case control study (15 aug 2014) - คณะแพทยศาสตร์...
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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.
Selection of controls
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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Selection of controls
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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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
Age
BMI (mean + SD)
Anticoagulant use
Underlying 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
Age
Anticoagulant use
Type 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 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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ResultsFactors related to hip fracture (women)
Factor Case vs Hosp cont Case vs Com cont
OR (95%CI) P-value OR (95%CI) P-value
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
ResultsFactors related to hip fracture (women)
Multivariate analysis: adjusted for age (continue)Factor Case vs Hosp cont Case vs Com cont
OR (95%CI) P-value OR (95%CI) P-value
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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ResultsFactors related to hip fracture (women)
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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ResultsFactors related to hip fracture (men)
Factor Case vs Hosp cont Case vs Com cont
OR (95%CI) P-value OR (95%CI) P-value
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
ResultsFactors related to hip fracture (men)
Factor Case vs Hosp cont Case vs Com cont
OR (95%CI) P-value OR (95%CI) P-value
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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ResultsFactors related to hip fracture (men)
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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DiscussionFactors related to hip fracture (women)
Factors converselyassociated with hip fracture according to other literature
OR (95%CI)
ReferencesOR (95%CI)
Diuretics: 2.10 (0.62-7.14)** Cummings: 0.8 (0.6-1.2)
* Hospital control
** Community control
DiscussionFactors related to hip fracture (women)
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
** Community control
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DiscussionFactors related to hip fracture (men)
Factors consistent associated with hip fracture according to other
literatureOR (95%CI)
ReferencesOR (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
** Community control
DiscussionFactors related to hip fracture (men)
Factors converselyassociated with hip
fracture according to other literature
OR (95%CI)
ReferencesOR (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
** Community control
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DiscussionFactors related to hip fracture (men)
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
** Community 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