School of Biomedical Sciences Charles Sturt University ......factors, such as Mannose binding lectin...

School of Biomedical Sciences

Charles Sturt University

Rheumatoid Arthritis and Risk of Infection: The Role of

Disease-Modifying Anti-inflammatory Drugs

Hamid Reza Ravanbod

MBBS, M.Sc. (Public Health), M.Sc. (Podiatric Surgery)

Submitted in partial fulfilment of the requirements for the degree of

Doctor of Philosophy

First submitted August 2019

Revised March 2020

i

CERTIFICATE OF AUTHORSHIP

Hamidreza Ravanbod

ii

PUBLICATIONS FROM THIS WORK

1. Hamid Reza Ravanbod, H.R., Jazayeri, J.A., Russell, K.G., and Carroll, G.J. (2017).

Serious infections in rheumatoid arthritis and strategies for their prevention - A review

and discussion of implications for clinical practice. Journal of Immunology, Infection

& Inflammatory Diseases, 2(3). https://scientonline.org/open-access/serious-infections-

in-rheumatoid-arthritis-and-strategies-for-their-prevention-a-review-and-discussion-

of-implications-for-clinical-practice.pdf

2. Serious and non-serious infections in recipients of conventional synthetic and biologic

DMARDs in rheumatoid arthritis; an examination of self-reported data from the ARAD

registry (in preparation).

iii

ETHICS APPROVAL

This research was approved by the Human Research Ethics Committee (HREC), Charles Sturt

University. Protocol number: 2014/080

(Appendix L).

iv

ACKNOWLEDGEMENTS

I would like to express my sincere thanks and gratitude to my supervisors, Dr. Jalal Jazayeri

(CSU, principal supervisor) and Dr. Graeme Carroll (UWA) for their guidance and supports

during this research. I also wish to express my gratitude to late Professor Kenneth Russell for

his expert advice and contributions to the statistics in the fifth chapter. While this PhD research

has never been easy, it has always been a privilege to undertake it. Thank you, both, for helping

me along the way.

I would also like to thank Dr Sandra Savocchia, Dr Christopher Scott, Ms. Vibhasha Chand,

and Mr Abishek Santhakumar for their various advice or assistance with technical matters.

I would like to acknowledge Kara Gilbert for proofreading this thesis, in accordance with the

ethical standards for editing and proofreading contained in the Australian Standards for

Editing Practices (2nd ed.) (2013) as set out by the Institute of Professional Editors (IPEd) in

relation to editing and proofreading research these.

Special thanks to my family, parents, and my children, who supported me and gave me time to

finish this project, and to my employers, who provided me with an ongoing income to support

my family during my university studies.

v

LIST OF ABBREVIATIONS

ABT: Abatacept

ACPA: Anti-citrullinated peptide antibody

ACR: American College of Rheumatology

AD: Anno Domini

ADA: Adalimumab

AIC: Akaike information criterion

AIRR: Annualised internal rate of return

ANC: Absolute neutrophil count

APC: Antigen-presenting cells

APRIL: A proliferation-inducing ligand

ARAD: Australian Rheumatology Association Database;

bDMARDs: Biologic disease-modifying anti-rheumatic drugs

BJM: Bone, joint, muscle

BMI: Body mass index

BSRBR: British Society for Rheumatology Biologics Register

CABG: Coronary artery bypass grafting

CCP: Cyclic citrullinated peptide,

CHD: Coronary heart disease

CMV: Cytomegalovirus

CNS: Central nervous system

COPD: Chronic obstructive pulmonary disease

CoQ10: Coenzyme Q 10

CRP: C-reactive protein

CS: Corticosteroid

csDMARDs: Conventional synthetic biologic disease-modifying anti-rheumatic drugs

CSF: Colony-stimulating factor

CSU: Charles Sturt University

CV: Cardiovascular

CVID: Common variable immunodeficiency

CYA: Cyclosporine A

DF: Degree of freedom

DM: Diabetes mellitus

vi

DMARD: Disease-modifying anti-rheumatic drugs

DREAM: Dutch Rheumatoid Arthritis Monitoring Registry (Netherlands)

EENT: Eye, ear, nose, throat,

EOW: Every other week

ESR: Erythrocyte sedimentation rate

ETN: Etanercept

GAG: Glycosaminoglycans

GCONV: Global convergence variable

GDR: German RABBIT Registry Review

GISEA: Registry (Italian Group for the Study of Early Arthritis)

GIT: Gastrointestinal tract (GIT)

GM CSF: granulocyte-macrophage colony-stimulating factor

HAQ Score: Health assessment questionnaire score

HB: Hepatitis B

HCQ: Hydroxychloroquine

HLA: Human leukocyte antigen

HREC: Human Research Ethics Committee

T1DM: Insulin-dependent diabetes mellitus

IHD: Ischemic heart disease

ILD: Interstitial lung disease

IM: Intramuscular

IMIDs: Immune-mediated inflammatory diseases

INX: Infliximab

IR: Incidence rate

IRR: Incidence rate ratio

IUIS: International Union of Immunological Societies

IV: Intravenous

JAK: Janus kinase inhibitors

-2 Log L: Deviance in the model

LDA: Low disease activity

LEF: Leflunomide

lr: Likelihood ratio

LRTI: Lower respiratory tract infection

MBDA: Multi biomarker disease activity

vii

MBL: Mannose binding lectin (MBL)

MCP: Metacarpophalangeal

MCSF: Macrophage colony-stimulating factor,

MHDA: Moderate to high disease activity

MI: Myocardial infarction

MMPs: Matrix metalloproteinases

MSK: Musculoskeletal

MTP: Metatarsophalangeal

MTX: Methotrexate

NF-KB: Nuclear factor kappa-Β,

T2DM: Non-insulin dependent diabetes mellitus

NK: Natural killer cell

NSAIDs: Non-steroidal, anti-inflammatory drug

NTM: Non-tuberculous mycobacterial

OCP: Oral contraceptive pill

OIs: Opportunistic infections

PG: Proteoglycan

PIP: Proximal interphalangeal

PML: Leukoencephalopathy

PRISMA: Preferred reporting items for systematic reviews

PYs: Person-years

RA: Rheumatic arthritis

RABBIT: Rheumatoid arthritis (RA) observation of biologic therapy

RANKL: Receptor activator of nuclear factor kappa-Β ligand

RCT: Randomised control (or controlled) trial

RF: Rheumatic factor

RTX: Rituximab

RX: A medical prescription

SAS software: Statistical Analysis System software

SC: Schwarz criterion

SD: Standard deviation

SERENE: Study evaluating rituximab’s efficacy in MTX iNadequate rEsponders

SI: Serious infection

SIE: Serious infection event

viii

SSTIs: Skin and soft tissue infections

TB: Tuberculosis

TCZ: Tocilizumab

TKI: Tyrosine kinase inhibitor

TNF-α: Tumour necrosis factor-α

TNFI: TNF inhibitor

TOF: Tofacitinib

UK: United Kingdom

US: Ultrasound

USA: United States of America

UTI: Urinary tract infection

UWA: University of Western Australia

ix

WHOLE THESIS ABSTRACT

The development of infection is far more common in rheumatoid arthritis (RA) patients than in

the general population. It is probably one of the most important consequences of RA. It is shown

that RA can also increase the rate of serious infection (SI), from less than one per hundred

patient years (100PYs) in the normal population to around five per 100PYs in the RA

population. The risk of infection in RA increases due to several factors. Some of these include

(i) the nature of RA disease and the pathophysiological changes in the immune system, (ii) RA

medications, a number of which suppress the immune system, and (iii) coexisting genetic

factors, such as Mannose binding lectin (MBL) deficiency, which increases the risk of

immunodeficiency through well-known or unknown mechanism(s).

In this project, data were collected from the Australian Rheumatology Association Database

(ARAD), in which a cohort of 3569 RA patients (960 males and 2609 females), who had

completed related questionnaires 28176 times (during 200 to 2014) were investigated for the

development of infections. Among the 3569 patients, 459 patients were eliminated because they

had filled out the questionnaire only once, after which 3110 patients remained. Eight duplicates

were eliminated, leaving 27709 visits from 3110 patients. All these visits were examined, to

capture self-reported infections in different organs and the medications that were being taken

at the time. ARAD reports were statistically analysed using the Chi-square test, Fisher’s exact

test and logistic or multinomial logistical regression modelling. The thesis is divided into five

chapters:

Chapter 1 provides a detailed overview of the entire thesis, including a comprehensive

background of the topic and the project hypothesis, goals, objectives, and strategies.

Chapter 2 outlines a comprehensive systematic review in which the implications of the

development of infection in RA patients and strategies for the prevention of infection are

discussed. This chapter was published as a review article in 2017. This chapter provides a

background on the subject of this thesis and provides a comprehensive review of the relevant

studies that have been undertaken in this area.

Chapter 3 outlines a descriptive analysis of the infection status of RA patients, in which the

role of disease modifying anti-inflammatory drugs (DMARDs) are investigated. ARAD

x

reports are examined with respect to demographic and treatment categories. Observed

differences were then subjected to descriptive statistical appraisal. This chapter is an

introduction to the more complex inferential analysis outlined in chapter 4.

Chapter 4 outlines an inferential analysis of the association between the risk of infection and

each anti-RA medication. The analysis provides valuable information concerning the

relative frequency of self-reported infections in users of diverse anti-rheumatic therapies.

Various organs, including eyes, ears, nose, throat, lungs, urinary tract, heart, gastrointestinal

tract, and the central nervous system (CNS) are examined, as well as systemic infections of

a viral and pyogenic nature (sepsis /septicaemia). This provides an introduction to the use of

adjusted equations for predicting the risk of infection, presented in the next chapter.

Chapter 5 presents more complex assessments around the incidence of serious infection, its

demographic characteristics, and potential risk factors. Patient reports taken from 27709

visits by 3110 patients during 2001 to 2014 were searched for evidence of hospitalisation or

intravenous (IV) infusion for infection. Resultant data were tested using inferential and

descriptive analyses, and odds ratios for potential risk factors were calculated. A few studies

indicate that RA disease and anti RA medication can specifically increase the risk of serious

infections. Serious infection (SI) is still the number one cause of death in RA, globally, and

so investigating the basis for SIs is important because of the risk of immediate mortality,

ongoing morbidity, and health economic burdens. Moreover, an increased understanding of

SIs may lead to the development of improved strategies for the prevention of infection. In

Chapter 5, serious infection, with all its potential risk factors, is discussed and analysed in

detail.

Based on the systematic literature research, we have found that SI is far more common in RA

than in the general population. In addition, anti RA medications have different impacts on

serious infections, with corticosteroids demonstrating a huge impact on infection followed by

bDMARDs and csDMARDs. The time of prescribing bDMARDs in the first year or after,

higher dosage of bDMARDs, and combination therapy with bDMARDs all increase the risk

of infection. Although it seems that, in the Australian database, csDMARDs alone, during

prescription, can evoke higher rates of infection than bDMARDs alone; this difference is

statistically significant in self-reports of heart infection, lung infection (p-value = 0.0156),

urinary system infection (p-value = 0.0002), and GIT infection. Both csDMARDs and

xi

bDMARDs are associated with a higher risk of infection in RA. All in all, without isolating

the first year of taking bDMARDs, it seems that bDMARDs causes less infection but more

serious infection. The impact of various medications on infection depends on the type and

severity of infection.

xii

TABLE OF CONTENTS

Certificate of authorship ................................................................................................................................. i

Publications from this work ........................................................................................................................... ii

Ethics approval ............................................................................................................................................. iii

List of Abbreviations ...................................................................................................................................... v

Whole Thesis Abstract .................................................................................................................................. ix

Table of Contents ....................................................................................................................................... xiii

chapter 1 ....................................................................................................................................................... 1

Abstract ................................................................................................................................. 2

1. Introduction ...................................................................................................................... 3

1.1 Overview .................................................................................................................................................. 3 1.2 Overview of the thesis rationale ............................................................................................................. 3 1.3. Background information ......................................................................................................................... 3

1.3.1. Rheumatoid arthritis ......................................................................................................... 3

1.3.2. Diagnosis and prevalence in RA ....................................................................................... 4

1.3.3. Consequences and medication in RA ............................................................................... 5

1.3.4. Pathophysiology in RA ..................................................................................................... 5

1.4. Molecular pathogenesis ......................................................................................................................... 6 1.4.1. Mechanism of actions of bDMARDs and csDMARDs .................................................... 7

1.4.2. Mechanism of action of bDMARDs ................................................................................. 8

1.4.3. TNFα ................................................................................................................................ 9

1.4.4. TNFα inhibitors ................................................................................................................ 9

1.5. Major risk factors .................................................................................................................................. 11 1.6. Signs and symptoms and laboratory tests ............................................................................................ 11 1.7. Complications ....................................................................................................................................... 13 1.8. Moderate and serious infections .......................................................................................................... 14 1.9. Medical treatment ................................................................................................................................ 15

1.9.1. Medication and risk of infection in the literature ............................................................ 17

1.10 Discussion ............................................................................................................................................ 19 1.11 Organisation of this thesis ................................................................................................................... 23 1.12 Hypotheses to be examined in this thesis ........................................................................................... 24 1.13 Significance of undertaking this review ............................................................................................... 25

2. Methods ........................................................................................................................... 25

3. Summary of the Results ................................................................................................. 26

3.1. Strengths of this research ..................................................................................................................... 27

xiv

3.2. Limitations ............................................................................................................................................ 28

4. Conclusion ....................................................................................................................... 28

References ........................................................................................................................... 30

Chapter 2 .................................................................................................................................................... 37

Abstract ............................................................................................................................... 38

1. Introduction .................................................................................................................... 39

2. Methods ........................................................................................................................... 40

2.1. Search strategy and selection criteria .................................................................................................. 40

3. Results and discussions .................................................................................................. 40

3.1. Study selection ..................................................................................................................................... 40 3.3. Risk factor categories ........................................................................................................................... 44 3.4. The impact of medications (non‐biologics) .......................................................................................... 45 3.5. Corticosteroids ..................................................................................................................................... 46 3.6 Synthetic DMARDS ................................................................................................................................ 46 3.7 The impact of medications (biologics) ................................................................................................... 48 3.8. TNF‐α Inhibitors .................................................................................................................................... 48 3.9. Abatacept (ABT), Rituximab, Anakinra, Tofacitinib and Tocilizumab ................................................... 49 3.10. Risks associated with combination therapies ..................................................................................... 51 3.11. Tuberculosis (TB) and non‐tuberculous mycobacterial (NTM) infections .......................................... 52 3.12. Serological and other laboratory parameters that influence SI risk ................................................... 52 3.13. Mannose Binding Lectin (MBL) and other immune deficiencies ........................................................ 52 3.14. Implications for Clinical Practice ......................................................................................................... 54

3.14.1. Age ............................................................................................................................... 54

3.14.2. Corticosteroid (CS) Use and Dosage ............................................................................ 54

3.14.3. Doses of biologic agents ............................................................................................... 54

3.14.4. Vaccination ................................................................................................................... 55

3.14.5. Comorbidities related and unrelated to RA .................................................................. 55

4. Conclusion ....................................................................................................................... 55

References ........................................................................................................................... 57

Chapter 3 .................................................................................................................................................... 63

Abstract ............................................................................................................................... 64

1. Introduction .................................................................................................................... 66

1.1. DMARDs ................................................................................................................................................ 68 1.2. bDMARDs .............................................................................................................................................. 68 1.3. Aims and Objectives ............................................................................................................................. 69

2. Methods ........................................................................................................................... 70

2.1. Data Collection ..................................................................................................................................... 70 2.2. Statistical Analysis ................................................................................................................................ 70

3. Results and discussions .................................................................................................. 70

xv

3.1. Demography of whole RA population .................................................................................................. 70 3.2. Demography of patients taking purely bDMARDs ................................................................................ 71 3.3. Demography of patients receiving csDMARDs alone ........................................................................... 74 3.4. Comparison of patients receiving bDMARDs and patients on csDMARDs alone ................................. 77

3.4.1. Prednisolone comparison ................................................................................................ 78

3.4.2. Alcohol comparison ........................................................................................................ 79

3.4.3. Smoking comparison ...................................................................................................... 80

3.4.4. Sex distribution comparison ........................................................................................... 82

3.4.5. T2DM comparison .......................................................................................................... 85

3.4.6. T1DM comparison .......................................................................................................... 86

3.4.7. Skin and nail infections comparison ............................................................................... 87

3.4.8. Eyes, Ears, nose, Throat (EENT) Infections – a comparison ......................................... 89

3.4.9. Heart infections comparison ........................................................................................... 91

3.4.10. Lung infections comparison ......................................................................................... 92

3.4.11. Gasterointestinal tract (GIT) infections ........................................................................ 95

3.4.12. Urinary tract infections (UTI) ........................................................................................................... 97 3.4.13. Musculoskeletal infections (MSK) ................................................................................................... 99 3.4.14. Artificial joint infections ................................................................................................................ 102 3.4.15. Nervous system infections ............................................................................................................ 103 3.4.16. Tuberculosis (TB) infection ............................................................................................................ 103 3.3.17. Blood infections ............................................................................................................................. 104 3.4.18. Viral Infections ............................................................................................................................... 106 3.5. Chapter discussion and conclusion ..................................................................................................... 108

References: ........................................................................................................................ 117

Chapter 4 .................................................................................................................................................. 125

Abstract ............................................................................................................................. 126

1. Introduction .................................................................................................................. 128

1.1. Aims .................................................................................................................................................... 131 1.2. Hypothesis .......................................................................................................................................... 131

2. Methods ......................................................................................................................... 132

2.1 Data Collection .................................................................................................................................... 132 2.2. Statistical Analysis .............................................................................................................................. 132

3. Results and Discussions ............................................................................................... 132

3.1. Different organ infections .................................................................................................................. 135 3.2. Eye, Ears, Nose and Throat (EENT) infection ‐ analysis of Anti‐RA medicines .................................... 136

3.2.1. Wald Chi-square, Likelihood ratio test and Score test to test significance of differences

................................................................................................................................................ 137

3.2.2. Effects of medications on Eye Ear Nose and Throat (EENT) infection ....................... 137

3.3. Chest or lung infection ‐ analysis of anti‐RA medicines ...................................................................... 142 3.3.1. Wald Chi-square, likelihood ratio test and score test to test significance of differences

................................................................................................................................................ 143

3.3.2. Effects of different medications on lung infection ....................................................... 143

xvi

3.4. Skin and Nail infection ‐ analysis of Anti‐RA medicines ...................................................................... 150 3.4.1. Effects of different medications on skin and nail infection .......................................... 151

3.5. Artificial (Prosthetic) Joint infection ‐ analysis of Anti‐RA medicines ................................................. 156 3.5.1. Wald Chi-square, Likelihood ratio test and Score test to test significance of differences

................................................................................................................................................ 158

3.5.2. Effects of different medications on artificial (prosthetic) joint infection ..................... 158

3.6. Bone, joint and muscle (BJM) infection ‐ analysis of anti‐RA medicines ............................................ 162 3.6.1. Wald Chi-squared, Likelihood ratio test and Score test to test significance of differences

................................................................................................................................................ 163

3.6.2. Effects of different medications on bone, joint and muscle infection ........................... 163

3.7. Blood infection ‐ analysis of Anti‐RA medicines ................................................................................. 170 3.7.1. Wald Chi-square, Likelihood ratio test and Score test to test the significance of

differences .............................................................................................................................. 171

3.7.2. Effects of different medications on blood infection ..................................................... 171

3.8. Gastro‐intestinal tract infection ‐ analysis of medication confounders ............................................. 176 3.8.1. Wald Chi-square, Likelihood ratio test and Score test ................................................. 178

3.8.2. Effects of different medications on GIT infections ...................................................... 178

3.9. Nervous System infection ‐ analysis of medication confounders ....................................................... 182 3.10. TB infection ‐ analysis of medication confounders ........................................................................... 183 3.11. Urinary tract infection ‐ analysis of medication confounders .......................................................... 184

3.11.1. Wald Chi-square, Likelihood ratio test and Score test to test significance of differences

................................................................................................................................................ 186

3.11.2. Effects of medications on Urinary tract infection ....................................................... 186

3.12. Viral infection ‐ analysis of medication confounders ....................................................................... 193

3.12.3 Chapter Conclusion ............................................................................................... 198

References: ........................................................................................................................ 203

Chapter 5 .................................................................................................................................................. 206

Abstract ............................................................................................................................. 207

1. Introduction .................................................................................................................. 208

1.1. Aims .................................................................................................................................................... 209 1.2. Hypothesis .......................................................................................................................................... 210

2. Methods ......................................................................................................................... 210

2.1. Data Collection ................................................................................................................................... 210 2.2. Statistical Analysis .............................................................................................................................. 211

3.0 Results and discussion ................................................................................................ 212

3.1. Analysis of Rheumatoid Arthritis (RA) and Serious Infections (SIs) in Australia ................................. 212 3.2. Age and gender ................................................................................................................................... 215 3.3. Length of time in the program ........................................................................................................... 217 3.4. Time in the program as a function of Gender .................................................................................... 218 3.5. Distribution of age groups .................................................................................................................. 218 3.6. Incidence and rate of SIs. .................................................................................................................... 219 3.7. Incidence of SIs ................................................................................................................................... 220

xvii

3.7.1. Rates of serious infections ............................................................................................ 221

3.7.2. Predictor variables ........................................................................................................ 222

3.8. Prediction of Serious infection ........................................................................................................... 224

4. Discussion ...................................................................................................................... 227

5. Chapter conclusion ....................................................................................................... 230

Thesis summary and Remarks .................................................................................................................... 232

Summary of main findings ......................................................................................................................... 233 Concluding remarks ................................................................................................................................... 236

References: ........................................................................................................................ 238

Appendices ............................................................................................................................................... 241

Description of data in appendix ...................................................................................... 243

Taking different medication levels ............................................................................................................ 243 Response levels ......................................................................................................................................... 243

Appendix A: Output of SAS for EENT Infection ........................................................................................... 244

Appendix B: OUTPUT of SAS for Lung Infection .......................................................................................... 268

Appendix C: Output of SAS for Nail and skin infection ................................................................................ 301

Appendix D: Output of SAS for artificial joint infection ............................................................................... 328

Appendix E: Output of SAS for bone muscle joint infection ........................................................................ 351

Appendix F: Output of SAS for blood infection ........................................................................................... 385

Appendix G: Output of SAS for GIT Infection .............................................................................................. 411

Appendix H: Output of SAS for Nervous system infection ........................................................................... 433

Appendix I: Output of SAS for TB infection ................................................................................................. 461

Appendix J: Output of SAS for Urinary Tract Infection ................................................................................ 485

Appendix K: Output of SAS for viral infection ............................................................................................. 509

Appendix L: Ethical approval for the thesis ................................................................................................ 535

APPENDIX M: Sample of ARAD questionnaire ............................................................................................ 536

1

CHAPTER 1

Introduction and overview

2

Abstract

Objective: To provide a comprehensive background to the project and to summarise the goals

and approaches of this thesis.

Methods: After a systematic review, ARAD patients’ records from 2001to 2014 were tested

using a series of descriptive and inferential statistical analysis. Initially the data was once

divided to (i) those with serious infection and those with non-serious infection, Then the

development of serious infection was evaluated in patients taking bDMARDs and compared

with those who were taking csDMARDs. Afterward in each section, these groups were

compared for their features and risk of infection.

Results: In the systematic review 31 articles met the criteria for further analysis and showed

increased association of serious infection with taking prednisolone, bDMARDs and to a lesser

extent csDMARDs. The risk of infection is reported to be higher in the first year of taking

bDMARDs compared to the following years.

ARAD data is analyzed by a series of descriptive and inferential analyses. In the descriptive

analysis the mean age for RA patients was found to be 61.47; for the group taking csDMARDs

it was 59.24 and for those taking bDMARDs it was 62.62 years respectively. ENT infections,

with a frequency of 14.75%, were the most common infection type in RA. Heart infection, lung

infection, urinary tract infection, and GIT infection were statistically more frequent in users of

csDMARDs compared to bDMARDs. Cyclosporine and Prednisolone were almost associated

with all types of infections in RA. Age, gender, alcohol consumption, etc. are potentially

associated with increased risk of SIs.

Conclusion: Based on the systematic research, SI is far more common in RA than in the

general population. Based on ARAD data, for most types of infection, csDMARDs alone are

associated with higher rates of diverse infection, whereas bDMARDs alone are more strongly

associated with serious infections.

According to the ARAD analysis, the most common infection in RA in Australia is EENT

infection (14.75%). The risk of any serious infection is almost 2.92% in ARAD and for females

this risk starts at younger ages. Among various risk factors, smoking is linked to serious

infections.

3

1. Introduction

1.1 Overview

This chapter presents a summary of key information important to the rationale for the thesis.

Information about rheumatoid arthritis (RA) is presented, followed by background information

about infection in RA. Biologic DMARDs and csDMARDs are defined and their role in the

treatment of RA, together with their capacity to predispose to infection, is outlined. The role of

different risk factors in increasing the risk of infection is briefly reviewed, followed by

background information about medications. The aims, hypotheses, and significance of the study

conclude the introduction.

1.2 Overview of the thesis rationale

Linkages between RA and serious infection have been hypothesised and continue to be refined

as our understanding of RA, its pathogenesis and methods of treatment continue to evolve. A

growing body of research indicates that sometimes using effective treatments, such as

bDMARDs, is associated with unwanted effects, including minor and major infections, some

of which are serious and can be life-threatening or fatal[1].

1.3. Background information

1.3.1. Rheumatoid arthritis

According to Arthritis Australia, RA is an autoimmune disease which causes swelling and pain

of the joints. RA disease causes inflammation and joint damage in the smaller joints in the

hands and feet, through damage to the lining of the joints. Rarely, in RA, larger joints, such as

the knees and hip joints, can also be affected, too [1]. Symptoms vary from person to person

and may include symmetric joint pain, swelling and tenderness, with morning stiffness [2].

RA is usually diagnosed from its symptoms, a physical examination and testsm such as blood

tests for inflammatory factors and antibodies (anti-CCP), including rheumatoid factor. X-rays

can also help to see if joints are damaged[3].

4

1.3.2. Diagnosis and prevalence in RA

Historically, the precise time at which RA emerged is difficult to determine and is mainly based

on both assumptions and empirical analysis; however, it seems that RA is mainly a disease of

the modern world. Probably the earliest evidence of RA start from portraits by artists of the

Flemish school, during the mid-15th to early 16th centuries. These depictions hint at the

existence of rheumatoid-like deformities in the European models used by these artists[4].

According to Australian guidelines, the diagnosis of RA is made on the basis of clinical

presentation, in association with autoantibodies and evidence of systemic inflammation.

Common features of RA are discussed in the following sections.

Common features of rheumatoid arthritis [5]:

Early morning stiffness for longer than one hour

Family history of inflammatory arthritis

Joint swelling in more than five joints and symmetry of the affected area

Rheumatoid factor positivity, compression tenderness in hands and feet

Anti CCP positivity, chronicity of symptoms for more than 6 weeks

Bony erosion apparent in X-rays of the hands, wrists and feet

Presence of rheumatoid nodules and raised inflammatory markers (ESR and CRP)

Systemic features, such as fatigue and weight loss, are relatively common

The incidence of RA is generally variable and, overall, the number of people affected by RA is

not large. Among all countries, Japan and France have the lowest incidence rates of 8 per

100,000 and 8.8 per 100,000, respectively, while the highest incidence rate is reported to occur

in the United States, with 44.6 per 100,000. Rheumatoid arthritis incidence rates may alter

marginally, as they are exaggerated by time of reporting and the gap between symptom onset

and report to a population-based registry[6].

The prevalence of RA among blacks is lower compared to whites. Although the prevalence of

RA among the black population is lower, there is no evidence that the disease manifestations

differ. However, there is even some evidence to suggest that RA in blacks is less severe, in

5

terms of extra-articular manifestations and disability. Rheumatoid arthritis is also more

common in women and is relatively uncommon in young men (<35 years). The French Afro-

Caribbean population have specific manifestations of the disease, such as high female

predominance, high immune seropositivity and low tobacco use. As these data are extracted

from France, which has a mixed population, it seems that geographical varieties play a small

role in these differences[7]. In another study in North America, the prevalence of RA among

Eskimos has been also reported as 0.8 % (the method of diagnosing RA in this study was based

on clinical signs and symptoms plus serology, without the benefit of x-rays)[8]. Also, the

prevalence rate of RA among Chinese people is different. In a study by Zeng et. al. (2002) the

prevalence rate of RA in China is almost 0.2-0.3% of the population[9].

1.3.3. Consequences and medication in RA

Rheumatoid arthritis (RA) has been a major cause of disability and loss of productivity among

different populations, including Australians[10]. Multiple comorbidities and extra-articular

disease manifestations may accompany rheumatoid arthritis (RA) [10].

Prior to 1990 or thereabouts, a conventional multi-disciplinary management approach to this

chronic disease was usually followed. Increasingly, in the last three decades, the emphasis has

shifted to the use of multiple and increasingly sophisticated pharmacological measures, with

early and aggressive management strategies advocated. One of the disadvantages of this

approach has been a rise in the rates of non-serious and also serious infections in a not

inconsiderable subset of RA patients receiving these new treatments [10].

1.3.4. Pathophysiology in RA

In rheumatoid arthritis (RA), the site of the initial inflammatory process is the synovial lining

of diarthrodial joints. In these joints, usually synovial fluid is the source of food for the articular

cartilage and lubricates the cartilage matrix. During the inflammatory process, the synovial

tissue undergoes increased vascularization and infiltration by activated macrophages,

lymphocytes, and plasma cells. As the disease advances, a pannus forms from the progressive

overgrowth of this tissue, which then threatens the adjacent cartilage and bone (6).

Although the aetiology and pathogenesis of RA have yet to be completely elucidated, several

factors have been identified that contribute importantly to the disease process. These factors

6

include genetic contributors, environmental factors, the interaction of genes and environment,

and cellular abnormalities. A series of immune system factors such as tumour necrosis factor

(TNF), have been identified which, when induced, is elaborated and then interacts with target

cells, and appears to drive inflammation and tissue damage. Accordingly, medications, such as

infliximab (Remicade), adalimumab (Humira), etanercept (Etanercept), certolizumab pegol

(Cimzia), and golimumab (Simponi), have been produced which can prevent the interaction of

TNF with its endogenous receptors or bind and neutralize their activity in the extracellular

environment [12].

1.4. Molecular pathogenesis

The mechanism of action by which progressive inflammation and damage occurs is a complex

cellular interplay between several key cell types and processes. Usually RA initiates with

abnormal presentation of self-antigen by antigen-presenting cells (APC), such as B cells,

dendritic cells, or macrophages, which leads, in turn, to the activation of autoreactive T

lymphocytes[13]. As the disease progresses, the sub lining of the synovium is infiltrated by T

cells, B cells, macrophages, and plasma cells. T cells, once activated, build up in the affected

joint and secrete lymphokines such as interleukin-2 and interferon, and other pro-inflammatory

cytokines. In addition to acting as APC, B cells produce RF and other autoantibodies, secrete

pro-inflammatory cytokines, such as tumour necrosis factor (TNF)-α, and activate T cells. In

addition, macrophages secret cytokines and stimulate synoviocytes to release enzymes, which

may damage cartilage and bone[14].

Several other cell types accumulate and stimulate in the synovial membrane of RA patients via

activated endothelial cells, including synovial fibroblasts and osteoclasts, both of which can

promote bone degradation. Synovial fibroblasts contribute to cartilage and joint destruction

through the expression of matrix-degrading enzymes, such as matrix metalloproteinases

(MMPs), and are activated by a variety of cytokines, including TNF-α and interleukin-1. The

identification and understanding of this process has led to the development of several novel

therapeutic strategies that target these cytokines[15]. Osteoclasts resorb bone matrix and are

complemented by osteoblasts that produce bone matrix. Macrophage colony-stimulating factor

(MCSF) and the receptor antagonist of NF-KB ligand (RANKL) are required for the growth

and differentiation needed by osteoclasts to become fully developed. An abnormal activation

of osteoclasts leads to the bone destruction observed in RA patients, in whom osteoclast

7

formation in inflamed joints is promoted by pro-inflammatory cytokines through their influence

on RANKL expression. Figure 1.1 represents a current model of the hypothesized pathogenesis

of RA[15].

Figure 1.1 Schematic picture of pathogenesis in RA.

1.4.1. Mechanism of actions of bDMARDs and csDMARDs

These drugs are immune-suppressive and are designed to slow cartilage damage. There are two

types of DMARDs; (i) conventional synthetic DMARD commonly reoffered to as csDMARDs,

examples of which include methotrexate, sulfasalazine, hydroxychloroquine, or leflunomide

and (ii) Biologic DMARDs (bDMARDs) which only came to market in the early 1990s[16].

These include Lenercept, etanercept, abatacept, infliximab, rituximab, tocilizumab etc. some of

these drugs are monoclonal antibody based are produced in prokaryotic or eukaryotic cells

using hybridoma technology (Table 2.1). Such monoclonal antibodies are engineered to have

specific targets and pharmacodynamic properties. In addition, these drugs are engineered to

improve their pharmacokinetics and pharmacodynamics properties such as long stability and

serum half-life to reduce their frequency of administration. Such modifications include addition

of Fc potion of human IgG antibodies or by PEGylation, addition of polyethylene glycol[16].

8

1.4.2. Mechanism of action of bDMARDs There are a range of bDMARDs, which have been developed as anti-inflammatory drugs,

targeting a range of proinflammatory cytokines (Figure 1.2). Generally, cytokines are targeted

in four ways[17]:

1. Anti-cytokine antibodies

2. Receptor-blocking antibodies

3. Soluble receptors: TNF-α soluble receptors bind to and inactivate TNF-α, thus reducing

the TNF-α pool available for membrane-bound receptors and signal transduction

4. Receptor antagonists

A B C D 5.

6.

7.

8.

9.

10.

Figure 1.2 Mode of action of anti-cytokines (A) normal cytokine-receptor interactions

(B) neutralization of cytokines with either soluble receptors or monoclonal antibodies (C)

receptor antagonist block receptor so that no inflammatory signal is sent (D) suppression of

inflammatory cytokines by activating anti-inflammatory pathways[18],[19] .

Anti-inflammatory drugs each have their own mechanism of action leading to

interfering/blockage of the critical pathways in the inflammatory cascade[16]. For example,

Methotrexate stimulates adenosine release from fibroblasts[16]

Anti -TNFα inhibitors all bind to the cytokine TNF and inhibit its interaction with the TNF receptors [20]

Hydroxychloroquine has mild immunomodulatory action that inhibits intracellular toll-like receptor TLR9 [21].

9

1.4.3. TNFα

Tumour necrosis factor-α (TNF-α) is a 26 kDa membrane bound cell signalling cytokine, which

has several roles in the immune system. These include: (i) antitumor activity (ii) immune system

modulation (iii) inflammation (iv) anorexia, (v) cachexia, (vi) septic shock, (vii) viral

replication and (viii) haematopoiesis. In arthritis, these cytokines collectively induce

chondrocytes to produce metalloproteinases (MMPs), which contribute to cartilage and bone

erosion[22].

Overexpression of TNFα plays a key role in the pathogenesis of many chronic inflammatory

and rheumatic diseases, including rheumatoid arthritis, ankylosing spondylitis, psoriatic

arthritis, Crohn’s disease, as well in pulmonary inflammation and emphysema and myocarditis

etc[22].

1.4.4. TNFα inhibitors

Several biologics have been designed to block the proinflammatory activity of TNFα (Figure

1.3). These include etanercept, infliximab and adalimumab. Such biologics have shown to

reduce symptoms and improve function and quality of life [23]. There are two main strategies

for inhibiting TNF:

1. Monoclonal anti-TNF antibodies

2. Soluble TNF receptors (sTNF-R) -recombinant protein

10

Figure 1.3 Structure of some of the TNFα inhibitors: etanercept, recombinant fusion protein

with two p75 TNF receptors that is solubilised by linking to the Fc portion of human IgG1;

pegsunercept, a soluble tumour necrosis factor receptor which is PEGylated; onerecept,

recombinant human TNFα binding protein-1; adalimumab; infliximab, an IgG1 monoclonal

antibody; and, CDP571, a humanised monoclonal antibody to TNFα[24] , [22].

11

1.5. Major risk factors

There are several risk factors which may ignite the above-mentioned molecular pathways in

RA. Environmental risk factors is one of them. The primary known environmental risk factor

for RA is cigarette smoking, however, an unanticipated finding also shows that taking the oral

contraceptive for 7 or more consecutive years is associated with a lower risk of RA [25].

Smoking usually is associated with sero-positive, not seronegative, RA [25]. An increase in the

duration of smoking years increases the risk of developing seropositive RA [26]. Former

smokers are also at risk. Studies show that former smokers remain at risk of RA for anywhere

between 10 and 19 years after smoking cessation. Another risk factor is air pollution. In a study

by Hart et.al. (2009), the prevalence of RA is higher in the regions of the USA which have

greater air pollution [27]. By gathering the results of these two studies, Hart et.al., in their study,

concluded that inhaled particulate matter from traffic pollution might contribute to the risk of

developing RA [27].

Alcohol consumption, birth weight, and early life hygiene are other well-known risk

factors[28]. It has been suggested that there is a dose-dependent inverse risk associated with

alcohol consumption and RA [28]. Also, in an analysis of women, it was revealed that women

with a higher birth weight (>4.54 kg) had a two- fold increased risk of adult onset RA[28]. In

addition, in a number of studies, oral contraceptive pill (OCP) consumption is associated with

a lower risk for RA (Kłodziński & Wisłowska, 2018)[28]. A comprehensive list of risk

protective and causative risk factors is presented in Table 1.1[8].

1.6. Signs and symptoms and laboratory tests

The main symptoms of RA are pain and stiffness. There are usually four distinct phases in RA:

an initial phase (no clinical manifestations), an early inflammatory phase (clinical

manifestations); a destructive phase (erosions and disease progression); and an ongoing phase

(irreversible joint destruction). Two major overlapping subpopulations in RA include

individuals who are positive for the presence of rheumatoid factor (RF) and individuals who

are positive for the presence of antibodies that can bind cyclic citrullinated peptides (CCP).

Patients with neither of these biomarkers tend to have a more benign course and are referred to

as having “seronegative” RA [6].

12

Table 1.1 List of risk factors in RA [29]

Risk Factors

Increase Chance of disease

Protein tyrosine phosphatase, non-receptor type 22 (PTPN22) Peptidyl arginine deiminase 4 (PADI4) DNA methylation changes CD40, CC chemokine ligand 21 (CCL21), CC chemokine receptor 6 (CCR6) Tumour necrosis factor receptor-associated factor-1 (TRAF1/C5) Interleukin-6 receptor (IL6R) MHC regions especially amino acids at positions 70 and 71. Fc gamma receptor (FCGR) Tumour necrosis factor receptor-associated factor-1 (TRAF1/C5) Signal transducer and activator of transcription 4 (STAT4) Exposure to tobacco smoke Female sex Low vitamin D intake and levels Obesity Occupational dust (silica) High sodium, red meat and iron consumption Air pollution

Possible protective effect:

HLA DRB1*1301 (decreased risk for ACPA positive RA) Statin use Healthy diet Consumption of fish Consumption of alcohol Hormone replacement

Rheumatoid factor is a type of antibody present in around 80% of RA patients. It is believed

that this antibody attacks healthy tissue and causes inflammation. This factor is assessed and

measured in the blood stream and once it passes a certain amount, RF is reported positive.

Previously, RF was the only way to diagnose RA but, nowadays, other antibodies, such as anti

CCP and antinuclear antibodies, are also being used[29]. Anti-CCP is another destructive

antibody in RA causing inflammation and damage to the joints and they may be positive long

before symptoms manifest in RA[29]. Antinuclear antibodies, such as ANA, are also antibodies

which are circulating normally in the body, and when their amount increases, they can attack

normal tissue and are indicators of autoimmune diseases[6]. ESR and CRP are mainly useful

to measure the level of inflammation in a particular patient and cannot be used to diagnose RA

(table 1.2) [30].

13

Table 1.2 A list of signs and symptoms and diagnostic laboratory tests in RA[6].

Signs and symptoms Disrupted sleep Low grade fever Fatigue Depression and mood changes Dry eyes and mouth Weight loss Joint pain (more small joints in hands and feet) Joint swelling (more small joints in hands and feet) Joint stiffness (more small joints in hands and feet) Red joint (more small joints in hands and feet) Warm joints (more small joints in hands and feet) Joint deformity (more small joints in hands and feet) Numbness and tingling (feet and hands) Subcutaneous Nodules Dry eyes and mouth Depression and mood changes Muscle aches Lack of appetite Loss of energy Limping Hoarseness Painful walking

Laboratory tests ESR CRP RF Anti-CCP Antinuclear Antibody (ANA)

1.7. Complications

Although RA is not a terminal disease, related information indicates a gap in mortality between

individuals with RA and the general population. For example, RA increases the prevalence of

ischemic heart disease (IHD) and pulmonary disease, particularly interstitial lung disease

(ILD), type 1 diabetes, obesity, infection in different organs, serious infection, and hypertension

[29][5][31]. Rheumatoid arthritis (RA) may require special attention due to the particularly

14

devastating effects in organs such as lung, heart, CNS, or lymphatic system that sometimes

ensue[29].

1.8. Moderate and serious infections

Probably one of the most important consequences in RA is the development of infections.

Medicine Net has a well worded definition for infection: “Infections may be localized, or may

become systemic (body wide)”[32]. Table 1.3 lists a series of most common microbes which

frequently cause infection in RA.

Table1.3 Common RA-associated microbes[33].

Name of microbe

Proteus mirabilis

Epstein-Barr Virus

Mycoplasma spp.

Prophyromonas spp.

Periodontal disease (PD) is probably one of the most commonly infections associated with

RA. This association has been considered since the early 1820s. Almost twenty different

bacterial species can cause PD. P. gingivalis, Prevotella intermedia, Tannerella forsythia,

and Aggregatibacter actinomycetemcomitans are the most common ones. There is, however,

another possibility that PD can increase the incidence of RA[33].

It has been shown that a range of bacterial and viral infections can manifest rheumatic disease

symptoms, including reactive arthritis. These infections include gastrointestinal or

genitourinary infections with Salmonella, Shigella, Campylobacter, Yersinia, and Chlamydia

trachomatis, HIV, parvovirus and hepatitis viruses B and C [34].

15

Table 1.4 Incidence cohort of RA patients, followed from 1955 to 1994 at the Mayo Clinic [32]

Infection Rate ratio Urinary tract infection 1.1 Septicaemia 1.5 Pneumonia 1.6

Lower respiratory tract infection 1.9 Other 2.0 Intra-abdominal 2.8 Skin or soft tissue 3.3 Osteomyelitis 10.6 Septic arthritis 14.9

RA can also increase the rate of serious infection (SI), from less than one per hundred patient

years (100PYs) in the normal population to around 5 per 100PYs in RA, overall (Table 1.4)

[5]. The risk of infection in RA increases due to several changes. Some of these changes include

RA disease and pathophysiology of changes in the immune system, RA medications, a number

of which suppress the immune system, and, finally, sometimes there are coexisting genetic

factors, such as Mannose Binding Lectin (MBL) deficiency, which increases the risk of

immunodeficiency through well-known or unknown mechanisms [35][33].

The risk for the development of serious infection (SI) can also increase in RA. In the literature,

the term serious infection is usually used for an infection which requires specific interventions,

such as hospitalisation or intravenous antibiotics or both, or any infection which results in death

or severe disability. In this study, data have been collected from participants in the ARAD

database, who have self-reported details of their illness, treatment, and course over time,

including complications, such as infections. While infection can happen in any organ, based on

the literature, the most predominant infections in RA include (i) bronchopulmonary (ii)

urogenital (iii) soft tissue and (iv) skin, bone/joint sepsis and gastrointestinal infections [31].

Less common infections in RA include the CNS, the cardiovascular system and the lymphatic

system[31].

1.9. Medical treatment

The primary goals in treating patients with rheumatoid arthritis (RA) are to reduce pain and

stiffness, slow disease development and improve function. Medications, such as non-biologic

16

and biologic disease-modifying antirheumatic drugs (DMARDs), can reduce pain, retard

disease progression, and improve functional outcomes[36] whenever it proves possible to

reduce dosages or eliminate these medications. However, studies show that there are probably

some positive associations when taking biological medications if there should be a serious

infection. Richter et.al., in a study published in 2015, performed an observational cohort study

of 947 patients with serious infection in a total cohort of 11,150 participants in the German

registry (RABBIT) [35]. He and his colleagues observed that persons exposed to bDMARDs at

the time of an SI had a reduced risk of sepsis (septicaemia) and mortality[37].

Wherever DMARDs are discussed in this study, DMARDs is divided to csDMARDs and

bDMARDs. csDMARDs, or Conventional synthetic DMARDs, include: 1- IM Methotrexate

2- Hydroxychloroquine, 3- Sulphasalazine, 4- Arava (Leflunomide), 5- Azathioprine, 6-

Cyclosporin. bDMARDs, or biologics or biological DMARDs, include: 1-

Humera/Adalimumab, 2- Etanercept/Etanercept, 3- Kineret/Anakinra, 4- Remicade/Infliximab,

5- Mabthera/Rituximab, 6- Orencia/Abatacept, 7- Actemra/Tocilizumab, 8-

Simponi/Golimumab, 9- Cimzia/Certolizumab Pegol. Prednisolone, IM gold and penicillamine

do not belong to any group and are studied separately.

Mechanism of action of csDMARDs

Methotrexate (MTX), usually the first drug of choice for people with RA, stimulates

adenosine release from fibroblasts. When csDMARDs, such as MTX, are ineffective or

partially ineffective, other treatments options will involve bDMARDs.

Mechanism of actions of bDMARDs and csDMARDs

These drugs are immune-suppressive and are designed to slow cartilage damage [38]. There are

two types of DMARDs; (i) conventional synthetic DMARD, commonly reoffered to as

csDMARDs, examples of which include methotrexate, sulfasalazine, hydroxychloroquine and

leflunomide, and (ii) biologic DMARDs (bDMARDs), which only came to market in the early

1990s. These include lenercept, etanercept, abatacept, infliximab, rituximab and tocilizumab.

Some of these drugs are monoclonal antibody-based and are produced in prokaryotic or

eukaryotic cells using hybridoma technology (Table 2.1). Such monoclonal antibodies are

engineered to have specific targets and pharmacodynamic properties[38].

17

In addition, these drugs are engineered to improve their pharmacokinetics and

pharmacodynamics properties, such as long stability and serum half-life, to reduce their

frequency of administration. Such modifications include the addition of the Fc portion of

human IgG antibodies or the addition of polyethylene by glycol PEGylation [39].

Mechanism of action of bDMARDs

Cytokines are therapeutic targets for a range of bDMARDs, which are designed to reduce their

production (overexpression) or function. There are four ways in which cytokines are targeted.

These include the application of (i) anti-cytokine antibodies (ii) receptor-blocking antibodies

(iii) soluble receptors and (iv) receptor antagonists. Most bDMARDs fall into one of these

categories. Examples include infliximab, lenercept and etanercept and adalimumab among

others inhibiting the “second signal” required for T-cell activation, and depleting B-cells or

inhibiting factors that active B-cells (rituximab and belimumab) [17].

Considering the difference between anti-RA medication, different countries have developed

different therapeutic guidelines, based on factors such as the availability of medication and the

health economy. Table 1.5 shows a comparison between the different treatment modalities in

Australia, the United States and Canada.

1.9.1. Medication and risk of infection in the literature

Richter et al, in their study in 2015, concluded that bDMARDs supress the immune system

[37]. In some studies, bDMARDs are very safe and adding or not adding human antibodies will

not change this safety. For example, in a study by Wong Pack published in 2016, the authors

examined the incidence of serious infections in RA patients treated either with the combination

of denosumab and an immunosuppressive biologic DMARD or with an immunosuppressive

biologic DMARD alone [37]. Denosumab is a human monoclonal antibody which is used to

treat osteoporosis arising from multiple different causes. The sample included patients over 18

years of age with RA, registered in the practice 3 months before and after the index date, and

who had received 1 injection/infusion or filled a prescription for an immunosuppressive

biologic DMARD therapy for RA. Among all 308 patients in the sample, the authors concluded

that there is a low incidence of SIs in RA patients receiving bDMARDs, including patients

who currently are taking bDMARDs (Table 1.5) [43].

18

Table 1.5 Cross-comparison of RA therapy between Australia, the United States and Canada Countries Therapeutic Guidelines Australian

Initially, start treatment with simple analgesics, such as paracetamol, and supplements, such as Omega-3. Also, patient education and referral to physiotherapist and podiatrist are essential. Pharmaceutical therapy starts initially with NSAIDs and COX-2 inhibitors. If, in spite of using these medicines, swelling is persistent beyond six weeks, the patient needs to be referred to a rheumatologist to start DMARDs or low dose glucocorticoids. Referral to a rheumatologist can happen initially after multiple swollen joints are detected or if six weeks of NSAID therapy does not improve signs and symptoms. Advanced therapy in RA includes combination of DMARDs, leflunomide or cyclosporin or taking biologic agents, anti-TNFs, anakinra and rituximab[40].

American Use a treat-to-target strategy. Start with monotherapy (with MTX) rather than double therapy or triple therapy. In moderate or high disease activity without previous DMARDs, patient should take DMARD monotherapy, which is better than double or triple therapy. If the disease is still active, a combination of DMARDs or a TNFi or a non-TNF biologic, in no particular order, is preferred. If the disease activity remains moderate or high, use TNFI monotherapy or TNFi plus MTX. If disease activity persists and is moderate to high, add low dose glucocorticoid. Depending on the activity of disease, the dosage of glucocorticoid can be increased but it should remain as low as possible[41].

Canadian Start DMARD as soon as possible, through combination with methotrexate (MTX) or monotherapy with MTX. If response is inadequate, then switch to DMARDs. Usually first choice is anti-TNF with MTX, then ABAT/RTX or TCZ. If there is still an inadequate response, switch to any biologic or switch to traditional DMARDs. Inadequate response is defined as not reaching targets by 3 to 6 months[42].

Notes. MTX: Methotrexate; Anti TNF: Tumor necrosis factor inhibitor; ABAT: Abatacept, RTX: Rituximab, TCZ: Tocilizumab

19

Table 1.6 Samples of bDMARDs and their molecular structure[44] Type Name Description

mAb based

Infliximab (Remicade®)

Mouse-human chimeric anti-human TNF mAb

Adalimumab (HumiraTM)

Fully human anti-human TNF mAb

CDP571 a humanised monoclonal antibody to TNF- α

Golimumab Tumor necrosis factor alpha (TNF-alpha) inhibitors

Recombinant Fusin protein

Etanercept (Enbrel®) p75sTNF-RII-Fc (dimeric)

Lenercept p55sTNF-RI-IgG1 (dimeric)

Pegylated Certolizumab (Cimzia) PEGylated anti-TNFα biologic

1.10 Discussion

Zamora-leoff et.al. (2016), in a retrospective study among 181 patients suffering from RA,

found that the risk of serious infection is the highest in the first year after diagnosis of interstitial

lung disease (ILD). They found that the most common types of infection among this group

included pneumonia, septicaemia, and opportunistic infections. It was also revealed that

prednisolone in doses more than 10 mg, with or without DMARDs, was associated with the

highest rate of infection. The authors of this study concluded that the underlying autoimmune

process and use of immunosuppressive drugs or both are potential risk factors for higher

infection rates among patients with RA-ILD [45].

Curtis et.al. (2018) investigated a sample of 17433 RA patients with hospitalised

pneumonia/sepsis SIs and 16796 with myocardial infarction (MI) and coronary heart disease

(CHD). They found that higher multi-biomarker disease activity (MBDA) scores were

associated with hospitalised infections, predominantly in the older, US RA population [31].

Morel et. al.(2017), in a study of 1491 patients with RA who were treated with tocilizumab,

found that a high absolute neutrophil count (ANC) (above 5.0 × 109 at baseline), a negative

anti-citrullinated peptide antibody (ACPA) and concomitant therapy with leflunomide (LEF)

are predictive factors of serious infection [46].

20

Accortt et.al. (2018), in a study of patients over 18 years old and with a disease activity index

score of two or more than two, found that, compared to low RA disease activity (LDA),

moderate‐to‐high RA disease activity (MHDA) had a greater number of serious infections. The

authors concluded that lower RA disease activity was associated with lower serious infection

rates and recommended that treating physicians strive for remission of RA rather than accept

an LDA [31].

Salmon et. al. (2015) revealed that, in practice, usually patients with rheumatoid arthritis treated

with abatacept (ABT) have more comorbidities and serious infections are slightly more

frequently observed. In the Orencia and Rheumatoid Arthritis (ORA) registry, predictive risk

factors for serious infections included age and a proceeding history of serious infections [47].

Hashimoto et. al. (2015), in a study of 370 patients with RA, demonstrated that, although the

current disease activity was similar in patients with SIs, patients with multiple SI had greater

radiographic joint damage and more advanced physical dysfunction. [48]. Rutherford et.al.

(2017) examined 19282 patients with rheumatoid arthritis for 46771 patient-years and they

found that the incidence of serious infection was lowest with certolizumab. Rituximab and

tocilizumab both have higher rates of infection and there is a possibility that patient factors as

opposed to the drug itself were responsible for the observed difference [49].

In another study by Tarp et al. (2017), the crude incidence rate (IR) per 100 patient-years for

serious infections was calculated for the sustained remission, low disease activity (LDA), and

moderate to high disease activity (MHDA) groups. [31]. Baradat et. al. (2017) published a

systematic review of 16 RCTs. In this systematic review, the rates of serious infection and death

were compared between patients with RA who were treated with a combination therapy of

methotrexate and biological disease-modifying antirheumatic drug (bDMARDs) and patients

with RA who were using biological disease-modifying antirheumatic drug (bDMARDs)

monotherapy. The authors in this study concluded that there was no significant difference

between the two groups. They confirmed that using methotrexate and bDMARDs combination

therapy in RA does not cause an increased risk of serious adverse events [50].

De Andrade (2017) published a study in which he concluded that there is no difference in the

rate of SIs between patients who were taking rituximab (RTX), on one hand, or bDMARDs

(such as TNF inhibitors), on the other. Silva-Fernandez et. al. (2016) presented another study

21

in which the authors demonstrate that there is no difference at all in the risk of SIs over the first

year of treatment in patients treated with RTX compared with those treated with a second TNFα

after discontinuing a first TNFα [51]. Subesinghe et.al (2018) published a report concerning

the recurrence rate of SI among RA patients registered with the British Society for

Rheumatology Biologics Register. Among 5289 subjects with at least one serious infection,

contributing to 19 431 patient-years follow-up, the first SI rate was 4.6% (95% CI: 4.5, 4.7),

increasing to 14.1% (95% CI: 13.5, 14.8) [49].

Pappas et.al. (2017) conducted an extended observation analysis in clinical trials and showed

that rituximab does not increase the risk of serious infection events (SIE) in patients with

rheumatoid arthritis (RA). They describe characteristics of rituximab-treated patients who

experienced a SIE versus those who did not. In this study, they concluded that retreatment with

rituximab infusions was not associated with a higher rate of SIEs. [52].

Henry et. al. (2017) showed that, among a sample of 1278 RA patients who were treated with

standard vs reduced doses of rituximab for 5 years, the SI rate was lower in those who received

reduced doses. [53].

Zhang et.al. (2017) investigated 688 patients with pure RA and examined the association

between the infections and disease outcome. The authors concluded that repeated exposure to

infectious agents during the disease duration might lead to poor outcome for RA. They advised

paying more attention to those patients who have repeatedly infectious agents during their

disease duration in order to improve their prognosis [33]. Jinno et.al. (2017) examined 792,921

hospitalisations for infection where there was a secondary diagnosis of RA and concluded that

the proportion of hospitalisations for infections among RA patients appeared to decline over

time for pneumonia and opportunistic infections (OIs). They also observed a slight decrease in

UTIs, a slight increase in skin and soft tissue infections (SSTIs), and an increase in

hospitalisations with sepsis. [54]. Bortoluzzi et.al. (2016) studied the databases of the

Lombardy Region in the period between 1/1/2004 and 31/12/2013. They concluded that among

4656 RA patients recorded in the database, treatment with bDMARDs was not associated with

an increase in hospitalised infection. The risk was lower with abatacept, which accords with the

perception that it has a better safety profile [55].

22

In a meta-analysis by Singh, Cameron, Noorbaloochi et.al. (2015), 525 serious infections were

identified in 59 studies. A total of 342 infections occurred with biologic therapy with or without

DMARD adjunct therapy and 183 infections occurred with conventional DMARD

therapy. They concluded that therapy with standard dose or increased dose biologics or

DMARD combination biologics increases the risk of serious infections. Therefore, they advised

that practitioners should consider risk factors, such as corticosteroid therapy, increased age and

comorbidities to estimate the individual risk of infection when undertaking treatment with

biologic DMARDs[56]. Unfortunately, from this Meta-analysis, it is not clear if traditional

DMARDs had included corticosteroid as well or whether the data were limited to Methotrexate

only.

In an article published by Salmon et.al. (2016), the authors concluded that factors predictive of

serious infections include: age, history of previous serious infections, diseases such as diabetes

and a lower number of previous anti-TNFα therapies. However, on multivariate analysis, only

a history of previous serious or recurrent infections (HR 1.94, 95% CI 1.18 to 3.20, p=0.009)

and age (HR per 10-year increase 1.44, 95% CI 1.17 to 1.76, p=0.001) were significantly

associated with a higher risk of serious infections. [47]. Subesinghe et.al.(2018) used data from

the British Society for Rheumatology Biologics Register -Rheumatoid Arthritis, to follow up

5289 subjects with at least one SI 19 431 patient-years. [49].

Tarp et.al. (2015), in a meta-analysis of 106 trials found that, compared with traditional

DMARDs, standard-dose biological drugs and high-dose biological drugs were associated with

an increased risk of serious infections, although low-dose biological drugs were not. [57].

In another study by Singh et.al. (2015), the authors performed a systematic review and meta-

analysis of patients with RA recorded in Copenhagen University Hospital. They identified 106

trials that reported serious infection among patients who were taking biologics. They concluded

that, of traditional DMARDs, standard-dose biological drugs and high-dose biological drugs,

only high-dose biological drugs were associated with an increased risk of serious infections. In

their analysis, low-dose biological drugs and csDMARDs were not. [57].

In a study by Kawashima et. al. (2017), the impacts of the long-term use of biologic agents on

serious infection were investigated. The authors showed that the incidence rate of serious

infections was not significantly different between biologics-treated and non-biologic or

23

csDMARDs-treated patients [48]. Prednisolone usage (1-4 mg/da) was significantly associated

with serious infections [48]. Curtis et.al. (2016) in their study among 3355 RA patients in the

sustained remission group and 3912 in the sustained LDA group, found that patients in

sustained remission have a lower risk of serious infections compared to those in sustained LDA

[31]. Diederik et.al (2017) in their study compared the effects of TNF inhibitors (TNFi) and

rituximab (RTX) on SI rate. The analysis included patients registered in the British Society for

Rheumatology Biologics Register (BSRBR)-RA. A total of 3419 patients who were taking

tumour necrosis factor inhibitor (TNFi) and 1396 patients who were taking rituximab (RTX)

were compared. Patients contributed almost 2765 and 1224 person-years (pyrs), respectively.

The risk of SIs was comparable in RA patients using rituximab or a TNFα, in the first year [52].

Altogether, it is difficult to reconcile the conflicting information that has arisen from many

disparate studies of infections complicating RA, since the patient cohorts are not always

comparable and the drugs under evaluation differ in respect to class or family, dose, duration

of therapy and adjunctive agents. Furthermore, the infections are not always well defined. Data

pertaining to deaths as a result of SIs tend to be limited or scanty. Assuming very little

difference in RA cohorts and that SIs can be relied upon, despite somewhat differing

definitions, the following conclusions can be drawn:

Higher rates of SIs are encountered in RA per se, irrespective of treatment.

SIs are a function of RA disease activity.

Corticosteroids are a potent risk factor for SIs. No safe dose has been defined.

Current widely used csDMARDs, such as HCQ, SAS, MTX and LEF, confer only a

modest risk for SIs.

SIs are increased in the first year of therapy with a bDMARD and rates taper,

thereafter, but probably remain above background risk throughout treatment.

1.11 Organisation of this thesis

This thesis interpolates materials from one paper by the authors Dr. Hamid Ravanbod, Dr Jalal

Jazayeri, Dr Graeme Carroll and Professor Ken Russell. In Chapter 2, the medical literature

concerning serious infections in rheumatoid arthritis is reviewed. In this chapter strategies to

prevent serious infection in RA are discussed. In total, 3,324 articles were reviewed to form

24

this chapter. Among these articles, 31 studies met the selection criteria such as large population

size, heterogeneous populations, and English language with adolescence RA.

In Chapters 3 and 4, there are descriptive and inferential analyses of the impact of all available

anti-RA medications on serious infections in different organs. The data for this analysis have

been gathered from the Australian Rheumatology Association Database (ARAD). Participants

with rheumatoid arthritis provided information for the database in response to structured

questionnaires. Also, some equations are generated to predict the risk of infection based on

some well-known cofactors. An example of the questionnaire is provided in the appendix.

Chapter 4 presents the inferential analysis of the RA and organ infections with some of the

potential risk factors among the Australian population.

