INTRODUCTION TO EPIDEMIOLO FOR POME 105. Lesson 3: R H THEKISO:SENIOR PAT TIME LECTURER INE OF...

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INTRODUCTION TO EPIDEMIOLO FOR POME 105. Lesson 3: R H THEKISO:SENIOR PAT TIME LECTURER INE OF PRESENTATION 1.Epidemiologic measures of association 2.Study designs allowing for association measurement 3.Confounding 4.Dealing with confounding 5.Group task for today

Transcript of INTRODUCTION TO EPIDEMIOLO FOR POME 105. Lesson 3: R H THEKISO:SENIOR PAT TIME LECTURER INE OF...

INTRODUCTION TO EPIDEMIOLO FOR POME 105.Lesson 3:

R H THEKISO:SENIOR PAT TIME LECTURER

INE OF PRESENTATION1.Epidemiologic measures of association2.Study designs allowing for association

measurement3.Confounding4.Dealing with confounding5.Group task for today

MEASURES OF ASSOCIATION

MEASURES OF ASSOCIATION ARE CLASSIFIED INTO VARIOUS MEASURES SUCH AS THE FOLOOWING

1. Odds ratio (OR) (relative odds) used in case control studies

2. Risk ratio or relative risk used in cohort studies

3. Risk difference or attributable risk (AR)4. Attributable fraction (AF)

Introduction to design of a case control

Cases Controls

Have the diseaseDo not have the

disease

Were exposed

Were not exposed

Were exposed

Were not exposed

Introduction to design of a case control

First select Cases controls (with (without disease) disease)

Then measure past exposure

Were exposed a b Were not exposed c d TOTALS a + c b + d

The odds of exposure in cases a a + c The odds of exposure in controls b b + d

Introduction to design of a case controlA hypothetical example of a case control study of Coronary heart disease(CHD) and cigarette smoking First select CHD cases controls (with disease) (without disease) Were exposed 112 176Then measure Past exposure Were not exposed 88 224 Total 200 400 Odds of exposure in cases 112 112+ 88 176 Odds of exposure in control 176 +224 0.560 0.440

Introduction to design of a case control

Explanation of the hypothetical example1. We start with 200 people with CHD(cases) and

compare them to 400 people without CHD (control)2. If there is a temporal relationship between smoking

and CHD,we would anticipate that a greater proportion of the CHD cases than of the controls would have been smokers(exposed)

3. Let us say we find that of the 200 CHD cases,112 were smokers and 88 non smokers AND Of the 400 controls,176 were smokers and 224 were non smokers

Odds ratio in case control

In case control studies, the odds ratio (OR) is the odds of exposure in cases divided by the odds of exposure in control i .e OR = a/a + c b/b +d

i.e OR = odds of exposure in cases odds of exposure in control i.e OR = 0.560 0.440 i.e OR = 1.273

Interpretation of odds ratio:1. If OR= 1 then the exposure is not related to disease2. If OR greater than 1,then the exposure is positively related to

the disease disease

exposure3. If OR less than 1,then the exposure is negatively related to

the disease disease

exposure

Introduction to design of a cohort studyDesign of a cohort study beginning with exposed and non exposed groups.

Exposed Not exposed

Disease develops Disease does not develop

Disease develops

Disease does not develop

Explanation of the design of a cohort study’

1. In a cohort study investigator selects exposed individuals and a group of non exposed individuals and follows the groups to compare the incidence of disease in the two groups

2. If a positive association exists between the exposure and the disease, we would expect that the proportion of the exposed group in whom the disease develops(incidence in the exposed group) would be greater than that of the non exposed group in whom the diseases develops)incidence in the non exposed group)

Introduction to a table of the design of a cohort study

Then follow to see whether Disease develops Disease does Totals Incidence rates of develop disease Exposed a b a + b aFirst a + bSelect Not exposed c d c + d c c + d

Table of a design of cohort study

Introduction to a table of the design of a cohort study

Calculation of the design of a cohort study.1. We begin with the exposed group and the non

exposed group2. Of the (a +b) exposed person the disease

develops in a but not in b.3. Thus the incidence of the disease among the

exposed is a/a +b4. Similarly, in the (c+d) non exposed persons in the

study, the disease develops I c but not in d.5. Thus the incidence of the disease among the non

exposed is c/c+d.

Hypothetical example of a cohort study of 3000 smokers and 5000 non smokers to investigate the relation of smoking to the development of coronary heart disease(CHD) over

Then follow to see whether Disease Disease does Totals Incidence rates develops not develop of disease/1000 Exposed 84 2916 3000 28.0 First select not 87 4913 5000 17.4 exposed Then calculate IR in exposed 84/3000=28.0 per 1000And calculate IR in non exposed 87/5000=17.4 per 1000

Relative risk or risk ratio= 28.0/17,4=1.61

Interpretation of RR

1. If RR =1,the numerator equals the denominator and the risk in exposed persons equals the risk in non exposed person. Therefore no evidence exists for any increased risk in exposed individuals or for any association of disease with the exposure in question.

