1. Transmission: Explain concepts. Transmission: Explain biological concepts related to PH clearly to achieve mastery of content Developmental: Application to PH problems Developmental: Structured thinking. Moving from simple to complex thinking & problem solving. Assessing validity. Introduction to Epidemiology The Biology of Public Health Perspectives on Teaching

2. Transmission: …have mastery of content and represent it accurately and efficiently. … provide clear objectives, adjust pace, answer questions, clarify, summarize, etc. Apprenticeship: …reveal inner workings of skilled performance & translate it into accessible language and an order set of tasks. Progression from simple to complex. Must know what learners can do on their own…. Developmental: Primary goal is to help learners develop increasingly sophisticated cognitive structures for comprehending content. Key is two skills: a) effective questioning that challenges learners to move to more complex forms of thinking, and b) “bridging knowledge”: questions, problems, cases, examples. Nurturing: via knowledge that they can succeed in learning if they try; achievement is a product of their own efforts and ability. Good teachers create a climate of caring and trust and setting challenging, but achievable goals and providing encouragement and support.

3. My Rules of Engagement 1.Think of yourself as a more experienced learner. 2.Be a leader, not a boss. Have a plan. Listen. 3.Be yourself; don’t try to be funny. 4.You must earn their trust. Never humiliate a student. 5.Don’t cram content. Leave time to discuss.

4. 6.Use stories to engage and show relevance. 7.Use problems to engage and challenge. 8.Change pace & methods. 9.Visualize concepts; reduce use of text on slides. 10. Reflect on your teaching before and after class.

5. Comments on ‘Engaging’ Use of PowerPoint

6. ODDS RATIO “THE RATIO OF THE ODDS OF HAVING THE TARGET DISORDER IN THE EXERIMENTAL GROUP RELATIVE TO THE ODDS IN FAVOUR OF HAVING THE TARGET DISORDER IN THE CONTROL GROUP (IN COHORT STUDIES OR SYSTEMATIC REVIEWS) OR THE ODDS IN FAVOUR OF BEING EXPOSED IN SUBJECTS WITH THE TARGET DISORDER DIVIDED BY THE ODDS IN FAVOUR OF BEING EXPOSED IN CONTROL SUBJECTS (WITHOUT THE TARGET DISORDER).”

7. Yes No Hepatitis 1 29 18 7 Yes No 19 36 Ate at Deli? A case-control study comparing odds of exposure. 18/1 7/29 Odds Ratio = 18/1 7/29 = 75 Odds of exposure Hepatitis cases were 75 times more likely to have eaten at the Deli. Cases Controls The Odds Ratio

8. Beginning With a Pair-Share Discussion of a Problem - An Embedded Flash Animation - Checking Understanding With an Audience Response System

9. Measuring Disease Frequency

10. The mayor of your town was startled to learn that there are 3 people who were recently diagnosed with hepatitis A in his neighborhood. He is concerned that this may just be the tip of the iceberg, and he is wondering if this signals an epidemic. He wants your help in assessing the magnitude of the problem. What information do you need in order to assess:  How big the problem is in town,  Whether there is an epidemic starting.  How the problem in your town compares to that of neighboring towns.

11. Population Examples:  Residents of Boston  Members of Blue Cross/Blue Shield  Postmenopausal women in Massachusetts  Coal miners in Pennsylvania  Adolescents in U.S. A group of people with some common characteristic (age, race, gender, place of residence). Residents of Marshfield, MA Residents of Marshfield, MA Sample : 19 who got hepatitis 38 who did not

12. Fixed population: membership is permanent and defined by some event. Example: Survivors of the atomic bomb blasts in Japan Dynamic Population: membership can be transient. Example: Residents of Boston

13. Basic Concepts  Ratio  Proportion  Rate

14. A number obtained by dividing one number by another. Ratio Example: the ratio of women to men in a class # women 120 = 8 # men 15 1 A ratio doesn’t have any dimensions or units. It just indicates the relative magnitude of the two entities. Women = Men = Women Men

15. A type of ratio that relates a part to a whole; often expressed as a percentage (%). Proportion Example: proportion of women in a class # women = 120 = 88.9% total # students 135 Men Women

16. A type of ratio that relates a part to a whole; often expressed as a percentage (%). Proportion Example: The proportion of students who developed a respiratory infection during the semester. # with colds 45 33.3% total # students 135 ==