In chapter 5, serious infection with all its potential risk factors is discussed and analysed in

detail. Globally, serious infection (SI) is still the main cause of death in RA and investigating

the basis for SIs is important because of the risk of immediate mortality, ongoing morbidity,

and health economic burdens. Moreover, an increased understanding of SIs may lead to the

development of improved strategies for prevention.

The risk of serious infection pertains to most if not all organs in RA is estimated to be in the

order of 5 per 100PYs in RA overall (35). It is important to know the most common sites and

the most common pathogens for these infections.

A thorough review has been undertaken to identify arrange of risk factors, which include

pathophysiology of RA, medications and immunodeficiencies. There is also a discussion of the

risk of SIs in RA, potential risk factors and a concise summary concerning the contributions of

csDMARDs and bDMARDs to SIs.

1.12 Hypotheses to be examined in this thesis

Infection (including serious infection) occurs commonly in RA worldwide.

Regarding medication-induced infection and serious infections in RA, bDMARDs are

the safest available anti-RA medications

25

There are several detectable risk factors for SI in RA and anti-RA medications are

amongst these risk factors. Genetic factors can also impact the risk of serious infection

among patients with RA.

Infections and Sis among patients registered with ARAD are common and taking anti-

RA medicines can impact the frequency of these infections.

The risk of infection is different between different patients and it is possible to predict

this risk based on cofactors.

The frequency of self-reported infection in different organs varies in ARAD

participants.

Commonly used anti-RA medications have different impacts on the risk of infection in

different organs.

1.13 Significance of undertaking this review

Treatment in RA targets pain relief, reduction of joint damage and improved joint function. A

growing number of medications are available for the treatment of RA. They are categorised as

csDMARDs and bDMARDs. Usually treatment plans can change depending on the disease

activity, severity of symptoms, signs and prognosis and sometimes expected medication side

effects (25). Conventional synthetic disease-modifying anti-rheumatic drugs (csDMARDs)

interfere with the immune system to suppress it, indirectly and non-specifically. On the other

hand, biologic DMARDs specifically suppress a pathway in the immune system. There is an

increasing trend to use bDMARDs rather than rely on csDMARDs alone in RA (25) (38).

In this study, the contributions of csDMARDs and bDMARDs to infections, including Sis, are

evaluated and compared. We further categorised infections according to organ type and

severity and compared the impact of medication on the development of infection in each

organ, separately.

2. Methods

A systematic review of all available articles concerning serious infection was conducted.

ARAD reports (patients’ responses to questionnaires) from 2001to 2014 were tested under

several descriptive analyses and results were analysed statistically by Chi-square test, and

Fisher test where appropriate or by means of logistic or multinomial logistical regression

26

modelling. Demographic, disease specific, treatment and infection record data were extracted

from the ARA Database which contains details of a cohort of 3569 RA patients (960 males and

2609 females), who had completed related questionnaires 28176 times (during 2001-2014).

Among the 3569 patients, 459 patients were eliminated because they had filled out the

questionnaire only once. We were therefore left with 3110 patients. After deducing eight

duplications and eliminating accordingly, there remained 27709 visits from 3110 patients.

In Chapter 3, using filtering procedure in SPSS, the whole data was divided into two groups;

those who were just taking bDMARDs without any csDMARDs and patients and those who

were taking just csDMARDs without taking any bDMARDs. The demographic distributions of

the risk factors were assessed and compared utilizing SAS software.

Furthermore, the whole dataset was tested by SAS software to work out the impact of each anti-

RA medicine on the frequency of different types of organ infection and the results are reported

in Chapter 4. For this purpose, data were fitted in the logistic regression model and results were

tested by using Chi square and Fisher tests. The odds ratio of effectiveness is also calculated

for the medicines that have significant effects on infection. Furthermore, each type of organ

infection was categorised based on the severity of the infection.

Chapter 5 presents more complex assessments around the incidence of serious infection, its

demographic characteristics, and potential risk factors. Patients’ reports among 27709 visits

from 3110 patients during 2001 to 2014 were searched for evidence of hospitalisation or IV

infusion for infection. Resultant data were tested by inferential and descriptive analyses and

odds ratios for potential risk factors were calculated.

In this section, a few equations were also created to help predict the likelihood of serious

infection in a single patient based on known risk factors.

3. Summary of the Results

In the systematic review of 3324 articles, only 31 articles met the criteria for the review.

Descriptive analysis of ARAD revealed the mean age amongst participants with RA was 61.47.

In the group taking csDMARDs the mean age was 59.24 years and, in the group taking

bDMARDs, the mean age was 62.62 years. The Wald P-value of the differences between both

groups of Ras, based on risk factors, is very large. Taking csDMARDs alone and bDMARDs

27

alone were associated with statistically significant difference in the rates of heart infection, lung

infection, urinary tract infection, and GIT infection.

During 2001 to 2014, the most frequent infections amongst RA participants was related to the

eye, ear, nose and throat or EENT (14.75%). Cyclosporine and prednisolone were associated

with increased rates for all types of infections, whereas bDMARDs, such as adalimumab, were

associated with a reduction in the frequency of nail/skin infection.

Just under 3% of the ARAD cohort reported SIs. Adalimumab and etanercept were the most

commonly used bDMARDs in patients who reported SIs, but they were also the most frequently

prescribed agents in this category. Age, gender, alcohol consumption, medication, diabetes,

kidney disease, liver disease, heart attack and, sometimes, previous coronary artery bypass

grafting (CABG) were each implicated in higher rates of SI.

3.1. Strengths of this research

The research reported here takes a more comprehensive approach to infections in RA, because

it does not focus exclusively on serious infections but, rather, includes self-reported infections

of diverse severity and categorises these infections according to internally defined levels of

severity. Emphasis has been placed on an anatomical and organ-based approach, so that the

factors responsible for greater numbers of infections in specific organs and anatomically

defined systems can be examined methodically. Although there are several studies comparing

csDMARDs and bDMARDs for the development of serious infections, these mainly focus on

SIs and so are narrower in scope. Moreover, they lack consensus. This study has the potential

to provide a more comprehensive analysis and assist in the acquisition of greater agreement. In

the inferential analyses, backward regression was performed. Accordingly, the effect of

medications has not only been examined separately, but also, the compounding impacts that

combinations of medications may have on each other have been considered.

In this study, a large sample has been analysed (28176 patient-visits). This provides

considerable statistical power and likely more generalisability regarding the findings. There is

also a section on descriptive analysis. This section provides a better understanding of the risk

factors and status of the study population and helps to ascertain how far the findings can be

generalised. Importantly, this large sample was followed for almost fourteen years (2001 to

28

2014), which allows longitudinal effects to be more readily captured, thereby strengthening the

estimations of SIs calculated in terms of person-years.

3.2. Limitations

The main limitations of this study relate to the design of the ARAD questionnaire and

the inherent weaknesses associated with the capture and use of self-reported data.

Participants may not have known that some illnesses suffered were in fact infections

or they may not have remembered to report them when completing a questionnaire

some months later for example. Furthermore, it was not possible to validate reported

information, since family practitioner confirmation, hospital records and

microbiological and other pathology and imaging were unavailable. Reporting in

respect to the nature of medications is likely to be reliable, but it is not possible to

assess medication compliance. There are also statistical limitations affecting data analysis.

In the descriptive analysis, reliance was placed on Chi-Square and P-value calculations. In

addition, the reliability of the conclusions in descriptive analysis is compromised by the fact

that participants needed to answer a very general question.

4. Conclusion

Based on the systematic research, SI is far more common in RA than in the general population.

Anti-RA medications have different impacts on this infection, with a huge impact from

corticosteroids followed by bDMARDs and csDMARDs. The time of prescribing bDMARDs

in the first year or after that, a higher dosage of bDMARDs and combination therapy with

bDMARDs all increase the risk of infection.

Compelling evidence has proved that in Australia, RA can increase risk of infection. Although

it seems that in the Australian database (ARAD data which is used in the current study) overall,

csDMARDs alone during prescription can evoke higher rates of infection than bDMARDs

alone; this difference is statistically significant only in self-reports of heart infection, lung

infection (P value 0.0156), urinary system infection (P-Value 0.0002), and GIT infection.

Both csDMARDs and bDMARDs are associated with higher risk of infection in RA. All in all,

without isolating the first year of taking bDMARDs, it seems that bDMARDs causes less

29

infection, but more serious infection. The impact of various medications on infection depends

on the type of infection and severity of infection.

Serious infection can occur in almost 2.92% of anti RA treatments in Australia, and for females

this risk starts in younger ages. Also, in Australia, the majority of patients who develop SIs are

taking biologics. It seems that previously taking bDMARDs is a higher risk for patients who

are currently taking bDMARDs and those who have never taken this medication. In addition,

among different risk factors, which are tested in this review, smoking has a significant

connection to the seriousness of infection.

In the next chapter, a comprehensive literature review has been conducted, not only to collect

the latest published information in the field but also to investigate the prevalence and status of

infection and SI in RA patients and to explore the potential risk factors., including anti-RA

medications. In addition, the implications of such infections in clinical practice are also

explored and discussed.

30

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pp. 258–265, Jul. 2015, doi: 10.1016/S0140-6736(14)61704-9.

[57] S. Tarp et al., “Risk of serious adverse effects of biological and targeted drugs in

patients with rheumatoid arthritis: a systematic review meta-analysis,” Rheumatology

(Oxford), vol. 56, no. 3, pp. 417–425, 01 2017, doi: 10.1093/rheumatology/kew442.

37

CHAPTER 2

Infections in rheumatoid arthritis and strategies for their

prevention: A review and discussion of implications for

clinical practice

38

Abstract

Objectives: Serious infections (SIs) in rheumatoid arthritis (RA) are common and may be life-

threatening. The goal of this chapter is to present a systematic review of the present literature

regarding prevalence and status of infection and SIs in RA and explore potential risk factors

including anti RA medications.

Methods: A systematic review was performed that included multiple databases, viz. PubMed,

Medline, Scopus, and Google Scholar. Search terms used were ‘Rheumatoid Arthritis AND

infection’. Searches were limited to the title of articles, human subjects and non-juvenile

arthritis and to those articles published in English.

Results: In total, 3,324 articles were found. After removing duplicates, 825 articles remained

for further screening, from which 141 articles were selected. These were further assessed and

110 were then excluded because 31 articles were case reports, 35 focused on young subjects

(<16 year and 44 studies focused on non-serious infection. Overall, only 31 studies met our

selection criteria.

Conclusion: SIs are far more common in RA than in the general population. Corticosteroids

are associated with an appreciable increase in SI risk. Most commonly used and currently

favoured synthetic DMARDs confer a small or no risk, biologic DMARDs confer moderate

risk in the first year of therapy and then a diminishing risk, thereafter, and higher dose biologic

or combination biologic therapy should be avoided since the SI risk is unacceptably high.

Undetectable mannose binding lectin (MBL) is a major risk factor for SI in RA, comparable to

prednisolone.

Keywords: infections, arthritis, serious infections, risk factors

39

1. Introduction

Rheumatoid Arthritis (RA) is a chronic, systemic autoimmune disorder. It affects over 1% of

the world population and confers significant economic burdens, not only on the individual, but

also society as a whole. The rheumatoid patient is exposed to many complications including

infections, cardiovascular disease and malignancy [1, 2]. Among these, serious infection (SI)

(infection that is life-threatening or fatal, requires hospitalization or intravenous antibiotics or

results in severe disability) is of special importance, because of the immediate risk of mortality,

ongoing morbidity and because of the health economic implications. Serious infections are still

the number one cause of death in RA, globally [2]. The most prevalent infections in RA include

bronchopulmonary, urogenital, soft tissue and skin, bone/joint sepsis and gastrointestinal

infections [1]. Infections in the lung and urogenital system or generalized sepsis in a patient

with RA is common and may be fatal, with incident frequencies up to ten times that in the

general population [2,3]. Less commonly, infection in RA may affect the central nervous

system (CNS), the cardiovascular system or the lymphatic system [4].

It is clear that in RA, there is a marked increase in rates of SI from less than one per 100PYs in

the normal population to around 5 per 100PYs in RA overall [5]. Bronchopulmonary, urogenital

and skin infections are the most common SIs. The main pathogens are S. pneumoniae, S. aureus,

gram-negative bacilli and anaerobes [6]. Some studies have investigated diverse risk factors,

such as RA disease pathophysiology, RA medication and immunodeficiency including

Mannose Binding Lectin (MBL) deficiency as potential causes for this higher incidence rate

[2,7-15] A failure to appreciate this de novo increase in frequency of SIs in RA can give rise to

a perception of more frequent SIs in csDMARDs and bDMARDs treated RA.

In this review, we have searched the literature in order to determine and analyse (i) the extent

to which RA patients are predisposed/susceptible to developing SIs (ii) the potential risk factors

associated with SIs in RA patients, and (iii) whether the rate of SIs is higher in patients who are

on medications, such as anti-TNF-α, and DMARDs. The goal was to identify, categorise and

evaluate the main causes of SIs in RA. In addition, methods and possible strategies to minimise

or prevent infection in RA and, in turn, reduce the rate of hospitalisation and out-of-hospital

treatments will be discussed.

40

2. Methods

2.1. Search strategy and selection criteria

A systematic review was performed that included multiple databases, viz. PubMed, Medline,

Scopus, and Google Scholar. Search terms used were ‘Rheumatoid Arthritis AND infection’.

Searches were limited to the title of articles, human subjects and non-juvenile arthritis and to

those articles published in English. The search timeframe was 1996-2015. Articles were only

included in this review if they investigated or discussed ‘infection in Rheumatoid arthritis’

specifically focusing on SI in patients over 16 years of age. Eleven cohorts, four reviews, one

cross-sectional study, one observational prospective study, five case-control studies, five

randomized controlled trials (RCT), three systematic reviews and two meta-analyses were

included. Even though diverse methodologies and a relatively long timeframe mitigating

against embracement of the modern biologic era were used, the advantages of inclusivity were

deemed to outweigh the inconsistencies in methodology. A PRISMA chart has been constructed

to show the systematic selection of the articles (Tables 2.1 and 2.2).

3. Results and discussions

3.1. Study selection

In total, 3,324 articles identified through PubMed, Medline, Scopus and Google Scholar

repository were found. After removing duplicates, 825 articles remained, from which upon

further screening, 684 articles were culled due to one or more of the following: (i) population

size was very small or the studies were not within the designated time frame; namely 1996 -

2015 (ii) heterogeneous populations, which made it difficult to identify infections pertaining

explicitly to RA and (iii) the language in which the articles were published was not English.

41

Figure 2. 1 Prisma chart showing search results and article selection

42

Table 2.1. Relationship between medications and infections in RA leading to defective cell-mediated immunity

Medications Bacteria Fungal Protozoan Viral

Corticosteroids, Cyclophosphamide and Azathioprine

Gram-positive: Staphylococcus aureus

Streptococcal spp

Nocardia spp

Gram-negative: E.coli

Klebsiella pneumoniae

Pseudomonas aeruginosa

Other Enterobacteriaceae

Candida Albicans

Aspergillus Spp

Human herpes virus

Measles virus -

Varicella zoster virus -

Adenovirus

Cytomegalovirus

Epstein-Barr virus

-Corticosteroids -Cyclophosphamide -Other alkylating agents -Azathioprine -Methotrexate -Cyclosporin A

Nocardia Spp

Listeria monocytogenes

Salmonella Spp

Mycobacterium Spp

Histoplasma capsule

Coccidioides immiti

Cryptococcus neoformans

Pneumocystis carina -

Strongyloidiasis stercoralis

-Azathioprine -Corticosteroids (high dose) -Cyclophosphamide

Haemophilus Influenzae

Streptococcus pneumoniae

43

Table 2.2. Summary of studies showing the rate of SIs in patients treated with synthetic and biologic DMARDs

*PYs- Patients years - For the period 1955 – 1994, rates may have declined over time #Mean follow-up (period of observation) was 1.4 years ^ ETA, INX and ADA

Study Group Study Design Rate of SIs per 100PYs (synthetic

DMARD)

Rate of SIs per 100PYs (biologic

DMARD) Reference

Galloway J B, et.al. (2011)

BSRBR Registry Review (UK) 3.20 (csDMARDs controls) 4.20 (all Bx DMARDs) ^ [63]

Doran, et.al. (2002) RA vs Population Controls 19.23 per 100 PYs* NA [30]

Listing, et.al. (2005) German RABBIT Registry Review (GDR)

2.28 (csDMARDs controls) 6.15 (INX) and 6.42(ETA) [17]

Lacaille, et.al. (2008) Large RA cohort (n=27,710) 4.5 -5.5 per 100PYs NA [27]

Greenberg, et.al. (2010) MTX vs controls (n=7,971) 3.1 – 3.2 per 100 PYs# NA [24]

Askling J, et.al. (2006) Swedish Biologics Register - 4.5 (INX, ETA and ADA) [39]

Atzeni, et.al. (2012) GISEA Registry (Italy) NA 3.18 (for INX, ADA and ETA) [48]

van Dartel SAA, et.al. (2012)

DREAM Registry (Netherlands) NA 2.91 (over 5 years) [14]

44

Nolla, et.al. (2000) reported that among RA patients, during 1990-1998, the most prevalent

bacterial infections were Staphylococcus aureus and Streptococcus pneumonia [16]. Both are

Gram-positive cocci. They also showed that skin infection was the principal source of infective

disease in RA patients and S. aureus was among the most important pathogens for septic

arthritis. S. pneumoniae was also a relevant pathogen in septic arthritis in RA patients, but it

was less frequent. The majority of cases of septic arthritis in RA were mono-articular with

involvement of the knee, elbow and wrist most often reported (Tables 2.1 and 2.3) [6].

Table 2.3. Serious Infection rates for diverse biologic agents (numbers per 100 PYs)

Anti TNF 4.90, [95%CI 4.4-5.4, 57 trials], n = 26492, Cum. Exp. = 29429 years

ABT 3.04, 95%CI 2.49-3.72,11 trials, n = 5953, Cum. Exp. = 6070 years

RITUX 3.72, 8 trials, n = 2926, Cum. Exp. = 2687 years

TCZ 5.45, 13 trials, n = 5547, Cum.Exp. = 4522 yrs.

TOF# 2.93, 14 trials, n = 5671, Cum.Exp. = 12,664 yrs.

ETA 4.06

ADA 5.04

GOL 5.31

INX 6.11

CERT 7.59

# denotes long term extension studies, Cum. Exp. denotes cumulative exposure. TNFi = Tumour Necrosis Factor inhibitor, ABT = Abatacept, RITUX = Rituximab, TCZ = Tocilizumab, TOF = Tofacitinib. Data extracted from Strand V et.al, Arthritis Research and Therapy 2015;17:36 [64]

3.3. Risk factor categories

Many studies have shown a greater than two-fold increased risk of SI in RA patients [1,2,7-20].

There are several contributing factors involved. Briefly these include:

The pathobiology of the disease itself;

Chronic comorbid conditions, such as diabetes mellitus, heart failure, lung or kidney

disease, bronchiectasis and alcoholism;

Age: elderly-onset RA patients are more vulnerable;

Drug dosage, duration of treatment, and side effects: it has been shown that some drugs

at high dosage and prolonged treatment therewith confer significant risk, especially in

older patients with RA;

45

The immunosuppressive nature of at least some of the drugs used to treat RA; these

include a range of medications, such as corticosteroids, synthetic DMARDs, and

biologic DMARDs;

Genetic factors: these include mannose binding lectin (MBL) deficiency, which has

recently been shown to contribute significantly to serious infections in RA and is the

commonest form of innate immune deficiency. In addition, hypogammaglobulinemia

(common variable immunodeficiency or CVID and selective IgA deficiency or SIgAD),

which are much less common but may, nevertheless, occasionally contribute to SIs.

Roberts, et.al. (2015) have reported immunoglobulin deficiency after rituximab for

lymphoma and rheumatoid arthritis [21,22]; and

Lifestyle factors, such as poor diet, reduced physical activity, smoking, and alcohol

consumption.

3.4. The impact of medications (non-biologics)

A summary of studies showing the rate of SIs in patients treated with synthetic and biologic

DMARDs is shown in Table 2.2. Galloway, et.al. (2011) showed the SI incidence rates to be

42/1000 and 32/1000 patient-years for anti-TNF and csDMARDs respectively. And the risk did

not differ significantly between the three agents; adalimumab, etanercept and infliximab. The

risk was highest during the first six months of therapy [23]. Greenberg et.al. showed that a

major risk factor for infection is the immunosuppressive therapy used. They also showed that

newer therapies for RA may lead to increased rates of infection by pathogens, such as

Mycobacterium tuberculosis [24]. In another study, to examine the association of methotrexate

(MTX) and tumour necrosis factor (TNF) antagonists with the risk of infectious illness,

Greenberg et.al. showed that MTX, TNF antagonists and prednisone at doses >10 mg daily

were associated with increased risks of infections overall. Low-dose prednisone and TNF

antagonists (but not MTX) increased the risk of opportunistic infections [24]. Van Dartel, et.al.

(2013) showed the incidence rates for a first serious infection in patients with RA per 100

patient-years were 2.61, 3.86 and 1.66, for adalimumab, infliximab and etanercept, respectively

[14]. The impacts of other medications are discussed below.

46

3.5. Corticosteroids

Corticosteroid (CS) use is a major contributor to SIs in RA. The effects are dose- and duration-

dependent [25]. Both high dosage and the duration of CS treatment confer significant risk,

especially in older patients with RA. The infection risk has been clearly shown to be dose

dependent, but whether there is a minimum safe dose with respect to serious infection risk is

unclear. Of considerable concern, a patient who has taken at least 5 mg of prednisolone daily

for three months has a 30% chance of hospitalization due to infection [26]. Therefore, in the

treatment of RA, in order to minimize the risk of an SI, the lowest possible dose of CS for the

shortest possible duration should be prescribed [25]. Increasingly, with the advent of more

effective synthetic and biologic DMARDs, the scope to progressively taper and switch from

CS to DMARDs alone has increased.

Listing, et.al. showed that there is evidence that glucocorticoids (GCs) increase the risk of

serious infections up to 4-fold in a dose- dependent manner. In addition, anti-TNF-α inhibitors

increase the serious infection risk up to two-fold. The risk of infection is substantial if patients

need higher dosages of GCs in addition to treatment with anti-TNF-α therapy. It was

recommended that such combination therapies should avoided, if possible, especially in

patients with additional risk factors such as older age or comorbid conditions [20].

3.6 Synthetic DMARDS

Whether synthetic DMARDs at recommended doses contribute to infections in RA is uncertain

and still a matter of conjecture. Lacaille et.al. (2008) conducted a retrospective, longitudinal

study of a population-based RA cohort in British Columbia, Canada (from January 1996 to

March 2003). In this study, a total of 27,710 RA patients provided 162,710 person-years of

follow-up. The authors showed that 92% of patients had at least one type of mild infection and

18% had a SI. Corticosteroids were shown to be unequivocally implicated in Sis, with an

adjusted rate ratio of 1.9 (CI 1.75-2.05) [27]. Importantly, these investigators showed that use

of DMARDs without corticosteroids was not associated with an increased risk for SI [adjusted

rate ratio of 0.92 (CI 0.85-1.00)]. They concluded that, unlike corticosteroids, synthetic

DMARDs, in general, do not elevate the risk of serious infection in RA. It is, however, worth

noting that, in their study, the SI rate for RA patients receiving cyclophosphamide (CYC) was

19.8 to 39.4 per 100 patient years of exposure, which is well above the rate seen for SIs in RA

47

overall (~ 4.4 to 5.5 per 100 Pys in their study), suggesting that some immunosuppressive

DMARDs might still be an exception to the rule. CYC of course is now rarely used as a

DMARD in uncomplicated RA.

Dixon et.al. (2016) [10] conducted a prospective study of participants in the British Society for

Rheumatology Biologics Register (BSRBR). They compared synthetic DMARD-treated

patients (n=1,354) with anti-TNF (biologic DMARD)–treated patients (n=7,664). After

adjustment for baseline risk, it was concluded that anti-TNF therapy was not associated with

increased risk of SI overall, compared with synthetic DMARD treatment. However, they did

show that anti-TNF therapy was associated with serious skin and soft tissue infections [10].

The impact of other DMARDs on the development of infections in RA patients has also been

investigated. The medication-related findings are set out below:

(i) Cyclosporine (CyA, Neoral), which is a fungal peptide, inhibits interleukin-2 and

proliferation of T-cells and promotes apoptosis in macrophages. When used in combination

with methotrexate for treatment of severe RA, CyA can increase the rate of urinary tract

infection (UTI) [7,28].

(ii) Methotrexate (MTX, Methoblastin)-related infections are varied and appear to be dose-

dependent. Because MTX is commonly used in combination with other drugs, it is often

difficult to assess the contribution of MTX alone. There have been several studies which have

investigated MTX and its role in the development of infection. For example, in a randomized

controlled trial (RCT), incorporating 571 RA patients who were treated with a mean MTX

dosage of 10.8 mg/week, without concomitant biological DMARDs, Sakai et.al. (2011) showed

that MTX did not confer an increased risk for serious infections in RA patients [15]. However,

there were limitations to this study, not least the lower mean dosage of MTX than that

commonly used in the United States, Australia and Europe. Boerbooms et.al. (1995) in a six-

year open prospective study and in a 12-month randomized double blind trial comparing MTX

with AZA, showed that the infection rate during MTX treatment was higher in severe RA than

in moderate RA. Once again, this highlights the likely contribution of inherent disease activity

to SI risk [29].

Doran et.al. [30] reported that the hazard ratio for SIs in RA patients treated with MTX was

0.96 while Greenberg et.al. (2010), who followed a total of 7,971 patients, showed that the rate

48

of infection per 100 person-years was increased among MTX users. They expanded their

studies to TNF antagonists and prednisolone and concluded that both MTX and Prednisolone,

at doses more than 10 mg daily, were associated with increased risks for infections overall [24].

Bernatsky et.al. (2007) in a cohort of 23,733 RA patients, showed that methotrexate increases

the rate of pneumonia (RR: 1.16, 95% CI: 1.02–1.33) [7] (Table 2.2).

3.7 The impact of medications (biologics)

Due to their modes of action and the fact that they target cells involved in the immune system,

there is an ongoing concern that these medicines may potentially increase the risk of SI in RA

[31-33]. In this study, the orally active tofacitinib, a tyrosine kinase inhibitor (TKI) is included,

but other TKIs in development have been excluded. We will now consider the groups of

biologic agents in turn.

3.8. TNF-α Inhibitors

Biologics such as Adalimumab, Certolizumab, Etanercept, Golimumab and Infliximab inhibit

TNF-α and thereby modulate the inflammatory process in RA. However, TNF-α is also

important for defence against common and uncommon infections. When TNF function is

inhibited, there is increased risk of diverse infections. These include (i) bacterial infections such

as Gram-positive and Gram-negative bacteria, Mycobacterium tuberculosis, atypical

mycobacterial infection, Listeriosis monocytogenes, (ii) viral infections e.g. cytomegalovirus

(CMV), and (iii) fungal infections e.g. Pneumocystis jirovecii, aspergillosis, histoplasmosis,

coccidioidomycosis and cryptococcal infections [34,35].

The evidence for re-activation of Mycobacterium tuberculosis infection in RA patients has been

discussed in at least two different studies [36]. All TNF inhibitors have a propensity to re-

activate tuberculosis. Infliximab appears to confer greater risk than Etanercept [31,34]. There

is also a significantly increased rate for Hepatitis B virus reactivation, especially when

immunosuppression is diminished or withdrawn. Therefore, a combination of treatments with

hepatitis B (HB) antiviral agents in conjunction with TNF inhibitors is suggested in patients

with evidence of previous HB infection [35]. In addition, there is a known, albeit small, increase

in risk for herpes zoster and a very small risk for leukoencephalopathy (PML) in TNF inhibitor

recipients [36,37]. Historically, the greatest risk for PML has been associated with use of

Natalizumab in multiple sclerosis, but the risk for TNF blockers and Rituximab in RA is not

49

negligible and will require further study to accurately quantify [31,38] and predict

susceptibility. A meta-analysis in 2006 revealed that anti- TNF treatment can also increase the

risk of serious pyogenic infections [8]. The German Biologics Registry investigators found the

risk of serious pyogenic infection to be two-fold [8,10,17]. In contrast to the above studies, the

BSRBR and the Swedish Arthritis Treatment group have reported that a non-significant relative

risk ratio exists for severe infections in patients treated with TNF inhibitors [10,39]. These

differences may be explained by the longevity of the studies. SIs appear to be much more

frequent within the first year of usage / observation. Thus, long term follow-up studies may

report lower rates of SI compared to short-term studies.

It is worth noting that van Dartel, et.al. (2012) found that Adalimumab and Infliximab conferred

higher, albeit similar risks for serious infection in RA patients, whereas Etanercept conferred

lower risk [14]. In addition, Trung, et.al. (2013) in their studies provided a table (Table 2.3) to

categorize the risk of infection with different anti-synovitis medications. In that study, it is

reported that Etanercept, Infliximab and Golimumab were associated with the highest rates of

serious infection. It was shown that Etanercept, Adalimumab, Abatacept and Tocilizumab were

associated with opportunistic infections and tuberculosis (TB) [40]. In a systematic review by

Greenberg et.al. (2002), it was demonstrated that some of the anti-TNF medicines increased

the rates of opportunistic infections while traditional immunosuppressants such as

corticosteroids and synthetic DMARDs were major risk factors for serious infection in RA

(Table 2.2) [2]. Moreover, Dixon et.al. (2006) in an observational study of a large cohort of RA

patients (n=7664) enrolled in the BSRBR, emphasized the important role that TNF has in host

defence in the skin and soft tissue [10]. In their study, patients who were treated with anti TNF-

α agents, as compared to synthetic DMARDs, developed more serious skin and soft tissue

infections. However, importantly, they found that the overall risk of serious infection for anti-

TNF medicines compared to synthetic DMARDs was the same in both groups [10].

3.9. Abatacept (ABT), Rituximab, Anakinra, Tofacitinib and Tocilizumab

ABT safety has been evaluated in several long-term extension (LTE) studies (duration usually

2-3 years). Within this timeframe, in respect to SIs, ABT performs well with SI rates of 1.6 to

3.6 per 100 PYs of treatment in age unstratified RA recipients [41,42]. Given that up to 60% of

these patients were also taking corticosteroids in doses not always clearly defined, the rates are

low for the most part and somewhat lower than for most other biologic agents (Table 2.3). The

50

elderly is more vulnerable as is true in respect to SIs in general and especially after there has

been an antecedent hospitalization for infection, whereupon rates of 26.5 per 100 PYs apply for

ABT and 36.1 for ETA [42,43]. Lahaye, et.al. found that SI rates in Abatacept recipients rose

progressively from 1.73 per 100PYs in persons under 50 to 4.65 in persons 50-64, 5.90 in

persons 65-74 and 10.38 per 100PYs in persons equal to or greater than 75 years of age [43].

Thus, whilst relatively safe in the young and up to extended middle age, the SI rates rose

concerningly for ABT in those over 65 years of age and especially when there has been an

antecedent hospitalization for an infection (Table 2.3).

For Rituximab, Tocilizumab and Tofacitinib, the rates of SI are comparable to those reported

for all TNF inhibitors. However, it should be noted that the cumulative exposure for most of

these agents, like the TNF inhibitors is limited and mostly does not exceed 2 years.

Furthermore, not enough additional data is available to evaluate associated SI risk factors in

these cohorts. For example, a breakdown for age, corticosteroid dosage and important

comorbidities such as diabetes, neutropoenia and lymphopenia is not available sufficiently

often to allow these parameters to be taken fully into account in respect to their independent or

additive effect on SI risk.

In the case of Infliximab (INX) and tocilizumab (TCZ) there is some data, which suggests that

SIs are dose dependent with higher rates seen with higher doses [44]. This has already been

referred to in respect to INX. For TCZ, SI rates of 3.4 per 100 patient’s years (100PYs) were

observed for comparator groups, 3.5 per 100 PYs for TCZ 4 mg/Kg and 4.9 per 100 PYs for

TCZ 8 mg/Kg [45]. In contrast, for Rituximab (RITUX), the SI rates for 500 mg x 2 versus

1000 mg x2 at 24-week intervals were similar at 2.62 and 1.96 SIs per 100PYs [46]. There is a

limitation in this systematic review, and it is not clear if discussed doses are adjusted for Body

mass index (BMI) or not.

Salliot, et.al. [47] investigated the risk of SIs during treatment of RA with rituximab, abatacept

and anakinra. SI frequencies were investigated using meta-analyses of randomized placebo-

controlled trials. It is important to remember that this approach inevitably is short term due to

the design of the trials. Moreover, sicker patients are often excluded. Nevertheless, no

significant increase in the risk for SIs attributable to these biologics was observed. The authors

concluded that, based on these randomized placebo-controlled trials, rituximab, abatacept and

51

anakinra have a relatively good safety profile for SIs. However, an increased risk for SIs was

observed for high doses of anakinra (⩾100 mg per day) in patients with comorbidities.

3.10. Risks associated with combination therapies

It is now common practice to combine synthetic DMARDs with biologic DMARDs, since

efficacy is greater. Some studies have shown that synthetic DMARDs in combination with anti-

TNF-α increase the rate of SIs. For example, Atzeni, et.al. (2012) in a case control study

examined 2,769 patients with long term RA [48]. Treatment with corticosteroids and other

synthetic DMARDs in combination with various anti-TNF agents, viz. Infliximab (INX),

Adalimumab (ADA) and Etanercept (ETN), was investigated. The authors found that the risk

of SI was significantly different across these medication groups (p<0.0001). In these patients,

the following factors were identified as significant infection predictors: (i) The concomitant use

of corticosteroids (p<0.046 with hazard ratio (HR) of 1.849) (ii) concomitant DMARD

treatment during anti- TNF therapy (p=0.004 with HR of 2.178) (iii) advanced age at the start

of anti-TNF treatment (p<0.0001 with HR of 1.03) and (iv) the use of INX or ADA rather than

ETN ( p<0.0001with HR 4.291 for INX vs ETA and p=0.023 with HR 1.942 for ADA vs ETA).

In this study the authors also found that treatment with anti-TNF was associated with a small,

but statistically significant risk of SI (HR of 1.03 and P < 0.0001). In Atzeni et.al’s study,

disease duration and the disease severity score were not found to be predictive of serious

infection [48].

In a systematic study by Campbell, et.al. (2011), the effect of tocilizumab (TCZ), in

combination with MTX, in patients with RA was investigated [49]. The researchers concluded

that this combination treatment for RA is associated with a small, but significantly increased

risk of adverse effects and infections. Their meta-analysis revealed that tocilizumab 8 mg/kg

compared with controls increased the risk of infection. This risk is comparable with other

biologic agents, although the risk of serious infection may be less than that for TNF antagonists.

Perhaps more so than any other biologic agent, the capacity of IL-6 antagonists to markedly

reduce CRP further compounds the difficulty in recognizing serious infection, since great

reliance is usually placed on the CRP concentration when determining the probability of an SI

in an unwell rheumatoid patient. Such delays may adversely affect patient outcomes.

52

3.11. Tuberculosis (TB) and non-tuberculous mycobacterial (NTM) infections

In a recent meta-analysis conducted by Winthrop, et.al. (2015), both TB and NTM infections

were shown to be increased in patients with RA who have been treated with a range of biologics

[36,50] These include Infliximab, Etanercept and Adalimumab, which target TNF-α, as well as

Rituximab, which targets CD20 receptors on the surface of B cells. All these agents have been

shown to re-activate TB and predispose to NTM infections, albeit at different rates. Infliximab

was implicated in TB and NTM infections (11 and 7 cases respectively). In contrast, in this

meta-analysis Abatacept was not shown to predispose to TB or NTM infections. It remains

important to carefully screen for latent TB, both clinically and otherwise (Mantoux skin testing,

Quantiferon GOLD testing) and where necessary, to treat these conditions appropriately before

initiating bDMARDs in RA.

3.12. Serological and other laboratory parameters that influence SI risk

Diverse cellular and serological abnormalities are known to increase susceptibility to infection.

These include neutropoenia, especially in the context of Felty’s syndrome, where high disease

activity is often a factor as well, lymphopenia, immunoglobulin deficiencies (innate and

acquired) and terminal complement deficiencies, although the frequency of Ig and terminal

complement deficiency is low or very low respectively. deficiency is far more common with

frequencies in the order of 5-8% in the population in general and 8 - 15% in rheumatoid

populations. The prevalence of serious infection in ARAD participants was 2.92 % of all patient

visits. The rate in other studies such as the study by Doran et al. in Minnesota US reported 9.6

infections/100 person-years (1). The reasons for this difference are partially due to the different

study designs, settings and therapeutic guidelines [2]. Periodontal infection occurs due to

almost twenty different bacterial species and occurs about two-fold higher in RA patients. In

addition, the prevalence of moderate to severe periodontitis in RA patients is almost 51% which

is more than age and gender matched patients with osteoarthritis (26%) [1]. Concurrent diseases

in patients with RA include depression (15%), asthma (6.6%), cardiovascular events (6%),

cancer (4.5%), and chronic obstructive pulmonary disease 3.5%.

3.13. Mannose Binding Lectin (MBL) and other immune deficiencies

Mannose Binding Lectin (MBL) deficiency is implicated in a variety of infections in neonates

and children, but less so in otherwise healthy adults [11,51-53] Mannose Binding Lectin (MBL)

53

is a component of the innate immune system. It is a carbohydrate binding protein produced by

the liver and is involved in innate immunity [54]. Structurally, this molecule comes in trimer

and tetramer forms and binds to the glycan on the pathogen’s cell surface mannose receptor.

Generally, immune-compromised patients and patients with chronic diseases or impaired

adaptive immune systems including those with Mannose Binding Lectin (MBL) deficiency

have increased risks of serious infection [11,55-57] Mannose Binding Lectin (MBL) has also

been shown to have roles in manifestations of RA disease and the development of other

complications of RA, such as cardiovascular disease [58].

In a recently reported study of risk factors for SIs in RA, both undetectable Mannose Binding

Lectin (MBL) and CS use (prednisolone at doses of 5 mg per day or more) were shown to

confer a 4-5-fold increased risk for SIs [53]. This takes on greater importance when it is

remembered that up to 15% of RA patients have undetectable Mannose Binding Lectin (MBL)

and that rates of CS use in RA, although they vary a great deal from centre to centre are still

high despite the availability of more efficacious DMARDs (up to 70%) [53]. In fact, apart from

severe neutropoenia, such as in Felty’s syndrome for example, no other laboratory marker

appears to confer greater SI risk then undetectable Mannose Binding Lectin (MBL). Common

variable immunodeficiency (CVID) is estimated to affect up to 1 in 25,000 individuals and can

be associated with auto-immune diseases including RA [59-61]. The exact risk associated with

CVID or its various disease expressions such as panhypogammaglobulinaemia, selectively

reduced immunoglobulins (e.g. IgA deficiency) and IgG subset deficiency in RA is unknown,

but given that these deficiencies are much less frequent than undetectable Mannose Binding

Lectin (MBL), they are likely to be relatively less important clinically.

Selective IgA deficiency or SIgAD, which is the most common of these immunoglobulin

deficiencies, occurs in less than 1 in 100 persons of Arabic descent and in less than 1 in 800

Caucasians in the UK. Although increased rates of severe respiratory tract infections are

observed in SIgAD persons, compared to unaffected controls (3-fold increased risk), life-

threatening infections were not recorded in this group [62] Elsewhere, risk factors predisposing

to the development of hypogammaglobulinemia and infections post-rituximab treatment have

been reported [63]. Terminal complement components C5 - C9, otherwise referred to as the

membrane attack complex also predispose to recurrent infection, especially with encapsulated

organisms, such as Neisseria, but do not appear to associate strongly with autoimmune

54

diseases and are relatively rare in Caucasians, although not in Afro-Americans and probably

not in native Africans.

3.14. Implications for Clinical Practice

The treating clinician needs to consider the following when choosing therapeutic agents for

patients with RA who are at risk for SIs:

3.14.1. Age

SIs are substantially increased in persons of advanced age -for example in a large USA

Medicare beneficiaries’ cohort, the SI rate in those over 65 was 14.2 per 100PYs compared to

4.8 in those less than 65 years of age [43]. A recently reported study by one of the authors

indicates that the risk of an SI increases by 19% for every 5 years increase in age and by 41%

for every 10-year increase in age [53]. The prescribing clinician should consider, the differing

relative risks for SIs when prescribing DMARDs for the old and the very old rheumatoid

patient.

3.14.2. Corticosteroid (CS) Use and Dosage

In RA patients, the SI risk is appreciably higher in recipients of CS. Furthermore, this risk is

most likely dose dependent. For example, in one study, a daily dose of 10 mg of Prednisolone

or more was associated with an odds ratio (OR) for an SI of 4.70, whereas a dose of 1-4.5 mg

per day was associated with an OR of 2.57 [9]. Initial use of CS at first presentation may be

unavoidable, but scope to wean the dose of CS should be explored, as a matter of priority, once

a satisfactory response to synthetic DMARD or biologic therapy has been achieved. The

minimum safe dose of CS is unknown. Indeed, in respect to SIs, there may be no safe minimum,

but until more definitive data is available, a dose of 3 mg/day may represent a reasonable

compromise target for maintenance of lower disease activity and at the same time minimization

of SI risk [64,65].

3.14.3. Doses of biologic agents

The dose of any therapeutic agent should be periodically reviewed. For some biologic agents,

where there is dose flexibility, lower SI risks have been convincingly demonstrated with lower

doses of the biologic agent. For example, for Infliximab (INX), a 3 mg/Kg dose confers less

risk than 6 mg/Kg and for Adalimumab (ADA), 40 mg every other week (EOW) confers less

55

risk than 40 mg qw. Similar observations have been made for Tocilizumab (TCZ) where 4

mg/kg was found to confer less SI risk than 8 mg/ kg [46]. Where SI risk is a major concern

and disease control will allow, reduced doses of DMARDs in general, including bDMARDs,

should be considered or monotherapy with a bDMARDs should be preferred.

3.14.4. Vaccination Pneumonias and lower respiratory tract infections in general are the most common SIs in all

RA patients irrespective of biologic or synthetic DMARD therapy. Pneumococcal vaccination

should be advised, unless contra-indicated, and follow-up post- vaccination serology performed

to confirm adequate immunity. When it becomes more widely available/accessible, the new

subunit zoster vaccine (Shingrix) should be considered, especially in those most at risk due to

age.

3.14.5. Comorbidities related and unrelated to RA

Amongst related disorders, consider Felty’s syndrome and other conditions that may give rise

to Neutropoenia. Consideration should also be given to innate immune deficiencies such as

Mannose Binding Lectin (MBL) deficiency and Hypogammaglobulinemia. Undetectable

Mannose Binding Lectin (MBL) concentrations carry a considerable risk for SIs in RA

(OR=4.67) comparable to 10 mg of prednisolone daily [3,9]. Since pneumonias are more often

fatal in Mannose Binding Lectin (MBL) deficient persons and since 8-15% of RA patients are

Mannose Binding Lectin (MBL) deficient (serum concentrations less than 50 ng/mL), there is

an even stronger case for pneumococcal vaccination in those with RA with undetectable

Mannose Binding Lectin (MBL). The treating clinician should consider determining the

Mannose Binding Lectin (MBL) concentration in advance of commencing CS, csDMARDs or

bDMARDs therapy, as this information taken together with age and CS usage will inform

decision making in respect to the nature and risks of therapy.

4. Conclusion

In conclusion, SIs are far more common in RA than in the general population, CS are associated

with an appreciable increase in SI risk (5 fold at doses of 10 mg per day or more), most

commonly used and currently favoured synthetic DMARDs confer a small or no risk, biologic

DMARDs confer moderate risk in the first year of therapy and then a diminishing risk

thereafter, and higher dose biologic or combination biologic therapy should be avoided since

56

the serious infection risk is unacceptably high. Combinations of CS and bDMARDs or of

csDMARDs and bDMARDs should be used with caution in those with a track record for one

or more SIs and perhaps also in the elderly. Undetectable Mannose Binding Lectin (MBL) is a

major risk factor for SI in RA, comparable to Prednisolone 10 mg per day or more and

measurement thereof will inform SI risk stratification.

57

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63

CHAPTER 3

Descriptive analysis of the infection status in rheumatoid

arthritis patients (using ARA data)

64

Abstract

Objectives: To conduct a descriptive analysis of the type and frequency of self-reported

infections in rheumatoid arthritis (RA) based upon reports to the Australian Rheumatology

Association Database (ARAD). These include the effects of anti-RA medications in the

development of infections across various organs.

Methods: ARAD reports (patients’ responses to questionnaires) from 2001 to 2014 were

examined in respect to demographic and treatment categories. Observed differences were

subjected to descriptive statistical appraisal.

Results: Based on this analysis, the mean age in RA is 61.47 years and, in the group taking

csDMARDs, it is 59.24 years and, in the group taking bDMARDs, it is 62.62 years. Also,

patient groups who were taking csDMARDs alone and bDMARDs alone were comparable

based on risk factors, such as taking prednisolone, smoking or alcohol consumption. Finally, in

comparison to bDMARDs, taking csDMARDs alone was significantly associated with higher

rate of infection in a few organs, such as lung, urinary system, and GIT.

Conclusion: Compelling evidence suggests that RA can increase the risk of infection and

potentially serious infection and that different medications are potentially associated with this

risk. The Australian RA population in ARAD shows that risk factors, such as smoking, can play

a role in the development of serious infection. Although it seems that csDMARDs alone is

connected to more rates of infection than bDMARDs alone, the difference is only significant

in a few types of infections. The findings in this analysis indicate that smoking is a likely

contributor to increased infection risk in RA. The Australian RA population in ARAD

shows that risk factors, such as smoking, can play a role in the development of serious

infection. Although it seems that csDMARDs alone is associated with higher

frequencies of infection than bDMARDs alone, the difference is only statistically

significant for a few types of infections. Accordingly, these apparent differences

require closer scrutiny. Importantly, the findings contrast with those reported in most

registries, where bDMARDs use is associated with higher rates of infection or at least

serious infection and at least in the first year of treatment. The different definitions

65

applied for infection in ARAD and the very long follow-up may account for the

differences observed.

66

1. Introduction Rheumatoid arthritis (RA) as a chronic multisystemic, immuno-inflammatory disease is a

common disease affecting millions of people worldwide [1]. In developed countries, RA affects

0ꞏ5–1ꞏ0% of adults, with 5–50 per 100 000 new cases annually[2]. The female to male ratio in

this disease is more than three to one. The risk of RA increases with age, perhaps pointing to

loss of tolerance as the immune system undergoes age-related loss of antigen discriminatory

capacity. Genetic and environmental risk factors also contribute to the risk of RA [3].

There are some well-known risk factors in this disease, including genetic susceptibility, gender,

age, smoking, infectious agents, hormonal factors and ethnic factors[4]. Roughly 50% of the

risk for RA is attributable to genetic factors. Around 30 genetic loci have been implicated in

RA. These suspicious genes have been classified, however, the pathogenesis of their influence

in developing RA is still unclear[4]. Smoking is also a main environmental risk factor.

Smoking-related tissue necrosis is thought to be of influence in the onset of excessive

inflammation and immune response to self-antigens [3]. Age and sex can also play

aetiopathogenetic roles.

The incidence, severity, and the outcome of the disease show inconsistencies between diverse

ethnical-origin units, which is related to socioeconomic levels, as well as genetic and

environmental factors. For example, patients in underdeveloped countries have poorer

prognosis. They demonstrate a more severe clinical course due to limited access to medical care

and medication, amongst other factors. Studies on RA has revealed that various genetic and

environmental factors can influence the disease in diverse ethnical groups[5].

RA signs and symptoms include persistent synovitis and systemic inflammation due to

autoantibodies particular to rheumatoid factor (RF) and antibodies to certain peptides. The

typical symptoms at onset are symptoms of synovitis (pain, swelling, loss of function, including

stiffness, restricted motion, and possible heat and redness in joints, if severe), which are most

often in a symmetrical pattern and sometimes accompanied by systemic symptoms, such as

lethargy /malaise, weight loss and sometimes fever[6].

Clinical onset of this disease is generally symmetrical involvement of the small joints, pain,

morning stiffness, and limitation of movement for more than one hour. RA may also involve

67

any joint, but most frequently it involves the meta-carpophalangeal (MCP) joints, the proximal

interphalangeal (PIP) joints, the wrists, the metatarsophalangeal (MTP) joints and the knee

joints[6]. Articular symptoms are usually symmetric and systemic pattern. The large joints

which may be involved include the shoulders, elbows, knees and ankles. The small joints

include the MCP, PIP, MTP, thumb interphalangeal joint and wrists[7]. The clinical

presentation of RA varies, although an insidious onset of pain accompanied by symmetric

swelling of the small joints is the most common symptom cluster at the outset [7]. Rheumatoid

arthritis also increases the risks of several other comorbidities including cardiac disease,

depression, lymphoma and other malignancies [7]. Complications are not limited to the joints

and can involve extra-articular tissues including vasculitis and ophthalmic,

neurologic, and cutaneous complications[8]. RA is not directly life-threatening, but

uncontrolled active rheumatoid arthritis can also lead to joint damage, decreased quality of life,

disability, and cardiovascular comorbidities[9].

Complications are not limited to the joints and can involve extra-articular tissues, including the

serosal surfaces (pleural and pericardial effusions), bone marrow ( anaemias and cytopaenias)

the lungs ( interstitial lung disease ), blood vessels (vasculitis), the eyes (episcleritis and

scleritis with blindness due to occasional perforation), neurologic and cutaneous

complications[9]. RA is not directly life-threatening but uncontrolled active rheumatoid

arthritis can be very debilitating, reduce the quality of life, contribute to substantial disability,

and contribute to cardiovascular comorbidities [10]. Importantly, infection is the commonest

cause of death in RA; the disease, its treatment and probably co-existent immunodeficiencies

all likely contribute to this increased risk [10].

The pathophysiology of RA is yet to be elucidated completely, but it seems that molecular and

cellular pathways of inflammation with involvement of both B cells and T cells play important

roles. Distinct autoantibodies are always present in the sera of patients[10]. Rheumatoid factor

(RF), both IgM rheumatoid factors (IgM-RF) and IgG rheumatoid factors (IgG-RF) are present

in different stages of RA pathogenesis. The IgM rheumatoid factors (IgM-RF) are the main RF

class found in RA and they can be detected in 60–80% of established cases of RA and 50–60%

of RA patients in the early stages of the disease[11]. This implies that RF is probably an

outcome of non-specific immune activation[11].

68

Modern treatment of RA has been substantially improved by the introduction of biologic

therapies. For most patients, these drugs represent an effective and safe management strategy;

however, serious infections connected with biologic therapies are a major concern for both

patients and clinicians. Registry data from the UK and Sweden have shown an augmented risk

of serious infection in new anti-TNF starters, especially in the first 6–12 months of treatment

[12]. Infections are usually due to the same organisms seen commonly in the general population,

and a small number of infections are due to opportunistic infections (OI)[12]. Treatment in RA

is usually based on immune suppression through csDMARDs) or (bDMARDs)[13].

1.1. DMARDs Disease-modifying anti-rheumatic drugs (DMARDs) are drugs which reduce the level of

inflammation, slow joint damage and decrease the systemic effects of RA. There are three major

groups; these include:

conventional synthetic DMARDs (csDMARDs),

targeted synthetic DMARDs (csDMARDs), and

biological DMARDs (bDMARDs)[14].

csDMARDs alone or Conventional synthetic DMARDs include: 1- Methotrexate (oral or

parenteral), 2- Hydroxychloroquine, 3- Sulphasalazine, 4- Leflunomide, 5- Azathioprine, 6-

Cyclosporin.

1.2. bDMARDs These are engineered medications and are designed to regulate the immune response.

Hereditarily-engineered proteins initiating from human genes form biologic drugs targeting

the specific portions of the immune system that fuel inflammation. csDMARDs alone, such as

methotrexate, are less targeted [15].

Biologics are usually used singly or in combination with other non-biologics. What

distinguishes biologics, besides how they work and what they target, is their makeup, how they

are delivered, and some risks, although all of them probably confer an increased risk for

infection. Different groups of biologics include: Tumor necrosis factor inhibitors (TNF-

Inhibitors) which block tumor necrosis factor, one of the chemical messengers of inflammation

69

that drives joint inflammation and destruction. Interleukin-1 (IL-1) blocker ( for example

anakinra) which blocks IL-1, the factor with a major role in inflammation, B-cell inhibitor

(rituximab), T-cell inhibitors (abatacept), humira/adalimumab, etanercept or brenzys

/etanercept, remicade/infliximab, actemra/tocilizumab, simponi/golimumab [15].

Others with different mechanisms of action include actemra/tocilizumab (a monoclonal

antibody directed against IL-6 receptor), Interleukin-1 (IL-1) receptor antagonist (for example,

anakinra), which blocks IL-1, B cell depletors (rituximab) and T-cell inhibitors (abatacept). A

new family of Jak inhibitors, which can be taken orally, is now emerging and is in clinical use

with growing uptake. These include tofacitinib and baricitinib.

It should be noted that bDMARDs are not used concomitantly because of concerns regarding

still higher rates of serious infection, however, the evidence base underpinning this fear is not

strong and newer agents with low infection propensity have not been combined with older

agents and studied rigorously in clinical trials. This section will cover the demographic

characteristics of RA, SI, different types of infections and their severities. In addition, there will

be a descriptive assessment of the association between various modalities of treatment in RA

and the severity of different types of infection. Through a comprehensive descriptive analysis,

potential associations and other relationships may emerge. Later in this section observed

differences and potential relationships will be evaluated statistically and discussed in detail.

1.3. Aims and Objectives The aim of this study is to increase knowledge about the pattern of RA in the Australian

population and to determine the frequency and significance of self-reported infections. Specific

objectives in this section include:

• Describe the demographic characteristics of the ARA database and to report the type, severity and frequency of self-reported SIs as well as the relevant potential risk factors for infection.

• Describe the different types of infections in RA and their association with the major treatment modalities, csDMARDs alone or bDMARDs alone.

• Provide essential tools for other researchers from other parts of the world to perform similar analyses and compare demographic characteristics in different parts of the world.

70

• Discuss potential associations between different available modalities for treatment and different types of infections and their severity.

2. Methods

2.1. Data Collection The data were collected from the ARAD, in which a cohort of 3569 RA patients (960 males

and 2609 females) who had completed related questionnaires 28176 times (during 2001-2014),

were investigated for the development of infections. Among the 3569 patients, 459 patients

were eliminated because they had filled out the questionnaire only once. We were left with

3110 patients. Eight duplicates were eliminated, leaving 27709 visits from 3110 patients. All

these visits were examined to capture self-reported infections in different organs and the

medications that were being taken at the time.

2.2. Statistical Analysis Amongst the 3110 Rheumatoid Arthritis patients who had taken part in the study and had filled

in the questionnaire more than once, the central tendency for age and sex distribution was

calculated. In the first step all data were entered in excel. Single, faulty and duplicate reports

were eliminated. Then data was divided into two groups of patients who were taking either just

csDMARDs alone and patients who were taking just bDMARDs alone. Overall, 1653 visits

from 405 patients applied to those taking csDMARDs alone and 323 visits from 80 patients

applied to those taking bDMARDs alone. All the patients who were taking both csDMARDs

and bDMARDs concurrently were eliminated from the analysis at this stage. Both csDMARDs-

alone and bDMARDs-alone participants and the overall RA population were examined closely

and compared in respect to sex distribution, age distribution, smoking history, alcohol

consumption, and different organ infection. Possible differences were tested with the chi-

squared test and the Fisher test, wherever it was applicable.

3. Results and discussions

3.1. Demography of whole RA population The amount and frequency of smoking [16], alcohol consumption [17], T1DM, T2DM[18]

and prednisolone consumption[19] all can play a role in the incidence of infection among RA

71

patients. In addition, a patient’s age and sex can have a different distribution among RA

patients and the normal population. Therefore, in the following tables (tables 3.1 to 3.6), these

differences are explored and compared.

The mean and median were used as points of estimate and accuracy was measured by Standard

Error (Table 3.1). This shows the distribution of age, the number of cigarettes smoked and the

duration of smoking, as well as the amount of alcohol consumed.

Table 3.1 Comparison demography of RA (Data collected from ARAD)

Variable Mean SD Median

Age in RA 61.48 12.31 63.00

Number of cigarettes smoked among smokers 14.89 13.23 15.00

Duration of smoking among smokers 17.26 13.95 16.00

Alcohol Consumption units among alcohol consumers

1.32 0.47 1.00

A sample of 1653 visits, pertaining to 405 patients who were receiving csDMARDs alone and

80 patients who were receiving bDMARDs alone, was examined for the impact of several

predictor variables: age, gender, alcohol consumption, smoking history and prednisolone

intake. The results were compared through chi-squared tests, in the next stage. This is

described below.

3.2. Demography of patients taking purely bDMARDs Only eighty patients were just taking bDMARDs alone during the period of this study (2001 to

2014) without any concurrent csDMARDs. Among this number, 63.75 % were female and

36.25% were male. Smoking as a risk factor for infection was also measured in this population

[16].

Table 3.2 Central tendency among patients who received bDMARDs alone


Age

Average number of cigarettes smoked by smokers

Smoking duration (yrs) among smokers

62.62

25.90

21.53

12.57

21.67

12.26

62.50

20.00

22.00

72

The mean age for the patients receiving bDMARDs alone was 62 years old (Table 3.2). 64 %

of the patients who were taking bDMARDs alone were female and 36% were male (Table 3.3).

Table 3.3 Sex distribution among patients who received bDMARDs alone

Sex Frequency % Cumulative frequency Cumulative %

Male 29 36.25 29 36.25

Female 51 63.75 80 100.00

These differences in the sex distribution are best appreciated in the pie chart (Figure 3.1).

Note. Sex1= Male; Sex 2= Female

Figure 3.1 Sex distribution among patients receiving bDMARDs alone

The majority of patients (almost 82%) who were taking bDMARDs alone were non-smokers.

According to centraltendency data, patients, on average, were smokers for 21 years and

consumed almost 26 cigarettes per day (Table 3.2, Table 3.4)

Male36%

Female64%

SEX DISTRIBUTION AMONG BDMARDS ALONE

Male Female

73

Table 3.4 Smoking status amongst patients receiving bDMARDs alone

It is apparent that the majority of patients who received bDMARDs alone were alcohol

consumers (Table 3.5).

Table 3.5 Status of alcohol consumption among patients who were taking bDMARDs

Alcohol consumption among patients who received bDMARDs alone

Alcohol Frequency % Cumulative

Frequency

Cumulative

%

Never 32 40.00 32 40.00

Sometimes 39 48.75 71 88.75

Everyday 9 11.25 80 100.00

Note. Frequency missing = 325

A small majority of patients (almost 52%) who were taking bDMARDs alone were taking

prednisolone as well (Table 3.6)

Table 3.6 Status of taking methyl prednisolone among patients taking bDMARDs alone

Also,

according to

the following

tables,

approximately 7.5% of patients on bDMARDs have current T1DM, whereas 12.5% have

current T2DM (Table 3.7, Table 3.8).

Smoking status amongst patients who received bDMARDs alone

Smoking status Frequency % Cumulative Frequency Cumulative %

No 66 82.50 66 82.50

Yes 14 17.50 80 100.00


Prednisolone status Frequency % Cumulative Frequency Cumulative %

Never taken 25 31.25 25 31.25

Currently taking 42 52.50 67 83.75

Stopped taking 12 15.00 79 98.75

Don’t know 1 1.25 80 100.00


74

Table 3.7 Status of T1DM among patients who were taking bDMARDs alone

T2DM was observed to be more common amongst patients who were receiving bDMARDs

alone (Table 3.8).

Table 3.8 Frequency of T2DM amongst patients who were receiving bDMARDs alone

3.3. Demography of patients receiving csDMARDs alone During the period of this study (2001 to 2014), 405 patients were taking csDMARDs alone

without any csDMARDs. The mean age was around 59 years (Table 3.9).

According to the following table (Table 3.9), among all patients with RA, smokers were, on

average, smoking for 12 years about 10 cigarettes per day.

Table 3.9 Mean and central tendency in csDMARDs alone


Age

Smoking status among smokers

Smoking duration (yrs) among smokers

59.24

10.49

12.40

12.69

12.12

14.46

60.00

10.00

9.00

Amongst the 405 patients who were receiving csDMARDs alone, 77.04% were female. In

comparison to bDMARDs alone, the proportion of females receiving csDMARDs alone was

higher (Table 3.10).

T1DM Frequency % Cumulative

Frequency

Cumulative

%

Never 73 91.25 73 91.25

Current 6 7.50 79 98.75

Past 1 1.25 80 100.00

Frequency missing = 325

T2DM Frequency % Cumulative frequency Cumulative %

Never 68 85.00 68 85.00

Current 10 12.50 78 97.50

Past 2 2.50 80 100.00


75

Table 3.10 Sex distribution among patients who received csDMARDs alone

Gender

Frequency % Cumulative frequency Cumulative %

Male 93 22.96 93 22.96

Female 312 77.04 405 100.00

The difference in sex distribution is best appreciated in the following Figure (Figure 3.2). It

can be seen that females predominate.

.

