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

3. 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. The “Not everything that glitters is gold” Principle The “Not everything that glitters is gold” Principle

4. 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.

5. 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. The “Just because your friend sleeps in class and never fails her courses does not mean that sleeping in class does not cause F grades” Principle The “Just because your friend sleeps in class and never fails her courses does not mean that sleeping in class does not cause F grades” Principle

6. Reasons for associations Confounding Confounding  E is associated with C and C causes D Bias Bias  F causes D, but we thought F was an E Reverse causality Reverse causality  “D” causes “E” Sampling error (chance) Sampling error (chance) Causation Causation  E1  D  E1 + E2  D  E1 or E2  D  E1 + E2 OR E3+E4  D

7. Osteoporosis risk is higher among women who live alone. Osteoporosis risk is higher among women who live alone. Death rates are low in AK and high in FL. Death rates are low in AK and high in FL. African American women have higher infant mortality than others in the US. African American women have higher infant mortality than others in the US.

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

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

10. Confounding YES confounding module example: YES confounding module example:  Hypothetical cohort study  20,000 men followed for 10 yrs  RQ: Are bedsores related to mortality among elderly patients with hip fractures?

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

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

13. 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? 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? What about if the participants all had problems of very low severity?

14. 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

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

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

17. 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

18. Bedsores So…. So…. Bedsores are unrelated to mortality among those with severe problems. Bedsores are unrelated to mortality among those with severe problems. Bedsores are unrelated to mortality among those with problems of less severity. Bedsores are unrelated to mortality among those with problems of less severity. …. …. the adjusted RR = 1, and the unadjusted RR = 2.9 the adjusted RR = 1, and the unadjusted RR = 2.9

19. Confounding Confounding is an alternate explanation for an observed association of interest. Confounding is an alternate explanation for an observed association of interest. BedsoresDeath Severity of medical problems

20. Reasons for associations Confounding Confounding  E is associated with C and C causes D Bias Bias  F causes D, but we thought F was an E Reverse causality Reverse causality  “D” causes “E” Sampling error (chance) Sampling error (chance) Causation Causation  E1  D  E1 + E2  D  E1 or E2  D  E1 + E2 OR E3+E4  D

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

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

23. Selection bias Healthy worker effect Healthy worker effect  People who are working are more likely to be healthier than non-workers Non-response Non-response  People who participate in a study may be different from people who do not Attrition Attrition  People who drop out of a study may be less different from those who stay in the study Berkson’s Berkson’s  Hospital controls in a case-control study

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

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

26. 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. 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.

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

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

29. Reasons for associations Confounding Confounding  E is associated with C and C causes D Bias Bias  F causes D, but we thought F was an E Reverse causality Reverse causality  “D” causes “E” Sampling error (chance) Sampling error (chance) Causation Causation  E1  D  E1 + E2  D  E1 or E2  D  E1 + E2 OR E3+E4  D

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

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

32. Reasons for associations Confounding Confounding  E is associated with C and C causes D Bias Bias  F causes D, but we thought F was an E Reverse causality Reverse causality  “D” causes “E” Sampling error (chance) Sampling error (chance) Causation Causation  E1  D  E1 + E2  D  E1 or E2  D  E1 + E2 OR E3+E4  D

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

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

35. RR in sample1 D+D- E+252550 E-252550 5050100

36. RR in sample2 D+D- E+203050 E-302050 5050100

37. RR in sample3 D+D- E+302050 E-153550 4555100

38. Reasons for associations Confounding Confounding  E is associated with C and C causes D Bias Bias  F causes D, but we thought F was an E Reverse causality Reverse causality  “D” causes “E” Sampling error (chance) Sampling error (chance) Causation Causation  E1  D  E1 + E2  D  E1 or E2  D  E1 + E2 OR E3+E4  D

39. Causal pathways Necessary, sufficient—rare, if at all Necessary, sufficient—rare, if at all Not necessary, sufficient—also rare Not necessary, sufficient—also rare Necessary, not sufficient—TB Necessary, not sufficient—TB Not necessary, not sufficient--Most causes fall into this category--heart disease, obesity Not necessary, not sufficient--Most causes fall into this category--heart disease, obesity

40. Reasons for associations Confounding Confounding  E is associated with C and C causes D Bias Bias  F causes D, but we thought F was an E Reverse causality Reverse causality  “D” causes “E” Sampling error (chance) Sampling error (chance) Causation Causation  E1  D  E1 + E2  D  E1 or E2  D  E1 + E2 OR E3+E4  D

41. The process of assessing causality Observe patterns Observe patterns Generate hypothesis Generate hypothesis Design study to test hypothesis Design study to test hypothesis Conduct study Conduct study Interpret the results…the big question is did the exposure cause the disease? Interpret the results…the big question is did the exposure cause the disease?  Are there alternate non-causal explanations for the results we found?  If not, then is this the whole story?

