CASE CONTROL STUDY - Mahidol University · Case-control study Patarawan Woratanarat, M.D., Ph.D....

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Case-control study

Patarawan Woratanarat, M.D., Ph.D. (Clin. Epid.)

Department of Orthopaedics

Faculty of Medicine Ramathibodi Hospital

Objectives

� To understand

� A concept of case-control study

� Conduct a case-control study

� Selection of study population

� The principle of measurement

� Data collection

� Analysis

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Types of research

� Qualitative / quantitative

� Descriptive

� Exploratory/observational: case-control, cohort, cross-sectional study

� Experimental: RCT

4 groups

Think about your research question?

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Study designs

� Guideline

� Therapy � RCT/Systematic review

� Diagnosis � Cross-section

� Screening � Cross-section

� Prognosis � Cohort

� Causation � Cohort, case-control

A concept of case-control study

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Study designs

� Case-control study

Direction of the study

Population

People with disease

People withoutdisease

Exposed

Exposed

Not exposed

Not exposed

Study designs


Direction of the study

Population

THR patientsWith DVT

THR patientWithout DVT

Spinal anesthesia

Spinal anesthesia

General anesthesia

General anesthesia

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Case-control studies

� Advantages

� Valuable for rare conditions

� Short duration

� Inexpensive

� Small sample size

� Yield odds ratio

� Disadvantages

� Limit to one outcome

� Potential selection bias

� Measurement bias

� Survivor bias

� Do not establish a temporal sequence

� Do not yield absolute risk estimates

Conducting a case-control study

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Research process

� Phase I: Identify the research question

� Phase II: Design the study

� Phase III: Methods

� Phase IV: Data analysis

� Phase V: Communication

Research question

� Hypothesis: � a statement in which an attempt is made to

generalize about the nature of the universe in which we live.

� To act as a guide in interpreting the wider meanings of a particular data set

� Research question� Identifies the issue to be addressed by the

research , it does not have to be stated in a testable form

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Research question

� Research problem� Clinical experience, theory, literature

� Research question should be� Important

� Answerable

� Feasible

� Identify� Target population

� Variables

Research question

� Hypothesis: non directional

� Ho: There is no difference in the reduction of DVT in Thai patients who undergo elective total hip replacement under spinal anesthesia compared with general anesthesia

� Ha: There is a difference in the reduction of DVT in Thai patients who undergo elective total hip replacement under spinal anesthesia compared with general anesthesia

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Research question

� Hypothesis: directional

� Ho: spinal anesthesia does not reduce risk of DVT in Thai patients who undergo elective total hip replacement from 10% to 3% when compared with general anesthesia.

� Ha: spinal anesthesia reduce risk of DVT in Thai patients who undergo elective total hip replacement from 10% to 3% when compared with general anesthesia.

J Arthroplasty. 1999;14(4):456-63.Clin Orthop. 1989;247: 163-7.

Research question

� Research question

� Does spinal anesthesia reduce risk of DVT in Thai patients who undergo elective total hip replacement?

� Objective

� To determine the effect of spinal anesthesia to the occurrence of DVT in patients who undergo elective total hip replacement.

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Literature review

� Literature search

� Source

� Primary: Medline, CINAHL, Ovid, Springer, Science direct

� Secondary: Cochrane database, Uptodate, DARE, ACP journal club, Tripdatabase, e-medicine

� Critical appraisal

Group discussion

Gr 1: New (incident) case or prevalence case

Gr 2: Case - definition, inclusion & exclusion criteria

Gr 3: Control – definition, inclusion & exclusion criteria

Gr 4: Matching – yes/no and why?

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Design the study

� Population and sample

� Target population/reference population

� Study population

Target population

Accessible population

Study population

Thailand

Bangkok

Ramathibodi Hospital

Sampling bias…….

Selection of cases

� Definition

� Diseases, ICD-10

� Example: osteoporotic hip fracture definition

� Thai adults, age ≥≥≥≥ 51 years old whom are admitted in orthopedic wards with the first episode of osteoporotic hip fracture, ie. fracture of femoral neck, intertrochanter, subtrochanter sustained from low-velocity accident.

� (ICD-10, S72.0-72.9)

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Selection of cases

� Probability samples (Random selection)

� Simple random sampling

� Systematic sampling

� Stratified random sampling

� Cluster sampling

� Non probability samples

� Convenience sampling

� Quota sampling

� Proposive sampling

� Snowball sampling (chain referral)

Sampling techniques

Selection of cases

� Whole population

� Hospital

� Incident cases

� Avoid prevalent cases (distort exposure)

� Example:

New case of spinal stenosisFloor activity

Chronic spinal stenosisFloor activity X

5 years

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Selection of controls

� Definition: no outcome (case definition)

� Example:

� Thai adults who are neighborhoods ofcases aged 51 years and were not directrelatives of cases. No fracture offorearm, spine, and hip.