Figure 3.2 Gender distribution among patients who took csDMARDs alone

The majority of patients receiving csDMARDs alone were non-smokers. According to central

tendency data (Table 3.2), the smokers in this group consumed almost 10 cigarette s per day

with an average smoking duration of 12 years.

Table 3.11 Smoking status in those receiving csDMARDs alone

Smoking status

Smoker Frequency % Cumulative frequency Cumulative %

Missing 1 0.25 1 0.25

No 367 90.62 368 90.86

Yes 37 9.14 405 100.00

The majority of patients, up to around 90%, were non-smokers, whereas almost 70% were

consumers of alcohol (Table 3.9, Table 3.11, Table 3.12).

Male23%

Female77%

SEX DISTRIBUTION IN CSDMARDS ALONE

Male Female

76

Table 3.12. Alcohol consumption in those receiving csDMARDs alone

Alcohol Frequency % Cumulative frequency Cumulative %

Never 123 30.37 123 30.37

Sometimes 209 51.60 332 81.98

Everyday 73 18.02 405 100.00

The significance of differences observed between the csDMARDs-alone and bDMARDs-alone

groups were examined by statistical testing. Prednisolone was noted to be used by 46% of

patients receiving csDMARDs alone (Table 3.13).

Table 3.13 Prednisolone usage amongst those receiving csDMARDs alone

Prednisolone status Frequency % Cumulative frequency Cumulative %

Never 110 27.16 110 27.16

Currently 187 46.17 297 73.33

Stopped 107 26.42 404 99.75

Don’t know 1 0.25 405 100.00

T1DM, as another well-known risk for infection, was reported in just 3.71% of all patients who

were taking csDMARDs [20] (Table 3.14).

Table 3.14. Insulin Dependent Diabetes Mellitus in CsDMARDs alone

Frequency of T1DM in patients taking (csDMARDs)

T1DM Frequency % Cumulative

Frequency

Cumulative

%

Never 390 96.30 390 96.30

Current 14 3.46 404 99.75

Past 1 0.25 405 100.00

The frequency of T2DM reported was slightly higher than for T1DM, but still less than 10%

(Table 3.15).

77

Table 3.15 Non-insulin dependent diabetes mellitus in those receiving csDMARDs alone

3.4. Comparison of patients receiving bDMARDs and patients on csDMARDs alone

In order to compare the frequency of qualitative variables, for each variable a separately chi-

square table was used (Table 3.17, Table 3.19, Table 3.21, Table 3.23, Table 3.25, Table 3.27,

Table 3.29, Table 3.35, Table 3.41, Table 3.45, Table 3.55, Table 3.59). In these tables, the

significance of the differences is tested by different test methods, including the Wald chi-square

test (pearson Chi-Square), the Continuity-Adjusted Chi-Square test, the Mantel-Haenszel Chi-

Square test, the Likelihood Ratio Chi-Square test, the Phi Coefficient, and Cramer’s V. Among

these tests, the Wald Chi-Square, which is also known as the Pearson Chi-Square, is the most

commonly used test. The null hypothesis is that the frequency of the variable is similar for

recipients of biologic DMARDs alone and recipients of conventional synthetic DMARDs alone

recipients. A criticism of this test is that the Wald Chi-square fixes the row and column margin

totals which, in effect, makes an assumption about the distribution of the variables in the

population being studied (Table 3.17)[21].

The second test in this table (Table 3.17) is the Continuity-Adjusted Chi-Square test statistic.

This test consists of the Pearson Chi-Square modified with an adjustment for continuity and is

dependent on the sample size. The Mantel-Haenszel Chi-Square test is usually related to the

Pearson Chi-Square test. In the 2x2 case, as the sample size gets larger, the Mantel-Haenszel

and Wald Chi Square statistics tests converge[22]. In the case of 2xC or Rx2 tables, if the

variable with more than two categories is ordinal, the Mantel-Haenszel Chi-square is a test for

trend while the Pearson Chi-square remains a general test for association[22]. This test is

currently calculated and reported in SAS, but it was not evaluated further in this study. The

Likelihood Ratio Chi-Square is asymptotically equivalent to the Pearson Chi-Square and

T2DM Frequency % Cumulative Frequency Cumulative %

don’t know 1 0.25 1 0.25

Never 379 93.58 380 93.83

Current 20 4.94 400 98.77

Past 5 1.23 405 100.00

78

Mantel-Haenszel Chi-Square but not usually used when analysing 2x2 tables. It is used in

logistic regression and log linear modelling which involves contingency tables[22].

[22]. Cramer’s V is derived from the chi-square and in the 2 x2 table, which is identical to the

Phi coefficient. The contingency coefficient, the Phi coefficient, and Cramer’s V are well-suited

for nominal variables in which the order of the levels is meaningless[23].

3.4.1. Prednisolone comparison

Prednisolone is one of the anti-rheumatic medications known to play a role in infection (Chapter

2, section 3.12.2.). It is important to check if prednisolone intake differs between patients who

are taking csDMARDs and patients who are taking bDMARDs (Table 3.16).

Table 3.16 Comparison of prednisolone consumption among patients receiving csDMARDs

alone and bDMARDs alone

Group Response

Status Never

Taking

Currently

taking

Stopped

taking

Not

Known

Total

csDMARDs

Frequency 110 187 107 1 405

% 22.68 38.56 22.06 0.21 83.51

Row % 27.16 46.17 26.42 0.25

Column % 81.48 81.66 89.92 50.00

bDMARDs

Frequency 25 42 12 1 80

% 5.15 8.66 2.47 0.21 16.49

Row % 31.25 52.50 15 1.25

Column % 18.52 18.34 10.08 50.00

Total Frequency 135 229 119 2 485

% 27.84 47.22 24.54 0.41 100

Based on the p-value of the Chi-square in the ARAD sample, there is not a significant

difference between the csDMARDs and bDMARDs groups in taking prednisolone. As the

number of samples in 25 % of calculating cells was less than 5, other tests (chi- square,

likelihood ratio, Mantel- Haenszel, phi Coefficient, Contingency coefficient, Cramer’s V)

were also checked, and all of these tests align with the original finding (Table 3.17) [19].

79

Table 3.17 Chi-squared test for difference in frequency of prednisolone usage between

csDMARDs-alone recipients and bDMARDs-alone recipients, sample size=485

Statistic DF Value Probability

Chi-Square 3.00 6.15 0.10

Likelihood Ratio Chi-Square 3.00 6.15 0.10

Mantel-Haenszel Chi-Square 1.00 2.25 0.13

Phi Coefficient 0.11 -

Contingency Coefficient 0.11 -

Cramer's V 0.11 -

3.4.2. Alcohol comparison

There are also other factors which may play a role with respect to infection susceptibility and

also impact on general health. Alcohol consumption and cigarette smoking are two such factors.

Alcohol consumption in recipients of csDMARDs alone and bDMARDs alone was examined.

The results are shown in Table 3.18 and Table 3.19.

Table 3.18 Comparison of alcohol consumption among patients receiving csDMARDs alone

and bDMARDs alone

Group Response

Status Never

taking

Sometimes Everyday Total

CsDMARDs Frequency 123 209 73 405

% 25.36 43.09 15.05 83.51

Row % 30.37 51.60 18.02

Column % 79.35 84.27 89.02

bDMARDs Frequency 32 39 9 80

% 6.60 8.04 1.86 16.49

Row % 40.00 48.75 11.25

Column % 20.65 15.73 10.98

Total Frequency 155 248 82 485

% 31.96 51.13 16.91 100

80

Notes. Sample size is a combination of 405 patients who were taking csDMARDs alone and 80 patients who were

bDMARDs alone. Based on the chi-square test, the differences in alcohol consumption between csDMARDs alone

and bDMARDs alone were not statistically significant (Table 3.19).

Table 3.19 Chi-squared for differences in frequency of alcohol use between recipients of

csDMARDs alone and recipients of bDMARDs alone, sample size=485.

3.4.3. Smoking comparison

Smoking is another potential risk factor for infection and deterioration in patient health status.

Statistically, it seems that more people in the csDMARDs group intend to smoke (Table 3.20a).

However, this is not statistically significant different from the other group (Table 3.21).

Table 3.20a Comparison smoking status among patients receiving csDMARDs alone and

bDMARDs alone

Group Response

Status No Yes Missing Total

csDMARDs

Frequency 367 37 1 405

% 75.67 7.63 0.21 83.51

Row % 90.62 9.14 0.25

Column % 84.76 72.55 100.00

bDMARDs


% 13.61 2.89 0.00 16.49

Row % 82.50 17.50 0.00

Column % 15.24 27.45 0.00


% 89.28 10.52 0.21 100.00


Chi-Square 2.00 3.86 0.15



Phi Coefficient 0.09

Contingency Coefficient 0.09

Cramer's V 0.09

81

Different studies reveal the strong connection between smoking and RA. For example,

Criswell et al. (2002), in a cohort study, showed that those who stopped smoking could have

a reduced risk of RA, particularly among postmenopausal women [24]. In a case study by

Padyukov et al. (2004), it was shown that the risk of RA with SE of HLA-DR is strongly

influenced by the presence of an environmental factor (e.g., smoking) in the population at risk

[16]. Costenbader et al. (2006) showed in a cohort study that past and current smoking were

related to the development of RA, in particular seropositive RA [25]. In a meta-analysis by

Sugiyama et al. (2010), it was shown that smoking is a risk for RA, especially seropositive

RA in men. For women, the risk for smokers is about 1.3 times greater than for non- smokers

[26]. Di Giuseppe et al. (2014) showed that lifelong cigarette smoking was positively

associated with the risk of RA, even among smokers with a low lifelong exposure [27].

Furthermore, other studies show a connection between the effectiveness of smoking cession

and better responsiveness of bio-treatment [28]. Sustained smoking cessation within four

years of RA diagnosis is connected to a reduction in mortality risk, this rate is same as non-

smokers. However, smoking more than 5 years after RA diagnosis increased mortality well

above the risk of non-RA patients [29].

According to the ARAD, the rate of smoking between 2001 and 2014 was 10.5% (328/3111).

This was almost 8.9% of all patient visits (2484/27712). Table 3.20b shows the rate of

smoking in the general population in Australia during the same time.

Table 3.20b Comparing rate of smokers during the years 2001 to 2013, Australia [30]

Year %Total smokers

2001 22

2004 20

2007 19

2010 18

2013 15

It seems that the rate of smokers in RA is less than the rate of smokers among the general

population in Australia during those years. Two major possibilities for this discrepancy include

82

the facts that (1) ARAD is a subjective report and data are not reliable, and (2)there are several

risk factors for causing RA, and risk factors other than smoking play a more significant role in

Australia, especially since the rate of RA disease in Australia is higher than in many other

countries [31].

Based on the information in the smoking and alcohol consumption tables, the apparent

differences in smoking history and alcohol consumption status are not confirmed, statistically.

Accordingly, they are unlikely to account for any differences in infections observed between

the csDMARDs and bDMARD groups. There is marginal evidence for differences with respect

to smoking between the csDMARDs-alone and bDMARDs-alone groups (Table 3.21). In this

test, almost 33% of cells had an expected count of less than five, which means that further

statistical testing is required. For this purpose, the likelihood ratio test was performed, and this

test confirmed the results[32].

Table 3.21 Chi-squared for differences in frequency of smoking between csDMARDs-alone

and bDMARDs-alone groups, sample size=485

3.4.4. Sex distribution comparison

Sex differences can also play a major role in many conditions. Therefore, it is important to

determine if there is a sex difference between patients taking csDMARDs and those taking

bDMARDs (Table 3.22).

Statistical tests DF Value Probability Chi-Square 2.00 5.14 0.08 Likelihood Ratio Chi-Square 2.00 4.72 0.09 Mantel-Haenszel Chi-Square 1.00 4.07 0.04 Phi Coefficient 0.10 Contingency Coefficient 0.10 Cramer's V 0.10

83

Table 3.22 Comparison sex distribution among patients receiving csDMARDs alone and

bDMARDs alone (sample Size = 485)

Group Response

Status Male Female Total

csDMARDs Frequency 93 312 405

% 19.18 64.33 83.51

Row % 22.96 77.04

Column % 76.23 85.95

bDMARDs Frequency 29 51 80

% 5.98 10.52 16.49

Row % 36.25 63.75

Column % 23.77 14.05

Total Frequency 122 363 485

% 25.15 74.85 100.00

Based on the information in Table 3.23, the Chi-Square test result for sex difference is highly

significant, at the level of 0.05. This means that the sex distribution amongst recipients of

csDMARDs and bDMARDs is different. Accordingly, gender may confound inferences made

in relation to these two groups. In order to confirm this difference and confirm that the small

size of the population is not responsible, a Fisher's test was performed (Table 3.24)[18].

Table 3.23 Chi-squared for differences in frequency of sex distribution among recipients of

csDMARDs alone and recipients of bDMARDs alone


Chi-Square 1.00 6.26 0.01


Continuity Adj. Chi-Square 1.00 5.58 0.02


Phi Coefficient -0.11


Cramer's V -0.11

84

Based on the Fisher’s test, it was confirmed that the two populations have a different sex

distribution (Table 3.24).

Table 3.24 Fisher test for differences in frequency of sex distribution between recipients of

csDMARDs alone and recipients of bDMARDs alone

Based on the statistical tests, the sex distribution is different between csDMARDs and

bDMARDs. In the following pie chart, all sex distributions are presented to make this

comparison easier (Figure 3.3). According to this chart, in both csDMARDs and bDMARDs,

the population of the female sex is greater than the male sex and this difference is greater

amongst those taking csDMARDs (Figure 3.3).

Figure 3.3 Sex distribution among csDMARDs alone and bDMARDs alone

Biologic Male6%

csDMARDs Male19%

bDMARDS Female11%

csDMARDs Female64%

Sex differences in csDMARDs alone and bDMARDs alone

Biologic Male

csDMARDs Male

bDMARDS Female

csDMARDs Female

Fisher's Exact Test

Cell (1,1) Frequency (F) 93.00

Left-sided Pr <= F 0.01

Right-sided Pr >= F 0.99

Table Probability (P) 0.01

Two-sided Pr <= P 0.02

85

3.4.5. T2DM comparison

Table 3.25 summarises the differences in the frequency of T2DM in recipients of csDMARDs

and bDMARDs.

Table 3.25 Comparison of T2DM among patients receiving csDMARDs alone and bDMARDs

alone

Group Response

Status No known Never Current Past Total

CsDMARDs Frequency 1 379 20 5 405

% 0.21 78.14 4.12 1.03 83.51

Row % 0.25 93.58 4.94 1.23

Column % 100 84.79 66.67 71.43

bDMARDs Frequency 0 68 10 2 80

% 0.00 14.02 2.06 0.41 16.49

Row % 0.00 85.00 12.50 2.5

Column % 0.00 15.21 33.33 28.57

Total Frequency 1 447 30 7 485

% 0.21 92.16 6.19 1.44 100.00

There is marginal evidence that the frequency of T2DM in ARAD participants is different

between patients taking csDMARDs alone and those taking bDMARDs alone. The difference

was not significant at the 0.05 level of significance (Table 3.26).

Table 3.26 Chi-squared for differences in frequency of T2DM between csDMARDs alone and

bDMARDs alone, sample size= 485


Chi-Square 3.00 7.65 0.05





Cramer's V 0.13

86

3.4.6. T1DM comparison

Table 3.27 summarises the differences in frequency of T2DM in recipients of csDMARDs

alone and bDMARDs alone.

Table 3.27 Comparison T1DM among patients on csDMARDs alone and bDMARDs alone

Group Response

Status No known Never Current Past

CsDMARDs Frequency 390 14 1 405

% 80.41 2.89 0.21 83.51

Row % 96.30 3.46 0.25

Column % 84.23 70.00 50.00

bDMARDs Frequency 73 6 1 80

% 15.05 1.24 0.21 16.49

Row percentage 91.25 7.50 1.25

Column % 15.77 30.00 50.00


% 95.46 4.12 0.41 100.00

Based on the chi-square test, there is no difference in the frequency of T1DM between patients

who are taking csDMARDs alone and those who are taking bDMARDs alone. However, the

population size is low and there is a possibility that using just Chi-squared reduces the accuracy

of this test. Therefore, other tests (chi- square, likelihood ratio, Mantel- Haenszel, phi

Coefficient, Contingency coefficient, Cramer’s V) have also been assessed (Table 3.28) [18].

Table 3.28 Chi-squared for differences in frequency of T1DM between csDMARDs alone

and bDMARDs alone, Sample size=485


Chi-Square 2.00 4.46 0.11





Cramer's V 0.10

87

In the following, we study each organ infection in csDMARDs versus bDMARDs in more

detail. In different sections, we discuss different levels of infection, including mild, moderate

and severe. Based on the ARAD questionnaire (Appendix M), mild infection is defined as an

infection which does not change activities and the patient did not see a doctor and did not

require prescription medicine for treatment. Moderate infection is defined as an infection

which changes activities occasionally and the patient needed a prescription medication for the

symptoms. Severe infection is an infection which can cause a major change in activities and

the patient needed to see a doctor and received prescription medication, however, the

medication only provided partial relief.

3.4.7. Skin and nail infections comparison

Skin and nail infections are amongst the commonest infections in RA. Skin and nail infection

was reported for three different levels of severity. The relationship between the frequency of

these levels and type of medication are reviewed in Tables 3.29 and 3.30

Table 3.29 Table of frequency of skin and nail infections in recipients of csDMARDs alone

recipients.

Skin and nail infection in csDMARDs alone

Severity Frequency % Cumulative

Frequency

Cumulative

%

Mild 76 53.15 76 53.15

Moderate 56 39.16 132 92.31

Severe 11 7.69 143 100.00


Severe skin and nail infection occurred more frequently in patients who were taking bDMARDs

alone compared to csDMARDs alone (Tables 3.29 and 3.30). In contrast, other types of

infections were either similar or more frequently observed in csDMARDs recipients (Tables

3.29-3.30).

88

Table 3.30 Table of frequency of skin and nail infections in bDMARDs alone

Skin and nail infection in Biologics


Frequency

Cumulative

%

Mild 16 53.33 16 53.33

Moderate 11 36.67 27 90.00

Severe 3 10.00 30 100.00


Almost 44 % of patients who were taking csDMARDs alone have reported mild levels of

skin/nail infection. This is about 53% of all patients who were taking csDMARDs alone and

82% of all patients who reported mild skin infection (Table 3.31). Almost 9% of patients who

were taking bDMARDs alone reported mild infection; this is about 53% of all patients who

were taking bDMARDs alone and almost 17% of all patients who reported mild infection

(Table 3.31). Almost 6 % of patients who were taking CsDMARDs alone have reported severe

level of skin infection. This is about 7.69% of all patients who were taking csDMARDs alone

and 78.5% of all patients who reported severe skin infection (Table 3.31).

Almost 2% of patients who were taking bDMARDs alone reported severe skin infection, this

is about 10% of all patients who were on bDMARDs alone and almost 21% of all patients who

reported severe infection (Table 3.31).

Table 3.31 Differences in frequency of skin infections in csDMARDs and bDMARDs alone

Group

Response Status Mild Moderate Severe Total

csDMARDs

Frequency 76 56 11 143 % 43.93 32.37 6.36 82.66 Row percentage 53.15 39.16 7.69 Column % 82.61 83.58 78.57

bDMARDs

Frequency 16 11 3 30 % 9.25 6.36 1.73 17.34 Row % 53.33 36.67 10.00 Column % 17.39 16.42 21.43

Total Frequency 92 67 14 173 % 53.18 38.73 8.09 100.00

Based on the table 3.32, the point of estimate was used on the Chi-square and calculating p-

values. This shows that the null hypothesis cannot be rejected at the 0.05 level of significance.

Therefore, the frequency of self-reported infection is similar in both groups (Table 3.32).

89

Table 3.32 Table and figures showing differences in frequency of skin/nail infections in

recipients of csDMARDs alone and bDMARDs alone, Sample size=173

3.4.8. Eyes, Ears, nose, Throat (EENT) Infections – a comparison

EENT infections are among the most common infections in RA. EENT infection also was

reported for three different levels of severity.

The data suggest that csDMARDs can increase the frequency of mild and moderate EENT

infection, while bDMARDs appeared to increase the frequency of severe EENT infection

(Table 3.33, Table 3.34).

Table 3.33 Frequency of eye, ear, nose & throat infections in patients taking csDMARDs alone

Ear Nose Throat infection in csDMARDs alone

Eent Infection Frequency % Cumulative

Frequency

Cumulative

%

Mild 83 44.39 83 44.39

Moderate 82 43.85 165 88.24

Severe 22 11.76 187 100.00


According to Table 3.33 and Table 3.34, severe EENT infection occurs more often in patients

who are taking bDMARDs alone compared to those who take csDMARDs alone. The

frequency of other types of infections was similar in both groups (Table 3.33-3.34)


Chi-Square 2.00 0.20 0.90





Cramer’s V 0.03

90

Table 3.34 Frequency of Ear Nose Throat infections in in patients taking bDMARDs alone

Ear Nose Throat infection in recipients of bDMARDs

EENT

infection

Frequency % Cumulative

Frequency

Cumulative

%

Mild 10 41.67 10 41.67

Moderate 10 41.67 20 83.33

Severe 4 16.67 24 100.00


Almost 10% of patients who were taking csDMARDs alone reported severe EENT infection,

this is about 12% of all patients who were taking csDMARDs alone (Table 3.35).

In contrast, 16 % of patients who were taking bDMARDs reported severe EENT infection

(Table 3.35). The figures in Table 3.35 are just descriptive, and don’t allow the strength of

the association to be confirmed.

Table 3.35 Table of differences in frequency of ear, nose, and throat infections in csDMARDs

alone and bDMARDs alone

Group

Response

Status Mild Moderate Severe Total

csDMARDs


% 39.34 38.86 10.43 88.63

Row % 44.39 43.85 11.76

Column% 89.25 89.13 84.62

bDMARDs


% 4.74 4.74 1.90 11.37

Row% 41.67 41.67 16.67

Column % 10.75 10.87 15.38


% 44.08 43.60 12.32 100.00

Calculating Chi- Square and P-Value for differences in EENT infection reveals that frequency

of self-reported infection is similar in both bDMARDs alone and csDMARDs alone. The large

value of the chi-square statistic, 0.4737, and the p-value of 0.7891 indicate that the null

91

hypothesis cannot be rejected at the 0.05 level of significance. Therefore, we conclude that

frequency of eye, ear, nose and throat (EENT) infection is similar in both groups (Table 3.36).

A criticism of this test is that it fixes the row and column margin totals, which in effect makes

an assumption about the distribution of the variables in the population being studied.

Table 3.36 Chi-squared for differences in frequency of EENT infections between csDMARDs

alone and bDMARDs alone, sample size=211


Chi-Square 2.00 0.47 0.79





Cramer's V 0.05

3.4.9. Heart infections comparison

Heart infection also was reported for three different levels of severity. Based on estimation of

the frequency of heart infection, the frequency of heart infection among patients taking

bDMARDs alone and csDMARDs alone weas different, with a significantly lower frequency

of infection among patients on bDMARDs (Table 3.37). Amongst the large number of

reports, 1646 participants did not report any heart infection.

Table 3.37 Table of frequency of Heart infections in csDMARDs alone

Heart infection in csDMARDs alone Heart infection Frequency % Cumulative Frequency Cumulative % Moderate 3 42.86 3 42.86 Severe 4 57.14 7 100.00 Note. Frequency Missing = 1646

Amongst bDMARD-alone recipients, only one case of mild heart infection was reported,

whereas in csDMARDs-alone recipients, a few patients reported moderate or severe

infections (Table 3.38). Patients with a moderate level of infection were 58,66 and 66 years

old, while those with a severe level of infection were 59, 60, 65 and 69 years old.

92

Table 3.38 Table of frequency of Heart infections in bDMARDs alone

Heart infection in Biologics

Heart

infection


Frequency

Cumulative

%

Mild 1 100.00 1 100.00


As the difference between csDMARDs and bDMARDs in heart infection appeared to be

possibly significant, the differences are demonstrated in a cylindrical graph (Figure 3.4). As it

is shown in the graph, the number of patients who were taking biologics and reported heart

infection was close to zero or negligible (Figure 3.4). The only patient in this group was 67

years old.

Figure 3.4 Comparison of the rate of heart infection in recipients of csDMARDs alone and

bDMARDs alone (with logarithm base).

3.4.10. Lung infections comparison

Lung infection was also reported for three different levels of severity. Lung infection is also

an important infection in rheumatoid arthritis and, based on Table 3.39, almost 61.29 % of the

participants in the csDMARDs group reported moderate lung infection (Table 3.39).

0.01

0.1

1

10

100

Mild Moderate Severe No infection

Number of patients in

Hundreds

Heart Infection

csDMARDs BDMARDs

93

Table 3.39 Table of frequency of Lung infections in csDMARDs alone

Lung infection in csDMARDs alone

Lung infection Frequency % Cumulative

Frequency

Cumulative

%

Mild 25 16.13 25 16.13

Moderate 95 61.29 120 77.42

Severe 35 22.58 155 100.00


According to Table 3.39 and Table 3.40, severe lung infection occurs almost twice as frequently

in patients who are taking csDMARDs alone compared to those who were taking bDMARDs

alone.

Table 3.40 Table of frequency of Lung infections in recipients of bDMARDs alone

Lung infection in bDMARDs recipients

Lung infection Frequency % Cumulative

Frequency

Cumulative

%

Mild 11 40.74 11 40.74

Moderate 10 37.04 21 77.78

Severe 6 22.22 27 100.00


Almost 19 % of patients who were taking csDMARDs alone reported severe lung infection.

This is about 22 % of all patients who were taking csDMARDs alone and almost 85% of all

patients who reported lung infection (Table 3.41). Almost 3% of patients who were taking

bDMARDs alone reported severe lung infection, this is about 22% of all patients who were on

bDMARDs alone and almost 15% of all patients who reported severe lung infection (Table

3.41). These figures are just descriptive, and any statistical differences need to be confirmed

with Chi-squared tests (Table 3.41).

94

Table 3.41 Table and Figure of differences in frequency of Lung infections in recipients of

csDMARDs alone and bDMARDs alone

Group by response

Group Mild Moderate Severe Total

Lung infection in recipients of

csDMARDs alone


% 13.74 52.20 19.23 85.16

Row % 16.13 61.29 22.58

Column % 69.44 90.48 85.37

Lung infection in recipients of

bDMARDs alone


% 6.04 5.49 3.30 14.84

Row % 40.74 37.04 22.22

Column % 30.56 9.52 14.63

Total 36 105 41 182

19.78 57.69 22.53 100

The difference in lung infection between those taking csDMARDs alone and bDMARDs alone

is statistically significant (p-value 0.01), at the level of 0.05 (Table 3.42).

The null hypothesis is that the frequency of self-reported infection is similar in both users of

bDMARDs alone and csDMARDs alone. The large value of the chi-square statistic, 9.3875,

and the small amount of p-value of 0.01 indicate that the null hypothesis can be rejected at the

0.05 level of significance. Therefore, it can be concluded that the frequency of lung infection

is different in both csDMARDs alone and bDMARDs alone. In other words, the frequency of

lung infection is significantly higher among patients who are taking csDMARDs alone (Figure

3.5).

Table 3.42 Chi-squared for differences in frequency of lung infections between csDMARDs

alone and bDMARDs alone, sample size= 182

Statistic DF Value Prob Chi-Square 2.00 9.39 0.01 Likelihood Ratio Chi-Square 2.00 8.32 0.02 Mantel-Haenszel Chi-Square 1.00 3.38 0.07 Phi Coefficient 0.23 Contingency Coefficient 0.22 Cramer's V 0.23

95

The difference in lung infection in between csDMARDs and bDMARDs is demonstrated in

the following chart (Figure 3.5).

Figure 3.5 Comparison of the frequency of lung infection for recipients of csDMARDs alone

and bDMARDs alone (with logarithm base).

3.4.11. Gasterointestinal tract (GIT) infections

GIT infection also was reported for three different levels of severity. Based on the frequency

table below, it can be seen that the frequency of gastrointestinal tract (GIT) infection among

patients who were taking biologic DMARDs was much lower than that for recipients of

csDMARDs alone (Tables 3.43- 3.44).

Table 3.43 Table of frequency of GIT infections in recipients of csDMARDs alone

Gastero Intestinal Tract (GIT) infection in csDMARDs alone

GIT Infection Frequency % Cumulative

Frequency

Cumulative

%

Mild 7 29.17 7 29.17

Moderate 8 33.33 15 62.50

Severe 9 37.50 24 100.00


0.01

0.1

1

10

100


Number of patients in Hundreds

Lung Infection

CsDMARDs BDMARDs

96

In recipients of bDMARDs, the only GIT infection reported was of moderate severity, while

in recipients of csDMARDs, there were numerous reports of GIT infections in the mild,

moderate and severe categories (Tables 3.43- 3.44).

Table 3.44 Table of frequency of GIT infections in in recipients of bDMARDs alone

As GIT infection was different between the csDMARDs and bDMARDs groups, we compared

this type of infection in these two groups (see Figure 3.6). Based on this figure, csDMARDs is

the major contributing factor for this type of infection and, with a minor difference between

groups, most of the patients reported a severe type of infection.

Figure 3.6 Comparison of the frequency of GIT infection in patients taking csDMARDs alone

and bDMARDs alone

0.01

0.1

1

10

100


Number of patients in Hundreds

GIT infection

csDMARDs BDMARDs

Gastro-intestinal Tract (GIT) infection in recipients of bDMARDs alone

Biologics

GIT

infection


Frequency

Cumulative

%

Moderate 1 100.00 1 100.00


97

3.4.12. Urinary tract infections (UTI) Urinary tract infection was reported at three different levels of severity. Based on Table 3.45,

moderate infection is the most common type of UTI in recipients of csDMARDs, followed by

mild and severe infections (Table 3.45).

Table 3.45 Table of frequency of UTI in recipients of csDMARDs alone

Urinary System infection in recipients of csDMARDs alone

Kidney and Urinary

infection


Frequency

Cumulative

%

Mild 12 15.19 12 15.19

Moderate 56 70.89 68 86.08

Severe 11 13.92 79 100.00


Almost 6% of patients who were taking bDMARDs alone reported severe urinary system

infection. This is about 25% of all patients who were receiving bDMARDs alone and almost

35% of all patients who reported severe urinary tract infection. These is descriptive information

and will be tested in the following section (Table 3.46).

Table 3.46 Table and figure showing differences in frequency of urinary tract infections in

recipients of csDMARDs alone and bDMARDs alone

Urinary tract infection recipients of bDMARDs

Kidney and Urinary

infection


Frequency

Cumulative

%

Mild 12 50.00 12 50.00

Moderate 6 25.00 18 75.00

Severe 6 25.00 24 100.00


In order to compare UTIs in recipients of csDMARDs alone and bDMARDs alone, the tables

were combined. (Table 3.47).

98

Table 3.47 Table and Figure of differences in frequency of urinary tract infections in

csDMARDs alone and bDMARDs alone recipients

Group

Response


csDMARDs


% 11.65 54.37 10.68 76.70

Row % 15.19 70.89 13.92

Column % 50.00 90.32 64.71

bDMARDs

Frequency 12 6 6 24

% 11.65 5.83 5.83 23.30

Row % 50.00 25.00 25.00

Column % 50.00 9.68 35.29


% 23.30 60.19 16.50 100.00

Differences between UTIs in recipients of csDMARDs alone and bDMARDs alone were tested

by Chi-square and p-value. The null hypothesis is that the frequency of Urinary tract infection

(UTI) differs between recipients of bDMARDs alone and csDMARDs alone. Examined for the

three categories, notably mild, moderate, and severe. The large value of the chi-square statistic,

17.3798, and the low p-value of 0.0002 indicate that the null hypothesis should be rejected at

the 0.05 level of significance. Therefore, it was concluded that the frequency of UTI is different

for these two groups, and that the observed difference is highly statistically significant. In other

words, the frequency of moderate and severe UTI is significantly higher among recipients of

csDMARDs alone. The associations observed for moderate and severe UTIs in recipients of

csDMARDs alone were not apparent for mild UTIs.

Table 3.48 Chi-squared for differences in frequency of urinary tract infections between

csDMARDs alone and bDMARDs alone, sample size=103


Chi-Square 2.00 17.38 0.00





Cramer's V 0.41

99

Almost 10% of patients who were taking csDMARDs alone reported severe urinary tract

infections, this is about 14% of all patients who were receiving csDMARDs alone and almost

64% of all patients who reported urinary tract infection (Figure 3.7)

Figure 3.7 Frequency of urinary tract infections in recipients of csDMARDs alone and

bDMARDs alone

UTI is more prevalent among patients on csDMARDs. UTI is also more prevalent among the

female sex. As there is a significant difference between female and male distribution in between

csDMARDs and bDMARDs, the current difference in UTI can be partly and completely due to

this difference in the sex distribution[33].

3.4.13. Musculoskeletal infections (MSK) MSK infection also was reported for three different levels of severity. The frequencies for MSK

infection in recipients of csDMARDs alone and bDMARDs alone were very similar. Moderate

MSK infection was more frequent in recipients of csDMARDs alone. (Tables 3.49-3.50).

0.01

0.1

1

10

100

mild moderate severe No infection

Number of patients in

Hundreds

Urinary System infection

csDMARDs BDMARDs

100

Table 3.49 Table of frequency of musculoskeletal system infection in recipient of csDMARDs

alone

Musculoskeletal system infection in csDMARDs alone

Bone Joint and

Muscle infection


Frequency

Cumulative

%

Mild 6 19.35 6 19.35

Moderate 16 51.61 22 70.97

Severe 9 29.03 31 100.00


The figures in Tables 3.49 to 3.51 are just descriptive and need to be tested further by

application of a Chi-squared test.

Table 3.50 Table of frequency of musculoskeletal system infection in bDMARDs alone

Musculoskeletal system infection in recipient of bDMARDs

Bone Joint and

Muscle infection


Frequency

Cumulative

%

Mild 4 33.33 4 33.33

Moderate 4 33.33 8 66.67

Severe 4 33.33 12 100.00


Almost 21 % of all patients who were receiving csDMARDs alone or bDMARDs alone

reported severe MSK infection; this is about 29 % of all patients who were on csDMARDs

alone and almost 69% of all patients who reported severe MSK infection. Almost 9% of

patients who were receiving bDMARDs alone reported severe MSK infection, this is about

33% of all patients who were on bDMARDs alone and almost 30% of all patients who reported

severe MSK infection (Table 3.51)

101

Table 3.51 Table and figure of differences in frequency of muscular skeletal system infections

in recipients of csDMARDs alone and bDMARDs alone

Group Response


csDMARDs

Frequency 6 16 9 31

% 13.95 37.21 20.93 72.09

Row % 19.35 51.61 29.03

Column % 60.00 80.00 69.23

bDMARDs

Frequency 4 4 4 12

% 9.30 9.30 9.30 27.91

Row% 33.33 33.33 33.33

Column % 40.00 20.00 30.77


% 23.26 46.51 30.23 100.00

The null hypothesis is that the frequency of musculoskeletal (MSK) infection differs between

recipients of bDMARDs alone and csDMARDs alone. MSK infection was categorized into

mild, moderate and severe. The low value of the chi-square statistic, 1.4013, and the p-value

of 0.4963 indicate that the null hypothesis should be rejected at the 0.05 level of significance.

Therefore, it can be concluded that frequency of MSK infection is similar in both groups

(Table 3.52).

Table 3.52 Chi-squared for differences in frequency of Musculoskeletal system infections

between recipients of csDMARDs alone and bDMARDs alone, sample size=4

Statistic DF Value Prob Chi-Square 2.00 1.40 0.50 Likelihood Ratio Chi-Square 2.00 1.39 0.50 Mantel-Haenszel Chi-Square 1.00 0.15 0.70 Phi Coefficient 0.18 Contingency Coefficient 0.18 Cramer's V 0.18 WARNING: 33% of the cells have expected counts less than 5. Chi-Square may not be a valid test.

102

3.4.14. Artificial joint infections Artificial joint infection also was reported for three different levels of severity. Almost 80% of

the artificial joint infections in recipients of csDMARDs were classified as severe infection

(Table 3.53).

Table 3.53 Table of frequency of Artificial Joint infection in csDMARDs alone

Artificial joint infection in csDMARDs alone

Artificial Joint

Infection


Frequency

Cumulative

%

Moderate 1 20.00 1 20.00

Severe 4 80.00 5 100.00


The only artificial joint infection which was reported in recipients of bDMARDs was

classified in the self-report as mild infection (Table 3.54).

Table 3.54 Table of frequency of artificial joint infection in bDMARDs alone

Artificial joint infection in Biologics

Artificial Joint

Infection


Frequency

Cumulative

%

Mild 1 100.00 1 100.00


Based on the above frequency table (Table 3.54) artificial joint infections in recipients of

bDMARDs alone were numerically less frequent than those in recipients of csDMARDs alone.

Using estimation methods and testing methods is not appropriate here because the only

infection among patients who were taking bDMARDs alone was categorised as mild, whereas

those self-reported by recipients of csDMARDs alone were categorized as moderate or severe

(Table 3.54). Furthermore, the numbers are too small to allow meaningful comparison.

However, it should be indicated that artificial joint infection is almost always an emergency

case and needs hospital admission. Therefore, there should not be tolerance of mild or moderate

infection. In other words, the subjective data here do not concur with reality but, still, our

conclusion remains that csDMARDs cause more artificial joint infection than bDMARDs.

103

3.4.15. Nervous system infections Nervous system infection also was reported for three different levels of severity. Based on

Table 3.55 Nervous system infection is not common in RA and the only episode of this

infection caused mild symptoms and occurred in one patient who was taking csDMARDs alone

(Table 3.55).

Table 3.55 Table of frequency of Nervous System infection in csDMARDs alone

Nervous system infection in csDMARDs alone

Infection Neuro Frequency % Cumulative

Frequency

Cumulative

%

Mild 1 100.00 1 100.00


There is no report of nervous system infection in patients who were taking bDMARDs alone

(Table 3.56).

Table 3.56 Table of frequency of Nervous system infection in recipients of bDMARDs alone

Nervous system infection in Biologics

Biologic Infection

Neuro


Frequency

Cumulative

%

0 0 0 0

Frequency Missing = 1653

3.4.16. Tuberculosis (TB) infection Tuberculosis (TB) also was reported at three different levels of severity. However, there were

just two reports of moderate to severe level tuberculous infection. Based on the frequency table

tuberculous infection was very uncommon in RA (Table 3.57-3.58).

104

Table 3.57 Table of frequency of tuberculous infection in recipients of csDMARDs alone

TB infection in csDMARDs alone

TB Infection Frequency % Cumulative

Frequency

Cumulative

%

Moderate 2 100.00 2 100.00


Only two episodes of tuberculous infection with moderate symptoms were reported and these

infections were restricted to recipients of csDMARDs alone (Tables 3.57-3.58).

Table 3.58 Table of frequency of tuberculous infection in recipients of bDMARDs alone

TB infection in patients taking Biologics

TB Infection Frequency % Cumulative

Frequency

Cumulative

%

0 0 0 0


3.3.17. Blood infections Blood infection also was reported at three different levels of severity. The majority of blood

infections in recipients of csDMARDs were of moderate severity (54.55%) (Table 3.59).

Table 3.59 Table of frequency of blood infection in csDMARDs alone

Blood infection in recipients csDMARDs alone


Frequency

Cumulative

%

Mild 1 9.09 1 9.09

Moderate 6 54.55 7 63.64

Severe 4 36.36 11 100.00


In contrast, the majority of blood infection reports in recipients of bDMARDs were reported to

be mild (50%) (Table 3.60).

105

Table 3.60 Table of frequency of blood infection in recipients of bDMARDs alone

Blood infection in recipients of bDMARDs alone


Frequency

Cumulative

%

Mild 2 50.00 2 50.00

Moderate 1 25.00 3 75.00

Severe 1 25.00 4 100.00


Almost 27 % of patients who were taking csDMARDs alone reported severe blood infection,

this is about 36 % of all patients who were taking csDMARDs alone and almost 80% of all

patients who reported severe blood infection. However, it should be indicated that infection is

almost always emergency and needs hospital admission. Therefore, there is no mild or moderate

infections. In other words, subjective data here does not coordinate with reality, but still our

conclusion stays similar and states that csDMARDs cause more blood infection than

bDMARDs. Almost 7% of patients who were taking bDMARDs alone reported severe blood

infection. This was about 25% of all patients who were taking bDMARDs alone and almost

20% of all patients who reported severe blood infection (Tables 3.61).

Table 3.61 Table and figure for differences in frequency of blood infections in recipients of

csDMARDs alone and bDMARDs alone

Group Response


csDMARDs

Frequency 1 6 4 11

% 6.67 40.00 26.67 73.33

Row % 9.09 54.55 36.36 -

Column % 33.33 85.71 80.00 -

bDMARDs

Frequency 2 1 1 4

% 13.33 6.67 6.67 26.67

Row % 50.00 25.00 25.00

Column % 66.67 14.29 20.00


% 20.00 46.67 33.33 100.00

106

The figures from tables 3.59 to 3.61 are just descriptive, and they do not reveal anything about

the association. In order to estimate the association, we need to do Chi-squared tests. In order

to undertake the Chi-squared test, the null hypothesis is that the frequency of blood infection is

greater in the recipients of csDMARDs alone. Blood infection reports allowed categorisation

into three groups, notably mild, moderate, and severe. The large value of the chi-square

statistic, 3.1169, and the moderately high p-value of 0.2105 indicate that the null hypothesis

cannot be rejected at the 0.05 level of significance. Therefore, we conclude that the frequency

of blood infection is similar in the two groups. In this test, 83% of cells were less than 5, so

other tests (chi- square, likelihood ratio, Mantel- Haenszel, phi Coefficient, Contingency

coefficient, Cramer’s V) need to be applied to check the veracity of the findings (Table 3.62).

Table 3.62 Chi-squared for differences in frequency of Blood infections between recipients of

csDMARDs alone and bDMARDs alone, sample size= 15

3.4.18. Viral Infections Viral infection was also reported at three different levels of severity. The majority of reported

viral infections in both the csDMARDs and bDMARD recipients were moderate in severity

(Tables 3.63 to 3.64).

Table 3.63 Table of frequency of viral infection in recipients of csDMARDs alone

Viral infection in csDMARDs alone

Viral

Infection


Frequency

Cumulative

%

Mild 30 34.88 30 34.88

Moderate 42 48.84 72 83.72

Severe 14 16.28 86 100.00


Statistics used DF Value Probability (p-value)

Chi-Square 2.00 3.12 0.21 Likelihood Ratio Chi-Square 2.00 2.83 0.24 Mantel-Haenszel Chi-Square 1.00 1.45 0.23

Phi Coefficient 0.46 Contingency Coefficient 0.41

Cramer's V 0.46

107

Table 3.64 Table of frequency of viral infection in recipients of bDMARDs alone

Viral infection in Biologics

Viral

infection


Frequency

Cumulative

%

Mild 5 35.71 5 35.71

Moderate 7 50.00 12 85.71

Severe 2 14.29 14 100.00


Almost 14 % of patients who were taking csDMARDs alone reported severe viral infection.

This is about 16% of all patients who were on csDMARDs alone and almost 87% of all patients

who reported severe viral infection (Table 3.65).

Almost 2% of patients who were taking bDMARDs alone reported severe viral infection. This

is about 14% of all patients who were on bDMARDs alone and almost 12.5% of all patients

who reported severe viral infection (Table 3.65).

Table 3.65 Frequency of viral infections in recipients of csDMARDs alone and bDMARDs

alone

Group Response


csDMARDs


% 30.00 42.00 14.00 86.00

Row % 34.88 48.84 16.28

Column % 85.71 85.71 87.50

bDMARDs

Frequency 5 7 2 14

% 5.00 7.00 2.00 14.00

Row % 35.71 50.00 14.29

Column% 14.29 14.29 12.50


% 35.00 49.00 16.00 100.00

108

The null hypothesis is that the frequency of viral infections is similar in recipients of

bDMARDs alone and csDMARDs alone. Viral infections were categorised into three groups,

notably mild, moderate, and severe. The values of the chi-square statistic, 0.0356, and the very

high p-value of 0.9824, indicate that the null hypothesis should be confirmed at the 0.05 level

of significance. Therefore, we conclude that the frequency of viral infection is similar for

recipients of csDMARDs alone and bDMARDs alone. In this test, 33% of cells contained a

number less than 5. By checking the likelihood ratio and performing other tests (chi- square,

likelihood ratio, Mantel- Haenszel, phi Coefficient, Contingency coefficient, Cramer’s V) the

findings can be confirmed (Table 3.66).

Table 3.66 Chi-squared test for differences in the frequency of viral infections between

patients who were receiving csDMARDs alone and bDMARDs alone, sample size = 100


Chi-Square 2.00 0.04 0.98





Cramer's V 0.02

3.5. Chapter discussion and conclusion

In this section, the demography of the ARAD data has been discussed. Demography analysis

of ARAD data can allow for the comparison of differences between the Australian population

and other populations around the world. This comparison is, potentially, helpful for other

practitioners when they want to generalise the results of studies. This valuable information also

provides a good tool to assess the reliability of inferential analysis findings when ARAD reports

are examined (53).

Different studies reveal the strong connection between smoking and RA. For example,

Criswell et al. (2002), in a cohort study, showed that abstinence from smoking may reduce

the risk of RA among postmenopausal women [24]. A case control study by Padyukov et al.

(2004) showed the risk of RA positive with the SE of HLA-DR is strongly influenced by the

presence of an environmental factor (e.g., smoking) in the population at risk [16].

109

In 2006, Costenbader et al., in a cohort study, showed that past and current smoking were

related to the development of RA, seropositive RA. In this study it was shown that both

smoking intensity and duration were directly related to risk of infection [25]. In a meta-

analysis by Sugiyama et al. (2010), it was shown that smoking is a risk for RA, especially

seropositive RA in men. For women, the risk for smokers is about 1.3 times greater than for

non- smokers [26]. Di Giuseppe et al. (2014) showed that life-long cigarette smoking was

associated with the risk of RA, even among smokers with a low life-long exposure [27].

Furthermore other studies show the connection between effectiveness of smoking cession

and better responsiveness of bio-treatment [28].

Sustained smoking cessation within four years of RA diagnosis is connected to a reduction in

mortality risk, this rate is same as non-smokers. However, smoking more than five years after

RA diagnosis increased mortality beyond the risk of non-RA patients [29]. According to the

ARAD, the rate of smoking between 2001 to 2014 is 10.5% (328/3111). This is almost 8.9%

of all patient visits (2484/27712). Table 3.67 shows the rate of smoking in the general

population in Australia during the same time.

Table 3.67 comparing rate of smokers during the years 2001 to 2013, Australia [30]

Year %Total smokers

2001 22

2004 20

2007 19

2010 18

2013 15

It seems that the rate of smokers in RA is less than rate of smokers among general population

in Australia during those years. Two major possibilities for this discrepancy include (1) ARAD

is a subjective report and data are not reliable; and (2) there are several risk factors for causing

RA and risk factors other than smoking play more significant roles in Australia, especially since

the rate of RA disease in Australia is higher than many other countries [31].

With resoect to alcohol consumption, a few studies have demonstrated that consuming a

moderate amount of alcohol is associated with a reduction in the signs and symptoms of

110

arthritis in RA [17] [34]. Overall, based on ARAD reports, the amount of alcohol consumed

by RA participants is lower compared to that in the general population (1.32 compared to 2.72

standard drinks per day)[35]. According to the British Society of Rheumatology, alcohol

abusers are unsuitable for methotrexate therapy [36]. Rheumatologists should inform RA

patients receiving methotrexate (MTX) to limit alcohol intake and to consider changing MTX

to a safer medication [36]. Based on this advice, most of the patients consuming excessive

alcohol should have been shifted from MTX to bDMARDs; in other words we would expect

to see a meaningful difference in drinking alcohol between patients on csDMARDs compared

to patients on bDMARDs. However, in ARAD this difference between the two groups is not

significant. There is a guideline in Australia to assess alcohol intake in patients before

prescribing MTX, but few data question the contribution of alcohol to the risk of

hepatotoxicity [37] [38].

Based on ARAD, there is a marginal difference in alcohol consumption between bDMARD

and csDMARD users (Mantel-Haenszel Chi-Square P value 0.05). There are several

possibilities for this discrepancy. Australian prescribers may not be permitted to change

medication based on alcohol consumption. According to Australian therapeutic guidelines,

prescribers should assess a patient’s alcohol intake before prescribing methotrexate.

According to this guideline, if methotrexate is prescribed for an alcohol abuser, closer kidney

and liver assessments are required [39]. In addition, Rajakulendran et al. (2008) includes

other medications, such as leflunomide, in this alcohol restriction as well [37]. Other potential

reasons for this difference include (1)n relatively few patients are heavy alcohol users,(2)

subjective data about drinking alcohol is not reliable, and (3) prescribing biologics was not

that common during the study period and (4) most of the alcohol abusers remained in the

csDMARDs group. However, the last possibility was not significant in ARAD.

Based on the findings presented in Tables 3.37-3.38 and in Figure 3.4, it can be seen that the

rate of heart infection is very low among RA patients. The very small numbers reported makes

it difficult to compare the frequency of such infections between recipients of csDMARDs alone

and bDMARDs alone. There is a trend toward higher rates of self-reported moderate or severe

heart infection in csDMARDs users. However, these findings need to be interpreted with

considerable caution, since they are self-reported and participants may not have grasped the

distinction between infection involving the heart and other diverse heart conditions.

111

Lung infections were reported frequently in recipients of both csDMARDs and bDMARDs.

Interestingly, recipients of bDMARDs alone reported lower rates and milder or less severe lung

infections (Table 3.39-3.42). Whether this may be due to a protective effect of bDMARDs is

unclear, but this is considered unlikely, even though there may be better outcomes in bDMARD

recipients for more severe lung infections. An alternative possibility is that certain synthetic

DMARDs, such as methotrexate andlLeflunomide may have conferred greater lung infection

susceptibility. Participants may not have been able to easily distinguish between viral infections

affecting the respiratory tract and lung infections, which may have resulted in unequal

variations in assignment to these two categories of infection.

Urinary tract infections were found to be more common and more severe amongst recipients of

csDMARDs alone (65% compared to 25%) (Table 3.45-3.47). Here again, certain csDMARDs

may have increased the propensity to UTIs to a greater degree than bDMARDs. Prednisolone

use and in particular dosage may also be relevant in this regard. Gastrointestinal tract (GIT)

infection was relatively uncommon and not unequivocally associated with any particular

treatment group. (Table 3.43-3.44).

In summary, the csDMARDs-alone and bDMARDs-alone treatment groups in the ARAD

dataset were found to be well matched and, thus, quite comparable. With respect to self-

reported infections of differing severity, lung infections (LRTIs) and urinary tract infections

(UTIs) were strongly associated with use of csDMARDs, implying either a biologic DMARD

protective effect, which is considered improbable, or a greater propensity to these infections

due to the use of one or more synthetic DMARDs, such as methotrexate or leflunomide for

example, both of which have been implicated in LRTI.

The descriptive analysis of ARAD reports during 2001 to 2014 shows that, when infections of

differing severity are compared between csDMARDs and bDMARD recipients, bDMARDs

alone are associated with less risk of infections among Australian patients with RA than

csDMARDs alone.

Overall, the type of infection, differences in the severity of infections and whether the

frequencies differ significantly statistically between patients who are taking csDMARDs alone

and patients who are taking bDMARDs alone are shown in table 3.68 and figure 4.1.

112

Table 3.68 Comparison of the frequencies of different infections of varying severity between

recipients of csDMARDs alone and bDMARDs alone

Distribution of the severity of infection %

Frequency of the type of infection (%) Type of

infection Severity csDMARDs bDMARDs p-value of

difference LRTI

Mild 16.13 40.74 0.0156

9.8

Moderate 61.29 37.04 Severe 22.58 22.22

GITI

Mild 29.17 0 NA

2.82 Moderate 33.33 100

Severe 37.5 0 UTI

Mild 15.19 50 0.0002

6.33 Moderate 70.89 25

Severe 13.92 25 Viral

Mild 34.88 35.71 0.9819

7.52 Moderate 48.84 50

Severe 16.28 14.29 Skin/nail

Mild 53.15 53.33 0.9072

13 Moderate 39.16 36.67

Severe 7.69 10 EENT

Mild 44.39 41.67 0.8032

14.75 Moderate 43.85 41.67

Severe 11.76 16.67 Heart Mild 0 0

NA

0. 38 Moderate 42.86 100 Severe 57.14 0

MSK, Bone, Joint

Mild 19.35 33.33 0.4982

3.11 Moderate 51.61 33.33

Severe 29.03 33.33 Artificial joint

Mild 0 100 NA

0.61 Moderate 20 0

Severe 80 0 Blood

Mild 9.09 50 0.2426

0.707 Moderate 54.55 25

Severe 36.36 25 LRTI: Lower respiratory tract infection; GITI: Gastrointestinal tract infection; UTI: Urinary Tract Infection

In the above tables, p-values indicate whether there is a significant difference in the frequency

of infections between recipients of csDMARDs alone and bDMARDs alone.

The literature review also shows that both csDMARDs and bDMARDs can increase the risk

of serious infection and non-serious infection. However, according to the literature review,

113

the rate of infection by different medicines is slightly different and, overall, bDMARDs are

causing more infections. FORWARD is one of the largest National Databank for Rheumatic

Diseases across the world. According to this database, TNFIs have almost the highest rate of

serious infection 26.9 (95% CI 24.5‐29.6) compared to non TNFIs 23.3 (95% CI 19.0‐28.5),

and csDMARDs 22.4 (95% CI 19.2‐26.1) [18].

In the same study, the smoking rate was compared between csDMARDs and bDMARDs (p

value 0.738) and ARAD. No significant difference was identified [18].

It has also been demonstrated that, compared to patients with OA, orthopaedic surgery in RA

is associated with a higher risk of infection[40]. This risk also increases in patients who are

taking bDMARDs or cs DAMARDs[41]. Due to this risk, the American College of

Rheumatology advises stopping TNFα inhibitors one week or more prior to surgery[41]. The

British Rheumatology Society also, for the same reason, recommends withholding therapy for

3 to 5 times the half-life of the drug[42], and the Canadian Rheumatology Association

reduces this period to 2 half-lives of the drug[43].

Among all the different anti-RA medications, csDMARDs (methotrexate,

hydroxychloroquine, sulfasalazine, and azathioprine) are safer[40]. Among bDMARDs, anti-

TNFα inhibitor therapy significantly increases the risk of surgical site infection and should be

stopped for more than two weeks prior to orthopaedic surgery[40]. Infliximab and etanercept

from bDMARDs are usually prescribed in longer disease duration and are associated with

further risk of acute surgical site infection (SSI)[44]. Withholding medications before and

after a procedure depends on the pharmacokinetic properties of the individual medication

and the region of the world[45].

Other risk factors associated with an increased risk of infection include steroid doses over 15

mg/day, coronary artery disease, and being underweight [44]. Therefore, it is important to

taper prednisone in the peri-operative management strategy [44]. Sometimes, the risk of

csDMARDs and bDMARDs, compared to other risk factors, is ignorable [44].

Based on the different studies in the literature, the risk of infection is not always the same

among all bDMARDs. For example, TNF inhibitors and methotrexate are both associated

114

with increased incidence of infections, much more than biologics [46]. However, in a cohort

study in the USA conducted with 609 patients with RA before the introduction of biologics,

the infection rate was reaching almost 19.64/100 patient-years and, after bDMARDs, was

reduced to 12.87/100 patient-years in matched controls. In this study, septic arthritis (14.89;

95% CI: 6.12-73) was the most common infection, followed by osteomyelitis (10.63; 95% CI:

3.39-126)[47]. TNFIs also seem to be associated with an almost 2- to 4-fold increased risk of

serious bacterial infections and a slight increase in non-serious infection. Still, a combination

of TNF inhibitors with methotrexate can increase the risk of serious infection, significantly.

[48][49].

Etanercept and infliximab are other samples of biologics. The risk of serious infection in

monotherapy with these medications is the same as for methotrexate [50][51]. These serious

infections include bacterial infection, fungal infection, bronchitis, cellulitis, herpes zoster

infection, pneumonia, peritonitis, pyelonephritis, sepsis, and tuberculosis [50]. In both

etanercept and infliximab, if there is a combination therapy with methotrexate, the risk

reaches higher than the risk with methotrexate alone (P 0.05 for both infliximab doses)[52].

On the other hand, adalimumab from bDMARDs assumes to cause limited incidence of

serious infections. The overall rate of infections in the pooled adalimumab (1.55/patient-year)

is similar to methotrexate monotherapy (1.38/patient year)[53].

With the use adalimumab, the incidence of serious infections is almost 2.03/100 patient-years

[48]and, from the most common to the least common, infections include pneumonia, urinary

tract infections, and septic arthritis. The safety of adalimumab has been approved in other

studies. For example, in a study on 10,000 patients with approximately 12,500 patient-years

of adalimumab exposure only 5.1/100 patient-years developed serious infection[49].

Anakinra is another sample from bDMARDs. Anakinra has been connected to serious

infection in organs, such as lung and skin (5.37/100 patient-years in compare to 1.65/100

patient-years)[54]. However, it seems that most of this connection to infection occurs when a

patient is taking a baseline corticosteroid, otherwise the serious infection rate was

substantially lower (2.87/100 patient-years compare to 7.13/100 patient-years)[54].

115

Abatacept also has a higher rate of serious infection compared to many other bDMARDs (78).

The rate of serious infection in abatacept sometimes is higher than the infection rate in the

methotrexate monotherapy group (2.5% versus 0.9%; 95% CI: 0.3-3.6) [55]. Overall, the

incidence of both serious and nonserious bacterial infections increases in abatacept and it is

better to avoid prescribing abatacept and TNFIs together [55].

Rituximab, in comparison to other bDMARDs, has been associated with less serious

infection. However, there are a few reports regarding a 4- to 7-fold increased risk of

reactivation of latent tuberculosis when using TNFIs together with infliximab, and this rate is

even more than the combination of etanercept and TNFIs[56].

Overall, as a result of the modes of action in medicine, among bDMARDs TNF inhibitors,

anakinra, abatacept, and rituximab can change immune response, leaving patients at an

increased risk of infection. This risk increases by combining some bDMARDs and TNFIs.

For example, when infliximab is added to methotrexate in compare to methotrexate

monotherapy, the risk of serious infection increases, significantly [52].

The most common types of infections in bDMARDs are respiratory tract infections (including

pneumonia), following by skin and soft-tissue infections and urinary tract infections [52].

There is also a risk of tuberculosis with TNFIs. Some evidence reveals that that this risk is the

highest with infliximab and less with anakinra[52]. Rituximab and abatacept seem to have a

lower risk of viral serious infection compared to TNFIs. However, in long term studies, this

was not approved [57][58]. Rituximab monotherapy seems to be associated with serious

infection when it is prescribed for a longer period of time but, overall, the rate is lower than

for many other bDMARDs (94). However, decreases in the levels of IgM during prolonged

treatment with rituximab is associated with a higher incidence of opportunistic infections,

such as non-Hodgkin's lymphoma (NHL) [58][59]. In most studies, corticosteroids (CS) are

assessed among csDMARDs. CS use is associated with an increased relative risk (RR) (1.67,

1.47–1.87) of infection[60]. MTX in a Canadian study was associated with slight increase of

risk of pneumonia (RR 1.2; 95% CI 1.0–1.3) [61], while another study from US indicated a

decreased infection risk in MTX users [49]. In conclusion, the slightly increased risk of

infection in MTX is counterbalanced by the effective control of rheumatic disease, leading to

improved function.

116

In one study, the incidence of severe infection with LEF can reach up to 3.3% person-year

[62][21]. Hydroxychloroquine (HCQ), sulfasalazine (SSZ), and cyclosporine A (CsA) in a US

study were not associated with risk of infection[21]. If there is an association, that is very

mild unless the patient is suffering from other conditions, such as transplanted organs[63].

In summary, according to the literature and ARAD results, both bDMARDs and

csDMARDs will increase the risk of bacterial infection, especially pneumonia. With some

exceptions, it seems that, overall, bDMARDs are associated with higher rates of infection

compared to csDMARDs. However, the ARAD analysis depends on the severity of

infection, this ratio can change or the differences not be regarded significant (Table 3.68).

The reason for this discrepancy might be due to geographical differences; for example, in

TB infection, TB risks in TB-endemic areas with TNFIs is much higher than other

regions[64].

117

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125

CHAPTER 4

Inferential Analysis of Infection in Rheumatoid

Arthritis

126

Abstract

Objective: To conduct an inferential analysis of the association between the risk of infection

and each Anti-RA medication. The current analysis provides valuable information concerning

the relative frequency of self-reported infections in users of diverse anti-rheumatic therapies.

Various organs including eye, ear, nose, throat, lungs, urinary tract, heart, gastrointestinal tract,

CNS were examined as well as systemic infections of a viral and pyogenic nature (sepsis /

septicaemia) are investigated.

Methods Self-reported and unverified data concerning infections was collected from 3110

Australian Rheumatology Association Database (ARAD) participants, who reported

sequentially from 2001 to 2014. Through the processes of data cleaning all duplicated

answers, single answers and faulty reports were deleted. Overall 27,709 visit reports were

available. Data was tested by multinominal logistic regression in SAS software. Mild,

moderate and severe infections assigned according to a priori descriptive criteria were

categorised in relation to organ involvement / body system affected and examined in relation

to current therapy.

