1. Carnegie Institution for Science 1530 P Street, NW Washington, DC 20005 April 18-21, 2011 Teach Epidemiology Professional Development Workshop Day 2

2. 2

3. 3 Teach Epidemiology

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

5. 5 Teach Epidemiology US Obesity Trends

6. Definitions: Definitions: Obesity: Body Mass Index (BMI) of 30 or higher. Obesity: Body Mass Index (BMI) of 30 or higher. Body Mass Index (BMI): A measure of an adult’s weight in relation to his or her height, specifically the adult’s weight in kilograms divided by the square of his or her height in meters. Body Mass Index (BMI): A measure of an adult’s weight in relation to his or her height, specifically the adult’s weight in kilograms divided by the square of his or her height in meters. Obesity Trends Among U.S. Adults between 1985 and 2009

7. Source of the data: The data shown in these maps were collected through CDC’s Behavioral Risk Factor Surveillance System (BRFSS). Each year, state health departments use standard procedures to collect data through a series of telephone interviews with U.S. adults. Prevalence estimates generated for the maps may vary slightly from those generated for the states by BRFSS (http://aps.nccd.cdc.gov/brfss) as slightly different analytic methods are used.

8. In 1990, among states participating in the Behavioral Risk Factor Surveillance System, ten states had a prevalence of obesity less than 10% and no states had prevalence equal to or greater than 15%. By 1999, no state had prevalence less than 10%, eighteen states had a prevalence of obesity between 20-24%, and no state had prevalence equal to or greater than 25%. In 2009, only one state (Colorado) and the District of Columbia had a prevalence of obesity less than 20%. Thirty-three states had a prevalence equal to or greater than 25%; nine of these states (Alabama, Arkansas, Kentucky, Louisiana, Mississippi, Missouri, Oklahoma, Tennessee, and West Virginia) had a prevalence of obesity equal to or greater than 30%.

9. Citations BRFSS, Behavioral Risk Factor Surveillance System http: //www.cdc.gov/brfss/ Mokdad AH, et al. The spread of the obesity epidemic in the United States, 1991—1998 JAMA 1999; 282:16:1519–22. Mokdad AH, et al. The continuing epidemics of obesity and diabetes in the United States. JAMA. 2001; 286:10:1519–22. Mokdad AH, et al. Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001. JAMA 2003: 289:1: 76–9 Vital Signs: State-Specific Obesity Prevalence Among Adults —United States, 2009 MMWR 2010;59(30).

10. Source: Behavioral Risk Factor Surveillance System, CDC. 1999 Obesity Trends* Among U.S. Adults BRFSS, 1990, 1999, 2009 (*BMI 30, or about 30 lbs. overweight for 5’4” person) 2009 1990 No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%

11. Source: Behavioral Risk Factor Surveillance System, CDC. Obesity Trends* Among U.S. Adults BRFSS, 1985 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14%

12. Source: Behavioral Risk Factor Surveillance System, CDC. Obesity Trends* Among U.S. Adults BRFSS, 1986 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14%

13. Source: Behavioral Risk Factor Surveillance System, CDC. Obesity Trends* Among U.S. Adults BRFSS, 1987 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14%

14. Source: Behavioral Risk Factor Surveillance System, CDC. Obesity Trends* Among U.S. Adults BRFSS, 1988 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14%

15. Source: Behavioral Risk Factor Surveillance System, CDC. Obesity Trends* Among U.S. Adults BRFSS, 1989 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14%

16. Source: Behavioral Risk Factor Surveillance System, CDC. Obesity Trends* Among U.S. Adults BRFSS, 1990 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14%

17. Source: Behavioral Risk Factor Surveillance System, CDC. Obesity Trends* Among U.S. Adults BRFSS, 1991 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14% 15%–19%

18. Source: Behavioral Risk Factor Surveillance System, CDC. Obesity Trends* Among U.S. Adults BRFSS, 1992 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14% 15%–19%

19. Source: Behavioral Risk Factor Surveillance System, CDC. Obesity Trends* Among U.S. Adults BRFSS, 1993 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14% 15%–19%

20. Source: Behavioral Risk Factor Surveillance System, CDC. Obesity Trends* Among U.S. Adults BRFSS, 1994 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14% 15%–19%

21. Source: Behavioral Risk Factor Surveillance System, CDC. Obesity Trends* Among U.S. Adults BRFSS, 1995 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14% 15%–19%