� Sampling

� Site: the same as cases

� Hospital or community

� Has an opportunity to expose to the exposure

� Can be cases in the future

� Example:

� Controls of CACx: male?, child?

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Population vs Hospitalbased case-control study

� Population based

� Can define source of population

� Cases and controls are from the same source

� Exposure in the controls represent real situations

� Hospital based

� Convenience

� Good cooperation

� Baseline characteristics are similar to cases

� Convenience for searching available exposure data

Examples

A case-control study

� Risk factors for Hip fracture

� Drugs vs. road traffic accident

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Risk factors for hip fracture

� Frequency matching case-control study; 1:1 of cases :controls. Matched by sex and age + 5 years (not less than 51years old).

� Hospital controls: same hospital

� Community controls: neighborhoods� Search for address registry and national ID

� Pick up people who was in required age and lived within 1 km from case’s address

Risk factors for hip fracture

Total recent activity scores

Cases vs Hospital controls

Cases vs Community controls

OR (95%CI) P-value OR (95%CI) P-value

Inactive* 0.80

(0.51-1.25)

0.341 0.32

(0.20-0.50)

<0.0001

Active 0.53

(0.32-0.87)

0.012 0.20

(0.12-0.34)

<0.0001

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DRUG vs. ROAD TRAFFIC INJURYDRUG vs. ROAD TRAFFIC INJURY

• Case verification by ER nurses• Informed consent

3. Alcohol breath testBlood for alcohol levelUrine collection

5. Case admissioninterview by ward nurseswithin 72 hours

4. Notification To ward & Research center

6. Specimen & questionnairepickup by Research center (Rama)

Mobile unit

1. Verify site from case RTI area

2. Search gas stations

3. Contact gas stations

4. Data collection

Controls

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Hospital-based?

� Recommendation� Unspecified disease (reflect real

exposure)

� New patients

� Low number of underlying diseases

� Avoid disease that correlated with the interesting exposure

� Example: Patients, aged 51 years, who are newly admitted (not 1st admission) in other wards in the same hospital and were not severely ill.

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Matched??

� Advantages� More reliable for a

comparison between case and controls

� Need small samples

� More specific controls

� Discard confounding factors which were matched

� Disadvantages� Time and budget consuming

� Hard to find a specific control– discard matched case

� Unable to find a relationship between matched variables and outcome

� Residual difference if match for continuous or ordinal data

� Overmatching: cannot find the difference between cases and controls

Matched

� 1:1

� 1:2 – 1:4

� Decreased sample size of cases

Alpha Power Po OR Match N of cases

0.05 0.8 0.03 3 2

4

216

116

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Frequency matching

� Match 2-3 variables

� Example

� Controls were matched to the cases according to sex and age + 5 years. And they were admitted to the same hospital within 90 days before or after the admission date of the cases.

Nested case-control study

Cohort studyDisease free + collect baseline characteristics

Follow-up

Diseases Disease free

Review previously collected dataObtaining additional exposures

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Group discussion

Gr 1: What is the primary exposure, why?

Gr 2: Study factors and measurements

Gr 3: Data collection

Gr 4: Sample size calculation – what do you need to prepare?

Measurement of exposures

� Define exposures

� Try to retrieve hard data

� Measurement methods

� Interview

� Questionnaire

� Medical records

� Others: data registry, VDO, x-ray, etc.

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Measurement of exposures

� Measurement

� Validity = accuracy

� Recall bias

� Incomplete data

� Precision

Precision

Methods

� Data collection

� Methods: interview (questionnaire), physical examination, laboratory test

� Sources: medical records, x-ray, patients

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Sample size

� Formula

� Power and sample size program

� PS

� EpiInfo

� Internet access

Sample size

� Think about outcome first

� Categorical data eg. death: proportion

� 1 or 2 group?

� 2 proportions

� Paired/unpaired

� How clinical difference it is?

� 2 groups: How clinical difference they are?

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Sample size

� Formula

� 2 proportions

� N = [2(Z(alpha)+ Z(beta))2P(1-P)]

(P1-P2)2

Note: P = (P1+P2)/2

Sample size

� Determine

� Alpha error

� Usually 0.05 or 0.1

� Beta error (1-power of study)

� Usually 0.2 or 0.1

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Sample size calculation

Alphaerror

Betaerror

Physical activity among

controls

Odds ratio of physical

activity

N

0.05 0.2 0.8 0.62 401

0.05 0.2 0.8 0.6 349

0.05 0.2 0.8 0.55 253

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Group discussion

Gr 1: analysis plan for primary exposureGr 2: analysis plan for study factorsGr 3: What is odds ratio?Gr 4: How can you apply the results?

