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For peer review only

Beliefs about medicines and non-adherence in patients with

stroke, diabetes mellitus and rheumatoid arthritis in China

Journal: BMJ Open

Manuscript ID bmjopen-2017-017293

Article Type: Research

Date Submitted by the Author: 12-Apr-2017

Complete List of Authors: Wei, Li; University College London School of Pharmacy, BMA house, Tavistock Square Chapman, Sarah; UCL School of Pharmacy, Li, Xiaomei; Anhui Provincial Hospital, Anhui Medical University Li, Xin; The Second Hospital, Tianjin Medical University Li, Sumei; Anhui Provincial Hospital, Anhui Medical University Chen, Ruoling; University of Wolverhampton, Centre for Health and Social Care Improvement

Bo, Nie; University College London School of Pharmacy Chater, Angel; University College London, Centre for Behavioural Medicine Horne, Robert; University College London School of Pharmacy

<b>Primary Subject Heading</b>:

Global health

Secondary Subject Heading: Medical management

Keywords: beliefs about medicines, medicine non-adherence, chronic diseases

For peer review only - http://bmjopen.bmj.com/site/about/guidelines.xhtml

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Beliefs about medicines and non-adherence in patients with stroke, diabetes mellitus

and rheumatoid arthritis in China

Li Wei, MD, PhD1, Sarah Chapman PhD

1, Xiaomei Li MD

2, Xin Li MD, PhD

3, Sumei Li

MD4, Ruoling Chen MD, PhD

5, Bo Nie MSc

1, Angel Chater PhD

1, and Rob Horne PhD

1

Short running title: Beliefs about medicines and medicine non-adherence

Key words: beliefs about medicines, medicine non-adherence, chronic diseases

1Department of Practice and Policy, UCL School of Pharmacy, Mezzanine Floor, Entrance A,

BMA House, Tavistock Square, London, WC1H 9JP

2Department of Rheumatology and Immunology, Anhui Provincial Hospital, Anhui Medical

University, Hefei230001, China

3Department of Neurology, The Second Hospital, Tianjin Medical University, Tianjin

300211, China

4Department of Endocrinology, Anhui Provincial Hospital, Anhui Medical University, Hefei

230001, China

5Centre for Health and Social Care Improvement, Faculty of Education Health and

Wellbeing, University of Wolverhampton, Wolverhampton, WV1 1DT, UK

Corresponding author:

Dr Li Wei

Department of Practice and Policy, UCL School of Pharmacy, 29-39 Brunswick Square,

London, WC1N 1AX

Tel: 44 2078741275

E-mail: [email protected]

This study was funded by the Research Innovation Fund, UCL School of Pharmacy.

Word count (abstract): 300; Word count (full text): 2750; Tables: 5; Figures: 1

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Abstract

Objectives: To investigate beliefs about medicines and their association with medicine

adherence in patients with chronic diseases in China.

Design: A cross-sectional questionnaire-based study

Setting: Two large urban hospitals in Hefei and Tianjin, China

Participants: Hospital inpatients (313 stroke patients) and outpatients (315 diabetic patients

and 339 rheumatoid arthritis (RA) patients) were recruited between January 2014 and

September 2014.

Outcome measures: The Beliefs about Medicines Questionnaire (BMQ), assessing patients’

beliefs about the specific medicine (Specific-Necessity and Specific-Concerns) prescribed for

their conditions (stroke/diabetes/RA) and more general background beliefs about

pharmaceuticals as a class of treatment (BMQ-General Benefit, Harm and Overuse); the

Perceived Sensitivity to Medicines scale (PSM) assessed patients’ beliefs about how sensitive

they were to the effects of medicines and the Medication Adherence Report Scale. The

association between non-adherence and beliefs about medicines was assessed using a logistic

regression model.

Results: Patients with diabetes mellitus had a stronger perceived need for treatment [mean

(SD) Specific-Necessity score, 3.75 (0.40)] than patients with stroke [3.69 (0.53)] and RA

[3.66 (0.44)] (p=0.049). Moderate correlations were observed between Specific-Concerns and

General-Overuse, General-Harm and PSM (Pearson correlation coefficients, 0.39, 0.49 and

0.49, respectively, p<0.01). 311 patients were non-adherent to their medicine [159 (51.0%) in

the stroke group, 60 (26.7%) in the diabetes mellitus group and 62 (19.8%) in the RA group,

p<0.01]. Across the whole sample, after adjusting for demographic characteristics non-

adherence was associated with patients who had higher concerns about their medicines (OR,

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1.35, 95% CI 1.07-1.71) and patients who believed that they were personally sensitive to the

effects of medications (OR 1.44, 95% CI 1.16-1.85).

Conclusion: The BMQ is a useful tool to identify patients at risk of non-adherence. In future,

adherence intervention studies may use the BMQ to screen for patients who are at risk of

non-adherence and to map interventional support.

Funding: Research Innovation Fund, UCL School of Pharmacy.

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Strengths and limitations of this study

• First large study of beliefs about medicines in China

• High response rate, reflecting generalisability of the study finding

• A cross-sectional design implicating that the association between non-adherence and

beliefs about medicines did not necessarily lead a causal relationship.

• Self-reported adherence may not be the best measurement for medicine adherence.

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Introduction

Medicine plays an essential role in chronic disease management. However, it is recognised

that only half of patients with chronic diseases take their medicines as prescribed.1 Stroke,

diabetes mellitus and rheumatoid arthritis (RA) are three common chronic diseases which

together affect over 10% of China’s population of 1.40 billion.2-5

Stroke is the leading cause

of adult death and disability in China with an annual mortality rate of approximately 1.6

million, or 157 per 100,000.6

Patients with chronic diseases often require multiple medicine

treatments for their conditions and any associated symptoms and comorbidities. To obtain

full benefit from treatment, patients need to adhere to these complex medicine regimens in

order to control their disease and maintain health. However, in reality medicine management

is sub-optimal and relates to physician inertia and patients’ poor adherence with therapy.7

The

elderly, with high rates of co-morbidity and co-prescribing, tend to have poor adherence and

are at particular risk of unwanted adverse effects from drugs.8,9

Studies outside China have identified patients’ beliefs about medicines as an important

determinant of non-adherence.10

A recent meta-analysis of 96 peer-reviewed studies

involving over 24,000 patients across 24 long-term conditions and 18 countries should that

non-adherence was related to patients beliefs about medicines, measured by the Beliefs about

Medicines Questionnaire (BMQ).10

These studies indicated that there was often a disconnect

between the patients and prescribers view of the medicine. Many patients doubted their

personal need for the treatment or harboured concerns and these beliefs were associated with

non-adherence. Beliefs about medicines may also be relevant in China, particularly as there

some evidence that trust between patients and health professionals may have diminished after

China’s economic reform.11,12

Patients often do not trust their doctors and doubt the treatment

including therapeutic treatment they received. It is possible that this may translate into

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scepticism about prescribed medicines and non-adherence. The aim of this study was to

understand beliefs about medicines in Chinese patients with stroke, diabetes mellitus and RA

and to investigate whether these beliefs are associated with medicine non-adherence.

Methods

A cross-sectional survey was conducted in two large teaching hospitals in China between

January 2014 and November 2014. The study was approved by the local research ethics

committees.

Study populations and recruitment

Patients with stroke were recruited from The Second Hospital, Tianjin Medical University,

Tianjin, China and patients with diabetes mellitus and RA were recruited from Anhui

Provincial Hospital, Anhui Medical University, Hefei, China. Both hospitals are large

teaching hospitals and are level 3 general hospitals, the highest classification for quality care

given by the China’s National Health and Family Planning Commission (NHFPC) for all

public hospitals.13

Patients who had a clinical diagnosis of stroke, diabetes mellitus or RA

who were judged by the healthcare professionals as able to answer questions were invited by

the healthcare professionals to take part in the study when they were admitted to hospital or

came to hospital clinics during the study period. They were asked to complete a questionnaire

which took approximately 10-15 minutes. Study information was given to patients and verbal

consent was obtained before they started to fill in the questionnaire. All invited patients

returned their questionnaires. Since there is a lack of research on beliefs about medicines and

medicine non-adherence in China, a target sample size of 300 patients for each condition was

chosen based on typical sample sizes used in similar surveys. A recent meta-analysis showed

that small-moderate effect of psychological beliefs on medicine adherence was observed in

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94 studies with an average sample size of 266.10

Data collection started in January 2014 and

lasted around 4 months for each condition and was stopped once the target numbers were

reached (November 2014).

Questionnaire measurement

The questionnaire consisted of demographic information, the BMQ questionnaire,14

perceived

sensitivity to medicines scale (PSM) and medicine adherence report scale (MARS).15

All

scales were translated and back translated in accordance with the Originator’s conditions to

create a Chinese version, (BMQ-Chinese © R Horne MARS-Chinese © R Horne, PSM-

Chinese © R Horne). The BMQ and MARS were translated into Chinese by Dr Li Wei, the

principal investigator for this study. They were then sent to the co-authors, Profs Xiaomei Li

and Xin Li for comments and further adaptation resulting in a final Chinese version of the

BMQ.