17. Rate A type of ratio in which the denominator also takes into account the dimension of time. Example: 120 miles in 2 hours 120 miles = 60 miles per hr. 2 hours Example: 60 gallons in 3 hours 60 gal. = 20 gal. per hr. 3 hours

18. Rate A type of ratio in which the denominator also takes into account the dimension of time. Example: the rate of myocardial infarctions (heart attacks) in a study population taking low dose aspirin. 254.8 per 100,000 person-years

19. Counts of Disease If events aren’t recorded, there is no way to detect trends.

20. The simple count of HIV+ people provides the basis for significant discussions among city officials and health care providers. HIV+ people in our town 2001 5 2002 7 2003 10 2004 3 2005 5 2006 19 Counts of Disease Simple counts are essential to public health planners and policy makers by providing a direct measure of the need for resources for specific problems.

21. HIV+ Is HIV more of a problem in our town? But Count Data Alone Are Insufficient for Making Comparisons Obviously, you need to take into account the time frame and size of each population. Our Town 75 Next Town 35

22. Measures of Disease Frequency Prevalence (a proportion) Incidence Cumulative incidence (a proportion) Incidence rate (a rate)

23. The focus is on existing disease at a specific time, not the development of new cases. The proportion of a population that has disease at a given time. Prevalence

24. The focus is on existing disease at a specific point in time. Imagine you took a snapshot of a class and labeled those suffering from hay fever or other allergies with a red “A”. The proportion of a population or group that has disease at a specific “point” in time. Point Prevalence A A A A A

25. The proportion of a population that has disease during a given period of time. 198019811979 Prevalence = 310 (cataracts) 2,477 (total) =.125 = 12.5% Period Prevalence 310 had cataracts 310 had cataracts Eye exam survey of 2,477 people x x xx x x x xxx x x

26. Prevalence of HIV in MA in 2003 8,263 HIV+ Total MA population = 5.7 million in 2003 = 0.00145 = 0.145% = 14.5 per 10,000 Express it this way.

27. Numerator: # new cases during a span of time. Denominator: includes only people “at risk”. The focus is on measuring the probability of developing disease during a span of time. Frequency of new cases during a span of time in people “at risk”. Incidence XXXXXXXXXX

28. 200320042005 Prevalence In 2003 = 0.00145% Prevalence versus Incidence 20062007200820092010 Prevalence is the probability of having disease at a point in time. X X XX X XX X X X XX Incidence: Frequency of new cases during a span of time in people at risk. Incidence is the probability of developing disease during a span of time.

29. Incidence Both focus on # new cases of disease (numerator) during a period of observation. The difference is the way they handle time. Cumulative incidence (a proportion) Incidence rate (a true rate)

30. Cumulative Incidence  A proportion  A fixed block of observation time  Assumes complete follow-up for all subjects.  You don’t know the precise “time at risk” for each person.  The time period is described in words (“… during spring semester.”) x xxx x xx x xx xx x x x xx x x x x x x Jan. 2007 (50 students) May 2007 (45 students) CI = 25/50 = 50% during spring semester Example: 25 colds in a class of 50 during spring semester.

31. In reality, people are moving in and out of Boston, and some will die (& no longer be members of the population). But there is no way to know the details of this. The best we can do is assume that the number of people in the population stays the same and they are always at risk. TB Incidence in Boston During 2005?

32. We need to assume the population is fixed, i.e. all people were followed for the entire block of time. CI = # new cases 2005 est. pop. size Cumulative incidence (a proportion) TB Incidence in Boston During 2005?

33. Cumulative Incidence of AIDS in MA During 2004 CI = 523 new AIDS cases = 9.2/100,000 Population at risk: about 5.7 million from 1/1/04 to 1/31/04

34. Which has greater rate of relief? Which has greater proportion of relief? New drug Old drug X X X X X X X X X X X X 1 2 3 4 5 6 7 8 9 10 o o o o o o o o Hours Here, the outcome of interest is relief of pain.

