1. Hadpop Calculations

2. Odds ratio What study applicable? Q. It is suggested that obesity increases the chances on an individual becoming infected with erysipelas. A sample of 165 obese people was taken with 68 having suffered previously with erysipelas. This compares with a sample including 257 normal people with 61 having been infected. Construct a table and work out the odds ration of developing erysipelas as a consequence of obesity. Interpret your result.

3. Odds ratio Odds = A x D / B x C =68x197 / 97x 61 =2.3 This is greater than 1, and we can therefore conclude that being exposed to this infection increases the odds of developing obesity. Case control study – identify outcome, people with infection, compare exposure obesity. E.g (HAVE AIDS, DO ANAL?) erysipelasNo erysipelas obese68 (A)97 (B) normal61 (C)197 (D)

4. IRR What study? What purpose? Over a 3 year period 1500 former employee’s of ‘asbestos uk’ were followed up. They were separated into 2 cohorts, 1000 who were exposed to aspestos, 500 who weren’t. In the exposed cohort 600 developed mesothelioma, in the unexposed 3 developed mesothelioma. This compares to the national average of 120 people per year per 1000. Q. Calculate the IRR and interpret.

5. IRR Cohort Identify exposure – compare outcome, e.g (ANAL? – AIDS) Exposed = 3 x 1000 = 3000 people years Not = 3 x 500 = 1500 py IRR exp, 600/3000 = 200 per 1000 py IRR not exp, 3 / 1500 = 2 per 1000 py, 200/2 Answer = 100 Greater than 1, is a much higher incidence

6. SMR What purpose? Q.120 people die in Scotland from cardiac related problems. This compares with an average of 33. Calculate the SMR and interpret.

7. SMR Ans = 120 x 100 / 33 =363.6 Greater than 100, so we can conclude that living in Scotland's increases your risk of cardiac related mortality.

8. Causality Identifies the chance that an exposure caused a disease. Using the IRR from the previous question calculate the confidence intervals for this study. Interpret Error factor = 3, (a small study has a big error factor) Hint Ub, Lb,

9. CI UB = er x ratio, =3 x 100, 300 LB = ratio / er, = 100/3, 33

10. Generic things to say about C95% I can say with 95% certainty that the true underlying value lies between x – y, the range makes me think... The value for the null hypothesis is 1, this is included so is a possible value, p>0.05 and i fail to reject the null hypothesis. The result is not statistically significant 1 is just included in the range, and so there is a tendency to significance 1 is not in the range, i can reject the null hypothesis, p<0.05 the results are significant

11. This example Range is 300-33. Large range, say with 95% certainty true value lies in this range. Does not include 1, reject the null, p<0.05 the results are significant