1. Centers for Disease Control and Prevention Morgantown, West Virginia June 20-24, 2011 Teach Epidemiology Professional Development Workshop Day 4

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3. 3 Teach Epidemiology

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

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6. 6 Time Check 8:15 AM

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8. 8 Teach Epidemiology

9. Teach Epidemiology Day 4 Morgantown, WV Diane Marie M St. George, PhD University of MD School of Medicine Dept of Epidemiology and Public Health

10. 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. 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.

11. 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.

12. 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. 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.

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

14. Confounding in our lives Age-adjusted rates of… Rates of lung cancer adjusted for smoking

15. Osteoporosis risk is higher among women who live alone than among women who live with others.

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

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

18. 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?

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

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

21. 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?

22. Bedsores and Mortality DiedDid not die Bedsores55 severe 24 not 51 severe 694 not 824 No bedsores 5 severe 281 not 5 severe 8285 not 8576 36590359400

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

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

25. Bedsores and Mortality stratified by Medical Severity SEVERE + DiedDidn’t die Bedsoresab No sorescd RR = 1.0 SEVERE- DiedDidn’t die Bedsoresab No sorescd RR = 1.0

26. 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

27. Controlling confounding Study design phase  Matching  Restriction  Random assignment Study analysis phase  Stratification  Statistical adjustment

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

29. Bias Case Studies In groups, review the assigned case studies.

30. 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 of cases are asked about their loved ones’ usage patterns.

31. 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.

32. 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

33. 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 Hospital controls may not represent the source population for the cases

34. 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 (or probe exposed more than unexposed)

35. 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

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

37. 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

38. 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

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

40. Sampling error/chance E and D are associated in a sample, but not in the population from which the sample was drawn.

41. RR in the population D+D- E+5050100 E-5050100 100100200

42. RR in sample 1 D+D- E+252550 E-252550 5050100

43. RR in sample 2 D+D- E+45550 E-153550 5050100

44. RR in sample 3 D+D- E+203050 E-302050 5050100

45. Minimize sampling error (chance) Random selection Adequate sample size

46. 46 Time Check 9:45 AM

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48. 48 Teach Epidemiology

49. 49 Time Check 10:00 AM

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51. 51 Teach Epidemiology

52. 52 Time Check 11:00 AM

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54. 54 Teach Epidemiology

55. 55 Time Check 11:30 AM

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57. 57 Teach Epidemiology

58. 58 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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60. 60 Teach Epidemiology Enduring Epidemiological Understandings

61. 61 Teach Epidemiology Enduring Epidemiological Understandings

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63. 63 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

64. 64 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

65. 65 1.Cause 2.Confounding 3.Reverse Time Order 4. Chance 5.Bias Possible Explanations for Finding an Association

66. 66 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.

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

68. 68 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

69. 69 Sample of 100

70. 70 Sample of 100, 25 are Sick

71. 71 Diagram 2x2 Table DZ X X ab c d Types of Causal Relationships

72. 72 DZ X X ab c d Diagram 2x2 Table Types of Causal Relationships

73. 73 Handout

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75. 75 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

76. X1X1 76 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

77. X1X1 77 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

78. X1X1 78 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

79. 79 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

80. 80 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

81. 81 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

82. 82 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

83. 83 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.

84. 84 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.

85. 85 Is the association causal?

86. 86 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

87. 87 Teach Epidemiology Enduring Epidemiological Understandings

88. 88 Time Check Noon AM

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90. 90 Teach Epidemiology

91. 91 Time Check 1:00 PM

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93. 93 Teach Epidemiology

94. 94 1.Cause 2.Confounding 3.Reverse Time Order 4. Chance 5.Bias Possible Explanations for Finding an Association

95. 95 All the people in a particular group. Population Possible Explanations for Finding an Association

96. 96 A selection of people from a population. Sample Possible Explanations for Finding an Association

97. 97 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

98. 98 Sample Population Process of predicting from what is observed to what is not observed. Observed Not Observed Inference

99. 99 Deck of 100 cards Population

100. 100 a 25 cards b c d Population

101. 101 = Population a 25 cards bc d = ab cd Odd # Even # No Marijuana Population Total

102. 102 = Population a 25 cards bc d = 25 50 Total Odd # Even # No Marijuana Population

103. 103 = 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

104. 104 = Population = 25 50 Total a 25 cards bc d Risk 25 / 50 or 50% Odd # Even # No Marijuana Population

105. 105 = Population a 25 cards bc d = 25 50 TotalRiskRelative Risk 25 / 50 or 50 % 50 % / 50% = = 1 50 % ____ Odd # Even # No Marijuana Population

106. 106 25 cards Population

107. 107 To occur accidentally. To occur without design. Chance A coincidence. Possible Explanations for Finding an Association

108. 108 Chance

109. 109 Chance

110. 110 Population Sample b Sample of 20 cards 25 cards Sample

111. 111 Population Sample b Sample of 20 cards 25 cards 10 Total 55 55 Odd # Even # No Marijuana Sample

112. 112 Population Sample b Sample of 20 cards 25 cards 10 Total 55 55 Risk 5 / 10 or 50 % Odd # Even # No Marijuana Sample