35. Incidence Rate of HIV Seropositivity in Prostitutes ******************* Follow-up ******************** Subject 198919901991199219931994Disease-free Yrs 1---------+---------------- --------1 2---------?1 3 +---------------- --------2 4---------?1 5 ?3 6 ?5 7 ----------------- --------6 8--------- ?5 9 +---------------- --------1 10---------+---------------- ?1 IR = 4 new AIDS cases = 0.15 = 15/100 P-Yrs 26 person-yrs Sum = 26 yrs

36. Incidence Rate Total # new cases Total amount of disease-free observation time for a group

37. Incidence Rate Subject A- B- C- D- E- F- G- H- I- J- K- L- x x x 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 IR = 3 = 28 107.7 p-ys 1000 p-yrs Total =107.7 person-yrs Time at Risk 8.3 11.0 14.0 10.2 3.0 7.0 10.0 3.0 9.0 6.2 12.0 X = when they got disease CI = 3 12 over 14 yrs

38. CI versus IR? Which has greater rate of relief? Which has greater proportion of relief? New drug Old drug X X X X X X X X X X X X 1 2 3 4 5 6 7 8 9 10 o o o o o o o o CI = 6/10 = 60% over 10 years IR = 6/49 p-yrs = 12.2/100 P-yrs CI = 6/10 = 60% over 10 years IR = 6/85 p-yrs = 7/100 P-yrs

39. Obesity Risk of Non-fatal Myocardial Infarction Association? <21 21-23 23-25 25-29 >29 BMI: wgt kg hgt m 2 # MIs (non-fatal) 41 57 56 67 85 person-years of observation 177,356 194,243 155,717 148,541 99,573 rate of MI per 100,000 P-Yrs (incidence) 23.1 29.3 36.0 45.1 85.4 126 lb @ 5’6” = 21 175 lb @ 5’6” = 29 126 lb @ 5’6” = 21 175 lb @ 5’6” = 29 The Nurse’s Health Study

40. 200320042005 20062007200820092010 X X XX X XX X X X XX Incidence: Frequency of new cases during a span of time in people at risk. Incidence is the probability of developing disease during a span of time. Incidence provides a way of measuring the risk of becoming diseased.

41. Summary –Measures of Disease Frequency Prevalence (a proportion) = People# People with disease at a point in time Total People# People in the study population Cumulative Incidence (a proportion) = People# new cases in a specified period Total People# People (at risk) in the study population Incidence Rate (a rate) = People# new cases of disease People-TimeTotal observation time in a group at risk

42. The proportion of exposed people who develop disease. (Not really a rate; it’s a special type of cumulative incidence.) Attack Rate TB exposure (a cumulative incidence) Passengers on Honolulu to Baltimore flight within 2 rows of index case Positive TB tests

43. Case Fatality Rate The proportion of diseased people who die - in this case 2/6 = 33%. (Again, not a rate, but a special type of cumulative incidence.) A measure of the severity or risk of dying from the disease if you have it. Example:, 33% of people who got SARS died. SARS cases

44. If the prevalence, incidence, and average duration of disease have been relatively constant, this relationship can be used to predict the effects of changing incidence or average duration. Prevalence Depends on Incidence & Duration of Disease Prevalence = Incidence x Average Duration of Disease P = I x D

45. Not surprisingly, brief, acute illnesses such as viral gastroenteritis do not have high prevalence, because they don’t last long. In contrast, diseases like diabetes have greater prevalence because they aren’t rapidly fatal, but there is not real cure; they are just controlled. Prevalence = Incidence x Average Duration of Disease P = I x D Average Duration of Disease Affects Prevalence

47. Since D = P/IR then D = 23/100,000 persons= 0.5 years 46/100,000 person-years Conclusion: People with lung cancer survive an average of 6 months from diagnosis to death. Calculating the Mean Duration of Disease If Prevalence = Incidence x Avg. Duration, then Avg. Duration = Prevalence Incidence Example: Lung cancer: If incidence = 46 new cancers per 100,000 P-Yrs (i.e., in a population of 100,000 you expect 46 cases per year), and prevalence = 23 per 100,000 population. What is the average duration of lung cancer?

48. Use of an Audience Response System to: Assess understanding, Reinforce concepts, Identify & clarify misconceptions, and Build confidence. (And have fun.)

