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MARIEL LOPEZ & MARITZA RENEAU Foreign Languages
Warm-UpIdentification and Classification of Outcome
Medical condition Psychological or social problem Positive
Identification of ExposureHigher probabilityProtective effect
Warm-up
Medical condition Psychological or social problem
Positive
Outcome
Risk Factor-Possible effect
Higher probability
Protective effect
Warm-up Medical condition Psychological or
social problem Positive
Lung cancer Teen pregnancy Good academic performance
Outcome
Risk Factor-Possible effect
Higher probability
Protective effect
Smoking eat breakfast
Parents with low level of education-
Warm-upEnduring Epidemiological Understanding:
Making group comparison and identifying associationGeneral model Specific model : Smoking and lung cancer
Warm-up
Exposure Disease
Association of interest
Warm-up
Smoking Lung cancer
Association of interest
What do you think is the best method to demonstrate a causal relation? Choose the best answera.Experimental studyb.Observational study.
Warm-up
Smoking Lung cancer
Association of interest
What do you think is the best method to demonstrate a causal relation? Choose the best answera.Observational study. Choose the best answer
a. Case-controlb. Cohort c. Cross-sectional
Warm-upCohort study- handout
DesignAdvantages and disadvantages
Warm-up
Smoking Lung cancer
Association of interest
Can you think of some examples of other exposures or lifestyle choices that might be the real culprits in causing lung cancer?
Enduring Epidemiological Understanding
Explaining Association and Judging Causation
LESSON OBJECTIVESTo Understand ConfoundingTo Calculate and Interpret Relative RiskTo use Stratification in order to Identify
Confounding Variables
In what phase of the study can stratification be used?
a. Design b. Analysis
Introduction- Confounding VariableBedsores and Mortality
Bedsores Mortality
Association of interest
Can you think of some examples of other exposures or lifestyle choices that might be the real culprits in causing Mortality?
Medical Severit
yCV
Bedsores and Mortality StudyObjective: The association between bedsores
and death among elderly hip fracture patients.
Sample: 9,400 patients aged 60 and over, admitted with hip fracture to one of 20 study hospitals.
Methods: Medical charts were reviewed by research nurses in order to identify exposure and outcome.
Analysis – Bedsores and MortalityRR- Unadjusted
Died Did not die Total
Bedsores 79 745 824
No bedsores 286 8,290 8,576
Total 365 9,035 9,400
# of people with bedsore who died
# of people with a bedsore who did not die
Total # of people with a bedsore
# of people without a bedsore who died
# of people without a bedsore who did not die
Total # of people without a bedsore
Proportion of people with a bedsore who died
Proportion of people without a bedsore who died
Analysis – Bedsores and MortalityRR- Unadjusted
Died Did not die Total
Bedsores 79 745 824
No bedsores 286 8,290 8,576
Total 365 9,035 9,400
# of people with bedsore who died 79
# of people with a bedsore who did not die
745
Total # of people with a bedsore 824
# of people without a bedsore who died 286
# of people without a bedsore who did not die
8,290
Total # of people without a bedsore 8,576
Proportion of people with a bedsore who died
79/824=9.6%
Proportion of people without a bedsore who died
286/8,576=3.3%
RR=.096/.033=2.9
Introduction- Confounding VariableBedsores and Mortality
Bedsores Mortality
Association of interest
Can you think of some examples of other exposures or lifestyle choices that might be the real culprits in causing Mortality?
Medical Severit
yCV
Analysis – Bedsores and MortalityAdjusted by Medical Severity (PCV)
Died Did not die Total
Bedsores 55 51 106
No bedsores
5 5 10
Total 60 56 116
High Medical Severity Group – 5 or more diseases when admitted to hospital
Low Medical Severity Group- <5
Died Did not die Total
Bedsores
24 694 718
No bedsores
281 8,285 8,566
Total 305 8,979 9,284
RR=55/106= 1.04 5/10
RR=24/718= 1.02 281/8,566
RR U=.096/.033=2.9
Bedsores and MortalityPCV Medical SeverityIs Medical Severity a confounding variable?
According to the stratification analysis….According to the definition
CV Outcome
We would expect that the people with HMS would have a higher probability of death that people with LMS
CV RFWe would expect that people with HMS would
have a higher probability of bedsores that people with LMS.
Analysis – Bedsores and MortalityAdjusted by Medical Severity (PCV)
Died Did not die Total
Bedsores 55 51 106
No bedsores
5 5 10
Total 60 56 116
High Medical Severity Group – 5 or more diseases when admitted to hospital
Low Medical Severity Group- <5
Died Did not die Total
Bedsores
24 694 718
No bedsores
281 8,285 8,566
Total 305 8,979 9,284
Proportion of HMS who died= 60/116= 51.7%
Proportion of HMS who died= 305/9,284= 3.3%
MS Mortality
Analysis – Bedsores and MortalityAdjusted by Medical Severity (PCV)
Died Did not die Total
Bedsores 55 51 106
No bedsores
5 5 10
Total 60 56 116
High Medical Severity Group – 5 or more diseases when admitted to hospital
Low Medical Severity Group- <5
Died Did not die Total
Bedsores
24 694 718
No bedsores
281 8,285 8,566
Total 305 8,979 9,284
Proportion of people with bedsores among those with HMS 106/116= 91.4%
MS Bedsores
Proportion of people with bedsores among those with LMS 718/9,284= 7.7%
ConclusionThe fact that the adjusted RR was different
from the unadjusted RR is evidence that there is confounding.
Another symptom of confounding was identified by showing that there was an association both between bedsores and MS and dying and MS.
There was no association between bedsores and mortality.
More…..In our example, there is confounding by MS
but does that mean that the association between bedsores and dying is not real?If your answer is no, why do you say so?
More…..In our example, there is confounding by MS
but does that mean that the association between bedsores and dying is not real?Answer: No. Patients with bedsores really do
have a higher risk of dying but it is not because they have bedsores.
Bedsores are guilty by association!
ActivityStudent handout
