1. Problem 1a: Measure and interpret association between number of births and cataract. 2. Set up a 2x2 table for each birth category comparing cases and controls to calculate odds ratios (OR): For 1-3 babies: Cases=31, Controls=134 For 4-6 babies: Cases=49, Controls=114 For 7-11 babies: Cases=15, Controls=14 3. Calculate odds of being a case in each group: Odds(1-3)=31/134=0.2313 Odds(4-6)=49/114=0.4298 Odds(7-11)=15/14=1.0714 4. Choose reference group as 1-3 babies to calculate OR: OR(4-6) = 0.4298 / 0.2313 = 1.857 OR(7-11) = 1.0714 / 0.2313 = 4.632 5. Interpretation: Women with 4-6 babies have about 1.86 times higher odds and women with 7-11 babies have about 4.63 times higher odds of cataract compared to women with 1-3 babies, indicating increased risk with higher parity. 6. Problem 1b: Plan a follow-up study to test causality. 7. Design a prospective cohort study recruiting young women without cataract. Measure number of births over time and incidence of cataract. 8. Record confounding variables such as age, socioeconomic status, nutrition, sun exposure, and genetics. 9. Use multivariable regression to adjust for confounders. 10. Follow participants longitudinally to establish temporal relationship between parity and cataract occurrence. 11. Problem 1c: Potential biases and their effect on OR. 12. Selection bias: Cases and controls might not be representative; could over or underestimate OR. 13. Recall bias: Misreporting number of births may distort exposure classification and bias OR. 14. Confounding: Unmeasured factors linked to both parity and cataract could skew results. 15. Information bias: Misclassification of cataract diagnosis affects OR accuracy. 16. Problem 2a: Association between smoking and heart disease. 17. Organize data into 2x2 table: Smokers diseased=90, non-diseased=210 Non-smokers diseased=30, non-diseased=270 18. Calculate incidence rates: Incidence smokers = 90/(90+210) = 0.3 Incidence non-smokers = 30/(30+270) = 0.1 19. Calculate risk ratio (RR) = 0.3 / 0.1 = 3.0 20. Calculate odds ratio (OR) = (90*270)/(210*30) = 3.857 21. Interpretation: Smokers have 3 times the risk and about 3.9 times the odds of developing heart disease compared to non-smokers. 22. Problem 2b: Design retrospective cohort study. 23. Identify cohort based on past smoking status from records. 24. Collect data on heart disease incidence over 10 years retrospectively. 25. Measure confounders such as age, diet, exercise, and medical history. 26. Compare incidence rates of heart disease in smokers vs non-smokers. 27. Adjust for confounders using statistical methods to estimate association between smoking and heart disease.