Results: The most frequent infections reported by ARAD participants were EENT system

infections (eye, ear, nose and throat,14.75%) followed by skin and nail infections (13%), lung

infections (9.83%), and viral infections of any type (7.52%). Based on the same database, the

most commonly used bDMARDs were Etanercept, followed by Adalimumab. Amongst

csDMARDs the most commonly used medications were: Methotrexate, Hydroxychloroquine

and Sulphasalazine. Among all these medications safest medication in most common

infections were as following. Etanercept and Methotrexate the safest for EENT infection,

Etanercept and Adalimumab the safest in lung infection, and Leflunomide safest option in

skin and nail infections (Table 4.53).

Conclusion: Both csDMARDs and bDMARDs are shown to be associated with higher risk

of infection in RA. It seems that prednisolone (with consumption prevalence of 3.33%)

followed by cyclosporine (with a consumption prevalence of 0.05%) are the most common

medications in most of the moderate to severe infections throughout body. In comparison to

127

csDMARDs, if we consider prevalence of consumption, bDMARDs are rarely causing

moderate or severe infections. Some medications are playing a paradoxical role. For example,

although taking Adalimumab usually increases the risk of infection in skin and nail infection,

in comparison to other medications, it was found to be associated to a reduction in prevalence

of artificial joint or GIT infections. Overall, bDMARDs seems to be safer with lower risk of

infection as compare to csDMARDs.

128

1. Introduction

Rheumatoid arthritis (RA) affects approximately 0.8% of adults and is a cause of significant

morbidity and mortality [1]. RA also affects approximately three times more females than males

[3]. Disease onset is commonly between 40 and 70 years of age, though it can begin at any age.

Understanding the pathogenesis of RA has progressed over the past few decades resulted in the

development of more effective anti-RA medications [4].

Conventionally, in RA, non-steroidal anti-inflammatory drugs, glucocorticoids, and disease-

modifying antirheumatic drugs (DMARDs) are used to treat the disease [5]. The most widely

used DMARD is methotrexate (MTX), which is the basis of most treatment programs for

rheumatoid arthritis [6]. MTX has the highest retention rates compared to other available

medications [7].

Despite progress in developing more efficacious treatment for RA, the risk of infections in

patients receiving biologic or conventional treatments has not been substantially reduced [7].

One theory for this disturbing statistic is that immunosuppressive or disease-modifying

treatments are often required in those most vulnerable to infections, such as elderly patients and

patients with multiple comorbidities. This naturally increases the risk of infection after

treatment with anti-RA medications. Rheumatologists should be aware of the specific patterns

of infection risk that treatment with anti-RA medications confer [8]. This is especially important

with newer treatment modalities. By understanding the risk of infection in different organs and

the severity of those infections, potential risk factors and their connection to other treatments,

health practitioners can adjust medications and institute preventative measures accordingly. For

example, measures such as appropriate screening for and treatment of chronic hepatitis B

virus infection, to ensure optimal vaccination against respiratory pathogens (influenza virus and

pneumococcus) and, where appropriate, offer chemoprophylaxis in patients susceptible to

Pneumocystis jirovecii pneumonia [9]. Patients who have had a splenectomy or in whom there

is chronic sino-pulmonary infection, including bronchiectasis can be identified for increased

vigilance, vaccination and fast-tracking when infections flare or develop [9].

129

Conventionally, in RA, analgesics, non-steroidal anti-inflammatory drugs, glucocorticoids

(GC), and disease-modifying anti-rheumatic drugs (DMARDs) are used to treat the disease.

The former two only suppress symptoms, whereas GC and DMARDs suppress symptoms and

importantly also modify the course of the disease. The most widely used DMARD is

methotrexate (MTX). MTX usually forms the basis of most treatment programs for rheumatoid

arthritis.58–60. It is noteworthy that MTX has the highest retention rates compared to other

available medications [3].

Previous and ongoing research into therapeutic possibilities for RA has led to the development

of potent, biologic medications. Using effective medication should be associated with goal-

oriented treatment plans with regular appraisals of disease activity[10]. The treatment goals for

RA have shifted from mainly symptomatic relief to minimising or eliminating disease activity

and in turn altering the progression of the disease. This can potentially improve long-term

outcomes and reduce morbidity rates [10]. Better treatment strategies have significantly

moderated the severity of RA in the overall population. This has resulted in lower rates of joint

replacement and reduced hospital admissions for RA.

Lower frequencies for vasculitis have also been reported [11]. Better use of csDMARDs

treatment has probably contributed to this improvement because the beginning of the decline in

these measures of disease was noted prior to the use of biologic DMARDs however bDMARDs

may well have reinforced these effects. The use of bDMARDs has further reduced symptoms

and has improved functional and work capacity[12]. The pro-inflammatory cytokines, such as

IL-1, IL-6, and tumour necrosis factor (TNF), have been shown to play an integral role in RA

pathogenesis. Therefore, the development of biologic agents, which target these mediators

could be expected to impact disease activity significantly. IL-1 antagonists proved to be

disappointing, but TNF and IL-6R blockers and IL-6 monoclonal antibodies have shown much

superior efficacy[13].

The approach to the RA treatment has changed during the time and is different among different

nations. During 2001 to 2004, National RA treatment in Australia was based on taking simple

analgesics (e.g., paracetamol), Omega-3 supplements, patient education, physical therapy and

exercise, applying Ice and/or heat, and enhanced primary care referrals (e.g., occupational

therapy and physiotherapy[14].

130

For medicine, usually NSAIDs or COX-2 inhibitors were prescribed in the early stages. If

symptoms continued, the patient was referred to a rheumatologist, where csDMARDs plus a

low dose corticosteroid was prescribed. If disease signs and symptoms were still not

controlled, a rheumatologist could start advanced therapy with DMARDs, leflunomide,

cyclosporine or even the biologic agents, anakinra, anti-TNFs and rituximab[14].

At the same time, almost another 22 different RA management guidelines (American,

APLAR, Australian, Brazilian, British Columbia, British Society for Rheumatology:

established and early, Canadian, EULAR, French, German, Hong Kong, Indian, Latin

American, Mexican, England, Scotland, South African, Spanish, Swedish, Treat to target,

Turkish) show that several general principles were followed. In all these guidelines, remission

or low disease activity is the preferred target. csDMARDs usually started as soon as possible

after the diagnosis and disease activity monitoring, regularly. There is an emphasis in all of

these guidelines that methotrexate is the best initial treatment, and that this can be usefully

enhanced with temporary glucocorticoid treatment. Biologic DMARDs were usually used in

persistently active disease in patients who have already received methotrexate/other

csDMARDs. As soon as the patient achieved a sustained remission, biologics can be

tapered[15].

There are a few minor differences about the value and place for using combinations of

csDMARDs. For example, EULAR guidance is uncertain about using csDMARDs, however,

according to ACR guidance, using csDMARDs is essential. NICE guidance recommends only

starting biologics in patients with disease that has not responded to intensive therapy, using a

combination of conventional DMARDs[16].

Another difference in these guidelines is in the treatment of moderately active RA. While,

different guidelines have ignored to separate moderately active RA from others, the ACR

guidance strongly recommends considering treating moderate RA disease intensively[15].

According to the Australian guidelines for RA treatment in 2020, the guidelines were changed

briefly. In the Australian guideline, the initial treatment starts with simple analgaesics (e.g.,

paracetamol), and omega-3 supplements. In the meantime, patient education (e.g., Arthritis

131

Australia), physical exercise, applying ice and/or heat, and enhanced primary care referrals

(e.g., physiotherapy, occupational therapy, podiatry, psychology and others) are important[19].

Using NSAIDs or COX-2 inhibitors should start after assessment of potential side effects in

first line treatment. If advanced therapy is required, a rheumatologist can prescribe a

combination of csDMARDs (eg. methotrexate, hydroxychloroquine, sulfasalazine) with

biological agents[19].

In this chapter, the impact of risk factors and, in particular, of anti-RA medications on the

frequency of infections in different organs/systems is examined in detail. In addition, based on

patient reports, the severity of infections, the frequency of different types of infections and the

association with different anti-RA medications have been investigated.

1.1. Aims

The aims of this chapter are to determine the:

• frequency of self-reported infections in different organs in RA;

• frequency of prescribed anti-RA medication uses among patients in ARAD;

• impact of different anti- RA medications on self-reported infections; and

• impact of different anti-RA medications on infection severity.

1.2. Hypothesis

The aims are based on the following hypothesis:

Infections are very common in RA and there may be differential effects of anti-rheumatic drugs

on the type and severity of infections that occur in RA.

The following topics will be discussed: 1- Frequency of different types of infection in RA and

categorization of these types of infections, 2- Frequency of different prescribed anti RA

medications, 3- Impacts of anti-RA medications on different types of infection, and 4- impacts

of anti-RA medications on severity of infections.

132

2. Methods

2.1 Data Collection

Data were collected from the Australian Rheumatology Association Database (ARAD), in

which a cohort of 3569 RA patients (960 males and 2609 females), who had regularly

completed questionnaires (28,176 person-reports in total during 2001-2014) and had self-

reported in respect to infections, were investigated for the type and severity of infection and

how these related to currently used anti-RA medications. Among the 3569 patients, 459

patients were eliminated because they had only completed a questionnaire once. After removing

8 duplications 27,709 reports from 3110 patients remained. All underwent a series of inferential

analysis with descriptive tests and logistic regression using SAS software. Only patients who

were currently taking anti-RA medications were selected and the number and severity of

infections in different organs were analysed.

2.2. Statistical Analysis

All 27,709 visits from 3110 patients were subjected to a series of inferential analyses with

descriptive tests and logistic regression in SAS software to extract the required data. Currently

used anti-RA medications were selected and the impacts on different levels of severity of

infection were analysed.

3. Results and Discussions

Based on Table 4.1 and Figure 4.1, the most prevalent infection in RA was EENT infection,

followed by skin and nail infection and lung infection. These data are based on patient reports

from 2001 to 2014. The guidelines in each region and the changes in prescription habits during

time influence these frequencies significantly.

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Figure 4.1 Frequency of self-reported organ infections in RA based on patient visits during

2001-2014

In order to have a better estimation of the meaning of association between a particular type of

infection and medications, it is necessary to know the frequency with which medications have

been used by ARAD participants.

Based on ARAD, the most common to the least common prescribed medications during 2001

to 2014 among RA patients were: Etanercept, Adalimumab, Methotrexate and Folic acid,

Hydroxychloroquine, Sulphasalazine, Rituximab, Abatacept, Prednisolone, Tocilizumab,

Infliximab, Leflunomide, Anakinra, Azathioprine, Cyclosporine, IM Gold and Penicillamine

(Figure 4.2). In this sample, investigation is based on the patient visit reports. The category of

currently taking medication does not include visits of patient who were previously taking or

had stopped taking that particular type of medication. Also, the data inputs entirely depend on

the patient reports and may include a smaller sample than other studies in these categories.

0.12% 0.16% 0.38% 0.61% 0.71%

2.82% 3.11%

6.33%

7.52%

9.83%

13%

14.75%

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

14.00%

16.00%

Frequency of self‐reported infections

134

Table 4.1 Frequency of taking each medication based on patient visit data

Type of

Medication

Name of

Medication

Population Currently

taking%

Population Never Taken%

csDMARDs

Azathioprine 0.068% (19/27711)

4.64% (1286/27711)

Cyclosporine 0.05% (16/27711)

4.41% (1224/27711)

Leflunomide (Arava (Leflunomide))

1.18% (327/27711)

1.01% (281/27711)

Methotrexate/Folic Acid

19.19% (5318/27711)

58.41% (16188/27711)

Hydroxychloroquine 17.06% (4730/27711)

40.19% (11139/27711)

Sulphasalazine 10.63% (2947/27711)

40.96% (11352/27711)

bDMARDs

Abatacept 3.66% (1016/27711)

92.77% (25708/27711)

Adalimumab 22.18% (6149/27711)

57.71% (15993/27711)

Anakinra 0.14% (39/27711)

96.56% (26758/27711)

Certolizumab 0.88% (246/27711)

97.35% (26979/27711)

Etanercept 30.42% 47.61% Golimumab 1.72%

(479/27711) 96.08% (26625/27711)

Infliximab 2.67% (742/27711)

89.86% (24903/27711)

Rituximab 4.26% (1183/27711)

9.34% (2589/27711)

Tocilizumab 2.72% (756/27711)

94.83% (26281/27711)

Independent

Group

Prednisolone 3.33% (924/27711)

0.45% (127/27711)

IM Gold Injection 0.05% (14/27711)

3.52% (978/27711)

Penicillamine 0.0036% (1/27711)

0.47% (1303/27711)

135

Figure 4.2 Frequency of medication use in RA patients

In order to calculate the effect of medication on different organ infections, logistic regression

was used first and, if there were differences, pairwise chi-squares were calculated. This method

is better than pairwise chi-square at the outset because, with pairwise chi-square, the risk of

mistakes in each test with a p value of 0.05 is up to 5%. When this test is performed more often,

the potential error risk adds up and, for example, if 10 pairwise Chi-square tests are done, the

risk of one wrong answer approaches almost 40%.

By doing logistic regression, the characteristics of the overall test can be evaluated first,

followed by pairwise tests for each factor. Logistic regression takes into account the duration

of medication uses as well, because all occasions upon which the patient has reported are taken

into account[20].

3.1. Different organ infections

In the following part of this chapter we discuss infection in different organs separately.

Multinomial logistic regression was used to compare different severities of each organ

infection with the control group (those who did not have this type of infection). The reason

for using this model was because the outcome was a non-binary categorical variable. Using

pair-wise Chi-square test without applying the model could potentially increase errors

0.003 0.05 0.05 0.06 0.14 1.1 2.6 2.7 3.3 3.6 4.26

10

1719

22

30

Frequency of medications in RA

136

because the number of pairwise tests would be far too high. After checking this test, we assess

convergence status table for the model. It shows if the test meets the criteria for accuracy and

the variables fit the statistical model (Appendices tables A1-A5, B1-B5, C1-C5, D1-D5, E1-

E5, F1-F5, G1-G5, H1-H5, I1-I5, J1-J5, K1-K5).

3.2. Eye, Ears, Nose and Throat (EENT) infection - analysis of Anti-RA medicines

Amongst 21,506 observations 1050/21506 (4.88%) reported at least mild EENT infection,

1829/21506 (8.5%) reported moderate infection, and 406/21506 (1.88%) reported severe

infection, whereas 18221/21506 (84.72 %) reported no EENT infection at all. Overall the

results show a significant difference [(chi square (χ2) of 431.3 with a p-value < 0.0001)]

between the variable effects on the EENT infection (Appendix Tables A.7) [20].

The convergence status table for the model (Appendix Tables A.5-A.7) shows that the test

meets the criteria for accuracy and the variables fit the statistical model. Overall the test shows

a significant difference (likelihood ratio Chi-square of 431.2272 with a p-value of less than

0.0001) for variable effects in relation to EENT infection. This means that one or more of the

medications under study are really associated with EENT infection [20] (Appendix table A.7).

The Wald Chi-square for the overall test is also highly significant (0.0001), with a Chi-square

of almost 415 among 144 degrees of freedom. In other words, the impact on the EENT infection

is not the same in different groups. This Chi-square p-value is almost equivalent to the p-value

in the overall Pearson test. Indeed, the logistic regression result is much the same as the

frequency table (Table 3.35) result because it is a large sample. As the model used is a logistic

regression and not a linear regression model, Chi-square is used to test comparisons.

The Score test (Lagrange multiplier test) requires estimating only a single model. The test

statistic is calculated based on the slope of the likelihood function at the observed values of the

variables in the model. Usually the score tests are compared when we add parameters and it

gives us an estimation of how far the accuracy of the test improves by adding new parameters

or deleting existing parameters [21] (Appendix table A.7). During the backward stepwise model

in the next part of the model, the effects of the medications are dropped one by one to see how

much change occurs in the chi-square and to obtain an estimation of the amount of impact of

that medication on increasing EENT infection [22] (Appendix table A8-A.31).

137

3.2.1. Wald Chi-square, Likelihood ratio test and Score test to test significance of

differences

As the size of the study population in this study is high enough, any of these three tests can be

used, but if the size of the sample is small, then all three tests need to be used to increase the

reliability of the conclusions. Among these tests, the likelihood ratio test is the most reliable

test, because it stays unchanged even if the data is reparametrized [22] (Appendix table A.7).

3.2.2. Effects of medications on Eye Ear Nose and Throat (EENT) infection

As the medication effects in this model are all qualitative, the degree of effect (impact) on the

particular infection can be easily worked out by comparing these categorical variables a

backward procedure in multinominal logistic regression was used. According to the summary

table for the backward procedure (Table 4.2 and Appendix Table A.32), the least significant

effect is from Azathioprine followed by Certolizumab, Penicillamine, IM Gold Injection,

Rituximab, and Golimumab. However, the effect of all of these medications was minimal.

Accordingly, they were dropped from the model (Table 4.2 and Appendix Table A.32).

Table 4.2 Summary of backward elimination of anti-RA medications and risk of EENT

infection

Summary of Backward Elimination

Step Effect

Removed

DF Number

In

Wald

Chi-Square

Pr > ChiSq Variable

Label

1.00 Azathioprine 9.00 17.00 4.99 0.84 Azathioprine

2.00 Certolizumab 9.00 16.00 5.45 0.79 Certolizumab

3.00 Penicillamine 9.00 15.00 7.20 0.62 Penicillamine

4.00 IM Gold injection 9.00 14.00 9.19 0.42 IM Gold

injection

5.00 Rituximab 9.00 13.00 13.65 0.14 Rituximab

6.00 Golimumab 6.00 12.00 11.22 0.08 Golimumab

According to Table 4.3, the following medications have significant association with either

producing or reducing the risk of EENT infection in RA. These medications include Etanercept,

Adalimumab, Anakinra, Infliximab, Abatacept, Tocilizumab, F Methotrexate (plus Folic acid),

138

Hydroxychloroquine, Sulphasalazine, Leflunomide, Cyclosporine, and Prednisolone (Table 4.3

and Appendix Table A.33).

Table 4.3 Medications implicated in the promotion of EENT infection

Type 3 Analysis of Effects

Effect DF Chi-Square Pr > ChiSq

Abatacept 9.00 18.02 0.04

Adalimumab 9.00 22.41 0.01

Anakinra 9.00 18.27 0.03

Cyclosporine 9.00 47.34 <.0001

Etanercept 9.00 52.14 <0.0001

Folic acid plus

Methotrexate

3.00 9.42 0.02

Hydroxychloroquine 9.00 23.37 0.01

Infliximab 9.00 31.02 0.0003

Leflunomide 9.00 17.53 0.04

Prednisolone 9.00 29.48 0.0005

Sulphasalazine 9.00 26.74 0.0015

Tocilizumab 9.00 18.10 0.03

In Table 4.4, the effect of each medication was examined in turn:

Etanercept (ETA): The current use of Etanercept (Etanercept) seems to marginally increases

the overall risk of mild EENT infection up to 18 times (CI: 0.989 to 1.43, P value 0.06), but

somewhat paradoxically, amongst all biologics used, the use of Etanercept was associated with

a significant reduction in the chance of severe EENT infection (Table 4.4-4.5 and Appendix

Tables A.34- A.35).

Adalimumab (ADA): The current use of Adalimumab is associated with an increase (P Value:

0.0021) in the risk of mild and moderate EENT infection. The amount of this increase is in turn

almost 33 times greater for mild (CI: 1.110 to 1.605, P value 0.0021) and 20 times greater for

moderate (CI: 1.041 to 1.390, P value 0.0122) EENT infections (Table 4.4-4.5 and Appendix

Tables A.34- A.35).

Infliximab (INX): The current use of Infliximab is associated with a higher risk of mild (P

value of 0.0002) and moderate EENT infection (P value of 0.0007). For mild EENT infection,

139

the amount of this increase is up to almost 90 times (CI: 1.352 to 2.682) greater compared to

participants who have never taken Infliximab, whereas for moderate EENT infection it was 60

times greater (CI: 1.220 to 2.109) (Table 4.4-4.5 and Appendix Tables A.34- A.35).

Abatacept (ABT): The current use of Abatacept is associated with an increase in the risk of

mild EENT infection (P value: 0.0335). The amount of this increase is 40 times greater than in

patients who have never taken this medication (CI: 1.027 to 1.908) (Table 4.4-4.5 and Appendix

Tables A.34- A.35). The risk for moderate and severe EENT infections was not significant.

Tocilizumab (TOC): The current use of Tocilizumab is associated with an increase in the risk

of mild (P value: 0.0036) and moderate (P value: 0.0143) EENT infection. The amount of this

increase is almost 64 times greater in mild EENT infection (CI: 1.175 to 2.283) and 39 times

greater (CI: 1.068 to 1.812) for moderate infection, compared to patients who have never taken

this medication. (Table 4.4-4.5 and Appendix Tables A.34- A.35).

Methotrexate (plus Folic acid): The use of Methotrexate (plus Folic acid) increased the risk

of infection more than 16 times, but among all csDMARDs users, this medication is associated

with a reduction in the risk of moderate (P value: 0.0049, CI: 0.752 to 0.950) EENT infection

(Table 4.4-4.5 and Appendix Tables A.34- A.35).

Hydroxychloroquine (HCQ) and Sulphasalazine (SAS): Modest increases in rates of EENT

infection were observed for both agents, but these increases were not statistically significant.

(Table 4.4-4.5 and Appendix Tables A.34- A.35).

Leflunomide (LEF): The current use of Leflunomide is associated with an increase in risk of

mild EENT infection (P value: 0.017). The amount of this increase is 31 times greater than in

patients who have never taken this medication (CI:1.065 to 1.613) (Table 4.4-4.5 and Appendix

Tables A.34- A.35). Changes in the rates of moderate and severe EENT infection for LEF were

not significant.

Cyclosporine A (CYA): The current use of cyclosporine A is associated with an increase in

the risk of moderate (P value: 0.0001) and severe (P value: 0.0202) EENT infection. The

amount of this increase is almost 180 times for moderate infection (CI: 1.833 to 4.403) and 170

times (CI: 1.173 to 6.577) for severe infections compared to patients who have never taken this

medication (Table 4.4-4.5 and Appendix Tables A.34- A.35).

Prednisolone: The current use of prednisolone is associated with a significant increase in the

risk of severe EENT infection (P value: 0.0329). The amount of this increase is almost 48 times

140

greater than that in patients who have never taken this medication (CI: 1.032 to 2.118) (Table

4.4-4.5 and Appendix Tables A.34- A.35).

In summary, multiple DMARDs were found to be associated significantly with mild or

moderate EENT infections, whereas only cyclosporin and prednisolone were found to be

associated with severe EENT infection.

Conclusion: The findings demonstrate differential risk for EENT infections for users of both

csDMARDs and bDMARDs. Cyclosporine A and prednisolone confer high risk for example in

comparison to HCQ, SAS and to a lesser extent MTX/FA and LEF. Amongst bDMARDs users,

TNF inhibitors and Tocilizumab confer high risk compared to Abatacept. Whether the

differences between TNF inhibitors are clinically important is doubtful.

Methotrexate (plus Folic acid) confer lower risk for EENT infection, whereas cyclosporine,

prednisolone and infliximab are associated with the highest rates for EENT infections (Table

4.4-4.5 and Appendix Tables A.34- A.35).

141

Table 4.4 Analysis of maximum likelihood estimate in EENT infection

Analysis of Maximum Likelihood Estimates

Parameter Medication

Status

EENT

Infection

DF Estimate Standard

Error

Wald

Chi-

Square

Pr > ChiSq

Adalimumab

currently taking Mild 1.00 0.29 0.09 9.42 0.0021

currently taking Moderate 1.00 0.19 0.07 6.28 0.0122

currently taking Severe 1.00 -0.08 0.15 0.29 0.5873

Cyclosporin


currently taking Moderate 1.00 1.04 0.22 21.80 <.0001

currently taking Severe 1.00 1.02 0.44 5.39edrt 0.0202

Etanercept

Stopped taking Severe 1.00 -0.40 0.15 7.47 0.01

Don’t know mild 1.00 1.30 0.54 5.73 0.02

Don’t know Moderate 1.00 1.92 0.34 31.40 <.0001

currently taking mild 1.00 0.17 0.09 3.38 0.07

currently taking Severe 1.00 -0.34 0.14 5.47 0.02

Infliximab

Stopped taking Moderate 1.00 -0.21 0.11 3.50 0.06

currently taking mild 1.00 0.64 0.17 13.59 0.0002


Folic acid

plus

Methotrexate

currently taking Moderate 1.00 -0.17 0.06 7.92 0.0049

Cyclosporine

Stopped taking Moderate 1.00 0.20 0.07 8.71 0.0032

Stopped taking Severe 1.00 0.47 0.13 12.38 0.0004

currently taking Moderate 1.00 1.04 0.22 21.80 <.0001

currently taking Severe 1.00 1.02 0.44 5.40 0.02

Arava

(Leflunomide)




Prednisolone

Stopped taking mild 1.00 0.33 0.11 9.34 0.0022

Stopped taking Moderate 1.00 0.26 0.08 9.27 0.0023

Stopped taking Severe 1.00 0.50 0.18 7.37 0.01


142

Table 4.5 Estimation of Odd’s ratios in EENT infection

Odds Ratio Estimates Effect EENT

Infection Point Estimate

95% Wald Confidence Limits

Adalimumab- currently taking vs never taken

Mild 1.335 1.110 1.605 Moderate 1.203 1.041 1.390 Severe 0.923 0.692 1.232

Etanercept - currently taking vs never taken


Infliximab currently taking vs never taken


Cyclosporine - -currently taking vs never taken


Prednisolone currently taking vs never taken


3.3. Chest or lung infection - analysis of anti-RA medicines

Amongst 21506 observations, 371/21506 (1.72 %) were self-reported mild infections,

1379/21506 (6.41%) were self-reported moderate infections and 624/21506 (2.9%) were self-

reported severe infections. In contrast, for 19132/21506 (88.96 %) patient visits, no infections

were reported. In this model, categories of reported chest or lung infection were compared to

participants who reported no chest or lung infection. A multinomial logistic regression model

was used. The reason for using this model is because the outcome is a non-binary categorical

variable. Using pairwise Chi-square test without using the model can increase potential

mistakes because the number of comparisons is high (14). The model convergence status table

(Appendix Tables B.5-B.7) is used to assess that the test meets the criteria for accuracy and the

variables fit the statistical model (14) (Appendix table B.7).

In the model fit statistics table (Appendix table B.7), the likelihood ratio or lr (difference

between -2 Log L or Deviance in the model which contains just the intercept and the one which

contains both the intercept and covariates) is 383.2851. The P value is highly significant. This

shows that a model with covariates is making the test more rigorous and that covariates are

143

actually impacting the cofactors in the lung infection. Other tests such as SC and AIC are also

used to recheck this conclusion [22] (Appendix table B.6).

The Wald Chi-square test for overall test is also highly significant (0.0001) with a Chi-square

of almost 397 among 162 degrees of freedom. In other words, the impact on lung infection is

not the same in different groups. This Chi-square P value is almost equivalent to the p value in

the overall Pearson test. Indeed, the logistic regression result is much the same as that for the

frequency table (Table 3.41) result because it is a large sample. As the model used is logistic

regression and not a linear regression, the Chi-square test permits comparison. [21] (Appendix

table B.7).



variables in the model. Usually the score tests are compared when parameters are added, and it

gives us an estimation of how far the accuracy of the test improves by adding new parameters

or deleting existing ones [21] (Appendix table B.7).

During the backward stepwise model in the next part of the model, the effects of the medications

are dropped one by one to see how much change occurs in the Chi-square and to get an

estimation of the amount of impact of that medication on the frequency of lung infection[22]

(Appendix table B8-B.31).

3.3.1. Wald Chi-square, likelihood ratio test and score test to test significance of

differences

As the size of the study population in this study was large enough, any of these three tests can

be used. Had the size of the sample been small, then it would have been necessary to use all

three tests to increase the reliability of the conclusions. Among these tests, the likelihood ratio

test is the most reliable test, because it remains unchanged even if reparameterization is

necessary [22] (Appendix table B.7).

3.3.2. Effects of different medications on lung infection

The effects of each variable on the lung infection was investigated directly by studying its

coefficient. backward procedure in logistic regression, with three levels of mild, moderate and

severe infections was applied because the outcome was categorical. The backward stepwise

144

procedure was used here because it is more accurate than the forward procedure and considers

the accumulating effect of all variables and starts with a bigger model. The model-fit statistics

show that, as using this large model is still fitted to the data, it can be used accordingly. Also,

there is no collinearity and no two variables are identical. This makes it easier to use the

backward model.

As the variables are all categorical variables, the effects of each variable on lung infection can

be examined by studying its coefficient, directly [21].

For this section, a backward procedure in multinominal logistic regression was preferred.

Logistic regression is required, because the outcome is categorical and as lung infection has

three categories of severity, viz: mild, moderate and severe and a no infection category as well,

a multinominal logistic regression is appropriate. Also, using backward stepwise is preferable

here because it is more accurate than a forward procedure and considers the accumulating effect

of all variables and starts with a bigger model. As the model fit statistics show that using this

large model is still well fitted to the data, it can be used with confidence. Furthermore, there is

no collinearity and no two variables are identical. This makes it easier to use the backward

model [22].

According to the summary table of results derived from use of the backward procedure, the

least significant effect is from Certolizumab followed by Penicillamine, Methotrexate (plus

Folic acid), Azathioprine, Rituximab, Infliximab, Tocilizumab, Golimumab, Arava

(Leflunomide), and Adalimumab. However, the effect of all these medications was found to be

minimal and so they were eliminated from the model (Table 4.6 and Appendix Table B.32).

145

Table 4.6 Summary of backward elimination of anti-RA medications and risk of lung infection


Step

Effect

Removed DF

Number

In

Wald

Chi-Square Pr > ChiSq

1 Certolizumab 9 18 5.2822 0.8090

2 Penicillamine 9 17 8.9767 0.4394

3 Methotrexate and Folic acid 3 16 3.0046 0.3909

4 Azathioprine 9 15 10.4127 0.3181

5 Rituximab 9 14 10.6214 0.3026

6 Infliximab 9 13 12.4421 0.1895

7 Tocilizumab 9 12 14.5987 0.1026

8 Golimumab 6 11 11.7349 0.0682

9 Arava (Leflunomide) 9 10 16.1280 0.0643

10 Adalimumab 9 9 16.1807 0.0632

According to the type 3 analysis of effects, the following medications have significant

association with increasing or reducing the propensity for lung infection in RA. These

medications include Etanercept, Anakinra, Abatacept, Hydroxychloroquine,

Hydroxychloroquine, Sulphasalazine, Cyclosporine, Prednisolone, and IM Gold injections

(Table 4.7 and Appendix Table B.33).

146

Table 4.7 Anti-rheumatic medications and propensity for lung infection


Effect DF

Wald


Etanercept 9 31.4874 0.0002

Anakinra 9 20.0990 0.0173

Abatacept 9 34.9246 <.0001

Methotrexate 9 20.5746 0.0147

Hydroxychloroq

uine

9 24.4648 0.0036

Sulphasalazine 9 20.8255 0.0134

Cyclosporine 9 20.6307 0.0144

Prednisolone 9 67.5034 <.0001

IM Gold 9 19.8810 0.0187

In the analysis of maximum likelihood, the statistical findings in respect to each medication

examined are shown in detail, in this section we just discuss significant effect of medications

which are currently being taken by the patient:

Abatacept: Currently taking ABT was found to be associated with a significant increase in the

propensity for moderate lung infection (P value: <.0001). The size of this increase equates to

almost 70 times more in the case of moderate infection compared to patients who don’t take

Abatacept at all (CI: 1.36 to 2.161) (Table 4.8-4.59 and Appendix Tables B.34- B.35).

Hydroxychloroquine: Currently taking HCQ is strongly associated with an increase in the rate

of severe infection in RA (P value: 0.0001). Taking this medication is associated with 53 times

greater risk for severe infection (CI: 1.23 to 1.91). The effect of taking Hydroxychloroquine in

increasing moderate level of infection is marginal (P value: 0.049) and can reach to 17 times

more risk (Table 4.8-4.59 and Appendix Tables B.34- B.35).

147

Sulphasalazine: Currently taking SAL is marginally associated with an increase in the rate of

severe infection in RA (P value: 0.0689). However, the amount of this increase is ignorable

(Table 4.8-4.59 and Appendix Tables B.34- B.35).

Cyclosporine A: Current use of CYA was found to be associated with an increased propensity

for mild (P value 0.0012) and moderate (P value 0.0064) lung infection. Taking this medication

is associated with 243 times greater risk for mild infection and 105 times greater risk for

moderate infection (Table 4.8-4.59 and Appendix Tables B.34- B.35).

Prednisolone: Currently taking prednisolone was found to be associated with an increased

propensity for all categories of lung infection. Taking prednisolone was associated with a 63

times greater propensity for mild infection and a 33- and 140-times greater propensity for

moderate and severe lung infection,, respectively (Table 4.8-4.59 and Appendix Tables B.34-

B.35).

Methotrexate: Taking methotrexate was also associated with significant increase in the rate

of moderate infection (p‐value < 0. 0.0166). Taking this medication is associated with 4 times

greater risk for moderate infection (CI: 1.313 to 15.218).

IM Gold Injection: No impact of IM Gold injections on lung infection was identified (Table

4.8-4.59 and Appendix Tables B.34- B.35).

In summary, multiple DMARDs were found to be associated with mild or moderate lung

infection, whereas only use of Prednisolone was found to be associated with severe lung

infection.

148

Table 4.8 Analysis of maximum likelihood estimate in lung infection


Parameter InfLung DF Estimate Standard

Error Wald

Chi-Square Pr > ChiSq Etanercept currently taking Mild 1 -0.1632 0.1350 1.4614 0.2267 Etanercept currently taking Moderate 1 -0.0438 0.0677 0.4200 0.5169 Etanercept currently taking Severe 1 -0.1177 0.1018 1.3382 0.2473 Anakinra currently taking Mild 1 1.1898 1.0395 1.3102 0.2524 Anakinra currently taking Moderate 1 1.0875 0.6364 2.9208 0.0874 Anakinra currently taking Severe 1 -11.1525 348.5 0.0010 0.9745 Abatacept currently taking Mild 1 0.3420 0.2178 2.4657 0.1164 Abatacept currently taking Moderate 1 0.5392 0.1180 20.8696 <.0001 Abatacept currently taking Severe 1 -0.0929 0.2079 0.1996 0.6550

Hydroxychloroquine

currently taking Mild 1 0.2042 0.1458 1.9617 0.1613

Hydroxychloroquine

currently taking Moderate 1 0.1582 0.0804 3.8734 0.0491

Hydroxychloroquine

currently taking Severe 1 0.4302 0.1114 14.9202 0.0001

Sulphasalazine currently taking Mild 1 0.3020 0.1660 3.3084 0.0689 Sulphasalazine currently taking Moderate 1 0.0113 0.1022 0.0122 0.9120 Sulphasalazine currently taking Severe 1 0.00650 0.1447 0.0020 0.9641 Cyclosporine currently taking Mild 1 1.2314 0.3793 10.5374 0.0012 Cyclosporine currently taking Moderate 1 0.7209 0.2642 7.4466 0.0064 Cyclosporine currently taking Severe 1 0.2533 0.4633 0.2990 0.5845 Prednisolone currently taking Mild 1 0.4916 0.1786 7.5773 0.0059 Prednisolone currently taking Moderate 1 0.3192 0.0919 12.0656 0.0005 Prednisolone currently taking Severe 1 0.8943 0.1574 32.2591 <.0001

IM Gold currently taking Mild 1 -1.3342 1.0067 1.7565 0.1851 IM Gold currently taking Moderate 1 0.2764 0.2666 1.0746 0.2999 IM Gold currently taking Severe 1 -0.1061 0.4610 0.0529 0.8181

Conclusion: Differential effects on the propensity to lung infections were observed with

csDMARDs and bDMARDs. Prednisolone, CYA and HCQ all increased this propensity,

whereas SAS and IM Gold did not or there was insufficient data to draw firm conclusions.

Amongst bDMARDs, ABT was significantly associated with an increased frequency of

moderate lung infections, whereas ETA and Anakinra were somewhat surprisingly associated

with possible reduced rates of lung infection. Amongst serious infections in RA, lung infections

are the most common. In patients with chronic lung diseases, such as COPD, bronchiectasis

and in those with a past history of one or more attacks of pneumonia, for example, this new

data could be factored into treatment selection.

149

Table 4.9- Estimation of odds ratios in lung infection

Odds Ratio Estimates

Effect InfLung Point

Estimate 95% Wald

Confidence Limits Etanercept currently taking vs never taking 1 0.849 0.652 1.107 Etanercept currently taking vs never taking 2 0.957 0.838 1.093 Etanercept currently taking vs never taking 3 0.889 0.728 1.085 Anakinra currently taking vs never taking 1 3.286 0.428 25.207 Anakinra currently taking vs never taking 2 2.967 0.852 10.327 Anakinra currently taking vs never taking 3 <0.001 <0.001 >999.99

9 Abatacept currently taking vs never taking 1 1.408 0.919 2.157 Abatacept currently taking vs never taking 2 1.715 1.361 2.161 Abatacept currently taking vs never taking 3 0.911 0.606 1.370

Hydroxychloroquine currently taking vs never taking

1 1.227 0.922 1.632


2 1.171 1.001 1.371


3 1.538 1.236 1.913

Sulphasalazine currently taking vs never taking 1 1.353 0.977 1.873 Sulphasalazine currently taking vs never taking 2 1.011 0.828 1.236 Sulphasalazine currently taking vs never taking 3 1.007 0.758 1.337 Cyclosporine currently taking vs never taking 1 3.426 1.629 7.206 Cyclosporine currently taking vs never taking 2 2.056 1.225 3.451 Cyclosporine currently taking vs never taking 3 1.288 0.520 3.194 Prednisolone currently taking vs never taking 1 1.635 1.152 2.320 Prednisolone currently taking vs never taking 2 1.376 1.149 1.648 Prednisolone currently taking vs never taking 3 2.446 1.796 3.330

IM Gold currently taking vs never taking 1 0.263 0.037 1.894 IM Gold currently taking vs never taking 2 1.318 0.782 2.223 IM Gold currently taking vs never taking 3 0.899 0.364 2.220

150

3.4. Skin and Nail infection - analysis of Anti-RA medicines

Amongst 21506 patient-visit observations 1253/21506 (5.82 %) self-reported mild infection,

1039/21506 (4.83%) self-reported moderate infection and 361/21506 (1.67%) self-reported

severe infection. In contrast, for 18853/21506 (87.66 %) patient-visits, no infections were

reported. In this model, participants who developed different severities of skin and nail infection

were compared to participants who did not develop this type of infection (Appendix tables C1-

C3). A multinomial logistic regression model was used to evaluate these reports. The reason

for using this model is because the outcome is a non-binary categorical variable. Using pairwise

Chi-square test without using the model can increase potential mistakes because the number of

comparisons is large[20].

The model convergence status table (Appendix Tables C.5-C.7) shows that the test meets the

criteria for accuracy and the variables fit the statistical model. Overall, the test shows that anti-

RA medicines have an effect on the risk of skin and nail infection (lr Chi-square of 386.3201

with a P value of less than 0.0001) [20] (Appendix table C.7).

In the model fit statistics table (Appendix table C.7), the likelihood ratio or lr (difference

between -2 Log L or Deviance in the model which just contains intercept and the one which

contains intercept and covariates) is 386.3201. The P value is highly significant (Appendix table

C.7). This shows that a model with covariates is making the test more robust and that covariates

are actually impacting cofactors in skin and nail infection. Other tests, such as SC and AIC, are

also used to recheck this conclusion [22] (Appendix table C.6).

Wald Chi-square for overall test is also highly significant (0.0015), with a Chi-square of almost

85 among 50 degrees of freedom. In other words, the impact on the skin infection is not the

same in different groups. This Chi-square is almost equivalent to the p-value in the overall

Pearson test. Indeed, the logistic regression result is much the same as the frequency table

(Tables 3.29-3.30) result because it is a large sample. As the model used was a logistic

regression and not a linear regression model, the Chi-square test was used for comparison. [21]

(Appendix table C.7).

151



variables in the model. Usually the score tests are compared when parameters are added, which

provides an estimation of how far the accuracy of the test improves by adding new parameters

or deleting existing parameters [21] (Appendix table C.7).

During the backward stepwise procedure in the next part of the model, the effects of the

medications are eliminated, one by one, to see how much change occurs in the Chi-square and

to get an estimation of the amount of impact of that medication in increasing skin and nail

infections [22] (Appendix tables C8-C.31).

3.4.1. Effects of different medications on skin and nail infection

For this section, a backward elimination procedure was preferred, utilising multinominal

logistic regression. Logistic regression is required because the outcome is categorical and

because nail and skin infection have three categories, viz: mild, moderate and severe.

Furthermore, there is a no infection category. Accordingly, multinominal logistic regression is

a more appropriate model (Appendix Table C.4). A backward stepwise procedure is used here

because it is more accurate than a forward procedure and considers the accumulating effect of

all variables and also because it starts with a bigger model. As the model fit statistics (Appendix

Table C.6) show, using this large model is still well fitted to the data, so it can be used

appropriately. Moreover, since there is no collinearity and no two variables are identical, it is

easier to use the backward model [22].

According to the summary of the backward procedure, as shown in Table 4.10,, the least

significant effect is from Certolizumab, followed by Hydroxychloroquine, IM Gold injection,

Abatacept, Tocilizumab, Penicillamine, Golimumab, Anakinra, and Azathioprine. However,

the effect of all these medications was minimal and so they were eliminated from the model

(Table 4.10 and Appendix Table C.32).

152

Table 4.10 Summary of backward elimination of anti-RA medications and risk of skin

and nail infection.


Step Effect

Removed

DF Number

In

Wald

Chi-Square

Pr > ChiSq

1.00 Certolizumab 9.00 17.00 7.14 0.62

2.00 Hydroxychloroquine 9.00 16.00 7.26 0.61

3.00 IM Gold injection 9.00 15.00 9.70 0.38

4.00 Abatacept 9.00 14.00 11.59 0.24

5.00 Tocilizumab 9.00 13.00 10.33 0.32

6.00 Penicillamine 9.00 12.00 12.72 0.18

7.00 Golimumab 6.00 11.00 9.98 0.13

8.00 Anakinra 9.00 10.00 14.79 0.10

9.00 Azathioprine 9.00 9.00 15.79 0.07

10.00 Cyclosporine 9.00 8.00 15.94 0.07

According to the type 3 analysis of effects, as shown in Table 4.11, the following medications

have significant impact on producing or reducing the risk of skin and nail infection in RA.

These medications include: Etanercept, Adalimumab, Infliximab, Rituximab, Methotrexate

(plus Folic acid), Sulphasalazine, Leflunomide, Prednisolone (Table 4.11 and Appendix Table

C.33).

Table 4.11 Medications associated with a propensity to increase skin and nail infections


Effect DF Wald Chi-Square Pr > ChiSq

Etanercept 9.00 24.94 0.00

Adalimumab 9.00 21.42 0.01

Infliximab 9.00 29.90 0.00

Rituximab 9.00 24.22 0.00

Methotrexate (plus Folic acid) 3.00 25.61 <.0001

Sulphasalazine 9.00 34.68 <.0001

Leflunomide 9.00 26.58 0.00

Prednisolone 9.00 38.01 <.0001

153

In the analysis of maximum likelihood, the effect of each medication was examined in more

detail:

Etanercept: The use of ETA was associated with a reduced frequency of skin and nail

infections. Moderate infections were observed to occur more than 19-fold less often (CI: 0.679

to 0.971) and severe infections 30 times less often (CI: 0.522 to 0.956). Among all biologics

taken Etanercept was associated with a reduction in moderate and severe skin and nail infection

(Table 4.12-4.13 and Appendix Tables C.34- C.35).

Adalimumab: Currently taking Adalimumab was associated with a slight increase in the

frequency of mild infection (P Value: 0.0076). The amount of this increase is almost 24 times

greater than that for patients who have never taken Adalimumab (CI: 1.061 to 1.468) (Table

4.12-4.13 and Appendix Tables C.34- C.35).

Infliximab: Currently taking Infliximab was associated with an increased frequency for severe

skin and nail infection. (P value of 0.0404). The amount of this increase is up to almost 72 times

more than that for participants who have never taken Infliximab. (CI: 1.024 to 2.911) (Table

4.12-4.13 and Appendix Tables C.34- C.35).

Rituximab: Current use of Rituximab was associated with an increased frequency of skin and

nail infection with increases more than 34 to 40 times in moderate and mild infection, but

among all biologics, the use of Rituximab was associated with a reduced risk in mild (p value

0.0032) and moderate (p value 0.0149) skin and nail infection. (Table 4.12-4.13 and Appendix

Tables C.34- C.35).

Methotrexate (plus Folic acid): Currently taking Methotrexate (plus Folic acid) can reduce

the frequency of both mild and moderate skin and nail infection. The reduction in mild infection

is up to almost 26 times lower than in those who have never taken Methotrexate (plus Folic

acid). In moderate skin and nail infection, it is approximately 20 times lower (Table 4.12-4.13

and Appendix Tables C.34- C.35).

Sulphasalazine: Currently taking SAS reduces the frequency of mild skin and nail infection.

According to points estimate, the frequency of skin and nail infection is 29 times (1/0.710) less

than that for patients who have never taken this agent (CI:0.566 to 0.891) (Table 4.12-4.13 and

Appendix Tables C.34- C.35).

Leflunomide: Currently taking LEF increases the risk of mild and moderate skin and nail

infection. According to points estimate the frequency of skin and nail infection is 35 times

154

greater for mild (CI:1.131 to 1.629) and 25 times greater for moderate infection (CI:1.015 to

1.528) (Table 4.12-4.13 and Appendix Tables C.34- C.35).

Prednisolone: Currently taking Prednisolone increases the frequency of severe infection.

According to points estimate, this infection rate is more than 160 times more than that for

patients who have never taken prednisolone (CI: 1.688 to 4.055) (Table 4.12-4.13 and Appendix

Tables C.34- C.35).

In summary, they use of multiple biologic and conventional synthetic DMARDs was found to

be associated with increased rates of mild and moderate skin and nail infections, whereas

prednisolone use and infliximab were associated with an increased frequency of severe skin

and nail infections.

155

Table 4.12 Analysis of maximum likelihood estimate in skin and nail infection



Status

Skin and

Nail

Infection


Error

Wald

Chi-

Square

Pr > ChiSq

Etanercept

currently

taking

Mild 1.00 0.001 0.08 0.001 0.99

Moderate 1.00 -0.21 0.09 5.22 0.02

Severe 1.00 -0.35 0.15 5.06 0.02

Adalimumab

currently

taking

Mild 1.00 0.22 0.08 7.13 0.01

Moderate 1.00 0.01 0.09 0.01 0.93

Severe 1.00 -0.08 0.16 0.29 0.59

Infliximab

currently

taking

Mild 1.00 0.22 0.18 1.59 0.21

Moderate 1.00 -0.11 0.21 0.29 0.59

Severe 1.00 0.55 0.27 4.20 0.04

Rituximab

currently

taking

Mild 1.00 -0.50 0.17 8.70 0.001

Moderate 1.00 -0.41 0.17 5.93 0.01

Severe 1.00 -0.45 0.26 3.02 0.08

Methotrexate

(plus Folic

acid)

currently

taking

Mild 1.00 -0.30 0.07 17.08 <.0001

Moderate 1.00 -0.18 0.08 5.37 0.02

Severe 1.00 0.21 0.12 3.27 0.07

Sulphasalazine

currently

taking

Mild 1.00 -0.34 0.12 8.77 0.001

Moderate 1.00 -0.11 0.12 0.86 0.35

Severe 1.00 0.09 0.19 0.21 0.65

Leflunomide

currently

taking

Mild 1.00 0.31 0.09 10.74 0.001

Moderate 1.00 0.22 0.10 4.40 0.04

Severe 1.00 0.18 0.17 1.14 0.29

Prednisolone

currently

taking

Mild 1.00 0.10 0.09 1.31 0.25

Moderate 1.00 0.01 0.10 0.02 0.90

Severe 1.00 0.96 0.22 18.49 <.0001

156

Conclusion: Differential effects on the frequency of skin and nail infections were observed

amongst users of csDMARDs and bDMARDs. Prednisolone substantially increased the

frequency of severe skin and nail infections, whereas less consistent effects were observed with

other non-biologic agents. Amongst bDMARDs, severe skin and nail infections were higher in

recipients of Infliximab, whereas lower skin and nail infection rates were observed with

Etanercept. Taking Leflunomide can also increase the risk of mild and moderate skin and nail

infection.

Table 4.13 Estimation of odds ratios in skin and nail infection

Odds Ratio Estimates Effect Skin and Nail

Infection Point Estimate


Etanercept - currently taking vs never taken


Adalimumab - currently taking vs never taken


Infliximab - currently taking vs never taken


Rituximab - currently taking vs never taken


Methotrexate/ Methotrexate (plus Folic acid) - currently taking vs never taken


Sulphasalazine - currently taking vs never taken


Leflunomide - currently taking vs never taken


Prednisolone - currently taking vs never taken


3.5. Artificial (Prosthetic) Joint infection - analysis of Anti-RA medicines

Amongst 21506 respondent observations, 19/21506 (0.088 %) reported mild prosthetic joint

infection, 39/21506 (0.18%) self-reported moderate prosthetic joint infection, and 78/21506

(0.36%) reported severe prosthetic joint infection. For 21370/21506 (99 %) patient-visits, no

infections were reported.

157

In this model, the different categories of prosthesis infection were compared to the control

group (RA respondents who did not have prosthetic joint infection). A multinomial logistic

regression model was used. The reason for using this model was because the outcome was a

non-binary categorical variable. Using pairwise Chi-square test without using the model could

increase potential error, therefore we use regression model.

The model convergence status table shows that the test meets the criteria for accuracy and the

variables fit the model of statistics. Overall, the test shows that medications have significant

different impacts on causing artificial joint infection in RA (lr Chi-square of 208.9481with a P

value of 0.0018) .

The model convergence status table (Appendix Tables D.5-D.7) shows that the test meets the

criteria for accuracy and the variables fit the statistical model. Overall, the test shows significant

differential effects of anti-RA medications on prosthetic joint infection that (lr Chi-square of

208.9481with a P value of 0.0018) on the Artificial joint infection [20] (Appendix table D.7).

In the model fit statistics (Table 4.14), the likelihood ratio or lr is 208.411 and the P value is

significant. This shows that a model with covariates is making the test more robust and that

covariates are actual impacting cofactors in respect to prosthetic joint infection. Other tests,

such as SC and AIC, are also used to recheck this conclusion [22] (Table 4.14 and Appendix

table D.6).

Table 4.14 Estimation of the impact of confounders

Model Fit Statistics

Criterion Intercept Only Intercept and

Covariates

AIC 1,913.34 2,010.39

SC 1,937.27 3,254.66

-2 Log L 1,907.34 1,698.39

The Wald Chi-square for overall test is also highly significant (0.0001) with a Chi-square of

almost 200.8923 among 153 degrees of freedom. In the other words, the impact on the Artificial

Joint infection is not the same in different groups. This Chi-square P value is almost equivalent

to the p value in the overall Pearson test. Indeed, the logistic regression result is much same as

158

the frequency table (Table 3.53-3.54) result because it is a large sample. As our model is logistic

regression and not a linear regression, we are using Chi-square test for our comparison. [21]

(Appendix D.7).



variables in the model. Usually we compare the score tests when we add parameters and it gives

us an estimation of how far the accuracy of test improves by adding new parameters or deleting

existing parameters. If we compare this test in backward regression, the major drop is

happening in Cyclosporine, Azathioprine, Tocilizumab and Prednisolone [21] (Appendix D.7).

During the backward stepwise procedure in the next part of the model, the effects of the

medications are dropped, one by one, to see how much change happen in the Chi-square and to

get an estimation of the association between the medication and changes in the artificial joint

infection [22] (Appendix D.8-D.31).


differences

As the size of the study population in our study is enough, we can use any of these three tests,

but if the size of the sample is small then we have to check all these three tests to increase

reliability of the conclusions. Among these tests, likelihood ratio test is the most reliable test,

because it stays unchanged even if we reparametrize what we are testing [22] (Appendix D.7).

3.5.2. Effects of different medications on artificial (prosthetic) joint infection

As the medication effects in this model are all qualitative, it is possible to work out the degree

of effect (impact) on prosthetic joint infection by comparing these categorical variables.

For this section, a backward procedure in multinominal logistic regression was preferred (Table

4.15-4.16). Logistic regression was required because the outcome is categorical and as artificial

joint infection has three categories of severity, notably mild, moderate and severe and a no

infection report, multinominal logistic regression is the appropriate model. Also, backward

stepwise is used here because it is more accurate than the forward procedure and considers the

accumulating effect of all variables and starts with a bigger model (Appendix Table D.4). As

the model fit statistics (Appendix Table D.6) show that using this large model is still fitted to

159

the data, it can be used appropriately. Also, there is not any collinearity and none of any two

variables are identical. This makes it easier to use the backward model [22].

Table 4.15 Estimation of fitness of tests in artificial joint infection

Testing Global Null Hypothesis: BETA=0

Test Chi-Square DF Pr > ChiSq

Likelihood Ratio 208.95 153.00 0.001

Score 312.91 153.00 <.0001

Wald 200.89 153.00 0.01

According to the summary of the backward procedure (Table 4.16 and Appendix Table D.32),

the least significant effect is from Certolizumab followed by Golimumab, Anakinra, Abatacept,

Methotrexate (plus Folic acid), Azathioprine, Infliximab, Sulphasalazine, Prednisolone,

Etanercept, Leflunomide, Penicillamine, Hydroxychloroquine, Tocilizumab, and Cyclosporine.

However, the effect of all these medications was found to be minimal and they were dropped

from the model.

160

Table 4.16 Summary of backward elimination of anti-RA medications and risk of artificial joint

infection

Summary of backward elimination

Step Effect

Removed

DF Number

In

Wald

Chi-Square

Pr > ChiSq

1.00 Certolizumab 9.00 17.00 0.001 1.00

2.00 Golimumab 6.00 16.00 0.02 1.00

3.00 Anakinra 9.00 15.00 1.66 1.00

4.00 Abatacept 9.00 14.00 2.96 0.97

5.00 Folic acid plus

Methotrexate

3.00 13.00 0.90 0.82

6.00 Azathioprine 9.00 12.00 5.69 0.77

7.00 Infliximab 9.00 11.00 4.45 0.88

8.00 Sulphasalazine 9.00 10.00 5.63 0.78

9.00 Prednisolone 9.00 9.00 7.02 0.64

10.00 Etanercept 9.00 8.00 8.48 0.49

11.00 Leflunomide 9.00 7.00 7.73 0.56

12.00 Penicillamine 9.00 6.00 9.13 0.43


14.00 Tocilizumab 9.00 4.00 12.22 0.20

15.00 Cyclosporine 9.00 3.00 16.02 0.07

According to type 3 analysis of effects (Table 4.17), the following medications had significant

association with either increasing or reducing the risk of artificial joint infection in RA. These

medications were: Adalimumab, Rituximab, and IM Gold injection (Table 4.17 and Appendix

Table D.32).

Table 4.17 Medications implicated in the development of artificial (prosthetic) joint infection


Effect DF Wald

Chi-Square

Pr > ChiSq

Adalimumab 9.00 17.24 0.05

Rituximab 9.00 17.54 0.04

IM Gold injection 9.00 30.83 0.001

161

In the analysis of maximum likelihood, the effect of each medication was investigated in more

detail:

Adalimumab: Taking Adalimumab is associated with reduction in the risk of moderate

artificial joint infection (P value 0.035). However, if patient does not need to take adalimumab,

he / she will have a lower risk for infection (by up to 70 times) (CI: 0.083 to 0.916). (Table

4.18-4.19 and Appendix Tables D.34- D.35).

Rituximab: Currently taking Rituximab is not associated with either a reduction or increase in

prosthetic joint infection (Table 4.18-4.19 and Appendix Tables D.34- D.35).

IM Gold injection: Currently taking parenteral Gold is not associated with either an increase

or decrease in prosthetic joint infection (Table 4.18-4.19 and Appendix Tables D.34- D.35).

Table 4.18- Analysis of Maximum likelihood estimate in artificial joint infection

Analysis of Maximum Likelihood Estimates Parameter Medication

Status Artificial Joint Infection

DF Estimate Standard Error

Wald Chi-Square

Pr > ChiSq

Adalimumab

currently taking

Mild 1.00 -0.38 0.66 0.34 0.56 Moderate 1.00 -1.29 0.61 4.42 0.04 Severe 1.00 -0.07 0.28 0.06 0.81

Rituximab

currently taking

Mild 1.00 0.54 0.81 0.44 0.51 Moderate 1.00 -0.97 1.03 0.88 0.35 Severe 1.00 -0.46 0.74 0.38 0.54

IM Gold injection

currently taking

Mild 1.00 -11.94 1,057.40 0.001 0.99 Moderate 1.00 -12.03 728.10 0.003 0.99 Severe 1.00 -11.57 520.60 0.002 0.98

Conclusion: According to the above information and analysis, Adalimumab has a significant

reduction impact in comparison with other bDMARDs. In people with risk of artificial joint

infection, Adalimumab is the safest (Table 4.18-4.19 and Appendix Tables D.1- D.35).

162

Table 4.19- Estimation of Odds ratios in Artificial Joint infection

Odds Ratio Estimates Effect Artificial

joint infection

Point Estimate


Adalimumab currently taking Versus never taken

Mild 0.682 0.187 2.488


Moderate 0.276 0.083 0.916


Severe 0.937 0.545 1.612

Rituximab currently taking Versus never taken Mild 1.710 0.352 8.307 Rituximab currently taking Versus never taken Moderate 0.378 0.050 2.870 Rituximab currently taking Versus never taken Severe 0.633 0.149 2.690 IM Gold injection currently taking Versus never taken

Mild <0.001 <0.001 >999.999

IM Gold injection currently taking Versus never taken

Moderate <0.001 <0.001 >999.999

IM Gold injection currently taking Versus never taken

Severe <0.001 <0.001 >999.999

3.6. Bone, joint and muscle (BJM) infection - analysis of anti-RA medicines

Amongst 21506 observations, 82/21506 (0.38 %) self-reported mild infection, 213/21506

(0.99%) self-reported moderate infection, 243/21506 (1.12%) self-reported severe infection,

and for 20968/21506 (97.49 %) patient-visits, no infections were reported. In this model,

ARAD participants who self-reported bone, joint and muscle infection of differing severity

were compared with ARAD participants in whom there was no such infection. The statistical

model used is the multinomial logistic regression model (Appendix Tables D1-D4). The reason

for using this model is because the outcome is a non-binary categorical variable. Using pairwise

Chi-square test without using the model can increase potential mistakes because the number of

comparisons is large[20]. The model convergence status table (Appendix Tables D.5-D.7)

shows that the test meets the criteria for accuracy and the variables fit the model of statistics.

Overall, the test shows significant differences (lr Chi-square of 283.1804 with a P value of

<.0001) between variable effects on bone, joint, and muscle infections (Appendix table D.7).

In the model fit statistics table (Appendix table D.7), the likelihood ratio or lr (difference

between -2 Log L or Deviance in the model which just contains the intercept and one which

contains both the intercept and covariates) is 283.1804. The P value is highly significant

(Appendix table D.1-D.7). This shows that a model with covariates is strengthening the test and

demonstrates that covariates are the actual impacting cofactors in BJM bone, joint and muscle

163

infection. Other tests such as SC and AIC are also used to recheck this conclusion (Appendix

table D.6). Wald Chi-square for overall test is also highly significant (0.0001) with a Chi-square

of almost 274 among 153 degrees of freedom. In other words, the impact on BJM bone, joint

and muscle infection is not the same in different groups. This Chi-square P value is almost

equivalent to the p value in the overall Pearson test. Indeed, the logistic regression result is

much the same as in the frequency table (Tables 3.49-3.50) result, because it is a large sample.

The model used is a logistic regression model and not a linear regression, Chi-square test was

used for comparison. [21] (Appendix D.7).



variables in the model. Usually the score tests are compared when parameters are added, which

provides an estimation of how far the accuracy of the test improves by adding new parameters

or deleting existing parameters [21] (Appendix D.7). During the backward stepwise procedure

in the next part of the model, the effects of the medications are dropped, one by one, to see how

much there is a change in the Chi-square and to get an estimation of the amount of impact of

that medication in increasing bone, joint and muscle (BJM) infection [22] (Appendix D8-

D.31).

3.6.1. Wald Chi-squared, Likelihood ratio test and Score test to test significance of

differences

As the size of the study population in this study is large enough, any of these three tests can be

used, but if the size of the sample were small, then it would be necessary to check all three tests

to increase the reliability of the conclusions. Among these tests, the likelihood ratio test is the

most reliable test, because it stays unchanged even if the data under analysis is reparametrized

[22] (Appendix D.7).

