2. Leicester Warwick Medical School Health and Disease in Populations Revision Paul Burton

3. What you don’t need to know!!!!! Detailed knowledge of how to fill in a death certificate Detailed knowledge of census questions Detailed knowledge of exactly what is in/not in specific information sources Knowledge of web site addresses Wording of Helsinki declaration, Hippocratic Oath Mathematical formulae for error factors

4. Cohort Studies Exposed Unexposed Time Count events and pyrs

5. A worked example 1,000 children followed from birth to age 5 yrs 300 at least one parent smoked in the home 700 neither parent smoked in the home p-y SMOKE = 300  5 pyr = 1,500 p-y p-y NON-SMOKE = 700  5 pyr = 3,500 p-y

6. A worked example Smoke exposed, 75 diagnosed asthma Smoke unexposed, 105 diagnosed asthma IR SMOKERS = 75/1,500 = 50 per 1,000 p-y IR NON-SMOKERS = 105/3,500 = 30 per 1,000 p-y

7. A worked example IRR = 1.667 e.f. = e.f. = 1.35 95% CI: 1.667÷1.35 to 1.667  1.35 i.e. 1.23 to 2.25

8. Case-control Studies Case Non-Case (Control) Exposed? Time

9. Analysis 95%CI: OR  e.f., OR  e.f.

10. Creutzfeld Jacob Disease (CJD) and occupation Odds ratio = (9×104)/(3×13) = 24 95% CI: 24÷4.29, 24×4.29 = (5.59, 103.0)

11. How many controls? Unlike an IRR, the precision of an OR is affected by the number of healthy people (x and z): So, it is worth increasing the number of controls - up to a point (typically up to 4-6 times as many controls as there are cases)

12. Multiple levels of exposure

13. Retrospective v prospective? Confusing terminology: two different issues (1) Does the analysis look forwards or backwards? (2) Are the data collected as and when they occur ( i.e. prospectively) or from historical review - questionnaire, case-notes or other health records – ( i.e. retrospectively). Cohort analysis always looks forwards in time: Given exposure status at baseline, how many events occurred over time in how many person years and what is the incidence rate ratio? Simple case-control analysis is usually expressed as being backwards in time: Given case-control status now, what is the ratio of the odds of exposure at baseline?

14. Retrospective v prospective? Confusing terminology: two different issues (1) Does the analysis look forwards or backwards? (2) Are the data collected as and when they occur ( i.e. prospectively) or from historical review - questionnaire, case-notes or other health records – ( i.e. retrospectively). Conventional cohort study: prospective Historical cohort study: retrospective Conventional case-control study: retrospective

15. Comparison of cohort and case-control studies

16. Statistical inference on a rate ratio Population 1: d 1 cases in P 1 person years Population 2: d 2 cases in P 2 person years Rate ratio = d 1 /P 1  d 2 /P 2

17. An example 80 deaths in 8,000 pyrs (male) 50 deaths in 10,000 pyrs (female) Rate M = 10 per 1,000 p-y; Rate F = 5 per 1,000 p-y Observed rate ratio (M/F) = 2.0 95% CI: [2÷1.43 to 2×1.43] = [1.40 to 2.86] Best guess for true rate ratio=2.0, and 95% certain that true rate ratio lies between 1.40 and 2.86. This range does not include 1.00 so able to reject hypothesis of equality (p<0.05)

18. Statistical inference on an SMR Observe O deaths Expect E deaths (based on age-specific rates in the standard population and age-specific population sizes in the test population) SMR = (O/E)  100

19. For example On basis of age specific rates in standard population expect 50 deaths in test population. Observe 60. (O=60, E=50) SMR = (60/50)×100 = 120 95% CI for SMR = 120 ÷/× 1.29 = 93 to 155. CI includes 100 so data consistent with equality of death rate in test and standard populations (p>0.05). But also consistent with e.g. a 50% excess so certainly doesn’t prove equality.