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

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 Cases Controls Have the disease Do 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 control A 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 Then measure Past exposure Were not exposed Total Odds of exposure in cases Odds of exposure in control

Introduction to design of a case control Explanation of the hypothetical example 1.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 = i.e OR = 1.273

Interpretation of odds ratio: 1.If OR= 1 then the exposure is not related to disease 2.If OR greater than 1,then the exposure is positively related to the disease disease exposure 3.If OR less than 1,then the exposure is negatively related to the disease disease exposure

Introduction to design of a cohort study Design of a cohort study beginning with exposed and non exposed groups. ExposedNot 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 a First a + b Select 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 group 2.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 +b 4.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 First select not exposed Then calculate IR in exposed 84/3000=28.0 per 1000 And 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 risk 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 can we determine whether the excess risk is associated with the exposure Excess 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 left The 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 fraction The incidence of disease that is attributable to the exposure in the exposed group is calculated as follows We 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 exposed 1.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 per 1000 =10.6 per 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=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 coffee drinking Increased risk of pancreatic cancer Increased risk of pancreatic cancer smoking

In the figure shown Smoking is known to be a risk factor pancreatic cancer Smoking is associated with coffee drinking but is not a result of coffee drinking. Observed association If an association is observed between coffee drinking and cancer of the pancreas it may be that Coffee 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 5 th edition 2.Recruit volunteers and collect data for the study: Is there an association between gender and headaches in students at SMUHS 3.The sample size should be 213 (based on what you have learned from this lecture), but we will increase this to 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)