Analysis

� Type of data

� Nominal scale: yes/no, male/female

� Ordinal scale (non equal distance between unit): mild/moderate/severe

� Interval scale (equal distance between unit): visual analog scale, range of motion

� Normal/non normal distribution

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Statistical analysis

Parametric

Study group Continuous data Categorical data

1 group Mean + Standard deviation Proportion, percentage

2 group

- Independent Unpaired T-test Chi-square

- Matched pair, pre-/post) Paired T-test McNemar’s Chi-square

> 2 groups Analysis of variance Chi-square

Statistical analysis

nonparametric

Study group Continuous data Categorical data

1 group Sign test Proportion, percentage

2 Groups

- Independent Mann-Whitney U test Fisher’s exact

- Matched pair or pre-, post- Wilcoxon sign-rank test McNemar’s Chi-square

> 2 กลุ่ม Kruskall-Wallis Fisher’s exact

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Case control study

Exposure Disease No disease

Total No. of cases

Poor work

Good work

Total

+ a b a+b > 10 80 10 90

- c d c+d < 10 20 90 110

a+c b+d n 100 100 200

Term General Example Definition

Odds ratio ad/bc 80x90/20x10

= 36

The odds of exposure in case/the odds of exposure in control (odds of having disease comparing exposed and unexposed)

[a/(a+b)] / [b/(a+b)] = a/b = ad[c/(c+d)] / [d/(c+d)] c/d bc

Stata . cci 80 20 10 90

Proportion

| Exposed Unexposed | Total Exposed

-----------------+------------------------+-------- ----------------

Cases | 80 20 | 100 0 .8000

Controls | 10 90 | 100 0.1000

-----------------+------------------------+-------- ----------------

Total | 90 110 | 200 0 .4500

| |

| Point estimate | [95% Conf. Interval]

|------------------------+------------------------

Odds ratio | 36 | 14.97669 89.7686 (exact)

Attr. frac. ex. | .9722222 | .9332 296 .9888602 (exact)

Attr. frac. pop | .7777778 |

+-------------------------------------------------

chi2(1) = 98.99 Pr>chi2 = 0.0000

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McNemar test

Cases Controls Total

meditat+ meditat-

meditat+ 200 (a) 100 (b) 300

meditat- 150 (c) 450 (d) 600

Concordance pairs

χ2 = Σ (|O-E|-1/2)2/E

••Meditation vs. Degenerative spine Meditation vs. Degenerative spine

. . mcci mcci 200 100 150 450200 100 150 450

| Controls || Controls |

Cases | Exposed Unexposed | Tot alCases | Exposed Unexposed | Tot al

----------------------------------++------------------------------------------------++---------- ----------

Exposed | Exposed | 200 100 200 100 | | 300300

Unexposed | Unexposed | 150 450 150 450 | | 600600

----------------------------------++------------------------------------------------++---------- ----------

Total | Total | 350 550 350 550 | | 900900

McNemar's chiMcNemar's chi22((11) = ) = 1010..00 00 Prob > chiProb > chi2 2 = = 00..00160016

Exact McNemar significance probability = Exact McNemar significance probability = 00.. 00190019

Proportion with factorProportion with factor

Cases .Cases .33333333333333

Controls .Controls .3888889 3888889 [[9595% Conf. Interval]% Conf. Interval]

------------------ ----------------------------------------

difference difference --..0555556 0555556 --..0909079 0909079 --..02020320202032

ratio .ratio .8571429 8571429 ..7789666 7789666 ..94316489431648

rel. diff. rel. diff. --..0909091 0909091 --..1497595 1497595 --..03205870320587

odds ratio .odds ratio .66666676666667 ..512362 512362 ..86434298643429 (exact)(exact)

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Logistic regression

� Categorical outcome� Extraneous variables associated with outcome

� Multiple� Continous/categorical data

� For case-control study� Matched: conditional logistic regression� Unmatched: unconditional logistic regression

� Output: Odds ratio, adjusted odds ratio

Logistic regression

� Probability of having disease� P = 1

1 + e (a+b1x1+…..+bixj)

� 95% confidence interval: � Significant value: should no include 1� Precision: narrow� Ex: Odds ratio = 5.3 (95% CI: 3.4,8.5)� Ex: Odds ratio = 5.3 (95% CI: 1.2, 16.9)

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ปัจจยั SE Adjusted OR (95% CI) P-value

ผลแอลกอฮอลท์างลมหายใจ(mg/dl)

> 50 35.62 68.89 ( 25.01-189.78) <0.001

< 50 1

ประเภทของยา

ยาที�มฤีทธิ �ต่อจติประสาท 0.88 3.05( 1.73-5.37) <0.001

ยาอื�นๆ 1

Ethical considerations

� Scientifically accepted

� First do no harm

� Risk/Benificence

� Institutional Board Review

� Informed consent� Contact persons, background, what

patient will be done/have to do, risk/benefit, patient’s rights.

