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Teach Epidemiology

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  1. Welcome to 3 Teach Epidemiology Professional Development Workshop Centers for Disease Control and Prevention July 14-18, 2008

  2. Time Check 9:00 AM 15 Minutes

  3. Teach Epidemiology Teach Epidemiology

  4. Is Epidemiology in Your Future? Teach Epidemiology

  5. Making Group Comparisons and Identifying Associations Ralph Cordell, Ph.D. Acting Associate Director of Science Division of Partnership and Strategic Alliances National Center for Health Marketing Teach Epidemiology

  6. Time Check 9:15 PM 45 Minutes

  7. Teach Epidemiology Teach Epidemiology

  8. Time Check 10:00 AM 135 Minutes

  9. Teach Epidemiology Teach Epidemiology

  10. Teach Epidemiology Young Epidemiology Scholars Professional Development Workshop July 16, 2008 Diane Marie M. St. George, PhD

  11. Enduring Understandings 7-9 Explaining associations and judging causation

  12. EU7: One possible explanation for finding an association is that the exposure causes the outcome. Because studies are complicated by factors not controlled by the observer, other explanations also must be considered, including confounding, chance, and bias. • The “Not everything that glitters is gold” Principle

  13. EU8: Judgments about whether an exposure causes a disease are developed by examining a body of epidemiologic evidence, as well as evidence from other scientific disciplines.

  14. EU9: While a given exposure may be necessary to cause an outcome, the presence of a single factor is seldom sufficient. Most outcomes are caused by a combination of exposures that may include genetic make-up, behaviors, social, economic, and cultural factors and the environment. • The “Just because your friend sleeps in class and never fails her courses does not mean that sleeping in class does not cause F grades” Principle

  15. Reasons for associations • Confounding • Bias • Reverse causality • Sampling error (chance) • Causation

  16. Osteoporosis risk is higher among women who live alone. • Death rates are low in AK and high in FL. • Those who work on farms are more likely to have a heart attack than those who do not. • In GA, African American women have the lowest mammography screening rates.

  17. Confounding • Confounding is an alternate explanation for an observed association of interest. Number of persons in the home Osteoporosis Age

  18. Confounding • Confounding is an alternate explanation for an observed association of interest. Exposure Outcome Confounder

  19. Confounding • Hypothetical cohort study • 9400 newborns followed for 10 yrs • RQ: Is exposure to manufacturing chemical by-products related to low vaccination rates among children?

  20. Pollution and low vaccination rates RR = (79 / 824) / (286 / 8576) = 2.9

  21. Pollution and vaccination rates • Could there be some other explanation for the observed association?

  22. Pollution and vaccination rates • If health care access had been the reason for the association between pollution and vaccination rates, what might the RR be if all children had no access? • What about if the children all had health care access?

  23. Pollution and vaccination rates

  24. Pollution and vaccination rates (Access) RR = (55 / 106) / (5 / 10) = 1.0

  25. Pollution and vaccinationrates (No access) RR = (24 / 718) / (281 / 8566) = 1.0

  26. Conclusions • Exposure to manufacturing waste is unrelated to vaccination rates among children with no health care access. • Exposure to manufacturing waste is unrelated to vaccination rates among children with health care access. • So…

  27. Confounding • Exists when confounder related to exposure • Exists when confounder related to outcome • Confounders can be risk factors (not just “nuisance” factors), e.g. lung CA

  28. Handling confounding • Restriction • Matching • Random assignment • Stratification • Statistical adjustment

  29. Confounding • Confounding is an alternate explanation for an observed association of interest. Low vaccination rates Manufacturing waste No health care access

  30. Reasons for associations • Confounding • Bias • Reverse causality • Sampling error (chance) • Causation

  31. Cohort study

  32. Bias • Errors are mistakes that are: • randomly distributed • not expected to impact the MA • less modifiable • Biases are mistakes that are: • not randomly distributed • may impact the MA • more modifiable

  33. Types of bias • Selection bias • The process for selecting/keeping subjects causes mistakes • Information bias • The process for collecting information from the subjects causes mistakes

  34. Information bias • Misclassification, e.g. non-exposed as exposed or cases as controls • Recall bias • Cases are more likely than controls to recall past exposures • Interviewer bias • Interviewers probe cases more than controls (exposed more than unexposed)

  35. Birth defects and diet • In a study of birth defects, mothers of children with and without infantile cataracts are asked about dietary habits during pregnancy.

  36. Pesticides and cancer mortality • In a study of the relationship between home pesticide use and cancer mortality, controls are asked about pesticide use and family members are asked about their loved ones’ usage patterns.

  37. Induced abortion & breast CA • Positive association found in 5 studies • No association found in 6 studies • Negative association found in 1 study

  38. Minimize bias • Can only be done in the planning and implementation phase • Standardized processes for data collection • Masking • Clear, comprehensive case definitions • Incentives for participation/retention

  39. Selection bias • People who agree to participate in a study may be different from people who do not • People who drop out of a study may be different from those who stay in the study

  40. Time Check 12:15 PM 30 Minutes

  41. Teach Epidemiology Revised Teach Epidemiology

  42. Time Check 12:45 PM 15 Minutes

  43. Teach Epidemiology Revised Teach Epidemiology

  44. Teach Epidemiology Revised Teach Epidemiology

  45. Time Check 1:30 PM 15 Minutes