3.6.2. Effects of different medications on bone, joint and muscle infection

As the medication effects in this model are all qualitative, the degree of effect (impact) on an

infection can be easily determined by comparing these categorical variables [21]. For this

section, a backward procedure in multinominal logistic regression was preferred. Logistic

regression was used because the outcome is categorical. As bone, joint and muscle infection

has three categories of severity, notably: mild, moderate and severe together with a no infection

category, multinominal logistic regression is an appropriate model to use (Appendix Table

164

D.4). Also, backward stepwise is used here because it is more accurate than is a forward

procedure. It also considers the accumulating effect of all variables and starts with a bigger

model. As the model fit statistics (Appendix Table D.6) show that using this large model is still

fitted to the data, it can be appropriately used. Also, there is no collinearity and no two variables

are identical. This makes it easier to use the backward model [22].

According to the summary table in the backward procedure, the least significant effect is from

Certolizumab followed by Azathioprine, Anakinra, Golimumab, Tocilizumab, Cyclosporine,

Methotrexate, Rituximab, Abatacept, Sulphasalazine, Etanercept, Adalimumab and IM Gold

injection. However, the effect of all these medications was found to be minimal, so they were

dropped from the model (Table 4.20 and Appendix Table D.32).

165

Table 4.20- Summary of backward elimination of anti-RA medications and risk of Bone,

Joint and Muscle infections


Step

Effect

Removed DF

Number

In

Wald


Variable

Label

1 Certolizumab 9 18 1.6031 0.9963 Certolizumab

2 Azathioprine 9 17 3.8457 0.9213 Azathioprine

3 Anakinra 9 16 4.3211 0.8890

4 Golimumab 6 15 3.0321 0.8048 Golimumab

5 Tocilizumab 9 14 5.4188 0.7964 Tocilizumab

6 Cyclosporin 9 13 6.0931 0.7306 Cyclosporin

7 Methotrexate 9 12 8.0079 0.5334 Methotrexate

8 Rituximab 9 11 10.8068 0.2892 Rituximab

9 Abatacept 9 10 11.4004 0.2493 Abatacept

10 Sulphasalazine 9 9 14.0763 0.1196 Sulphasalazin

e

11 Etanercept 9 8 15.3543 0.0817

12 Adalimumab 9 7 15.0322 0.0901

13 IM Gold 9 6 16.1101 0.0646 IM Gold

According to type 3 analysis of effects table, the following medications have significant impact

on either increasing or reducing the risk of bone, joint and muscle (BJM) infection in RA. These

medications include: Infliximab, Methotrexate (plus Folic acid), Hydroxychloroquine,

Leflunomide, Prednisolone and Penicillamine (Table 4.21 and Appendix Table D.33).

166

Table 4.21 Effect of medications in causing bone, joint and muscle infection


Effect DF

Wald


Infliximab 9 24.8305 0.0032

Methotrexate (plus Folic acid) 3 8.3854 0.0387

Hydroxychloroquine 9 25.4841 0.0025

Leflunomide 9 35.2574 <.0001

Prednisolone 9 33.2572 0.0001

Penicillamine 9 28.5823 0.0008

In the analysis of maximum likelihood table, the effect of each medication was investigated in

more detail. The results are outline below:

Infliximab: Currently taking Infliximab was found to be marginally associated with an

increased frequency of severe BJM infection (P value: 0.07). The amount of this increase can

reach 82 times more than patient who were not treated with this medicine at all (Table 4.22-

4.23 and Appendix Tables D.34- D.35).

Methotrexate (plus Folic acid): Currently taking MTX / Folic acid was found to reduce

moderate BJM infection, significantly (P value: 0.02). The amount of this reduction is almost

66% of patient who did not need to take MTX, at all. (CI: 0.46 to 0.94) (Table 4.22-4.23 and

Appendix Tables D.34- D.35).

Hydroxychloroquine: There is no evidence that currently taking this medication can affect

BJM infection (Table 4.22-4.23 and Appendix Tables D.34- D.35).

Leflunomide: Currently taking Leflunomide was associated with an increased frequency of

severe BJM infection (P value: 0.0014). The amount of this increase is 87 times greater than in

patients who have never taken this medication at all (CI: 1.276 to 2.759) (Table 4.22-4.23 and

Appendix Tables D.34- D.35).

167

Prednisolone: Currently taking Prednisolone was associated with significant increased

frequency of moderate and severe bone, joint and muscle infection. The amount of this increase

is in turn 87 and 152 times more than in patients who have never taken prednisolone at all

(Table 4.22-4.23 and Appendix Tables D.34- D.35).

Penicillamine: There is no evidence that currently taking Penicillamine can affect BJM

infection (Table 4.22-4.23 and Appendix Tables D.34- D.35).

In summary, the use of infliximab, leflunomide and prednisolone were associated with

statistically significant increases in the frequency of severe BJM infections.

168

Table 4.22-Analysis of maximum likelihood estimate in bone, joint and muscle (BJM)

infection


Parameter Bone/Joint/Muscle infection DF Estimate

Standard Error

Wald Chi-Square Pr > ChiSq

Infliximab currently taking 1 1 -0.2314 0.7289 0.1007 0.7510 Infliximab currently taking 2 1 -0.0882 0.4578 0.0372 0.8471 Infliximab currently taking 3 1 0.5992 0.3308 3.2819 0.0700

Methotrexate (plus Folic

acid)

currently taking 1 1 -0.4576 0.2957 2.3950 0.1217


acid)



acid)

currently taking 3 1 0.1239 0.1458 0.7217 0.3956

Hydroxychloroquine


Hydroxychloroquine


Hydroxychloroquine


Arava (Leflunomide)


Arava (Leflunomide)


Arava (Leflunomide)


Prednisolone currently taking 1 1 0.0363 0.3124 0.0135 0.9075 Prednisolone currently taking 2 1 0.6298 0.2394 6.9233 0.0085 Prednisolone currently taking 3 1 0.9273 0.2630 12.4305 0.0004 Penicillamine currently taking 1 1 1.6260 1.0242 2.5204 0.1124 Penicillamine currently taking 2 1 -

13.0777 913.3 0.0002 0.9886

Penicillamine currently taking 3 1 -12.9395

847.3 0.0002 0.9878

169

Conclusion:

Differential effects on the frequency of BJM infections were observed amongst users of

csDMARDs and bDMARDs. Amongst csDMARDs, Prednisolone and leflunomide were

associated with a significant increased frequency of severe BJM infections, whereas

methotrexate was associated with reduced frequency of moderate BJM infections. Amongst

bDMARDs, infliximab was associated with an increased frequency of BJM infections in most

categories and severe infection type was significant. The data eres less clear for

hydroxychloroquine and penicillamine, but low numbers may have limited the capacity for

analysis (Table 4.22-4.23 and Appendix Tables D.1- D.35).

Table 4.23- Estimation of Odds ratios in Bone, Joint and Muscle infection


Effect Bone/Joint/Muscle infection

Point Estimate


Infliximab currently taking Versus never taking 1 0.793 0.190 3.311 Infliximab currently taking Versus never taking 2 0.916 0.373 2.246 Infliximab currently taking Versus never taking 3 1.821 0.952 3.482 Methotrexate (plus Folic acid) currently taking

Versus never taking Mild 0.633 0.354 1.130

Methotrexate (plus Folic acid) currently taking Versus never taking

Moderate 0.664 0.468 0.943

Methotrexate (plus Folic acid) currently taking Versus never taking

Severe 1.132 0.851 1.506

Hydroxychloroquine currently taking Versus never taking

Mild 0.757 0.404 1.419


Moderate 1.306 0.901 1.893


Severe 1.140 0.816 1.594

Arava (Leflunomide) currently taking Versus never taking

Mild 1.209 0.629 2.325


Moderate 1.203 0.801 1.808


Severe 1.876 1.276 2.759

Prednisolone currently taking Versus never taking Mild 1.037 0.562 1.913 Prednisolone currently taking Versus never taking Moderate 1.877 1.174 3.001 Prednisolone currently taking Versus never taking Severe 2.528 1.510 4.232 Penicillamine currently taking Versus never taking Mild 5.084 0.683 37.844 Penicillamine currently taking Versus never taking Moderate <0.001 <0.001 >999.999 Penicillamine currently taking Versus never taking Severe <0.001 <0.001 >999.999

170

3.7. Blood infection - analysis of Anti-RA medicines



whereas 21304/21506 (99.06 %) reported no infection. In this model, participants with different

categories of blood infection (presumed sepsis or septicaemia) were compared to those who did

not report any infection (Appendix tables E1-E3). A multinomial logistic regression model was

used. The reason for using this model is that the outcome is a non-binary categorical variable.

Using pairwise Chi-square test without using the model can increase potential mistakes because

the number of comparisons is large [20].

Table 4.24 Estimation of fitness of tests in blood infection


Criterion Intercept Only Intercept and Covariates

AIC 2,670.26 2,718.38

SC 2,694.19 3,962.65

-2 Log L 2,664.26 2,406.38

Table 4.25 Estimation of fitness of tests in blood infection



Likelihood Ratio 257.8845 153 <.0001

Score 284.8579 153 <.0001

Wald 233.2076 153 <.0001

The model convergence status table (Appendix Tables E.5-E.7) shows that the test meets the

criteria for accuracy and the variables fit the model of statistics. Overall the test shows a

significant difference (lr Chi-square of 257.8845 with a P value of <.0001) between variable

effects on blood infection [20] (Appendix table E.7).

In the model fit statistics table (Appendix table E.7), the likelihood ratio or lr (difference


contains both the intercept and covariates) is 257. The P value is highly significant (Appendix

table E.1-E.7). This shows that in a model with covariates, the test strengthened. Furthermore,

171

the covariates were found to be impacting cofactors in blood infection. Other tests such as SC

and AIC were also used to recheck this conclusion [22] (Appendix table E.6).

Wald Chi-Square for overall test is also highly significant (<.0001) with a Chi-square of almost

233 among 153 degrees of freedom. In other words, the impact on the blood infection is not the

same for different groups. This Chi-square P value is almost equivalent to the p value in the

overall Pearson test. Indeed, the logistic regression result is much the same as the frequency

table (Tables 3.59-3.60) result because it is a large sample. As the model is a logistic regression

and not a linear regression, the Chi-square test has been used to examine the comparison. [21]

(Appendix E.7).



variables in the model. Usually the score tests are compared when parameters are added. They

provide an estimation of how far the accuracy of the test improves when new parameters are

added to or removed from existing parameters [21] (Appendix E.7). During the backward

stepwise model in the next part of the model, the effects of the medications are dropped one by

one to see how much change occurs in the chi-square and to get an estimate of the amount of

impact of that medication in increasing blood infection [22] (Appendix E.8-E.31).

3.7.1. Wald Chi-square, Likelihood ratio test and Score test to test the significance

of differences

As the size of the study population in this study was large enough, any of these three tests can

be used, but if the size of the sample were small, then it would be necessary to check all three

tests to increase the reliability of the conclusions. Among these tests, the likelihood ratio test is

the most reliable test, because it stays unchanged even if re-parametrization of the data is

undertaken [22] (Appendix E.7).

3.7.2. Effects of different medications on blood infection

As the medication effects in this model are all qualitative, the degree of effect (impact) on blood

infection can be easily determined by comparing these categorical variables [21].

172


Logistic regression is required because the outcome is categorical and also because blood

infection is divided into three categories of severity, viz: mild, moderate and severe. Moreover,

there is a no infection category, (self-reported absence of infection). Accordingly, a multi

nominal logistic regression model was deemed most appropriate (Appendix Table E.4).

Furthermore, a backward stepwise approach is used here too, because it is more accurate than

a forward procedure and considers the accumulating effect of all variables and starts with a

bigger model. As the model fit statistics (Appendix Table E.6) show that using this large model

is still well fitted to the data, it can be used appropriately. Additionally, there is no collinearity

and none of any two variables are identical. This makes it easier to use the backward model

(Tables 4.26-4.29).

According to the summary table in the backward procedure (Table 4.26 and Appendix Table

E.32), the least significant effect is from Anakinra followed by Certolizumab, Infliximab,

Rituximab, Leflunomide, Penicillamine, Golimumab, Cyclosporine, Sulphasalazine,

Azathioprine, Abatacept, Tocilizumab, Methotrexate (plus Folic acid), IM Gold injection,

Adalimumab and Etanercept. However, the effect of all of these medications was minimal and

so they were dropped from the model.

173

Table 4.26 Summary of backward elimination of anti-RA medications in respect to Blood

infection (sepsis or septicaemia)


Step Effect Removed DF Number In Wald Chi-Square Pr > ChiSq

1.00 Anakinra 9.00 17.00 0.28 1.00

2.00 Certolizumab 9.00 16.00 1.68 1.00

3.00 Infliximab 9.00 15.00 3.55 0.94

4.00 Rituximab 9.00 14.00 4.87 0.85

5.00 Leflunomide 9.00 13.00 5.34 0.80

6.00 Penicillamine 9.00 12.00 5.65 0.77

7.00 Golimumab 6.00 11.00 3.51 0.74

8.00 Cyclosporine 9.00 10.00 7.10 0.63

9.00 Sulphasalazine 9.00 9.00 7.13 0.62

10.00 Azathioprine 9.00 8.00 9.52 0.39

11.00 Abatacept 9.00 7.00 12.24 0.20

12.00 Tocilizumab 9.00 6.00 12.21 0.20

13.00 Methotrexate (plus Folic

acid)

3.00 5.00 4.80 0.19

14.00 IM Gold injection 9.00 4.00 15.55 0.08

15.00 Adalimumab 9.00 3.00 16.53 0.06

16.00 Etanercept 9.00 2.00 14.05 0.12

The only medications for which there was evidence of an effect on blood infection were:

Hydroxychloroquine and Prednisolone (Table 4.27 and Appendix Table A.32).

Table 4.27 Effect of medications in causing Blood infection


Effect DF Wald

Chi-Square

Pr > ChiSq


Prednisolone 9.00 49.54 <.0001

174

Hydroxychloroquine: Taking Hydroxychloroquine was associated with a significant reduction

in reporting blood infection. Although taking this medication (CI: 0.22 to 0.84 P value: 0.03)

(Table 4.27-4.28 and Appendix Tables E.34- E.35).

Prednisolone: Use of prednisolone is strongly associated with an increased frequency of severe

blood infections. These severe blood infections were increased by up to almost 431 times

compared to participants who were never users of prednisolone (CI: 2.147 to 13.142) (Table

4.27-4.28 and Appendix Tables E.34- E.35).

In summary, use of Prednisolone carries considerable risk in respect to septicaemia.

Table 4.28 Analysis of maximum likelihood estimate in blood infection


Parameter Medicati

on Status

Blood

Infectio

n

DF Estimat

e

Standar

d Error

Wald

Chi-

Squar

e

Pr > ChiS

q

Hydroxychloroquine

currently

taking

Mild 1.0

0

-1.94 1.03 3.51 0.06

Moderat

e

1.0

0

-0.29 0.34 0.72 0.40

Severe 1.0

0

-0.86 0.35 6.07 0.01

Prednisolone

Mild 1.0

0

-0.40 0.56 0.50 0.48

currently

taking

Moderat

e

1.0

0

0.23 0.33 0.47 0.49

Severe 1.0

0

1.67 0.46 13.06 0.001

175

Conclusion: Hydroxychloroquine and especially prednisolone substantially increase rates of

severe blood infection and have a significant association with risk of blood infection. While

hydroxychloroquine is associated with a reduction in this risk, taking prednisolone is

significantly associated with a sharp increase in this risk (Table 4.28-4.29 and Appendix Tables

E.1- E.35).

Table 4.29 Estimation of Odds ratios in blood infection


Effect Blood

Infection

Point

Estimate

95% Wald

Confidence Limits

Hydroxychloroquine - currently taking vs

never taken

1.00 0.14 0.02 1.09

2.00 0.75 0.39 1.45

3.00 0.43 0.22 0.84

Prednisolone - currently taking vs never taken

1.00 0.67 0.22 2.02

2.00 1.25 0.66 2.39

3.00 5.31 2.15 13.14

176

3.8. Gastro-intestinal tract infection - analysis of medication confounders

Among 21506 observations, 118/21506 (0.54 %) self-reported mild infection, 241/21506

(1.12%) self-reported moderate infection and 155/21506 (0.72%) self-reported severe infection,

whereas 20992/21506 (97.6 %) reported no infection. In this model, participants with different

categories of gastrointestinal infection were compared with ARAD participants, who did not

self-report this type of infection (Appendix tables F1-F3). The statistical model used was

Multinomial logistic regression. The reason for using this model is that the outcome is a non-

binary categorical variable. Using pairwise Chi-square test without using the model can

increase potential mistakes because the number of comparisons is very large [20].

The model convergence status table (Appendix Tables F.5-F.7) shows that the test meets the

criteria for accuracy and the variables fit the statistical model. Overall the test shows significant

effects (lr Chi-square of 233.3227 with a P value of <.0001) from anti RA medicines on GIT

infection [20] (Appendix table F.7).

In the model fit statistics table (Appendix table F.7), the likelihood ratio or lr (difference


contains both the intercept and covariates) is 233.3227. The P value is highly significant (Table

4.30 and Appendix table F.1-F.7). This shows that a model with covariates is strengthening the

test and that the covariates are actually impacting cofactors in GIT infections. Other tests such

as SC and AIC are also used to recheck this conclusion [22] (Table 4.30 and Appendix table

F.6).




AIC 5,944.02 6,016.70

SC 5,967.95 7,260.97

-2 Log L 5,938.02 5,704.70

177

Wald Chi-square for overall test is also highly significant (0.0001) with a Chi-square of almost

233.3227 among 153 degrees of freedom. In other words, the impact on the GIT infection is

not the same in different groups. This Chi-square P value is almost equivalent to the p value in

the overall Pearson test. Indeed, the logistic regression result is much the same as the frequency

table (Tables 3.59-3.60) result because it is a large sample. As a logistic regression and not a

linear regression has been used, the Chi-Square test has been used for comparison. [21] (Table

4.31 and Appendix F.7).

Table 4.31 Estimation of fitness of tests in GIT infection




Score 267.8657 153 <.0001

Wald 231.4222 153 <.0001



variables in the model. Usually, the score tests are compared when new parameters are added.

It gives an estimate of how far the accuracy of the test improves when new parameters are

added, or existing parameters are deleted [21] (Appendix F.7).

During the backward stepwise phase in the next part of the model, the effects of the medications

are dropped one by one to see how much there is a change in the Chi-square and to get an

estimate of the impact of that medication with regard to increasing or decreasing GIT infection

[22] (Appendix F8-F.31).

178

3.8.1. Wald Chi-square, Likelihood ratio test and Score test


used, but if the size of the sample is small then all three tests need to be used to increase the


test, because it remains unchanged even if the data is reparametrized [22] (Appendix F.7).

3.8.2. Effects of different medications on GIT infections

As the medication effects in this model are all qualitative, the degree of effect (impact) on any

infection can be easily worked out by comparing these categorical variables [21].

For this section, a backward procedure in multinominal logistic regression is preferred. Logistic

regression is required because the outcome is categorical and as GIT infection has three

categories, notably mild, moderate and severe as well as a no infection category, multinominal

logistic regression is an appropriate model (Appendix Table F.4). Also, backward stepwise is

used here because it is more accurate than a forward procedure and considers the accumulating

effect of all variables and starts with a bigger model. As the model fit statistics (Appendix Table

F.6) show that using this large model is still fitted to the data, it can be used appropriately. Also,

there is no collinearity and none of any two variables are identical. This makes it easier to use

the backward model [22].

According to the summary of the backward procedure (Table 4.32), the least significant effect

is from Certolizumab followed by Azathioprine, IM Gold injection, Golimumab, Tocilizumab,

Etanercept, Leflunomide, Anakinra, Penicillamine, Abatacept, Methotrexate (plus Folic acid),

Rituximab, Hydroxychloroquine, and Sulphasalazine (Table 4.32 and Appendix Table F.32).

179

Table 4.32 Summary of backward elimination of anti RA medications and risk of GIT infection

Summary of backward elimination

Step Effect Removed DF Number In Wald Chi-Square Pr > ChiSq

1.00 Certolizumab 9.00 17.00 1.04 1.00

2.00 Azathioprine 9.00 16.00 3.60 0.94

3.00 IM Gold injections 9.00 15.00 4.16 0.90

4.00 Golimumab 6.00 14.00 2.60 0.86

5.00 Tocilizumab 9.00 13.00 4.03 0.91

6.00 Etanercept 9.00 12.00 4.74 0.86

7.00 Leflunomide 9.00 11.00 7.01 0.64

8.00 Anakinra 9.00 10.00 8.19 0.52

9.00 Penicillamine 9.00 9.00 9.03 0.44

10.00 Abatacept 9.00 8.00 9.71 0.37

11.00 Folic acid plus Methotrexate 3.00 7.00 3.37 0.34

12.00 Rituximab 9.00 6.00 11.20 0.26


14.00 Sulphasalazine 9.00 4.00 13.32 0.15

However, the effect of all these medications was minimal, so they were dropped from the

model. The only medications with significant effects were: Adalimumab, Infliximab,

Cyclosporine, and Prednisolone (Table 4.33 and Appendix Table F.33).

Table 4.33 Medications associated with either increased or decreased GIT infection


Effect DF Wald

Chi-Square

Pr > ChiSq

Adalimumab 9.00 17.65 0.04

Infliximab 9.00 20.52 0.02

Cyclosporine 9.00 45.78 <.0001


180

In the analysis of maximum likelihood table, the effect of each medication was examined in

more detail:

Adalimumab: Currently taking Adalimumab was found to be significantly associated with a

reduction in the chance of mild GIT infection. However, if patient does not need to take

Adalimumab at all, the risk could be even less and can get up to 48 times less (CI: 0.314 to

0.888) (Table 4.34-4.35 and Appendix Tables F.34- F.35).

Infliximab: Currently taking Infliximab is associated with a significant increase (P Value:

0.0235) in the risk of moderate infection. The amount of this increase is almost 99 times more

than patients who never took this medication (Table 4.34-4.35 and Appendix Tables F.34-

F.35).

Cyclosporine: Currently taking this medication is associated with a higher risk of mild (P value

of <0.0001) and moderate (P value of <0.0001) GIT infection. This approaches 500 times

compared to patients who don’t take this medication (Table 4.34-4.35 and Appendix Tables

F.34- F.35).

Prednisolone: Currently taking prednisolone is associated with an increase in the risk of severe

GIT infection (P value: 0.0505) and there is marginal evidence (P value 0.065) that it can also

increase moderate GIT infection. This increase is more than 50 times compared to patients who

have never taken Prednisolone (Table 4.34-4.35 and Appendix Tables F.34- F.35).

In summary, use of Prednisolone was associated with an increased frequency of severe GIT

infection and use of both Infliximab and Cyclosporin were associated with increased rates of

moderate GIT infection

181

Table 4.34 Analysis of Maximum likelihood estimates in GIT infection



Status

GIT

Infection


Error

Wald

Chi-

Square

Pr > ChiSq

Adalimumab

currently

taking

1.00 1.00 -0.64 0.26 5.81 0.02

2.00 1.00 0.13 0.17 0.62 0.43

3.00 1.00 -0.10 0.22 0.22 0.64

Infliximab

currently

taking

1.00 1.00 -0.61 0.72 0.71 0.40

2.00 1.00 0.69 0.31 5.13 0.02

3.00 1.00 0.24 0.46 0.27 0.61

Cyclosporine

currently

taking

1.00 1.00 1.89 0.47 16.32 <.0001

2.00 1.00 1.83 0.36 26.26 <.0001

3.00 1.00 0.75 0.72 1.09 0.30

Prednisolone

currently

taking

1.00 1.00 0.19 0.30 0.39 0.53

2.00 1.00 0.44 0.24 3.39 0.07

3.00 1.00 0.54 0.28 3.83 0.05

Conclusion: Differential effects on the frequency of GIT infections were observed amongst

users of csDMARDs and bDMARDs. Amongst csDMARDs, Cyclosporine was associated with

an increase in mild and moderate self-reported GIT infections, whereas prednisolone was

associated with an increase in severe self-reported GIT infections. Amongst bDMARDs, use of

infliximab was associated with an increase in moderate self-reported GIT infections, whereas

adalimumab was associated with a protective effect for mild, but not moderate or severe GIT

infections. The clinical relevance of this latter finding is uncertain. Once again, the potential

for corticosteroid therapy to confer risk for severe infection in multiple systems was evident.

182

Table 4.35 Estimation of odds ratios in GIT infection


Effect GIT

Infection

Point Estimate 95% Wald

Confidence Limits

Adalimumab - currently taking vs never taking

1.00 0.53 0.31 0.89

2.00 1.14 0.82 1.58

3.00 0.90 0.58 1.40

Infliximab - currently taking vs never taking

1.00 0.55 0.13 2.22

2.00 2.00 1.10 3.64

3.00 1.27 0.51 3.16

Cyclosporine - currently taking vs never taking

1.00 6.64 2.65 16.65

2.00 6.21 3.09 12.48

3.00 2.12 0.52 8.71

Prednisolone - currently taking vs never taking

1.00 1.21 0.67 2.17

2.00 1.55 0.97 2.48

3.00 1.72 1.00 2.97

3.9. Nervous System infection - analysis of medication confounders



whereas 21476/21506 (99.86 %) reported no infection. In this model, nervous system infections

of different severity were compared to those in participants who did not have such infection

(Appendix tables G1-G3). The model used was a Multinomial logistic regression model. The

reason for using this model is because the outcome is a non-binary categorical variable. Using

pairwise Chi-square test without using the model can increase potential mistakes because the

number of comparisons is very large [20]. The model convergence status table (Appendix

Tables G.5-G.7) shows that the test does not meet the criteria for accuracy and the variables do

not fit the model of statistics.

Table 4.36 Estimation of the impact of confounders.



AIC 525.79 719.40

SC 549.71 1,963.67

-2 Log L 519.79 407.40

183

Overall, the test does not show any significant effect of anti-RA medicines on nervous system

infections (lr Chi-Square of 112.3885 with a P value of 0.9943) [20] (Table 4.36-4.37 and

Appendix table G.7).

Table 4.37 Estimation of fitness of tests in nervous system infection




Score 170.52 153.00 0.16

Wald 83.37 153.00 1.00

This means that the test is not reliable due to several potential reasons, including sample size,

and so it is not possible to draw reliable conclusions from the analysis.

3.10. TB infection - analysis of medication confounders

A multinomial logistic regression model was used to compare different the severities of TB

infection with a control group (respondents who have not had TB infection). The reason for

using this model was because the outcome was a non-binary categorical variable. The results

indicate that amongst 21506 observations 3/21506 (0.013 %) had mild TB infection, 6/21506

(0.027%) had moderate TB infection and 2/21506 (0.0092%) reported severe TB infection,

whereas 21495/21506 (99.94 %) reported no infection at all. The model convergence status

table shows that the test does not meet the criteria for accuracy and the variables do not fit the

model of statistics. Overall the test does not show significant difference (lr Chi-square of

93.7402 with a P value of 1) in between variable effects on the TB infection. This means that

the test is not reliable due to several potential reasons including sample size and we cannot

judge the conclusion out of such analysis.




AIC 216.60 428.86

SC 240.53 1,673.13

-2 Log L 210.60 116.86

184

Overall, the test does not show a significant difference (lr Chi-square of 93.7402 with a P value

of 1) between variable effects on TB infection (Table 4.39 and Appendix table H.7).

Table 4.39 Estimation of fitness of tests in TB infection




Score 191.37 153.00 0.02

Wald 26.52 153.00 1.00

This means that the test is not reliable due to several potential reasons including sample size

and so it is not possible to draw reliable conclusions from the analysis.

3.11. Urinary tract infection - analysis of medication confounders


(3.87%) self-reported moderate infection, and 256/21506 (1.19%) self-reported severe

infection, whereas 20127/21506 (93.58 %) reported no infection. In this model, persons with

different categories of urinary tract infection are compared with people who don’t have this

type of infection (Appendix tables I1-I3). A multinomial logistic regression model was

employed to analyse the data. The reason for using this model is because the outcome is a non-

binary categorical variable. Using pairwise Chi-square test without using the model can

increase potential mistakes because the number of comparisons is very large [20]. The model

convergence status table (Appendix Tables I.5-I.7) shows that the test meets the criteria for

accuracy and the variables fit the model of statistics. Overall the test shows a significant

difference (lr Chi-square of 442.0070 with a P value of less than 0.0001) between variable

effects on urinary tract infection [20] (Table 4.40-4.41 and Appendix table I.7).



Criterion Intercept Only Intercept and covariates

AIC 12856.1 12720.09

SC 12880.03 13964.36

-2Log L 12850.1 12408.09

185

In the model fit statistics table (Appendix table I.7), the likelihood ratio or lr (difference


contains both the intercept and covariates) is 442. The P value is highly significant (Appendix

table I.1-I.7). This shows that a model with covariates strengthens the test and that the

covariates are actually impacting cofactors in urinary tract infection. Other tests such as SC and

AIC are also used to recheck this conclusion [22] (Tables 4.40-4.41 and Appendix table I.6).

Table 4.41 Estimation of fitness of tests in Urinary tract infection




Score 494.1448 153 <.0001

Wald 442.5757 153 <.0001

The Wald Chi-Square for overall test is also highly significant (0.0001) with a Chi-square of

almost 442 among 153 degrees of freedom. In other words, the impact on urinary tract infection

is not the same in different groups. This Chi-Square P value is almost equivalent to the p value

in the overall Pearson test. Indeed, the logistic regression result is much the same as in the

frequency table (Table 3.47) result, because it is a large sample. As a logistic regression and

not a linear regression model was used, Chi-square test for comparison. [21] (Appendix table

I.7).





added, or existing parameters are deleted [21] (Table 4.41 and Appendix table I.7).

During the backward stepwise phase in the next part of the model, the effects of the medications

are dropped one by one to see how much there is a change in the Chi-square and to get an

estimate of the impact of that medication with regard to increasing or decreasing GIT infection

[22] (Appendix tables I8-I.31).

186


differences

As the size of the study population in this study is enough, we can use any of these three tests,

but if the size of the sample is small then it is necessary to check all three tests to increase the


test, because it stays unchanged even if the testing is reparametrized 3.10.1. Effects of different

medications on urinary tract infection [22] (Appendix I.7).

3.11.2. Effects of medications on Urinary tract infection

As the medication effects in this model are all qualitative, the degree of effect (impact) on

urinary tract infections infection can be easily worked out by comparing the categorical

variables [21].


Logistic regression is required because the outcome is categorical and as urinary tract infection

has three categories, notably mild, moderate and severe infection as well as a no infection

report, multinominal logistic regression is an appropriate model (Appendix Table I.4). The

backward stepwise procedure is used here because it is more accurate than a forward procedure

and considers the accumulating effect of all variables and starts with a bigger model. As the

model fit statistics (Appendix Table I.6) show that this large model is still fitted to the data, it

is appropriate for use. Also, there is no collinearity and no two variables are identical. This

makes it easier to use the backward model [22].

According to the summary table in the backward procedure (Table 4.42), the least significant

effect is from Abatacept followed by Anakinra, Certolizumab, Golimumab, Tocilizumab,

Sulphasalazine, Adalimumab, Rituximab, and Folic acid plus Methotrexate (plus Folic acid).

However, the effect of all these medications was found to be minimal and they were therefore

dropped from the model. There was evidence for an effect of the following medications in

respect to Urinary tract infection: Etanercept, Infliximab, Hydroxychloroquine, Leflunomide,

Azathioprine, Cyclosporine, Prednisolone, IM Gold Injection, and Penicillamine (Table 4.42

and Appendix Table I.32).

187

Table 4.42 Summary of backward elimination of anti RA medications and risk of urinary tract

infection


Step Effect

Removed

DF Number

In

Wald

Chi-Square

Pr > ChiSq

1.00 Abatacept 9.00 17.00 2.97 0.97

2.00 Anakinra 9.00 16.00 3.68 0.93

3.00 Certolizumab 9.00 15.00 6.06 0.73

4.00 Golimumab 6.00 14.00 4.11 0.66

5.00 Tocilizumab 9.00 13.00 9.34 0.41

6.00 Sulphasalazine 9.00 12.00 10.71 0.30

7.00 Adalimumab 9.00 11.00 13.75 0.13

8.00 Rituximab 9.00 10.00 15.73 0.07

9.00 Folic acid plus

Methotrexate

3.00 9.00 7.47 0.06

In the analysis of maximum likelihood table, the effect of each medication was examined in

more detail.

188

Table 4.43 Analysis of Maximum likelihood estimate in Urinary tract infection



Status

Urinary

system

Infection


Error

Wald

Chi-

Square

Pr > ChiSq

Infliximab

currently

taking

Mild 1.00 0.33 0.34 0.98 0.32

Moderate 1.00 0.02 0.22 0.01 0.91

Severe 1.00 -1.49 0.74 4.06 0.04

Hydroxychloroquine

currently

taking

Mild 1.00 0.07 0.17 0.16 0.69

Moderate 1.00 0.08 0.11 0.53 0.47

Severe 1.00 -0.18 0.20 0.81 0.37

Leflunomide

currently

taking

Mild 1.00 -0.21 0.19 1.17 0.28

Moderate 1.00 -0.14 0.12 1.19 0.28

Severe 1.00 -0.53 0.24 5.01 0.03

Azathioprine

currently

taking

Mild 1.00 -11.88 269.90 0.001 0.96

Moderate 1.00 -0.80 0.59 1.84 0.17

Severe 1.00 -11.93 256.60 0.001 0.96

Cyclosporine

currently

taking

Mild 1.00 1.52 0.38 16.37 <.0001

Moderate 1.00 1.06 0.29 13.23 0.001

Severe 1.00 0.69 0.53 1.68 0.20

Prednisolone

currently

taking

Mild 1.00 -0.03 0.19 0.03 0.86

Moderate 1.00 0.36 0.12 9.16 0.001

Severe 1.00 0.78 0.24 10.51 0.001

IM Gold injection

currently

taking

Mild 1.00 -0.03 0.72 0.001 0.97

Moderate 1.00 0.48 0.33 2.10 0.15

Severe 1.00 1.11 0.44 6.56 0.01

Penicillamine

currently

taking

Mild 1.00 -12.05 451.70 0.001 0.98

Moderate 1.00 -12.12 270.80 0.001 0.96

Severe 1.00 -12.08 478.60 0.001 0.98

Based on the likelihood estimate table and odd’s ratio the following results are concluded:

Etanercept: There is no evidence that currently taking Etanercept is associated with urinary

tract infection (Table 4.43-4.45 and Appendix Tables I.34- I.35).

189

Infliximab: Currently taking Infliximab is associated with a reduced (P Value: 0.0438)

frequency of Urinary tract infection. However, patients who have never taken Infliximab have

almost 80 times less chance for Urinary tract infection (CI: 0.053 to 0.960) (Table 4.43-4.45

and Appendix Tables I.34- I.35).

Hydroxychloroquine: There is no evidence that currently taking Hydroxychloroquine is

associated with urinary tract infection (Table 4.43-4.45 and Appendix Tables I.34- I.35).

Leflunomide: Currently using Leflunomide is associated with a reduced reports of severe

urinary tract infection. The amount of this reduction was almost 58% compared to non-users of

LEF (CI: 0.367 to 0.936 P value: 0.0252) (Table 4.43-4.45 and Appendix Tables I.34- I.35).

Azathioprine: There is no evidence that currently taking Azathioprine is associated with

urinary tract infection (Table 4.43-4.45 and Appendix Tables I.34- I.35).

Cyclosporine: Currently taking Cyclosporine is associated with an increased frequency of mild

and moderate urinary tract infection. Compared to participants who have never taken

Cyclosporine, the extent of this increase is 358-fold for mild and 187-fold for moderate urinary

tract infection.

Prednisolone: Currently taking Prednisolone is associated with an increased frequency of

moderate and severe urinary tract infection. Compared to participants who have never taken

Prednisolone, the extent of the increase is 43-fold for moderate and 117-fold for severe urinary

tract infection (Table 4.43-4.45 and Appendix Tables I.34- I.35).

IM Gold Injection: Currently receiving parenteral Gold is associated with an increase in the

frequency of severe urinary tract infection (P value: 0.0104). Compared to participants who

have never received parenteral Gold, the extent of the increase is 204-fold (CI: 1.299 to 7.151)

(Table 4.43-4.45 and Appendix Tables I.34- I.35).

Penicillamine: There was no evidence that currently taking Penicillamine is associated with an

increased frequency of urinary tract infection (Table 4.43-4.44 and Appendix Tables I.34- I.35).

Conclusion: In summary use of Prednisolone and Cyclosporin was found to be associated with

moderate and severe UTIs in the case of the former and moderate and mild, but not severe UTIs

in the case of the latter. Use of Infliximab and Leflunomide appeared to have protective effects

in respect to UTI.

190

Table 4.44 Estimation of Odds ratios in participants with urinary tract infection


Effect Urinary system

Infection

Point

Estimate

95% Wald

Confidence Limits

Etanercept - currently taking vs

never taken

Mild 1.39 1.04 1.84

Moderate 0.90 0.75 1.06

Severe 0.91 0.65 1.26

Infliximab - currently taking vs

never taken

Mild 1.39 0.72 2.69

Moderate 1.03 0.67 1.58

Severe 0.23 0.05 0.96

Hydroxychloroquine - currently

taking vs never taken

Mild 1.07 0.76 1.51

Moderate 1.08 0.87 1.34

Severe 0.84 0.57 1.23

Leflunomide - currently taking vs

never taken

Mild 0.81 0.56 1.19

Moderate 0.87 0.68 1.12

Severe 0.59 0.37 0.94

Azathioprine - currently taking vs

never taken

Mild <0.001 <0.001 >999.99

Moderate 0.45 0.14 1.43

Severe <0.001 <0.001 >999.99

Cyclosporine - currently taking vs

never taken

Mild 4.59 2.19 9.60

Moderate 2.88 1.63 5.09

Severe 1.99 0.70 5.66

Prednisolone - currently taking vs

never taken

Mild 0.97 0.67 1.40

Moderate 1.43 1.14 1.81

Severe 2.17 1.36 3.47

IM Gold - currently taking vs never

taken

Mild 0.97 0.24 3.98

Moderate 1.62 0.84 3.11

Severe 3.05 1.30 7.15

Penicillamine - currently taking vs

never taken

Mild <0.001 <0.001 >999.99

Moderate <0.001 <0.001 >999.99

Severe <0.001 <0.001 >999.99

As the anatomy of urinary system is different between two sexes it will be appropriate to assess

if these differences can modify the effect of tablets. In other word if the effect of Anti RA

191

medication on UTI is the same for male and female sexes. In table 4.45 the interaction between

anti RA medication and sex in UTI has been assessed.

Table 4.45 Estimation of interaction between anti RA medication and sex


Effect DF Wald

Chi-Square

Pr > ChiSq

Interaction with Etanercept 2 17.4683 0.0002

Interaction with Tocilizumab 2 11.0376 0.0040

Interaction with Hydroxychloroquine 3 8.9707 0.0297

Interaction with Leflunomide 2 9.9919 0.0068

Interaction with Prednisolone 2 15.1467 0.0005

Table 4.45 shows that sex modifies effects of Etanercept, Tocilizumab, Hydroxychloroquine,

Leflunomide and Prednisolone. In order to find the differences in more details, table 4.46

presents the pairwise test in each anti RA medication. Based on the results of this table

Leflunomide is associated with increased rate of infection in male sex, but all other discussed

anti RA medications (Etanercept, Tocilizumab, Hydroxychloroquine and prednisolone) were

associated with less frequent UTI in male than in female (Table 4.46).

With Etanercept (odds ratio 0.10) and Tocilizumab (odds ratio 0.0044) and prednisolone (odds

ratio 0.049), the effect of these medications is stronger in the female sex than the male sex.

However, Leflunomide (odd’s ratio of 0.88) can significantly increase UTI in the male sex.

192

Table 4.46 Significant Sex interactions with anti-RA medications


Parameter


Error

Wald

Chi-

Square

Pr > ChiSq

Intercept male 1 -1.2366 0.2185 32.0290 <.0001

Intercept female 1 1.7962 0.2227 65.0781 <.0001

Sex male 1 0.3896 0.4426 0.7748 0.3787

Sex female 0 0 . . .

Interaction with

Etanercept male

currently

taking 1 -1.0522 0.4816 4.7733 0.0289

Interaction with

Tocilizumab male

currently

taking 1 -4.1742 1.3291 9.8643 0.0017

Interaction with

Hydroxychloroquine male

currently

taking 1 -0.5432 0.5263 1.0652 0.3020

Interaction with

Leflunomide male

currently

taking 1 1.1168 0.5347 4.3624 0.0367

Interaction with

Prednisolone male

currently

taking 1 -1.7637 0.5440 10.5125 0.0012

Conclusion:

Differential effects on the frequency of urinary tract infections were observed amongst users of

csDMARDs and bDMARDs. Cyclosporine, IM Gold and Prednisolone increased the risk for

urinary tract infections, whereas Leflunomide protected against severe urinary tract infections

mainly in female sex but can significantly increase risk of UTI in male sex. None of the

evaluated biologic agents increased the frequency of UTIs in both sexes and Infliximab had an

unequivocal protective effect, the clinical significance of which is uncertain.

193

3.12. Viral infection - analysis of medication confounders


(3.89%) self-reported moderate viral infection; 305/21506 (1.41%) self-reported severe viral

infection, whereas for 19929/21506 (92.66 %) patient-visits, no infections were reported. In

this model different categories of viral infection were compared with people who did not self-

report such infection (Table 4.47) (Appendix tables I1-I3). A multinomial logistic regression

model was used to analyse the data. The reason for using this model is because the outcome is

a non-binary categorical variable. Using pairwise Chi-square test without using the model can

increase potential mistakes because the number of comparisons is very large [20].




AIC 14,465.34 14,470.56

SC 14,489.27 15,714.83

-2 Log L 14,459.34 14,158.56

The model convergence status table (Appendix Tables I.5-I.7) shows that the test meets the

criteria for accuracy and the variables fit the model of statistics. Overall the test shows

significant difference (lr Chi-square of 300.7784 with a P value of less than 0.0001) between

variable effects on VIRAL infections [20] (Table 4.47) (Appendix table I.7).

Table 4.48 Estimation of fitness of tests in viral infection




Score 331.3978 153 <.0001

Wald 311.3192 153 <.0001

In the model fit statistics table (Appendix table I.7), the likelihood ratio or lr (difference

between -2 Log L or deviance in the model (which just contains intercept and the one which

contains intercept and covariates) is 300.7784 (Table 4.48). The P value is highly significant

(Table 4.48) (Appendix table I.1-I.7). This shows that a model with covariates is making the

test more significant and covariates are actually impacting cofactors in respect to VIRAL

194

infections. Other tests such as SC and AIC are also used to recheck this conclusion

[22](Appendix table I.6).

Wald Chi-square for overall test is also highly significant (0.0001) with a Chi-square of almost

300 among 153 degrees of freedom. In other words, the impact on VIRAL infection is not the

same in different groups. This Chi-square P value is almost equivalent to the p value in the

overall Pearson test. Indeed, the logistic regression result is much the same as it is in the

frequency table (Tables 3.63-3.64) because it is a large sample. As the model is a logistic

regression and not a linear regression model, the Chi-square test has been used for comparison.

[21] (Table 4.48) (Appendix table I.7).





added, or existing parameters are deleted [21] (Table 4.48) (Appendix table I.7). During the

backward stepwise phase in the next part of the model, the effects of the medications are

dropped one by one to see how much there is a change in the Chi-square and to get an estimate

of the impact of that medication with regard to increasing or decreasing VIRAL infection [22]

(Appendix table I.8-I.31).

3.12.1. Selection between Wald Chi-square, Likelihood ratio test and Score test to test

significance of differences


used, but if the size of the sample is small then it is necessary to check all three tests to increase

the reliability of the conclusions. Among these tests, the likelihood ratio test is the most reliable

test, because it stays unchanged even if the data being tested is reparametrized what we are

testing [22] (Table 4.49) (Appendix table I.7).

3.12.2. Effects of different medications on the frequency of viral infections

As the variables are all categorical variables, the effects of each variable on viral infection can

be examined by studying its coefficient, directly [21].

195


Logistic regression is required because the outcome is categorical and as viral infection has

three categories, notably: mild, moderate and severe infection as well as no infection report,

multinominal logistic regression is an appropriate model (Table 4.49) (Appendix Table I.4).

Also, a backward stepwise procedure is used here because it is more accurate than a forward

procedure and considers the accumulating effect of all variables and starts with a bigger model.

As the model fit statistics (Appendix Table I.6) show that this large model is still fitted to the

data, it is appropriate for use. Furthermore, there is no collinearity and no two variables are

identical. This makes it easier to use the backward model [22].

As can be seen in the summary table for the backward procedure analysis (Table 4.49 and

Appendix Table I.32), the least significant effect is from Penicillamine, Certolizumab,

Golimumab, Leflunomide, Abatacept, IM Gold injection, Azathioprine, Sulphasalazine,

Anakinra, Tocilizumab, Adalimumab, Rituximab, and Infliximab.

Table 4.49 Summary of backward elimination of anti-RA medications and risk of viral

infection

Step Effect

Removed

DF Number

In

Wald Chi-Square Pr > ChiSq

1 Penicillamine 9 17 4.95 0.84

2 Certolizumab 9 16 8.19 0.51

3 Golimumab 6 15 5.47 0.48

4 Leflunomide 9 14 7.71 0.56

5 Abatacept 9 13 10.03 0.35

6 IM Gold injection 9 12 10.95 0.28

7 Azathioprine 9 11 11.21 0.26

8 Sulphasalazine 9 10 9.91 0.36

9 Anakinra 9 9 10.14 0.34

10 Tocilizumab 9 8 11.97 0.21

11 Adalimumab 9 7 15.06 0.09

12 Rituximab 9 6 16.70 0.05

13 Infliximab 9 5 13.14 0.15

196

However, the effect of all these medications was minimal and they were, therefore, dropped

from the model. According to type 3 analysis of effects (Table 4.32), medications which

significantly affected the frequency of viral infection in RA include: Etanercept, Methotrexate

(plus Folic acid), Hydroxychloroquine, Cyclosporine, and Prednisolone (Table 4.51 and

Appendix Table 4.32).

Table 4.50Medications which increase the frequency of viral infection

In the analysis of maximum likelihood table, the effect of each medication is examined in more

detail:

Etanercept: There is marginal evidence that currently taking Etanercept is associated

with an increase in mild viral infection (P value 0.0573). This increase is almost 24

times more than in people who never taken this medication (CI: 0.993 to 1.550) (Table

4.51-4.52 and Appendix Tables I.34- I.35).

Methotrexate (plus Folic acid): Currently taking Methotrexate (plus Folic acid) is

associated with a reduction in mild and moderate viral infection. However, if the patient

does not take this medication at all, the risk will be almost 25 times less (Table 4.51-

4.52 and Appendix Tables I.34- I.35).

Hydroxychloroquine: Currently taking Hydroxychloroquine is associated with an

increase in moderate viral infection (P value 0.0117). This risk is almost 30 times more

than in people who never took this medication (Table 4.51-4.52 and Appendix Tables

I.34- I.35).

Cyclosporine: Currently taking Cyclosporine is strongly associated with an increase in

the risk of moderate viral infection (P value: 0.0008). The amount of this increase is 167


Effect DF Wald

Chi-Square

Pr > ChiSq

Etanercept 9.00 43.98 <.0001

Folic acid plus Methotrexate 3.00 13.78 0.001


Cyclosporine 9.00 29.31 0.001


197

times more than in patients who have never taken Cyclosporine (CI 1.503 to 4.777)


Prednisolone: Currently taking Prednisolone is associated with increased rates of

moderate and severe viral infection. The extent of this increase is 41-fold greater in the

case of moderate and 108-fold greater in the case of severe viral infection respectively


Table 4.51 Analysis of maximum likelihood estimate in Viral infection



Status

Viral

Infection


Error

Wald Chi-

Square

Pr > C

hiSq

Etanercept

currently

taking

Mild 1.00 0.22 0.11 3.61 0.06

Moderate 1.00 -0.07 0.09 0.66 0.42

Severe 1.00 0.02 0.14 0.01 0.91

Methotrexate (plus

Folic acid)

currently

taking

Mild 1.00 -0.28 0.12 5.25 0.02

Moderate 1.00 -0.25 0.09 8.16 0.001

Severe 1.00 -0.13 0.14 0.88 0.35

Hydroxychloroquine

currently

taking

Mild 1.00 0.22 0.14 2.55 0.11

Moderate 1.00 0.26 0.11 6.35 0.01

Severe 1.00 0.14 0.17 0.66 0.42

Cyclosporine

currently

taking

Mild 1.00 0.001 0.59 0.001 1.00

Moderate 1.00 0.99 0.30 11.16 0.001

Severe 1.00 0.06 0.72 0.01 0.94

Prednisolone

currently

taking

Mild 1.00 0.001 0.15 0.001 0.98

Moderate 1.00 0.35 0.12 7.81 0.01

Severe 1.00 0.74 0.22 11.18 0.001

Conclusion:

Differential effects on the frequency of viral infections were observed amongst users of

csDMARDs and bDMARDs. Amongst csDMARDs recipients, Cyclosporine and Prednisolone

substantially increased the risk for viral infections, whereas only a modest increase was

198

observed with Methotrexate (plus Folic acid) and Hydroxychloroquine. Amongst bDMARDs

recipients, Etanercept increased viral infections slightly. Overall no major effect was observed

amongst bDMARDs users (Table 4.51-4.52 and Appendix Tables I.34- I.35).

Table 4.52 Estimation of odds ratios in viral infection


Effect Viral

Infection

Point

Estimate

95% Wald

Confidence Limits

Hydroxychloroquine - currently

taking vs never taken

Mild 1.25 0.95 1.64

Moderate 1.30 1.06 1.60

Severe 1.15 0.82 1.60

Cyclosporine - currently taking vs

never taken

Mild 1.00 0.32 3.16

Moderate 2.68 1.50 4.78

Severe 1.06 0.26 4.31

Prednisolone - currently taking vs

never taken

Mild 1.00 0.74 1.34

Moderate 1.41 1.11 1.80

Severe 2.09 1.36 3.22

3.12.3 Chapter Conclusion

Infections of diverse severity were observed commonly in the ARAD cohort, in keeping with

the high rates expected for active RA in a rheumatoid population biased toward the higher end

of the age spectrum, where moderately high levels of functional impairment are operative and

comorbidities are common. Based on ARAD data from 2001 to 2014, the highest to lowest

rates of major organ infections reported by questionnaire respondents receiving diverse

therapies were: EENT infection, skin and nail infection, lung infection, viral infection, kidney

and urinary tract infection (Figure 4.1). As might be expected, the frequency of use of one or

more than one anti-RA medications of various types was high amongst RA participants in

ARAD. This accords with the targeting of patients who were about to commence a biologic

therapy and in whom prior usage of csDMARDs was government-mandated, thereby ensuring

use of multiple therapies not only in the quest for disease control, but also to satisfy prescribing

restrictions.

199

Anti-RA medications were found to be used with divergent frequencies across the full range of

RA respondents. From the most frequent to the least frequent medication, anti-RA medications

used were Etanercept, Adalimumab, Methotrexate, Hydroxychloroquine, Sulphasalazine,

Rituximab, Abatacept, Prednisolone, Tocilizumab, Infliximab and Leflunomide respectively

(Table 4.1). Overall, although a number of differences exist between current RA treatment

guidelines, there are some general principles. Remission or low disease activity is the preferred

target. csDMARDs should be started soon after diagnosis and, usually, methotrexate is in the

first line. It is important to monitor disease activity regularly and, if disease remains active

persistently, biologics therapies should be used, as well[15].

Strengths of this study include large numbers of participants derived from real-world

experience in community clinical practice and the considerable number of sequential visits.

Substantial confidence concerning the primary diagnosis of RA is a further strength, since

subsets of patients have been classified under ACR criteria with strong diagnostic

concordance apparent. The large numbers aid in statistical analysis and the robustness of the

statistical modelling strengthens the analysis.

This study has several limitations. Importantly, infections were self-reported and unvalidated,

so their veracity cannot be substantiated. No microbiological reports or family physician

corroborations were available. A different form of categorisation, whilst readily understood

(notably: mild or moderate or severe), was, on the one hand, helpful but, on the other hand,

not, since it precluded comparison with the more widely utilised categories of (SI, frequently

found in publications and meta-analyses. Furthermore, SIs could not be easily deduced, since

hospitalisation data was not available across the full duration of the study period, notably

2001 to 2014. A further pitfall was the lack of comparability between users of bDMARDs and

those who had not taken such medication. Those who did not progress to bDMARDs had less

severe disease that was, for the most part, amenable to simpler therapy and, thus, differences

in respect to type and severity of infection may relate to differences in disease severity and

not the type of medications taken. The agents used for treatment varied considerably and

reflected the timing of introduction for clinical use and also prescriber preferences. Thus, the

number of bDMARDs for different categories is skewed toward TNF inhibitors, which in a

number of cases limited comparability with other bDMARDs due to uneven numbers of users

200

and sometimes very small numbers of recipients. Furthermore, this study was carried out in

the pre-orally active csDMARDs era.

All in all, the reports for infection in RA came to a total of 757/1947 (38.88 %) in comparison

to 1190/1947 (61.11%) in whom there was no infection.

• In EENT infection, most participants reported infection of moderate severity.

• In lung infection, most participants reported infections in a moderate (6.41%) or severe

category (2.9%).

• In skin and nail infection, most participants reported either mild (5.82%) or moderate

(4.83%) infection.

• In artificial joint infection, most participants reported infections in either a severe

(0.36%) or moderate (0.18%) category.

• In bone, joint and muscle infections, most participants reported severe (1.12%) or

moderate (0.99%) infections.

• In blood infection, most participants reported infections of either severe (0.51%) or

moderate (0.31%) severity.

• In GIT infection, most participants reported infections in either a moderate (1.12%) or

severe (0.72%) category.

• In urinary tract infection, most participants reported infections in either a mild (1.34%)

or moderate (3.87%) category.

• In viral infection, most participants reported infections in either a mild (2.022%) or

moderate (3.89%) category.

• Nervous system and mycobacterium tuberculosis (TB) infections were very rare.

Based on the findings in this study, the csDMARDs and bDMARDs drugs that either protect

against or predispose to infection in RA can be tabulated as shown in the table below (Table

4.53). Details are provided for different organ systems. Prednisolone was found to strongly

predispose to moderate or severe infections in multiple organ systems, notably: EENT; lung;

skin and nail; bone, joint and muscle; blood; GIT; urinary tract and in respect to viral infections.

This finding accords with clinical experience and the well documented capacity of

corticosteroids to predispose to infection in many systems. Other csDMARDs associated

strongly with moderate or severe infection were cyclosporine for multiple systems (EENT,

201

lung, GIT, urinary tract and viral infections), hydroxychloroquine for blood infection alone and

parenteral Gold for urinary tract infection alone. As cyclosporine has potent

immunosuppressive properties, it is not surprising that it is implicated in infections in multiple

organ systems. Whether the hydroxychloroquine and parenteral gold observations are clinically

important is uncertain.

Amongst bDMARDs, only Infliximab was associated with moderate or severe infections in

multiple organ systems (EENT, skin and nail, GIT). Adalimumab was associated with moderate

or severe infection in the skin and nails alone. Etanercept, Certolizumab, Golimumab,

Abatacept, Tocilizumab and Rituximab were not associated with moderate or severe infection

in any system. The possibility that these agents do predispose to moderate or severe infection

when used in combination with other agents cannot be discounted. Since, for some of these

infection,s the numbers of patients who contracted such infections was less than 100 (Blood

infection, GIT infection, Nervous system infection) and the number of observations over time

correspondingly smaller, there is a need to exercise caution in applying these sample results to

the wider population.

Several csDMARDs appear to protect against infections in selected systems. For example,

methotrexate was associated with a protective effect in EENT and viral infections, leflunomide

was associated with a protective effect in urinary tract infection alone and hydroxychloroquine

against viral infection alone. Whether such effects are clinically meaningful is also uncertain,

but somewhat doubtful. Amongst bDMARDs, etanercept appeared to protect against EENT,

lung and viral infection, whereas adalimumab protected against lung, artificial joint and GIT

infection. Infliximab was associated with a protective effect in urinary tract infection. Again,

such effects are of uncertain significance and need to be independently validated.

202

Table 4.53 Summary of anti RA medication impacts on different types of infection

Type of infection Safest Medications Medications associated with higher

rates of moderate or severe infections

EENT Etanercept, Methotrexate Cyclosporine, Prednisolone, Infliximab

Lung Etanercept and Adalimumab Cyclosporine and Prednisolone

Skin and nail Leflunomide Prednisolone, Infliximab, Adalimumab

Artificial Joint Adalimumab Not enough information

Bone, Joint and muscle Not enough information Leflunomide and Prednisolone

Blood infection Not enough information Hydroxychloroquine and Prednisolone

GIT Adalimumab Cyclosporine, Prednisolone,

Infliximab.

Nervous system Not enough information Not enough information

TB Not enough information Not enough information

Urinary tract infection Leflunomide and Infliximab Cyclosporine, Prednisolone and IM

Gold

Viral Infection Etanercept, Methotrexate

and Hydroxychloroquine

Cyclosporine and Prednisolone

These findings need to be validated in independent studies. However, they do provide new

insights into the likely differential effects of csDMARDs and bDMARDs on diverse infections.

They confirm known and suspected risks associated with corticosteroid use and potent TNF

inhibitors, such as infliximab. These different effects were observed across multiple anatomical

systems. Some protective effects were observed which, if confirmed in further studies, might

allow an opportunity for selection of one agent over another, particularly where a high risk for

infections is known to apply, based on comorbidities or previous infection history. Thus, it may

be possible, taking all factors into consideration, to make an informed choice, rather than one

more arbitrary, thereby enhancing patient safety without compromising clinical outcomes. This

application of a personalised medicine approach has the potential to reduce the morbidity and

mortality associated with non-serious and very importantly with serious infections of diverse

aetiologies.

203

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206

CHAPTER 5

Serious Infections in Rheumatoid Arthritis

207

Abstract

Objective: The main goal of this study is to evaluate self-reported infections in patients

suffering from rheumatoid arthritis and to determine the level of impact from different

csDMARDs and bDMARDs as potential risk factors.

Method: ARAD reports were collected from 2001 to 2014 and cleaned by deleting all

duplicated answers, single answers and faulty/incomplete patients reports. Overall 27,709 visits

from 3110 patients during 2001 to 2014 were collected. Based on our definition for serious

infection, patients’ reports were searched for evidence of hospitalisation or IV infusion for

infection.

Results: Out of 27,709 visits during 2001 to 2014, 811 patients had reported serious infection,

a prevalence of almost 3 %. Also, among all patient who took bDMARDs, adalimumab and

etanercept were the most common medications with association with serious infection. Other

factors such as age and gender, alcohol consumption, biologics, prednisolone, diseases such as

diabetes, kidney disease, liver disease, heart attack and sometimes previous coronary artery

bypass grafting (CABG) were all shown to have contribution in the development of SIs. These

risk factors have been used to generate an equation which assists in predicting the development

of SIs due to a range of risk factors.

Conclusion: There is clearly an increasing trend for serious infection (SI) among patients who

were treated with biologics.

208

1. Introduction

Rheumatoid arthritis (RA) is a chronic multisystem, immuno-inflammatory disease, the

cardinal features of which are joint deformity and damage in most, but not all cases. Destructive

polyarthritis is common and can be severely disabling and diminish quality of life. The major

clinical manifestation of RA is persistent and progressive synovitis, mostly in the peripheral

joints, leading to resorption of cartilage and subchondral bone. Joint disease in RA is usually

symmetrical, polyarticular and when destructive the joint damage is usually irreversible [1].

The prevalence of the disease increases with increasing age, but it may happen at any age, with

the peak incidence between the fourth and sixth decades. RA may be diagnosed as early as 3

months from onset up to 2 years when the disease is established. Depending on the diagnosis

the prevalence of RA is up to 0.5–1% of the world’s population. The female sex is usually up

to three times more susceptible to the disease than the male sex [1][2].

RA can cause chronic pain and joint destruction, premature mortality, and elevated risk of

disability, with high costs for victims and for society. It is a heterogeneous disease comprising

several subsets of patients with variations in pathogenesis, but it usually stems from due to a

sustained specific immune response directed against unknown self-antigens. The characteristic

of this autoimmune reaction is a cellular infiltration and synovial inflammation resulting in

tissue damage. The major pathophysiological events in RA include mononuclear cell

infiltration in the sub intimal layer, hyperplastic changes in synovial lining cells and formation

of a destructive type of synovial tissue known as pannus that invades the interface between

cartilage and bone. Chronic synovitis can progress to the destruction of adjacent bone and

cartilage, leading to joint deformity and disability [1] [3].

All aspects of RA treatment have changed in the past 25 years. The pathogenic basis of RA also

plays a role in its treatment. As early onset of structural damages is usual in RA, late treatment

can cause more than 50% disability in this disease. Therefore, early treatment of the disease is

an important objective [4]. Treatment in RA usually has three main goals including elimination

of pain, prevention of joint damage and improvement of joint function. Usually treatment plans

change depending on the disease activity, severity of symptoms, signs and prognosis [5].

209

Disease-modifying anti-rheumatic drugs (DMARDs) are medicines which are generally used

to control RA. They interfere with the immune system to suppress the overactive immune

system in RA, decreasing inflammation and progression of the disease process. These drugs are

categorized as biologics and non-biologics, where non-biologics are indirect and nonspecific

immune suppressants and biologics interfere with a specific aspect of the immune system

(Chiurchiù & Maccarrone, 2011)[6]. [7].