42. So, what should we do? Goal is to understand causality Goal is to understand causality Use guidelines to help us make sense of the evidence Use guidelines to help us make sense of the evidence

43. Key Guidelines Temporality: a necessary condition Temporality: a necessary condition Consistency Consistency Dose-response Dose-response Consideration of alternate explanations Consideration of alternate explanations Coherence Coherence

44. Enduring Understandings 7, 8, and 9

45. 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. The “Not everything that glitters is gold” Principle The “Not everything that glitters is gold” Principle

46. 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.

47. 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. The “Just because your friend sleeps in class and never fails her courses does not mean that sleeping in class does not cause F grades” Principle The “Just because your friend sleeps in class and never fails her courses does not mean that sleeping in class does not cause F grades” Principle

49. 49 1.Cause 2.Confounding 3.Reverse Time Order 4. Chance 5.Bias Possible Explanations for Finding an Association

50. 50 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

51. 51 Sample of 100

52. 52 Sample of 100, 25 are Sick

53. 53 Diagram 2x2 Table DZ X X ab c d Types of Causal Relationships

54. 54 DZ X X ab c d Diagram 2x2 Table Types of Causal Relationships

55. 55 Handout

56. 56 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

57. 57 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

58. 58 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

59. 59 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

60. 60 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.

61. 61 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.

62. 62 Is the association causal?

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 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

64. 64

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

66. 66 All the people in a particular group. Population Possible Explanations for Finding an Association

67. 67 A selection of people from a population. Sample Possible Explanations for Finding an Association

68. 68 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

69. 69 Sample Population Process of predicting from what is observed to what is not observed. Observed Not Observed Inference

70. 70 Deck of 100 cards Population

71. 71 a 25 cards b c d Population

72. 72 = Population a 25 cards bc d = ab cd Odd # Even # No Marijuana Population Total

73. 73 = Population a 25 cards bc d = 25 50 Total Odd # Even # No Marijuana Population

74. 74 = 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

75. 75 = Population = 25 50 Total a 25 cards bc d Risk 25 / 50 or 50% Odd # Even # No Marijuana Population

76. 76 = Population a 25 cards bc d = 25 50 TotalRiskRelative Risk 25 / 50 or 50 % 50 % / 50% = = 1 50 % ____ Odd # Even # No Marijuana Population

77. 77 25 cards Population

78. 78 To occur accidentally. To occur without design. Chance A coincidence. Possible Explanations for Finding an Association

79. 79 Chance

80. 80 Chance

81. 81 Population Sample b Sample of 20 cards 25 cards Sample

82. 82 Population Sample b Sample of 20 cards 25 cards 10 Total 55 55 Odd # Even # No Marijuana Sample

83. 83 Population Sample b Sample of 20 cards 25 cards 10 Total 55 55 Risk 5 / 10 or 50 % Odd # Even # No Marijuana Sample

84. 84 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 % ____

85. 85 b Sample of 20 cards Total Risk 5 / 10 = 50 % 50 1 Relative Risk By Chance CDC % ___ % = Odd # Even # No Marijuana Sample

86. 86 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

87. 87 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

88. 88

89. An Association: TV and Aggressive Acts

90. Worksheet

91. “… the study of the distribution and determinants of health-related states or events …” A Study Finds More Links Between TV and Violence March 29, 2002 By GINA KOLATA The New York Times ON THE WEB

92. Study Designs Experimental Studies Observational Studies Randomized Controlled Trials Other Experimental Studies Cohort Studies Case-Control Studies Cross-Sectional Studies Ecologic Studies Cohort Studies

93. A study in which a group of people is followed over time The group is made up of people who have the exposure of interest and people who do not have the exposure of interest Exposed and unexposed people are followed over time to determine whether they experience the outcome Cohort Study

94. When epidemiologists ask a question, it is often of the form: Does ______________ cause ______________? Exposure - Outcome (exposure)(outcome)