49. PH officials used surveillance data to determine the number of new cases of tuberculosis in Boston during 2008, and they computed the frequency of new TB cases using census data as an estimate of the population size in 2008. What measure of disease frequency did they calculate? 1.Prevalence 2.Cumulative incidence 3.Incidence rate 4.Attack rate 5.Case fatality rate

50. Which measure of disease frequency best describes the percentage of men found to have previously undiagnosed prostate cancer at autopsy? 1.Prevalence 2.Cumulative incidence 3.Incidence rate 4.Attack rate 5.Case fatality rate

51. Investigators reported that 136 deaths occurred among 272 persons who had been infected with avian flu. What measure of disease frequency does this represent? 1.Person-yrs 2.Incidence rate 3.Case-fatality rate 4.Prevalence

52. Which measure of disease frequency best describes the percentage of malaria patients who are found to have chloroquine-resistant malaria? 1.Prevalence 2.Cumulative incidence 3.Incidence rate 4.Attack rate 5.Case fatality rate

53. Which measure of disease frequency best describes the rate at which myocardial infarctions occurred among smokers, expressed as the number per 100,000 person-years of observation? 1.Prevalence 2.Cumulative incidence 3.Incidence rate 4.Attack rate 5.Case fatality rate

54. An “In-class Quiz.”

55. Study population of 1,000. Dashed line = disease present (Lung Cancer) Patients 1, 2, 3, & 4) had the disease before the study began. During the year of the study, 6 new cases occur (start of dashed lines). Among the total of 10 cases, there were 6 deaths during the year. The 990 other individuals in the study did not become ill or die. 1994 1995 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan 1<---------------------------------------------------------------------------------------------------------Alive 2<---------------------------------------Dead 3<--------------------------------------------------------------Dead 4<-----Dead 5 -----------------------------------------------------------------------------------------------------Alive 6 ----------------------------Dead 7 -------------------------------------------------------------------------Alive 8 ------------Dead 9 ------------------------------------------------------------Alive 10 ---------------------------------------Dead Prevalence of disease on: Jan. 1, 1994? July 1, 1994? Dec. 31, 1994? X X X X X X What was the cumulative incidence during 1994? What was the case- fatality rate during 1994?

56. Study population of 1,000. Dashed line = disease present (Lung Cancer) Patients 1, 2, 3, & 4) had the disease before the study began. During the year of the study, 6 new cases occur (start of dashed lines). Among the total of 10 cases, there were 6 deaths during the year The 990 other individuals in the study did not become ill or die. 1994 1995 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan 1<---------------------------------------------------------------------------------------------------------Alive 2<---------------------------------------Dead 3<--------------------------------------------------------------Dead 4<-----Dead 5 -----------------------------------------------------------------------------------------------------Alive 6 ----------------------------Dead 7 -------------------------------------------------------------------------Alive 8 ------------Dead 9 ------------------------------------------------------------Alive 10 ---------------------------------------Dead 4/1,000 5/996 4/994 X X X X X X Prevalence of disease on: Jan. 1, 1994? July 1, 1994? Dec. 31, 1994?

57. Study population of 1,000. Dashed line = disease present (Lung Cancer) Patients 1, 2, 3, & 4) had the disease before the study began. During the year of the study, 6 new cases occur (start of dashed lines). Among the total of 10 cases, there were 6 deaths during the year The 990 other individuals in the study did not become ill or die. 1994 1995 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan 1<---------------------------------------------------------------------------------------------------------Alive 2<---------------------------------------Dead 3<--------------------------------------------------------------Dead 4<-----Dead 5 -----------------------------------------------------------------------------------------------------Alive 6 ----------------------------Dead 7 -------------------------------------------------------------------------Alive 8 ------------Dead 9 ------------------------------------------------------------Alive 10 ---------------------------------------Dead X X X X X X 6/996 What was the cumulative incidence during 1994?

58. Study population of 1,000. Dashed line = disease present (Lung Cancer) Patients 1, 2, 3, & 4) had the disease before the study began. During the year of the study, 6 new cases occur (start of dashed lines). Among the total of 10 cases, there were 6 deaths during the year. The 990 other individuals in the study did not become ill or die. 1994 1995 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan 1<---------------------------------------------------------------------------------------------------------Alive 2<---------------------------------------Dead 3<--------------------------------------------------------------Dead 4<-----Dead 5 -----------------------------------------------------------------------------------------------------Alive 6 ----------------------------Dead 7 -------------------------------------------------------------------------Alive 8 ------------Dead 9 ------------------------------------------------------------Alive 10 ---------------------------------------Dead X X X X X X 6/10 or 60% What was the case- fatality rate during 1994?