Non-biologic medicines suppress the immune system indirectly, while biologics suppress

immune system directly by interfering with a specific mediator. For example, methotrexate acts

as a folic acid analogue to inhibit different pathways in the immune system, but the inhibition

of tumor necrosis factor (TNF) ) by biologic DMARDs is achieved with a monoclonal antibody

such as infliximab (Remicade), adalimumab (Humira), certolizumab pegol (Cimzia), and

golimumab (Simponi), or with a circulating receptor fusion protein such as etanercept

(Etanercept or Brenzys)[8] [6][7].

All the currently available conventional synthetic DMARDs are associated with limited

efficacy and many of them cause important side effects. Due to these side effects, the majority

of patients have to stop non biologic DMARDs within 1-2 years. Aletaha and Smolen found

that, among 593 patients with RA, comprising 1319 courses of DMARD therapy over 2378

patient-years of treatment, retention rates were less than 24 months in most cases and treatment

courses were terminated mostly due to adverse effects and toxicity (42%) and sometimes lack

of sufficient efficacy (37%) [6][7].

1.1. Aims

The aims of this study were to:

• determine the frequency of serious infections (SIs) amongst ARAD participants who

were mostly taking bDMARDs;

• Investigate for apparent differences, if present, amongst patients taking bDMARDs

and those taking csDMARDs;

• Identify and evaluate potential clinical risk factors for infections e.g. age,

comorbidities, use of corticosteroids, synthetic DMARDs and biologic DMARDs,

and assess ARAD data to investigate possible roles of behavioural, environmental

and genetic risk factors in the development of serious infections in RA patients.

210

1.2. Hypothesis

The aims are based on the following hypotheses:

• Infections and specifically serious infections are common in RA and likely modulated

by medication use;

• It is possible to predict the approximate risk of infection based on risk factors that a

patient possesses; and

• Type of medication, duration of medication usage and dosage of the medication can all

impact the frequency of infection.

In order to assess these points, the ARAD data has been statistically analysed for the rate of

serious infections (SIs). This study includes the rate, as well as the type of infection in more

than 3000 RA patients.

The following points will be discussed:

(i) Descriptive analysis of infection in RA including the comparison of the SIs between

patients on csDMARDs and bDMARDs;

(ii) Comparison of the frequency of use of different biologics in serious infection;

(iii) Assessing the status of prednisolone and methotrexate in producing serious

infection;

(iv) Discussing the different potential risk factors for serious infection in more details;

and

(v) Predicting risk of serious infection based on the impacts from different risk factors.

2. Methods

2.1. Data Collection

The data were collected from the ARAD, in which a cohort of 3569 RA patients (960 males

and 2609 females) who had completed related questionnaires 28176 times (during 2001-2014)

were investigated for the development of SIs associated with a range of risk factors. Among the

3569 patients, 459 patients were eliminated because they had filled out the questionnaire only

once. We were therefore left with 3110 patients. After deducing eight duplications, at the end

we came up to 27709 visits and, amongst these visits, 811 were classified as having developed

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SIs and these were used to calculate SIs/100 patient-years. Using the ARAD database, RA

patients with serious infections were identified, and their age, sex, type of infection, and type

of medications used were all extracted for further statistical analysis.

2.2. Statistical Analysis

This data was subjected to a series of descriptive and inferential statistical analyses, including

the determination of summaries of the frequencies, ratios, proportions, incidence rates, and the

rate of possible complications, as well as age and gender. In addition, a series of descriptions,

including comparison of central location in frequencies and dispersion in both intervention and

control variables, were undertaken. To facilitate the discussion around possible clinical risk

factors for the development of serious infections in RA patients, relative risks and odds ratios

were also calculated. In appropriate circumstances, these calculations can examine the

association between variables and side effects.

We calculated patient-years of treatment for diverse treatment categories by dividing the

number of SIs associated with each therapeutic by the sum of the lengths of time or cumulative

exposure time during which each patient was taking that medicine. This enables assessment of

the association between different bDMARDs and SIs while considering the number of patients

and the duration of the period that they were taking these therapies. Furthermore, categorical

statistical analysis using Chi-squared test between medications such as bDMARDs and a range

of potential risk factors were calculated. The results of the Chi-squared tests helped determine

the true association between intervention factors and SIs in this research. In this study we

reviewed the following risk factors: gender, alcohol intake, prednisolone, diabetes (non-insulin-

dependent diabetes mellitus (T2DM) as well as insulin dependent diabetes mellitus (T1DM),

lung, kidney, and liver disease, heart attack, coronary artery bypass grafting (CABG), and

stenting status. From these chi-squared values, the relevant p-values were calculated.

A generalized linear mixed model was applied to the data from the 28679 visits by the 3110

patients who visited more than once. Based on that, a model was constructed which expressed

the natural logarithm of the odds for SI in terms of five predictor variables: age (in years), sex

(M or F), alcohol, biologics (currently on any biologic? yes/no) and prednisolone (currently

taking prednisolone? yes/no).

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A histogram was created to demonstrate the age (in years) of each participant on his/her first

visit. A series of statistical analyses was carried out to determine the number and the percentage

of male/female patients who had self-reported their use of various biologics as (i) Never taken

or Don't know, (ii) Currently taking, or (iii) Stopped taking.

Wherever DMARDs are discussed in this study, DMARDs is divided into csDMARDs and

bDMARDs. csDMARDs or conventional synthetic DMARDs include: 1- Methotrexate – oral

or parenteral, 2- Hydroxychloroquine (Hydroxychloroquine), 3- Sulphasalazine, 4-

Leflunomide, 5- Azathioprine, 6- Cyclosporine. bDMARDs or Biologics or biological

DMARDs include: 1- Humira/Adalimumab, 2- Etanercept/Etanercept, 3- Kineret/Anakinra, 4-

Remicade/Infliximab, 5- Mabthera/Rituximab, 6- Orencia/Abatacept, 7- Actemra/Tocilizumab,

8- Simponi/Golimumab, 9- Cimzia/Certolizumab Pegol.

Prednisolone, IM gold and Penicillamine do not belong to any group and are studied separately.

3.0 Results and discussion

Amongst the 27709 visits made by RA patients who had taken part in the study and had filled

in the questionnaire more than once, 811 visits were confirmed to relate to patients who had

developed serious infections, a prevalence of 2.92 %. Our data were examined for the impact

of several predictor variables: medications, age, gender and length of time in program. Some

combinations of these variables were also considered. This is described below.

3.1. Analysis of Rheumatoid Arthritis (RA) and Serious Infections (SIs) in Australia

Analysis of the association between the frequency of taking different anti RA medications and

development of serious infection. Among all different anti RA medications, it seems that taking

most of the medication between RA and RA with SIs are same, except for Adalimumab (Arrow

in the table) which is the most frequent medication in Sis followed by Etanercept (figure 5.1).

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Duration in years

Figure 5.1 Differences in biologics between Rheumatoid Arthritis and Rheumatoid Arthritis

with Serious infection. Adalimumab is indicated by arrow

Figure 5.2 shows the absolute number of self-reported SIs amongst recipients of prednisolone.

The three coloured lines depict the categories of prednisolone usage, notably current, previous

and never used. Here, we are comparing frequency of taking prednisolone in patients with

serious infection compare to RA group. As it is apparent in the serious infection group,

‘currently taking prednisolone’ has the highest rate, followed by’ stopped taking’. The

frequency of ‘Never taking’ prednisolone is almost same in both group and its graph has

become flat (Figure 5.2). This indicates a significant role for prednisolone in serious infection.

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Duration in years

Figure 5.2 Serious infection and prednisolone usage

The situation changes with methotrexate. Figure 5.3 is comparing the frequency of taking

methotrexate in patients with serious infection compare to RA group. As it is apparent in serious

infection group never taken methotrexate has the highest rate followed by patients who were

taking Methoteraxate and recently stopped taking. The frequency of “currently taking”

Methotrexate is almost same in both groups and its graph has become almost flat (Figure 5.3).

This indicates a small role for methotrexate in serious infection (Figure 5).

Figure 5.3 Methotrexate status in Serious infection compare to RA

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3.2. Age and gender

In the database, there were 3569 participants (960 males and 2609 females) who completed a

relevant questionnaire 28168 times. A boxplot of the ages at first visit for the two sexes is shown

in Figure 5.4.

Figure 5.4 Boxplot showing the ages of patients at their first visit, broken down by gender

Sex1= Male Sex 2= Female

Figure 5. 5 Sex distribution in RA, Sample size: 3111

216

The mean and standard deviation of the ages of the males were 59.3 and 11.7 years respectively

and the mean and standard deviation of the ages of the females were 56.1 and 12.9 years

respectively. The differences were statistically significant (P value <0.0001). A boxplot of the ages

of the two genders appears in Figure 5.6. As can be seen in the boxplot, the age distribution was

very similar in the two sexes. However, disease occurs slightly earlier and with a wider age

dispersion among females.

Figure 5.6. Boxplot of the ages (in years) at the time of entry to the registry, broken down by the

two sexes

According to Figure 5.7, disease onset in most patients was reported between the ages of 50 and

60 years.

217

Figure 5.7 Histogram of frequency of age groups for RA

3.3. Length of time in the program

A histogram showing the number of months during which participants reported is shown in Figure

5.8. The longest report was for almost 150 months.

Sex (1= Male, 2= Female)

Figure 5.8. Boxplot of participation Time in the ARAD Program, broken down by gender

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3.4. Time in the program as a function of Gender

The mean and standard deviation of the times in the program for males were 52.3 and 32.3 months,

respectively, and the mean and standard deviation for the duration of participation for ages of

females were 53.9 and 38.9 months, respectively. A boxplot of participation times in the program

for the two genders appears in Figure 5.8. According to this boxplot, with almost similar standard

deviation between both sexes, on average, in this study, females participated for a longer duration.

3.5. Distribution of age groups

In this section, the mean and standard deviation in respect to age for csDMARDs with bDMARDs

recipients and among three main participant groups of bDMARDs are compared.

Among three main participant groups of bDMARDs 64.5 and 14.0 for bDMARDs treated

participants, 59.1 and 17.4 for biologic 2, and finally 59.35 and 15.35 for biologic 3. A boxplot of

the age upon entry and the biologic status at the last visit appears in Figure 5.9. According to this

boxplot there are small differences in the age distribution among patients who take biologics. These

differences can potentially increase risk of type 1 error in this study.

Figure 5.9 Boxplot of distribution of ages for the main three biologic groups

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3.6. Incidence and rate of SIs.

A cohort of 3569 RA patients (960 males and 2609 females), who had completed related

questionnaires 28176 times (during 2001-2014), were investigated for the development of SIs

associated with previously identified risk factors. After eliminating eight duplicate visit records,

the records of 28168 visits remained. The data were studied from two perspectives; the incidence

of SIs and the rate of SIs.

Visits were classified as indicating the presence of an SI or not. An investigation of the incidence

of SIs examines each individual visit and so, potentially, it looked at all 28168 visits (for the 3569

patients). However, to look at the rate of SIs per 100 patient years requires a count of the number

of SIs over a period that is not instantaneous, so records from at least two visits by a patient are

needed to measure the passage of time. As 459 patients had completed the questionnaire only once,

they were eliminated from the analysis of the rate of SIs. This left 3110 patients (2275 females and

835 males) and the records of 27709 visits. In these records, 811 visits were identified where the

patient had an SI. (Figure 5.10)

Sex1= Male Sex 2= Female

Figure 5. 10 Sex distribution in RA with serious infection

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3.7. Incidence of SIs

According to the registries from other countries, such as Britain, the rate of SIs in bDMARDs

recipients is highest in the first year. Therefore, in ARAD we are separating the first year’s visit to

assess bDMARDs in the first year.

There were 9087 visits, overall. Each visit was classified based on the presence of SI and

descriptions for that infection including infected organ, type of DMARD and statistical significance

of the difference. The following table shows the various combinations of SI/no SI with ever/never

having taken bDMARDs (Table 5.1).

Table 5.1 Relationship between bDMARDs and Serious infections

Biologics

Yes No Total

Yes 191 98 289

No 6308 2490 8798

Total 6499 2588 9087

A Pearson’s chi-squared test of independence was performed to assess the data. The null

hypothesis that the two variables are independent is rejected at the 5% level of significance (X-

squared = 4.0495, df = 1, p-value = 0.04418). There is slight evidence of an association between

whether a visit is classified as “SI” and whether the patient has ever had a biologic.

In addition, the statistics for taking bDMARDs based on the gender of the patient has been

calculated in table 5.2. Based on the large Chi square result and insignificant p-value (0.38) at level

of 0.05, the frequency of taking bDMARDs is similar in between male and female sex (Table 5.2).

Table 5.2 Biologic status of the patients in the study

Pearson's Chi-squared test data: X-squared = 1.9067, df = 2, p-value = 0.3854

Biologic status Never Taken

or Don't Know

Currently

Using

Stopped

taking

Total

Number and % of

patients

521

(16.75%)

2160

(69.45%)

429

(13.8%)

3110

Gender

Male 151 565 119 835

Female 370 1595 310 2275

Serious Infection

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3.7.1. Rates of serious infections

When considering rates of SI, at least two visits by a patient are required; among the 3569 patients,

459 patients were eliminated because they had completed a questionnaire only once. We were

therefore left with 3110 patients (2275 females and 835 males). Amongst these visits, 811 RA

patients with serious infections were identified. The SI patients did not differ appreciably from the

overall group with regard to gender or distribution of ages at the first visit. In the table 5.5 the status

of bDMARDs treatment and status of Sis has been demonstrated. In order to calculate 100 patient

year, the number of patients was divided by duration in years multiplied by 100. The reason for

using patient 100 year is to provide more accurate comparisons among groups when follow-up time

(i.e., patient exposure time) is not the same in all groups (Table 5.3).

Table 5.3 Numbers of SIs, total elapsed time (in months) between first and final visits for the

patients, and the corresponding rate of SIs per 100 patient-years

The risk of SI in ARAD is 26 % or 811 SI out of 3110 patients with RA. In the following table

we review some of the differences between these two populations (Table 5.4).

bDMARDs status Never Taken or

Don't Know

Currently Using Stopped taking

No. of SIs 89 585 137

Total time (in months) 27600 115764 22821

Rate of SIs/100 patient years 3.870 6.06 7.20

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Table 5.4 Demographic characteristics of participants who self-reported an SI and participants who did not (Data collected from ARAD)


Age in non-SI 61.48 12.31 63.00

Age in SI reporters 59.73 12.22 61.00

Number of Cigarettes / D in non-SI 14.89 13.23 15.00

Number of Cigarettes /D in SI

reporters

19.20 15.63 15.00

Duration of Smoking in non-SI 17.26 13.95 16.00

Duration of Smoking in SI reporters 21.41 12.28 20.00

ALCOHOL CONSUMPTION >2 or <2 in non-SI

1.32 0.47 1.00

ALCOHOL CONSUMPTION >2 or <2 in SI reporters

0.66 0.47 1.00

3.7.2. Predictor variables

The predictor variables considered possibly to influence the rate of SIs per patient were age (in

years) at first visit, gender, alcohol (ever taken/never taken), bDMARDs use (ever/never taken any

of Anakinra, Etanercept, Adalimumab, Infliximab, Certolizumab, Golimumab, Rituximab,

Abatacept or Tocilizumab), prednisolone (ever/never taken), diabetes (ever/never had non-insulin-

dependent diabetes mellitus (T2DM) or insulin dependent diabetes mellitus (T1DM)), lung disease

(ever/never suffered), kidney disease (ever/never), liver disease (ever/never), heart attack

(ever/never), coronary artery bypass grafting (CABG) (ever/never), and stenting status

(ever/never).

One patient’s records had to be removed from this analysis, as there were contradictory answers to

alcohol status over her various visits. This left 3109 patients (2274 females and 835 males).

The overall SI rate among these 3109 patients was 5.8597 SIs per 100 patient years (PYs). For

those who had ever taken a bDMARDs, the rate was 6.2610 SIs per 100 years. For those who had

never taken bDMARDs, the rate was 3.8386 SIs per 100 PYs.

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It is reasonable to assume that the number of SIs experienced by a patient in the time of observation

might follow a Poisson distribution, with the rate of incidence varying from patient to patient as a

function of the potential risk factors described in the previous paragraph. A Generalized Linear

Model with a logarithmic link was used to model the number of SIs in terms of total time observed

and the predictor variables listed in the previous paragraph. The number of SIs per patient was

found to be slightly less variable than would be expected of data from a Poisson distribution, so

appropriate adjustments were made to the analysis when testing for significance of terms in the

model. The variable (ever had CABG) was found not to have a significant effect on the number of

SIs, either alone or in conjunction with other variables, and was deleted from the model.

The following model was selected as providing the best fit to the data:

log(rate) = 0.5562 + 0.003984 × (age at first visit) – 0.9394 (if male) + 0.6160 (if ever taken a

biologic) + 0.3044 (if ever taken prednisolone) + 0.08837 (if ever had diabetes) + 0.2388 (if ever

had lung disease) + 0.2926 (if ever had liver disease) + 0.5205 (if ever had heart attack) + 0.4225

(if ever had stenting) + 0.01517 × (age at first visit) (if male) + 0.2727 (if male and had ever had

lung disease) – 0.5768 (if male, and had ever had liver disease) – 0.4945 (if ever taken biologic

and ever had heart attack) + 0.5872 (if ever taken a biologic and ever had heart attack) + 0.5872 (if

ever had diabetes and ever had a heart attack) – 1.0865 (if ever had diabetes and ever had stenting).

(eq. 1)

The equation is an expression for the natural logarithm of the rate of SI per 100 PYs. It can be

converted to an expression for the rate by taking antilogarithms[9]:

Rate = e0.5562 × e0.003984 × (age at first visit) × e–0.9394 (if male) × e0.6160 (if ever taken a biologic) × e0.3044

(if ever taken prednisolone) × e0.08837 (if ever had diabetes) × e0.2388 (if ever had lung disease) ×

e0.2926 (if ever had liver disease) × e0.5205 (if ever had heart attack) × e0.4225 (if ever had stenting) ×

e0.01517 × (age at first visit) (if male) × e0.2727 (if male and had ever had lung disease) × e–0.5768 (if male,

and had ever had liver disease) × e–0.4945 (if ever taken biologic and ever had heart attack) × e0.5872

(if ever had diabetes and ever had a heart attack) × e–1.0865 (if ever had diabetes and ever had

stenting). (eq. 2)

Note that, except for the first two terms on the right-hand side, all other terms on the right-hand

side appear only if one or two conditions are satisfied (simultaneously). For example, “-0.9394 (if

male)” means that 0.9394 is subtracted from the right-hand side if the patient is male; if the patient

is female, nothing is done. The expression “+ 0.2727 (if male and had ever had lung disease)”

224

means that 0.2727 is added to the right-hand side if the patient is male and had ever had lung

disease; if the patient is female or had never had lung disease, nothing is done.

In order to predict the rate of serious infection in each patient we need to have an estimation of the

risk factors for serious infection. In the following table we summarize the connection between

different risk factors (predictors) and biologic medication.

3.8. Prediction of Serious infection

A generalised linear model (GLM) was utilised to calculate the frequency of SI based on the

estimated impact from each risk factor. For visits up to 12 months, there were 9087 visits overall.

The following equation was used in the model:

In (P/1-P) = 〆+B1X1+B2X2+B3X3+B4X4 where P stands for prevalence.

log(p/(1-p)) = - 4.293 – 0.042 × initial age - 0.358 (if patient is male) – 3.284 (if patient has ever

taken a biologic) + 0.263 (if patient drinks alcohol every day) + 2.626 (if patient has ever taken

prednisone) – 0.909 (if patient has ever had diabetes) + 4.877 (if patient has ever had lung disease)

+ 9.341 (if patient has ever had kidney disease) + 2.885 (if patient has ever had liver disease) –

6.317 (if patient has ever had heart attack) – 2.673 (if patient has ever had angioplasty) + 0.039 ×

initial age (if patient has ever had a bDMARDs) – 0.089 × initial age (if patient has ever had lung

disease) – 0.156 × initial age (if patient has ever had kidney disease) + 0.118 × initial age (if patient

has ever had a heart attack) + 2.083 (if patient is male and has ever had lung disease) + 2.091 (if

patient is male and has ever had kidney disease) + 3.465 (if patient is male and has ever had a heart

attack) – 3.075 (if patient is male and has ever had angioplasty) – 1.494 (if patient has ever had a

biologic and has ever had prednisone) – 0.932 (if patient has ever had a biologic and has ever had

lung disease) + 1.076 (if patient has ever had a biologic and has ever had kidney disease) + 2.641

(if patient has ever had a biologic and has ever had liver disease) – 4.076 (if patient has ever had a

biologic and has ever had a heart attack) + 6.305 (if patient has ever had a bDMARD and has ever

had angioplasty) + 1.368 (if patient drinks alcohol every day and has ever had lung disease) – 1.542

(if patient has ever had prednisone and has ever had kidney disease) – 5.710 (if patient has ever

had prednisone and has ever had liver disease) – 4.055 (if patient has ever had diabetes and has

ever had a heart attack) + 1.605 (if patient has ever had lung disease and has ever had kidney

disease) – 5.113 (if patient has ever had lung disease and has ever had a heart attack) + 5.009 (if

patient has ever had liver disease and has ever had a heart attack).

225

The model does not contain anything involving “if the patient has ever had a graft”, because no

term involving this predictor variable was found to be statistically significant. For example, a male

patient who was 62 years on his first visit, drinks alcohol every day, has taken a bDMARD but has

never taken prednisolone, has had lung disease but none of the other diseases considered and has

had a heart attack, but has not had angioplasty.

Then we have:

log(p/(1-p)) = - 4.293 – 0.042 × 62 - 0.358 – 3.284 + 0.263 + 4.877 – 6.317 + 0.039 × 62 – 0.089

× 62 + 0.118 × 62 + 2.083 + 3.465 – 0.932 – 4.076 + 1.368 – 5.113 = -10.705,

and so, odds = p/(1-p) = e-10.705 = 2.24 × 10-5. P=e/1+e= 2.24 × 10-5/1+2.24 × 10-5

Here is a table that shows the actual SI status on each visit, and the predicted status (odds less than

1 implies “Not SI”, odds greater than 1 implies “SI”).

Predicted

Yes No

SI Yes 3 286 289

No 14 8784 8798

17 9070 9087

The model was very reliable in predicting a “non-SI” when the patient did not have an SI but was

virtually useless in predicting an SI when the patient had an SI.

This is a plot of the predicted probability that a visit will be an “SI” vs the actual result of the visit.

Probabilities less than 0.5 correspond to odds of less than 1, while probabilities greater than 1

correspond to odds of more than 1. We can see that the probabilities cover a very wide range even

though we would like them to be very close to 0 or 1 (for “non-SI and “SI” respectively). For visits

after 12 months, the following table of SI/not SI vs biologic ever/never can be constructed:

bDMARD

SI yes no

yes 480 69 549

no 15105 3426 18531

15585 3495 19080

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There is a significant association between SI and bDMARD use (X-squared = 12.095, df = 1, p-

value = 0.0005055). Of the people who have ever taken a bDMARD, the proportion whose visit is

associated with an SI is 480/15585 = 3.08%, whereas, of the people who have never taken a

biologic, the proportion whose visit is associated with an SI is 69/3495 = 1.83%. There is a greater

proportion of visits associated with an SI amongst those who have ever taken a biologic than

amongst those who have never taken a biologic.

When the full statistical analysis involving all predictor variables was performed, the best model

was found to be log(p/(1-p)) = - 7.968 + 0.025 × initial age - 2.043 (if patient is male) + 0.579 (if

patient has ever taken a biologic) – 1.062 (if patient drinks alcohol every day) + 0.719 (if patient

has ever taken prednisone) – 2.747 (if patient has ever had diabetes) + 0.978 (if patient has ever

had lung disease) + 1.772 (if patient has ever had kidney disease) - 0.258 (if patient has ever had

liver disease) + 1.711 (if patient has ever had a heart attack) – 0.813 (if patient has ever had a graft)

+ 3.692 (if patient has ever had angioplasty) + 0.033 × initial age (if patient is male) + 0.043 ×

initial age (if patient has ever had diabetes) – 0.060 × initial age (if patient has ever had angioplasty)

– 1.987 (if patient is male and has ever had kidney disease) -2.653 (if patient is male and has ever

had a graft) + 0.893 (if patient is male and has ever had angioplasty) + 1.057 (if patient has ever

had a biologic and drinks alcohol every day) + 1.004 (if patient has ever had a biologic and has

ever had diabetes) - 1.850 (if patient has ever had a biologic and has ever had a heart attack) –

0.651 (if patient drinks alcohol every day and has ever had lung disease) – 1.432 (if patient drinks

alcohol every day and has ever had kidney disease) + 1.657 (if patient drinks alcohol every day and

has ever had liver disease) + 0.937 (if patient drinks alcohol every day and has ever had a heart

attack) + 2.691 (if patient drinks alcohol every day and has ever had a graft) – 1.383 (if patient

drinks alcohol every day and has ever had angioplasty) + 3.464 (if patient has ever had prednisone

and has ever had a graft) – 1.154 (if patient has ever had diabetes and has ever had lung disease) +

1.299 (if patient has ever had diabetes and has ever had liver disease) – 0.880 (if patient has ever

had diabetes and has ever had a heart attack) + 0.695 (if patient has ever had lung disease and has

ever had a heart attack) - 1.806 (if patient has ever had lung disease and has ever had a graft) +

0.983 (if patient has ever had a heart attack and has ever had angioplasty).

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The following is the table of predictions made by the model for each visit:

Predicted

Yes No

SI Yes 1 548 549

No 2 18529 18531

Total

3 19077 19080

The model predicted virtually every visit to be a “non-SI”, whether an SI was present.

Fig. 5.12 displays a plot of predicted probabilities for a given visit being an “SI” vs the actual

result of the visit. It may seem that the models for predicting that a visit is associated with an SI,

due to small amount of available data are not of much value, but as SI is potentially fatal and has

dangerous consequences, it is worthwhile to try and predict it.

4. Discussion

The model for those 2966 patients who had at least two visits in the first 12 months, for the rate (in

SIs per 100 PYs) is:

log(rate) = 0.1583 + 0.0244 × initial age - 0.5759 (if patient is male) + 0.2543 (if has ever taken a

biologic) + (1.8543 - 0.02356 × initial age) (if has ever taken prednisone) + 0.7903 (if male and

has ever taken a biologic).

We could take antilog and rewrite this equation as:

rate = e 0.1583 × e 0.0244 × initial age × e- 0.5759 (if patient is male) × e 0.2543 (if has ever taken a biologic)

× e (1.8543 - 0.02356 × initial age) (if has ever taken prednisone) + e 0.7903 (if male and has ever taken a

biologic)

In this model, the rate increases with increasing age with or without taking prednisolone, it

increases if the patient has ever taken a bDMARD, and it increases further if the patient is a male

who has ever taken a biologic (Figure 5.11).

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Figure 5. 11 A plot of the number of SIs predicted by the model for the first 12 months against

the observed (actual) number of SIs

Guide Table for figure 5.11 Frequency of predicted serious infections

No. of SIs 0 1 2 3 Total Frequency 2726 225 14 1 2966

In figure 5.12 the highest individual predicted number is about 0.14, whereas one person had three

SIs. (That patient was female, aged 70 at the initial visit, and had taken biologics and prednisolone

(Figure 5.12).

Figure 5.12 Rates of SIs per 100 patient years vs age at initial visit (in years) for males and females based on infections in the first 12 months after the initial visit

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The effects of bDMARDs on serious infections may vary according to duration of bDMARD use.

Therefore, predicted risk has been studied in the first year and then also after the first year. In

Figure 5.13, the plot of the predicted number of SIs for the patients versus the observed numbers

of SIs, after the first year of exposure to bDMARD medication has been demonstrated (Figure

5.13).

Figure 5.13 A plot of the number of SIs predicted by the model for more than 12 months exposure

against the observed (actual) number of SIs.

Guide Table for figure 5.13 - Frequency of predicted serious infections.

No. of SIs 0 1 2 3 4 5 Total

Frequency 2283 346 74 14 2 1 2720

As can be seen in figure 5.14, the shape of the graph for the frequency of serious infection in

females rises to only a modest extent with age, whereas in males it tends to increase sharply with

age (Figure 5.13).

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Figure 5.14 Rates of SIs per 100 patient years vs age at initial visit (in years) for males and females based

on infections more than 12 months after the initial visit

These rates of SIs during the first year and then after the first-year show that in the first 12 months

of treatment with bDMARDs, the rates are about twice (10-12 per 100PYs) those observed for the

whole period of exposure (approximately 6 per 100PYs). From those 2720 patients who had at

least one visit after the first 12 months, the rate (in SIs per 100 patient years) is:

log(rate) = 0.3939 + 0.0075 × initial age + (-1.2990 + 0.0228 × initial age) (if patient is male) +

0.4017 (if has ever taken a bDMARD) + 0.4619 (if has ever taken prednisone).

If we take antilog, we can rewrite this equation as:

rate = e 0.3939 × e 0.0075 × initial age × e (-1.2990 + 0.0228 × initial age) (if patient is male) × e 0.4017 (if has ever

taken a bDMARD) × e 0.4619 (if has ever taken prednisolone),

5. Chapter conclusion

The risk of serious infections among patients in ARAD during 2001 to 2014 is about 3%. In this

chapter the level of impact from different biologics as potential risk factors for serious infection

was studied. Data suggest that the rates of SI increases with increasing age with or without taking

prednisolone, it increases if the patient has ever taken a biologic, and it increases further if the

patient is a male who has ever taken a biologic. However, medication is not the only risk for SI

and other risks, such as having chronic diseases or other factors which compromise immune

system, can play a potential role and change the results, as well.

231

In compare to other registries, the rate of SI in Australia is higher. For instance, in south

American countries according to a study which published in Aug. 2019 almost 2591 out of 13380

patient/years were taking bDMARDs and 1126 treated with csDMARDs. The SI IR was 30.54

(CI 27.18-34.30) for all bDMARDs and 5.15 (CI 3.36-7.89) for csDMARDs. In this study the

aIRR between the two groups was 2.03 ([1.05, 3.9] p = 0.034) for the first 6 months of treatment

but subsequently increased to 8.26 ([4.32, 15.76] p < 0.001). The SI IR for bDMARDs decreased

over time in both registries, dropping from 36.59 (28.41-47.12) in 2012 to 7.27 (4.79-11.05) in

2016.[10]

In another study from British Society for Rheumatology Biologics Register - Rheumatoid Arthritis

in total, 5289 subjects 19 431 patient-years had at least one SI. The baseline annual rate of first SI

was 4.6% (95% CI: 4.5, 4.7), increasing to 14.1% (95% CI: 13.5, 14.8) following an index

infection. Respiratory infections were the most frequent (44% of all events). Recurrent infections

mirrored the organ class of the index infection. Sepsis, increasing age and polypharmacy were

significant predictors of infection recurrence in a fully adjusted model. The system class of index

infection was associated with the risk of a recurrent event; subjects who experienced sepsis had the

highest risk of subsequent SI within 12 months, 19.7% (95% CI: 15.1, 25.7). [11]

Finally, in a study from five different registries (USA, Sweden, UK, Japan, and CORRONA

International (multiple countries)) from 2000 to 2017 the results showed that age/sex-standardised

rates of hospitalised infection were quite consistent across registries (range 1.14-1.62 per 100

patient-years). Higher and more consistent rates were observed when adding standardisation for

HAQ score (registry range 1.86-2.18, trials rate 2.92) or restricting to a treatment initiation sub

cohort followed for 18 months (registry range 0.99-2.84, trials rate 2.74).[12]

232

THESIS SUMMARY AND REMARKS

233

Summary of main findings

In this model, the rate of SIs increases with increasing age, but at a greater rate (from a smaller

start) if the patient is male. It also increases if the patient has ever taken a bDMARD and increases

if the patient has ever taken prednisolone. Figure 5.12 shows a plot of the predicted individual rate

of SIs versus the observed individual rate of SIs. Once again, we have a very large number of

individuals who had no SIs is observed, so the model tends to predict a low rate[13].

A Generalized Linear Mixed Model was applied to the data from the 28168 visits by the 3569

patients in the study. A model was constructed which expressed the natural logarithm of the odds

for SI in terms of x predictor variables. The number of SIs is greatest for Etanercept, Adalimumab

and Abatacept respectively, but when the rate per 100 PYs is considered, this order changes to

Adalimumab, Etanercept and Anakinra[14].

It may mean that Etanercept is potentially more dangerous than Etanercept, but in practice the

dosage of medicine also plays a role. If a bDMARDs is prescribed for a shorter time at a lower

dosage, it puts the patient potentially at lower risk and is safer. However, even one SI report in this

situation is statistically more significant than the same report for another medicine with a longer

duration and higher dosage. Also, doctors’ preferences in prescribing one medication and the

selection of patients for these biologics all can contribute to the results.

In addition, for Anakinra there was only a limited number of patients available and this can reduce

the reliability of the results[15]. For Anakinra, there was only a small number of patients exposed

to the drug (n= ZZ), which calls into question the validity of the findings for this agent. Finally,

medication is not the only risk for SI in most of the reports and other risks such as having chronic

diseases or other factors which compromise immune system can play a potential role and change

the results (Table 5.5)[16]. Around 83% of the patients have received a biologic. There are more

women than men in each biologic status category, and the ratios of women to men are not

significantly different across the three categories (Table 5.5)[16].

The results for alcohol consumption are likely relevant. Patients who were using a bDMARD

treatment tended to consume alcohol more often (Table 5.5, P = QQ). Possible reasons for this

observation include the use of alcohol to combat pain in those with more active disease, who are

more likely to progress to bDMARDs.

234

A further possibility is that disease activity may be greater in alcohol users and so they may

progress to bDMARDs more quickly or more often. Liver enzyme induction in higher consumers

of alcohol may diminish responsiveness to csDMARDs and lead to more frequent progression to

bDMARDs. Germane to this possible explanation is the tendency for anti-TB drugs, such as

Rifampicin to induce liver enzymes and reduce responsiveness to corticosteroids, which in turn can

lead to more active RA in patients who were previously stable[17].

There is also a statistically significant association between taking bDMARDs and taking

prednisolone. Most of the patients who were taking bDMARDs were also taking prednisolone

concurrently or had taken prednisolone previously. This is most likely explained on the basis of

rheumatoid disease severity. RA patients not well controlled on multiple or sequential csDMARDs

are more likely to have received adjunctive corticosteroids prior to qualifying for a bDMARD.

Furthermore, depending on the clinical response to the bDMARD, they may or may not have been

able to discontinue prednisolone. In any event, since this parameter includes previous usage, the

associated use would have been captured[17].

Increased disease severity in RA is a risk factor for SIs but this risk cannot be easily disentangled

from other risk factors. In other conditions, such as insulin dependent diabetes mellitus (T1DM)

and non-insulin dependent diabetes mellitus (T2DM), lung disease and in patients with previous

stent operations for coronary heart disease, the frequency with which bDMARDs were used is

lower (Table 5.5). Any association between bDMARD status and the other factors is not

statistically significant. One possible reason for this lack of association is the relatively small

numbers of participants with these conditions in this RA cohort. However, the possibility of

confounding by indication also exists, since prescribers may have avoided bDMARD usage in

certain patients with concerning comorbidities. For example, the known propensity for diabetics to

develop infections might have led to treating Rheumatologists exercising restraint in respect to

prescribing bDMARDs in the context of T2DM and T1DM[17].

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Table 5. 5- Relationship between bDMARD use and potential cofactors

Predictor Variables bDMARDs Never taken Taken at some time Total Alcohol

Never/Don’t know 135 785 920 current 297 1501 1789 past 89 303 392 total 521 2589 3110

Prednisolone


T1DM


T2DM


Lung Disease


Kidney Disease


Liver Disease Never/Don’t know 497 2386 2883 current 9 93 102 past 15 110 125 total 521 2589 3110

MI Never/Don’t know 464 2394 2858 current 16 56 72 past 41 139 180 total 521 2589 3110

CABG Never/Don’t know 499 2528 3027 current 8 14 22 past 14 47 61 total 521 2589 3110

C Stenting Never/Don’t know 480 2448 2928 current 20 69 89 past 21 72 93 total 3110

Pearson's Chi-squared test X-squared = 96.663, df = 2, p-value < 2.2e-16X-squared = 173.82, df = 2, p-value < 2.2e-16 A

large number of people, who are taking biologics, are simultaneously taking prednisolone Insulin dependent Diabetes

(T1DM) X-squared = 0.91075, df = 2, p-value = 0.6342None Insulin dependent Diabetes (T2DM) X-squared = 5.0186, df =

2, p-value = 0.08132Lung Disease X-squared = 5.051, df = 2, p-value = 0.08002 Kidney Disease X-squared = 0.071819, df =

2, p-value = 0.9647

Liver disease X-squared = 7.1119, df = 2, p-value = 0.02855

MI: Myocardial infarction X-squared = 6.7789, df = 2, p-value = 0.03373

Coronary artery bypass grafting (CABG) X-squared = 7.9029, df = 2, p-value = 0.01923

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Coronary stenting; X-squared = 4.6234, df = 2, p-value = 0.0

Concluding remarks

In the descriptive statistics, there is always a possibility that people who contribute to the

research are not randomly selected or one sex contributes more than the other sex in answering

the questions. In some studies, the number of patients who contribute to the study also may

cause limitations. However, in this project, there was access to an adequate number of

participants. Furthermore, there were no selection criteria applied by ARAD designers that

might have led to preferential selection of some patients instead of others. Still, criteria such as

the contribution of one gender more than the other or the ability of patients to answer

questionnaires may have contributed to some bias[11].

RA in Australia is predominantly a female disease. The mean and median age for male patients

is greater than the corresponding ages for female patients, which means that RA in females

tends to begin at a younger age than in males. The prevalence of RA peaks in the 60s or seventh

decade of life. The relevance of RA among those in the population younger than 60 is greater

than in the population over 60. Many patients are diagnosed with RA when they are in their

50s[11].

Most of the patients with RA who participated in the ARA database were already taking or

were about to begin bDMARDs, however, this is because the database was conceived as a

bDMARD registry and non-bDMARD recipients were recruited later to supplement the cohort.

The prevalence of serious infection in ARAD participants was 2.92 %. Rates were appreciably

higher in the first year of treatment at around 12 per 100PYs in this study. Similar increased

rates in the first year have been observed in other registries. Thereafter, rates were about half

that in the first year at approximately 6 per 100PYs, which again accords with that reported in

other registries. Males had higher rates of SI and this increased sharply with age, whereas in

females there was a modest, but steady increase with age and even in advanced age, the rates

in females did not approach those in males over 65 years. The major risk factors contributing

to high rates of SI were advancing age, use of bDMARDs and use of Prednisolone.

Comorbidities were not found to be major contributors to SIs[17].

As indicated previously, it is difficult to determine the rate of SIs in RA, since it is a function

of disease severity as well as other factors. Within ARAD at least, the csDMARDs group was

recruited post-hoc and likely consists of patients unmatched for disease activity, since they did

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not require bDMARDs. Moreover, there may have been different rates of corticosteroid use

in csDMARDs users. Nevertheless, serious infections in this group were in the order of 3-4 per

100PYs, which is clearly lower than that in the first year of bDMARD therapy and lower than

that in long term bDMARD recipients. These differences cannot be taken as proof of a

substantial difference, but they are consistent with an increased propensity for SIs in bDMARD

users[12].

Strengths of this analysis include the large size of the database and the randomness or lack of

bias in recruitment, the opportunity to assess participants over a relatively long period of time

(2001-2014) and the capture of events that might have been overlooked if reporting were not

done by the participants, but rather by busy clinicians, whose reporting compliance may have

been suboptimal.

Limitations include the unmatched nature of csDMARDs and bDMARD participants, thus

confounding valid comparisons, the inability to verify self-reported infections of any severity

(no input from family practitioners, no hospital records available, no microbiological

confirmation of infections), but particularly SIs and the inability to capture SIs that resulted in

death or severe disability that precluded further reporting.

Future studies both within and without ARAD have the potential to verify the findings reported

here and to extend them. For example, the extent to which SIs increase in participants who

transition from csDMARDs to bDMARDs within ARAD could be compared as there will have

been an adequate period of observation during the pre-bDMARD era in these patients. Such a

study would have the added benefit that the participants could be their own control, which in

turn would provide greater rigor. Whether SI rates decline in bDMARD recipients after 5-10

years could also be examined. Deeper analysis of newly discovered clinical risk factors might

also be possible. With linkage in time to biobanks, it may also become possible to examine the

role of genetic and acquired immunity[17].

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APPENDICES

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Appendices 241

Description of data in appendix ...................................................................................... 243

Taking different medication levels ............................................................................................................ 243 Response levels ......................................................................................................................................... 243

Appendix A: Output of SAS for EENT Infection 244

Appendix B: OUTPUT of SAS for Lung Infection 268

Appendix C: Output of SAS for Nail and skin infection 301

Appendix D: Output of SAS for artificial joint infection 328

Appendix E: Output of SAS for bone muscle joint infection 351

Appendix F: Output of SAS for blood infection 385

Appendix G: Output of SAS for GIT Infection 411

Appendix H: Output of SAS for Nervous system infection 433

Appendix I: Output of SAS for TB infection 461

Appendix J: Output of SAS for Urinary Tract Infection 485

Appendix K: Output of SAS for viral infection 509

Appendix L: Ethical approval for the thesis 535

APPENDIX M: Sample of ARAD questionnaire 536

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Description of data in appendix

Taking different medication levels

1=Never taking 2=Currently taking 3=Stopped taking 4=Don’t know

Response levels

1=Mild 2=Moderate 3=Severe 4=Missing

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APPENDIX A: OUTPUT OF SAS FOR

EENT INFECTION

Table A.1- Model information for EENT infection

Model Information

Data Set WORK.IMPORT2

Response Variable InfEent InfEent

Number of Response Levels 4

Model generalized logit

Optimization Technique Newton-Raphson

Table A.2- Observation status for EENT infection

Number of Observations Read 27711

Number of Observations Used 21506

Table A.3- response value for EENT infection

Response Profile

Ordered

Value InfEent

Total

Frequency

Mild 1 1050

Moderate 2 1829

Severe 3 406

Missing 4 18221

Logits modelled use InfEent='4' as the reference category.

Note: 6205 observations were deleted due to missing values for

the response or explanatory variables.

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Table A.4- Backward Elimination Procedure for EENT infection

Backward Elimination Procedure

Class Level Information Class Value Design Variables Etanercept 3 1 0 0 0 4 0 1 0 0 currently taking 0 0 1 0 never taking 0 0 0 1

Adalimumab 3 1 0 0 0 4 0 1 0 0 currently taking 0 0 1 0 never taking 0 0 0 1

Anakinra 3 1 0 0 0 4 0 1 0 0 currently taking 0 0 1 0 never taking 0 0 0 1

Infliximab 3 1 0 0 0 4 0 1 0 0 currently taking 0 0 1 0 never taking 0 0 0 1

Rituximab 3 1 0 0 0 4 0 1 0 0 currently taking 0 0 1 0 never taking 0 0 0 1

Abatacept 3 1 0 0 0 4 0 1 0 0 currently taking 0 0 1 0 never taking 0 0 0 1

Tocilizumab 3 1 0 0 0 4 0 1 0 0 currently taking 0 0 1 0 never taking 0 0 0 1

Golimumab 3 1 0 0 currently taking 0 1 0 never taking 0 0 1

Certolizumab 3 1 0 0 0 4 0 1 0 0 currently taking 0 0 1 0 never taking 0 0 0 1

Folic Acid currently taking 1 0 never taking 0 1

Hydroxychloroquine 3 1 0 0 0 4 0 1 0 0 currently taking 0 0 1 0 never taking 0 0 0 1

Sulphasalazine 3 1 0 0 0 4 0 1 0 0 currently taking 0 0 1 0 never taking 0 0 0 1

Arava (Leflunomide) 3 1 0 0 0 4 0 1 0 0 currently taking 0 0 1 0 never taking 0 0 0 1

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Class Level Information Class Value Design Variables Azathioprine 3 1 0 0 0 4 0 1 0 0 currently taking 0 0 1 0 never taking 0 0 0 1

Cyclosporin 3 1 0 0 0 4 0 1 0 0 currently taking 0 0 1 0 never taking 0 0 0 1

Prednisolone 3 1 0 0 0 4 0 1 0 0 currently taking 0 0 1 0 never taking 0 0 0 1

IM Gold injection 3 1 0 0 0 4 0 1 0 0 currently taking 0 0 1 0 never taking 0 0 0 1

Penicillamine 3 1 0 0 0 4 0 1 0 0 currently taking 0 0 1 0 never taking 0 0 0 1

Step 0. The following effects were entered:

Intercept Etanercept Adalimumab Anakinra Infliximab Rituximab Abatacept Tocilizumab

Golimumab Certolizumab Folic Acid Hydroxychloroquine Sulphasalazine Arava

(Leflunomide) Azathioprine Cyclosporin Prednisolone IM Gold injection Penicillamine

Table A.5- Model Convergence status for EENT infection

Model Convergence Status

Convergence criterion (GCONV=1E-8) satisfied.

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Table A.6- Model Fit statistics for EENT infection



AIC 24626.355 24501.128

SC 24650.284 25745.398

-2 Log L 24620.355 24189.128

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Table A.7- Testing null hypothesis for EENT infection



Likelihood Ratio

431.2272

153

<.0001

Score

463.0664

153

<.0001

Wald

419.5882

153

<.0001

Table A.8- Model Fit statistics for removing covariant step 1

Step 1. Effect Azathioprine is removed:






AIC 24626.355 24488.566

SC 24650.284 25661.051

-2 Log L 24620.355 24194.566

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Table A.10- Testing Null hypothesis after removing covariant step 1




Score 457.8861 144 <.0001

Wald 415.1007 144 <.0001

Table A.11- Residual removing covariant step 1

Residual Chi-Square Test

Chi-Square DF Pr > ChiSq

5.0524 9 0.8297


Step 2. Effect Certolizumab is removed:



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Table A.13- Model Fit statistics after removing covariant step 2



AIC 24626.355 24476.658

SC 24650.284 25577.358

-2 Log L 24620.355 24200.658





Score 450.9468 135 <.0001

Wald 408.7712 135 <.0001




10.9161 18 0.8979

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Step 3. Effect Penicillamine is removed:






AIC 24626.35 24473.673

SC 24650.28 25502.589

-2 Log L 24620.35 24215.673





Score 440.0301 126 <.0001

Wald 401.9656 126 <.0001

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22.0077 27 0.7370


Step 4. Effect IM Gold injection is removed:






AIC 24626.355 24465.880

SC 24650.284 25423.011

-2 Log L 24620.355 24225.880

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Score 430.4313 117 <.0001

Wald 392.2553 117 <.0001




31.2787 36 0.6926


Step 5. Effect Rituximab is removed:



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AIC 24626.355 24461.305

SC 24650.284 25346.650

-2 Log L 24620.355 24239.305





Score 415.9895 108 <.0001

Wald 378.4582 108 <.0001




44.9975 45 0.4721

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Step 6. Effect Golimumab is removed:






AIC 24626.355 24462.511

SC 24650.284 25300.000

-2 Log L 24620.355 24252.511





Score 403.4935 102 <.0001

Wald 366.8141 102 <.0001

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57.2672 51 0.2539

Table A.32- Summary of backward elimination in EENT

Note: No (additional) effects met the 0.05 significance level for removal from the model.


Step Effect Removed

DF Number In

Wald Chi-Square

Pr > ChiSq Variable Label



3 Penicillamine 9 15 7.1956 0.6168 Penicillamine

4 IM Gold injection 9 14 9.1915 0.4198 IM Gold injection



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Table A.33- Type 3 analysis of effects in EENT


Effect DF

Wald


Etanercept 9 52.1431 <.0001

Adalimumab 9 22.4139 0.0077

Anakinra 9 18.2690 0.0322

Infliximab 9 31.0160 0.0003

Abatacept 9 18.0153 0.0350

Tocilizumab 9 18.1032 0.0340

Folic Acid 3 9.4165 0.0242



Arava (Leflunomide) 9 17.5339 0.0410

Cyclosporin 9 47.3358 <.0001


Table A.34- Analysis of maximum likelihood estimates in EENT


Parameter

Eent

Infection DF Estimate

Standard

Error

Wald

Chi-

Square Pr > ChiSq

Intercept Mild 1 -3.4872 0.1190 859.2759 <.0001

Intercept Mod 1 -2.9786 0.0928 1031.0501 <.0001

Intercept Severe 1 -4.3609 0.1917 517.2695 <.0001

Abatacept 3 Mild 1 0.5166 0.1769 8.5260 0.0035

Abatacept 3 Mod 1 0.1147 0.1534 0.5587 0.4548

Abatacept 3 Severe 1 -0.4339 0.3573 1.4747 0.2246

Abatacept 4 Mild 1 0.2751 0.8455 0.1059 0.7449

Abatacept 4 Mod 1 -0.6022 0.6388 0.8887 0.3458

Abatacept 4 Severe 1 1.3251 1.0615 1.5582 0.2119

Abatacept currently

taking

Mild 1 0.3362 0.1582 4.5176 0.0335

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Parameter

Eent


Standard

Error

Wald

Chi-

Square Pr > ChiSq

Abatacept currently

taking

Mod 1 0.1491 0.1240 1.4460 0.2292

Abatacept currently

taking

Severe 1 -0.2016 0.2673 0.5686 0.4508

Abatacept never taking Mild 0 0 . . .

Abatacept never taking Mod 0 0 . . .

Abatacept never taking Severe 0 0 . . .

Adalimumab 3 Mild 1 0.0104 0.0914 0.0129 0.9094

Adalimumab 3 Mod 1 0.1823 0.0686 7.0504 0.0079

Adalimumab 3 Severe 1 0.1418 0.1403 1.0222 0.3120

Adalimumab 4 Mild 1 -0.5402 0.6756 0.6394 0.4239

Adalimumab 4 Mod 1 -

0.00090

0.4440 0.0000 0.9984

Adalimumab 4 Severe 1 -

10.2462

147.6 0.0048 0.9447

Adalimumab currently

taking

Mild 1 0.2887 0.0941 9.4206 0.0021


taking

Mod 1 0.1847 0.0737 6.2813 0.0122


taking

Severe 1 -0.0798 0.1470 0.2946 0.5873

Adalimumab never taking Mild 0 0 . . .

Adalimumab never taking Mod 0 0 . . .

Adalimumab never taking Severe 0 0 . . .

Anakinra 3 Mild 1 0.1448 0.2523 0.3295 0.5659

Anakinra 3 Mod 1 -0.0761 0.2191 0.1205 0.7285

Anakinra 3 Severe 1 0.4597 0.3413 1.8146 0.1780

Anakinra 4 Mild 1 -0.7187 0.6297 1.3026 0.2537

Anakinra 4 Mod 1 0.0275 0.4047 0.0046 0.9459

Anakinra 4 Severe 1 -0.4484 1.0513 0.1819 0.6697

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Parameter

Eent


Standard

Error

Wald

Chi-

Square Pr > ChiSq

Anakinra currently

taking

Mild 1 -

11.9697

512.1 0.0005 0.9814

Anakinra currently

taking

Mod 1 1.7999 0.4745 14.3901 0.0001

Anakinra currently

taking

Severe 1 -

12.2422

809.5 0.0002 0.9879

Anakinra never taking Mild 0 0 . . .

Anakinra never taking Mod 0 0 . . .

Anakinra never taking Severe 0 0 . . .

Arava (Leflunomide) 3 Mild 1 0.1098 0.0935 1.3804 0.2400

Arava (Leflunomide) 3 Mod 1 0.1933 0.0729 7.0343 0.0080

Arava (Leflunomide) 3 Severe 1 0.1484 0.1434 1.0712 0.3007

Arava (Leflunomide) 4 Mild 1 -0.2856 0.5428 0.2768 0.5988


Arava (Leflunomide) 4 Severe 1 -0.7134 1.0581 0.4546 0.5002

Arava (Leflunomide) currently

taking

Mild 1 0.2705 0.1060 6.5075 0.0107


taking

Mod 1 0.1492 0.0858 3.0250 0.0820


taking

Severe 1 0.00639 0.1726 0.0014 0.9705

Arava (Leflunomide) never taking Mild 0 0 . . .

Arava (Leflunomide) never taking Mod 0 0 . . .

Arava (Leflunomide) never taking Severe 0 0 . . .

Cyclosporin 3 Mild 1 0.0263 0.0937 0.0789 0.7788

Cyclosporin 3 Mod 1 0.2042 0.0692 8.7084 0.0032

Cyclosporin 3 Severe 1 0.4662 0.1325 12.3789 0.0004

Cyclosporin 4 Mild 1 -0.2418 0.3214 0.5662 0.4518

Cyclosporin 4 Mod 1 0.0673 0.2205 0.0931 0.7603

Cyclosporin 4 Severe 1 -1.0526 0.7303 2.0770 0.1495

of 577


Parameter

Eent


Standard

Error

Wald

Chi-

Square Pr > ChiSq

Cyclosporin currently

taking

Mild 1 0.5290 0.3373 2.4603 0.1168


taking

Mod 1 1.0439 0.2236 21.7983 <.0001


taking

Severe 1 1.0216 0.4398 5.3965 0.0202

Cyclosporin never taking Mild 0 0 . . .

Cyclosporin never taking Mod 0 0 . . .

Cyclosporin never taking Severe 0 0 . . .

Etanercept 3 Mild 1 -0.0509 0.0911 0.3118 0.5766

Etanercept 3 Mod 1 -0.0713 0.0705 1.0220 0.3120

Etanercept 3 Severe 1 -0.3981 0.1457 7.4653 0.0063

Etanercept 4 Mild 1 1.3033 0.5444 5.7307 0.0167

Etanercept 4 Mod 1 1.9227 0.3431 31.3968 <.0001

Etanercept 4 Severe 1 1.3439 0.8633 2.4234 0.1195

Etanercept currently

taking

Mild 1 0.1730 0.0941 3.3831 0.0659


taking

Mod 1 0.0891 0.0722 1.5232 0.2171


taking

Severe 1 -0.3383 0.1446 5.4736 0.0193

Etanercept never taking Mild 0 0 . . .

Etanercept never taking Mod 0 0 . . .

Etanercept never taking Severe 0 0 . . .

Folic Acid and

Methotrexate

currently

taking

Mild 1 -0.1059 0.0761 1.9365 0.1641

Folic Acid and

Methotrexate

currently

taking

Mod 1 -0.1683 0.0598 7.9220 0.0049

Folic Acid and

Methotrexate

currently

taking

Severe 1 -0.0493 0.1190 0.1713 0.6789

of 577


Parameter

Eent


Standard

Error

Wald

Chi-

Square Pr > ChiSq

Folic Acid and

Methotrexate

never taking Mild 0 0 . . .

Folic Acid and

Methotrexate

never taking Mod 0 0 . . .

Folic Acid and

Methotrexate

never taking Severe 0 0 . . .

Hydroxychloroquine 3 Mild 1 0.1431 0.0736 3.7794 0.0519

Hydroxychloroquine 3 Mod 1 0.2299 0.0575 15.9873 <.0001

Hydroxychloroquine 3 Severe 1 0.1860 0.1175 2.5057 0.1134

Hydroxychloroquine 4 Mild 1 -0.0695 0.4165 0.0278 0.8676

Hydroxychloroquine 4 Mod 1 -0.0273 0.3338 0.0067 0.9348


Hydroxychloroquine currently

taking

Mild 1 0.0100 0.0960 0.0109 0.9168


taking

Mod 1 0.0789 0.0753 1.0991 0.2945


taking

Severe 1 0.0332 0.1546 0.0463 0.8297

Hydroxychloroquine never taking Mild 0 0 . . .

Hydroxychloroquine never taking Mod 0 0 . . .

Hydroxychloroquine never taking Severe 0 0 . . .

Infliximab 3 Mild 1 0.0552 0.1337 0.1707 0.6795

Infliximab 3 Mod 1 -0.2055 0.1098 3.5047 0.0612

Infliximab 3 Severe 1 0.0478 0.1974 0.0585 0.8088

Infliximab 4 Mild 1 0.4422 0.4291 1.0621 0.3027

Infliximab 4 Mod 1 -0.1974 0.3745 0.2779 0.5981

Infliximab 4 Severe 1 -0.8062 0.8952 0.8110 0.3678

Infliximab currently

taking

Mild 1 0.6440 0.1747 13.5909 0.0002


taking

Mod 1 0.4727 0.1396 11.4614 0.0007

of 577


Parameter

Eent


Standard

Error

Wald

Chi-

Square Pr > ChiSq


taking

Severe 1 -0.3706 0.3711 0.9971 0.3180

Infliximab never taking Mild 0 0 . . .

Infliximab never taking Mod 0 0 . . .

Infliximab never taking Severe 0 0 . . .

Prednisolone 3 Mild 1 0.3310 0.1083 9.3359 0.0022

Prednisolone 3 Mod 1 0.2552 0.0838 9.2693 0.0023

Prednisolone 3 Severe 1 0.4980 0.1834 7.3738 0.0066




Prednisolone currently

taking

Mild 1 0.1671 0.1087 2.3642 0.1241


taking

Mod 1 0.1308 0.0838 2.4345 0.1187


taking

Severe 1 0.3911 0.1833 4.5509 0.0329

Prednisolone never taking Mild 0 0 . . .

Prednisolone never taking Mod 0 0 . . .

Prednisolone never taking Severe 0 0 . . .

Sulphasalazine 3 Mild 1 0.0933 0.0714 1.7093 0.1911

Sulphasalazine 3 Mod 1 0.2229 0.0554 16.1883 <.0001

Sulphasalazine 3 Severe 1 0.1577 0.1136 1.9284 0.1649


Sulphasalazine 4 Mod 1 -0.2181 0.2582 0.7132 0.3984


Sulphasalazine currently

taking

Mild 1 0.1470 0.1112 1.7468 0.1863


taking

Mod 1 0.00403 0.0926 0.0019 0.9653

of 577


Parameter

Eent


Standard

Error

Wald

Chi-

Square Pr > ChiSq


taking

Severe 1 -0.0445 0.1896 0.0551 0.8144

Sulphasalazine never taking Mild 0 0 . . .

Sulphasalazine never taking Mod 0 0 . . .

Sulphasalazine never taking Severe 0 0 . . .

Tocilizumab 3 Mild 1 0.1595 0.2454 0.4224 0.5157

Tocilizumab 3 Mod 1 0.1835 0.1951 0.8847 0.3469

Tocilizumab 3 Severe 1 0.7127 0.3269 4.7534 0.0292

Tocilizumab 4 Mild 1 -

11.4739

529.0 0.0005 0.9827

Tocilizumab 4 Mod 1 -

11.5154

222.6 0.0027 0.9587

Tocilizumab 4 Severe 1 -

10.9097

820.5 0.0002 0.9894

Tocilizumab currently

taking

Mild 1 0.4933 0.1695 8.4682 0.0036


taking

Mod 1 0.3301 0.1348 5.9962 0.0143


taking

Severe 1 0.1795 0.2814 0.4069 0.5236

Tocilizumab never taking Mild 0 0 . . .

Tocilizumab never taking Mod 0 0 . . .

Tocilizumab never taking Severe 0 0 . . .

of 577

Table A.35- Odds ratio estimates in EENT


Effect InfEent Point Estimate

95% Wald

Confidence Limits

Etanercept 3 vs never taking 1 0.950 0.795 1.136






Etanercept currently taking vs never taking 1 1.189 0.989 1.430



Adalimumab 3 vs never taking 1 1.010 0.845 1.209





Adalimumab 4 vs never taking 3 <0.001 <0.001 >999.999

Adalimumab currently taking vs never taking 1 1.335 1.110 1.605



Anakinra 3 vs never taking 1 1.156 0.705 1.895






Anakinra currently taking vs never taking 1 <0.001 <0.001 >999.999

Anakinra currently taking vs never taking 2 6.049 2.387 15.330


Infliximab 3 vs never taking 1 1.057 0.813 1.373


of 577



95% Wald

Confidence Limits





Infliximab currently taking vs never taking 1 1.904 1.352 2.682



Abatacept 3 vs never taking 1 1.676 1.185 2.371






Abatacept currently taking vs never taking 1 1.400 1.027 1.908



Tocilizumab 3 vs never taking 1 1.173 0.725 1.897



Tocilizumab 4 vs never taking 1 <0.001 <0.001 >999.999



Tocilizumab currently taking vs never taking 1 1.638 1.175 2.283



Folic Acid currently taking vs never taking 1 0.899 0.775 1.044



Hydroxychloroquine 3 vs never taking 1 1.154 0.999 1.333




of 577



95% Wald

Confidence Limits



Hydroxychloroquine currently taking vs never taking 1 1.010 0.837 1.219



Sulphasalazine 3 vs never taking 1 1.098 0.955 1.263






Sulphasalazine currently taking vs never taking 1 1.158 0.931 1.440



Arava (Leflunomide) 3 vs never taking 1 1.116 0.929 1.341






Arava (Leflunomide) currently taking vs never taking 1 1.311 1.065 1.613



Cyclosporin 3 vs never taking 1 1.027 0.854 1.234






Cyclosporin currently taking vs never taking 1 1.697 0.876 3.287



of 577



95% Wald

Confidence Limits

Prednisolone 3 vs never taking 1 1.392 1.126 1.722






Prednisolone currently taking vs never taking 1 1.182 0.955 1.462



of 577

APPENDIX B: OUTPUT OF SAS FOR

LUNG INFECTION

Table B.1- Complete statistics for Lung infection

Model Information


Response Variable InfLung InfLung




Table B.2- Observation status for Lung infection



Table B.3- response value for Lung infection

Response Profile

Ordered

Value InfLung

Total

Frequency

1 1 371

2 2 1379

3 3 624

4 4 19132

Logits modelled use InfLung='4' as the reference category.