22. Source: Behavioral Risk Factor Surveillance System, CDC. Obesity Trends* Among U.S. Adults BRFSS, 1996 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14% 15%–19%

23. Source: Behavioral Risk Factor Surveillance System, CDC. Obesity Trends* Among U.S. Adults BRFSS, 1997 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14% 15%–19% ≥20%

24. Source: Behavioral Risk Factor Surveillance System, CDC. Obesity Trends* Among U.S. Adults BRFSS, 1998 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14% 15%–19% ≥20%

25. Source: Behavioral Risk Factor Surveillance System, CDC. Obesity Trends* Among U.S. Adults BRFSS, 1999 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14% 15%–19% ≥20%

26. Source: Behavioral Risk Factor Surveillance System, CDC. Obesity Trends* Among U.S. Adults BRFSS, 2000 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14% 15%–19% ≥20%

27. Source: Behavioral Risk Factor Surveillance System, CDC. Obesity Trends* Among U.S. Adults BRFSS, 2001 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14% 15%–19% 20%–24% ≥25%

28. Source: Behavioral Risk Factor Surveillance System, CDC. (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) Obesity Trends* Among U.S. Adults BRFSS, 2002 No Data <10% 10%–14% 15%–19% 20%–24% ≥25%

29. Source: Behavioral Risk Factor Surveillance System, CDC. Obesity Trends* Among U.S. Adults BRFSS, 2003 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14% 15%–19% 20%–24% ≥25%

30. Source: Behavioral Risk Factor Surveillance System, CDC. Obesity Trends* Among U.S. Adults BRFSS, 2004 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14% 15%–19% 20%–24% ≥25%

31. Source: Behavioral Risk Factor Surveillance System, CDC. Obesity Trends* Among U.S. Adults BRFSS, 2005 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%

32. Source: Behavioral Risk Factor Surveillance System, CDC. Obesity Trends* Among U.S. Adults BRFSS, 2006 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%

33. Source: Behavioral Risk Factor Surveillance System, CDC. Obesity Trends* Among U.S. Adults BRFSS, 2007 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%

34. Source: Behavioral Risk Factor Surveillance System, CDC. Obesity Trends* Among U.S. Adults BRFSS, 2008 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%

35. Source: Behavioral Risk Factor Surveillance System, CDC. Obesity Trends* Among U.S. Adults BRFSS, 2009 (*BMI ≥30, or ~ 30 lbs. overweight for 5’ 4” person) No Data <10% 10%–14% 15%–19% 20%–24% 25%–29% ≥30%

36. 36 Time Check 9:45 AM

37. 37

38. 38 Teach Epidemiology

39. 39 Time Check 10:15 AM

40. 40

41. 41 Teach Epidemiology

42. 42 Teach Epidemiology Teachers Team-Teaching Teachers (TTTT) TTTT 1 (Class 1 –pages 6-12)

43. 43 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 Metacognition Teach Epidemiology Epi – Grades 6-12

44. 44 Teach Epidemiology Enduring Epidemiological Understandings

45. 45 Time Check 10:45 AM

46. 46

47. 47 Teach Epidemiology

48. 48 Time Check 11:00 AM

49. 49

50. 50 Teach Epidemiology

51. 51 Teach Epidemiology Teachers Team-Teaching Teachers (TTTT) TTTT 2

52. 52 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 Metacognition Teach Epidemiology Epi – Grades 6-12

53. 53 Teach Epidemiology Enduring Epidemiological Understandings

54. 54 Time Check 11:30 AM

55. 55

56. 56 Teach Epidemiology

57. 57 Teach Epidemiology Epi – Grades 6-12 TTTT 3

58. 58 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 Metacognition Teach Epidemiology Epi – Grades 6-12

