Association & causation.pptx

This power presentation explains association & causation in details with examples.

1. Association & Causation Presented By: Dr. Sadhana Meena (JR3 ) Department of Community Medicine

2. Index Definition Types with examples Association to Causation Causal Relationships Criteria to judge association

3. What is Association? Defined as: Statistical dependence between two variables, that is, the degree to which the rate of disease in person with a specific exposure is either higher or lower than the rate of disease among those without that exposure.

4. Why do we need Association ? Descriptive studies It suggest an etiological hypothesis. Analytical and experimental studies Test the hypotheses derived from descriptive studies and confirm or refute the observed association.

5. Common sequence of studies in human population

6. Conceptually, a two-step process is followed in carrying out studies and evaluating evidence. We determine whether there is an association between an exposure or characteristic and the risk of a disease. If an association is demonstrated, we determine whether the observed association is likely to be a causal one Association does not imply a causal relationship CORRELATION indicates the degree of association between two characteristics. Correlation coefficient range from -1.0 to +1.0.

7. Types Of Association Spurious association Indirect association Direct association One-to-one causal association Multifactorial causation

8. Spurious Association Observed association between a disease and suspected factor may not be real, which appears due to improper comparison. Example: Neonatal mortality was observed to be more in the newborns in a hospital than those born at home. This is likely to lead to a conclusion that home delivery is better for the health of new born . However , this conclusion is spurious or art factual, because in general , hospitals attract women at high risk for delivery.

9. Indirect Association It is a statistical association between a characteristic (or variable) of interest and a disease due to the presence of another factor, known or unknown, that is common to both the characteristic and the disease. The third factor is also known as “CONFOUNDING” variable.

10. Direct Association The association between the two attributes is not through the third attributes . Classified Into Two Types One-to-one Causal Relationship: The variables are stated to be causal related ( A&B ) if a change in A is followed by a change in B. When the disease is present, the factor must also be present . 2. Multi factorial causation: Alternative causal factors each acting independently. E.g . In lung cancer more than one factor (e.g. air pollution, smoking, heredity) can produce the disease independently.

11. What is Cause? Cause of a specific disease event can be defined as “ an antecedent event, condition or characteristics that was necessary for the occurrence of the disease at the moment it occurred, given that the other conditions are fixed.”

12. Types of casual pathway

13. What is Causal Relationship? It can be defined as – “change in the Frequency or Quality of exposure of characteristic results in corresponding change in the Frequency of the disease or outcome of interest .”

14. Types of Causal Relationship Four types are possible: I. Necessary and Sufficient One-to-one relationship II. Necessary, but not Sufficient III.Sufficient , but not Necessary IV.Neither Sufficient nor Necessary  Multifactorial relationship

15. Factors for D isease Causation Sufficient Factors: One which inevitably produces disease i.e., presence always result in disease. Ex- Rabies virus for rabies Necessary Factors: Without which disease does not occur, but by itself, not sufficient to cause disease. Ex- Mycobacterium TB for TB 

16. I. Necessary and Sufficient • Without that factor, the disease never develops (Necessary) • In presence of that factor, the disease always develops (Sufficient) • Rare situation

17. II. Necessary, but not Sufficient • Each factor is necessary, but not, in itself sufficient to cause disease. • Multiple factors required, o ften required in a specific sequence. • Example - Carcinogenesis, Tuberculosis 

18. III. Sufficient, but not Necessary • Factors independently can produce the disease. • Examples- Radiation or Benzene exposure can produce leukaemia. • Although both factors are not needed, other cofactors are probably needed. 

19. IV. Neither Sufficient nor Necessary • Most complex model • Most accurately represent the causal relationship operating in CHRONIC DISEASE.

20. Guidelines for Assessing Causation (Hill’s Criteria, 1965)  Temporal Relationship Strength of association Dose- response relationship Replication of findings Biologic plausibility Consideration of alternate explanation Cessation of exposure Consistency with other knowledge Specificity of the association

21. 1.Temporal Relationship • Exposure precedes disease development with adequate elapsed time or Latency period • Often easier to establish by Prospective cohort study . The temporal relationship of exposure and disease is important not only for clarifying the order in which the two occur but also in regard to the length of the interval between exposure and disease.

22. 2.Strength of association • Measured by the relative risk (or odds ratio) • Stronger the association, the more likely it is that the relation is causal.

23. 3.Dose- response relationship  As the dose of the exposure increases, the risk of disease also increases. It is a strong evidence for a causal relationship 

24. 4.Replication of findings • It is supportive if the same finding can be replicated in subgroups or different populations and/or by using various study designs.

25. 5. Biological plausibility • Refers to knowledge of biological (or social) model or mechanism that explains the cause-effect association. • Epidemiologic studies often identify cause-effect relationships before a biological mechanism is identified. • Example: Teratogenic viruses and Rubella and congenital cataracts ; ii. Thalidomide and limb defects 

26. 6.Consideration of alternate explanation • Take into account the extent to which the researchers has considered alternative explanations for the outcome.

27. 7.Cessation of exposure • The risk of disease to decline when exposure to the factor is reduced or eliminated. • Cessation data if available provide helpful supporting evidence for a causal association.

28. 8.Consistency with other knowledge • If a relationship is causal, the findings will be consistent with the other data. • Ex- Lung Cancer and Cigarette smoking • Absence of consistency would not completely rule out the hypothesis

29. 9.Specificity of the association • Association is specific when a certain exposure is associated with only one disease . • Weakest point of all the guidelines. • When found, it provides additional support for a causal inference.

30. Modifications of the Guidelines