Demographic information included name, age, gender, education, occupation and duration of

the condition. The BMQ questionnaire has two parts: the BMQ-Specific assessing beliefs

about medicine used for a particular condition and the BMQ-General assessing beliefs about

medicines in general. The details of the BMQ questionnaire can be found in a previous

publication.8 In brief, BMQ-Specific comprised two scales: a 5-item treatment necessity scale

Specific-Necessity and a 6-item treatment concern scale Specific-Concern. BMQ-General

used three subscales: 1) General-Overuse (4 items), 2) General-Harm (4 items), and 3)

General-Benefit (4 items). Perceived Sensitivity to Medicines (PSM) scale contained 5 items

assessing perceptions of personal susceptibility to the effects of medicines. All BMQ items

and PSM were scored on a Likert type scale (where 1 = strongly disagree, 2 = disagree, 3 =

uncertain, 4 = agree and 5 = strongly agree). A mean item score was then calculated as the

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sum of each item score divided by the number of items [for example, mean score of Specific-

Necessity= (N1+N2+N3+N4+N5)/5]. The Cronbach’s αs indicated that all scale measures

were internally consistent in the study sample with αnecessity = 0.64, αconcern = 0.75, αoveruse=

0.54, αharm = 0.55, αbenefit = 0.58 and αpsm = 0.85. Medicine adherence was measured using the

MARS scale consisting of 5 items with 25 score points in total.

Before the study commenced, 30 people including doctors, nurses, medical students and

patients were asked to complete the questionnaire to assess whether the Chinese version of

the BMQ, PSM and MARS would be easily understood by Chinese patients. Feedback was

wholly positive; however we recognised that patients were likely to be elderly and may have

poor literacy. Therefore, arrangements were made to provide help should patients have any

difficulty completing the questionnaire. Patients in both centres completed the questionnaire

either by themselves or with help from the healthcare professionals in the medical ward or

clinic they attended. Healthcare professionals received a briefing from the local investigators

on how to complete the questionnaire including instructions on how to explain the meaning

of the questionnaire items without influencing patients’ responses.

Definition of adherence and non-adherence

Adherence rate was calculated as summed from each item scored by each patient divided by

the maximum points of 25 using the 5-item MARS scale. Adherent patients were defined as

having ≥ 80% adherence rate (i.e. ≥ 20 points) which is a commonly used cut-off point in

medicine benefit/safety studies and is also used conventionally in clinical trials of safety and

efficacy that have been used to support a new drug registration.16

Non-adherent patients were

defined as having <80% adherence rate.

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Statistical analysis

Data were presented as mean (SD) for continuous variables and as frequencies (%) for

categorical variables. Chi-squared and analysis of variance (ANOVA) tests were performed

to determine significant differences between the three diseases. Correlations were used to

examine relationships between BMQ-General and BMQ-Specific subscales. Logistic

regression analysis was employed to assess the association between non-adherence and

medicine beliefs. Five percent of patients had at least one missing value in their BMQ

answers and the total missing values were 0.3% for the whole BMQ items. A sensitivity

analysis was done to include all patients with missing data replaced with imputed data in the

multivariable analysis concluding all variables listed in Table 1. The multiple imputation

analysis included all variables used in the final analysis with 10 imputations by using the

Markov Chain Monte Carlo method to produce imputed data. All statistical analyses were

carried out using SAS (version 9.4).

Results

Patient characteristics

There were 967 patients in the study (313 with stroke, 315 with diabetes mellitus and 339

with RA). Table 1 shows the demographic and clinical information by disease groups.

Patients with RA were significantly younger than patients with stroke and diabetes mellitus

(mean age 49.7 vs 65.8 and 62.5, respectively). The RA group was 85% female while only

44% of stroke patients and 45% of diabetes mellitus patients were female. Patients with RA

were 5 times more likely to report having received no formal education than patients with

stroke and diabetes mellitus (23.3% vs 4.8% and 4.1%, respectively). Over half of patients in

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the stroke group had less than 1 year disease duration while more than half of the patients in

the RA and diabetes mellitus groups had more than 5 years disease duration.

BMQ results

The results of BMQ subscales are shown in Table 2. There were no significant differences in

the mean scores of Specific-Concerns and General-Harm between the three groups. However,

differences were observed for Specific-Necessity, General-Overuse, General-Benefit and

PSM among the three groups. Patients with RA had the highest score of 3.75 (SD 0.40) for

Specific-Necessity and lowest scores of 2.95 (SD 0.51) for General-Overuse and 3.55 (SD

0.45) for General-Benefit. The mean scores of PSM were 2.89 (SD 0.65), 2.35 (SD 0.64) and

2.70 (SD 0.68). The attitudinal analysis categorised patients into four groups (high/low

necessity and high/low concerns) according to the midpoint of the Specific-Necessity

Specific-Concerns scales shows that 45% of patients were classified as ‘Ambivalent’ (Figure

1).

Patients’ general beliefs about medicines were associated with their evaluations of the

specific treatments they were taking. Table 3 shows the correlation between BMQ-General

and BMQ-Specific. Specific-Necessity was positively association with General-Benefit

(Pearson correlation coefficients = 0.31, p<0.01). Moderate correlations were observed

between Specific-Concerns and General-Overuse, General-Harm and PSM (Pearson

correlation coefficients, 0.39, 0.49 and 0.49, respectively, p<0.01). General-Overuse was also

correlated with General-Harm and PSM (Pearson correlation coefficients, 0.45 and 0.39,

respectively, p<0.01). The Pearson correlation coefficient was 0.39 (p<0.01) for General-

Harm and PSM.

Association between beliefs about medicines and non-adherence

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Three hundred and eleven patients were non-adherent to their medicine [159 (51.0%) in the

stroke group, 60 (26.7%) in the diabetes mellitus group and 62 (19.8%) in the RA group,

p<0.01]. After adjusting for demographic characteristics, Specific-Concerns and PSM were

significantly associated with non-adherence (adjusted ORs, 1.35, 95% CI 1.07-1.71 and 1.44,

95% CI 1.16-1.80).There were negative associations between non-adherence and Specific-

Necessity and General-Benefit. However, the association was not statistically significant

(Table 4). The subgroup analyses showed that the point estimates of Specific-Concerns were

positively associated with non-adherence across the three conditions i.e. the more concerns

about the medicines, the more non-adherent to medicines, with adjusted ORs ranging from

1.15 for diabetes mellitus to 1.43 for stroke (Table 5). The unadjusted results showed similar

results across the three disease conditions. The sensitivity analysis showed that Specific-

Concerns and PSM were positively associated with non-adherence (adjusted ORs, 1.15 95%

CI 1.04-1.26 and 1.40, 95% CI 1.04-1.26, respectively) and General-Benefit was negatively

associated with non-adherence (adjusted OR 0.79, 95% CI 0.71-0.87). Compared with

patients in the Accepting group, patients in the other attitudinal groups were more likely to be

the non-adherent (adjusted ORs, 1.50 95% CI 1.10-2.05 for the Ambivalent group, 1.27 95%

CI 0.60-2.68 for the Skeptical group and 1.27 95% CI 0.69-2.34 for the Indifferent group.

Discussion

This is the first large study of this kind that investigates beliefs about medicines and medicine

non-adherence in China in three common chronic diseases. Both unadjusted and adjusted

results revealed that Specific-Concerns, General-Benefit and PSM were significantly

associated with non-adherence i.e. the higher the concern about the medicine or the higher

perceived sensitivity to medicines or the lower of the belief in general medicine benefits, the

lower the adherence to medicines.

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Our findings were consistent with the evidence from a recent meta-analysis which showed

that higher adherence was associated with fewer concerns about treatment10

but not consistent

with previous findings that higher belief in personal need for treatment was associated with

higher adherence; Specific-Necessity was not associated with non-adherence in our overall

analysis. Subgroup analyses showed that patients with stroke and diabetes mellitus showed

the same direction association as previous studies i.e, patients with higher necessity were less

likely to be non-adherent. However, an opposite non-statistically significant result of

Specific-Necessity and adherence for patients with RA (Our result of 1.34 (95% CI 0.73-

2.46) for non-adherence equals 0.75, 95% CI 0.41-1.38 for adherence) was observed in the

study in comparison with the meta-analysis result which had a pooled OR of 3.28, 95%CI

1.11-9.71 for adherence.10

The point estimate could be due to chance and further studies need

to confirm the finding. A recent study found that General-Benefit may be negatively

associated with non-adherence among 398 patients with epilepsy from the UK primary care

population (adjusted OR, 0.92, 95%CI 0.63-1.34)17

and this association was confirmed by our

study with a significant OR of 0.75 (95%CI 0.56-1.02). There was no correlation between

Necessity and Concern (Table 2) in our study population and this was supported by a recent

Swedish study conducted in 578 stroke patients.18

They reported a Spearman’s correlation

coefficient of 0.075 (p=0.08). Compared to patients with high necessity and low concerns,

patients with high necessity and high concerns, low necessity and high concerns, and low

necessity and low concerns were all associated with non-adherence and this was supported by

studies conducted in patients with inflammatory bowel disease and renal dysfunction.19, 20

High perceived sensitivity to medicines was reported to be associated with non-adherence

and higher medical care utilisation, increased symptom reporting and greater information-

seeking about medication.17, 21

Our study also showed PSM was associated with non-

adherence.

