1. The Health Education Center at Lankenau Hospital 100 Lancaster Avenue, Wynnewood, PA 19096 July 20-24, 2009 Teach Epidemiology Professional Development Workshop Day 3

2. 2 Teach Epidemiology

3. 3 Time Check 9:15 AM

4. 4

5. 5 Teach Epidemiology

6. 6 Teaching Epidemiology Group 3 Class 1 Pages 16-21)

7. 7 Time Check 10:00 AM

8. 8

9. 9 Teach Epidemiology

10. 10 Teach Epidemiology Teaching Epidemiology Group 1 Pages 35-36

11. 11 Time Check 10:45 AM

12. 12

13. 13 Teach Epidemiology

14. 14 Time Check 11:00 AM

15. 15

16. 16 Teach Epidemiology

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

18.  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.

19. 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.

20.  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.

21. Reasons for associations   Confounding   Bias   Reverse causality   Sampling error (chance)   Causation

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

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

24. Confounding  Confounding is an alternate explanation for an observed association of interest. ExposureOutcome Confounder

25. 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?

26. Bedsores and Mortality D+D- E+79745824 E-28682908576 36590359400 RR = (79 / 824) / (286 / 8576) = 2.9

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

28. 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?

29. Bedsores and Mortality Died Did not die Bedsores 55 severe 24 not 51 severe 694 not 824 No bedsores 5 severe 281 not 5 severe 8285 not 8576 36590359400

30. Bedsores and Mortality (Severe) Died Did not die Bedsores5551106 No bedsores 5510 6056116 RR = (55 / 106) / (5 / 10) = 1.0

31. Bedsores and Mortality (Not severe) Died Did not die Bedsores24694718 No bedsores 28182858566 30589799284 RR = (24 / 718) / (281 / 8566) = 1.0

32. Bedsores and Mortality stratified by Medical Severity SEVERE + Died Didn’t die Bedsoresab No sores cd RR = 1.0 SEVERE-Died Didn’t die Bedsoresab No sores cd RR = 1.0 SEVERE+Died Didn’t die Bedsoresab No sores cd RR = 2.9 SEVERE-Died Didn’t die Bedsoresab No sores cd RR = 2.9

33. 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

34. Confounding  Confounding is an alternate explanation for an observed association of interest. BedsoresDeath in the hospital Severity of medical problems

35. Controlling confounding  Study design phase  Matching  Restriction  Random assignment  Study analysis phase  Stratification  Statistical adjustment

36. Reasons for associations  Confounding  Bias  Reverse causality  Sampling error (chance)  Causation

37. 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

38. 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

39. 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

40. 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)

41. 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.

42. 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.

43. 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

44. Reasons for associations  Confounding  Bias  Reverse causality  Sampling error (chance)  Causation

45. 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

46. 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

47. 47

48. 48 Time Check 12:15 AM

49. 49

50. 50 Teach Epidemiology

51. 51 Time Check 12:45 PM

52. 52

53. 53 Teach Epidemiology

54. 54 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.

55. 55 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

56. 56 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

57. 57 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

58. 58 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

59. 59 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

60. 60 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

61. 61 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

62. 62 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

63. 63 Teach Epidemiology Enduring Epidemiological Understandings

64. 64

65. 65 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

66. 66 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

67. 67 1.Cause 2.Confounding 3.Reverse Time Order 4. Chance 5.Bias Possible Explanations for Finding an Association

68. 68 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.

69. 69 1.Cause 2.Confounding 3.Reverse Time Order 4. Chance 5.Bias Possible Explanations for Finding an Association

70. 70 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

71. 71 Sample of 100

72. 72 Sample of 100, 25 are Sick

73. 73 Diagram 2x2 Table DZ X X ab c d Types of Causal Relationships

74. 74 DZ X X ab c d Diagram 2x2 Table Types of Causal Relationships

75. 75 Handout

76. 76

77. 77 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

78. X1X1 78 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

79. X1X1 79 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

80. X1X1 80 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

81. 81 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

82. 82 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

83. 83 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

84. 84 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

85. 85 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.

86. 86 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.

87. 87

88. 88 Is the association causal?

89. 89 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

90. 90

91. 91 1.Cause 2.Confounding 3.Reverse Time Order 4. Chance 5.Bias Possible Explanations for Finding an Association

92. 92 All the people in a particular group. Population Possible Explanations for Finding an Association

93. 93 A selection of people from a population. Sample Possible Explanations for Finding an Association

94. 94 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

95. 95 Sample Population Process of predicting from what is observed to what is not observed. Observed Not Observed Inference

96. 96 Deck of 100 cards Population

97. 97 a 25 cards b c d Population

98. 98 = Population a 25 cards bc d = ab cd Odd # Even # No Marijuana Population Total

99. 99 = Population a 25 cards bc d = 25 50 Total Odd # Even # No Marijuana Population

100. 100 = 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

101. 101 = Population = 25 50 Total a 25 cards bc d Risk 25 / 50 or 50% Odd # Even # No Marijuana Population

