Purpose of Epi Studies Discover factors associated with diseases, physical conditions and behaviors Identify the causal factors Show the efficacy of intervening in both individuals and populations

Reasons to do epidemiologic studies Generate hypotheses Test hypotheses

Generating hypotheses Study done to look for potential exposures which are related to an exposure or disease You are simply looking for associations not for causation

Testing hypotheses Seeking to confirm a particular exposure is related to a particular disease These studies are attempting to prove causation

Types of study designs Observational studies Ecological Cross-sectional Case-control Cohort Interventional studies Clinical trials Community trials

Case control study The case control study, also commonly called a retrospective study, follows a paradigm that proceeds from effect to cause. In a case control study, individuals with a particular condition or disease (the case) are selected for comparison with a series of individuals in whom the condition or disease is absent (the controls) Cases and controls are compared with respect to existing or past attributes or exposures thought to be relevant to the development of the condition or disease under study

Selection of cases Sources Hospitals Physicians ’ office Disease registries Vital statistics bureau

Selection of cases Definition Should truly be a case: validation of diagnosis using objective criteria Should represent a defined eligible population: inclusions, exclusions should be specified clearly Cases generally should have been eligible to have exposure Detection issues that may be important

Selection of controls Objective select subjects who represent those who might have become cases in the study; that is, from the same source population as cases Sources population hospital neighborhood others

General population controls Useful when the series of cases is population- based Select from a population registry or random digit dialing Advantage generalizability avoids referral bias Disadvantages potentially costly cooperation may be low

Hospital controls Select from other hospital admissions Advantage feasibility high cooperation rate less recall bias makes cases and controls similar with respect to some determinants of hospitalization Disadvantage selection bias disease control may have more risk factors shared risk factors

Neighborhood controls Similar to general population but also matches on factors related to geography Select by a modification of random digit dialing on “ walking algorithm ”

Guidelines of selection of controls Choice of controls depends on hypothesis Goal: avoid selection bias Draw controls form same reference population as cases Subjects not at risk for disease should be excluded from control group If cases are excluded because they are not at risk for exposure, similar criteria should be applied to controls

Guidelines of selection of controls For hospital controls, pick conditions likely to have same referral reason individuals with medical conditions known to be associated with the exposure under study usually should be excluded form the control series If controls with certain diseases associated with exposure are excluded, consider whether persons with these diseases should also be excluded from case group

Guidelines of selection of controls Ideally, controls should undergo the same diagnostic procedures as cases. However, this often isn't practical Exposure status and cofounders must be able to be measured comparably in cases and controls. Case or control status must be defined before exposure determined If no one control group is best, consider more than one

Multiple control groups Increased cost Useful when no single control group is best Useful to compare findings among groups If findings contrast may help determine etiology

Selection bias Diagnosis bias Knowledge of the subjects ’ prior exposure history may influence the diagnosis of a disease Non-response bias Non-respondents may differ from respondents with respect to vital exposures Volunteer bias May not be representative of the disease rate in the population

Criterion of a confounder The extraneous factor must be a risk factor for the disease The extraneous factor must be associated with the exposure under study in the population form which the cases derive A confounding variable must not be a n intermediate step in the causal path between exposure and disease

Control of confounder Randomization Randomly placing study subjects into study groups, to try to ensure equal distribution of the confounding variable Restriction Restricting admission criteria to prohibit variation of the potential confounder in your study groups

Control of confounder Matching Matching subjects in the study groups according to the value of the suspected or known confounding variable to ensure equal distribution Analysis strategies An attempt to control confounding through the use of certain methods that can be employed at the time of statistical analysis

Analysis strategies to control confounder Stratification To evaluate the effect of an exposure within strata of the confounder define homogenous categories or narrow ranges of the confounding variable can combine stratum-specific effects into an overall effect by standard statistical principles and methods Multivariate analysis The construction of mathematics models to describe simultaneously the effect of exposure and the effect of the other factors that may be confounding the effect