1. Centers for Disease Control and Prevention Global Health Odyssey Museum Tom Harkin Global Communications Center June 6-10, 2011 Teach Epidemiology Professional Development Workshop Day 4

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3. 3 Teach Epidemiology

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5. http://www.cdc.gov/ MMWR

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7. 7 Critical Reviews

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22. 22 Time Check 8:15 AM

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24. 24 Teach Epidemiology

25. 25 Teach Epidemiology Teachers Team-Teaching Teachers (TTTT)

26. 26 National Research Council, Learning and Understanding Teach Epidemiology Enduring Epidemiological Understandings Knowledge that “… is connected and organized, and … ‘conditionalized’ to specify the context in which it is applicable.”

27. The goal of every epidemiological study is to harvest valid and precise information about the relationship between an exposure and a disease in a population. The various study designs merely represent different ways of harvesting this information. Essentials in Epidemiology in Public Health Ann Aschengrau and George R. Seage III Making Group Comparisons and Identifying Associations Teach Epidemiology

28. 28 Time Check 9:00 AM

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30. 30 Teach Epidemiology

31. Vocab Review 1.Genotype – combination of alleles 2.Alleles – variations of a gene 3.Homozygous – both alleles are the same 4.Heterozygous – both alleles are different

32. P – First people living near ports, then further inland. P – Europe T –1340’s-1350’s huron2.aaps.k12.mi.us

33. *Statistics in 1300’s? *Activity! *Hypotheses??? 1. Bacon fat?? What kind of study? 2. Genotypes?? What kind of study? LivedDied Did Drink Didn’t Drink LivedDied Had ‘a’ Didn’t have ‘a’

34. Natural Selection 1.Overpopulation 2.Genetic Variation 3.Struggle to survive – Selective Pressure 4.Differential Survival and Reproduction

35. Anecdote “A previously healthy 36 year-old man with clinically diagnosed CMV infection in September 1980 was seen in April of 1981 because of a 4-month history of fever, dyspnea and cough. On admission, he was found to have P. carinii pneumonia, oral candidiasis, and CMV retinitis. A complement-fixation CMV titer in April 1981 was 1928. The patient has been treated with 2 short courses of TMP/SMX that have been limited because of a sulfa-induced neutropenia. He is being treated for candidiasis with topical nystatin” –MMWR 1981

36. Population P – healthy homosexual males, intravenous drug users P – World wide (including U.S.) T – Early 1980’s

37. Exception – Steve CrohnSteve Crohn 1.Fits perfectly into the HIV high-risk category 2.Male partner died of HIV 3.He never got sick. 4.WHY???

38. CCR5 and Delta 32 Mutation *Maybe related to black plague resistance. *Maybe related to HIV resistance *What kind of study? HIV +HIV - No Delta 3220 1 or 2 Delta 321090

39. 39 National Research Council, Learning and Understanding Teach Epidemiology Enduring Epidemiological Understandings Knowledge that “… is connected and organized, and … ‘conditionalized’ to specify the context in which it is applicable.”

40. The goal of every epidemiological study is to harvest valid and precise information about the relationship between an exposure and a disease in a population. The various study designs merely represent different ways of harvesting this information. Essentials in Epidemiology in Public Health Ann Aschengrau and George R. Seage III Making Group Comparisons and Identifying Associations Teach Epidemiology

41. 41 Time Check 9:45 AM

42. 42

43. 43 Teach Epidemiology

44. Marian R Passannante, PhD Associate Professor University of Medicine and Dentistry of New Jersey New Jersey Medical School School of Public Health Teach Epidemiology EPI-501

45. 45 Teach Epidemiology Enduring Epidemiological Understandings EPI-501

46. 46 Teach Epidemiology Enduring Epidemiological Understandings An association is found- why? Chance Confounding Bias Real

47. 47 Teach Epidemiology Enduring Epidemiological Understandings Chance First, choose a statistical method to test the association between an exposure and an outcome.

48. 48 Teach Epidemiology Enduring Epidemiological Understandings Statins and the Risk of Colorectal Cancer Outcome Exposure+- + AB -CD

49. 49 Teach Epidemiology Enduring Epidemiological Understandings Chance First, choose a statistical method to test the association between an exposure and an outcome. Exposure: Statin use status Outcome: Colorectal Cancer status

50. 50 Teach Epidemiology Enduring Epidemiological Understandings Chance First, choose a statistical method to test the association between an exposure and an outcome. “background Statins are… effective lipid-lowering agents. Statins inhibit the growth of colon-cancer cell lines, and secondary analyses of some, but not all, clinical trials suggest that they reduce the risk of colorectal cancer.”

51. 51 Teach Epidemiology Enduring Epidemiological Understandings Chance First, choose a statistical method to test the association between an exposure and an outcome. “methods The Molecular Epidemiology of Colorectal Cancer study is a population-based case–control study of patients who received a diagnosis of colorectal cancer in northern Israel between 1998 and 2004 and controls matched according to age, sex, clinic, and ethnic group. We used a structured interview to determine the use of statins in the two groups and verified self-reported statin use by examining prescription records in a subgroup of patients for whom prescription records were available."