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Our study has some strengths. Firstly, this was the first large BMQ study conducted in China.

Secondly, we choose two large teaching hospitals for this study and collected data continually

until the target numbers were reached for each condition. Therefore, our study population

was likely to represent the disease population from each region. Thirdly, China has higher

prevalent rates of stoke and diabetes mellitus than Western Europe and stroke is also the

leading cause of death in China. In light of the current deteriorated relationship between

health professional and patients (i.e. patients do not trust their doctors and other health

professionals, doctors, nurses are often abused verbally and physically by patients,11, 22, 23

more studies are needed from the patients’ perspective. More consideration from the patient

perspective should be taken into account in term of chronic disease management and the

relationship between patients and health professionals. However, our study was an

observational study. Therefore the results may be confounded by unmeasured factors such as

comorbidity. Self-reported adherence may not be the best measurement for medicine

adherence as patients may, for example, underestimate how often they forget their treatment.

However, a strong correlation was found for medicine adherence measured by self-reported

questionnaire, electronic prescriptions and serum biomarkers in a recent intervention study

suggesting that self-report is a reliable measure.24

Also our study found relatively low

reliability of the BMQ general scales and further investigation and refinement might be

needed of these Chinese translations. The difference in non-adherence between stroke and

other conditions needs to be explored in future studies as disease itself may play an important

role in medicine adherence.

In conclusion, we found that the BMQ is a useful tool to identify psychological factors that

are linked to non-adherence in patients with stroke, diabetes and RA. Future studies should

use the BMQ to screen patients to identify those who are at high risk of non-adherence and

map their treatment plan accordingly.

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CONFLICT OF INTEREST/STUDY SUPPORT:

Specific author contributions: LW, RC and RH were involved in conception and design and

interpretation of data. LW did the statistical analysis and wrote the first draft of the article.

SC, RC and BN contributed to data analysis. LXM, LX and LSM collected data. SC, AC,

LXM, LX and LSM were involved in interpretation of data. All authors were responsible for

re-drafting of the article and approved the final version.

Guarantor of the article: LW is the guarantor

Financial support: This study was funded by the Research Innovation Fund, UCL School of

Pharmacy. The funder had no role in the design, conduct or data interpretation of the study.

Professor Rob Horne was supported by the National Institute for Health Research (NIHR)

Collaboration for Leadership in Applied Health Research and Care (CLAHRC) North

Thames at Bart’s Health NHS Trust.

Potential competing interests: RH declares: (1) Speaker engagements with honoraria and

consultancy payments from the pharmaceutical companies; (2) Founder and shareholder of a

UCL-Business, university spin-out company (Spoonful of Sugar Ltd) providing consultancy

on medication-related behaviours to healthcare policy makers, providers and industry; and (3)

Originator of BMQ.

References:

1. Medicines adherence: involving patients in decisions about prescribed medicines and

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on 30 January 2017)

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2. Liu L, Wang D, Wong KS, Wang Y. Stroke and stroke care in China: huge burden,

significant workload, and a national priority. Stroke; a journal of cerebral circulation.

2011;42(12):3651-4.

3. Feigin VL, Forouzanfar MH, Krishnamurthi R, Mensah GA, Connor M, Bennett DA, et al.

Global and regional burden of stroke during 1990-2010: findings from the Global Burden of

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4. Li R, Sun J, Ren LM, Wang HY, Liu WH, Zhang XW, et al. Epidemiology of eight

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5. Total population, 2013. http://www.who.int/countries/chn/en/ (accessed on 30 January

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6. Xiaomei Wu, Bo Zhu, Lingyu Fu, Hailong Wang, Bo Zhou, SafengZou, et al.

Prevalence, Incidence, and Mortality of Stroke in the Chinese Island Populations: A

Systematic Review PLOS Published: November 8, 2013•DOI: 10.1371/journal.pone.0078629

7. Will JC, Zhang Z, Ritchey MD, Loustalot F. Medication Adherence and Incident

Preventable Hospitalizations for Hypertension. American journal of preventive medicine.

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Adherence to dabigatran therapy and longitudinal patient outcomes: insights from the

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Adherence to statin therapy and the incidence of ischemic stroke in patients with diabetes.

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10. Horne, R, Chapman SC, Parham R, Freemantle N, Forbes A, Cooper V. Understanding

patients’ adherence –related beliefs about medicines prescribed for long-term conditions: a

meta-analytic review of the necessity-concerns framework. Plos One. 2013 Dec

2;8(12)e80633. Doi:10.1371/journal.pone.0080633.

11. Tucker JD, Cheng Y, Wong B, Gong N, Nie JB, Zhu W, et al; Patient-Physician Trust

Project Team. Patient-physician mistrust and violence against physicians in Guangdong

Province, China: a qualitative study. BMJ Open. 2015 Oct 6;5(10):e008221. doi:

10.1136/bmjopen-2015-008221.

12. Chan CS, Cheng Y, Cong Y, Du Z, Hu S, Kerrigan A, et al. Patient-physician trust in

China: a workshop summary. Lancet. 2016 Oct;388 Suppl 1:S72. doi: 10.1016/S0140-

6736(16)31999-7.

13. National Health and Family Planning Commission of the PRC http://en.nhfpc.gov.cn/

(last access 30 January 2017)

14. Horne R, Weinman J. Patients' beliefs about prescribed medicines and their role in

adherence to treatment in chronic physical illness. J Psychosom Res. 1999 Dec;47(6):555-67.

15. Horne R. The nature, determinants and effects of medication beliefs in chronic illness

(PhD thesis) University of London 1997.

16. Wei L, Wang J, Thompson P, Wong S, Struthers AD, MacDonald TM. Adherence to

statin treatment and readmission of patients after myocardial infarction: a six year follow up

study. Heart 2002;88:229-33.

17. Chapman SC, Horne R, Chater A, Hukins D, Smithson WH. Patients' perspectives on

antiepileptic medication: relationships between beliefs about medicines and adherence among

patients with epilepsy in UK primary care. Epilepsy Behav. 2014 Feb;31:312-20. doi:

10.1016/j.yebeh.2013.10.016. Epub 2013 Nov 27.

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18. Sjolander M, Eriksson M, Glader EL. The association between patients' beliefs about

medicines and adherence to drug treatment after stroke: a cross-sectional questionnaire

survey. BMJ open. 2013;3(9):e003551.

19. Horne R, Parham R, Driscoll R, Robinson A. Patients' attitudes to medicines and

adherence to maintenance treatment in inflammatory bowel disease. Inflamm Bowel Dis.

2009 Jun;15(6):837-44. doi: 10.1002/ibd.20846.

20. Chater AM, Parham R, Riley S, Hutchison AJ, Horne R. Profiling patient attitudes to

phosphate binding medication: a route to personalising treatment and adherence support.

Psychol Health. 2014;29(12):1407-20. doi: 10.1080/08870446.2014.942663. Epub 2014 Aug.

21. Faasse K, Grey A, Horne R, Petrie KJ. High perceived sensitivity to medicines is

associated with higher medical care utilisation, increased symptom reporting and greater

information-seeking about medication. Pharmacoepidemiol Drug Saf. 2015 Jun;24(6):592-9.

doi: 10.1002/pds.3751. Epub 2015 Apr 7.

22. Li ZG, Leng MX. Doctor-patient relationship research summary. Chinese Hospital

Management. 2009;29(332):40-3.

23. Doctors face verbal abuse or violence. http://www.cmdae.org/en/index.php (Accessed 30

January 2017).

24. Lyons I, Barber N, Raynor DK, Wei L. The Medicines Advice Service Evaluation

(MASE): a randomised controlled trial of a pharmacist-led telephone based intervention

designed to improve medication adherence. BMJ Qual Saf. 2016 Oct;25(10):759-69. doi:

10.1136/bmjqs-2015-004670.