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Results

� Results

� Dummy table

� Demographic data

� Main results

� Univariate analysis

� Multivariate analysis

Results (tentative)

� Dummy tables

Demographic data Case N = 81)

Control (N = 81)

P-value

Age, years (mean + SD)Male (%)Income, Baht (%)- 0 – 10,000- > 10,000 – 19,999- > 20,000 – 29,999- > 30,000Educational level (%)- No- Primary school- High school- Bachelor- Higher

*

Table 1 Demographic data

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Results (tentative)

� Dummy tables

Factors Case (N = 81)

Control (N = 81)

P-value

BMI (mean + SD)Anitcoagulant use Underlying disease (%)Type of anesthesia (%)- Spinal - General

*

Table 2 Factors related to DVT in THR patients

Results (tentative)

� Dummy tables

Factors Odds ratio 95% confidence interval

P-value

AgeBMI (mean + SD)Anticoagulant useUnderlying disease (%)Type of anesthesia (%)- Spinal - General

*

Table 3 Univariate analysis of factors related to DVT in THR patients

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Results (tentative)

� Dummy tables

Factors Adjusted odds ratio

95% confidence interval

P-value

AgeAnticoagulant useType of anesthesia (%)- Spinal - General

*

Table 4 Multivariate analysis of factors related to DVT in THR patients

Budget

� Researchers

� Statisticians

� Data collection/entry

� Materials: printing expenses, etc.

� Investigations

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Time line

� Proposal writing

� Data collection

� Data entry

� Data analysis

� Results

� Writing a paper

Month1 2 3 4 5

Applicability

� Expected usefulness of this study

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QUESTIONS

‘ Epidemiology of hip fracture in Thais ’

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Introduction

� Hip fracture

� Neck

� Intertrochanter

� Subtrochanter

� Most common in elderly people

� Incidence:

White > Asians > Black

Neck

Intertrochanter

Subtrochanter

Introduction

� Recently increased incidence of hip fracture

� Cause of morbidity(50-70%) and mortality(20%) among elderly

� Contribute significantly to health care costs0

200

400

600

800

1000

1960-

1980

1983-

1985

HongKongNorway

Incidence of hip fracture (per 100000)

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Rationale

� Big problems of hip fracture all over the world

� No support data of hip fracture in Thailand

� Incidence

� Risks & prevention

� Differences in incidence and risks among countries, race, and types of fracture.

Objectives

� To determine factors related to hip fracture in Thai adults, age 51 years or over, separately by sex.

� To compare factors related to intertrochanteric fracture and femoral neck fracture in Thai adults, age 51 years or over, separately by sex.

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Literature reviewLiterature reviewFactors related to hip fractureFactors related to hip fracture

Conceptual frameworkConceptual framework

Hip fracture

High BMDEstrogen

Calcium Physical Activity

FallingSmoking

Cancer renal diseasemal-absorption

Drugs-sedatives-antihistamineAlcoholPoor mental status

CVAParkinsonism

Race

Steroid, traditional med.

IncreaseDecrease

BMI

Diuretics

MethodologyFactors related to hip fracture

� Setting: hospitals in Bkk and its vicinity

� Matched case:control = 1:1 by age + 5 y and sex

� Population: Thai adults age > 51 y� Cases: ICD 9 (820.0-820.9) by orthopaedists

� Hospital controls: patients in other wards admitted w/i 90 days from case admission date, w/o fx

� Community controls: neighborhood of cases w/o fx

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MethodologyFactors related to hip fracture

� Main exposure variables:

� Physical activities, calcium intake

� Sample size: 401 (alpha error 0.05, beta error 0.2, OR of

physical activity 0.62, physical activity among controls 0.8)

� Data collection: interview with questionnaire

� Ethical consideration: verbal informed consent

� Statistical analysis: logistic regression (STATA 7.0 program)

ResultsFactors related to hip fracture (women)

Baseline characteristics

Case (%)

N = 231

Hospital controls (%)

N = 226

Community controls (%)

N = 224

Total (%)

N = 681

Age (years)

(mean+SD)

Race

Thai

Chinese

BMI (kg/m2)

(mean+SD)

Low

Medium

High

Mental status

Normal

Poor

75.3+9.1

141 (61.0)

90 (39.0)

22.2+4.0

83 (35.9)

62 (26.8)

86 (37.2)

203 (87.9)

28 (12.1)

74.4+8.5

187 (82.7)

39 (17.3)

23.5+4.1

58 (25.7)

80 (35.4)

88 (38.9)

207 (91.6)

19 (8.4)

73.9+8.4

176 (78.6)

48 (21.4)

23.5+4.6

76 (33.3)

75 (33.6)

73 (33.5)

220 (98.2)

4 (1.8)

74.6+8.7

504 (74.0)

177 (26.0)

23.1+4.3

217 (31.9)

217 (31.9)

247 (36.3)

630 (92.5)

51 (7.5)

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Factor Case vs Hosp cont Case vs Com cont


Race

Thai*

Chinese

Recent activity

Inactive*

Active

Very active

Past activity

Inactive*

Active

Very active

3.06 (1.95-4.81)

0.74 (0.46-1.20)

0.57 (0.33-0.99)

0.95 (0.60-1.49)

1.01 (0.61-1.68)

<0.0001

0.231

0.047

0.824

0.949

2.33 (1.36-3.99)

0.31 (0.17-0.57)

0.22 (0.11-0.44)

0.78 (0.46-1.33)

0.18 (0.09-0.37)

0.002

0.372

<0.0001

<0.0001

<0.0001

Multivariate analysis: adjusted for age


Multivariate analysis: adjusted for age (continue)Factor Case vs Hosp cont Case vs Com cont


BMI

Low*

Medium

High

Calcium

Low*

Medium

High

CVA

Diuretics

Antihistamine

Traditional med.