52. 52 Teach Epidemiology Enduring Epidemiological Understandings Chance First, choose a statistical method to test the association between an exposure and an outcome. Exposure: Statin use status Outcome: Colorectal Cancer status Statistical method: Chi-square To test whether the proportion of cases who took statins differs from the proportion of controls who took statins. Statin Use CaseControlTotal +ABA + B -CDC + D TotalA+CB+DA+B+C+ D

53. 53 Teach Epidemiology Enduring Epidemiological Understandings Chance Second, choose a level of risk (called  or the alpha level) that we are willing to take when conducting that test. Most common alpha levels: 0.05 or 0.01 Third, conduct the statistical test and get a p value.

54. 54 Teach Epidemiology Enduring Epidemiological Understandings Chi-square (  2 ) test  2 =  [(0-E) 2 /E] O = observed # of events in a cell E = expected # of events in a cell if there were no association between the independent and dependent variables Expected Value = (Row Total x Column Total) Grand Total

55. 55 Teach Epidemiology Enduring Epidemiological Understandings Chance If the percentage of cases who took statins is the same as the percentage of controls who took statins, what % would you expect to find among cases and controls? Exposure: Statin use status Outcome: Colorectal Cancer status Statistical method: Chi-square Statin Use CaseControlTotal + A ?% B ?% 354 8.9% -CD3614 91.1% Total195320153968

56. 56 Teach Epidemiology Enduring Epidemiological Understandings Chance If the percentage of cases who took statins is the same as the percentage of controls who took statins, what % would you expect to find among cases and controls? Exposure: Statin use status Outcome: Colorectal Cancer status Statistical method: Chi-square Statin Use CaseControlTotal + A 8.9 % B 8.9% 354 8.9% -CD3614 Total195320153968

57. 57 Teach Epidemiology Enduring Epidemiological Understandings Chance If the percentage of cases who took statins is the same as the percentage of controls who took statins, what % would you expect to find among cases and controls? Exposure: Statin use status Outcome: Colorectal Cancer status Statistical method: Chi-square Statin Use CaseControlTotal +AB Expecte d Value 354 8.9% -CD3614 Total195320153968

58. 58 Teach Epidemiology Enduring Epidemiological Understandings Chance If the percentage of cases who took statins is the same as the percentage of controls who took statins, what % would you expect to find among cases and controls? Exposure: Statin use status Outcome: Colorectal Cancer status Statistical method: Chi-square Statin Use CaseControlTotal +AB Expecte d Value 354 8.9% -CD3614 Total195320153968

59. 59 Teach Epidemiology Enduring Epidemiological Understandings Chance Expected Values appear in red below: Exposure: Statin use status Outcome: Colorectal Cancer status Statistical method: Chi-square  2 =  [(0-E) 2 /E] Statin Use CaseControlTotal +A 173.8 B 179.3 354 8.9% -C 1779.2 D 1835.7 3614 91.1% TotalA+C 1953 B+D 20153968 100%

60. 60 Teach Epidemiology Enduring Epidemiological Understandings Chance Expected Values appear in red below: Exposure: Statin use status Outcome: Colorectal Cancer status Statistical method: Chi-square To test whether the proportion of cases who took statins differs from the proportion of controls who took statins, chi-square compares what was observed and what one would expect to find if there were no association. The chi-square test result will provide a p value. We will compare our p value to our preset level of risk or alpha level. Statin Use CaseControlTotal +A 120 173.8 B 234 179.3 354 8.9% -C 1833 1778.8 D 1731 1835.2 3614 91.% Total195320153968 100%

61. 61 Teach Epidemiology Enduring Epidemiological Understandings Chance What does a p value tell us?

62. 62 Teach Epidemiology Enduring Epidemiological Understandings Chance What does a p value tell us? If you conducted a statistical test and got a p value of.04 it would mean that 4 times out of 100 you might find a significant association like the one observed in your study by chance alone. Significance testing provides a p value and this value tell you how likely the result you found is due to chance alone. Let’s look at the analysis results for the statin study….

63. 63 Teach Epidemiology Enduring Epidemiological Understandings Chance Third, we conduct the statistical test and get a p value.

64. 64 Teach Epidemiology Enduring Epidemiological Understandings Chance Third, we conduct the statistical test and get a p value. If the p value is less than alpha (say.05) we say that our exposure and outcome are significantly associated. With p <.0001, we can say that there is a strong association between statin use and disease outcome. How would you describe the direction of the relationship?

65. 65 Teach Epidemiology Enduring Epidemiological Understandings Chance How would you measure the strength of the association between statin use and colorectal cancer in this case-control study?