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Table 1. Patient characteristics by conditions

Stroke

n=313 (100%)

Diabetes

n=315 (100%)

RA

n=339 (100%)

Age 65.8 (13.7) 62.5 (13.9) 49.7 (12.8)

Gender†*

Women 136 (43.5%) 141 (44.8%) 287 (85.2%)

Men 177 (56.5%) 174 (55.2%) 50 (14.8%)

Education

Illiteracy (no formal

education)

15 (4.8%) 13 (4.1%) 79 (23.3%)

Primary school 66 (21.1%) 62 (19.7%) 93 (27.4%)

Junior high school 60 (19.2%) 91 (28.9%) 91 (26.8%)

High school/college 116 (37.1%) 77 (24.4%) 48 (14.2%)

University or above 54 (17.2%) 70 (22.2%) 25 (7.4%)

Unknown 2 (0.6%) 2 (0.6%) 3 (0.9%)

Occupation

Farmer 5 (1.6%) 22 (7.0%) 110 (32.5%)

Worker/clerk 83 (26.5%) 117 (37.1%) 38 (11.2%)

Housewife/unemployment 45 (14.4%) 12 (3.8%) 113 (33.3%)

Retirement 129 (41.2%) 54 (17.1%) 40 (11.8%)

Health professional 19 (6.1%) 29 (9.2%) 15 (4.4%)

Civil service 29 (9.3%) 72 (22.9%) 11 (3.2%)

Unknown 3 (1.0%) 9 (2.9%) 12 (3.5%)

Duration of the disease

<= 1 year 178 (56.9%) 20 (6.4%) 55 (16.2%)

1-5 years 91 (29.1%) 74 (23.5%) 104 (30.7%)

> 5 year 44 (14.1%) 220 (69.8%) 177 (52.2%)

unknown 0 (0.0%) 1 (0.3%) 3 (0.9%) †Excluding missing data; *<0.05

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Table 2. Results of BMQ and PSM by disease conditions

Stroke

n=313 (100%)

Diabetes

n=315 (100%)

RA

n=339 (100%)

Specific-Necessity (mean, SD) †

* 3.69 (0.53) 3.75 (0.40) 3.66 (0.44)

Specific-Concerns (mean, SD) †

3.03 (0.71) 3.15 (0.58) 3.07 (0.58)

General-Overuse (mean, SD) †

* 3.22 (0.62) 3.12 (0.50) 2.95 (0.51)

General-Harm (mean, SD) †

2.94 (0.78) 2.95 (0.50) 2.99 (0.43)

General-Benefit (mean, SD) †

* 3.70 (0.53) 3.69 (0.42) 3.55 (0.45)

PSM (mean, SD) †

* 2.89 (0.65) 2.35 (0.64) 2.70 (0.68)

MARS score (mean, SD*) 18.81 (4.16) 21.51 (3.07) 21.47 (3.86) †Excluding missing data; PSM=Perceived Sensitivity to Medicines; *<0.05

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Table 3. Correlation matrix between BMQ-Specific, BMQ-General and PSM

Necessity Concerns Overuse Harm Benefit PSM

Overall

Necessity 1.00

Concern 0.02(0.45) 1.00

Overuse -0.01 (0.87) 0.39 (<0.01) 1.00

Harm -0.05 (0.16) 0.49 (<0.01) 0.45 (<0.01) 1.00

Benefit 0.31(<0.01) -0.09 (0.01) 0.11 (<0.01) 0.04 (0.21) 1.00

PSM 0.04 (0.22) 0.45 (<0.01) 0.39 (<0.01) 0.38<0.01) 0.05 (0.14) 1.00

Stroke

Necessity 1.00

Concern 0.04 (0.43) 1.00

Overuse 0.06 (0.31) 0.50 (<0.01) 1.00

Harm -0.06 (0.27) 0.66 (<0.01) 0.57 (<0.01) 1.00

Benefit 0.36 (<0.01) 0.01 (0.88) 0.24 (<0.01) 0.13 (0.12) 1.00

PSM 0.23 (<0.01) 0.52 (<0.01) 0.51 (<0.01) 0.50 (<0.01) 0.22 (<0.01) 1.00

Diabetes mellitus

Necessity 1.00

Concern 0.02 (0.59) 1.00

Overuse -0.05 (0.40) 0.26 (<0.01) 1.00

Harm -0.08 (0.20) 0.21 (<0.01) 0.44 (<0.01) 1.00

Benefit 0.19 (<0.01) -0.07 (0.26) 0.18 (<0.01) 0.08 (0.19) 1.00

PSM -0.08 (0.20) 0.36 (<0.01) 0.38 (<0.01) 0.44 (<0.01) 0.07 (0.23) 1.00

RA

Necessity 1.00

Concerns 0.01 (0.91) 1.00

Overuse -0.08 (0.13) 0.41 (<0.01) 1.00

Harm 0.02 (0.72) 0.42 (<0.01) 0.30 (<0.01) 1.00

Benefit 0.30 (<0.01) -0.21 (<0.01) -0.20 (<0.01) -0.15 (0.21) 1.00

PSM -0.04 (0.52) 0.54 (<0.01) 0.29 (<0.01) 0.27 (<0.01) -0.13 (0.02) 1.00

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Table 4. Odd ratios of medicine non-adherence

Unadjusted

OR (95%CI)

Adjusted*

OR (95%CI)

Specific-Necessity 0.90, 0.67-1.21 0.93, 0.68-1.27

Specific-Concerns 1.27, 1.02-1.58 1.35, 1.07-1.71

General-Overuse 1.32, 1.04-1.69 1.15, 0.88-1.50

General-Harm 1.09, 0.87-1.38 1.12, 0.89-1.43

General-Benefit 0.84, 0.63-1.12 0.75, 0.56-1.02

PSM 1.72, 1.41-2.09 1.44, 1.16-1.85

*Adjusted for age, gender, education, occupation, duration of the disease and different

diseases

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Table 5. Odd ratios of medication non-adherence by disease groups

Stroke Diabetes RA

Unadjusted

OR (95%CI)

Adjusted*

OR (95%CI)

Unadjusted

OR (95%CI)

Adjusted*

OR (95%CI)

Unadjusted

OR (95%CI)

Adjusted*

OR (95%CI)

Specific-

Necessity

0.83, 0.54-1.26 0.92, 0.59-1.43 0.71, 0.35-1.40 0.92, 0.43-1.97 1.31, 0.75-2.27 1.34, 0.73-2.46

Specific-

Concerns

1.43, 1.04-1.96 1.43, 1.02-2.00 1.16, 0.71-1.88 1.15, 0.67-1.98 1.38, 0.91-2.12 1.32, 0.84-2.10

General-

Overuse

1.26, 0.88-1.81 1.24, 0.85-1.82 1.12, 0.64-1.96 1.10, 0.61-2.00 1.06, 0.66-1.71 0.98, 0.60-1.60

General-

Harm

1.26, 0.95-1.68 1.30, 0.96-1.77 0.58, 0.32-1.05 0.59, 0.32-1.11 1.27, 0.72-2.24 1.27, 0.70-2.30

General-

Benefit

0.87, 0.57-1.32 0.83, 0.53-1.29 0.78, 0.41-1.50 0.83, 0.41-1.69 0.64, 0.37-1.09 0.65, 0.37-1.13

PSM 1.76, 1.22-2.53 1.79, 1.23-2.60 1.60, 1.05-2.44 1.73, 1.10-2.70 1.06, 0.74-1.51 1.01, 0.70-1.48

*Adjusted for age, gender, education, occupation, duration of the disease

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Figure 1. Attitudinal analysis of BMQ-Specific.

Skeptical

n = 40 (4.3%)

Ambivalent

n = 422 (44.8%)

Indifferent

n = 58 (6.2%)

Accepting

n = 421 (44.7%)

High NecessityLow Necessity

High Concerns

Low Concerns

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stroke, diabetes mellitus and rheumatoid arthritis: A cross-

sectional study in China

Journal: BMJ Open

Manuscript ID bmjopen-2017-017293.R1


Date Submitted by the Author: 01-Jun-2017

Complete List of Authors: Wei, Li; University College London School of Pharmacy, BMA house, Tavistock Square Chapman, Sarah; UCL School of Pharmacy,

Li, Xiaomei; Anhui Provincial Hospital, Anhui Medical University Li, Xin; The Second Hospital, Tianjin Medical University Li, Sumei; Anhui Provincial Hospital, Anhui Medical University Chen, Ruoling; University of Wolverhampton, Centre for Health and Social Care Improvement Bo, Nie; University College London School of Pharmacy Chater, Angel; University College London, Centre for Behavioural Medicine Horne, Robert; University College London School of Pharmacy


Global health




BMJ Open on A


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j.com/

BM



ownloaded from



1


and rheumatoid arthritis: A cross-sectional study in China


1 2, Xiaomei Li MD

3, Xin Li MD, PhD

4, Sumei Li


6, Bo Nie MSc

1, Angel Chater PhD

1 7, and Rob Horne PhD

1





2Department of Pharmacy & Pharmacology, University of Bath, Claverton Down Road, Bath,

BA2 7AY 3Department of Rheumatology and Immunology, Anhui Provincial Hospital, Anhui Medical



300211, China


230001, China



7Centre for Health, Wellbeing and Behaviour Change, Faculty of Education and Sport

University of Bedfordshire, Polhill Avenue, Bedford, MK41 9EA


Dr Li Wei


London, WC1N 1AX

Tel: 44 2078741275




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Abstract







September 2014.








regression model.