0.52 (0.32-0.85)

0.70 (0.44-1.11)

1.08 (0.37-1.74)

1.11 (0.68-1.81)

-

-

-

-

0.010

0.131

0.728

0.653

-

-

-

-

1.12 (0.63-1.98)

0.90 (0.50-1.62)

0.36 (0.19-0.68)

0.66 (0.37-1.18)

8.98 (2.27-35.45)

3.40 (1.06-10.89)

13.45 (1.37-131.27)

6.06 (2.02-18.22)

0.690

0.740

0.002

0.167

0.002

0.039

0.025

0.001

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ResultsResultsFactors related to hip fractureFactors related to hip fracture (women)(women)

Multivariate final model Multivariate final model

Hip fracture

High BMDEstrogen

Calcium** Physical Activityrecent* **past**

FallingNo of liveborn**

Diuretics **

Drugs-sedatives**-antihistamine**Poor mental status**

CVA**

Race* **

Traditional med.**

* Hosp. control** Com. control

Increase Decrease

BMI*

ResultsFactors related to hip fracture (men)

Baseline characteristics

Case (%)

N = 187

Hospital controls (%)

N = 186

Community controls (%)

N = 177

Total (%)

N = 550

Age (years)

(mean+SD)

Race

Thai

Chinese

BMI (kg/m2)

(mean+SD)

Low

Medium

High

Mental status

Normal

Poor

71.2+9.8

115 (61.5)

72 (38.5)

21.9+3.4

50 (26.7)

53 (28.3)

84 (44.9)

169 (90.4)

18 (9.6)

70.4+9.6

142 (76.3)

44 (23.7)

21.6+4.1

66 (35.5)

52 (28.0)

68 (36.6)

172 (92.5)

14 (7.5)

69.7+8.6

127 (71.7)

50 (28.3)

22.4+3.8

52 (29.4)

48 (27.1)

77 (43.5)

160 (96.0)

7 (4.0)

70.4+9.4

384 (69.8)

166 (30.2)

22.0+3.8

168 (30.6)

153 (27.8)

229 (41.6)

511 (92.9)

39 (7.1)

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Race

Thai*

Chinese

Recent activity

Inactive*

Active

Very active

Past activity

Inactive*

Active

Very active

1.91 (1.16-3.15)

0.69 (0.39-1.22)

0.75 (0.42-1.35)

0.70 (0.41-1.18)

0.44 (0.23-0.84)

0.011

0.208

0.350

0.184

0.013

2.17 (1.16-4.05)

0.30 (0.15-0.61)

0.50 (0.21-1.18)

0.26 (0.13-0.50)

0.04 (0.01-0.14)

0.014

0.008

0.114

<0.0001

<0.0001

Multivariate analysis: adjusted for age, BMI, calcium, drugs




Smoking

Smoker*

Exsmoker

Nonsmoker

Walking act before fx

Independent*

Partially dep.

Totally dep.

History of fx

CVA

-

-

-

3.05 (1.42-6.53)

-

-

-

0.004

2.58 (1.23-5.43)

0.43 (0.05-3.36)

3.32 (1.31-8.38)

0.18 (0.01-3.20)

3.90 (1.26-12.11)

14.91 (3.12-71.11)

0.012

0.425

0.011

0.248

0.018

<0.0001

Multivariate analysis: (continue)

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ResultsResultsFactors related to hip fractureFactors related to hip fracture (men)(men)

Conceptual frameworkConceptual framework

Hip fracture

High BMDEstrogen

Calcium Physical Activityrecent**past* **

FallingSmoking**

CVA* **

Race* *** Hosp control** Com. control

IncreaseDecrease

Walking activity before fx**

DiscussionFactors related to hip fracture (women)

Factors consistent associated with hip fracture according to

other literature

OR (95%CI)

References

OR (95%CI)

BMI: 0.52 (0.32-0.85)*

Physical activity

Recent:0.57(0.33-0.99)*, 0.22(0.11-0.44)**

Past: 0.18 (0.09-0.37)**

CVA: 8.98 (2.27-35.45)**

Mayer HE: 0.68 (0.63-0.72)

Michaelsson: 0.39 (0.24-0.62)

