This document discusses descriptive and analytic epidemiology studies. Descriptive studies generate hypotheses and answer questions of who, what, when and where regarding disease. Analytic studies test hypotheses and answer why and how questions. Case control studies are an example of an analytic study. They are retrospective and compare exposure between cases (with disease) and controls (without disease). Exposure rates are calculated and odds ratios are used to estimate disease risk associated with an exposure. Bias can occur from confounding, recall, selection, or information collection.

2. Epidemiologic studies
Observational Experimental
Descriptive Analytical RCT Field Community
Ecological Cross Case control Cohort
sectional

3. Descriptive or Analytic Studies?
 Descriptive studies
• Generate hypotheses
• Answer what, who, where, and when
 Analytic studies
• Test hypotheses
• Answer why and how

4. Descriptive v/s Analytical Epidemiology
Descriptive Epidemiology refers
to the studies that generate
hypotheses and answer the
questions who, what, when and
where of the disease or infection
Analytic Epidemiology refers to
the studies that are conducted to
test for hypotheses and to
generate conclusions on the
particular disease
Hypothesis
Descriptive epidemiology is able
to generate a hypothesis
Analytic epidemiology is able to
conduct a test for the hypothesis
Interventions
Intervention studies are not
performed in descriptive
epidemiology
Interventions are analyzed in
analytic epidemiology

5. Analytic studies
 Determine:
1. Whether or not a statical association exists
between a disease and a suspected factor
2. If one exists, the strength of association

7. Case Control Study
 Retrospective study
 Three features:
1. Both exposure and outcome have occurred
before start of study
2. The study proceeds backwards from effect to
cause
3. It uses control and comparison group to
support or to refute an inference
 Confounding factors

8. Framework of case control study
(2 x 2 contingency table)
Suspected or risk
factors
Cases
(Disease present)
Controls
(Disease absent)
Present a b
Absent c d
a + c b + d

9. Basic steps
1. Selection of cases and controls
2. Matching
3. Measurement of exposure
4. Analysis and interpretation

10. Selection of cases
 Definition of cases
1. Diagnostic criteria
2. Eligibility criteria
 Sources of cases
1. Hospitals
2. General population

11. Selection of controls
1. Hospital controls
2. Relatives
3. Neighbourhood controls
4. General population

12. Matching
 Process by which we select controls in such a
way that they are similar to cases with regards
to certain selected variables, which are known
to influence the disease And which, if not
adequately matched for comparability, could
distort or confound the result

13. Measurement of exposure
 Information about exposure should be
obtained in precisely the same manner for
both cases and controls
 Obtained by interviews, questionnaires or by
studying past records

14. Analysis
1. Exposure rates among cases and controls to
suspected factor
2. Estimation of disease risk associated with
exposure (ODDS ratio)

15. Exposure rates

16.  Frequency rate of lung cancer was definitely
higher among smokers than non smokers
 Next step to ascertain statistical association by
calculating p-value
 For discrete variable : Chi-square test
 For continuous variable : standard error of
difference between two means

17.  If p value is less than or equal to 0.05, regards
as “statistically significant”
 Smaller the p value, the greater the
significance or probability that association is
not due to chance only

18. Estimation of risk
Incidence among exposed
 Relative risk =
Incidence among non exposed
But not used in case control study

19. Odds ratio (Cross product ratio)
 Closely related to relative risk
 Derivation of Odds ratio is based on 3
assumptions:
1. Disease being investigated must be relatively
rare
2. Cases must be representative of those with
the disease
3. Controls must be representative of those
without the disease

20.  Odds ratio = ad / bc
 8.1
 Interpretation: smokers having 8.1 times
higher risk than non smokers

21. Bias in case control studies
 Due to confounding: removed by matching
 Memory or recall bias
 Selection bias
 Berkesonian bias: arises because of the
different rates of admission to hospitals for
people with different diseases
 Interviewer bias: removed by double blinding