59. Descriptive Epidemiology - Using Interactive PowerPoint

60. Evolution of Medical Information 1. Description & hypothesis generation 2. Hypothesis testing to establish valid associations 3. Evaluation of efficacy of treatment or prevention

61. Differences: If the frequency of disease differs in two circumstances, it may be due to a factor that differs in the two circumstances. Example: stomach cancer in Japan & US Similarities: If a high frequency of disease is found in several different circumstances & one can identify a common factor, then the common factor may be responsible. Example: AIDS in IV drug users, or recipients of transfusions, & hemophiliacs. Correlations: If the frequency of disease varies in relation to some factor, then that factor may be a cause of the disease. Example: differences in colon cancer vary with per capita meat consumption. Hypotheses arise from observation of …

62. Descriptive information provides clues. What factors might be associated with disease? Are there similarities among the diseased? Are there differences between diseased & well people? What correlates with disease?  Person: characteristics?  Place: specific locations or settings?  Time: does it vary over time?

64. X X X X X X X X X X

65. Hepatitis Outbreak Marshfield, MA had an outbreak of hepatitis A. How did they identify the source?

66. What Might Provide Clues (hypotheses)? Door 1 Door 2 Door 4Door 3 Door 5 Done LL IN EC B SM

67. Interview Some Cases Back

68. Epidemic Curve Back

69. Spot Map – Residence of Hepatitis Cases Back

70. Rick’s Deli McDonald’s Jaime’s Pub Papa Gino’s Friendly’s They hypothesized that the source was probably an infected food handler at: Based on these clues: Knowledge of biology of hepatitis A (transmission, incubation) Time course: epidemic curve of “point source” Diverse age, occupation, location Interview with a series of cases & similarities in restaurant use

71. Measures of Association - An In-Class Quiz

72. Is There An Association? Exposure (Risk Factor) Outcome

73. 2) Calculate the difference in incidence between the two groups. (Subtract incidence in control group from the incidence in the exposed group). Options For Comparing Incidence 1)Calculate the ratio of the incidences for the two groups. (Divide incidence in exposed group by the incidence in the control group). Or IeI0IeI0 I e - I 0 For Cohort Type Studies

74. Yes No Wound Infection 1 78 79 7 124 131Yes No 8 202 210 Subjects RR = 7/131 = 5.3 = 4.2 1/79 1.3 RR = 7/131 = 5.3 = 4.2 1/79 1.3 Cumulative Incidence 5.3% 1.3% (7/131) (1/79) Had Incidental Appendectomy Measuring Association with Relative Risk

75. RR = = 5.3% 1.3% = 4.2 Interpretation: “In this study the risk of wound infection was 4.2 times greater in patients who had incidental appendectomy compared to those who did not have appendectomy.” 5.3% 1.3% Also had appendectomy No appendectomy Relative Risk in Appendectomy Study A ratio; no dimensions.

76. RR = = 5.3% = 1.0 5.3% What If Relative Risk = 1.0 ? 5.3% Exposed group Unexposed group

77. Yes No Myocardial Infarction Yes No 378 21,693 22,071 subjects 139 10,898 11,037 exposed Aspirin Use 239 10,795 11,034 not exposed RR =.0126 = 0.55.0221 RR =.0126 = 0.55.0221 I exposed = 139/11,037 =.0126 I unexposed = 239/11034 =.0221 I exposed = 139/11,037 =.0126 I unexposed = 239/11034 =.0221 What If Relative Risk < 1.0 ?

78. Yes No Myocardial Infarction Yes No 378 21,693 22,071 139 10,898 11,037 Aspirin Use 239 10,795 11,034 RR =.0126 = 0.55.0221 RR =.0126 = 0.55.0221 Subjects who used aspirin had 0.55 times the risk of myocardial infarction compared to those who did not use aspirin. Interpretation of Relative Risk < 1.0

79. Relative Risk = 55.2 /100,000 P-Yr. = 55.2 = 0.47 116.6 /100,000 P-Yr. 116.6 1.Women using hormone replacement therapy had 0.47 times the risk of coronary disease compared to women who did not use HRT. 2.Women using hormone replacement therapy had 0.47 times more risk of coronary disease compared to women who did not use HRT. 3.Women using hormone replacement therapy had 0.47 times less risk of coronary disease compared to women who did not use HRT. Which is the correct interpretation of the relative risk = 0.47?

