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Are exposures associated with disease?

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  1. Are exposures associated with disease? Epidemiology matters: a new introduction to methodological foundations Chapter 6

  2. Seven steps • Define the population of interest • Conceptualize and create measures of exposures and health indicators • Take a sample of the population • Estimate measures of association between exposures and health indicators of interest • Rigorously evaluate whether the association observed suggests a causal association • Assess the evidence for causes working together • Assess the extent to which the result matters, is externally valid, to other populations Epidemiology Matters – Chapter 1

  3. Associations • Ratio measures • Difference measures • Population attributable risk proportion • Summary Epidemiology Matters – Chapter 6

  4. Associations • Ratio measures • Difference measures • Population attributable risk proportion • Summary Epidemiology Matters – Chapter 6

  5. Associations • First we start with measures of disease occurrence and frequency • Association now involves the comparison of two measures Epidemiology Matters – Chapter 6

  6. Example: Farrlandia associations Farrlandia population • 10,000 people without heart disease • Follow population for 5 years • 3,000 people smoke • 410 of smokers develop heart disease • No loss to follow-up or change in smoking status over time Epidemiology Matters – Chapter 6

  7. Example: Farrlandia associations Risk of heart disease among 3,000 smokers and 7,000 non-smokers, over 5 years Epidemiology Matters – Chapter 6

  8. Example: Farrlandia risk Incidence (risk) • Risk of disease among exposed(smokers) diseased smokers population at baseline Epidemiology Matters – Chapter 6

  9. Example: Farrlandia risk Incidence (risk) • Risk of disease among unexposed (non-smokers) diseased non-smokers population at baseline Epidemiology Matters – Chapter 6

  10. Example: Farrlandia risk Incidence of heart disease among smokers = 13.7% Incidence of heart disease among non-smokers = 5% How much larger is 13.7% than 5%? Is the difference between 13.7% and 5% meaningful? Epidemiology Matters – Chapter 6

  11. Associations • Ratio measures • Difference measures • Population attributable risk proportion • Summary Epidemiology Matters – Chapter 6

  12. Ratios A way to quantify the magnitude of difference between two measures of disease Epidemiology Matters – Chapter 6

  13. Ratios • Risk ratios • 95% confidence interval for a risk ratio • Example of 95% confidence intervals for a risk ratio • Central Limit Theory assumptions and confidence intervals • Rate ratios • Odds ratios • 95% confidence interval for the odds ratio Epidemiology Matters – Chapter 6

  14. Risk ratio Numerator • Conditional risk of disease among exposed Denominator • Conditional risk of disease among unexposed Epidemiology Matters – Chapter 6

  15. a b a+b c d c+d a+c b+d a+b+c+d Epidemiology Matters – Chapter 6

  16. Risk ratio NumeratorRisk of disease in exposed DenominatorRisk of disease in unexposed a a+b = c c+d Epidemiology Matters – Chapter 6

  17. Disease incidence over time Non-diseased Diseased Exposed Unexposed Epidemiology Matters – Chapter 6

  18. Disease incidence over time Epidemiology Matters – Chapter 6

  19. Disease incidence over time Epidemiology Matters – Chapter 6

  20. Disease incidence over time Epidemiology Matters – Chapter 6

  21. 2 x 2 table Epidemiology Matters – Chapter 6

  22. Risk ratio NumeratorRisk of disease in exposed DenominatorRisk of disease in unexposed a a+b = c c+d Epidemiology Matters – Chapter 6

  23. 2 x 2 table 8 --- 10 -------- 5 --- 10 Risk ratio = = 1.6 Epidemiology Matters – Chapter 6

  24. 2 x 2 table 8 --- 10 -------- 5 --- 10 Risk ratio = a --- a+b -------- c --- c+d Risk ratio = Epidemiology Matters – Chapter 6

  25. Risk ratio interpretation • Ratios >1.0 indicate rate ishigheramong exposed than unexposed • Ratios = 1.0 indicate noassociation • Ratios < 1.0 indicate rate is loweramong exposed than unexposed Epidemiology Matters – Chapter 6

  26. Risk ratio95% confidence interval • Sample, by chance, will often not represent exact disease and exposure experience of population • Confidence intervals help to understand variability in study estimates due to chance in sampling process Epidemiology Matters – Chapter 6

  27. Steps: risk ratio95% confidence interval • Take natural log of risk ratio ln (Risk ratio) 2. Estimate standard error (SE) Epidemiology Matters – Chapter 6

  28. Steps: risk ratio95% confidence interval • Estimate upper and lower bounds on log scale • 95% confidence interval upper bound ln(Risk ratio) + 1.96(SE[ln(Risk ratio)]) • 95% confidence interval lower bound ln(Risk ratio)- 1.96(SE[ln(Risk ratio)]) Epidemiology Matters – Chapter 6

  29. Steps: risk ratio95% confidence interval • Exponentiate upper and lower bounds • Report and interpret estimate and confidence interval Sample: In these data, the exposed individuals had [risk ratio estimate]times the risk of the outcome compared with the unexposed, with a 95% confidence interval for the observed risk ratio ranging from [lowerbound]to [upper bound]. Epidemiology Matters – Chapter 6