Jaglal SB

0.54 (0.41-0.90)

0.66 (0.45-0.96)

Grisso JA: 3.00 (1.30-7.00)

* Hospital control* Hospital control

** Community control** Community control

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Factors converselyassociated with hip fracture according to other literature

OR (95%CI)

References

OR (95%CI)

Diuretics: 2.10 (0.62-7.14)** Cummings: 0.8 (0.6-1.2)




New factors associated with hip fracture

Reference

OR (95%CI)

Chinese race: 3.06 (1.95-4.81)*

2.33 (1.36-3.99)**

-



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DiscussionFactors related to hip fracture (men)

Factors consistent associated with hip fracture according to other

literature

OR (95%CI)

References

OR (95%CI)

Physical activity

Recent: 0.30 (0.15-0.61)*

Past: 0.44 (0.23-0.84)8, 0.04 (0.01-0.14)**

CVA: 3.05 (1.42-6.53)*, 14.91 (3.12-71.11)**

Cummings (men & women)

0.50 (0.30-1.00)

0.50 (0.20-1.20)

Grisso: 3.2 (1.9-5.3)




Factors converselyassociated with hip

fracture according to other literature

OR (95%CI)

References

OR (95%CI)

Smoking

Exsmoker: 1.33 (1.04-1.70)**

Eversmoker: 0.68 (0.39-1.71)**

Cummings

Exsmoker: 1.4 (0.6-2.5)

Eversmoker: 1.6 (1.0-2.6)



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New factors associated with hip fracture

Reference

OR (95%CI)

Chinese race: 1.91 (1.16-3.15)*

2.32 (1.22-4.40)**

-



DiscussionFactors related to hip fracture

� Limitation of the study� Selection bias: hospital controls

� Recall bias: calcium, past physical activity

� Measurement bias: calcium, BMI, physical activity

� Misclassification bias: underlying diseases, drugs

� Ascertainment bias: underlying diseases, drugs

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Conclusion

� The important factors related to hip fracture are physical activity, Chinese race, and CVA.

� Physical activity and CVA also related to both IT & FN. Chinese race and sedative drugs are associated with FN whereas impaired walking ability is associated with IT.

Recommendation

� ICD register for evaluation and monitoring hip fracture incidence in Thailand.

� It is time to prevent hip fracture by exercise, prevent and give good care for CVA

� Verify calcium as a protective factor by prospective study with log diary.(CEA, CBA)

� Genetic study for verify risk (Chinese race)

� Cost-effectiveness analysis

� Cost-utility analysis

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MMEDICINALEDICINAL DDRUGRUG UUSESE ANDAND RTI:RTI:A Case-Control Study.

Patarawan Woratanarat, MD, PhD.Patarawan Woratanarat, MD, PhD.

Atiporn Ingsathit, MD, PhD.Atiporn Ingsathit, MD, PhD.

Paibul Suriyawongpaisal, MD, Paibul Suriyawongpaisal, MD, MMSc.MMSc.

Faculty of Medicine Ramathibodi HospitalFaculty of Medicine Ramathibodi Hospital

Introduction

Vehicle factor

Human factors:Driving behaviorPhysical status AlcoholDrugsDrugs

Road environmentClimate

Road traffic injury (RTI)

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Introduction

� Medicinal drugs compared with blood alcohol level

� Benzodiazepine: 50-79 mg%

� Benzodiazepine + Alcohol: risk 112 times

� Antidepressant/Barbiturates: 80-100 mg%

� Diphenhydramine: 50-100 mg%

Odds ratio between medicinal drugs & RTI

3.5

2.1

0.4

4.4

2

0.4

3.3

1.2 1.50.7

5.1

3

0.30.9

2.4

32.4

1.6

15.515.5

17.6

0.8

0

10

20

30

40

Amp Coc Can BZD Opi Alc

Not fault

Road block(Movig)Road block(Mathijssen)Non-severe

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Odds ratio between medicinal drugs & RTI

3.52.1 0.44.4 2 0.4 3.31.21.50.7

5.1 3 0.3 0.92.4

32.4

1.6

15.515.517.6

0.86.1

24

0.2

112.2

179

0.60

50

100

150

200

Amp Coc Can BZD Opi Alc Multi Alc+drugs

Not fault


Introduction

� Other factors as risks of RTI� Male

� Young age

� Alcohol

� Driving behavior

� Physical status

� Road environment

� Climate

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Objectives

� To assess the relationship between medicinal drug use and road traffic accident

Materials & Methods


� All drivers (general and private)

� March 1, 2006 – November 30, 2006

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Materials & Methods

� Case

� All drivers with RTI admitted to the hospital within 24 hours after a crash

� Exclude: dead cases, unable to give consent/specimens/verbal responses

Materials & Methods

� Control

� All drivers stopped by gas stations without RTI requiring hospitalization within 6 months