102. 102 = Population a 25 cards bc d = 25 50 TotalRiskRelative Risk 25 / 50 or 50 % 50 % / 50% = = 1 50 % ____ Odd # Even # No Marijuana Population

103. 103 25 cards Population

104. 104 To occur accidentally. To occur without design. Chance A coincidence. Possible Explanations for Finding an Association

105. 105 Chance

106. 106 Chance

107. 107 Population Sample b Sample of 20 cards 25 cards Sample

108. 108 Population Sample b Sample of 20 cards 25 cards 10 Total 55 55 Odd # Even # No Marijuana Sample

109. 109 Population Sample b Sample of 20 cards 25 cards 10 Total 55 55 Risk 5 / 10 or 50 % Odd # Even # No Marijuana Sample

110. 110 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 % ____

111. 111 b Sample of 20 cards Total Risk 5 / 10 = 50 % 50 1 Relative Risk By Chance CDC % ___ % = Odd # Even # No Marijuana Sample

112. 112 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

113. 113 Relative Risks Greater than 1Less than 1 Chance

114. 114 Study Links Having an Odd Address to Marijuana Use Ties, Links, Relationships, and Associations

115. 115 Relative Risks Greater than 1Less than 1 Possible Explanations for Finding an Association

116. 116 Study Links Having an Even Address to Marijuana Use Ties, Links, Relationships, and Associations

117. 117 Relative Risks Greater than 1Less than 1 1 By Chance 25 cards Chance

118. 118 b Sample of 20 cards Total Risk 5 / 10 = 50 % 50 Relative Risk 50 % ___ % = Odd # Even # No Marijuana Different Sample Sizes

119. 119 Relative Risks Greater than 1Less than 1 1 By Chance 25 cards Chance 50 cards

120. 120 b Sample of 20 cards Total Risk 5 / 10 = 50 % 50 Relative Risk 75 % ___ % = Odd # Even # No Marijuana Different Sample Sizes

121. 121 Relative Risks Greater than 1Less than 1 1 By Chance 25 cards Chance 75 cards

122. 122 b Sample of 20 cards Total Risk 5 / 10 = 50 % 50 1 Relative Risk 99 % ___ % = Odd # Even # No Marijuana Different Sample Sizes

123. 123 Relative Risks Greater than 1Less than 1 1 By Chance 25 cards Chance 99 cards

124. 124 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

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

127. 127

128. Teach Epidemiology Explaining Associations and Judging Causation

129. 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

130. 130

131. 131 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

132. 132 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

133. 133 “In what circumstances can we pass from this observed association to a verdict of causation?” Teach Epidemiology Explaining Associations and Judging Causation

134. 134 “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

135. 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

136. Handout Teach Epidemiology Explaining Associations and Judging Causation

137. 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

138. Teach Epidemiology Explaining Associations and Judging Causation Handout

139. 139 Stress causes ulcers. Helicobacter pylori causes ulcers. Teach Epidemiology Explaining Associations and Judging Causation

140. 140 * * * * * * * * * Teach Epidemiology Explaining Associations and Judging Causation

141. 141 Teach Epidemiology Explaining Associations and Judging Causation

142. 142

143. 143 “Does Playing Video Games Cause Asthma?” Teach Epidemiology Explaining Associations and Judging Causation Handout

144. 144

145. 145 Time Check 3:30 PM

146. 146

147. 147 Teach Epidemiology

148. 148 Rules 1.Teach epidemiology 2.As a group, create a 20-minute lesson during which we will develop a deeper understanding of an enduring epidemiological understanding. 3.Focus on the portion of the unit that is assigned. Use that portion of the unit as the starting point for creating your 20-minute lesson. 4.When teaching assume the foundational epidemiological knowledge from the preceding days of the workshop. 5.Try to get us to uncover the enduring epidemiological understanding. Try to only tell us something when absolutely necessary. 6.End each lesson by placing it in the context of the appropriate enduring epidemiological understanding. 7.Be certain that the lesson is taught in 20 minutes or less. 8.Teach epidemiology. Teach Epidemiology Teaching Epidemiology

149. 149 They can then use that ability to think about their own thinking … to grasp how other people might learn. They know what has to come first, and they can distinguish between foundational concepts and elaborations or illustrations of those ideas. They realize where people are likely to face difficulties developing their own comprehension, and they can use that understanding to simplify and clarify complex topics for others, tell the right story, or raise a powerfully provocative question. Ken Bain, What the Best College Teachers Do Teach Epidemiology Teaching Epidemiology Metacognition

150. 150 To create “… a professional community that discusses new teacher materials and strategies and that supports the risk taking and struggle entailed in transforming practice.” Teach Epidemiology Teaching Epidemiology

151. 151 Teach Epidemiology Teaching Epidemiology Group 2 Pages 32-36

152. 152 Teach Epidemiology Teaching Epidemiology Group 3 Procedures 2, 4, and 5