66. 66 Teach Epidemiology Enduring Epidemiological Understandings Chance How would you describe the relationship? Which measure of risk would you calculate? The odds ratio: 120 x 1781 =.50 234 x 1833 “In analyses including 1953 patients with colorectal cancer and 2015 controls, the use of statins for at least five years (vs. the nonuse of statins) was associated with a significantly reduced … risk of colorectal cancer (odds ratio, 0.50; 95 percent confidence interval, 0.40 to 0.63).”

67. 67 Teach Epidemiology Enduring Epidemiological Understandings Chance Although you may have found a statistically significant association, it is always possible that your sample is not really a good representation of the total population and the result you got was by chance alone. How to avoid this problem?

68. 68 Teach Epidemiology Enduring Epidemiological Understandings Chance Although you may have found a statistically significant association, it is always possible that your sample is not really a good representation of the total population and the result you got was by chance alone. How to avoid this problem? Increase your sample size

69. Time Check 69

70. 70 Teach Epidemiology Enduring Epidemiological Understandings An association is found- why? Chance Confounding Bias Real

71. 71 Teach Epidemiology Enduring Epidemiological Understandings Confounding Is alcohol consumption associated with lung cancer? Exposure Outcome Alcohol consumption lung cancer Confounding occurs when a third factor is associated with both an exposure and an outcome. This third factor may create the appearance of a causal association between the exposure and outcome even if it isn’t really there.

72. 72 Teach Epidemiology Enduring Epidemiological Understandings Confounding Confounding occurs when a third factor is associated with both an exposure and an outcome. Exposure Outcome Alcohol consumption lung cancer Cigarette Smoking Confounder Smoking is associated with alcohol use and lung cancer.

73. 73 Teach Epidemiology Enduring Epidemiological Understandings Confounding Can you think of another relationship between an exposure and an outcome where there might be a confounder? Exposure Outcome Potential Confounder

74. 74 Teach Epidemiology Enduring Epidemiological Understandings Confounding Is there an association between OC use and MI? OC use MI (heart attack) Exposure Outcome Cigarette Smoking Potential Confounder

75. 75 Teach Epidemiology Enduring Epidemiological Understandings Confounding Can you think of a way to take a confounder into consideration when looking at the relationship between an exposure and an outcome? Exposure Outcome Potential Confounder

76. 76 Teach Epidemiology Enduring Epidemiological Understandings Control Confounding through Study Design Randomization (in experimental studies, randomly assign participants so that an equal proportion of those with the possible confounding factor will be in the exposure groups) Drug A Survival Drug B Age (Possible Confounder)

77. 77 Teach Epidemiology Enduring Epidemiological Understandings Control Confounding through Study Design Alcohol use lung cancer cigarette smoking Restriction (limit the study to those who do not have the confounding factor- e.g. never smokers) Matching (in a case-control study match cases with controls who have or do not have the potential confounder- e.g. lung cancer cases matched to controls who have the same smoking status)

78. 78 Teach Epidemiology Enduring Epidemiological Understandings Control Confounding through Analysis Alcohol use lung cancer cigarette smoking Stratification (Conduct the analysis separately for different levels of the possible confounder- smoking status) Statistical methods (A number of statistical methods allow for the assessment of the relationship between an exposure and an outcome, while controlling for possible confounders.)

79. 79 Teach Epidemiology Enduring Epidemiological Understandings An association is found- why? Chance Confounding Bias Real

80. 80 Teach Epidemiology Enduring Epidemiological Understandings Bias* a systematic deviation of results or inferences from the truth or processes leading to such systematic deviation; any systematic tendency in the collection, analysis, interpretation, publication, or review of data that can lead to conclusions that are systematically different from the truth. In epidemiology, does not imply intentional deviation. *Definition Source: Principles of Epidemiology in Public Health Practice Third Edition, U.S. DHHS, CDC

81. 81 Teach Epidemiology Enduring Epidemiological Understandings Bias Bias can appear in all types of epidemiologic studies Many types of bias have been identified by epidemiologists Two main forms of bias in epidemiologic studies Information Bias Selection Bias

82. 82 Teach Epidemiology Enduring Epidemiological Understandings Bias Information Bias* systematic difference in the collection of data regarding the participants in a study (e.g., about exposures in a case-control study, or about health outcomes in a cohort study) that leads to an incorrect result (e.g., risk ratio or odds ratio) or inference. Can you think of some situations that might result in information bias? *Definition Source: Principles of Epidemiology in Public Health Practice Third Edition, U.S. DHHS, CDC

83. 83 Teach Epidemiology Enduring Epidemiological Understandings Bias Information Bias Misclassification Bias Non-differential (inaccuracy of the data collection unrelated to exposure or disease status) Differential (rate of misclassification is different in different study groups) Recall Bias Observer Bias

84. 84 Teach Epidemiology Enduring Epidemiological Understandings Bias Misclassification Bias Non-differential (inaccuracy of the data collection- unrelated to exposure or disease status) some controls might be called cases some exposed people might be identified as non-exposed How might this impact ORs and RRs if there is a true relationship between exposure and outcome?