59. 59 Teach Epidemiology Enduring Epidemiological Understandings

60. 60 Time Check Noon

61. 61

62. 62 Teach Epidemiology

63. 63

64. 64 Communication and Discourse Another key feature of the work of scientists and engineers is scientific communication, which includes the practices of reading scientific reports, constructing written articles, and engaging in deliberative discourse with others. Researchers have demonstrated the centrality of reading to the practice of science, showing that, on average, scientists read for 553 hours per year or 23% of total work time. When the activities of speaking and writing are included as well, the scientists in their study spent on average 58% of their total working time in communication or working in the coordination space. More importantly, scientists and engineers were found to consider reading as essential to their work and as their primary source of creative stimulation. Thus the dominant practice in science and engineering is not ‘hands-on’ manipulation of the material world but rather a ‘minds-on’ social and cognitive engagement with ideas, evidence and argument. Reading, for instance, is an act of inquiry into meaning – an attempt to construct sense from the multiple forms of representation used in science – words, symbols, mathematics, charts, graphs and visualizations. Each individual must engage in a process of using his or her existing knowledge to interpret text and generate new understandings. Hence, a vital and important role for any education in the sciences and engineering is to explore how words and symbols are used to construct specific scientific meanings. A Framework for Science Education (Preliminary Public Draft / Public Comment Draft – July 12-August 2, 2010) Chapter 5: Dimension 3: Scientific and Engineering Practices, pages 7-8 Teach Epidemiology Epi – Grades 6-12

65. 65 View a News Item from an Epidemiologic Perspective Teach Epidemiology What do you mean - Teach Epidemiology?

66. 66 View a News Item from an Epidemiologic Perspective Teach Epidemiology What do you mean - Teach Epidemiology? Handout

67. Utah Behavioral Risk Factor Surveillance System

68. “I am about to give you Utah’s 12 questions about prescription pain medication. Do not write your name on the questions. I am going to ask you to answer the 12 questions with a #2 pencil and then immediately turn the questions over so that no one else can see your answers. You do not need to participate. If you do not wish to participate, you can pretend to answer the questions and turn the questions over or just turn the questions over. Your participation is voluntary, anonymous, and confidential. Let me repeat – You are not required to participate and nothing will happen to you if you do not. I will pass large envelopes around the room into which you can place your questions regardless of whether or not you answered them.” Behavioral Risk Factor Surveillance System

69. http://health.utah.gov/opha/publications/brfss/Questionnaires/08UTBRFSS.pdf Behavioral Risk Factor Surveillance System

70. 70 Question __________________________________________________ __________________________________________________ __________________________________________________ Answer Options A.___________________________________ B.___________________________________ C.___________________________________ D.___________________________________ E.___________________________________ F.___________________________________ G.___________________________________ H.___________________________________ Question / Answer Form Handout

83. 83 “… to see past the surface features of any problem to the deeper, more fundamental principles of the discipline.” National Research Council Learning and Understanding Fundamental Epidemiological Understandings

84. 84 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 Metacognition Teach Epidemiology Epi – Grades 6-12

85. 85

86. 86 Teach Epidemiology Enduring Epidemiological Understandings

87. 87 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 Metacognition Teach Epidemiology Epi – Grades 6-12

88. 88 Time Check 12:30 PM

89. 89

90. 90 Teach Epidemiology

91. 91 Time Check 1:30 PM

92. 92

93. 93 Teach Epidemiology

94. Teach Epidemiology Workshop—Day 2 Diane Marie M. St. George, PhD University of MD School of Medicine

95. Back to EU 2 and 3 Why study patterns of disease? Why is a description of the person, place, and time elements of a disease distribution important?

96. Epidemiologic Studies Descriptive epidemiology  Describes patterns of disease  Suggests hypotheses about relationships between “exposures” and “health-related conditions” Analytic epidemiology  Tests hypotheses  Evaluates relationships  Always in a search for causality  Knowing causation helps us to prevent and treat disease and promote health

97. Enduring Understandings 4. A hypothesis can be tested by comparing the frequency of disease in selected groups of people with and without an exposure to determine if the exposure and the disease are associated. 5. When an exposure is hypothesized to have a beneficial effect, studies can be designed in which a group of people is intentionally exposed to the hypothesized cause and compared to a group that is not exposed. 6. When an exposure is hypothesized to have a detrimental effect, it is not ethical to intentionally expose a group of people. In these circumstances, studies can be designed that observe groups of free- living people with and without the exposure.

98. Boys are more likely than girls to want to go to this field trip.  What are we comparing? Proportion of girls who will want to go on the trip to proportion of boys who will want to go on the trip.  What is the causal inference? Gender  Wanting to attend the field trip This “Teach Epi” thing…it will work better for Ms. Smith’s kids than mine.  What are we comparing? Proportion of Smith’s students who will engage with epidemiology to proportion of my students who will engage with epidemiology.  What is the causal inference? Class  Engagement with the science of epidemiology

99. Heart attacks Descriptive epidemiology showed the following patterns:  In certain Midwestern communities, increasing incidence of heart attacks over time  More heart attacks among farmworkers than non- farmworkers in those communities  More heart attacks among males than among females What is your hypothesis?