113. 113 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 % ____

114. 114 b Sample of 20 cards Total Risk 5 / 10 = 50 % 50 1 Relative Risk By Chance CDC % ___ % = Odd # Even # No Marijuana Sample

115. 115 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

116. 116 Relative Risks Greater than 1Less than 1 Chance

117. 117 Study Links Having an Odd Address to Marijuana Use Ties, Links, Relationships, and Associations

118. 118 Relative Risks Greater than 1Less than 1 Possible Explanations for Finding an Association

119. 119 Study Links Having an Even Address to Marijuana Use Ties, Links, Relationships, and Associations

120. 120 Relative Risks Greater than 1Less than 1 1 By Chance 25 cards Chance

121. 121 b Sample of 20 cards Total Risk 5 / 10 = 50 % 50 Relative Risk 50 % ___ % = Odd # Even # No Marijuana Different Sample Sizes

122. 122 Relative Risks Greater than 1Less than 1 1 By Chance 25 cards Chance 50 cards

123. 123 b Sample of 20 cards Total Risk 5 / 10 = 50 % 50 Relative Risk 75 % ___ % = Odd # Even # No Marijuana Different Sample Sizes

124. 124 Relative Risks Greater than 1Less than 1 1 By Chance 25 cards Chance 75 cards

125. 125 b Sample of 20 cards Total Risk 5 / 10 = 50 % 50 1 Relative Risk 99 % ___ % = Odd # Even # No Marijuana Different Sample Sizes

126. 126 Relative Risks Greater than 1Less than 1 1 By Chance 25 cards Chance 99 cards

127. 127 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

128. 128 Teach Epidemiology Enduring Epidemiological Understandings

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130. 130 Teach Epidemiology Enduring Epidemiological Understandings

131. Teach Epidemiology Explaining Associations and Judging Causation

132. Handout Teach Epidemiology Explaining Associations and Judging Causation

133. 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

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135. 135 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

136. 136 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

137. 137 “In what circumstances can we pass from this observed association to a verdict of causation?” Teach Epidemiology Explaining Associations and Judging Causation

138. 138 “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

139. 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

140. 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

141. Teach Epidemiology Explaining Associations and Judging Causation Handout

142. 142 Stress causes ulcers. Helicobacter pylori causes ulcers. Teach Epidemiology Explaining Associations and Judging Causation

143. 143 * * * * * * * * * Teach Epidemiology Explaining Associations and Judging Causation

144. 144 Teach Epidemiology Explaining Associations and Judging Causation

145. 145

146. 146 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.

147. 147 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

148. 148 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

149. 149 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

150. 150 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

151. 151 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

152. 152 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

153. 153 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

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 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

155. 155 Teach Epidemiology Enduring Epidemiological Understandings

156. 156

157. 157 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 Your Teach Epidemiology Stories

158. 158 Welcome to Teach Epidemiology Your Teach Epidemiology Stories To create “… a professional community that discusses new teacher materials and strategies and that supports the risk taking and struggle entailed in transforming practice.”

159. Centers for Disease Control and Prevention Morgantown, West Virginia June 20-24, 2011 Teach Epidemiology Professional Development Workshop

160. 160 Time Check 2:45 PM

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162. 162 Teach Epidemiology

163. 163 Time Check 3:00 PM

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165. 165 Teach Epidemiology

166. 166 Time Check 3:30 PM

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168. 168 Teach Epidemiology

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170. 170 Enduring Understandings

171. 171 Teach Epidemiology Enduring Understandings … the big ideas that reside at the heart of a discipline and have lasting value outside the classroom. Enduring Epidemiological Understandings … the big ideas that reside at the heart of epidemiology and have lasting value outside the classroom.

172. 172 Teach Epidemiology Enduring Epidemiological Understandings … the big ideas that reside at the heart of epidemiology and have lasting value outside the classroom.

173. 173 Teach Epidemiology Enduring Epidemiological Understandings … the big ideas that reside at the heart of epidemiology and have lasting value outside the classroom. Enduring Epidemiological Understandings

174. 174 Teach Epidemiology Enduring Epidemiological Understandings … the big ideas that reside at the heart of epidemiology and have lasting value outside the classroom. Enduring Epidemiological Understandings

175. 175 Teach Epidemiology Enduring Epidemiological Understandings … the big ideas that reside at the heart of epidemiology and have lasting value outside the classroom. Enduring Epidemiological Understandings

176. 176 Teach Epidemiology Enduring Epidemiological Understandings … the big ideas that reside at the heart of epidemiology and have lasting value outside the classroom. Enduring Epidemiological Understandings

177. 177 “… to see past the surface features of any problem to the deeper, more fundamental principles of the discipline.” National Research Council Learning and Understanding Enduring Understandings

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179. 179 Epidemiology Hypothesis Total RiskRelative Risk a b c d or % % ExposureOutcome ? Turned Up Together Healthy People - E E O O O O

180. 180 Total RiskRelative Risk a b c d or % % ExposureOutcome ? Associated Turned Up Together 1. 2. 3. 4. 5. Cause Confounding Reverse Time Order Chance Bias ? Epidemiology

181. Give people fish, they have food for a day, Teach people how to fish, they have food for a lifetime. Teach Epidemiology Enduring Epidemiological Understandings

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183. 183 Explore Public Health Career Paths http://www.asph.org/document.cfm?page=1038 Teach Epidemiology What do you mean - Teach Epidemiology?