95. Do diesel exhaust fumes from school buses cause asthma? Does eating chocolate cause acne? Are males at higher risk of automobile accidents? Does immunization with the measles vaccine prevent measles? Does acupuncture result in pain relief? Exposure - Outcome For example: When epidemiologists ask a question, it is often of the form: Does ______________ cause ______________? (exposure)(outcome)

96. A Study Finds More Links Between TV and Violence March 29, 2002 By GINA KOLATA Cohort Study Flow Diagram A designated group of persons who are followed or traced over a period of time Exposed Not Exposed Time - Cohort Outcome No Outcome Outcome No Outcome

97. By age 22 A Study Finds More Links Between TV and Violence March 29, 2002 By GINA KOLATA Cohort Study Flow Diagram A designated group of persons who are followed or traced over a period of time Watching TV for > 1 hrs per day Watching TV for < 1 hr per day At age 14 - Adolescents & Young Adults Aggressive Acts No Aggressive Acts Aggressive Acts No Aggressive Acts

98. Watched TV > 1 hour per day At age 14 154 reported aggressive acts 465 did not report aggressive acts Express it in Numbers By age 22

99. Express it in Numbers Exposed Outcome Total No Outcome Watched TV > 1 hour per day At age 14 154 reported aggressive acts 465 did not report aggressive acts By age 22

100. Exposed Outcome Total No Outcome At age 14 By age 22 Express it in Numbers Watched TV > 1 hour per day Aggressive Acts No Aggressive Acts 154465619 Total Watched TV > 1 hour per day 154 reported aggressive acts 465 did not report aggressive acts

101. Exposed Outcome Total No Outcome Risk Aggressive Acts No Aggressive Acts 154465619 Total 154 (154 + 465) = 154 619 24.9% = Watched TV > 1 hour per day

102. An unproven idea, based on observation or reasoning, that can be proven or disproven through investigation An educated guess Hypothesis Watching TV causes aggressive acts.

103. Exposed Outcome Total No Outcome Aggressive Acts No Aggressive Acts 154465619 Total Does watching TV cause aggressive acts? Risk 24.9% Watched TV > 1 hour per day 154 (154 + 465) = 24.9% 154 619 =

104. By 22 years Watching TV for > 1 hrs per day Watching TV for < 1 hr per day At 14 years - Aggressive Acts No Aggressive Acts Aggressive Acts No Aggressive Acts 24.9% risk of committing an aggressive act ? risk of committing an aggressive act Does watching TV cause aggressive acts? Adolescents & Young Adults

105. By 22 years Watching TV for > 1 hrs per day Watching TV for < 1 hr per day At 14 years - Aggressive Acts No Aggressive Acts Aggressive Acts No Aggressive Acts 24.9% risk of committing an aggressive act ? risk of committing an aggressive act Comparison Group Does watching TV cause aggressive acts? Adolescents & Young Adults

106. Exposed Outcome Total No Outcome Aggressive Acts No Aggressive Acts 154465619 Total Comparison Group Risk 24.9% Watched TV > 1 hour per day Watched TV < 1 hour per day 5 reported aggressive acts 83 did not report aggressive acts Watched TV < 1 hour per day At age 14By age 22

107. Exposed Outcome Total Aggressive Acts No Aggressive Acts 154465619 Total Comparison Group Risk 24.9% Watched TV > 1 hour per day Watched TV < 1 hour per day 5 reported aggressive acts 83 did not report aggressive acts Watched TV < 1 hour per day At age 14By age 22 583885.7%

108. Exposed Outcome Total No Outcome Aggressive Acts No Aggressive Acts 154465619 TotalRisk 24.9% Watched TV > 1 hour per day Watched TV < 1 hour per day 583885.7% Exposure Outcome Contingency Table

109. Exposed Outcome Total No Outcome Aggressive Acts No Aggressive Acts 154465619 TotalRisk 24.9% Watched TV > 1 hour per day Watched TV < 1 hour per day 583885.7% Does watching TV cause aggressive acts?