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Introduction













The






involving over 24,000 patients across 24 long-term conditions and 18 countries showed that





personal need for the treatment or harboured concerns and these beliefs are associated with






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Methods

A cross-sectional study was conducted in two large teaching hospitals in China between


committees.







public hospitals.13



the healthcare professionals to take part in the study when they were admitted to hospital

(stroke patients) or came to hospital clinics (diabetic and RA patients) during the study

period. Some patients may have co-morbidity. However, they were only included for the

condition they were hospitalised or the condition from the specific outpatient clinics. They

were asked to complete a questionnaire which took approximately 10-15 minutes. Study

information was given to patients and verbal consent was obtained before they started to fill

in the questionnaire. All invited patients returned their questionnaires. Since there is a lack of

research on beliefs about medicines and medicine non-adherence in China, a target sample

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size of 300 patients for each condition was chosen based on typical sample sizes used in

similar surveys. A recent meta-analysis showed that small-moderate effect of psychological

beliefs on medicine adherence was observed in 94 studies with an average sample size of

266.10

Data collection started in January 2014 and lasted around 4 months for each condition

and was stopped once the target numbers were reached (November 2014).



perceived


All






BMQ.










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were internally consistent in the study sample with αnecessity = 0.64, αconcern = 0.75, αoveruse=

0.54, αharm = 0.55, αbenefit = 0.58 and αpsm = 0.85. For BMQ-Specific, patients were only asked

for the treatment prescribed for the diagnosed condition in the past 12 months. Medicine

adherence was measured using the MARS scale consisting of 5 items with 25 score points in

total. For example, one item is “I forget to take them”.












Adherent patients were defined as measuring ≥ 80% on the measure of adherence (i.e. ≥ 20

points) to their medicine which is a commonly used cut-off point in medicine benefit/safety

studies and is also used conventionally in clinical trials of safety and efficacy that have been

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used to support a new drug registration.16

. Adherence was calculated as sum of the scores

from each item divided by the maximum points of 25 using the 5-item MARS scale. Non-

adherent patients were defined as having a score <80% adherence to medicine.

Attitudinal analysis

Participants were categorized into attitudinal groups (i.e. Skeptical, Ambivalent, Indifferent

and Accepting groups) based on whether they scored above or below the scale midpoint for

BMQ necessity and concerns scales. Non-adherence was investigated between the groups.17














Results

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BMQ results









Specific-Concerns scales shows that 45% of patients were classified as ‘Ambivalent’, 45%

as ‘Accepting’, 4% as ‘Skeptical’ and 6% as ‘Indifferent’ (Figure 1).



and BMQ-Specific. Specific-Necessity was positively associated with General-Benefit

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Harm and PSM.






95% CI 1.16-1.80). Negative associations between non-adherence and Specific-Necessity and

General-Benefit were not statistically significant (Table 4). The subgroup analyses showed

that the point estimates of Specific-Concerns were positively associated with non-adherence

across the three conditions i.e. the more concerns about the medicines, the more non-adherent

to medicines, with adjusted ORs ranging from 1.15 (95% CI 0.67-1.98) for diabetes mellitus

to 1.43 (95% CI 1.02-2.00) for stroke (Table 5). The unadjusted results showed similar results

across the three disease conditions. The sensitivity analysis showed that Specific-Concerns

and PSM were positively associated with non-adherence (adjusted ORs, 1.15 95% CI 1.04-

1.26 and 1.40, 95% CI 1.04-1.26, respectively) and General-Benefit was negatively




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Only patients in the Ambivalent group showed a statistical significance.

Discussion



results revealed that Specific-Concerns and PSM were significantly associated with non-

adherence i.e. the higher the concern about the medicine or the higher perceived sensitivity to

medicines or the lower of the belief in general medicine benefits, the lower the adherence to

medicines. Using the continuous MARS-5 scores showed similar relationships to those

reported in logistic regression. We noted differences between groups that might indicate

different attitudes towards the diseases and their treatments or be attributable to other factors.

The hospital out-patients (diabetes mellitus and RA) included some patients who had

previous hospitalisations and come back to hospital for regular monitoring.



but not consistent











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study with a borderline significant OR of 0.75 (95%CI 0.56-1.02). There was no correlation

between Necessity and Concern (Table 2) in our study population and this was supported by

a recent Swedish study conducted in 578 stroke patients.18

They reported a Spearman’s

correlation coefficient of 0.075 (p=0.08). Compared to patients with high necessity and low

concerns, patients with high necessity and high concerns, low necessity and high concerns,

and low necessity and low concerns were all associated with non-adherence and this was

supported by studies conducted in patients with inflammatory bowel disease and renal

dysfunction.19, 20

High perceived sensitivity to medicines was reported to be associated with

non-adherence and higher medical care utilisation, increased symptom reporting and greater

information-seeking about medication.17, 21

Our study also showed PSM was associated with

non-adherence.




was likely to represent the disease population from each region as large teaching hospitals

provide health care service to majority of Chinese patients. Thirdly, China has higher

prevalence rates of stoke and diabetes mellitus than Western Europe and stroke is also the

leading cause of death in China. In light of the current deteriorated relationship between

health professional and patients (i.e. patients do not trust their doctors and other health

professionals, doctors, nurses are often abused verbally and physically by patients,11, 22, 23

more studies are needed from the patients’ perspective. More consideration from the patient

perspective should be taken into account in term of chronic disease management and the

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relationship between patients and health professionals. However, our study was an

observational study. Therefore the results may be confounded by unmeasured factors such as

comorbidity. Self-reported adherence may not be the best measurement for medicine

adherence as patients may, for example, underestimate how often they forget their treatment.

However, a strong correlation was found for medicine adherence measured by self-reported

questionnaire, electronic prescriptions and serum biomarkers in a recent intervention study

suggesting that self-report is a reliable measure.24

Also our study found relatively low

reliability of the BMQ general scales and further investigation and refinement might be

needed of these Chinese translations. The difference in non-adherence between stroke and

other conditions needs to be explored in future studies as disease itself may play an important

role in medicine adherence.














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Originator of BMQ.

References:



on 30 January 2017)



2011;42(12):3651-4.







2011 Dec 16.

5. China Total population, 2015. http://www.who.int/countries/chn/en/ (accessed on 25 May

2017)

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6. Wu X, Zhu B, Fu L, Wang H, Zhou B, Zou S, et al. Prevalence, incidence, and mortality of

stroke in the chinese island populations: a systematic review. PLoS One. 2013 Nov

8;8(11):e78629. doi: 10.1371/journal.pone.0078629. eCollection 2013.


Preventable Hospitalizations for Hypertension. Am J Prev Med. 2016 Apr;50(4):489-99. doi:

10.1016/j.amepre.2015.08.021. Epub 2015 Oct 31.



veterans health administration. Am Heart J. 2014 Jun;167(6):810-7. doi:

10.1016/j.ahj.2014.03.023. Epub 2014 Apr 5.











10.1136/bmjopen-2015-008221.



6736(16)31999-7.

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study. Heart 2002;88:229-33.




10.1016/j.yebeh.2013.10.016. Epub 2013 Nov 27.



survey. BMJ open. 2013;3(9):e003551.



2009 Jun;15(6):837-44. doi: 10.1002/ibd.20846.






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doi: 10.1002/pds.3751. Epub 2015 Apr 7.


Management. 2009;29(332):40-3.


January 2017).




10.1136/bmjqs-2015-004670.

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Stroke

n=313 (100%)

Diabetes

n=315 (100%)

RA

n=339 (100%)

Age, mean (SD) * 65.8 (13.7) 62.5 (13.9) 49.7 (12.8)

Gender†*

Women 136 (43.5%) 141 (44.8%) 287 (85.2%)

Men 177 (56.5%) 174 (55.2%) 50 (14.8%)

Education*


education)

15 (4.8%) 13 (4.1%) 79 (23.3%)

Primary school 66 (21.1%) 62 (19.7%) 93 (27.4%)




Unknown 2 (0.6%) 2 (0.6%) 3 (0.9%)

Occupation*

Farmer 5 (1.6%) 22 (7.0%) 110 (32.5%)

Worker/clerk 83 (26.5%) 117 (37.1%) 38 (11.2%)


Retirement 129 (41.2%) 54 (17.1%) 40 (11.8%)


Civil service 29 (9.3%) 72 (22.9%) 11 (3.2%)

Unknown 3 (1.0%) 9 (2.9%) 12 (3.5%)

Duration of the disease*

<= 1 year 178 (56.9%) 20 (6.4%) 55 (16.2%)

1-5 years 91 (29.1%) 74 (23.5%) 104 (30.7%)

> 5 year 44 (14.1%) 220 (69.8%) 177 (52.2%)


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Stroke

n=313

Diabetes

n=315

RA

n=339


* 3.69 (0.53) 3.75 (0.40) 3.66 (0.44)