Note: 6205 observations were deleted due to missing values for the response or explanatory

variables.

Table B.4- Backward Elimination Procedure for Lung infection

of 577


Class Level Information

Class Value Design Variables

Etanercept 3 1 0 0 0

4 0 1 0 0

currently taking 0 0 1 0

b never taking 0 0 0 1

Adalimumab 3 1 0 0 0

4 0 1 0 0



Anakinra 3 1 0 0 0

4 0 1 0 0



Infliximab 3 1 0 0 0

4 0 1 0 0



Rituximab 3 1 0 0 0

4 0 1 0 0



Abatacept 3 1 0 0 0

4 0 1 0 0

of 577



Tocilizumab 3 1 0 0 0

4 0 1 0 0



Golimumab 3 1 0 0

currently taking 0 1 0

b never taking 0 0 1

Methotrexate 1 1 0 0 0

2 0 1 0 0

3 0 0 1 0

4 0 0 0 1

Certolizumab 3 1 0 0 0

4 0 1 0 0



Methotrexate (plus Folic acid) currently taking 1 0

b never taking 0 1

Hydroxychloroquine 3 1 0 0 0

4 0 1 0 0



Sulphasalazine 3 1 0 0 0

of 577

4 0 1 0 0



Arava (Leflunomide) 3 1 0 0 0

4 0 1 0 0



Azathioprine 3 1 0 0 0

4 0 1 0 0



Cyclosporine 3 1 0 0 0

4 0 1 0 0



Prednisolone 3 1 0 0 0

4 0 1 0 0



IM Gold 3 1 0 0 0

4 0 1 0 0



Penicillamine 3 1 0 0 0

of 577

4 0 1 0 0




Etanercept Adalimumab Anakinra Infliximab Rituximab Abatacept Tocilizumab

Golimumab Methotrexate Certolizumab Methotrexate (plus Folic acid)

Hydroxychloroquine Sulphasalazine Arava (Leflunomide) Azathioprine Cyclosporine

Prednisolone IM Gold Penicillamine

Table B.5- Model Convergence status for Lung infection


Quasi-complete separation of data points detected.

of 577

Table B.6- Model Fit statistics for Lung infection



AIC 19488.155 19401.988

SC 19512.083 20718.042

-2 Log L 19482.155 19071.988

Table B.7- Testing null hypothesis for Lung infection




Score 433.5687 162 <.0001

Wald 397.0385 162 <.0001

Table B.8- Model Fit statistics for removing covariant step 1




of 577




AIC 19488.155 19396.072

SC 19512.083 20640.342

-2 Log L 19482.155 19084.072

Table B.10- Testing Null hypothesis after removing covariant step 1




Score 413.5994 153 <.0001

Wald 391.7407 153 <.0001

Table B.11- Residual removing covariant step 1



13.3285 9 0.1483

of 577





Table B.13- Model Fit statistics after removing covariant step 2



AIC 19488.155 19393.250

SC 19512.083 20565.735

-2 Log L 19482.155 19099.250





Score 400.4674 144 <.0001

Wald 382.3359 144 <.0001

of 577




25.5437 18 0.1107


Step 3. Effect Methotrexate and Folic acid is removed:






AIC 19488.155 19390.300

SC 19512.083 20538.856

-2 Log L 19482.155 19102.300

of 577





Score 397.4990 141 <.0001

Wald 379.3969 141 <.0001




28.2177 21 0.1341





of 577




AIC 19488.155 19383.751

SC 19512.083 20460.523

-2 Log L 19482.155 19113.751





Score 387.3219 132 <.0001

Wald 368.8331 132 <.0001




38.6702 30 0.1333

of 577








AIC 19488.155 19376.800

SC 19512.083 20381.787

-2 Log L 19482.155 19124.800





Score 375.9798 123 <.0001

Wald 357.3572 123 <.0001

of 577




49.5870 39 0.1192


Step 6. Effect Infliximab is removed:






AIC 19488.155 19371.000

SC 19512.083 20304.202

-2 Log L 19482.155 19137.000

of 577





Score 362.5786 114 <.0001

Wald 344.7899 114 <.0001




62.0449 48 0.0838

Step 7. Effect Tocilizumab is removed:



of 577




AIC 19488.155 19367.574

SC 19512.083 20228.992

-2 Log L 19482.155 19151.574





Score 344.9075 105 <.0001

Wald 328.9232 105 <.0001




81.8928 57 0.0170

of 577







AIC 19488.155 19368.130

SC 19512.083 20181.691

-2 Log L 19482.155 19164.130





Score 331.5066 99 <.0001

Wald 316.0759 99 <.0001

of 577




94.1491 63 0.0067

Step 9. Effect Arava (Leflunomide) is removed:






AIC 19488.155 19366.491

SC 19512.083 20108.268

-2 Log L 19482.155 19180.491

of 577





Score 316.5298 90 <.0001

Wald 300.9549 90 <.0001




111.8224 72 0.0018

Step 10. Effect Adalimumab is removed:






AIC 19488.155 19366.037

SC 19512.083 20036.028

-2 Log L 19482.155 19198.037

of 577





Score 299.1489 81 <.0001

Wald 283.8184 81 <.0001




128.1890 81 0.0007

of 577

Table B.44- Summary of backward elimination in Lung


Step

Effect

Removed DF

Number

In

Wald

Chi-

Square Pr > ChiSq

Variable

Label



3 Methotrexate (plus

Folic acid)

3 16 3.0046 0.3909 Methotrexate (plus Folic

acid)



6 Infliximab 9 13 12.4421 0.1895



9 Arava (Leflunomide) 9 10 16.1280 0.0643 Arava (Leflunomide)

10 Adalimumab 9 9 16.1807 0.0632

of 577


Step

Effect

Removed DF

Number

In

Wald

Chi-

Square Pr > ChiSq

Variable

Label



3 Methotrexate (plus

Folic acid)

3 16 3.0046 0.3909 Methotrexate (plus Folic

acid)



6 Infliximab 9 13 12.4421 0.1895




10 Adalimumab 9 9 16.1807 0.0632

of 577

Table B.45- Type 3 analysis of effects in Lung


Effect DF

Wald


Etanercept 9 31.4874 0.0002

Anakinra 9 20.0990 0.0173

Abatacept 9 34.9246 <.0001

Methotrexate 9 20.5746 0.0147



Cyclosporine 9 20.6307 0.0144


IM Gold 9 19.8810 0.0187

Table B.46- Analysis of maximum likelihood estimates in Lung


Parameter InfLung DF Estimate

Standard

Error

Wald

Chi-

Square Pr > ChiSq

Intercept Mild 1 -

15.2999

132.2 0.0134 0.9079

Intercept Mod 1 -4.3102 0.6251 47.5499 <.0001


Abatacept 3 Mild 1 -0.0779 0.3188 0.0598 0.8068

Abatacept 3 Mod 1 0.3341 0.1584 4.4525 0.0349


Abatacept 4 Mild 1 1.0112 1.0300 0.9638 0.3262

of 577



Standard

Error

Wald

Chi-

Square Pr > ChiSq

Abatacept 4 Mod 1 -0.0585 0.7462 0.0062 0.9375


Abatacept currently

taking

Mild 1 0.3420 0.2178 2.4657 0.1164

Abatacept currently

taking

Mod 1 0.5392 0.1180 20.8696 <.0001

Abatacept currently

taking

Severe 1 -0.0929 0.2079 0.1996 0.6550

Abatacept b never

taking

Mild 0 0 . . .

Abatacept b never

taking

Mod 0 0 . . .

Abatacept b never

taking

Severe 0 0 . . .

Anakinra 3 Mild 1 0.5311 0.3487 2.3189 0.1278

Anakinra 3 Mod 1 0.6433 0.1815 12.5635 0.0004

Anakinra 3 Severe 1 -

0.00489

0.3302 0.0002 0.9882

Anakinra 4 Mild 1 0.1512 0.6315 0.0573 0.8108

Anakinra 4 Mod 1 -0.0709 0.3858 0.0337 0.8543

Anakinra 4 Severe 1 -0.9695 0.6978 1.9308 0.1647

Anakinra currently

taking

Mild 1 1.1898 1.0395 1.3102 0.2524

Anakinra currently

taking

Mod 1 1.0875 0.6364 2.9208 0.0874

of 577



Standard

Error

Wald

Chi-

Square Pr > ChiSq

Anakinra currently

taking

Severe 1 -

11.1525

348.5 0.0010 0.9745

Anakinra b never

taking

Mild 0 0 . . .

Anakinra b never

taking

Mod 0 0 . . .

Anakinra b never

taking

Severe 0 0 . . .

Cyclosporine 3 Mild 1 -0.1304 0.1673 0.6076 0.4357

Cyclosporine 3 Mod 1 0.0167 0.0825 0.0411 0.8393

Cyclosporine 3 Severe 1 -0.1421 0.1218 1.3629 0.2430

Cyclosporine 4 Mild 1 -0.6137 0.6260 0.9613 0.3269

Cyclosporine 4 Mod 1 -0.1896 0.2827 0.4496 0.5025

Cyclosporine 4 Severe 1 -0.1456 0.3622 0.1615 0.6878

Cyclosporine currently

taking

Mild 1 1.2314 0.3793 10.5374 0.0012


taking

Mod 1 0.7209 0.2642 7.4466 0.0064


taking

Severe 1 0.2533 0.4633 0.2990 0.5845

Cyclosporine b never

taking

Mild 0 0 . . .


taking

Mod 0 0 . . .

of 577



Standard

Error

Wald

Chi-

Square Pr > ChiSq


taking

Severe 0 0 . . .

Etanercept 3 Mild 1 0.2222 0.1357 2.6825 0.1015

Etanercept 3 Mod 1 -0.1754 0.0777 5.1023 0.0239


Etanercept 4 Mild 1 0.9834 0.9443 1.0845 0.2977

Etanercept 4 Mod 1 1.1483 0.4726 5.9037 0.0151



taking

Mild 1 -0.1632 0.1350 1.4614 0.2267


taking

Mod 1 -0.0438 0.0677 0.4200 0.5169


taking

Severe 1 -0.1177 0.1018 1.3382 0.2473

Etanercept b never

taking

Mild 0 0 . . .

Etanercept b never

taking

Mod 0 0 . . .

Etanercept b never

taking

Severe 0 0 . . .


Hydroxychloroquine 3 Mod 1 0.0739 0.0647 1.3065 0.2530



of 577



Standard

Error

Wald

Chi-

Square Pr > ChiSq




taking

Mild 1 0.2042 0.1458 1.9617 0.1613


taking

Mod 1 0.1582 0.0804 3.8734 0.0491


taking

Severe 1 0.4302 0.1114 14.9202 0.0001

Hydroxychloroquine b never

taking

Mild 0 0 . . .


taking

Mod 0 0 . . .


taking

Severe 0 0 . . .

IM Gold 3 Mild 1 -0.3051 0.1356 5.0582 0.0245

IM Gold 3 Mod 1 -0.0313 0.0676 0.2144 0.6433

IM Gold 3 Severe 1 0.1995 0.0945 4.4514 0.0349

IM Gold 4 Mild 1 0.7848 0.6264 1.5698 0.2102

IM Gold 4 Mod 1 0.6082 0.3463 3.0852 0.0790

IM Gold 4 Severe 1 0.7700 0.4271 3.2496 0.0714

IM Gold currently

taking

Mild 1 -1.3342 1.0067 1.7565 0.1851

IM Gold currently

taking

Mod 1 0.2764 0.2666 1.0746 0.2999

of 577



Standard

Error

Wald

Chi-

Square Pr > ChiSq

IM Gold currently

taking

Severe 1 -0.1061 0.4610 0.0529 0.8181

IM Gold b never

taking

Mild 0 0 . . .

IM Gold b never

taking

Mod 0 0 . . .

IM Gold b never

taking

Severe 0 0 . . .

Methotrexate 1 Mild 1 10.9145 132.2 0.0068 0.9342

Methotrexate 1 Mod 1 1.2418 0.6189 4.0258 0.0448

Methotrexate 1 Severe 1 -0.0534 0.4945 0.0117 0.9140

Methotrexate Currently

taking

Mild 1 11.1219 132.2 0.0071 0.9329


taking

Mod 1 1.4974 0.6251 5.7387 0.0166


taking

Severe 1 -0.0981 0.5177 0.0359 0.8497

Methotrexate 3 Mild 1 10.9202 132.2 0.0068 0.9342

Methotrexate 3 Mod 1 1.4460 0.6211 5.4198 0.0199

Methotrexate 3 Severe 1 0.1561 0.5001 0.0974 0.7549

Methotrexate 4 Mild 0 0 . . .

Methotrexate 4 Mod 0 0 . . .

Methotrexate 4 Severe 0 0 . . .


of 577



Standard

Error

Wald

Chi-

Square Pr > ChiSq



Prednisolone 4 Mild 1 -

10.5110

219.0 0.0023 0.9617

Prednisolone 4 Mod 1 -1.1526 1.0341 1.2422 0.2650

Prednisolone 4 Severe 1 -0.6292 1.0771 0.3413 0.5591


taking

Mild 1 0.4916 0.1786 7.5773 0.0059


taking

Mod 1 0.3192 0.0919 12.0656 0.0005


taking

Severe 1 0.8943 0.1574 32.2591 <.0001

Prednisolone b never

taking

Mild 0 0 . . .


taking

Mod 0 0 . . .


taking

Severe 0 0 . . .


Sulphasalazine 3 Mod 1 0.2041 0.0627 10.6112 0.0011



Sulphasalazine 4 Mod 1 -0.5335 0.3316 2.5882 0.1077


of 577



Standard

Error

Wald

Chi-

Square Pr > ChiSq


taking

Mild 1 0.3020 0.1660 3.3084 0.0689


taking

Mod 1 0.0113 0.1022 0.0122 0.9120


taking

Severe 1 0.00650 0.1447 0.0020 0.9641

Sulphasalazine b never

taking

Mild 0 0 . . .


taking

Mod 0 0 . . .


taking

Severe 0 0 . . .

Table B.47- Odds ratio estimates in Lung


Effect InfLung

Point

Estimate

95% Wald

Confidence Limits

Etanercept 3 Versus never taking Mild 1.249 0.957 1.629

Etanercept 3 Versus never taking Mod 0.839 0.721 0.977

Etanercept 3 Versus never taking Severe 1.004 0.812 1.243

Etanercept 4 Versus never taking Mild 2.674 0.420 17.018

Etanercept 4 Versus never taking Mod 3.153 1.249 7.961

Etanercept 4 Versus never taking Severe 5.905 2.226 15.664

Etanercept currently taking Versus never taking Mild 0.849 0.652 1.107

Etanercept currently taking Versus never taking Mod 0.957 0.838 1.093

of 577


Effect InfLung

Point

Estimate

95% Wald

Confidence Limits

Etanercept currently taking Versus never taking Severe 0.889 0.728 1.085

Anakinra 3 Versus never taking Mild 1.701 0.859 3.369

Anakinra 3 Versus never taking Mod 1.903 1.333 2.716

Anakinra 3 Versus never taking Severe 0.995 0.521 1.901

Anakinra 4 Versus never taking Mild 1.163 0.337 4.010

Anakinra 4 Versus never taking Mod 0.932 0.437 1.985

Anakinra 4 Versus never taking Severe 0.379 0.097 1.489

Anakinra currently taking Versus never taking Mild 3.286 0.428 25.207

Anakinra currently taking Versus never taking Mod 2.967 0.852 10.327

Anakinra currently taking Versus never taking Severe <0.001 <0.001 >999.999

Abatacept 3 Versus never taking Mild 0.925 0.495 1.728

Abatacept 3 Versus never taking Mod 1.397 1.024 1.905

Abatacept 3 Versus never taking Severe 1.768 1.198 2.608

Abatacept 4 Versus never taking Mild 2.749 0.365 20.695

Abatacept 4 Versus never taking Mod 0.943 0.218 4.071

Abatacept 4 Versus never taking Severe 1.084 0.153 7.660

Abatacept currently taking Versus never taking Mild 1.408 0.919 2.157

Abatacept currently taking Versus never taking Mod 1.715 1.361 2.161

Abatacept currently taking Versus never taking Severe 0.911 0.606 1.370

Methotrexate 1 vs 4 Mild >999.999 <0.001 >999.999

Methotrexate 1 Versus never taking Mod 3.462 1.029 11.643

Methotrexate 1 Versus never taking Severe 0.948 0.360 2.499

of 577


Effect InfLung

Point

Estimate

95% Wald

Confidence Limits

Methotrexate currently taking Versus never

taking

Mild >999.999 <0.001 >999.999


taking

Mod 4.470 1.313 15.218


taking

Severe 0.907 0.329 2.501

Methotrexate 3 vs 4 Mild >999.999 <0.001 >999.999

Methotrexate 3 vs 4 Mod 4.246 1.257 14.344

Methotrexate 3 vs 4 Severe 1.169 0.439 3.115

Hydroxychloroquine 3 Versus never

taking

Mild 1.049 0.826 1.333


taking

Mod 1.077 0.949 1.222


taking

Severe 1.039 0.858 1.258


taking

Mild 0.389 0.052 2.898


taking

Mod 1.186 0.598 2.349


taking

Severe 1.709 0.781 3.739

Hydroxychloroquine currently taking Versus

never taking

Mild 1.227 0.922 1.632


never taking

Mod 1.171 1.001 1.371


never taking

Severe 1.538 1.236 1.913

of 577


Effect InfLung

Point

Estimate

95% Wald

Confidence Limits

Sulphasalazine 3 Versus never taking Mild 1.010 0.800 1.275

Sulphasalazine 3 Versus never taking Mod 1.226 1.085 1.387

Sulphasalazine 3 Versus never taking Severe 1.107 0.925 1.325

Sulphasalazine 4 Versus never taking Mild 1.110 0.421 2.927

Sulphasalazine 4 Versus never taking Mod 0.587 0.306 1.124

Sulphasalazine 4 Versus never taking Severe 1.130 0.570 2.240

Sulphasalazine currently taking Versus never

taking

Mild 1.353 0.977 1.873


taking

Mod 1.011 0.828 1.236


taking

Severe 1.007 0.758 1.337

Cyclosporine 3 Versus never taking Mild 0.878 0.632 1.218

Cyclosporine 3 Versus never taking Mod 1.017 0.865 1.195

Cyclosporine 3 Versus never taking Severe 0.868 0.683 1.101

Cyclosporine 4 Versus never taking Mild 0.541 0.159 1.846

Cyclosporine 4 Versus never taking Mod 0.827 0.475 1.440

Cyclosporine 4 Versus never taking Severe 0.865 0.425 1.758

Cyclosporine currently taking Versus never

taking

Mild 3.426 1.629 7.206


taking

Mod 2.056 1.225 3.451


taking

Severe 1.288 0.520 3.194

Prednisolone 3 Versus never taking Mild 1.412 0.987 2.022

of 577


Effect InfLung

Point

Estimate

95% Wald

Confidence Limits

Prednisolone 3 Versus never taking Mod 1.195 0.994 1.437

Prednisolone 3 Versus never taking Severe 1.574 1.142 2.170

Prednisolone 4 Versus never taking Mild <0.001 <0.001 >999.999

Prednisolone 4 Versus never taking Mod 0.316 0.042 2.397

Prednisolone 4 Versus never taking Severe 0.533 0.065 4.401

Prednisolone currently taking Versus never

taking

Mild 1.635 1.152 2.320


taking

Mod 1.376 1.149 1.648


taking

Severe 2.446 1.796 3.330

IM Gold 3 Versus never taking Mild 0.737 0.565 0.962

IM Gold 3 Versus never taking Mod 0.969 0.849 1.106

IM Gold 3 Versus never taking Severe 1.221 1.014 1.469

IM Gold 4 Versus never taking Mild 2.192 0.642 7.482

IM Gold 4 Versus never taking Mod 1.837 0.932 3.621

IM Gold 4 Versus never taking Severe 2.160 0.935 4.989

IM Gold currently taking Versus never taking Mild 0.263 0.037 1.894

IM Gold currently taking Versus never taking Mod 1.318 0.782 2.223

IM Gold currently taking Versus never taking Severe 0.899 0.364 2.220

of 577

APPENDIX C: OUTPUT OF SAS FOR

NAIL AND SKIN INFECTION

Table C.1- Complete statistics for Nail and skin infection

Model Information


Response Variable Nail and skin infection InfSkin




Table C.2- Observation status for Nail and skin infection



of 577

Table C.3- response value for Nail and skin infection

Response Profile

Ordered

Value Skin and nail infection

Total

Frequency

1 Mild 1253

2 Moderate 1039

3 Severe 361

4 No report 18853

Logits modelled use InfSkin='4' as the reference category.


variables.

3= Never taken

4= Don’t know

of 577

Table C.4- Backward Elimination Procedure for Nail and skin infection





4 0 1 0 0


never taking 0 0 0 1


4 0 1 0 0



Anakinra 3 1 0 0 0

4 0 1 0 0




4 0 1 0 0



Rituximab 3 1 0 0 0

4 0 1 0 0



Abatacept 3 1 0 0 0

4 0 1 0 0




4 0 1 0 0



Golimumab 3 1 0 0


of 577

never taking 0 0 1


4 0 1 0 0



Folic Acid currently taking 1 0

never taking 0 1


4 0 1 0 0




4 0 1 0 0




4 0 1 0 0




4 0 1 0 0




4 0 1 0 0




4 0 1 0 0



IM Gold injection 3 1 0 0 0

4 0 1 0 0


of 577



4 0 1 0 0






(Leflunomide) Azathioprine Prednisolone IM Gold injection Penicillamine

Table C.5- Model Convergence status for Nail and skin infection



Table C.6- Model Fit statistics for Nail and skin infection



AIC 21341.861 21261.541

SC 21365.789 22505.811

-2 Log L 21335.861 20949.541

Table C.7- Testing null hypothesis for Nail and skin infection




Score 425.2533 153 <.0001

Wald 382.0017 153 <.0001

of 577

Table C.8- Model Fit statistics for removing covariant step 1







AIC 21341.861 21252.373

SC 21365.789 22424.858

-2 Log L 21335.861 20958.373

Table C.10- Testing Null hypothesis after removing covariant step 1




Score 417.3618 144 <.0001

of 577



Wald 374.6182 144 <.0001

Table C.11- Residual removing covariant step 1



7.5708 9 0.5779


Step 2. Effect Hydroxychloroquine is removed:



Table C.13- Model Fit statistics after removing covariant step 2



AIC 21341.861 21241.218

SC 21365.789 22341.919

-2 Log L 21335.861 20965.218





of 577



Score 410.3001 135 <.0001

Wald 367.1567 135 <.0001




14.9868 18 0.6629








AIC 21341.861 21232.298

SC 21365.789 22261.213

-2 Log L 21335.861 20974.298

of 577





Score 400.6553 126 <.0001

Wald 357.7151 126 <.0001




25.6149 27 0.5400

of 577


Step 4. Effect Abatacept is removed:






AIC 21341.861 21231.336

SC 21365.789 22188.467

-2 Log L 21335.861 20991.336





Score 385.9767 117 <.0001

Wald 345.3642 117 <.0001

of 577




40.5589 36 0.2763








AIC 21341.861 21224.235

SC 21365.789 22109.581

-2 Log L 21335.861 21002.235

of 577





Score 374.4255 108 <.0001

Wald 334.8860 108 <.0001




52.5641 45 0.2044





of 577




AIC 21341.861 21226.325

SC 21365.789 22039.885

-2 Log L 21335.861 21022.325





Score 353.7825 99 <.0001

Wald 320.6062 99 <.0001




67.5116 54 0.1024

of 577








AIC 21341.861 21223.598

SC 21365.789 21989.302

-2 Log L 21335.861 21031.598





Score 343.8880 93 <.0001

Wald 311.0294 93 <.0001

of 577




77.4503 60 0.0643


Step 8. Effect Anakinra is removed:






AIC 21341.861 21220.330

SC 21365.789 21914.249

-2 Log L 21335.861 21046.330

of 577





Score 325.8222 84 <.0001

Wald 295.3844 84 <.0001




94.3919 69 0.0229





of 577




AIC 21341.861 21224.705

SC 21365.789 21846.840

-2 Log L 21335.861 21068.705





Score 306.6692 75 <.0001

Wald 278.4445 75 <.0001




112.3163 78 0.0066








AIC 21341.861 21220.298

SC 21365.789 21770.648

of 577



-2 Log L 21335.861 21082.298





Score 289.6632 66 <.0001

Wald 262.6972 66 <.0001




129.4888 87 0.0021


of 577

Table C.48- Summary of backward elimination in Nail and skin infection


Step

Effect

Removed DF

Number

In

Wald


Variable

Label


2 Hydroxychloroquine 9 16 7.2627 0.6098 Hydroxychloroquine






8 Anakinra 9 10 14.7879 0.0969



Table C.49- Type 3 analysis of effects in Nail and skin infection


Effect DF

Wald


Etanercept 9 24.9423 0.0030

of 577


Effect DF

Wald


Adalimumab 9 21.4151 0.0109

Infliximab 9 29.8970 0.0005

Rituximab 9 24.2231 0.0040

Folic Acid 3 25.6065 <.0001

Sulphasalazine 9 34.6779 <.0001



Before studying the following tables please use the following codes:

Skin and nail infection level (Infskin) are 1= mild, 2= moderate, 3= severe, 4= not reported

Taking medication level just currently taking medication and never taken medication is

important for us, but we have also a few reports for 3= stopped taking medication, 4= don’t

know if patient took the medication or not.

Table C.50- Analysis of maximum likelihood estimates in Skin and nail infection


Parameter

Taking

medication

status

Skin and nail

infection DF Estimate

Standard

Error

Wald

Chi-

Square Pr > ChiSq

Intercept Mild 1 -2.8266 0.0946 893.5678 <.0001

Intercept Mod 1 -2.9696 0.1016 853.9240 <.0001


Adalimumab 3 Mild 1 0.1667 0.0834 3.9963 0.0456

Adalimumab 3 Mod 1 0.1512 0.0894 2.8596 0.0908


Adalimumab 4 Mild 1 -0.3524 0.6067 0.3375 0.5613

Adalimumab 4 Mod 1 -0.2622 0.5666 0.2142 0.6435


Adalimumab currently taking Mild 1 0.2213 0.0829 7.1270 0.0076

Adalimumab currently taking Mod 1 0.00778 0.0910 0.0073 0.9319

Adalimumab currently taking Severe 1 -0.0846 0.1565 0.2922 0.5888

of 577


Parameter

Taking

medication

status

Skin and nail


Standard

Error

Wald

Chi-

Square Pr > ChiSq




Arava

(Leflunomide)

3 Mild 1 0.0482 0.0837 0.3325 0.5642

Arava

(Leflunomide)

3 Mod 1 0.1231 0.0913 1.8168 0.1777

Arava

(Leflunomide)

3 Severe 1 -0.1156 0.1497 0.5969 0.4398

Arava

(Leflunomide)

4 Mild 1 -0.9108 0.6082 2.2430 0.1342

Arava

(Leflunomide)

4 Mod 1 0.2157 0.4031 0.2864 0.5926

Arava

(Leflunomide)

4 Severe 1 -0.9201 0.7794 1.3938 0.2378

Arava

(Leflunomide)

currently taking Mild 1 0.3055 0.0932 10.7435 0.0010

Arava

(Leflunomide)

currently taking Mod 1 0.2191 0.1044 4.4011 0.0359

Arava

(Leflunomide)


Arava

(Leflunomide)


Arava

(Leflunomide)


Arava

(Leflunomide)


Etanercept 3 Mild 1 -0.0175 0.0818 0.0459 0.8304

Etanercept 3 Mod 1 -0.0827 0.0890 0.8645 0.3525

Etanercept 3 Severe 1 -0.1033 0.1422 0.5275 0.4677

Etanercept 4 Mild 1 0.4892 0.5332 0.8418 0.3589

of 577


Parameter

Taking

medication

status

Skin and nail


Standard

Error

Wald

Chi-

Square Pr > ChiSq

Etanercept 4 Mod 1 1.1722 0.4277 7.5118 0.0061


Etanercept currently taking Mild 1 0.00155 0.0838 0.0003 0.9852

Etanercept currently taking Mod 1 -0.2081 0.0910 5.2237 0.0223

Etanercept currently taking Severe 1 -0.3476 0.1545 5.0621 0.0245




Infliximab 3 Mild 1 -0.1862 0.1367 1.8550 0.1732

Infliximab 3 Mod 1 0.2063 0.1283 2.5871 0.1077


Infliximab 4 Mild 1 0.4947 0.3630 1.8573 0.1729

Infliximab 4 Mod 1 0.8282 0.3319 6.2268 0.0126


Infliximab currently taking Mild 1 0.2213 0.1753 1.5940 0.2067

Infliximab currently taking Mod 1 -0.1129 0.2084 0.2935 0.5880

Infliximab currently taking Severe 1 0.5461 0.2665 4.1992 0.0404




Methotrexate and

Folic Acid

currently taking Mild 1 -0.3023 0.0732 17.0813 <.0001

Methotrexate and

Folic Acid

currently taking Mod 1 -0.1809 0.0780 5.3701 0.0205

Methotrexate and

Folic Acid


Methotrexate and

Folic Acid


Methotrexate and

Folic Acid


of 577


Parameter

Taking

medication

status

Skin and nail


Standard

Error

Wald

Chi-

Square Pr > ChiSq

Methotrexate and

Folic Acid


Prednisolone 3 Mild 1 -0.1068 0.0946 1.2757 0.2587






Prednisolone currently taking Mild 1 0.1044 0.0911 1.3132 0.2518

Prednisolone currently taking Mod 1 0.0129 0.0995 0.0169 0.8967

Prednisolone currently taking Severe 1 0.9617 0.2236 18.4899 <.0001




Rituximab 3 Mild 1 0.2088 0.1608 1.6855 0.1942

Rituximab 3 Mod 1 -0.2103 0.1883 1.2478 0.2640

Rituximab 3 Severe 1 -0.2355 0.2839 0.6882 0.4068

Rituximab 4 Mild 1 -0.5847 0.5650 1.0710 0.3007

Rituximab 4 Mod 1 -0.8432 0.5514 2.3389 0.1262

Rituximab 4 Severe 1 0.1096 0.5674 0.0373 0.8469

Rituximab currently taking Mild 1 -0.4992 0.1693 8.6962 0.0032

Rituximab currently taking Mod 1 -0.4075 0.1673 5.9319 0.0149

Rituximab currently taking Severe 1 -0.4535 0.2610 3.0184 0.0823

Rituximab never taking Mild 0 0 . . .

Rituximab never taking Mod 0 0 . . .

Rituximab never taking Severe 0 0 . . .




Sulphasalazine 4 Mild 1 -0.0553 0.2821 0.0385 0.8445


of 577


Parameter

Taking

medication

status

Skin and nail


Standard

Error

Wald

Chi-

Square Pr > ChiSq


Sulphasalazine currently taking Mild 1 -0.3428 0.1158 8.7686 0.0031

Sulphasalazine currently taking Mod 1 -0.1098 0.1187 0.8565 0.3547

Sulphasalazine currently taking Severe 1 0.0885 0.1946 0.2068 0.6493

Sulphasalazine never taking Mild 0 0 . . .

Sulphasalazine never taking Mod 0 0 . . .

Sulphasalazine never taking Severe 0 0 . . .

of 577

Table C.51- Odds ratio estimates in Skin and nail infection


Effect InfSkin

Point

Estimate

95% Wald

Confidence Limits

Etanercept 3 vs never taking Mild 0.983 0.837 1.154

Etanercept 3 vs never taking Mod 0.921 0.773 1.096

Etanercept 3 vs never taking Severe 0.902 0.682 1.192




Etanercept currently taking vs never taking Mild 1.002 0.850 1.180

Etanercept currently taking vs never taking Mod 0.812 0.679 0.971

Etanercept currently taking vs never taking Severe 0.706 0.522 0.956

Adalimumab 3 vs never taking Mild 1.181 1.003 1.391

Adalimumab 3 vs never taking Mod 1.163 0.976 1.386

Adalimumab 3 vs never taking Severe 1.442 1.096 1.899




Adalimumab currently taking vs never taking Mild 1.248 1.061 1.468

Adalimumab currently taking vs never taking Mod 1.008 0.843 1.205

Adalimumab currently taking vs never taking Severe 0.919 0.676 1.249

Infliximab 3 vs never taking Mild 0.830 0.635 1.085

Infliximab 3 vs never taking Mod 1.229 0.956 1.580

Infliximab 3 vs never taking Severe 1.618 1.121 2.337




Infliximab currently taking vs never taking Mild 1.248 0.885 1.759

Infliximab currently taking vs never taking Mod 0.893 0.594 1.344

Infliximab currently taking vs never taking Severe 1.727 1.024 2.911

Rituximab 3 vs never taking Mild 1.232 0.899 1.689

Rituximab 3 vs never taking Mod 0.810 0.560 1.172

Rituximab 3 vs never taking Severe 0.790 0.453 1.378

of 577


Effect InfSkin

Point

Estimate

95% Wald

Confidence Limits

Rituximab 4 vs never taking Mild 0.557 0.184 1.687

Rituximab 4 vs never taking Mod 0.430 0.146 1.268

Rituximab 4 vs never taking Severe 1.116 0.367 3.393

Rituximab currently taking vs never taking Mild 0.607 0.436 0.846

Rituximab currently taking vs never taking Mod 0.665 0.479 0.924

Rituximab currently taking vs never taking Severe 0.635 0.381 1.060

Methotrexate and Folic Acid currently taking vs never

taking

Mild 0.739 0.640 0.853


taking

Mod 0.835 0.716 0.972


taking

Severe 1.239 0.982 1.563

Sulphasalazine 3 vs never taking Mild 1.089 0.961 1.234

Sulphasalazine 3 vs never taking Mod 1.157 1.007 1.328

Sulphasalazine 3 vs never taking Severe 1.363 1.075 1.727

Sulphasalazine 4 vs never taking Mild 0.946 0.544 1.645

Sulphasalazine 4 vs never taking Mod 1.898 1.196 3.010

Sulphasalazine 4 vs never taking Severe 2.104 1.032 4.289

Sulphasalazine currently taking vs never taking Mild 0.710 0.566 0.891

Sulphasalazine currently taking vs never taking Mod 0.896 0.710 1.131

Sulphasalazine currently taking vs never taking Severe 1.093 0.746 1.600

Arava (Leflunomide) 3 vs never taking Mild 1.049 0.891 1.236

Arava (Leflunomide) 3 vs never taking Mod 1.131 0.946 1.353

Arava (Leflunomide) 3 vs never taking Severe 0.891 0.664 1.194




Arava (Leflunomide) currently taking vs never taking Mild 1.357 1.131 1.629

Arava (Leflunomide) currently taking vs never taking Mod 1.245 1.015 1.528

Arava (Leflunomide) currently taking vs never taking Severe 1.198 0.860 1.668

Prednisolone 3 vs never taking Mild 0.899 0.747 1.082

Prednisolone 3 vs never taking Mod 0.924 0.757 1.129

of 577


Effect InfSkin

Point

Estimate

95% Wald

Confidence Limits

Prednisolone 3 vs never taking Severe 1.901 1.212 2.981




Prednisolone currently taking vs never taking Mild 1.110 0.929 1.327

Prednisolone currently taking vs never taking Mod 1.013 0.833 1.231

Prednisolone currently taking vs never taking Severe 2.616 1.688 4.055

of 577

APPENDIX D: OUTPUT OF SAS FOR

ARTIFICIAL JOINT INFECTION

Table D.1- Complete statistics for Artificial Joint infection

Model Information


Response Variable TB Infection TB Infection




Table D.2- Observation status for Artificial Joint infection



Table D.3- response value for Artificial Joint infection

Response Profile

Ordered

Value TB Infection

Total

Frequency

1 1 1050

2 2 1829

3 3 406

4 4 18221

of 577

Table D.4- Backward Elimination Procedure for ARTIFICIAL JOINT infection

Logits modelled use TB Infection='4' as the reference category.

Note: 6205 observations were deleted due to missing values

for the response or explanatory variables.





4 0 1 0 0




4 0 1 0 0



Anakinra 3 1 0 0 0

4 0 1 0 0




4 0 1 0 0



Rituximab 3 1 0 0 0

4 0 1 0 0



Abatacept 3 1 0 0 0

4 0 1 0 0




4 0 1 0 0

of 577





Golimumab 3 1 0 0


never taking 0 0 1


4 0 1 0 0




never taking 0 1


4 0 1 0 0




4 0 1 0 0




4 0 1 0 0




4 0 1 0 0



Cyclosporin 3 1 0 0 0

4 0 1 0 0




of 577



4 0 1 0 0




4 0 1 0 0




4 0 1 0 0



of 577

Table D.5- Model Convergence status for ARTIFICIAL JOINT infection







Table D.6- Model Fit statistics for ARTIFICIAL JOINT infection



AIC 24626.355 24501.128

SC 24650.284 25745.398

-2 Log L 24620.355 24189.128

Table D.7- Testing null hypothesis for ARTIFICIAL JOINT infection




of 577



Score 463.0664 153 <.0001

Wald 419.5882 153 <.0001

Table D.8- Model Fit statistics for removing covariant step 1







AIC 24626.355 24488.566

SC 24650.284 25661.051

-2 Log L 24620.355 24194.566

Table D.10- Testing Null hypothesis after removing covariant step 1




Score 457.8861 144 <.0001

Wald 415.1007 144 <.0001

Table D.11- Residual removing covariant step 1



5.0524 9 0.8297

of 577





Table D.13- Model Fit statistics after removing covariant step 2



AIC 24626.355 24476.658

SC 24650.284 25577.358

-2 Log L 24620.355 24200.658





Score 450.9468 135 <.0001

Wald 408.7712 135 <.0001

of 577




10.9161 18 0.8979








AIC 24626.355 24473.673

SC 24650.284 25502.589

-2 Log L 24620.355 24215.673





Score 440.0301 126 <.0001

Wald 401.9656 126 <.0001

of 577




22.0077 27 0.7370








AIC 24626.355 24465.880

SC 24650.284 25423.011

-2 Log L 24620.355 24225.880





of 577



Score 430.4313 117 <.0001

Wald 392.2553 117 <.0001




31.2787 36 0.6926








AIC 24626.355 24461.305

SC 24650.284 25346.650

-2 Log L 24620.355 24239.305

of 577





Score 415.9895 108 <.0001

Wald 378.4582 108 <.0001




44.9975 45 0.4721





of 577




AIC 24626.355 24462.511

SC 24650.284 25300.000

-2 Log L 24620.355 24252.511





Score 403.4935 102 <.0001

Wald 366.8141 102 <.0001




57.2672 51 0.2539


of 577

Table D.32- Summary of backward elimination in ARTIFICIAL JOINT


Step

Effect

Removed DF

Number

In

Wald


Variable

Label







Table D.33- Type 3 analysis of effects in ARTIFICIAL JOINT


Effect DF

Wald


Etanercept 9 52.1431 <.0001

Adalimumab 9 22.4139 0.0077

Anakinra 9 18.2690 0.0322

Infliximab 9 31.0160 0.0003

Abatacept 9 18.0153 0.0350

Tocilizumab 9 18.1032 0.0340

Folic Acid 3 9.4165 0.0242




Cyclosporin 9 47.3358 <.0001


of 577

Table D.34- Analysis of maximum likelihood estimates in ARTIFICIAL JOINT


Parameter

TB


Standard

Error

Wald

Chi-

Square Pr > ChiSq

Intercept 1 1 -3.4872 0.1190 859.2759 <.0001

Intercept 2 1 -2.9786 0.0928 1031.0501 <.0001

Intercept 3 1 -4.3609 0.1917 517.2695 <.0001

Etanercept 3 1 1 -0.0509 0.0911 0.3118 0.5766

Etanercept 3 2 1 -0.0713 0.0705 1.0220 0.3120

Etanercept 3 3 1 -0.3981 0.1457 7.4653 0.0063

Etanercept 4 1 1 1.3033 0.5444 5.7307 0.0167

Etanercept 4 2 1 1.9227 0.3431 31.3968 <.0001

Etanercept 4 3 1 1.3439 0.8633 2.4234 0.1195


taking

1 1 0.1730 0.0941 3.3831 0.0659


taking

2 1 0.0891 0.0722 1.5232 0.2171


taking

3 1 -0.3383 0.1446 5.4736 0.0193

Etanercept never taking 1 0 0 . . .



Adalimumab 3 1 1 0.0104 0.0914 0.0129 0.9094

Adalimumab 3 2 1 0.1823 0.0686 7.0504 0.0079

Adalimumab 3 3 1 0.1418 0.1403 1.0222 0.3120

Adalimumab 4 1 1 -0.5402 0.6756 0.6394 0.4239

Adalimumab 4 2 1 -

0.00090

0.4440 0.0000 0.9984

Adalimumab 4 3 1 -

10.2462

147.6 0.0048 0.9447


taking

1 1 0.2887 0.0941 9.4206 0.0021


taking

2 1 0.1847 0.0737 6.2813 0.0122

of 577


Parameter

TB


Standard

Error

Wald

Chi-

Square Pr > ChiSq


taking

3 1 -0.0798 0.1470 0.2946 0.5873

Adalimumab never taking 1 0 0 . . .



Anakinra 3 1 1 0.1448 0.2523 0.3295 0.5659

Anakinra 3 2 1 -0.0761 0.2191 0.1205 0.7285

Anakinra 3 3 1 0.4597 0.3413 1.8146 0.1780

Anakinra 4 1 1 -0.7187 0.6297 1.3026 0.2537

Anakinra 4 2 1 0.0275 0.4047 0.0046 0.9459

Anakinra 4 3 1 -0.4484 1.0513 0.1819 0.6697

Anakinra currently

taking

1 1 -

11.9697

512.1 0.0005 0.9814

Anakinra currently

taking

2 1 1.7999 0.4745 14.3901 0.0001

Anakinra currently

taking

3 1 -

12.2422

809.5 0.0002 0.9879

Anakinra never taking 1 0 0 . . .



Infliximab 3 1 1 0.0552 0.1337 0.1707 0.6795

Infliximab 3 2 1 -0.2055 0.1098 3.5047 0.0612

Infliximab 3 3 1 0.0478 0.1974 0.0585 0.8088

Infliximab 4 1 1 0.4422 0.4291 1.0621 0.3027

Infliximab 4 2 1 -0.1974 0.3745 0.2779 0.5981

Infliximab 4 3 1 -0.8062 0.8952 0.8110 0.3678


taking

1 1 0.6440 0.1747 13.5909 0.0002


taking

2 1 0.4727 0.1396 11.4614 0.0007

of 577


Parameter

TB


Standard

Error

Wald

Chi-

Square Pr > ChiSq


taking

3 1 -0.3706 0.3711 0.9971 0.3180

Infliximab never taking 1 0 0 . . .



Abatacept 3 1 1 0.5166 0.1769 8.5260 0.0035

Abatacept 3 2 1 0.1147 0.1534 0.5587 0.4548

Abatacept 3 3 1 -0.4339 0.3573 1.4747 0.2246

Abatacept 4 1 1 0.2751 0.8455 0.1059 0.7449

Abatacept 4 2 1 -0.6022 0.6388 0.8887 0.3458

Abatacept 4 3 1 1.3251 1.0615 1.5582 0.2119

Abatacept currently

taking

1 1 0.3362 0.1582 4.5176 0.0335

Abatacept currently

taking

2 1 0.1491 0.1240 1.4460 0.2292

Abatacept currently

taking

3 1 -0.2016 0.2673 0.5686 0.4508

Abatacept never taking 1 0 0 . . .



Tocilizumab 3 1 1 0.1595 0.2454 0.4224 0.5157

Tocilizumab 3 2 1 0.1835 0.1951 0.8847 0.3469

Tocilizumab 3 3 1 0.7127 0.3269 4.7534 0.0292

Tocilizumab 4 1 1 -

11.4739

529.0 0.0005 0.9827

Tocilizumab 4 2 1 -

11.5154

222.6 0.0027 0.9587

Tocilizumab 4 3 1 -

10.9097

820.5 0.0002 0.9894


taking

1 1 0.4933 0.1695 8.4682 0.0036

of 577


Parameter

TB


Standard

Error

Wald

Chi-

Square Pr > ChiSq


taking

2 1 0.3301 0.1348 5.9962 0.0143


taking

3 1 0.1795 0.2814 0.4069 0.5236

Tocilizumab never taking 1 0 0 . . .



Folic Acid currently

taking

1 1 -0.1059 0.0761 1.9365 0.1641


taking

2 1 -0.1683 0.0598 7.9220 0.0049


taking

3 1 -0.0493 0.1190 0.1713 0.6789

Folic Acid never taking 1 0 0 . . .



Hydroxychloroquine 3 1 1 0.1431 0.0736 3.7794 0.0519

Hydroxychloroquine 3 2 1 0.2299 0.0575 15.9873 <.0001


Hydroxychloroquine 4 1 1 -0.0695 0.4165 0.0278 0.8676




taking

1 1 0.0100 0.0960 0.0109 0.9168


taking

2 1 0.0789 0.0753 1.0991 0.2945


taking

3 1 0.0332 0.1546 0.0463 0.8297

Hydroxychloroquine never taking 1 0 0 . . .



of 577


Parameter

TB


Standard

Error

Wald

Chi-

Square Pr > ChiSq

Sulphasalazine 3 1 1 0.0933 0.0714 1.7093 0.1911

Sulphasalazine 3 2 1 0.2229 0.0554 16.1883 <.0001

Sulphasalazine 3 3 1 0.1577 0.1136 1.9284 0.1649

Sulphasalazine 4 1 1 0.1273 0.3002 0.1799 0.6714

Sulphasalazine 4 2 1 -0.2181 0.2582 0.7132 0.3984

Sulphasalazine 4 3 1 0.6855 0.3912 3.0697 0.0798


taking

1 1 0.1470 0.1112 1.7468 0.1863


taking

2 1 0.00403 0.0926 0.0019 0.9653


taking

3 1 -0.0445 0.1896 0.0551 0.8144

Sulphasalazine never taking 1 0 0 . . .



Arava (Leflunomide) 3 1 1 0.1098 0.0935 1.3804 0.2400



Arava (Leflunomide) 4 1 1 -0.2856 0.5428 0.2768 0.5988




taking

1 1 0.2705 0.1060 6.5075 0.0107


taking

2 1 0.1492 0.0858 3.0250 0.0820


taking

3 1 0.00639 0.1726 0.0014 0.9705

Arava (Leflunomide) never taking 1 0 0 . . .



Cyclosporin 3 1 1 0.0263 0.0937 0.0789 0.7788

of 577


Parameter

TB


Standard

Error

Wald

Chi-

Square Pr > ChiSq

Cyclosporin 3 2 1 0.2042 0.0692 8.7084 0.0032

Cyclosporin 3 3 1 0.4662 0.1325 12.3789 0.0004

Cyclosporin 4 1 1 -0.2418 0.3214 0.5662 0.4518

Cyclosporin 4 2 1 0.0673 0.2205 0.0931 0.7603

Cyclosporin 4 3 1 -1.0526 0.7303 2.0770 0.1495


taking

1 1 0.5290 0.3373 2.4603 0.1168


taking

2 1 1.0439 0.2236 21.7983 <.0001


taking

3 1 1.0216 0.4398 5.3965 0.0202

Cyclosporin never taking 1 0 0 . . .



Prednisolone 3 1 1 0.3310 0.1083 9.3359 0.0022

Prednisolone 3 2 1 0.2552 0.0838 9.2693 0.0023

Prednisolone 3 3 1 0.4980 0.1834 7.3738 0.0066

Prednisolone 4 1 1 0.7838 0.5610 1.9520 0.1624

Prednisolone 4 2 1 0.5466 0.4327 1.5961 0.2065

Prednisolone 4 3 1 0.7162 1.0389 0.4753 0.4906


taking

1 1 0.1671 0.1087 2.3642 0.1241


taking

2 1 0.1308 0.0838 2.4345 0.1187


taking

3 1 0.3911 0.1833 4.5509 0.0329

Prednisolone never taking 1 0 0 . . .



of 577

Table D.35- Odds ratio estimates in ARTIFICIAL JOINT


Effect

TB

Infection

Point

Estimate

95% Wald

Confidence Limits































of 577


Effect

TB

Infection

Point

Estimate

95% Wald

Confidence Limits

































of 577


Effect

TB

Infection

Point

Estimate

95% Wald

Confidence Limits


Hydroxychloroquine currently taking vs never

taking

1 1.010 0.837 1.219


taking

2 1.082 0.934 1.254


taking

3 1.034 0.764 1.400
















Arava (Leflunomide) currently taking vs never

taking

1 1.311 1.065 1.613


taking

2 1.161 0.981 1.374


taking

3 1.006 0.717 1.412





of 577


Effect

TB

Infection

Point

Estimate

95% Wald

Confidence Limits















of 577

APPENDIX E: OUTPUT OF SAS FOR

BONE MUSCLE JOINT INFECTION

Table E.1- Complete statistics for bone muscle joint infection.

Model Information


Response Variable InfBone, Joint and muscle InfBone, Joint and muscle




Table E.2- Observation status for BONE MUSCLE JOINT infection



of 577

Table E.3- response value for BONE MUSCLE JOINT infection

Response Profile

Ordered

Value Bone/Joint/Muscle infection

Total

Frequency

1 1 82

2 2 213

3 3 243

4 4 20968

Table E.4- Backward Elimination Procedure for bone muscle joint infection




4 0 1 0 0




4 0 1 0 0



Anakinra 3 1 0 0 0

4 0 1 0 0



of 577




4 0 1 0 0



Rituximab 3 1 0 0 0

4 0 1 0 0



Abatacept 3 1 0 0 0

4 0 1 0 0




4 0 1 0 0



Golimumab 3 1 0 0


b never taking 0 0 1

Methotrexate 1 1 0 0 0

2 0 1 0 0

3 0 0 1 0

4 0 0 0 1

of 577




4 0 1 0 0



Methotrexate (plus Folic acid) currently taking 1 0

b never taking 0 1


4 0 1 0 0




4 0 1 0 0




4 0 1 0 0




4 0 1 0 0



of 577




4 0 1 0 0




4 0 1 0 0



IM Gold 3 1 0 0 0

4 0 1 0 0




4 0 1 0 0





Golimumab Methotrexate Certolizumab Methotrexate(plus Folic acid) Hydroxychloroquine

Sulphasalazine Arava (Leflunomide) Azathioprine Cyclosporin Prednisolone IM Gold

Penicillamine

of 577

Table E.5- Model Convergence status for BONE MUSCLE JOINT infection



Table E.6- Model Fit statistics for BONE MUSCLE JOINT infection



AIC 6126.457 6158.903

SC 6150.385 7474.957

-2 Log L 6120.457 5828.903

Table E.7- Testing null hypothesis for BONE MUSCLE JOINT infection




Score 316.8699 162 <.0001

Wald 282.4093 162 <.0001

of 577

Table E.8- Model Fit statistics for removing covariant step 1







AIC 6126.457 6146.339

SC 6150.385 7390.609

-2 Log L 6120.457 5834.339

Table E.10- Testing Null hypothesis after removing covariant step 1




Score 313.7027 153 <.0001

Wald 282.2026 153 <.0001

of 577

Table E.11- Residual removing covariant step 1



3.7370 9 0.9279





Table E.13- Model Fit statistics after removing covariant step 2



AIC 6126.457 6132.503

SC 6150.385 7304.988

-2 Log L 6120.457 5838.503





of 577



Score 307.4486 144 <.0001

Wald 277.8842 144 <.0001




7.6575 18 0.9833


Step 3. Effect Anakinra is removed






AIC 6126.457 6119.824

SC 6150.385 7220.524

-2 Log L 6120.457 5843.824





Score 299.1248 135 <.0001

Wald 271.6162 135 <.0001

of 577




12.9434 27 0.9896


Step 4. Effect IM Golimumab is removed:



of 577




AIC 6126.457 6111.659

SC 6150.385 7164.502

-2 Log L 6120.457 5847.659





Score 295.4929 129 <.0001

Wald 268.3543 129 <.0001




16.2276 33 0.9937

of 577








AIC 6126.457 6103.307

SC 6150.385 7084.366

-2 Log L 6120.457 5857.307





Score 289.4733 120 <.0001

Wald 265.8322 120 <.0001

of 577




24.2774 42 0.9870


Step 6. Effect Cyclosporin is removed:






AIC 6126.457 6090.488

SC 6150.385 6999.762

-2 Log L 6120.457 5862.488

of 577





Score 281.3060 111 <.0001

Wald 258.4273 111 <.0001




31.0446 51 0.9877



Step 7. Effect Methotrexate is removed:



Warning: The maximum likelihood estimate may not exist.

Warning: The LOGISTIC procedure continues in spite of the above warning. Results shown

are based on the last maximum likelihood iteration. Validity of the model fit is

questionable.

The LOGISTIC Procedure WARNING: The validity of the model fit is questionable.

Saturday, 14 December 2019 07:51:16 PM 365Table E.33- Model Fit statistics after removing covariant



AIC 6126.457 6081.126

SC 6150.385 6918.615

-2 Log L 6120.457 5871.126

Table E.34- Testing Null hypothesis after removing covariant




Score 269.9568 102 <.0001

Wald 249.6230 102 <.0001

Table E.35- Residual removing covariant



40.0344 60 0.9780

Table E.36- Model Fit statistics for removing covariant

The LOGISTIC Procedure WARNING: The validity of the model fit is questionable.

Saturday, 14 December 2019 07:51:16 PM 366Step 8. Effect Rituximab is removed:





are based on the last maximum likelihood iteration. Validity of the model fit is

questionable.

Table E.37- Model Fit statistics after removing covariant

367



AIC 6126.457 6074.207

SC 6150.385 6839.912

-2 Log L 6120.457 5882.207





Score 258.7575 93 <.0001

Wald 237.4385 93 <.0001




53.0612 69 0.9222





368




AIC 6126.457 6069.572

SC 6150.385 6763.492

-2 Log L 6120.457 5895.572





Score 244.5347 84 <.0001

Wald 226.2461 84 <.0001




65.8098 78 0.8359


Step 10. Effect Sulphasalazine is removed:

369






AIC 6126.457 6067.034

SC 6150.385 6689.169

-2 Log L 6120.457 5911.034





Score 229.3403 75 <.0001

Wald 210.2999 75 <.0001




81.1596 87 0.6562


370

Step 11. Effect Etanercept is removed:






AIC 6126.457 6065.927

SC 6150.385 6616.277

-2 Log L 6120.457 5927.927





Score 213.8622 66 <.0001

Wald 195.0069 66 <.0001




96.0334 96 0.4798


371







AIC 6126.457 6065.506

SC 6150.385 6544.071

-2 Log L 6120.457 5945.506





Score 191.8185 57 <.0001

Wald 176.7190 57 <.0001




110.9740 105 0.3262

372


Step 13. Effect IM Gold is removed:






AIC 6126.457 6063.434

SC 6150.385 6470.215

-2 Log L 6120.457 5961.434





Score 174.2918 48 <.0001

Wald 161.1437 48 <.0001




129.3306 114 0.1546

373


Table E.60- Summary of backward elimination in bone muscle joint

374


Step

Effect

Removed DF

Number

In

Wald


Variable

Label



3 Anakinra 9 16 4.3211 0.8890




7 Methotrexate 9 12 8.0079 0.5334 Methotrexate



10 Sulphasalazine 9 9 14.0763 0.1196 Sulphasalazine

11 Etanercept 9 8 15.3543 0.0817

12 Adalimumab 9 7 15.0322 0.0901

13 IM Gold 9 6 16.1101 0.0646 IM Gold

Table E.61- Type 3 analysis of effects in BONE MUSCLE JOINT


Effect DF

Wald


Infliximab 9 24.8305 0.0032

Methotrexate (plus Folic acid) 3 8.3854 0.0387


375


Effect DF

Wald


Arava (Leflunomide) 9 35.2574 <.0001


Penicillamine 9 28.5823 0.0008

Table E.62- Analysis of maximum likelihood estimates in BONE MUSCLE JOINT


Parameter

Bone/Joint/Muscl

e infection

D

F

Estimat

e

Standar

d

Error

Wald

Chi-

Square

Pr > ChiS

q

Intercept Mild 1 -5.1566 0.3087 278.964

7

<.0001

Intercept Mod 1 -4.9462 0.2433 413.256

7

<.0001

Intercept Severe 1 -5.3447 0.2712 388.337

9

<.0001

Arava

(Leflunomide)

3 Mild 1 -0.1188 0.2888 0.1693 0.6808

Arava

(Leflunomide)

3 Mod 1 -0.1982 0.1832 1.1705 0.2793

Arava

(Leflunomide)

3 Severe 1 -0.0531 0.1837 0.0835 0.7726

Arava

(Leflunomide)

4 Mild 1 2.0608 0.8039 6.5709 0.0104

Arava

(Leflunomide)

4 Mod 1 0.1281 0.7933 0.0261 0.8718

376


Parameter

Bone/Joint/Muscl

e infection

D

F

Estimat

e

Standar

d

Error

Wald

Chi-

Square

Pr > ChiS

q

Arava

(Leflunomide)

4 Severe 1 0.8231 0.6687 1.5153 0.2183

Arava

(Leflunomide)

currentl

y taking

Mild 1 0.1898 0.3336 0.3236 0.5694

Arava

(Leflunomide)

currentl

y taking

Mod 1 0.1851 0.2077 0.7935 0.3730

Arava

(Leflunomide)

currentl

y taking

Severe 1 0.6292 0.1968 10.2160 0.0014

Arava

(Leflunomide)

b never

taking

Mild 0 0 . . .

Arava

(Leflunomide)

b never

taking

Mod 0 0 . . .

Arava

(Leflunomide)

b never

taking

Severe 0 0 . . .

Hydroxychloroqui

ne

3 Mild 1 -0.2910 0.2544 1.3078 0.2528

Hydroxychloroqui

ne

3 Mod 1 0.00465 0.1644 0.0008 0.9774

Hydroxychloroqui

ne

3 Severe 1 -0.5410 0.1560 12.0268 0.0005

Hydroxychloroqui

ne

4 Mild 1 -

11.6261

412.2 0.0008 0.9775

Hydroxychloroqui

ne

4 Mod 1 0.8595 0.5838 2.1676 0.1409

Hydroxychloroqui

ne

4 Severe 1 0.6666 0.5145 1.6791 0.1950

377


Parameter

Bone/Joint/Muscl

e infection

D

F

Estimat

e

Standar

d

Error

Wald

Chi-

Square

Pr > ChiS

q

Hydroxychloroqui

ne

currentl

y taking

Mild 1 -0.2784 0.3205 0.7543 0.3851

Hydroxychloroqui

ne

currentl

y taking

Mod 1 0.2671 0.1893 1.9918 0.1582

Hydroxychloroqui

ne

currentl

y taking

Severe 1 0.1313 0.1708 0.5916 0.4418

Hydroxychloroqui

ne

b never

taking

Mild 0 0 . . .

Hydroxychloroqui

ne

b never

taking

Mod 0 0 . . .

Hydroxychloroqui

ne

b never

taking

Severe 0 0 . . .

Infliximab 3 Mild 1 0.2744 0.4349 0.3981 0.5281

Infliximab 3 Mod 1 0.0665 0.2839 0.0548 0.8149

Infliximab 3 Severe 1 0.8912 0.1977 20.3197 <.0001

Infliximab 4 Mild 1 -

11.3660

435.3 0.0007 0.9792

Infliximab 4 Mod 1 0.7831 0.5782 1.8342 0.1756


Infliximab currentl

y taking

Mild 1 -0.2314 0.7289 0.1007 0.7510

Infliximab currentl

y taking

Mod 1 -0.0882 0.4578 0.0372 0.8471

Infliximab currentl

y taking

Severe 1 0.5992 0.3308 3.2819 0.0700

378


Parameter

Bone/Joint/Muscl

e infection

D

F

Estimat

e

Standar

d

Error

Wald

Chi-

Square

Pr > ChiS

q

Infliximab b never

taking

Mild 0 0 . . .

Infliximab b never

taking

Mod 0 0 . . .

Infliximab b never

taking

Severe 0 0 . . .

Methotrexate (plus

Folic acid)

currentl

y taking

Mild 1 -0.4576 0.2957 2.3950 0.1217

Methotrexate (plus

Folic acid)

currentl

y taking

Mod 1 -0.4093 0.1788 5.2389 0.0221

Methotrexate (plus

Folic acid)

currentl

y taking

Severe 1 0.1239 0.1458 0.7217 0.3956

Methotrexate (plus

Folic acid)

b never

taking

Mild 0 0 . . .

Methotrexate (plus

Folic acid)

b never

taking

Mod 0 0 . . .