85. 85 Teach Epidemiology Enduring Epidemiological Understandings Bias Differential Misclassification (rate of misclassification is different in different study groups) Recall Bias (Case-Control Study) (differential recollection of exposure among cases and controls) Observer Bias (Cohort study/Trial) (if those assessing the disease outcome look more closely at the exposed group compared to the unexposed group)

86. 86 Teach Epidemiology Enduring Epidemiological Understandings How can we minimize these? Differential Misclassification (rate of misclassification is different in different study groups) Recall Bias (Case-Control Study) (differential recollection of exposure among cases and controls) Observer Bias (Cohort study/Trial) (if those assessing the disease outcome look more closely at the exposed group compared to the unexposed group)

87. 87 Teach Epidemiology Enduring Epidemiological Understandings How to minimize bias Differential (rate of misclassification is different in different study groups) Recall Bias (Case-Control Study) may be minimized with careful interview/survey item construction Observer Bias (Cohort study/Trial) can be controlled by blinding those who are assessing the outcome regarding the exposure status

88. 88 Teach Epidemiology Enduring Epidemiological Understandings Blinding* Blinding (masking)- intervention assignment is hidden from participants, trial investigators, or assessors. Single-blind- one of the three categories of individuals (normally participant) remains unaware of intervention assignment Double-blind- participants, investigators, and assessors usually all remain unaware of the intervention assignments Triple-blind- usually means a double-blind trial that also maintains a blind data analysis. * F Schulz, David A Grimes, Blinding in randomised trials: hiding who got what THE LANCET Vol 359 February 23, 2002 www.thelancet.com

89. Double vs Single Blinding F Schulz, David A Grimes, Blinding in randomised trials: hiding who got what THE LANCET Vol 359 February 23, 2002 www.thelancet.com 89

90. Potential Benefits accruing dependent on those individuals successfully blinded F Schulz, David A Grimes, Blinding in randomised trials: hiding who got what THE LANCET Vol 359 February 23, 2002 www.thelancet.com Individuals blinded Potential Benefits ParticipantsParticipants Less likely to have biased psychological or physical responses to intervention More likely to comply with trial regimens Less likely to seek additional adjunct interventions Less likely to leave trial without providing outcome data, leading to lost to follow-up Trial InvestigatorsLess likely to transfer their inclinations or attitudes to participants Less likely to differentially administer co-interventions Less likely to differentially adjust dose Less likely to differentially withdraw participants Less likely to differentially encourage or discourage participants to continue trial AssessorsLess likely to have biases affect their outcome assessments, especially with subjective outcomes of interest 90

91. 91 Teach Epidemiology Enduring Epidemiological Understandings Bias Selection Bias* systematic difference in the enrollment of participants in a study that leads to an incorrect result (e.g., risk ratio or odds ratio) or inference. *Definition Source: Principles of Epidemiology in Public Health Practice Third Edition, U.S. DHHS, CDC

92. 92 Teach Epidemiology Enduring Epidemiological Understandings Selection Bias systematic difference in the enrollment of participants in a study that leads to an incorrect result (e.g., risk ratio or odds ratio) or inference. Cross-sectional study Prevalence study of asthma Non-respondents Higher proportion of smokers Higher prevalence of asthma symptoms How might this impact prevalence estimates?

93. 93 Teach Epidemiology Enduring Epidemiological Understandings Selection Bias systematic difference in the enrollment of participants in a study that leads to an incorrect result (e.g., risk ratio or odds ratio) or inference. Case-Control Study: Study of smoking Lung Cancer Cases: Lung CA in hospital Controls: Heart attack (MI) patients in hospital How might this impact the OR?

94. 94 Teach Epidemiology Enduring Epidemiological Understandings Selection Bias systematic difference in the enrollment of participants in a study that leads to an incorrect result (e.g., risk ratio or odds ratio) or inference. Cohort Study: Study of H20 Exercise Heart Attack Participants in H 2 0 Class at Y Neighborhood non-participants How might this impact the Relative Risk?

95. 95 Teach Epidemiology Enduring Epidemiological Understandings EPI-501

96. 96 Teach Epidemiology Enduring Epidemiological Understandings Necessary and Sufficient Necessary- without the factor, the disease never develops Sufficient- in the presence of the factor the disease always develops Rare to have a factor be both necessary and sufficient. E.g. When a group of people are exposed to someone with active TB disease, not all of them get infected.

97. 97 Enduring Epidemiological Understandings Necessary but not sufficient compromised Exposure to TB Bacillus immune system (necessary but not sufficient) Malnutrition lack compromised Crowdedmedical care immune system Living conditions Source: modified from Figure 5.1 Bonita, Ruth. Basic epidemiology 2nd edition

98. 98 Teach Epidemiology Enduring Epidemiological Understandings Necessary and Sufficient Necessary- without the factor, the disease never develops Sufficient- in the presence of the factor the disease always develops Can you think of a factor that is both necessary and sufficient to cause disease?