110. Exposed Outcome Total No Outcome Aggressive Acts No Aggressive Acts 154465619 TotalRisk 24.9% Watched TV > 1 hour per day Watched TV < 1 hour per day 583885.7% Compared to those who watched TV for 1 hours per day were ____ times as likely to commit aggressive acts. 4.4 Does watching TV cause aggressive acts? Times as Likely

111. A way of quantifying the relationship between two risks Tells us the number of times one risk is larger or smaller than another Relative Risk Cartoon from Larry Gotnick’s The Cartoon Guide to Statistics, HarperPerennial, 1993

112. “… the control of health problems” What should be done? Exposed Outcome Total No Outcome Aggressive Acts No Aggressive Acts 154465619 TotalRisk 24.9% Watched TV > 1 hour per day Watched TV < 1 hour per day 583885.7% 4.4 Relative Risk

113. When things turn up together Association

114. Pretzels Auto Accidents Confounding Another Exposure Association Cause When an observed association between an exposure and an outcome is distorted because the exposure of interest is associated with some other exposure that causes the outcome Drinking Alcoholic Beverages Association of Interest

115. Confounding is the distortion of an exposure- outcome association brought about by the association of another factor with both outcome and exposure. A confounder confuses our conclusions about the relationship between an exposure and an outcome. Confounding

116. Pretzels Auto Accidents Another Exposure Association Cause “… the control of health problems” X Drinking Alcoholic Beverages X Association of Interest

117. Association When things turn up together Aggressive Acts No Aggressive Acts Total Watched TV < 1 hour per day Watched TV > 1 hour per day Relative Risk 619154465 Total Risk 24.9% 583885.7% 4.4

118. Confounding Association Cause ? Watching TV Aggressive Acts Association of Interest When an observed association between an exposure and an outcome is distorted because the exposure of interest is associated with some other exposure that causes the outcome

119. Watching TV Aggressive Acts Confounding Association Cause Living in a Violent Neighborhood Association of Interest When an observed association between an exposure and an outcome is distorted because the exposure of interest is associated with some other exposure that causes the outcome

120. Watching TV Aggressive Acts Confounding Association Cause Lack of Adequate Supervision Association of Interest When an observed association between an exposure and an outcome is distorted because the exposure of interest is associated with some other exposure that causes the outcome

121. Watching TV Aggressive Acts Association Cause Lack of Adequate Supervision X X “… the control of health problems” Association of Interest When an observed association between an exposure and an outcome is distorted because the exposure of interest is associated with some other exposure that causes the outcome

122. Assessment In a study of the hypothesis that drinking orange juice prevents the flu, 3,000 students at Wright High School, who did not have the flu on December 31, 2000, were followed from January 1 through March 31, 2001. By the end of the study, among the 1000 students who drank orange juice, 123 students had developed the flu. Among the 2000 students who did not drink orange juice, 342 students had developed the flu. Display the above data on a 2x2 table, calculate risks of flu, calculate the relative risk, and explain whether or not the results support the hypothesis that drinking orange juice prevents the flu.

123. 123

124. 124 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

125. 125 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

126. 126 “In what circumstances can we pass from this observed association to a verdict of causation?” Teach Epidemiology Explaining Associations and Judging Causation

127. 127 “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

128. 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 biological 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

129. 129 Stress causes ulcers. Helicobacter pylori causes ulcers. Teach Epidemiology Explaining Associations and Judging Causation

130. 130 * * * * * * * * * Teach Epidemiology Explaining Associations and Judging Causation

131. 131 Teach Epidemiology Explaining Associations and Judging Causation

132. 132

133. In the News Assemble into three-person teams Select an article Use the article to create a lesson plan to teach one or more of the Enduring Understandings to a specified class for 30 minutes Teach the lesson –Specify the student population and course –Engage us as though we were the students Help us to understand what you did to generate the lesson plan Teach Epidemiology

134. Article Choices Early childhood behavior and substance use Huffing and suicide Soft drinks and diabetes Circumcision and AIDS Prenatal smoking and attention deficit ADHD among girls Traffic and childhood asthma Breast-feeding and childhood obesity Depression and sexual risk-taking Family stress and childhood illness ADHD medications and mortality Teach Epidemiology

136. 136 1.Teach epidemiology. 2.As a group, create a 30-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 30-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.Teach epidemiology. 8.Metacognition--After the lesson, reflect on your preparation for and teaching of the lesson. Teach Epidemiology Teaching Epidemiology Rules

137. 137 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

138. 138 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

139. 139 Teach Epidemiology Teaching Epidemiology Group Assignments Births: Class 1, p. 6-12 War: Qs 11-21 Case-control: Class 1, p. 16-21 Confounding: p. 32-36 Bias: p. 25-29 and 30-32 Alpine Fizz: Procs 2, 4, 5