1. Deriving Biological Inferences From Epidemiologic Studies

2. Dr. Mostafa Arafa Associate Prof. of Family and Community medicine Faculty of Medicine & Medical Sciences King Khaled University, S.A. mostafaarafa@hotmail.com

3. Learning Objectives To learn the inferences about a disease’s etiology that can be derived from different epidemiologic studies. To learn the reasoning by which epidemiologists select the most plausible inference.

4. The first step in the epidemiologic analysis is the demonstration of a statistical relationship between a disease and a biological characteristic. The second step is to ascertain the meaning of that relationship.

5. Statistical associations can be explained as: 1- Artificial 2- Due to association of interrelated but non-causal variables 3- Due to uncontrolled confounding 4- Causal or etiological

6. Artificial Association Artificial association can result from biased methods of selecting cases and controls. It may also arise from biased methods of recording or obtaining information by interview. Errors in conduction or design of the study also may introduce spurious association.

7. Non-causal association An association between many variables can be observed and still be non-causal, because many variables can occur together without being a part of causal chain. Examination of interrelated associations is useful as they may suggest ways to reduce exposure to causal variables.

8. Confounding If any factor either increasing or decreasing the risk of a disease besides the exposure under study is unequally distributed in the groups that are compared with regards to the disease, this will give rise to difference in diseases frequency in the compared groups. Such distortion, termed confounding and variables are called confounder variables.

9. E Etiological Factor D Diseases CF Confounding factor

10. Cigarette smoking Lung cancer Alcohols

11. If any study did not control adequately control for potential confounders, the inferences drawn from the results may not be well founded. Studies in which there was inadequate control of all known confounders, the results of which may be explained by an unequal distribution of extraneous variables in the study groups and not by the effect of exposure on disease.

12. Methods used for controlling of confounders A)During the design of the study * Restriction to a specific group * Matching B)During analysis Stratification & multivariate analysis

13. Causal association The logician’s definition of “cause” is that a factor which must be necessary and sufficient for the occurrence of a disease. The concept “necessary and sufficient” implies there must be a one-to-one relationship between the factor and the disease.

14. Example of the sufficient cause for development of a disease A1 A2 A3 B C A4 A5 Cellular reaction Disease

15. Example for necessary cause for development of a disease A1 + A2 + A3 B C Cellular reaction Disease

16. Assessing Causality The following concepts are used in making a causal inference: Strength of association Consistency of observed association Specificity of the association Temporal sequence of events Dose-response relationship Biological plausibility of association Experimental evidence

17. Strength of association It is measured by Relative Risk, and Odds ratio. A strong association between exposure and outcome gives support to causal hypothesis. When a weak association is present, other information is needed to support causality.

18. Consistency of observed association Confirmation by repeated findings of an association in different studies, in different population, and in different settings strengths the inference of causal association. It is equivalent to replication of results in laboratory experiments.

19. Specificity of the association It has been postulated that one exposure should cause one disease and no other exposures should cause the disease. This has its roots in bacteriological models where one organism is associated with one disease. This could not be applied in chronic diseases as one exposure could lead to many adverse outcomes e.g. smoking and cancers, CVD, Birth- outcome.

20. Temporal sequence of events It is very obvious that an exposure must precede the disease to cause an effect. An example for that is the prenatal exposure and malformation. In case control studies the problem of temporality is quite obvious, however a cohort design can resolve the issues of temporality.

21. Dose – response relationships The risk of development of a disease should be related to the degree of exposure of the causal factor e.g. duration of estrogen use and the risk of endometrial cancer, dosage of smoking and lung cancer staging.

22. Biological plausibility A causal hypothesis must be viewed in the light of its biological plausibility. Statistical significant relationship should be understood in the view of biological significance. A good example is cigarette smoking and lung cancer relationship which was initially biologically implausible by some, but carcinogens in cigarettes were identified, which lent biological plausibility to the observed association.

23. Experimental evidence Randomized clinical trials is a well run trial that may confirm a causal relationship between an exposure and outcome. However ethical issues may prevent the conduction of such trials.

24. Summary Epidemiologic inferences lead to action, to changes in clinical practice, public policy, legislation, health education or new health directions. Epidemiologic studies can provide very strong support for hypotheses of either a causal or indirect association. Inferences from such studies must take into account all relevant biological information. Epidemiologic and other evidence can accumulate to point where a causal hypothesis becomes highly probable.