99. 99 Teach Epidemiology Enduring Epidemiological Understandings Necessary and Sufficient If the title of this article is correct…. Nature Medicine 11, 740 - 747 (2005) Published online: 12 June 2005; | doi:10.1038/nm1261 Smallpox vaccine– induced antibodies are necessary and sufficient for protection against monkeypox virus Necessary: Without the antibodies you won’t be protected Sufficient: With the antibodies you will be protected

100. Time Check 100

101. 101 Teach Epidemiology Enduring Epidemiological Understandings EPI-501

102. 102 Teach Epidemiology Enduring Epidemiological Understandings Assessing a Causal Relationship Source: Principles of Epidemiology in Public Health Practice Third Edition, U.S. DHHS, CDC

103. 103 Teach Epidemiology Enduring Epidemiological Understandings Odds Ratio of lung cancer by 75 by country Strength of Association: relationship must be clear Look for large Odds Ratios and Relative Risks Source: British Journal of Cancer (2004) 91, 1280– 1286. doi:10.1038/sj.bjc.66020 78 www.bjcancer.com Table 2www.bjcancer.com

104. 104 Teach Epidemiology Enduring Epidemiological Understandings Consistency: observation of the association must be repeatable in different populations at different times Ecological Studies The rising death rate from lung cancer in many counties along with a rise in cigarette consumption Case Control Studies* By 1959, 21 independent groups of investigators in 8 different countries. More studies followed with similar results. Cohort Studies* By 1959, large cohort studies in two countries by three independent groups. * Source: Cornfeld et al J. Nat. Cancer Inst. 22:173–203, 1959

105. 105 Teach Epidemiology Enduring Epidemiological Understandings Temporalit y: the cause (exposure) must precede the effect (the disease). Source: The American Cancer Society. Cancer Statistics 2010. http://www.cancer.org/research/c ancerfactsfigures/cancerfactsfigu res/cancer-facts-and-figures- 2010

106. 106 Teach Epidemiology Enduring Epidemiological Understandings Odds ratios of lung cancer by age 75, for current cigarette smokers stratified by amount smoked per day Biological Gradient: There must be a dose response. Source: British Journal of Cancer (2004) 91, 1280– 1286. doi:10.1038/sj.bjc.6602078 www.bjcancer.com Table 4 www.bjcancer.com

107. 107 Teach Epidemiology Enduring Epidemiological Understandings Assessing a Causal Relationship Biological Plausibility: the explanation must make sense biologically. http://www.txtwriter.com/onscience /articles/smokingcancer2.html

108. 108 Teach Epidemiology Enduring Epidemiological Understandings Assessing a Causal Relationship Source: Principles of Epidemiology in Public Health Practice Third Edition, U.S. DHHS, CDC

109. 109 Teach Epidemiology Enduring Epidemiological Understandings EPI-501

110. 110 Time Check 10:15 AM

111. 111

112. 112 Teach Epidemiology

113. 113 Time Check 10:30 AM

114. 114

115. 115 Teach Epidemiology

116. 116 Time Check 11:30 AM

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118. 118 Teach Epidemiology

119. 119 Time Check 12:30 PM

120. 120

121. 121 Teach Epidemiology

122. 122 Time Check 1:00 PM

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124. 124 Teach Epidemiology

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126. 126 Hypothesis Total RiskRelative Risk a b c d or % % ExposureOutcome ? Turned Up Together Healthy People - E E DZ Teach Epidemiology Where are we?

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128. 128 Teach Epidemiology Enduring Epidemiological Understandings

129. 129 Teach Epidemiology Enduring Epidemiological Understandings

130. 130

131. 131 Suicide Higher in Areas with Guns Family Meals Are Good for Mental Health Lack of High School Diploma Tied to US Death Rate Study Links Spanking to Aggression Study Concludes: Movies Influence Youth Smoking Study Links Iron Deficiency to Math Scores Kids Who Watch R-Rated Movies More Likely to Drink, Smoke Pollution Linked with Birth Defects in US Study Ties, Links, Relationships, and Associations Snacks Key to Kids’ TV- Linked Obesity: China Study Depressed Teens More Likely to Smoke

132. 132 Suicide Higher in Areas with Guns Family Meals Are Good for Mental Health Lack of High School Diploma Tied to US Death Rate Study Links Spanking to Aggression Study Concludes: Movies Influence Youth Smoking Study Links Iron Deficiency to Math Scores Kids Who Watch R-Rated Movies More Likely to Drink, Smoke Pollution Linked with Birth Defects in US Study Snacks Key to Kids’ TV- Linked Obesity: China Study Depressed Teens More Likely to Smoke Ties, Links, Relationships, and Associations

133. 133 1.Cause 2.Confounding 3.Reverse Time Order 4. Chance 5.Bias Possible Explanations for Finding an Association

134. 134 Epidemiology... the study of the distribution and determinants of health- related states or events in specified populations and the application of this study to the control of health problems. Leon Gordis, Epidemiology, 3 rd Edition, Elsevier Saunders, 2004.