100. Testing hypotheses about MI Hypothesis: Exposure to pesticides increases risk of MI. How might you go about evaluating this hypothesis?

101. Testing hypotheses about MI Hypothesis: Exposure to pesticides increases risk of MI. Evaluate the hypothesis using an:  Ecologic study

102. Ecologic study of pesticide exposure and MI Exposure is pesticide  Measured as proportion of land area devoted to wheat Outcome is MI  Measured as a rate per 100,000 Plot data on a graph What might you expect to see?

105. Ecologic Study Key element  Group-level estimates Quantify relationships  Graphical displays  Correlation coefficient Advantages  Study group-level variables, e.g. policies, laws, community socioeconomic status  Use existing data sources  Use fewer resources (time, money, subject burden) Disadvantage  Ecologic fallacy

106. Testing hypotheses about MI Hypothesis: Exposure to pesticides increases risk of MI. Evaluate the hypothesis using a:  Cross-sectional study

107. Cross-sectional study of pesticide exposure and MI Exposure is pesticide  Measured as pesticide application history Outcome is MI  Measured as yes or no Count responses What might you expect to see?

108. Pesticides and MI MI+MI- Pesticide+ Pesticide- 200

109. Pesticides and MI MI+MI- Pesticide+6090150 Pesticide-104050 70130200 So, is pesticide usage associated with MI?

110. Pesticides and MI MI+MI- Pesticide+6090150 Pesticide-104050 70130200 What is the prevalence of MI? What is the prevalence of MI among pesticide users? What is the prevalence of MI among non-users?

111. Pesticides and MI MI+MI- Pesticide+6090150 Pesticide-104050 70130200 What is the prevalence of MI? 70/200 = 35% What is the prevalence of MI among pesticide users? = 60/150 = 40% What is the prevalence of MI among non-users? = 10/50 = 20%

112. Cross-sectional Study Key element  Snapshot of one point in time Quantify association  Prevalence ratio Advantages  Individual data  Quick, cheap Disadvantages  Difficult to assess temporality because measure E and D simultaneously  Inefficient for E or D that are rare  Inefficient for D that are rapidly fatal or of short duration

113. Testing hypotheses about MI Hypothesis: Exposure to pesticides increases risk of MI. Evaluate the hypothesis using a:  Case-control study

114. Case-control study of pesticide exposure and MI Exposure is pesticide  Measured as pesticide application history Outcome is MI  Measured as yes or no Want to ensure that you have enough cases to do your study, so select for those with MI Find those without MI Ask them about exposures to pesticides What might you expect to see?

115. Pesticides and MI MI+MI- Pesticide+ Pesticide- 100 200

116. Pesticides and MI MI+MI- Pesticide+601070 Pesticide-4090130 100 200 What is the prevalence of MI?

117. Case-control Study Odds = probability an event will occur/probability that an event will not occur Odds of exposure in cases = (among cases) probability of being exposed/probability one was not exposed What is odds of exposure in controls? = (among controls) probability of being exposed/ probability one was not exposed What is Odds Ratio?

118. Pesticides and MI MI+MI- Pesticide+601070 Pesticide-4090130 100 200 What is the odds of exposure among the cases? What is the odds of exposure among the controls? What is the OR?

119. Pesticides and MI MI+MI- Pesticide+601070 Pesticide-4090130 100 200 What is the odds of exposure among the cases? (60/100)/(40/100) = 60/40 = 1.5 What is the odds of exposure among the controls? (10/90) =.11 What is the OR? ~ 13.5

120. Case-control Study Key elements  Compare individuals selected on the basis of disease status  Classic epidemiologic study design Quantify association  Odds Ratio Advantages  Can be less expensive and time-consuming than follow-up studies  Efficient for rare diseases Disadvantages  May be resource-intensive because of need to screen so many  Difficult to assess temporality  Recall bias

121. Testing hypotheses about MI Hypothesis: Exposure to pesticides increases risk of MI. Evaluate the hypothesis using a:  Cohort study

122. Cohort study of pesticide exposure and MI Exposure is pesticide  Measured as pesticide application history Outcome is MI  Measured as yes or no Want to ensure that you have enough exposed persons to do your study, so select for those with pesticide exposure Find those without pesticide exposure Follow them up over time to ascertain MI status What might you expect to see?