� Exclude: unable to give consent/specimens/verbal responses

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Materials & Methods

� Case: 3 Hospitals in Bangkok

Vajira

Lerdsin

Nopparat

Materials & Methods

� Control: gas station matched with cases (1:4) by

� Gender

� Place of accident (within 1 km)

� Time of accident (day/night)

� Type of vehicles

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Materials & Methods

� Psychoactive/illicit drugs

� Psychoactive/medicinal drugs:

� Antihistamine

� Hypnotics

� Antidepressants

� Anti-convulsants

� Cough-suppressants

� Muscle relaxants

Predictors :•Demographic profile•Vehicles •Behavior risk•Alcohol

Materials & Methods

� Measurement of study factors

� Structured questionnaire

� Direct observations(helmet, belt, colors)

� Alcohol Breathalyzer(Lion alcoholmeter 400 series)

� Blood test for alcohol: 5 cc

� Urine test (GC/MS) for various drugs: 50 cc.

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Materials & Methods

� Outcomes

� Severed RTI resulting in hospital admission

� Types of injuries, disability/death (ICD-10)

Materials & Methods

� Data collection

� Questionnaires

� Alcohol breath test

� Blood alcohol level (for case only)

� Urine drug test

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1. Case verification by ER nurses2. Informed consent

3. Alcohol breath testBlood for alcohol levelUrine sample collection

5. Case admissioninterview by ward nurseswithin 72 hours

4. Notification To ward & Research center

6. Specimen & questionnairepickup by Research center (Rama)

CaseCase

Mobile unit

1. Verify site from case RTI area

2. Search gas stations

3. Contact gas stations

4. Data collection

Controls

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Materials & Methods

� Data monitoring: site visit

� Data quality assurance: call subjects

� Data editing

� Double data entry: EpiInfo

Materials & Methods

� Data analysis

� Mean + SD, percentage

� Conditional logistic regression

� Univariate analysis

� Multivariate analysis (backward stepwise)

� PAR calculation (using data from survey study)

� Stata 9.0 (StataCorp, Texas)

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Sample size

P0 OR Case:Control ratio

Subject (N)

0.02

(Probability of hypnotics)

3 4 Case (250)

Control (1000)

0.02

(Probability of hypnotics)

2.5 4 Case (400)

Control (1600)

Results

200 CASES850 CONTROLS

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Diagram 1 Distribution of injury (cases)

41

106 5

59

21

5

0

10

20

30

40

50

60

Brain/head Chest Abdomen Spine Limbs Face Skin

Injury (%)

Table Table Table Table 1 1 1 1 Characteristics of cases and controlsCharacteristics of cases and controlsCharacteristics of cases and controlsCharacteristics of cases and controlsCharacteristics Cases

N = 200 (%)Controls

N = 850 (%)P-value

Age (years), mean (SD) 30.18(11.8) 35.45(10.6) <0.001*

Gender MaleFemale

189(94.5)11(5.5)

803(94.5)47(5.5)

-

Type of vehicles

Car/van/truck/bus 22(11.0) 91(10.7) 0.803

Motorcycle 178(89.0) 759(89.3)

Type of driving

General 161(80.5) 588(69.2) < 0.001*

Commercial 39 (19.5) 262 (30.8)

Experience of driving (years)

< 4 99(49.8) 176(20.7) < 0.001*

5-10 62(31.16) 353(41.5) 0.149

11-15 10(5.0) 96(11.3) 0.553

> 15 28(14.1) 225(26.5)

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Table Table Table Table 1 1 1 1 Characteristics of cases and controls (contCharacteristics of cases and controls (contCharacteristics of cases and controls (contCharacteristics of cases and controls (cont’ ))))

Characteristics Cases N = 200 (%)

ControlsN = 850

(%)

P-value

Timing of driving

Night time/dawn/dust 52(26.0) 66(7.8) <0.001*

Daytime 148(74.0) 784(92.2)

Duration of driving (minutes)

> 90 66(33.9) 189(22.7) <0.001*

41-90 49(25.1) 199(23.9) 0.030*

21-40 43(22.1) 223(26.7) 0.465

<20 37(19.0) 223(26.7)

Protective gear + head light

No 76(38.0) 184(21.7) <0.001*

Yes 124(62.0) 666(78.4)

Table 2 Single variable conditional logistic regression of one-month recall of drug and substances use

Drugs Cases N = 200

(%)

ControlsN = 850

(%)

OR (95% Cl)

P-value

Antihistamine/nasal decongestant

36 (18.0) 148 (17.4) 1.03 (0 .69, 1.55) 0.869

Cough suppressant 8 (4.0) 23 (2.7) 1.61 (0 .69, 3.76) 0.275

Muscle relaxant 12 (6.0) 55 (6.5) 0 .92 (0.48, 1.76) 0.803

Anti-anxiety 5 (2.5) 5 (0.6) 4.53 (1.20, 17.09)

