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Day 3. Teach Epidemiology. Professional Development Workshop. The Health Education Center at Lankenau Hospital 100 Lancaster Avenue, Wynnewood, PA 19096 July 20-24, 2009. Teach Epidemiology. Teach Epidemiology. Time Check 9:15 AM. Teach Epidemiology. Teach Epidemiology.

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Teach Epidemiology


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    1. Day 3 Teach Epidemiology Professional Development Workshop The Health Education Center at Lankenau Hospital100 Lancaster Avenue, Wynnewood, PA 19096July 20-24, 2009

    2. Teach Epidemiology Teach Epidemiology

    3. Time Check 9:15 AM

    4. Teach Epidemiology Teach Epidemiology

    5. Teaching Epidemiology Class 1 Pages 16-21) Group 3 Teach Epidemiology

    6. Time Check 10:00 AM

    7. Teach Epidemiology Teach Epidemiology

    8. Teaching Epidemiology Group 1 Pages 35-36 Teach Epidemiology

    9. Time Check 10:45 AM

    10. Teach Epidemiology Teach Epidemiology

    11. Time Check 11:00 AM

    12. Teach Epidemiology Teach Epidemiology

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

    14. 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.

    15. 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.

    16. 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.

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

    18. Osteoporosis risk is higher among women who live alone.

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

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

    21. Confounding • YES confounding module example: • Cohort study • 9,400 elderly in the hospital • RQ: Are bedsores related to mortality among elderly patients with hip fractures?

    22. Bedsores and Mortality RR = (79 / 824) / (286 / 8576) = 2.9

    23. Bedsores and Mortality • Avoid bedsores…Live forever!! • Could there be some other explanation for the observed association?

    24. Bedsores and mortality • If severity of medical problems had been the reason for the association between bedsores and mortality, what might the RR be if all study participants had very severe medical problems? • What about if the participants all had problems of very low severity?

    25. Bedsores and Mortality

    26. Bedsores and Mortality (Severe) RR = (55 / 106) / (5 / 10) = 1.0

    27. Bedsores and Mortality (Not severe) RR = (24 / 718) / (281 / 8566) = 1.0

    28. Bedsores and Mortality stratified by Medical Severity

    29. Bedsores • Bedsores are unrelated to mortality among those with severe problems. • Bedsores are unrelated to mortality among those with problems of less severity. • Adjusted RR = 1, and the unadjusted RR = 2.9

    30. Confounding • Confounding is an alternate explanation for an observed association of interest. Bedsores Death in the hospital Severity of medical problems

    31. Controlling confounding • Study design phase • Matching • Restriction • Random assignment • Study analysis phase • Stratification • Statistical adjustment

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

    33. 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

    34. 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

    35. Selection bias • People who are working are likely to be healthier than non-workers • People who 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 • Hospital controls may not represent the source population for the cases

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

    37. 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.

    38. 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.

    39. 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

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

    41. Reverse causality • Suspected disease actually precedes suspected cause • Pre-clinical disease  Exposure  Disease • For example: Memory deficits  Reading cessation  Alzheimer’s • Cross-sectional study • For example: Sexual activity/Marijuana

    42. Minimize effect of reverse causality • Done in the planning and implementation phase of a study • Pick study designs in which exposure is measured before disease onset • Assess disease status with as much accuracy as possible

    43. Time Check 12:15 AM

    44. Teach Epidemiology Teach Epidemiology