Methotrexate (plus

Folic acid)

b never

taking

Severe 0 0 . . .

Penicillamine 3 Mild 1 0.0335 0.3849 0.0076 0.9306

Penicillamine 3 Mod 1 0.3480 0.2056 2.8650 0.0905

Penicillamine 3 Severe 1 0.7969 0.1692 22.1732 <.0001

Penicillamine 4 Mild 1 -0.2000 1.0526 0.0361 0.8493

Penicillamine 4 Mod 1 -0.2949 0.6090 0.2346 0.6281

Penicillamine 4 Severe 1 -0.3479 0.5814 0.3581 0.5496

379


Parameter

Bone/Joint/Muscl

e infection

D

F

Estimat

e

Standar

d

Error

Wald

Chi-

Square

Pr > ChiS

q

Penicillamine currentl

y taking

Mild 1 1.6260 1.0242 2.5204 0.1124


y taking

Mod 1 -

13.0777

913.3 0.0002 0.9886


y taking

Severe 1 -

12.9395

847.3 0.0002 0.9878

Penicillamine b never

taking

Mild 0 0 . . .


taking

Mod 0 0 . . .


taking

Severe 0 0 . . .




Prednisolone 4 Mild 1 -

11.7508

537.3 0.0005 0.9826



Prednisolone currentl

y taking

Mild 1 0.0363 0.3124 0.0135 0.9075


y taking

Mod 1 0.6298 0.2394 6.9233 0.0085


y taking

Severe 1 0.9273 0.2630 12.4305 0.0004

380


Parameter

Bone/Joint/Muscl

e infection

D

F

Estimat

e

Standar

d

Error

Wald

Chi-

Square

Pr > ChiS

q


taking

Mild 0 0 . . .


taking

Mod 0 0 . . .


taking

Severe 0 0 . . .

Table E.63- Odds ratio estimates in BONE MUSCLE JOINT


Effect

Bone/Joint/Muscle

infection

Point

Estimate

95% Wald

Confidence Limits

Infliximab 3 Versus

never taking

1 1.316 0.561 3.086

Infliximab 3 Versus

never taking

2 1.069 0.613 1.864

Infliximab 3 Versus

never taking

3 2.438 1.655 3.592

Infliximab 4 Versus

never taking

1 <0.001 <0.001 >999.999

Infliximab 4 Versus

never taking

2 2.188 0.705 6.796

Infliximab 4 Versus

never taking

3 1.519 0.428 5.387

Infliximab currently taking

Versus never taking

1 0.793 0.190 3.311

381


Effect

Bone/Joint/Muscle

infection

Point

Estimate

95% Wald

Confidence Limits


Versus never taking

2 0.916 0.373 2.246


Versus never taking

3 1.821 0.952 3.482

Methotrexate (plus Folic acid)

currently taking Versus never

taking

1 0.633 0.354 1.130



taking

2 0.664 0.468 0.943



taking

3 1.132 0.851 1.506

Hydroxychloroquine 3

Versus never taking

1 0.748 0.454 1.231


Versus never taking

2 1.005 0.728 1.387


Versus never taking

3 0.582 0.429 0.790


Versus never taking

1 <0.001 <0.001 >999.999


Versus never taking

2 2.362 0.752 7.417


Versus never taking

3 1.948 0.711 5.339


taking Versus never taking

1 0.757 0.404 1.419

382


Effect

Bone/Joint/Muscle

infection

Point

Estimate

95% Wald

Confidence Limits



2 1.306 0.901 1.893



3 1.140 0.816 1.594

Arava (Leflunomide) 3

Versus never taking

1 0.888 0.504 1.564


Versus never taking

2 0.820 0.573 1.175


Versus never taking

3 0.948 0.662 1.359


Versus never taking

1 7.852 1.624 37.956


Versus never taking

2 1.137 0.240 5.381


Versus never taking

3 2.278 0.614 8.446

Arava (Leflunomide)


taking

1 1.209 0.629 2.325

Arava (Leflunomide)


taking

2 1.203 0.801 1.808

Arava (Leflunomide)


taking

3 1.876 1.276 2.759

Prednisolone 3

Versus never taking

1 0.632 0.321 1.244

383


Effect

Bone/Joint/Muscle

infection

Point

Estimate

95% Wald

Confidence Limits

Prednisolone 3

Versus never taking

2 1.229 0.749 2.019

Prednisolone 3

Versus never taking

3 1.642 0.959 2.813

Prednisolone 4

Versus never taking

1 <0.001 <0.001 >999.999

Prednisolone 4

Versus never taking

2 2.103 0.266 16.646

Prednisolone 4

Versus never taking

3 1.961 0.238 16.136

Prednisolone currently taking

Versus never taking

1 1.037 0.562 1.913


Versus never taking

2 1.877 1.174 3.001


Versus never taking

3 2.528 1.510 4.232

Penicillamine 3

Versus never taking

1 1.034 0.486 2.199

Penicillamine 3

Versus never taking

2 1.416 0.947 2.119

Penicillamine 3

Versus never taking

3 2.219 1.592 3.092

Penicillamine 4

Versus never taking

1 0.819 0.104 6.444

Penicillamine 4

Versus never taking

2 0.745 0.226 2.456

384


Effect

Bone/Joint/Muscle

infection

Point

Estimate

95% Wald

Confidence Limits

Penicillamine 4

Versus never taking

3 0.706 0.226 2.207

Penicillamine currently taking

Versus never taking

1 5.084 0.683 37.844


Versus never taking

2 <0.001 <0.001 >999.999


Versus never taking

3 <0.001 <0.001 >999.999

385

APPENDIX F: OUTPUT OF SAS FOR

BLOOD INFECTION

Table F.1- Complete statistics for blood infection.

Model Information


Response Variable InfBlood InfBlood




Table F.2- Observation status for BLOOD infection



Table F.3- response value for BLOOD infection

Response Profile

Ordered

Value InfBlood

Total

Frequency

1 1 21

2 2 70

3 3 111

4 4 21304

0 .

Logits modelled use InfBlood='4' as the reference category.

Note: 6205 observations were deleted due to missing values for

the response or explanatory variables.

386

Note: 1 response level was deleted due to missing or invalid values for its explanatory,

frequency, or weight variables.

Table F.4- Backward Elimination Procedure for BLOOD infection





4 0 1 0 0




4 0 1 0 0



Anakinra 3 1 0 0 0

4 0 1 0 0




4 0 1 0 0



Rituximab 3 1 0 0 0

4 0 1 0 0



Abatacept 3 1 0 0 0

4 0 1 0 0



387


4 0 1 0 0



Golimumab 3 1 0 0


never taking 0 0 1


4 0 1 0 0




never taking 0 1


4 0 1 0 0




4 0 1 0 0




4 0 1 0 0




4 0 1 0 0




4 0 1 0 0



388


4 0 1 0 0




4 0 1 0 0




4 0 1 0 0







Table F.5- Model Convergence status for BLOOD infection





are based on the last maximum likelihood iteration. Validity of the model fit is questionable

Table F.6- Model Fit statistics for BLOOD infection



AIC 2670.261 2718.376

389



SC 2694.189 3962.646

-2 Log L 2664.261 2406.376

Table F.7- Testing null hypothesis for BLOOD infection




Score 284.8579 153 <.0001

Wald 233.2076 153 <.0001

390

Table F.8- Model Fit statistics for removing covariant step 1










AIC 2670.261 2706.095

SC 2694.189 3878.580

-2 Log L 2664.261 2412.095

Table F.10- Testing Null hypothesis after removing covariant step 1




Score 282.2859 144 <.0001

Wald 231.6736 144 <.0001

391

Table F.11- Residual removing covariant step 1



3.1885 9 0.9563








Table F.13- Model Fit statistics after removing covariant step 2



AIC 2670.261 2701.408

SC 2694.189 3802.108

-2 Log L 2664.261 2425.408

392





Score 268.2359 135 <.0001

Wald 222.0689 135 <.0001




11.6827 18 0.8632








393




AIC 2670.261 2688.609

SC 2694.189 3717.524

-2 Log L 2664.261 2430.609





Score 261.5980 126 <.0001

Wald 217.2921 126 <.0001




16.0867 27 0.9513

394











AIC 2670.261 2678.948

SC 2694.189 3636.079

-2 Log L 2664.261 2438.948





Score 253.3066 117 <.0001

Wald 210.1667 117 <.0001

395




23.2706 36 0.9500











AIC 2670.261 2666.028

SC 2694.189 3551.373

-2 Log L 2664.261 2444.028





Score 246.5724 108 <.0001

Wald 203.9904 108 <.0001

396




28.7370 45 0.9717











AIC 2670.261 2658.846

SC 2694.189 3472.407

-2 Log L 2664.261 2454.846





Score 235.7326 99 <.0001

Wald 196.5912 99 <.0001

397




37.4641 54 0.9577











AIC 2670.261 2655.632

SC 2694.189 3421.337

-2 Log L 2664.261 2463.632





Score 228.1270 93 <.0001

Wald 193.4477 93 <.0001

398




44.5830 60 0.9316











AIC 2670.261 2653.141

SC 2694.189 3347.061

-2 Log L 2664.261 2479.141





Score 211.1646 84 <.0001

Wald 178.6733 84 <.0001

399




59.2165 69 0.7934








400




AIC 2670.261 2642.193

SC 2694.189 3264.328

-2 Log L 2664.261 2486.193





Score 200.9988 75 <.0001

Wald 169.6897 75 <.0001




67.3223 78 0.8005








401




AIC 2670.261 2635.960

SC 2694.189 3186.310

-2 Log L 2664.261 2497.960





Score 184.7483 66 <.0001

Wald 158.3863 66 <.0001




81.5794 87 0.6439









402



AIC 2670.261 2637.362

SC 2694.189 3115.928

-2 Log L 2664.261 2517.362





Score 164.0564 57 <.0001

Wald 139.9299 57 <.0001




103.8113 96 0.2753








403




AIC 2670.261 2632.264

SC 2694.189 3039.045

-2 Log L 2664.261 2530.264





Score 144.2262 48 <.0001

Wald 125.1312 48 <.0001




127.3818 105 0.0679

404


Step 13. Effect Folic Acid is removed:









AIC 2670.261 2630.847

SC 2694.189 3013.699

-2 Log L 2664.261 2534.847





Score 138.7698 45 <.0001

Wald 120.0137 45 <.0001




133.2998 108 0.0497

405











AIC 2670.261 2627.554

SC 2694.189 2938.621

-2 Log L 2664.261 2549.554





Score 122.2303 36 <.0001

Wald 104.6456 36 <.0001




151.0167 117 0.0187

406








AIC 2670.261 2629.777

SC 2694.189 2869.059

-2 Log L 2664.261 2569.777





Score 101.5949 27 <.0001

Wald 85.0867 27 <.0001




175.1574 126 0.0025





407




AIC 2670.261 2628.182

SC 2694.189 2795.680

-2 Log L 2664.261 2586.182





Score 84.6206 18 <.0001

Wald 69.6515 18 <.0001




191.7886 135 0.0010


Table F.71- Summary of backward elimination in BLOOD


Step

Effect

Removed DF

Number

In

Wald


Variable

Label

1 Anakinra 9 17 0.2808 1.0000


3 Infliximab 9 15 3.5451 0.9387


408


Step

Effect

Removed DF

Number

In

Wald


Variable

Label









13 Folic Acid 3 5 4.8032 0.1868 Folic Acid


15 Adalimumab 9 3 16.5298 0.0566

16 Etanercept 9 2 14.0456 0.1207

Table F.71- Type 3 analysis of effects in BLOOD


Effect DF

Wald




Table F.72- Analysis of maximum likelihood estimates in BLOOD


Parameter InfBlood DF Estimate

Standard

Error

Wald


Intercept Mild 1 -5.9671 0.4591 168.9262 <.0001

Intercept Mod 1 -5.4061 0.3070 309.9955 <.0001




409


Parameter InfBlood DF Estimate

Standard

Error

Wald


Hydroxychloroquine 3 Severe 1 -0.1629 0.2022 0.6495 0.4203




Hydroxychloroquine currently taking Mild 1 -1.9353 1.0331 3.5095 0.0610

Hydroxychloroquine currently taking Mod 1 -0.2859 0.3367 0.7211 0.3958

Hydroxychloroquine currently taking Severe 1 -0.8566 0.3478 6.0675 0.0138










Prednisolone currently taking Mild 1 -0.3973 0.5607 0.5020 0.4786


Prednisolone currently taking Severe 1 1.6700 0.4622 13.0559 0.0003




Table F.73- Odds ratio estimates in BLOOD


Effect InfBlood Point Estimate

95% Wald

Confidence Limits

Hydroxychloroquine 3 vs never taking Mild 0.305 0.110 0.843

Hydroxychloroquine 3 vs never taking Mod 0.622 0.365 1.061

Hydroxychloroquine 3 vs never taking Severe 0.850 0.572 1.263

Hydroxychloroquine 4 vs never taking Mild <0.001 <0.001 >999.999

410


Effect InfBlood Point Estimate

95% Wald

Confidence Limits



Hydroxychloroquine currently taking vs never taking Mild 0.144 0.019 1.094

Hydroxychloroquine currently taking vs never taking Mod 0.751 0.388 1.453

Hydroxychloroquine currently taking vs never taking Severe 0.425 0.215 0.839




Prednisolone 4 vs never taking Mild <0.001 <0.001 >999.999


Prednisolone 4 vs never taking Severe <0.001 <0.001 >999.999




411

APPENDIX G: OUTPUT OF SAS FOR

GIT INFECTION

Table G.1- complete statistics for GIT infection

Model Information


Response Variable InfGit InfGit




Table G.2- Observation status for GIT infection



Table G.3- response value for GIT infection

Response Profile

Ordered

Value InfGit

Total

Frequency

1 1 118

2 2 241

3 3 155

4 4 20992

412

Logits modelled use InfGit='4' as the reference category.


variables.

Table G.4- Backward Elimination Procedure for GIT infection





4 0 1 0 0




4 0 1 0 0



Anakinra 3 1 0 0 0

4 0 1 0 0




4 0 1 0 0



Rituximab 3 1 0 0 0

4 0 1 0 0



Abatacept 3 1 0 0 0

4 0 1 0 0

413




4 0 1 0 0



Golimumab 3 1 0 0


never taking 0 0 1


4 0 1 0 0




never taking 0 1


4 0 1 0 0




4 0 1 0 0




4 0 1 0 0




4 0 1 0 0




4 0 1 0 0

414




4 0 1 0 0




4 0 1 0 0




4 0 1 0 0







Table G.5- Model Convergence status for GIT infection






415

Table G.6- Model Fit statistics for GIT infection



AIC 5944.018 6016.696

SC 5967.947 7260.965

-2 Log L 5938.018 5704.696

Table G.7- Testing null hypothesis for GIT infection




Score 267.8657 153 <.0001

Wald 231.4222 153 <.0001

Table G.8- Model Fit statistics for removing covariant step 1







Table G.10- Testing Null hypothesis after removing covariant step 1




Score 262.5650 144 <.0001

Wald 229.5599 144 <.0001

416

Table G.11- Residual removing covariant step 1





Table G.13- Model Fit statistics after removing covariant step 2



AIC 5944.018 5995.805

SC 5967.947 7096.505

-2 Log L 5938.018 5719.805





Score 256.4742 135 <.0001

Wald 225.2282 135 <.0001




10.5472 18 0.9126



4.6014 9 0.8676

417








AIC 5944.018 5987.499

SC 5967.947 7016.414

-2 Log L 5938.018 5729.499





Score 249.5871 126 <.0001

Wald 218.5522 126 <.0001




18.1288 27 0.8995





418




AIC 5944.018 5981.489

SC 5967.947 6962.548

-2 Log L 5938.018 5735.489





Score 244.1877 120 <.0001

Wald 215.0861 120 <.0001




22.1197 33 0.9249

419








AIC 5944.018 5968.320

SC 5967.947 6877.594

-2 Log L 5938.018 5740.320





Score 238.1638 111 <.0001

Wald 209.0709 111 <.0001




26.6451 42 0.9688





420




AIC 5944.018 5956.458

SC 5967.947 6793.947

-2 Log L 5938.018 5746.458





Score 232.3845 102 <.0001

Wald 204.2680 102 <.0001




32.5729 51 0.9792








AIC 5944.018 5951.321

SC 5967.947 6717.025

-2 Log L 5938.018 5759.321

421





Score 222.8700 93 <.0001

Wald 195.3658 93 <.0001




42.0866 60 0.9618








AIC 5944.018 5941.440

SC 5967.947 6635.359

-2 Log L 5938.018 5767.440





422



Score 212.2333 84 <.0001

Wald 186.4767 84 <.0001




51.4198 69 0.9439








AIC 5944.018 5938.817

SC 5967.947 6560.952

-2 Log L 5938.018 5782.817





Score 199.4953 75 <.0001

Wald 176.5609 75 <.0001

423




63.9498 78 0.8742

424








AIC 5944.018 5932.444

SC 5967.947 6482.794

-2 Log L 5938.018 5794.444





Score 181.8225 66 <.0001

Wald 161.2515 66 <.0001




74.9017 87 0.8192





425




AIC 5944.018 5929.937

SC 5967.947 6456.359

-2 Log L 5938.018 5797.937





Score 178.1497 63 <.0001

Wald 157.6714 63 <.0001




77.7585 90 0.8178

426








AIC 5944.018 5923.849

SC 5967.947 6378.486

-2 Log L 5938.018 5809.849





Score 163.3382 54 <.0001

Wald 144.8243 54 <.0001




93.3813 99 0.6404


427







AIC 5944.018 5917.151

SC 5967.947 6300.003

-2 Log L 5938.018 5821.151





Score 151.1024 45 <.0001

Wald 132.5918 45 <.0001




105.9946 108 0.5366





428




AIC 5944.018 5913.025

SC 5967.947 6224.093

-2 Log L 5938.018 5835.025





Score 137.5680 36 <.0001

Wald 118.9900 36 <.0001




118.3833 117 0.4468

Table G.64- Summary of backward elimination in GIT



Step

Effect

Removed DF

Number

In

Wald


Variable

Label





429


Step

Effect

Removed DF

Number

In

Wald


Variable

Label


6 Etanercept 9 12 4.7392 0.8564


8 Anakinra 9 10 8.1882 0.5153







Table G.65- Type 3 analysis of effects in GIT


Effect DF

Wald


Adalimumab 9 17.6510 0.0394

Infliximab 9 20.5203 0.0150

Cyclosporin 9 45.7794 <.0001


Table G.66- Analysis of maximum likelihood estimates in GIT


Parameter InfGit DF Estimate

Standard

Error

Wald


Intercept Mild 1 -5.2849 0.2624 405.6943 <.0001

Intercept Mod 1 -5.0911 0.2188 541.1765 <.0001


Adalimumab 3 Mild 1 -0.2671 0.2430 1.2084 0.2716

Adalimumab 3 Mod 1 0.3088 0.1609 3.6832 0.0550

430



Standard

Error

Wald



Adalimumab 4 Mild 1 -10.7504 379.2 0.0008 0.9774

Adalimumab 4 Mod 1 -0.0400 0.8145 0.0024 0.9609


Adalimumab currently taking Mild 1 -0.6383 0.2648 5.8087 0.0159

Adalimumab currently taking Mod 1 0.1312 0.1664 0.6217 0.4304

Adalimumab currently taking Severe 1 -0.1035 0.2231 0.2151 0.6428





Cyclosporin 3 Mod 1 0.1697 0.1743 0.9487 0.3300


Cyclosporin 4 Mild 1 -0.1041 1.0088 0.0106 0.9178

Cyclosporin 4 Mod 1 -1.6749 1.0458 2.5652 0.1092


Cyclosporin currently taking Mild 1 1.8937 0.4688 16.3187 <.0001

Cyclosporin currently taking Mod 1 1.8260 0.3563 26.2594 <.0001

Cyclosporin currently taking Severe 1 0.7529 0.7200 1.0933 0.2957




Infliximab 3 Mild 1 0.1003 0.3783 0.0703 0.7909

Infliximab 3 Mod 1 0.2989 0.2397 1.5553 0.2124


Infliximab 4 Mild 1 -11.1291 340.5 0.0011 0.9739

Infliximab 4 Mod 1 1.3895 0.4631 9.0037 0.0027


Infliximab currently taking Mild 1 -0.6060 0.7169 0.7147 0.3979

Infliximab currently taking Mod 1 0.6928 0.3058 5.1333 0.0235

Infliximab currently taking Severe 1 0.2397 0.4640 0.2669 0.6054


431



Standard

Error

Wald










Prednisolone currently taking Mild 1 0.1868 0.3008 0.3856 0.5346






Table G.67- Odds ratio estimates in GIT


Effect InfGit Point Estimate

95% Wald

Confidence Limits




Adalimumab 4 vs never taking Mild <0.001 <0.001 >999.999



Adalimumab currently taking vs never taking Mild 0.528 0.314 0.888

Adalimumab currently taking vs never taking Mod 1.140 0.823 1.580

Adalimumab currently taking vs never taking Severe 0.902 0.582 1.396




432


Effect InfGit Point Estimate

95% Wald

Confidence Limits

Infliximab 4 vs never taking Mild <0.001 <0.001 >999.999






Cyclosporin 3 vs never taking Mild 1.228 0.737 2.047

Cyclosporin 3 vs never taking Mod 1.185 0.842 1.667

Cyclosporin 3 vs never taking Severe 0.981 0.637 1.511




Cyclosporin currently taking vs never taking Mild 6.644 2.651 16.651

Cyclosporin currently taking vs never taking Mod 6.209 3.088 12.484

Cyclosporin currently taking vs never taking Severe 2.123 0.518 8.707




Prednisolone 4 vs never taking Mild <0.001 <0.001 >999.999






433

APPENDIX H: OUTPUT OF SAS FOR

NERVOUS SYSTEM INFECTION

Table H.1- Complete statistics for Nervous system infection

Model Information


Response Variable InfNeuro InfNeuro




Table H.2- Observation status for Nervous system infection



434

Table H.3- response value for Nervous system infection

Response Profile

Ordered

Value InfNeuro

Total

Frequency

1 1 9

2 2 9

3 3 12

4 4 21476

Logits modelled use InfNeuro='4' as the reference category.


variables.

Table H.4- Backward Elimination Procedure for Nervous system infection





4 0 1 0 0




4 0 1 0 0



Anakinra 3 1 0 0 0

4 0 1 0 0


435



4 0 1 0 0



Rituximab 3 1 0 0 0

4 0 1 0 0



Abatacept 3 1 0 0 0

4 0 1 0 0




4 0 1 0 0



Golimumab 3 1 0 0


never taking 0 0 1


4 0 1 0 0




never taking 0 1


4 0 1 0 0




4 0 1 0 0


436



4 0 1 0 0




4 0 1 0 0




4 0 1 0 0




4 0 1 0 0




4 0 1 0 0




4 0 1 0 0







437

Table H.6- Model Fit statistics for Nervous system infection



AIC 525.786 719.397

SC 549.714 1963.667

-2 Log L 519.786 407.397

Table H.7- Testing null hypothesis for Nervous system infection



Likelihood Ratio 112.3885 153 0.9943

Score 170.5176 153 0.1578

Wald 83.3711 153 1.0000

Table H.8- Model Fit statistics for removing covariant step 1







Table H.11- Residual removing covariant step 1

438



0.9204 9 0.9996








439

Table H.13- Model Fit statistics after removing covariant step 2

Table H.14- Testing Null hypothesis after removing covariant step 2




AIC 525.786 686.901

SC 549.714 1787.601

-2 Log L 519.786 410.901




Score 158.9265 135 0.0781

Wald 81.8673 135 0.9999



1.9099 18 1.0000

440










Score 156.3178 129 0.0511

Wald 80.1217 129 0.9998



AIC 525.786 677.704

SC 549.714 1730.547

-2 Log L 519.786 413.704

441




3.7095 24 1.0000











AIC 525.786 663.254

SC 549.714 1644.313

-2 Log L 519.786 417.254

442





Score 149.3559 120 0.0358

Wald 78.8583 120 0.9986




5.9028 33 1.0000








443




AIC 525.786 649.106

SC 549.714 1558.380

-2 Log L 519.786 421.106





Score 143.4857 111 0.0206

Wald 77.1681 111 0.9939




9.2419 42 1.0000

444











AIC 525.786 634.968

SC 549.714 1472.457

-2 Log L 519.786 424.968





Score 132.3092 102 0.0234

Wald 74.4728 102 0.9815




12.5221 51 1.0000

445











AIC 525.786 622.390

SC 549.714 1388.095

-2 Log L 519.786 430.390

446





Score 127.7230 93 0.0099

Wald 75.4615 93 0.9078




15.8844 60 1.0000








447




AIC 525.786 606.681

SC 549.714 1300.601

-2 Log L 519.786 432.681





Score 124.9842 84 0.0025

Wald 75.0946 84 0.7457




18.2897 69 1.0000









448



AIC 525.786 592.141

SC 549.714 1214.276

-2 Log L 519.786 436.141





Score 120.9163 75 0.0006

Wald 72.3119 75 0.5665




21.4107 78 1.0000

449











AIC 525.786 585.500

SC 549.714 1135.851

-2 Log L 519.786 447.500





Score 103.0295 66 0.0024

Wald 68.9775 66 0.3771

450




31.5451 87 1.0000











AIC 525.786 576.716

SC 549.714 1055.281

-2 Log L 519.786 456.716

451





Score 84.0113 57 0.0115

Wald 60.0232 57 0.3667




44.0268 96 1.0000


Step 12. Effect Prednisolone is removed:









AIC 525.786 566.295

SC 549.714 973.076

-2 Log L 519.786 464.295

452





Score 78.2457 48 0.0038

Wald 58.2370 48 0.1479




50.5258 105 1.0000








453




AIC 525.786 555.825

SC 549.714 890.820

-2 Log L 519.786 471.825





Score 69.5512 39 0.0019

Wald 51.9824 39 0.0798




60.1603 114 1.0000









454



AIC 525.786 552.316

SC 549.714 863.383

-2 Log L 519.786 474.316





Score 67.3854 36 0.0012

Wald 49.9133 36 0.0615




62.5527 117 1.0000

455











AIC 525.786 551.109

SC 549.714 790.391

-2 Log L 519.786 491.109





Score 52.8187 27 0.0021

Wald 37.7840 27 0.0813

456




79.9812 126 0.9995

Table H.68(1)- Model Fit statistics for removing covariant step 16







Table H.68 (2)- Model Fit statistics after removing covariant step 16



AIC 525.786 541.646

SC 549.714 709.144

-2 Log L 519.786 499.646

457





Score 41.3631 18 0.0014

Wald 28.2599 18 0.0582




99.5067 135 0.9904











AIC 525.786 534.280

SC 549.714 629.993

-2 Log L 519.786 510.280

458





Score 16.0170 9 0.0665

Wald 10.8344 9 0.2872




123.0393 144 0.8963





459


-2 Log L = 519.786




170.5176 153 0.1578

Table H.78- Summary of backward elimination in Nervous system infection

Note: All effects have been removed from the model.


Step

Effect

Removed DF

Number

In

Wald

Chi-

Square Pr > ChiSq

Variable

Label


2 Anakinra 9 16 0.0051 1.0000








10 Adalimumab 9 8 4.0119 0.9106

11 Infliximab 9 7 4.8230 0.8495

12 Prednisolone 9 6 4.6980 0.8598 Prednisolone

13 Etanercept 9 5 6.7886 0.6591




460



Table H.79- Analysis of maximum likelihood estimates in Nervous system infection


Parameter InfNeuro DF Estimate

Standard

Error

Wald


Intercept 1 1 -7.7775 0.3334 544.1729 <.0001

Intercept 2 1 -7.7775 0.3334 544.1729 <.0001

Intercept 3 1 -7.4898 0.2888 672.7866 <.0001

461

APPENDIX I: OUTPUT OF SAS FOR

TB INFECTION

Table I.1- Complete statistics for TB infection

Model Information


Response Variable TB Infection TB Infection




Table I.2- Observation status for TB infection



Table I.3- response value for TB infection

Response Profile

Ordered

Value TB Infection

Total

Frequency

1 1 1050

2 2 1829

3 3 406

4 4 18221

Logits modelled use TB Infection='4' as the reference category.

462


variables.

Table I.4- Backward Elimination Procedure for TB infection





4 0 1 0 0




4 0 1 0 0



Anakinra 3 1 0 0 0

4 0 1 0 0




4 0 1 0 0



Rituximab 3 1 0 0 0

4 0 1 0 0



Abatacept 3 1 0 0 0

4 0 1 0 0




463

4 0 1 0 0



Golimumab 3 1 0 0


never taking 0 0 1


4 0 1 0 0




never taking 0 1


4 0 1 0 0




4 0 1 0 0




4 0 1 0 0




4 0 1 0 0




4 0 1 0 0




464

4 0 1 0 0




4 0 1 0 0




4 0 1 0 0







Table I.5- Model Convergence status for TB infection



Table I.6- Model Fit statistics for TB infection



AIC 24626.355 24501.128

SC 24650.284 25745.398

-2 Log L 24620.355 24189.128

465

Table I.7- Testing null hypothesis for TB infection




Score 463.0664 153 <.0001

Wald 419.5882 153 <.0001

Table I.8- Model Fit statistics for removing covariant step 1







AIC 24626.355 24488.566

SC 24650.284 25661.051

-2 Log L 24620.355 24194.566

Table I.10- Testing Null hypothesis after removing covariant step 1




Score 457.8861 144 <.0001

Wald 415.1007 144 <.0001

466

Table I.11- Residual removing covariant step 1



5.0524 9 0.8297

467





Table I.13- Model Fit statistics after removing covariant step 2



AIC 24626.355 24476.658

SC 24650.284 25577.358

-2 Log L 24620.355 24200.658





Score 450.9468 135 <.0001

Wald 408.7712 135 <.0001

468




10.9161 18 0.8979








AIC 24626.355 24473.673

SC 24650.284 25502.589

-2 Log L 24620.355 24215.673

469





Score 440.0301 126 <.0001

Wald 401.9656 126 <.0001




22.0077 27 0.7370





470




AIC 24626.355 24465.880

SC 24650.284 25423.011

-2 Log L 24620.355 24225.880





Score 430.4313 117 <.0001

Wald 392.2553 117 <.0001




31.2787 36 0.6926

471








AIC 24626.355 24461.305

SC 24650.284 25346.650

-2 Log L 24620.355 24239.305





Score 415.9895 108 <.0001

Wald 378.4582 108 <.0001

472




44.9975 45 0.4721








AIC 24626.355 24462.511

SC 24650.284 25300.000

-2 Log L 24620.355 24252.511





Score 403.4935 102 <.0001

Wald 366.8141 102 <.0001




57.2672 51 0.2539

473


Table I.32- Summary of backward elimination in TB infection


Step

Effect

Removed DF

Number

In

Wald


Variable

Label







474

Table I.33- Type 3 analysis of effects in TB infection


Effect DF

Wald


Etanercept 9 52.1431 <.0001

Adalimumab 9 22.4139 0.0077

Anakinra 9 18.2690 0.0322

Infliximab 9 31.0160 0.0003

Abatacept 9 18.0153 0.0350

Tocilizumab 9 18.1032 0.0340

Folic Acid 3 9.4165 0.0242




Cyclosporin 9 47.3358 <.0001


Table I.34- Analysis of maximum likelihood estimates in TB infection


Parameter

TB


Standard

Error

Wald

Chi-

Square Pr > ChiSq

Intercept 1 1 -3.4872 0.1190 859.2759 <.0001

Intercept 2 1 -2.9786 0.0928 1031.0501 <.0001

Intercept 3 1 -4.3609 0.1917 517.2695 <.0001

Etanercept 3 1 1 -0.0509 0.0911 0.3118 0.5766

Etanercept 3 2 1 -0.0713 0.0705 1.0220 0.3120

Etanercept 3 3 1 -0.3981 0.1457 7.4653 0.0063

Etanercept 4 1 1 1.3033 0.5444 5.7307 0.0167

Etanercept 4 2 1 1.9227 0.3431 31.3968 <.0001

Etanercept 4 3 1 1.3439 0.8633 2.4234 0.1195

475


Parameter

TB


Standard

Error

Wald

Chi-

Square Pr > ChiSq


taking

1 1 0.1730 0.0941 3.3831 0.0659


taking

2 1 0.0891 0.0722 1.5232 0.2171


taking

3 1 -0.3383 0.1446 5.4736 0.0193




Adalimumab 3 1 1 0.0104 0.0914 0.0129 0.9094

Adalimumab 3 2 1 0.1823 0.0686 7.0504 0.0079

Adalimumab 3 3 1 0.1418 0.1403 1.0222 0.3120

Adalimumab 4 1 1 -0.5402 0.6756 0.6394 0.4239

Adalimumab 4 2 1 -

0.00090

0.4440 0.0000 0.9984

Adalimumab 4 3 1 -

10.2462

147.6 0.0048 0.9447


taking

1 1 0.2887 0.0941 9.4206 0.0021


taking

2 1 0.1847 0.0737 6.2813 0.0122


taking

3 1 -0.0798 0.1470 0.2946 0.5873




Anakinra 3 1 1 0.1448 0.2523 0.3295 0.5659

Anakinra 3 2 1 -0.0761 0.2191 0.1205 0.7285

Anakinra 3 3 1 0.4597 0.3413 1.8146 0.1780

Anakinra 4 1 1 -0.7187 0.6297 1.3026 0.2537

Anakinra 4 2 1 0.0275 0.4047 0.0046 0.9459

476


Parameter

TB


Standard

Error

Wald

Chi-

Square Pr > ChiSq

Anakinra 4 3 1 -0.4484 1.0513 0.1819 0.6697

Anakinra currently

taking

1 1 -

11.9697

512.1 0.0005 0.9814

Anakinra currently

taking

2 1 1.7999 0.4745 14.3901 0.0001

Anakinra currently

taking

3 1 -

12.2422

809.5 0.0002 0.9879




Infliximab 3 1 1 0.0552 0.1337 0.1707 0.6795

Infliximab 3 2 1 -0.2055 0.1098 3.5047 0.0612

Infliximab 3 3 1 0.0478 0.1974 0.0585 0.8088

Infliximab 4 1 1 0.4422 0.4291 1.0621 0.3027

Infliximab 4 2 1 -0.1974 0.3745 0.2779 0.5981

Infliximab 4 3 1 -0.8062 0.8952 0.8110 0.3678


taking

1 1 0.6440 0.1747 13.5909 0.0002


taking

2 1 0.4727 0.1396 11.4614 0.0007


taking

3 1 -0.3706 0.3711 0.9971 0.3180




Abatacept 3 1 1 0.5166 0.1769 8.5260 0.0035

Abatacept 3 2 1 0.1147 0.1534 0.5587 0.4548

Abatacept 3 3 1 -0.4339 0.3573 1.4747 0.2246

Abatacept 4 1 1 0.2751 0.8455 0.1059 0.7449

Abatacept 4 2 1 -0.6022 0.6388 0.8887 0.3458

Abatacept 4 3 1 1.3251 1.0615 1.5582 0.2119

477


Parameter

TB


Standard

Error

Wald

Chi-

Square Pr > ChiSq

Abatacept currently

taking

1 1 0.3362 0.1582 4.5176 0.0335

Abatacept currently

taking

2 1 0.1491 0.1240 1.4460 0.2292

Abatacept currently

taking

3 1 -0.2016 0.2673 0.5686 0.4508




Tocilizumab 3 1 1 0.1595 0.2454 0.4224 0.5157

Tocilizumab 3 2 1 0.1835 0.1951 0.8847 0.3469

Tocilizumab 3 3 1 0.7127 0.3269 4.7534 0.0292

Tocilizumab 4 1 1 -

11.4739

529.0 0.0005 0.9827

Tocilizumab 4 2 1 -

11.5154

222.6 0.0027 0.9587

Tocilizumab 4 3 1 -

10.9097

820.5 0.0002 0.9894


taking

1 1 0.4933 0.1695 8.4682 0.0036


taking

2 1 0.3301 0.1348 5.9962 0.0143


taking

3 1 0.1795 0.2814 0.4069 0.5236





taking

1 1 -0.1059 0.0761 1.9365 0.1641


taking

2 1 -0.1683 0.0598 7.9220 0.0049

478


Parameter

TB


Standard

Error

Wald

Chi-

Square Pr > ChiSq


taking

3 1 -0.0493 0.1190 0.1713 0.6789











taking

1 1 0.0100 0.0960 0.0109 0.9168


taking

2 1 0.0789 0.0753 1.0991 0.2945


taking

3 1 0.0332 0.1546 0.0463 0.8297




Sulphasalazine 3 1 1 0.0933 0.0714 1.7093 0.1911

Sulphasalazine 3 2 1 0.2229 0.0554 16.1883 <.0001

Sulphasalazine 3 3 1 0.1577 0.1136 1.9284 0.1649

Sulphasalazine 4 1 1 0.1273 0.3002 0.1799 0.6714

Sulphasalazine 4 2 1 -0.2181 0.2582 0.7132 0.3984

Sulphasalazine 4 3 1 0.6855 0.3912 3.0697 0.0798


taking

1 1 0.1470 0.1112 1.7468 0.1863


taking

2 1 0.00403 0.0926 0.0019 0.9653

479


Parameter

TB


Standard

Error

Wald

Chi-

Square Pr > ChiSq


taking

3 1 -0.0445 0.1896 0.0551 0.8144











taking

1 1 0.2705 0.1060 6.5075 0.0107


taking

2 1 0.1492 0.0858 3.0250 0.0820


taking

3 1 0.00639 0.1726 0.0014 0.9705




Cyclosporin 3 1 1 0.0263 0.0937 0.0789 0.7788

Cyclosporin 3 2 1 0.2042 0.0692 8.7084 0.0032

Cyclosporin 3 3 1 0.4662 0.1325 12.3789 0.0004

Cyclosporin 4 1 1 -0.2418 0.3214 0.5662 0.4518

Cyclosporin 4 2 1 0.0673 0.2205 0.0931 0.7603

Cyclosporin 4 3 1 -1.0526 0.7303 2.0770 0.1495


taking

1 1 0.5290 0.3373 2.4603 0.1168


taking

2 1 1.0439 0.2236 21.7983 <.0001

480


Parameter

TB


Standard

Error

Wald

Chi-

Square Pr > ChiSq


taking

3 1 1.0216 0.4398 5.3965 0.0202




Prednisolone 3 1 1 0.3310 0.1083 9.3359 0.0022

Prednisolone 3 2 1 0.2552 0.0838 9.2693 0.0023

Prednisolone 3 3 1 0.4980 0.1834 7.3738 0.0066

Prednisolone 4 1 1 0.7838 0.5610 1.9520 0.1624

Prednisolone 4 2 1 0.5466 0.4327 1.5961 0.2065

Prednisolone 4 3 1 0.7162 1.0389 0.4753 0.4906


taking

1 1 0.1671 0.1087 2.3642 0.1241


taking

2 1 0.1308 0.0838 2.4345 0.1187


taking

3 1 0.3911 0.1833 4.5509 0.0329




Table I.35- Odds ratio estimates in TB infection


Effect

TB

Infection

Point

Estimate

95% Wald

Confidence Limits






481


Effect

TB

Infection

Point

Estimate

95% Wald

Confidence Limits

































482


Effect

TB

Infection

Point

Estimate

95% Wald

Confidence Limits




























taking

1 1.010 0.837 1.219


taking

2 1.082 0.934 1.254


taking

3 1.034 0.764 1.400

483


Effect

TB

Infection

Point

Estimate

95% Wald

Confidence Limits

















taking

1 1.311 1.065 1.613


taking

2 1.161 0.981 1.374


taking

3 1.006 0.717 1.412












484


Effect

TB

Infection

Point

Estimate

95% Wald

Confidence Limits








485

APPENDIX J: OUTPUT OF SAS FOR

URINARY TRACT INFECTION

Table J.1- Complete statistics for UTI

Model Information


Response Variable InfKidUri InfKidUri




Table J.2- Observation status for UTI



Table J.3- response value for UTI

Response Profile

Ordered

Value InfKidUri

Total

Frequency

1 1 290

2 2 833

3 3 256

4 4 20127

Logits modelled use InfKidUri='4' as the reference category.

486


variables.

Table J.4- Backward Elimination Procedure for UTI





4 0 1 0 0




4 0 1 0 0



Anakinra 3 1 0 0 0

4 0 1 0 0




4 0 1 0 0



Rituximab 3 1 0 0 0

4 0 1 0 0



Abatacept 3 1 0 0 0

4 0 1 0 0



487


4 0 1 0 0



Golimumab 3 1 0 0


never taking 0 0 1


4 0 1 0 0




never taking 0 1


4 0 1 0 0




4 0 1 0 0




4 0 1 0 0




4 0 1 0 0




4 0 1 0 0



488


4 0 1 0 0




4 0 1 0 0




4 0 1 0 0







Table J.5- Model Convergence status for UTI



489

Table J.6- Model Fit statistics for UTI



AIC 12856.104 12720.097

SC 12880.032 13964.367

-2 Log L 12850.104 12408.097

Table J.7- Testing null hypothesis for UTI




Score 494.1448 153 <.0001

Wald 442.5757 153 <.0001

490

Table J.8- Model Fit statistics status for removing covariant step 1




Table J.9- Model Fit statistics for removing covariant step 1



AIC 12856.104 12708.936

SC 12880.032 13881.421

-2 Log L 12850.104 12414.936

Table J.10- Testing Null hypothesis after removing covariant step 1




Score 486.6474 144 <.0001

Wald 438.5445 144 <.0001

491

Table J.11- Residual removing covariant step 1



5.2162 9 0.8151





Table J.13- Model Fit statistics after removing covariant step 2



AIC 12856.104 12698.658

SC 12880.032 13799.358

-2 Log L 12850.104 12422.658

492





Score 479.8413 135 <.0001

Wald 433.5090 135 <.0001




10.7595 18 0.9043








AIC 12856.104 12695.973

SC 12880.032 13724.888

-2 Log L 12850.104 12437.973





Score 467.3671 126 <.0001

Wald 425.1267 126 <.0001

493




21.4657 27 0.7640





494




AIC 12856.104 12687.586

SC 12880.032 13668.645

-2 Log L 12850.104 12441.586





Score 463.4235 120 <.0001

Wald 421.3840 120 <.0001




25.7755 33 0.8106

495








AIC 12856.104 12680.185

SC 12880.032 13589.459

-2 Log L 12850.104 12452.185





Score 449.3858 111 <.0001

Wald 408.9965 111 <.0001

496




37.0911 42 0.6860








AIC 12856.104 12673.942

SC 12880.032 13511.431

-2 Log L 12850.104 12463.942

497





Score 438.4015 102 <.0001

Wald 398.7265 102 <.0001




47.9903 51 0.5939





498




AIC 12856.104 12669.937

SC 12880.032 13435.642

-2 Log L 12850.104 12477.937





Score 422.5308 93 <.0001

Wald 384.7193 93 <.0001




62.5971 60 0.3842

499








AIC 12856.104 12667.611

SC 12880.032 13361.531

-2 Log L 12850.104 12493.611





Score 401.5030 84 <.0001

Wald 367.1355 84 <.0001

500




80.3612 69 0.1649








AIC 12856.104 12669.548

SC 12880.032 13339.540

-2 Log L 12850.104 12501.548

501





Score 394.3441 81 <.0001

Wald 359.6161 81 <.0001




87.7330 72 0.1001


Table J.44- Summary of backward elimination in UTI


Step

Effect

Removed DF

Number

In

Wald


Variable

Label


2 Anakinra 9 16 3.6765 0.9314





7 Adalimumab 9 11 13.7461 0.1316



502

Table J.45- Type 3 analysis of effects in UTI


Effect DF

Wald


Etanercept 9 27.3183 0.0012

Infliximab 9 24.4209 0.0037



Azathioprine 9 34.4145 <.0001

Cyclosporin 9 61.9727 <.0001


IM Gold injection 9 26.8635 0.0015

Penicillamine 9 46.2679 <.0001

Table J.46- Analysis of maximum likelihood estimates in UTI


Parameter InfKidUri DF Estimate

Standard

Error

Wald


Intercept Mild 1 -4.3801 0.1853 558.8895 <.0001

Intercept Mod 1 -3.6145 0.1223 872.9303 <.0001








Arava (Leflunomide) currently taking Mild 1 -0.2083 0.1928 1.1666 0.2801

Arava (Leflunomide) currently taking Mod 1 -0.1359 0.1248 1.1862 0.2761

Arava (Leflunomide) currently taking Severe 1 -0.5347 0.2388 5.0129 0.0252

Arava (Leflunomide) never taking Mild 0 0 . . .

Arava (Leflunomide) never taking Mod 0 0 . . .

503



Standard

Error

Wald


Arava (Leflunomide) never taking Severe 0 0 . . .

Azathioprine 3 Mild 1 -0.5205 0.2460 4.4756 0.0344

Azathioprine 3 Mod 1 -0.7160 0.1596 20.1196 <.0001

Azathioprine 3 Severe 1 0.4816 0.2146 5.0389 0.0248

Azathioprine 4 Mild 1 -0.6989 0.6277 1.2400 0.2655

Azathioprine 4 Mod 1 0.1760 0.3073 0.3283 0.5667

Azathioprine 4 Severe 1 0.6065 0.4734 1.6415 0.2001

Azathioprine currently taking Mild 1 -11.8768 269.9 0.0019 0.9649

Azathioprine currently taking Mod 1 -0.8026 0.5911 1.8433 0.1746

Azathioprine currently taking Severe 1 -11.9304 256.6 0.0022 0.9629

Azathioprine never taking Mild 0 0 . . .

Azathioprine never taking Mod 0 0 . . .

Azathioprine never taking Severe 0 0 . . .


Cyclosporin 3 Mod 1 0.1203 0.1042 1.3324 0.2484

Cyclosporin 3 Severe 1 -1.1810 0.2454 23.1502 <.0001


Cyclosporin 4 Mod 1 -0.5268 0.3731 1.9935 0.1580


Cyclosporin currently taking Mild 1 1.5236 0.3766 16.3680 <.0001

Cyclosporin currently taking Mod 1 1.0570 0.2906 13.2255 0.0003

Cyclosporin currently taking Severe 1 0.6898 0.5322 1.6798 0.1950




Etanercept 3 Mild 1 0.1829 0.1636 1.2501 0.2635

Etanercept 3 Mod 1 -0.2009 0.0961 4.3684 0.0366


Etanercept 4 Mild 1 -12.6298 554.8 0.0005 0.9818

Etanercept 4 Mod 1 -0.3715 0.8011 0.2151 0.6428


Etanercept currently taking Mild 1 0.3258 0.1452 5.0337 0.0249

504



Standard

Error

Wald


Etanercept currently taking Mod 1 -0.1108 0.0873 1.6099 0.2045

Etanercept currently taking Severe 1 -0.0992 0.1693 0.3431 0.5581









Hydroxychloroquine 4 Severe 1 -1.2246 1.0441 1.3755 0.2409

Hydroxychloroquine currently taking Mild 1 0.0696 0.1733 0.1611 0.6882

Hydroxychloroquine currently taking Mod 1 0.0797 0.1096 0.5288 0.4671

Hydroxychloroquine currently taking Severe 1 -0.1762 0.1963 0.8059 0.3693




IM Gold injection 3 Mild 1 0.4411 0.1444 9.3249 0.0023

IM Gold injection 3 Mod 1 0.1758 0.0890 3.9017 0.0482

IM Gold injection 3 Severe 1 0.1856 0.1582 1.3767 0.2407

IM Gold injection 4 Mild 1 0.2851 0.8555 0.1111 0.7389

IM Gold injection 4 Mod 1 0.7416 0.3618 4.2028 0.0404

IM Gold injection 4 Severe 1 -1.1430 0.9031 1.6021 0.2056

IM Gold injection currently taking Mild 1 -0.0266 0.7187 0.0014 0.9705

IM Gold injection currently taking Mod 1 0.4818 0.3327 2.0963 0.1477

IM Gold injection currently taking Severe 1 1.1145 0.4351 6.5630 0.0104

IM Gold injection never taking Mild 0 0 . . .

IM Gold injection never taking Mod 0 0 . . .

IM Gold injection never taking Severe 0 0 . . .

Infliximab 3 Mild 1 -0.4458 0.2930 2.3149 0.1281

Infliximab 3 Mod 1 -0.3481 0.1641 4.5005 0.0339


505



Standard

Error

Wald


Infliximab 4 Mild 1 -0.9060 1.0372 0.7631 0.3824

Infliximab 4 Mod 1 -0.5810 0.5126 1.2847 0.2570


Infliximab currently taking Mild 1 0.3314 0.3353 0.9770 0.3229

Infliximab currently taking Mod 1 0.0242 0.2202 0.0121 0.9123

Infliximab currently taking Severe 1 -1.4910 0.7397 4.0632 0.0438




Penicillamine 3 Mild 1 0.6192 0.1785 12.0339 0.0005

Penicillamine 3 Mod 1 0.4545 0.1140 15.8882 <.0001

Penicillamine 3 Severe 1 0.0475 0.2175 0.0477 0.8271

Penicillamine 4 Mild 1 -0.4054 0.6593 0.3781 0.5386

Penicillamine 4 Mod 1 1.0189 0.2755 13.6737 0.0002

Penicillamine 4 Severe 1 1.3537 0.4485 9.1120 0.0025

Penicillamine currently taking Mild 1 -12.0460 451.7 0.0007 0.9787

Penicillamine currently taking Mod 1 -12.1211 270.8 0.0020 0.9643

Penicillamine currently taking Severe 1 -12.0786 478.6 0.0006 0.9799

Penicillamine never taking Mild 0 0 . . .

Penicillamine never taking Mod 0 0 . . .

Penicillamine never taking Severe 0 0 . . .







Prednisolone currently taking Mild 1 -0.0322 0.1887 0.0291 0.8645





506



Standard

Error

Wald



Table J.47- Odds ratio estimates in UTI


Effect InfKidUri Point Estimate

95% Wald

Confidence Limits




Etanercept 4 vs never taking Mild <0.001 <0.001 >999.999



Etanercept currently taking vs never taking Mild 1.385 1.042 1.841

Etanercept currently taking vs never taking Mod 0.895 0.754 1.062

Etanercept currently taking vs never taking Severe 0.906 0.650 1.262
















507



95% Wald

Confidence Limits

Hydroxychloroquine currently taking vs never taking Mild 1.072 0.763 1.506

Hydroxychloroquine currently taking vs never taking Mod 1.083 0.874 1.343

Hydroxychloroquine currently taking vs never taking Severe 0.838 0.571 1.232







Arava (Leflunomide) currently taking vs never taking Mild 0.812 0.556 1.185

Arava (Leflunomide) currently taking vs never taking Mod 0.873 0.683 1.115

Arava (Leflunomide) currently taking vs never taking Severe 0.586 0.367 0.936

Azathioprine 3 vs never taking Mild 0.594 0.367 0.962

Azathioprine 3 vs never taking Mod 0.489 0.357 0.668

Azathioprine 3 vs never taking Severe 1.619 1.063 2.465

Azathioprine 4 vs never taking Mild 0.497 0.145 1.701

Azathioprine 4 vs never taking Mod 1.192 0.653 2.178

Azathioprine 4 vs never taking Severe 1.834 0.725 4.638

Azathioprine currently taking vs never taking Mild <0.001 <0.001 >999.999

Azathioprine currently taking vs never taking Mod 0.448 0.141 1.428

Azathioprine currently taking vs never taking Severe <0.001 <0.001 >999.999







Cyclosporin currently taking vs never taking Mild 4.589 2.193 9.599

Cyclosporin currently taking vs never taking Mod 2.878 1.628 5.087

Cyclosporin currently taking vs never taking Severe 1.993 0.702 5.658



508



95% Wald

Confidence Limits








IM Gold injection 3 vs never taking Mild 1.554 1.171 2.063

IM Gold injection 3 vs never taking Mod 1.192 1.001 1.419

IM Gold injection 3 vs never taking Severe 1.204 0.883 1.642

IM Gold injection 4 vs never taking Mild 1.330 0.249 7.113

IM Gold injection 4 vs never taking Mod 2.099 1.033 4.266

IM Gold injection 4 vs never taking Severe 0.319 0.054 1.872

IM Gold injection currently taking vs never taking Mild 0.974 0.238 3.983

IM Gold injection currently taking vs never taking Mod 1.619 0.843 3.108

IM Gold injection currently taking vs never taking Severe 3.048 1.299 7.151

Penicillamine 3 vs never taking Mild 1.857 1.309 2.636

Penicillamine 3 vs never taking Mod 1.575 1.260 1.970

Penicillamine 3 vs never taking Severe 1.049 0.685 1.606

Penicillamine 4 vs never taking Mild 0.667 0.183 2.427

Penicillamine 4 vs never taking Mod 2.770 1.614 4.754

Penicillamine 4 vs never taking Severe 3.872 1.608 9.325

Penicillamine currently taking vs never taking Mild <0.001 <0.001 >999.999

Penicillamine currently taking vs never taking Mod <0.001 <0.001 >999.999

Penicillamine currently taking vs never taking Severe <0.001 <0.001 >999.999

509

APPENDIX K: OUTPUT OF SAS FOR

VIRAL INFECTION

Table K.1- Complete statistics for viral infection

Model Information


Response Variable InfVir InfVir




Table K.2- Observation status for VIRAL INFECTION



Table K.3- response value for VIRAL INFECTION

Response Profile

Ordered

Value InfVir

Total

Frequency

1 1 435

2 2 837

3 3 305

4 4 19929

0 .

510

Logits modelled use InfVir='4' as the reference category.


variables.

Note1 response level was deleted due to missing or invalid values for its explanatory,

frequency, or weight variables

Table K.4- Backward Elimination Procedure for VIRAL INFECTION





4 0 1 0 0




4 0 1 0 0



Anakinra 3 1 0 0 0

4 0 1 0 0




4 0 1 0 0



Rituximab 3 1 0 0 0

4 0 1 0 0

511



Abatacept 3 1 0 0 0

4 0 1 0 0




4 0 1 0 0



Golimumab 3 1 0 0


never taking 0 0 1


4 0 1 0 0




never taking 0 1


4 0 1 0 0




4 0 1 0 0




4 0 1 0 0




4 0 1 0 0

512




4 0 1 0 0




4 0 1 0 0




4 0 1 0 0




4 0 1 0 0







513

Table K.5- Model Convergence status for VIRAL INFECTION



Table K.6- Model Fit statistics for VIRAL INFECTION



AIC 14465.339 14470.560

SC 14489.267 15714.830

-2 Log L 14459.339 14158.560

Table K.7- Testing null hypothesis for VIRAL INFECTION




Score 331.3978 153 <.0001

Wald 311.3192 153 <.0001

514

Table K.8- Model Fit statistics for removing covariant step 1







AIC 14465.339 14462.278

SC 14489.267 15634.763

-2 Log L 14459.339 14168.278

Table K.10- Testing Null hypothesis after removing covariant step 1




Score 325.8514 144 <.0001

Wald 306.2753 144 <.0001

515

Table K.11- Residual removing covariant step 1



7.0603 9 0.6308





Table K.13- Model Fit statistics after removing covariant step 2



AIC 14465.339 14452.290

SC 14489.267 15552.990

-2 Log L 14459.339 14176.290

516





Score 316.2107 135 <.0001

Wald 297.3478 135 <.0001




15.7431 18 0.6105





517




AIC 14465.339 14445.753

SC 14489.267 15498.596

-2 Log L 14459.339 14181.753





Score 311.0451 129 <.0001

Wald 292.3479 129 <.0001




21.2000 24 0.6269

518








AIC 14465.339 14443.433

SC 14489.267 15424.492

-2 Log L 14459.339 14197.433





Score 299.6898 120 <.0001

Wald 274.5846 120 <.0001

519




33.6212 33 0.4372








AIC 14465.339 14435.111

SC 14489.267 15344.385

-2 Log L 14459.339 14207.111





Score 291.0613 111 <.0001

Wald 266.0126 111 <.0001




44.0788 42 0.3837

520








AIC 14465.339 14427.297

SC 14489.267 15264.786

-2 Log L 14459.339 14217.297

521





Score 278.2277 102 <.0001

Wald 255.0262 102 <.0001




55.5679 51 0.3068





522




AIC 14465.339 14422.480

SC 14489.267 15188.185

-2 Log L 14459.339 14230.480





Score 264.9909 93 <.0001

Wald 242.7767 93 <.0001




67.4931 60 0.2365








AIC 14465.339 14413.609

523



SC 14489.267 15107.528

-2 Log L 14459.339 14239.609





Score 255.3106 84 <.0001

Wald 232.3624 84 <.0001




78.7472 69 0.1977

524








AIC 14465.339 14406.926

SC 14489.267 15029.060

-2 Log L 14459.339 14250.926





Score 242.5271 75 <.0001

Wald 221.0994 75 <.0001

525




88.8401 78 0.1885








AIC 14465.339 14402.102

SC 14489.267 14952.452

-2 Log L 14459.339 14264.102

526





Score 227.9013 66 <.0001

Wald 207.5631 66 <.0001




101.7314 87 0.1337








AIC 14465.339 14399.250

SC 14489.267 14877.815

-2 Log L 14459.339 14279.250





527



Score 211.6551 57 <.0001

Wald 191.8318 57 <.0001




116.7690 96 0.0735








AIC 14465.339 14402.145

SC 14489.267 14808.925

-2 Log L 14459.339 14300.145





Score 188.7845 48 <.0001

Wald 170.0883 48 <.0001

528




134.6372 105 0.0272





529




AIC 14465.339 14396.196

SC 14489.267 14731.191

-2 Log L 14459.339 14312.196





Score 173.8189 39 <.0001

Wald 156.0719 39 <.0001




150.7370 114 0.0121


530

Table K.60 - Summary of backward elimination in VIRAL INFECTION


Step

Effect

Removed DF

Number

In

Wald


Variable

Label









9 Anakinra 9 9 10.1434 0.3390


11 Adalimumab 9 7 15.0634 0.0892


13 Infliximab 9 5 13.1407 0.1563

Table K.61- Type 3 analysis of effects in VIRAL INFECTION


Effect DF

Wald


Etanercept 9 43.9767 <.0001

Folic Acid 3 13.7765 0.0032


Cyclosporin 9 29.3135 0.0006


531

Table K.62- Analysis of maximum likelihood estimates in VIRAL INFECTION


Parameter InfVir DF Estimate

Standard

Error

Wald


Intercept 1 1 -4.0046 0.1449 763.8821 <.0001

Intercept 2 1 -3.7221 0.1215 938.7083 <.0001

Intercept 3 1 -4.9163 0.2170 513.2153 <.0001

Etanercept 3 1 1 0.0300 0.1317 0.0520 0.8196

Etanercept 3 2 1 -0.0552 0.0924 0.3574 0.5499

Etanercept 3 3 1 -0.0276 0.1511 0.0334 0.8550

Etanercept 4 1 1 0.8042 0.7503 1.1486 0.2838

Etanercept 4 2 1 1.4693 0.3785 15.0703 0.0001

Etanercept 4 3 1 2.4269 0.4611 27.6982 <.0001

Etanercept currently taking 1 1 0.2158 0.1135 3.6129 0.0573

Etanercept currently taking 2 1 -0.0696 0.0858 0.6574 0.4175

Etanercept currently taking 3 1 0.0166 0.1397 0.0142 0.9053




Folic Acid currently taking 1 1 -0.2782 0.1215 5.2471 0.0220












Hydroxychloroquine currently taking 1 1 0.2225 0.1394 2.5473 0.1105




532


Parameter InfVir DF Estimate

Standard

Error

Wald




Cyclosporin 3 1 1 -0.1466 0.1488 0.9698 0.3247

Cyclosporin 3 2 1 0.3263 0.0938 12.0966 0.0005

Cyclosporin 3 3 1 0.2517 0.1518 2.7469 0.0974

Cyclosporin 4 1 1 -0.4712 0.5218 0.8156 0.3665

Cyclosporin 4 2 1 0.3039 0.2642 1.3229 0.2501

Cyclosporin 4 3 1 -1.0400 0.7326 2.0151 0.1557

Cyclosporin currently taking 1 1 0.000418 0.5873 0.0000 0.9994






Prednisolone 3 1 1 0.1061 0.1497 0.5023 0.4785

Prednisolone 3 2 1 0.4422 0.1240 12.7089 0.0004

Prednisolone 3 3 1 0.6023 0.2242 7.2156 0.0072

Prednisolone 4 1 1 0.6125 0.7489 0.6690 0.4134

Prednisolone 4 2 1 0.0692 0.6535 0.0112 0.9156

Prednisolone 4 3 1 -9.4512 139.2 0.0046 0.9459

Prednisolone currently taking 1 1 -0.00427 0.1498 0.0008 0.9772

Prednisolone currently taking 2 1 0.3461 0.1239 7.8094 0.0052

Prednisolone currently taking 3 1 0.7363 0.2202 11.1834 0.0008




533

Table K.63- Odds ratio estimates in VIRAL INFECTION


Effect InfVir Point Estimate

95% Wald

Confidence Limits































534


Effect InfVir Point Estimate

95% Wald

Confidence Limits






Prednisolone 4 vs never taking 3 <0.001 <0.001 >999.999




535

APPENDIX L: ETHICAL APPROVAL

FOR THE THESIS

536

APPENDIX M: SAMPLE OF ARAD

QUESTIONNAIRE

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