123. Pesticides and MI MI+MI- Pesticide+100 Pesticide-100 200

124. Pesticides and MI MI+MI- Pesticide+7030100 Pesticide-3565100 10595200 What is the incidence of MI among the pesticide users? What is the incidence of MI among the non-users? What is the risk ratio?

125. Pesticides and MI MI+MI- Pesticide+7030100 Pesticide-3565100 10595200 What is the incidence of MI among the pesticide users? = 70% What is the incidence of MI among the non-users? = 35% What is the risk ratio? = 2.0

126. Cohort Study Key element  Select based on exposure status and follow-up over time Quantify association  Relative risk (risk ratio) Advantages  Minimizes confusion about temporality  Ideal for rare exposures Disadvantages  May have to screen many to get exposed group  Large, time-consuming, expensive especially if disease is relatively rare and/or slow to develop  Attrition may result in selection bias  Inefficient for rare diseases

127. Testing hypotheses about MI Hypothesis: Exposure to pesticides increases risk of MI. Evaluate the hypothesis using a:  Randomized controlled trial

128. RCT study of pesticide exposure and MI Exposure is pesticide  Measured as pesticide exposure Outcome is MI  Measured as yes or no Want to ensure maximal control over study parameters, so you decide who gets exposed and who does not Follow up over time to ascertain MI status What might you expect to see?

129. Pesticides and MI MI+MI- Pesticide+ Pesticide- 200

130. Pesticides and MI MI+MI- Pesticide+7030100 Pesticide-3565100 10595200 What is the incidence of MI among the pesticide users? What is the incidence of MI among the non-users? What is the risk ratio?

131. Pesticides and MI MI+MI- Pesticide+7030100 Pesticide-3565100 10595200 What is the incidence of MI among the pesticide users? = 70% What is the incidence of MI among the non-users? = 35% What is the risk ratio? = 2.0

132. Randomized Controlled Trial Key elements  Assign treatments to individuals and follow up to ascertain disease status.  The researcher controls primary exposure under study. Exposures can be treatments (drug, surgery) or preventive measures (water fluoridation, exercise regimens).  Ethical considerations may preclude use of this design. Quantify association  Relative risk (risk ratio) Advantages  Random assignment serves to “equate” groups  Closest to “true experiment” Disadvantages  Expensive and time-consuming.  Subjects are often highly selected group because the requirements of participants can often be extensive.

133. Enduring Understandings 4. A hypothesis can be tested by comparing the frequency of disease in selected groups of people with and without an exposure to determine if the exposure and the disease are associated. 5. When an exposure is hypothesized to have a beneficial effect, studies can be designed in which a group of people is intentionally exposed to the hypothesized cause and compared to a group that is not exposed. 6. When an exposure is hypothesized to have a detrimental effect, it is not ethical to intentionally expose a group of people. In these circumstances, studies can be designed that observe groups of free- living people with and without the exposure.

134. 134 Time Check 3:15 PM

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136. 136 Teach Epidemiology

137. 137 Time Check 3:30 PM

138. 138

139. 139 Teach Epidemiology

140. 140 Teach Epidemiology Teachers Team-Teaching Teachers (TTTT) TTTT 1 (pages 1-33)

141. 141 Teach Epidemiology Teachers Team-Teaching Teachers (TTTT) TTTT 2 (pages 35-39)

142. 142 Teach Epidemiology Epi – Grades 6-12 TTTT 3

143. 143 Teach Epidemiology Epi – Grades 6-12

144. 144 TTTT Rules 1.Teach epidemiology. 2.As a team, create a 20-minute lesson during which you model a way to teach epidemiology for your workshop colleagues. 3.Make sure your lesson develops a deeper understanding of an enduring epidemiological understanding. 4.Assume the foundational epidemiological knowledge from the workshop. 5.Try to get us to uncover the enduring epidemiological understanding. 6.End each lesson by placing it in the context of the appropriate enduring epidemiological understanding. 7.Contribute to creating “… a professional community that discusses new teacher materials and strategies and that supports the risk taking and struggle entailed in transforming practice.” 8.Teach epidemiology. Teach Epidemiology Teachers Team-Teaching Teachers (TTTT)

145. 145 Time Check 4:30 PM