MARIEL LOPEZ & MARITZA RENEAU Foreign Languages

Warm-Up Identification and Classification of Outcome Medical condition Psychological or social problem Positive Identification of Exposure Higher probability Protective effect

Warm-up Medical conditionPsychological or social problem Positive Outcome Risk Factor-Possible effect Higher probabilityProtective effect

Warm-up Medical conditionPsychological or social problem Positive Lung cancerTeen pregnancyGood academic performance Outcome Risk Factor-Possible effect Higher probabilityProtective effect Smokingeat breakfast Parents with low level of education-

Warm-up Enduring Epidemiological Understanding: Making group comparison and identifying association General model Specific model : Smoking and lung cancer

Warm-up ExposureDisease 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 answer a.Experimental study b.Observational study.

Warm-up SmokingLung cancer Association of interest What do you think is the best method to demonstrate a causal relation? Choose the best answer a.Observational study. Choose the best answer a.Case-control b.Cohort c.Cross-sectional

Warm-up Cohort study- handout Design Advantages 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 OBJECTIVES To Understand Confounding To Calculate and Interpret Relative Risk To use Stratification in order to Identify Confounding Variables In what phase of the study can stratification be used? a.Design b.Analysis

Introduction- Confounding Variable Bedsores 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 Severity CV

Bedsores and Mortality Study Objective: 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 Mortality RR- Unadjusted DiedDid not dieTotal Bedsores No bedsores2868,2908,576 Total3659,0359,400 # 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 Mortality RR- Unadjusted DiedDid not dieTotal Bedsores No bedsores2868,2908,576 Total3659,0359,400 # of people with bedsore who died79 # of people with a bedsore who did not die745 Total # of people with a bedsore824 # of people without a bedsore who died286 # of people without a bedsore who did not die8,290 Total # of people without a bedsore8,576 Proportion of people with a bedsore who died79/824=9.6% Proportion of people without a bedsore who died 286/8,576=3.3% RR=.096/.033=2.9

Introduction- Confounding Variable Bedsores 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 Severity CV

Analysis – Bedsores and Mortality Adjusted by Medical Severity (PCV) DiedDid not dieTotal Bedsores No bedsores5510 Total High Medical Severity Group – 5 or more diseases when admitted to hospital Low Medical Severity Group- <5 DiedDid not dieTotal Bedsores No bedsores 2818,2858,566 Total3058,9799,284 RR=55/106= /10 RR=24/718= /8,566 RR U=.096/.033=2.9

Bedsores and Mortality PCV Medical Severity Is 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 RF We would expect that people with HMS would have a higher probability of bedsores that people with LMS.

Analysis – Bedsores and Mortality Adjusted by Medical Severity (PCV) DiedDid not dieTotal Bedsores No bedsores5510 Total High Medical Severity Group – 5 or more diseases when admitted to hospital Low Medical Severity Group- <5 DiedDid not dieTotal Bedsores No bedsores 2818,2858,566 Total3058,9799,284 Proportion of HMS who died= 60/116= 51.7% Proportion of HMS who died= 305/9,284= 3.3% MS Mortality

Analysis – Bedsores and Mortality Adjusted by Medical Severity (PCV) DiedDid not dieTotal Bedsores No bedsores5510 Total High Medical Severity Group – 5 or more diseases when admitted to hospital Low Medical Severity Group- <5 DiedDid not dieTotal Bedsores No bedsores 2818,2858,566 Total3058,9799,284 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%

Conclusion The 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!

Activity Student handout