3.03 (0.71) 3.15 (0.58) 3.07 (0.58)


* 3.22 (0.62) 3.12 (0.50) 2.95 (0.51)


2.94 (0.78) 2.95 (0.50) 2.99 (0.43)


* 3.70 (0.53) 3.69 (0.42) 3.55 (0.45)

PSM (mean, SD) †

* 2.89 (0.65) 2.35 (0.64) 2.70 (0.68)


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Overall

Necessity 1.00

Concern 0.02(0.45) 1.00

Overuse -0.01 (0.87) 0.39 (<0.01) 1.00

Harm -0.05 (0.16) 0.49 (<0.01) 0.45 (<0.01) 1.00

Benefit 0.31(<0.01) -0.09 (0.01) 0.11 (<0.01) 0.04 (0.21) 1.00

PSM 0.04 (0.22) 0.45 (<0.01) 0.39 (<0.01) 0.38<0.01) 0.05 (0.14) 1.00

Stroke

Necessity 1.00

Concern 0.04 (0.43) 1.00

Overuse 0.06 (0.31) 0.50 (<0.01) 1.00

Harm -0.06 (0.27) 0.66 (<0.01) 0.57 (<0.01) 1.00

Benefit 0.36 (<0.01) 0.01 (0.88) 0.24 (<0.01) 0.13 (0.12) 1.00

PSM 0.23 (<0.01) 0.52 (<0.01) 0.51 (<0.01) 0.50 (<0.01) 0.22 (<0.01) 1.00

Diabetes mellitus

Necessity 1.00

Concern 0.02 (0.59) 1.00

Overuse -0.05 (0.40) 0.26 (<0.01) 1.00

Harm -0.08 (0.20) 0.21 (<0.01) 0.44 (<0.01) 1.00

Benefit 0.19 (<0.01) -0.07 (0.26) 0.18 (<0.01) 0.08 (0.19) 1.00

PSM -0.08 (0.20) 0.36 (<0.01) 0.38 (<0.01) 0.44 (<0.01) 0.07 (0.23) 1.00

RA

Necessity 1.00

Concerns 0.01 (0.91) 1.00

Overuse -0.08 (0.13) 0.41 (<0.01) 1.00

Harm 0.02 (0.72) 0.42 (<0.01) 0.30 (<0.01) 1.00

Benefit 0.30 (<0.01) -0.21 (<0.01) -0.20 (<0.01) -0.15 (0.21) 1.00

PSM -0.04 (0.52) 0.54 (<0.01) 0.29 (<0.01) 0.27 (<0.01) -0.13 (0.02) 1.00

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Unadjusted

OR, 95% CI

Adjusted*

OR, 95% CI



General-Overuse 1.32, 1.04-1.69 1.15, 0.88-1.50

General-Harm 1.09, 0.87-1.38 1.12, 0.89-1.43

General-Benefit 0.84, 0.63-1.12 0.75, 0.56-1.02

PSM 1.72, 1.41-2.09 1.44, 1.16-1.85


diseases

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Stroke Diabetes RA

Unadjusted

OR, 95% CI

Adjusted*

OR, 95% CI

Unadjusted

OR, 95% CI

Adjusted*

OR, 95% CI

Unadjusted

OR, 95% CI

Adjusted*

OR, 95% CI

Specific-

Necessity

0.83, 0.54-1.26 0.92, 0.59-1.43 0.71, 0.35-1.40 0.92, 0.43-1.97 1.31, 0.75-2.27 1.34, 0.73-2.46

Specific-

Concerns

1.43, 1.04-1.96 1.43, 1.02-2.00 1.16, 0.71-1.88 1.15, 0.67-1.98 1.38, 0.91-2.12 1.32, 0.84-2.10

General-

Overuse

1.26, 0.88-1.81 1.24, 0.85-1.82 1.12, 0.64-1.96 1.10, 0.61-2.00 1.06, 0.66-1.71 0.98, 0.60-1.60

General-

Harm

1.26, 0.95-1.68 1.30, 0.96-1.77 0.58, 0.32-1.05 0.59, 0.32-1.11 1.27, 0.72-2.24 1.27, 0.70-2.30

General-

Benefit

0.87, 0.57-1.32 0.83, 0.53-1.29 0.78, 0.41-1.50 0.83, 0.41-1.69 0.64, 0.37-1.09 0.65, 0.37-1.13

PSM 1.76, 1.22-2.53 1.79, 1.23-2.60 1.60, 1.05-2.44 1.73, 1.10-2.70 1.06, 0.74-1.51 1.01, 0.70-1.48


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24

Figure legend


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STROBE 2007 (v4) Statement—Checklist of items that should be included in reports of cross-sectional studies

Section/Topic Item

# Recommendation Reported on page #

Title and abstract 1 (a) Indicate the study’s design with a commonly used term in the title or the abstract Page 2

(b) Provide in the abstract an informative and balanced summary of what was done and what was found Page 2

Introduction

Background/rationale 2 Explain the scientific background and rationale for the investigation being reported Page 5-6

Objectives 3 State specific objectives, including any prespecified hypotheses Page 6

Methods

Study design 4 Present key elements of study design early in the paper Page 6

Setting 5 Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data

collection

Page 6

Participants

6

(a) Give the eligibility criteria, and the sources and methods of selection of participants Pages 6-8

Variables 7 Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic criteria, if

applicable

Pages 7-8

Data sources/

measurement

8* For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe

comparability of assessment methods if there is more than one group

Page 7-8

Bias 9 Describe any efforts to address potential sources of bias Page 9

Study size 10 Explain how the study size was arrived at Page 6

Quantitative variables 11 Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen and

why

Page 9

Statistical methods 12 (a) Describe all statistical methods, including those used to control for confounding Page 9

(b) Describe any methods used to examine subgroups and interactions Page 11

(c) Explain how missing data were addressed Page 9

(d) If applicable, describe analytical methods taking account of sampling strategy n/a

(e) Describe any sensitivity analyses Pages 9 &12

Results

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Participants 13* (a) Report numbers of individuals at each stage of study—eg numbers potentially eligible, examined for eligibility,

confirmed eligible, included in the study, completing follow-up, and analysed

Page 9

(b) Give reasons for non-participation at each stage n/a

(c) Consider use of a flow diagram n/a

Descriptive data 14* (a) Give characteristics of study participants (eg demographic, clinical, social) and information on exposures and potential

confounders

Page 9 and Page 18

(b) Indicate number of participants with missing data for each variable of interest Page 18

Outcome data 15* Report numbers of outcome events or summary measures Pages 10-12

Main results 16 (a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (eg, 95% confidence

interval). Make clear which confounders were adjusted for and why they were included

Pages 22-23

(b) Report category boundaries when continuous variables were categorized Pages 8 & 10

(c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period n/a

Other analyses 17 Report other analyses done—eg analyses of subgroups and interactions, and sensitivity analyses Page 11

Discussion

Key results 18 Summarise key results with reference to study objectives Page 11

Limitations 19 Discuss limitations of the study, taking into account sources of potential bias or imprecision. Discuss both direction and

magnitude of any potential bias

Page 13

Interpretation 20 Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results from

similar studies, and other relevant evidence

Pages 11-14

Generalisability 21 Discuss the generalisability (external validity) of the study results Page 13

Other information

Funding 22 Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on

which the present article is based

Page 14

*Give information separately for cases and controls in case-control studies and, if applicable, for exposed and unexposed groups in cohort and cross-sectional studies.

Note: An Explanation and Elaboration article discusses each checklist item and gives methodological background and published examples of transparent reporting. The STROBE

checklist is best used in conjunction with this article (freely available on the Web sites of PLoS Medicine at http://www.plosmedicine.org/, Annals of Internal Medicine at

http://www.annals.org/, and Epidemiology at http://www.epidem.com/). Information on the STROBE Initiative is available at www.strobe-statement.org.

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stroke, diabetes mellitus and rheumatoid arthritis: A cross-

sectional study in China

Journal: BMJ Open

Manuscript ID bmjopen-2017-017293.R2


Date Submitted by the Author: 12-Jul-2017

Complete List of Authors: Wei, Li; University College London School of Pharmacy, BMA house, Tavistock Square Chapman, Sarah; UCL School of Pharmacy,

Li, Xiaomei; Anhui Provincial Hospital, Anhui Medical University Li, Xin; The Second Hospital, Tianjin Medical University Li, Sumei; Anhui Provincial Hospital, Anhui Medical University Chen, Ruoling; University of Wolverhampton, Centre for Health and Social Care Improvement Bo, Nie; University College London School of Pharmacy Chater, Angel; University College London, Centre for Behavioural Medicine Horne, Robert; University College London School of Pharmacy


Global health




BMJ Open on A


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j.com/

BM



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1


and rheumatoid arthritis: A cross-sectional study in China


1 2, Xiaomei Li MD

3, Xin Li MD, PhD

4, Sumei Li


6, Bo Nie MSc

1, Angel Chater PhD

1 7, and Rob Horne PhD

1





2Department of Pharmacy & Pharmacology, University of Bath, Claverton Down Road, Bath,

BA2 7AY 3Department of Rheumatology and Immunology, Anhui Provincial Hospital, Anhui Medical



300211, China


230001, China



7Centre for Health, Wellbeing and Behaviour Change, Faculty of Education and Sport

University of Bedfordshire, Polhill Avenue, Bedford, MK41 9EA


Dr Li Wei


London, WC1N 1AX

Tel: 44 2078741275




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Abstract







September 2014.








regression model.