184. 184 Explore Public Health Career Paths http://pathwaystopublichealth.org/ Teach Epidemiology What do you mean - Teach Epidemiology?

185. 185 Leverage the Science Olympiad Competition http://soinc.org/ Teach Epidemiology What do you mean - Teach Epidemiology?

186. 186 Create and Teach a New Epidemiology Lesson Teach Epidemiology What do you mean - Teach Epidemiology?

187. 187 Infuse Epidemiology into Existing Lesson about Something Else Teach Epidemiology What do you mean - Teach Epidemiology?

188. 188 Infuse Epidemiology into Existing Lesson about Something Else Teach Epidemiology What do you mean - Teach Epidemiology?

189. 189 Infuse Epidemiology into Existing Lesson about Something Else Teach Epidemiology What do you mean - Teach Epidemiology?

190. 190 Infuse Epidemiology into Existing Lesson about Something Else Teach Epidemiology What do you mean - Teach Epidemiology?

191. 191 Infuse Epidemiology into Existing Lesson about Something Else Teach Epidemiology What do you mean - Teach Epidemiology?

192. 192 Infuse Epidemiology into Existing Lesson about Something Else Teach Epidemiology What do you mean - Teach Epidemiology?

193. 193 Infuse Epidemiology into Existing Lesson about Something Else Teach Epidemiology What do you mean - Teach Epidemiology?

194. 194 Teach Epidemiology What do you mean - Teach Epidemiology? Teaching Existing Epidemiology Lessons

195. 195 Teaching Existing Epidemiology Lessons http://ccnmtl.columbia.edu/projects/epiville/ Teach Epidemiology What do you mean - Teach Epidemiology?

196. 196 Teaching Existing Epidemiology Lessons http://www.diseasedetectives.org/ Teach Epidemiology What do you mean - Teach Epidemiology?

197. 197 Teaching Existing Epidemiology Lessons http://www.cdc.gov/excite/ Teach Epidemiology What do you mean - Teach Epidemiology?

198. 198 Teaching Existing Epidemiology Lessons http://www2a.cdc.gov/epicasestudies/ Teach Epidemiology What do you mean - Teach Epidemiology?

199. 199 Teaching Existing Epidemiology Lessons http://www.cdc.gov/excite/ScienceAmbassador/ScienceAmbassador.htm Teach Epidemiology What do you mean - Teach Epidemiology?

200. 200 Teaching Existing Epidemiology Lessons http://www.buffetbusters.ca/ Teach Epidemiology What do you mean - Teach Epidemiology?

201. 201 Teaching Existing Epidemiology Lessons http://www.montclair.edu/Detectives/ Teach Epidemiology What do you mean - Teach Epidemiology?

202. 202 Teaching Existing Epidemiology Lessons http://www.montclair.edu/drugepi/ Teach Epidemiology What do you mean - Teach Epidemiology?

203. 203 Teaching Existing Epidemiology Lessons Teach Epidemiology What do you mean - Teach Epidemiology? http://www.collegeboard.com/yes/ft/iu/units.html

204. 204 View a News Item from an Epidemiologic Perspective http://www.nationalacademies.org/headlines/ Teach Epidemiology What do you mean - Teach Epidemiology?

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206. 206 1. 2. 3. 4. 5. 6. 7. 8.. Empowers students to be scientifically literate participants in the democratic decision-making process concerning public health policy. Empowers students to make more informed personal health-related decisions. Increases students’ media literacy and their understanding of public health messages. Increases students’ understanding of the basis for determining risk. Improves students’ mathematical and scientific literacy. Expands students’ understanding of scientific methods and develops their critical thinking skills. Provides students with another mechanism for exploring important, real world questions about their health and the health of others. Introduces students to an array of career paths related to the public’s health. Top 8 Reasons to Teach / Learn about Epidemiology Teach Epidemiology

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208. 208 Teach Epidemiology Innovation … an idea, practice or object that is perceived as new by an individual or other unit of adoption. Everett M. Rogers, Diffusion of Innovations Workshop Goal

209. 209 Diffusion The process by which an innovation is communicated through certain channels over time among the members of a social system (with the aim being to maximize the exposure and reach of innovations, strategies, or programs.) Everett M. Rogers, Diffusion of Innovations Teach Epidemiology Workshop Goal

210. 210 Workshop Goal Teach Epidemiology To increase the frequency with which epidemiology is taught to students in grades 6-12

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212. 212 Post-Workshop Assessment Teach Epidemiology

213. 213 Teach Epidemiology Handout Workshop Evaluation

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215. Centers for Disease Control and Prevention Morgantown, West Virginia June 20-24, 2011 Teach Epidemiology Professional Development Workshop Thank You

216. 216 Time Check 4:00 PM