CASE CONTROL STUDY (15 Aug 2014) - คณะแพทยศาสตร์ ...med.mahidol.ac.th/ceb/sites/default/files/public/pdf... · · 2017-10-25Qualitative / quantitative

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Case-control studyPatarawan Woratanarat, M.D., Ph.D. (Clin. Epid.)Department of OrthopaedicsFaculty 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 Case-control study

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 discussionGr 1: New (incident) case or prevalence caseGr 2: Case - definition, inclusion & exclusion

criteriaGr 3: Control – definition, inclusion & exclusion

criteriaGr 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 of

cases 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 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 24

216116

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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 discussionGr 1: What is the primary exposure, why?Gr 2: Study factors and measurementsGr 3: Data collectionGr 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 discussionGr 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 analysisParametric

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 analysisnonparametric

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 กลุม่ Kruskall-Wallis Fisher’s exact

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Case control studyExposure Disease No

diseaseTotal No. of

casesPoor 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 200Term 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 | .9332296 .9888602 (exact)

Attr. frac. pop | .7777778 |

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

chi2(1) = 98.99 Pr>chi2 = 0.0000

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McNemar testCases Controls Total

meditat+ meditat-meditat+ 200 (a) 100 (b) 300meditat- 150 (c) 450 (d) 600

Concordance pairs2 = (|O-E|-1/2)2/E

•Meditation vs. Degenerative spine

. mcci 200 100 150 450

| Controls |

Cases | Exposed Unexposed | Total

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

Exposed | 200 100 | 300

Unexposed | 150 450 | 600

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

Total | 350 550 | 900

McNemar's chi2(1) = 10.00 Prob > chi2 = 0.0016

Exact McNemar significance probability = 0.0019

Proportion with factor

Cases .3333333

Controls .3888889 [95% Conf. Interval]

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

difference -.0555556 -.0909079 -.0202032

ratio .8571429 .7789666 .9431648

rel. diff. -.0909091 -.1497595 -.0320587

odds ratio .6666667 .512362 .8643429 (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

ยาทีม่ฤีทธิต์่อจติประสาท 0.88 3.05( 1.73-5.37) <0.001

ยาอื่นๆ 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

- > 30,000

Educational level (%)

- No

- Primary school

- High school

- Bachelor

- Higher

*

Table 1 Demographic data

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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


Factors Odds ratio 95% confidence interval

P-value

Age

BMI (mean + SD)

Anticoagulant use

Underlying disease (%)


- Spinal

- General

*

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

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Factors Adjusted odds ratio

95% confidence interval

P-value

Age

Anticoagulant use


- 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 costs

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 reviewFactors related to hip fracture

Conceptual 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

ThaiChinese

BMI (kg/m2)(mean+SD)

Low MediumHigh

Mental statusNormalPoor

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


RaceThai*Chinese

Recent activityInactive*ActiveVery active

Past activityInactive*ActiveVery 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

0.2310.047

0.8240.949

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


BMILow*MediumHigh

CalciumLow*MediumHigh

CVADiureticsAntihistamineTraditional 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)

----

0.0100.131

0.7280.653

----

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)

0.6900.740

0.0020.1670.0020.0390.0250.001

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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

ThaiChinese

BMI (kg/m2)(mean+SD)

Low MediumHigh

Mental statusNormalPoor

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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RaceThai*Chinese

Recent activityInactive*ActiveVery active

Past activityInactive*ActiveVery 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

0.2080.350

0.1840.013

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

0.0080.114

<0.0001<0.0001

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




SmokingSmoker* ExsmokerNonsmoker

Walking act before fx

Independent*Partially dep.Totally dep.

History of fxCVA

-

-

-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)

0.0120.425

0.0110.2480.018

<0.0001

Multivariate analysis: (continue)

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Conceptual 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 literatureOR (95%CI)

ReferencesOR (95%CI)

BMI: 0.52 (0.32-0.85)*

Physical activityRecent: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

** Community control

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Factors converselyassociated with hip fracture according to other literature

OR (95%CI)


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

* Hospital control



New factors associated with hip fracture

ReferenceOR (95%CI)

Chinese race: 3.06 (1.95-4.81)*2.33 (1.36-3.99)**

-

* Hospital control


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DiscussionFactors related to hip fracture (men)

Factors consistent associated with hip fracture according to other

literatureOR (95%CI)


Physical activityRecent: 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)

* Hospital control



Factors converselyassociated with hip

fracture according to other literature

OR (95%CI)


SmokingExsmoker: 1.33 (1.04-1.70)**Eversmoker: 0.68 (0.39-1.71)**

CummingsExsmoker: 1.4 (0.6-2.5)Eversmoker: 1.6 (1.0-2.6)

* Hospital control


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New factors associated with hip fracture

ReferenceOR (95%CI)

Chinese race: 1.91 (1.16-3.15)*2.32 (1.22-4.40)**

-

* Hospital control


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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MEDICINAL DRUG USE AND RTI:A Case-Control Study.

Patarawan Woratanarat, MD, PhD.

Atiporn Ingsathit, MD, PhD.

Paibul Suriyawongpaisal, MD, MMSc.

Faculty of Medicine Ramathibodi Hospital

IntroductionVehicle factor

Human factors:Driving behaviorPhysical status AlcoholDrugs

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

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Odds ratio between medicinal drugs & RTI

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 Case-control study 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)

Case

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)

Table 1 Characteristics 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 vehiclesCar/van/truck/bus 22(11.0) 91(10.7) 0.803Motorcycle 178(89.0) 759(89.3)

Type of drivingGeneral 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) 0.14911-15 10(5.0) 96(11.3) 0.553> 15 28(14.1) 225(26.5)

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Table 1 Characteristics of cases and controls (cont’)

Characteristics Cases N = 200 (%)

ControlsN = 850

(%)

P-value

Timing of drivingNight 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 lightNo 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) 0.275Muscle relaxant 12 (6.0) 55 (6.5) 0 .92 (0.48, 1.76) 0.803Anti-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) 0.502Alcohol 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 drugsIllicit 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) 0.364Non-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) 0.9290 146 (73.00) 726

(85.51)1

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Table 4 Multivariate analysis for factors related to RTIFactors Adjusted OR (95% CI) p-valueIllicit 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

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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 byRoad Safety Fund, Dept Land TransportThai Health Promotion Foundation

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THANK YOU

Documents

CASE CONTROL STUDY (15 Aug 2014) - คณะแพทยศาสตร์ ...med.mahidol.ac.th/ceb/sites/default/files/public/pdf... · · 2017-10-25Qualitative / quantitative