  30. Example: risk ratio95% confidence interval • Measure association between family history of Alzheimer’s disease (AD) and incidence of AD among those aged >70 • Random sample of 1,000 individuals aged >70, no symptoms of AD • Followed for 20 years • Measure symptoms of AD every year • No losses to follow-up Epidemiology Matters – Chapter 6

  31. Example: risk ratio95% confidence interval a --- a+b -------- c --- c+d Risk ratio = Epidemiology Matters – Chapter 6

  32. Example: risk ratio95% confidence interval • Take natural log of risk ratio ln (Risk ratio) = ln(1.548) = 0.437 2. Estimate standard error (SE) Epidemiology Matters – Chapter 6

  33. Example: risk ratio95% confidence interval • Estimate upper and lower bounds on log scale • 95% confidence interval upper bound ln(Risk ratio) + 1.96(SE[ln(Risk ratio)]) 0.437 +1.96(0.1796) • 95% confidence interval lowerbound ln(Risk ratio)- 1.96(SE[ln(Risk ratio)]) 0.437 -1.96(0.1796) Epidemiology Matters – Chapter 6

  34. Steps: risk ratio95% confidence interval • Exponentiate upper and lower bounds • Report and interpret estimate and confidence interval Individuals >70 in Farrlandia with a family history of AD had 1.55 times the risk of developing AD over 20 years, with a 95% confidence interval for the risk ratio of 1.09 to 2.20. Epidemiology Matters – Chapter 6

  35. Central Limit Theory • Validity of confidence interval relies on Central Limit Theory (CLT) • Remember, assumptions of CLT • Large sample size • Each cell in 2 x 2 ≥ 5 Epidemiology Matters – Chapter 6

  36. Rate ratio • Risk ratios ideal with little or no loss to follow-up • Most studies have substantial loss to follow-up • Rate ratio more accurate representation of incidence when loss to follow-up an issue Epidemiology Matters – Chapter 6

  37. Rate ratio Epidemiology Matters – Chapter 6

  38. Rate ratio NumeratorRate of disease in exposed DenominatorRate of disease in unexposed = Epidemiology Matters – Chapter 6

  39. Rate ratio interpretation Similar to risk ratio • Ratios >1.0 indicate rate is higher among exposed than unexposed • Ratios = 1.0 indicate noassociation • Ratios < 1.0 indicate rate is lower among exposed than unexposed Epidemiology Matters – Chapter 6

  40. Steps: rate ratio95% confidence interval • Take natural log of rate ratio ln (Rate ratio) 2. Estimate standard error (SE) Epidemiology Matters – Chapter 6

  41. Steps: rate ratio95% confidence interval • Estimate upper and lower bounds on log scale • 95% confidence interval upper bound ln(Rate ratio) + 1.96(SE[ln(Rate ratio)]) • 95% confidence interval lower bound ln(Rate ratio)- 1.96(SE[ln(Rate ratio)]) Epidemiology Matters – Chapter 6

  42. Steps: rate ratio95% confidence interval • Exponentiate upper and lower bounds • Report and interpret estimate and confidence interval Sample: In these data, the exposed individuals had [rate ratio estimate] times the rate of the outcome compared with the unexposed, with a 95% confidence interval for the observed rate ratio ranging from [lower bound] to [upper bound]. Epidemiology Matters – Chapter 6

  43. Odds ratio • Appropriate measure of association for prospective study is risk or rate ratio • If sample individuals with and without disease and retrospectively assess exposure status, appropriate measure of association is odds ratio Epidemiology Matters – Chapter 6

  44. Example A Research question: Is smoking cigarettes during pregnancy a potential cause of offspring attention-deficit hyperactivity disorder (ADHD)? Sample: • Recruit 5,000 women during pregnancy who are smokers, and 5,000 women during pregnancy who are not smokers in Farrlandia • Prospective study • Assume no loss to follow-up Measures: Follow offspring at age 10 and determine which children developed ADHD and which did not Epidemiology Matters – Chapter 6

  45. Example A: risk ratio a --- a+b -------- c --- c+d Risk ratio = Epidemiology Matters – Chapter 6

  46. Example A: risk ratio interpretation From the prospective study, offspring of women who smoked in pregnancy have 1.5 times the risk of developing ADHD over 10 years compared to offspring of women who did not smoke in pregnancy. Epidemiology Matters – Chapter 6

  47. Odds ratio Numerator • Odds of disease in exposed Denominator • Odds of disease in unexposed Epidemiology Matters – Chapter 6

  48. Example A: odds ratio Epidemiology Matters – Chapter 6

  49. Example A: odds ratio • Odds of ADHD among exposed • Odds of ADHD among unexposed • Odds ratio Epidemiology Matters – Chapter 6

  50. Example A: odds ratiointerpretation The odds of developing ADHD in the first 10 years of life among those exposed are 1.53 times the odds of disease in the unexposed. Epidemiology Matters – Chapter 6