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Introduction













The






involving over 24,000 patients across 24 long-term conditions and 18 countries showed that





personal need for the treatment or harboured concerns and these beliefs are associated with






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Methods

A cross-sectional study was conducted in two large teaching hospitals in China between


committees.







public hospitals.13



the healthcare professionals to take part in the study when they were admitted to hospital

(stroke patients) or came to hospital clinics (diabetic and RA patients) during the study

period. Some patients may have co-morbidity. However, they were only included for the

condition they were hospitalised or the condition from the specific outpatient clinics. They

were asked to complete a questionnaire which took approximately 10-15 minutes. Study

information was given to patients and verbal consent was obtained before they started to fill

in the questionnaire. All invited patients returned their questionnaires. Since there is a lack of

research on beliefs about medicines and medicine non-adherence in China, a target sample

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size of 300 patients for each condition was chosen based on typical sample sizes used in

similar surveys. A recent meta-analysis showed that small-moderate effect of psychological

beliefs on medicine adherence was observed in 94 studies with an average sample size of

266.10

Data collection started in January 2014 and lasted around 4 months for each condition

and was stopped once the target numbers were reached (November 2014).



perceived


All






BMQ.










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were internally consistent in the study sample16

with high value of αpsm = 0.85, αconcern = 0.75

and αnecessity = 0.64, and low values of αoveruse= 0.54, αharm = 0.55 and αbenefit = 0.58. For BMQ-

Specific, patients were only asked for the treatment prescribed for the diagnosed condition in

the past 12 months. Medicine adherence was measured for the same period using the MARS

scale consisting of 5 items with 25 score points in total. For example, one item is “I forget to

take them”.












It is commonly accepted that it is not necessary to take 100% of a prescribed treatment, and

80% adherence is a commonly used cut-off to define adherence, especially in medicine

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benefit/safety studies.17

While the MARS scale does not allow direct assessment of the

percentage of prescribed treatment taken, we used a cut-off of 20 out of 25 on this scale to

define adherence, indicating that patients are taking a large proportion of their prescribed

doses. Adherence was calculated as sum of the scores from each item divided by the

maximum points of 25 using the 5-item MARS scale. Non-adherent patients were defined as

having a score <80% adherence to medicine.

Attitudinal analysis

Participants were categorized into attitudinal groups (i.e. Skeptical, Ambivalent, Indifferent

and Accepting groups) based on whether they scored above or below the scale midpoint for

BMQ necessity and concerns scales. Non-adherence was investigated between the groups.17














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Results











BMQ results









Specific-Concerns scales shows that 45% of patients were classified as ‘Ambivalent’, 45%

as ‘Accepting’, 4% as ‘Skeptical’ and 6% as ‘Indifferent’ (Figure 1).

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and BMQ-Specific. Specific-Necessity was positively associated with General-Benefit






Harm and PSM.






95% CI 1.16-1.80). Negative associations between non-adherence and Specific-Necessity and

General-Benefit were not statistically significant (Table 4). The subgroup analyses showed

that the point estimates of Specific-Concerns were positively associated with non-adherence

across the three conditions i.e. the more concerns about the medicines, the more non-adherent

to medicines, with adjusted ORs ranging from 1.15 (95% CI 0.67-1.98) for diabetes mellitus

to 1.43 (95% CI 1.02-2.00) for stroke (Table 5). The unadjusted results showed similar results

across the three disease conditions. The sensitivity analysis showed that Specific-Concerns

and PSM were positively associated with non-adherence (adjusted ORs, 1.15 95% CI 1.04-

1.26 and 1.40, 95% CI 1.04-1.26, respectively) and General-Benefit was negatively


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Only patients in the Ambivalent group showed a statistical significance.

Discussion



results revealed that Specific-Concerns and PSM were significantly associated with non-

adherence i.e. the higher the concern about the medicine or the higher perceived sensitivity to

medicines. Using the continuous MARS-5 scores showed similar relationships to those

reported in logistic regression (r=-0.09, p<0.01 for Specific-Concerns and r=-0.09, p<0.01 for

PSM). We noted differences between groups that might indicate different attitudes towards

the diseases and their treatments or be attributable to other factors. The hospital out-patients

(diabetes mellitus and RA) included some patients who had previous hospitalisations and

come back to hospital for regular monitoring.



but not consistent








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study with a borderline significant OR of 0.75 (95%CI 0.56-1.02). There was no correlation

between Necessity and Concern (Table 2) in our study population and this was supported by

a recent Swedish study conducted in 578 stroke patients.19

They reported a Spearman’s

correlation coefficient of 0.075 (p=0.08). Compared to patients with high necessity and low

concerns, patients with high necessity and high concerns, low necessity and high concerns,

and low necessity and low concerns were all associated with non-adherence and this was

supported by studies conducted in patients with inflammatory bowel disease and renal

dysfunction.20 21

High perceived sensitivity to medicines was reported to be associated with

non-adherence and higher medical care utilisation, increased symptom reporting and greater

information-seeking about medication.18, 22

Our study also showed PSM was associated with

non-adherence.




was more likely to represent the disease population from each region as large teaching

hospitals provide health care service to majority of Chinese patients. However, we

acknowledge that some patients with Transient Ischemic Attacks (TIAs), a subtype of stroke

may not be admitted to hospital. Therefore our stroke in-patients may under represent the

TIA patients. Thirdly, China has higher prevalence rates of stoke and diabetes mellitus than

Western Europe and stroke is also the leading cause of death in China. In light of the current

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deteriorated relationship between health professional and patients (i.e. patients do not trust

their doctors and other health professionals, doctors, nurses are often abused verbally and

physically by patients,11, 23, 24

more studies are needed from the patients’ perspective. More

consideration from the patient perspective should be taken into account in term of chronic

disease management and the relationship between patients and health professionals.

However, our study was an observational study. Therefore the results may be confounded by

unmeasured factors such as comorbidity. Self-reported adherence may not be the best

measurement for medicine adherence as patients may, for example, underestimate how often

they forget their treatment. However, a strong correlation was found for medicine adherence

measured by self-reported questionnaire, electronic prescriptions and serum biomarkers in a

recent intervention study suggesting that self-report is a reliable measure.25

Also our study

found relatively low reliability of the BMQ general scales and further investigation and

refinement might be needed of these Chinese translations. The difference in non-adherence

between stroke and other conditions needs to be explored in future studies as disease itself

may play an important role in medicine adherence.









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Originator of BMQ.

References:



on 30 January 2017)



2011;42(12):3651-4.




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2011 Dec 16.

5. China Total population, 2015. http://www.who.int/countries/chn/en/ (accessed on 25 May

2017)

6. Wu X, Zhu B, Fu L, Wang H, Zhou B, Zou S, et al. Prevalence, incidence, and mortality of

stroke in the chinese island populations: a systematic review. PLoS One. 2013 Nov

8;8(11):e78629. doi: 10.1371/journal.pone.0078629. eCollection 2013.


Preventable Hospitalizations for Hypertension. Am J Prev Med. 2016 Apr;50(4):489-99. doi:

10.1016/j.amepre.2015.08.021. Epub 2015 Oct 31.



veterans health administration. Am Heart J. 2014 Jun;167(6):810-7. doi:

10.1016/j.ahj.2014.03.023. Epub 2014 Apr 5.










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10.1136/bmjopen-2015-008221.



6736(16)31999-7.







16. Bland JM, Douglas G Altman, DG. Statistics notes: Cronbach's alpha. BMJ 1997;

314:572 doi: https://doi.org/10.1136/bmj.314.7080.572



study. Heart 2002;88:229-33.




10.1016/j.yebeh.2013.10.016. Epub 2013 Nov 27.



survey. BMJ open. 2013;3(9):e003551.

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2009 Jun;15(6):837-44. doi: 10.1002/ibd.20846.







doi: 10.1002/pds.3751. Epub 2015 Apr 7.


Management. 2009;29(332):40-3.


January 2017).




10.1136/bmjqs-2015-004670.