0.026*

Tea/coffee 97 (48.5) 537 (63.2) 0.53 (0.39, 0.73) <0.001*

Energy drinks 94 (47.0) 418 (49.2) 0.90 (0.65,1.23) 0.502

Alcohol 77 (38.5) 236 (27.8) 1.65 (1.18, 2.30) 0.003*

Any illicit psychoactive drug

11 (5.5) 38 (4.5) 1.43 (0.69, 2.95) 0.339

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Table 3 Single variable conditional logistic regression of drugs and substances

Drugs Casen(%)

Controln(%)

OR(95% Cl)

P-value

Antihistamine 4 (2.0) 35 (4.1) 0.48 (0.17, 1.37) 0.169

Cough suppressants 2 (1.0) 5 (0.6) 1.6 (0.31, 8.25) 0.574

Antidepressants 1 (0.5) 1 (0.1) 4 (0.25, 63.95) 0.327

Cannabis 4 (2.0) 20 (2.4) 0.78 (0.25, 2.40) 0.667

Amphetamine 32 (16.0) 22 (2.6) 8.88 (4.54, 17.39) <0.001

Alcohol breath test (mg%)

< 50 116 (58.0) 910 (93.5) 20.80 (9.78, 44.25) <0.001*

>50 84 (42.0) 63 (6.5) 1

Table 3 Single variable conditional logistic regression of drugs and substances (cont’)

Drugs Casen(%)

Controln(%)

OR(95% Cl)

P-value

Type of drugs

Illicit psychoactive drugs 38 (19.0) 65 (7.7) 3.21 (2.00, 5.15) <0.001*

Licit psychoactive drugs 16 (8.0) 58 (6.8) 1.31 (0.73, 2.34) 0.364

Non-psychoactive drugs 146 (73.0) 726 (85.5) 1

Number of drug use

> 1 6 (3.00) 27 (3.18) 2.59 (1.73, 3.87) <0.001*

1 48 (24.00) 96 (11.31) 1.04 (0.43, 2.55) 0.929

0 146 (73.00) 726 (85.51)

1

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Table 4 Multivariate analysis for factors related to RTI

Factors Adjusted OR (95% CI) p-value

Illicit psychoactive drugs vs. no drug 4.39 (2.13, 9.05) <0.001

Licit psychoactive drugs vs. no drug 4.71 (2.10, 10.53) <0.001

Alcohol breath test (> 50 mg% vs. < 50 mg%)

36.01 (13.54, 95.78) <0.001

Tea/coffee 0.49 (0.30, 0.82) 0.006

Experience of driving (years)

< 4 4.36 (2.18, 8.71) <0.001

5-10 1.41 (0.70, 2.84) 0.339

11-15 0.55 (0.16, 1.91) 0.348

> 15 1

Night time/dawn/dust vs. daytime driving 3.06 (1.56, 6.00) 0.001

Duration of driving (minutes)

> 90 5.41 (2.56, 11.43) <0.001

41-90 3.46 (1.63, 7.35) 0.001

21-40 1.19 (0.54, 2.63) 0.661

<20 1

Table 5 PAR for RTIFactor Adjusted OR

(95% CI)P-value Weight-

estimated prevalence

(%)

PAR

Type of drugs

Psychoactive drugs 4.52 (2.53,8.09) <0.001* 8.85 23.75

No drugs 1

Alcohol breath test (mg/dl)

> 50 35.81 (13.50, 95.00)

<0.001* 2.36 45.10

< 50 1

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Discussion

� Psychoactive drugs

� Antihistamine

� Amphetamine

� Low prevalence of BZD

� Alcohol

� Tea/coffee (Phillip P. Ann Intern Med

2006;144:785-91.)

Discussion

3.52.1 0.44.4 2 0.4 3.31.21.50.7

5.1 3 0.3 0.92.4

32.4

1.6

15.515.517.6

0.8

36

6.1

24

0.24

112.2

179

0.60

50

100

150

200

Amp Coc Can BZD Opi Alc Multi Alc+drugs

Not fault


This study

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Discussion

� Limitation

� Population

� Respondents vs. non-respondents

� Case severity

� Big city

� Measurement

� Speed

� Time between accident and specimen collection

� Urine GC/MS: cannot detect muscle relaxant

� Contamination of therapeutic use of opioid

Discussion

� Suggestion

� Review prescription of psychoactive drug use

� Control illicit drugs and alcohol use

� Land transportation’ s drivers – training

� Driver/rider’s license

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Conclusion

� Psychoactive drug use increased risk of RTI. It also contributed to RTI by 24%.

� It calls for legislative measures and/or publicity campaign to modify use of psychoactive drugs in addition to current measures for drink driving control.

Thank you for your attention

This study is funded by

�Road Safety Fund, Dept Land Transport

�Thai Health Promotion Foundation

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THANK YOU

CASE CONTROL STUDY - Mahidol University · Case-control study Patarawan Woratanarat, M.D., Ph.D....

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