135. 135 1.Cause 2.Confounding 3.Reverse Time Order 4. Chance 5.Bias Possible Explanations for Finding an Association

136. 136 Cause A factor that produces a change in another factor. William A. Oleckno, Essential Epidemiology: Principles and Applications, Waveland Press, 2002. Possible Explanations for Finding an Association

137. 137 Sample of 100

138. 138 Sample of 100, 25 are Sick

139. 139 Diagram 2x2 Table DZ X X ab c d Types of Causal Relationships

140. 140 DZ X X ab c d Diagram 2x2 Table Types of Causal Relationships

141. 141 Handout

142. 142

143. 143 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 DZ X1X1 X1X1 ab c d Diagram 2X12 Table Necessary and Sufficient

144. X1X1 144 DZ ab c d X1X1 X2X2 X3X3 ++ X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 Diagram 2X12 Table Necessary but Not Sufficient X1X1

145. X1X1 145 X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 DZ ab c d X2X2 X1X1 X3X3 Diagram 2X12 Table Not Necessary but Sufficient X1X1

146. X1X1 146 DZ ab c d X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X1X1 X4X4 X1X1 X7X7 X5X5 X6X6 ++ X2X2 X3X3 ++ X8X8 X9X9 ++ Not Necessary and Not Sufficient Diagram 2X12 Table X1X1

147. 147 X X X X X X X X X X X X X X XX X X X X X X X X X XDZ X X ab c d X Diagram 2x2 Table Necessary and Sufficient

148. 148 DZ X X ab c d X XX++ X X X X X X X X X X X X X X X XX X X X X X X X X X X X X X X X X X XX Diagram 2x2 Table Necessary but Not Sufficient

149. 149 X X X X X X X X X X X X X X X X DZ X X ab c d X X X X Diagram 2x2 Table Not Necessary but Sufficient

150. 150 DZ X X ab c d X X X X X X X X X X X X X X X X X X X X X X X X X X X X X XX X X X XX++ XX++ XX++ Not Necessary and Not Sufficient Diagram 2x2 Table

151. 151 a b c d Heart Attack No Heart Attack Lack of Fitness No Lack of Fitness Lack of fitness and physical activity causes heart attacks.

152. 152 a b c d Lead Poisoning No Lead Poisoning Lack of Supervision No Lack of Supervision Lack of supervision of small children causes lead poisoning.

153. 153 Is the association causal?

154. 154 Suicide Higher in Areas with Guns Family Meals Are Good for Mental Health Lack of High School Diploma Tied to US Death Rate Study Links Spanking to Aggression Study Concludes: Movies Influence Youth Smoking Study Links Iron Deficiency to Math Scores Kids Who Watch R-Rated Movies More Likely to Drink, Smoke Pollution Linked with Birth Defects in US Study Ties, Links, Relationships, and Associations 1.Cause 2.Confounding 3.Reverse Time Order 4. Chance 5.Bias Snacks Key to Kids’ TV- Linked Obesity: China Study Depressed Teens More Likely to Smoke

155. 155 Teach Epidemiology Enduring Epidemiological Understandings

156. 156

157. 157 1.Cause 2.Confounding 3.Reverse Time Order 4. Chance 5.Bias Possible Explanations for Finding an Association

158. 158 All the people in a particular group. Population Possible Explanations for Finding an Association

159. 159 A selection of people from a population. Sample Possible Explanations for Finding an Association

160. 160 Inference Process of predicting from what is observed in a sample to what is not observed in a population. To generalize back to the source population. Possible Explanations for Finding an Association

161. 161 Sample Population Process of predicting from what is observed to what is not observed. Observed Not Observed Inference

162. 162 Deck of 100 cards Population

163. 163 a 25 cards b c d Population

164. 164 = Population a 25 cards bc d = ab cd Odd # Even # No Marijuana Population Total

165. 165 = Population a 25 cards bc d = 25 50 Total Odd # Even # No Marijuana Population

166. 166 = Population = M&M’s No M&M’s Flu No Flu 25 50 Total = 25 50 Total a 25 cards bc d Odd # Even # No Marijuana Population

167. 167 = Population = 25 50 Total a 25 cards bc d Risk 25 / 50 or 50% Odd # Even # No Marijuana Population

168. 168 = Population a 25 cards bc d = 25 50 TotalRiskRelative Risk 25 / 50 or 50 % 50 % / 50% = = 1 50 % ____ Odd # Even # No Marijuana Population

169. 169 25 cards Population

170. 170 To occur accidentally. To occur without design. Chance A coincidence. Possible Explanations for Finding an Association

171. 171 Chance

172. 172 Chance

173. 173 Population Sample b Sample of 20 cards 25 cards Sample

174. 174 Population Sample b Sample of 20 cards 25 cards 10 Total 55 55 Odd # Even # No Marijuana Sample

175. 175 Population Sample b Sample of 20 cards 25 cards 10 Total 55 55 Risk 5 / 10 or 50 % Odd # Even # No Marijuana Sample