2. If RR>1,the numerator is greater than denominator and the risk in the exposed persons is greater than the risk in non exposed persons.This is evidence of positive association and may be causal.

3. If RR<1 the numerator is less than the denominator and the risk in exposed persons is less that the risk in non exposed .This is evidence of a negative association and it may be indicativeof a protective effect. Such finding can be observed in people who are given an effective vaccine(“exposed” to vaccine”

4. Therefore the results obtained of RR>1 indicates that smoking increases risk of development of CHD in the exposed.

5. The RR is important as a measure of the strength of association and is a major consideration in deriving a causal inference.

Risk difference or attributable riskWe have seen how the RR is important as a measure of the strength of association and a major consideration in deriving a causal inference

However a further question may be asked

“How can we determine whether the excess risk is associated with the exposureExcess risk is determined by subtracting the risk in those who are not exposed from the risk of those who are exposed e.g. from cigarette smoking

Determination of excess risk Incidence due to exposure

Incidence not due to exposure in exposed In non group exposed group Excess risk = Incidence in - Incidence in exposed group unexposed group The total risk of disease in the exposed is indicated by height of the full bar in the leftThe total risk of disease in the non exposed is indicated by height of the full bar in the left. The total risk of the disease is higher in the exposed group than in the non exposed

• Attributable fraction• WE have seen how The RR is important as a measure of the

strength of association and a major consideration in deriving a causal inference

• However a further question may be asked• “How much of the disease that occurs can be attributable to a

certain exposure"?. This is answered by another measure of risk, the attributable risk which is defined as the amount or proportion of disease incidence that can be attributed to a specific exposure. For example “how much of the lung cancer risk is experience by smokers is attributed to smoking or put in another way “how much of risk(incidence) can we hope to prevent if we are able to eliminate exposure to the agent in question?

Determination of attributable fractionThe incidence of disease that is attributable to the exposure in the exposed group is calculated as followsWe express the attributable risk as the proportion of the total incidence in the exposed group that is attributable to exposure by dividing the formula by incidence in the exposed Incidence in - Incidence in exposed group unexposed group Incidence in the exposed1. Example :If IR in the exposed group is 28.0 per 1000 and IR in the non

exposed is 17.4 per 1000 then 2. Risk difference is 28.0 per 1000 -17.4 per 1000 =10.6 per 1000.3. It means 10.6/1000 of the 28.0 /1000 in smokers are attributable to the fact

that these people smoke.4. OR as a proportion: 28.0-17.4/28=10.6/28=0,379*100=37.9%5. Thus 37.9% of the morbidity from CHD among smokers may be attributable

to smoking

Confounding in observational epidemiologic studies

Figure showing the association between increased coffee drinking and increased risk of pancreatic cancer

Increased risk of pancreatic cancer

Increased coffee drinking

Increased coffee drinking

Increased risk of pancreatic cancer

smoking

In the figure shownSmoking is known to be a risk factor pancreatic cancerSmoking is associated with coffee drinking but is not a result of coffee drinking .

Observed associationIf an association is observed between coffee drinking and cancer of the pancreas it may be thatCoffee actually causes cancer of the pancreas or That the observed association of coffee drinking and cancer of the pancreas may be as a result of confounding by cigarette a third factor that is both a risk factor for the disease and is associated with the exposure in question?

Dealing with confounding At the study design stage by: Matching (for confounders) cases to controls You must suspect (or know) what your confounders are in order to do

this At the data analysis stage by: Stratified analysis In the previous example, instead of using the crude death rate, they

should have stratified according to age groups Adjustment: Regression analysis Standardisation: Direct and indirect Restriction, randomisation

1. Read chapters 9,10,11,12 and chapter 15 and 5 of Leon Gordis 5th edition2. Recruit volunteers and collect data for the study: Is there an association

between gender and headaches in students at SMUHS3. The sample size should be 213 (based on what you have learned from this

lecture), but we will increase this to 240.4. This means each student should collect data from 6 SMUHS STUDENTS

5. Please use the prepared data collection sheet, which has been prepared for the collection of data from 6 participants.

6. After data collection, meet with your group members and summarise all your data on the data summary sheet provided.

7. Bring all these data collection and data summary sheets back to class, and present the data summary to the class

8. Using the data analysis sheet, analyse the summarised data for the whole class (i.e. n = 240)