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Stroke

n=313 (100%)

Diabetes

n=315 (100%)

RA

n=339 (100%)

Age, mean (SD) * 65.8 (13.7) 62.5 (13.9) 49.7 (12.8)

Gender†*

Women 136 (43.5%) 141 (44.8%) 287 (85.2%)

Men 177 (56.5%) 174 (55.2%) 50 (14.8%)

Education*


education)

15 (4.8%) 13 (4.1%) 79 (23.3%)

Primary school 66 (21.1%) 62 (19.7%) 93 (27.4%)




Unknown 2 (0.6%) 2 (0.6%) 3 (0.9%)

Occupation*

Farmer 5 (1.6%) 22 (7.0%) 110 (32.5%)

Worker/clerk 83 (26.5%) 117 (37.1%) 38 (11.2%)


Retirement 129 (41.2%) 54 (17.1%) 40 (11.8%)


Civil service 29 (9.3%) 72 (22.9%) 11 (3.2%)

Unknown 3 (1.0%) 9 (2.9%) 12 (3.5%)

Duration of the disease*

<= 1 year 178 (56.9%) 20 (6.4%) 55 (16.2%)

1-5 years 91 (29.1%) 74 (23.5%) 104 (30.7%)

> 5 year 44 (14.1%) 220 (69.8%) 177 (52.2%)


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Stroke

n=313

Diabetes

n=315

RA

n=339


* 3.69 (0.53) 3.75 (0.40) 3.66 (0.44)


3.03 (0.71) 3.15 (0.58) 3.07 (0.58)


* 3.22 (0.62) 3.12 (0.50) 2.95 (0.51)


2.94 (0.78) 2.95 (0.50) 2.99 (0.43)


* 3.70 (0.53) 3.69 (0.42) 3.55 (0.45)

PSM (mean, SD) †

* 2.89 (0.65) 2.35 (0.64) 2.70 (0.68)


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Overall

Necessity 1.00

Concern 0.02(0.45) 1.00

Overuse -0.01 (0.87) 0.39 (<0.01) 1.00

Harm -0.05 (0.16) 0.49 (<0.01) 0.45 (<0.01) 1.00

Benefit 0.31(<0.01) -0.09 (0.01) 0.11 (<0.01) 0.04 (0.21) 1.00

PSM 0.04 (0.22) 0.45 (<0.01) 0.39 (<0.01) 0.38<0.01) 0.05 (0.14) 1.00

Stroke

Necessity 1.00

Concern 0.04 (0.43) 1.00

Overuse 0.06 (0.31) 0.50 (<0.01) 1.00

Harm -0.06 (0.27) 0.66 (<0.01) 0.57 (<0.01) 1.00

Benefit 0.36 (<0.01) 0.01 (0.88) 0.24 (<0.01) 0.13 (0.12) 1.00

PSM 0.23 (<0.01) 0.52 (<0.01) 0.51 (<0.01) 0.50 (<0.01) 0.22 (<0.01) 1.00

Diabetes mellitus

Necessity 1.00

Concern 0.02 (0.59) 1.00

Overuse -0.05 (0.40) 0.26 (<0.01) 1.00

Harm -0.08 (0.20) 0.21 (<0.01) 0.44 (<0.01) 1.00

Benefit 0.19 (<0.01) -0.07 (0.26) 0.18 (<0.01) 0.08 (0.19) 1.00

PSM -0.08 (0.20) 0.36 (<0.01) 0.38 (<0.01) 0.44 (<0.01) 0.07 (0.23) 1.00

RA

Necessity 1.00

Concerns 0.01 (0.91) 1.00

Overuse -0.08 (0.13) 0.41 (<0.01) 1.00

Harm 0.02 (0.72) 0.42 (<0.01) 0.30 (<0.01) 1.00

Benefit 0.30 (<0.01) -0.21 (<0.01) -0.20 (<0.01) -0.15 (0.21) 1.00

PSM -0.04 (0.52) 0.54 (<0.01) 0.29 (<0.01) 0.27 (<0.01) -0.13 (0.02) 1.00

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Unadjusted

OR, 95% CI

Adjusted*

OR, 95% CI



General-Overuse 1.32, 1.04-1.69 1.15, 0.88-1.50

General-Harm 1.09, 0.87-1.38 1.12, 0.89-1.43

General-Benefit 0.84, 0.63-1.12 0.75, 0.56-1.02

PSM 1.72, 1.41-2.09 1.44, 1.16-1.85


diseases

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Stroke Diabetes RA

Unadjusted

OR, 95% CI

Adjusted*

OR, 95% CI

Unadjusted

OR, 95% CI

Adjusted*

OR, 95% CI

Unadjusted

OR, 95% CI

Adjusted*

OR, 95% CI

Specific-

Necessity

0.83, 0.54-1.26 0.92, 0.59-1.43 0.71, 0.35-1.40 0.92, 0.43-1.97 1.31, 0.75-2.27 1.34, 0.73-2.46

Specific-

Concerns

1.43, 1.04-1.96 1.43, 1.02-2.00 1.16, 0.71-1.88 1.15, 0.67-1.98 1.38, 0.91-2.12 1.32, 0.84-2.10

General-

Overuse

1.26, 0.88-1.81 1.24, 0.85-1.82 1.12, 0.64-1.96 1.10, 0.61-2.00 1.06, 0.66-1.71 0.98, 0.60-1.60

General-

Harm

1.26, 0.95-1.68 1.30, 0.96-1.77 0.58, 0.32-1.05 0.59, 0.32-1.11 1.27, 0.72-2.24 1.27, 0.70-2.30

General-

Benefit

0.87, 0.57-1.32 0.83, 0.53-1.29 0.78, 0.41-1.50 0.83, 0.41-1.69 0.64, 0.37-1.09 0.65, 0.37-1.13

PSM 1.76, 1.22-2.53 1.79, 1.23-2.60 1.60, 1.05-2.44 1.73, 1.10-2.70 1.06, 0.74-1.51 1.01, 0.70-1.48


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24

Figure legend


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254x190mm (300 x 300 DPI)

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STROBE 2007 (v4) Statement—Checklist of items that should be included in reports of cross-sectional studies

Section/Topic Item

# Recommendation Reported on page #

Title and abstract 1 (a) Indicate the study’s design with a commonly used term in the title or the abstract Page 2

(b) Provide in the abstract an informative and balanced summary of what was done and what was found Page 2

Introduction

Background/rationale 2 Explain the scientific background and rationale for the investigation being reported Page 5-6

Objectives 3 State specific objectives, including any prespecified hypotheses Page 6

Methods

Study design 4 Present key elements of study design early in the paper Page 6

Setting 5 Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data

collection

Page 6

Participants

6

(a) Give the eligibility criteria, and the sources and methods of selection of participants Pages 6-8

Variables 7 Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic criteria, if

applicable

Pages 7-8

Data sources/

measurement

8* For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe

comparability of assessment methods if there is more than one group

Page 7-8

Bias 9 Describe any efforts to address potential sources of bias Page 9

Study size 10 Explain how the study size was arrived at Page 6

Quantitative variables 11 Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen and

why

Page 9

Statistical methods 12 (a) Describe all statistical methods, including those used to control for confounding Page 9

(b) Describe any methods used to examine subgroups and interactions Page 11

(c) Explain how missing data were addressed Page 9

(d) If applicable, describe analytical methods taking account of sampling strategy n/a

(e) Describe any sensitivity analyses Pages 9 &12

Results

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Participants 13* (a) Report numbers of individuals at each stage of study—eg numbers potentially eligible, examined for eligibility,

confirmed eligible, included in the study, completing follow-up, and analysed

Page 9

(b) Give reasons for non-participation at each stage n/a

(c) Consider use of a flow diagram n/a

Descriptive data 14* (a) Give characteristics of study participants (eg demographic, clinical, social) and information on exposures and potential

confounders

Page 9 and Page 18

(b) Indicate number of participants with missing data for each variable of interest Page 18

Outcome data 15* Report numbers of outcome events or summary measures Pages 10-12

Main results 16 (a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (eg, 95% confidence

interval). Make clear which confounders were adjusted for and why they were included

Pages 22-23

(b) Report category boundaries when continuous variables were categorized Pages 8 & 10

(c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period n/a

Other analyses 17 Report other analyses done—eg analyses of subgroups and interactions, and sensitivity analyses Page 11

Discussion

Key results 18 Summarise key results with reference to study objectives Page 11

Limitations 19 Discuss limitations of the study, taking into account sources of potential bias or imprecision. Discuss both direction and

magnitude of any potential bias

Page 13

Interpretation 20 Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results from

similar studies, and other relevant evidence

Pages 11-14

Generalisability 21 Discuss the generalisability (external validity) of the study results Page 13

Other information

Funding 22 Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on

which the present article is based

Page 14

*Give information separately for cases and controls in case-control studies and, if applicable, for exposed and unexposed groups in cohort and cross-sectional studies.

Note: An Explanation and Elaboration article discusses each checklist item and gives methodological background and published examples of transparent reporting. The STROBE

checklist is best used in conjunction with this article (freely available on the Web sites of PLoS Medicine at http://www.plosmedicine.org/, Annals of Internal Medicine at

http://www.annals.org/, and Epidemiology at http://www.epidem.com/). Information on the STROBE Initiative is available at www.strobe-statement.org.

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