176. 176 Population Sample b Sample of 20 cards 25 cards 10 Total 55 55 Risk 5 / 10 or 50 % Odd # Even # No Marijuana Sample Relative Risk 50 % / 50% = = 1 50 % ____

177. 177 b Sample of 20 cards Total Risk 5 / 10 = 50 % 50 1 Relative Risk By Chance CDC % ___ % = Odd # Even # No Marijuana Sample

178. 178 10 Total 55 55 Risk 5 / 10 or 50 % Relative Risk How many students picked a sample with 5 people in each cell? = 1 50 % ____ Odd # Even # No Marijuana Chance By Chance

179. 179 Relative Risks Greater than 1Less than 1 Chance

180. 180 Study Links Having an Odd Address to Marijuana Use Ties, Links, Relationships, and Associations

181. 181 Relative Risks Greater than 1Less than 1 Possible Explanations for Finding an Association

182. 182 Study Links Having an Even Address to Marijuana Use Ties, Links, Relationships, and Associations

183. 183 Relative Risks Greater than 1Less than 1 1 By Chance 25 cards Chance

184. 184 b Sample of 20 cards Total Risk 5 / 10 = 50 % 50 Relative Risk 50 % ___ % = Odd # Even # No Marijuana Different Sample Sizes

185. 185 Relative Risks Greater than 1Less than 1 1 By Chance 25 cards Chance 50 cards

186. 186 b Sample of 20 cards Total Risk 5 / 10 = 50 % 50 Relative Risk 75 % ___ % = Odd # Even # No Marijuana Different Sample Sizes

187. 187 Relative Risks Greater than 1Less than 1 1 By Chance 25 cards Chance 75 cards

188. 188 b Sample of 20 cards Total Risk 5 / 10 = 50 % 50 1 Relative Risk 99 % ___ % = Odd # Even # No Marijuana Different Sample Sizes

189. 189 Relative Risks Greater than 1Less than 1 1 By Chance 25 cards Chance 99 cards

190. 190 Suicide Higher in Areas with Guns Family Meals Are Good for Mental Health Lack of High School Diploma Tied to US Death Rate Study Links Spanking to Aggression Study Concludes: Movies Influence Youth Smoking Study Links Iron Deficiency to Math Scores Kids Who Watch R-Rated Movies More Likely to Drink, Smoke 1.Cause 2.Confounding 3.Reverse Time Order 4. Chance 5.Bias Snacks Key to Kids’ TV- Linked Obesity: China Study Depressed Teens More Likely to Smoke Association is not necessarily causation. Ties, Links, Relationships, and Associations

191. 191 Teach Epidemiology Enduring Epidemiological Understandings

192. 192

193. 193 Teach Epidemiology Enduring Epidemiological Understandings

194. Teach Epidemiology Explaining Associations and Judging Causation

195. 1.Cause 2.Confounding 3.Reverse Time Order 4. Chance 5.Bias Teach Epidemiology Explaining Associations and Judging Causation Coffee and Cancer of the Pancreas

196. 196

197. 197 Guilt or Innocence?Causal or Not Causal? Does evidence from an aggregate of studies support a cause-effect relationship? Teach Epidemiology Explaining Associations and Judging Causation

198. 198 Sir Austin Bradford Hill “The Environment and Disease: Association or Causation?” Proceedings of the Royal Society of Medicine January 14, 1965 Teach Epidemiology Explaining Associations and Judging Causation Handout

199. 199 “In what circumstances can we pass from this observed association to a verdict of causation?” Teach Epidemiology Explaining Associations and Judging Causation

200. 200 “Here then are nine different viewpoints from all of which we should study association before we cry causation.” Teach Epidemiology Explaining Associations and Judging Causation

201. Does evidence from an aggregate of studies support a cause-effect relationship? 1. What is the strength of the association between the risk factor and the disease? 2. Can a biological gradient be demonstrated? 3. Is the finding consistent? Has it been replicated by others in other places? 4. Have studies established that the risk factor precedes the disease? 5. Is the risk factor associated with one disease or many different diseases? 6. Is the new finding coherent with earlier knowledge about the risk factor and the m disease? 7. Are the implications of the observed findings biologically sensible? 8. Is there experimental evidence, in humans or animals, in which the disease has m been produced by controlled administration of the risk factor? Teach Epidemiology Explaining Associations and Judging Causation

202. Handout Teach Epidemiology Explaining Associations and Judging Causation

203. Timeline Cohort Study Randomized Controlled Trial Timeline Case-Control Study Timeline Cross-Sectional Study Timeline E E O O O O E E E E Healthy People E Random Assignment E O O O O Healthy People E E O O O O Teach Epidemiology Explaining Associations and Judging Causation

204. Teach Epidemiology Explaining Associations and Judging Causation Handout

205. 205 Stress causes ulcers. Helicobacter pylori causes ulcers. Teach Epidemiology Explaining Associations and Judging Causation

206. 206 * * * * * * * * * Teach Epidemiology Explaining Associations and Judging Causation

207. 207 Teach Epidemiology Explaining Associations and Judging Causation

208. 208

209. 209 Epidemiology... the study of the distribution and determinants of health- related states or events in specified populations and the application of this study to the control of health problems. Leon Gordis, Epidemiology, 3 rd Edition, Elsevier Saunders, 2004.

210. 210 Outcome If an association was causal, …. Hypothesized Exposure X X … and you avoided or eliminated the hypothesized cause, what would happen to the outcome? causal, …. ? Control of Health Problems

211. 211 Outcome If the association was found due to confounding, …. Hypothesized Exposure Unobserved Exposure X … and you avoided or eliminated the hypothesized cause, what would happen to the outcome? ? found due to confounding, …. Control of Health Problems

212. 212 Hypothesized Exposure Outcome If an association was found due to reversed time-order, …. found due to reversed time order, …. X … and you avoided or eliminated the hypothesized cause, what would happen to the outcome? ? Control of Health Problems

213. 213 Outcome If an association was found due to chance, …. Hypothesized Exposure found due to chance, …. X … and you avoided or eliminated the hypothesized cause, what would happen to the outcome? ? Control of Health Problems

214. 214 Outcome If an association was found due to bias, …. Hypothesized Exposure ? found due to bias, …. X … and you avoided or eliminated the hypothesized cause, what would happen to the outcome? Control of Health Problems

215. 215 Outcome If an association was causal, …. Hypothesized Exposure X X … and you avoided or eliminated the hypothesized cause, what would happen to the outcome? causal, ….... the study of the distribution and determinants of health-related states or events in specified populations and the application of this study to the control of health problems. Control of Health Problems

216. 216 1.Cause 2.Confounding 3.Reverse Time Order 4. Chance 5.Bias... the study of the distribution and determinants of health-related states or events in specified populations and the application of this study to the control of health problems. Control of Health Problems

217. 217 Suicide Higher in Areas with Guns Family Meals Are Good for Mental Health Lack of High School Diploma Tied to US Death Rate Study Links Spanking to Aggression Study Concludes: Movies Influence Youth Smoking Study Links Iron Deficiency to Math Scores Kids Who Watch R-Rated Movies More Likely to Drink, Smoke Pollution Linked with Birth Defects in US Study 1.Cause 2.Confounding 3.Reverse Time Order 4. Chance 5.Bias Snacks Key to Kids’ TV- Linked Obesity: China Study Depressed Teens More Likely to Smoke Ties, Links, Relationships, and Associations

218. 218 Teach Epidemiology Enduring Epidemiological Understandings

219. 219 Time Check 2:45 PM

220. 220

221. 221 Teach Epidemiology

222. 222 Time Check 3:00 AM

223. 223

224. 224 Teach Epidemiology

225. 225 Time Check 3:30 PM

226. 226

227. 227 Teach Epidemiology

228. 228 Time Check 4:00 PM

229. 229

230. 230 Science Olympiad Pilot

231. 231 Leverage the Science Olympiad Competition http://soinc.org/ Teach Epidemiology What do you mean - Teach Epidemiology?

232. Think Like an Epidemiologist Challenge New Jersey Science Olympiad High School Finals March 17, 2009

233. Test the hypothesis: People who watch more TV eat more junk food. Handout Finish

234. Getting Ready 1

235. Asking Questions / Gathering Data 2

236. 2

237. 2

238. Analyzing Data / Testing Hypotheses 3

239. 3

240. Reporting Out 4

241. 4 Handouts Finish

243. 243 National Research Council, Learning and Understanding Teach Epidemiology Enduring Epidemiological Understandings Knowledge that “… is connected and organized, and … ‘conditionalized’ to specify the context in which it is applicable.”

244. Authentic Assessment Teach Epidemiology Epi – Grades 6-12 Are realistic; simulate the way a person’s understanding is tested in the real world Require judgment and innovation to address an unstructured problem, rather than following a set routine Ask students to “do” the subject rather than simply recall what was taught Replicate the context in which a person would be tested at work, in the community, or at home Are messy and murky Require a repertoire of knowledge and skill to be used efficiently and effectively Allow opportunities for rehearsal, practice, consultation, feedback, and refinement

245. 245

246. 246 http://www.njscienceolympiad.org/content/events/c/websites/epidemiology/index.html Teach Epidemiology Epi – Grades 6-12 Finish

247. Think Like an Epidemiologist Challenge New Jersey Science Olympiad, March 16, 2010 Thank you for stepping up, being a pioneer, and competing in the first Think Like an Epidemiologist Challenge trial event. You worked with others, developed epidemiologic knowledge and skills, and used judgment and innovation to actually "do" epidemiology under pressure. We hope you enjoyed the challenge. Name School Teach Epidemiology Robert Wood Johnson Foundation Detectives in the Classroom Special thanks to the Epidemiology Section of the American Public Health Association for allowing us to distribute their Section pins to the student participants in the 2010 Think Like an Epidemiologist Challenge. Finish

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