80. Which is the best interpretation of the risk ratio (relative risk)? 1. Women using hormone replacement therapy had 0.47 times the risk of coronary disease compared to women who did not use HRT. 2. Women using hormone replacement therapy had 0.47 times more risk of coronary disease compared to women who did not use HRT. 3. Women using hormone replacement therapy had 0.47 times less risk of coronary disease compared to women who did not use HRT.

81. The Nurse’s Health Study Obesity Non-fatal Myocardial Infarction ? # MIs (non-fatal) 41 57 56 67 85 Person-years of observation 177,356 194,243 155,717 148,541 99,573 Rate of MI per 100,000 P-Yrs (incidence) 23.1 29.3 36.0 45.1 85.4 Relative Risk 1.0 1.3 1.6 2.0 3.7 An “r x c” (row/column) Table – Multiple Rows & Columns <21 21-23 23-25 25-29 >29 BMI: wgt kg hgt m 2

82. How would you interpret the RR= 3.7 in the heaviest group? # MIs (non-fatal) 41 57 56 67 85 Person-years of observation 177,356 194,243 155,717 148,541 99,573 Rate of MI per 100,000 P-Yrs (incidence) 23.1 29.3 36.0 45.1 85.4 Relative Risk 1.0 1.3 1.6 2.0 3.7 <21 21-23 23-25 25-29 >29 BMI: wgt kg hgt m 2 1. The heaviest women had 3.7 times the risk compared to all the other women. 2. The heaviest women had 3.7 times the risk compared to the leanest women.

83. RD = Incidence in exposed - Incidence in unexposed Risk Difference = I e - I 0 The Risk Difference (Attributable Risk)

84. Yes No Wound Infection 1 78 79 7 124 131 Yes No 8 202 210 subjects Had Incidental Appendectomy Cumulative Incidence 5.3% 1.3% RD = 0.053 – 0.013 = 0.04 = 4 per 100 Risk Difference in Appendectomy Study

85. Even if appendectomy is not done, there is a risk of wound infection (1.3 per 100). … the RD is the excess risk in those who have the factor, i.e., the risk of wound infection that can be attributed to having an appendectomy, assuming there is a cause-effect relationship. Risk Difference Gives a Different Perspective on the Same Information Adding an appendectomy appears to increase the risk by (4 per 100 appendectomies), so... 1.3/100 5.3/100 Exposed

86. 85.4 23.1 Obesity Non-fatal Myocardial Infarction ? # MIs (non-fatal) 41 57 56 67 85 Person-years of observation 177,356 194,243 155,717 148,541 99,573 Rate of MI per 100,000 P-Yrs (incidence) 29.3 36.0 45.1 Relative Risk 1.0 1.3 1.6 2.0 3.7 Risk Difference= 85.4/100,000 - 23.1/100,000 = 62.3 excess cases / 100,000 P-Y in the heaviest group Risk Difference= 85.4/100,000 - 23.1/100,000 = 62.3 excess cases / 100,000 P-Y in the heaviest group Risk Difference in The Nurse’s Health Study <21 21-23 23-25 25-29 >29 BMI: wgt kg hgt m 2

87. Among the heaviest women there were 62 excess cases of heart disease per 100,000 person-years of follow up that could be attributed to their excess weight. Interpretation This suggests that if we followed 50,000 women with BMI > 29 for 2 years we might expect 62 excess myocardial infarctions due to their weight. (Or one could prevent 62 deaths by getting them to reduce their weight.)

88. The proportion (%) of disease in the exposed group that can be attributed to the exposure, i.e., the proportion of disease in the exposed group that could be prevented by eliminating the risk factor. AR% = AR x 100 I e.04 x 100 = 75%.053 What % of infections in the exposed group can be attributed to having the exposure? Exposed Not Exposed Attributable Risk % - The Attributable Proportion.013.053.04 Interpretation: 75% of infections in the exposed group could be attributed to doing an incidental appendectomy.

89. A Short In-class Quiz to Assess Understanding Reinforce concepts Build skill and confidence Clarify

90. A prospective cohort study was used to compare lung cancer mortality in smokers and non-smokers.  Among 20,000 non smokers there were 20 deaths from lung cancer during 5 years of study.  Among 5,000 smokers there were 100 deaths from lung cancer during the 5 year study period. 1)Organize this information in a 2x2 table. 2)Calculate the cumulative incidence of death (per 1,000) due to lung cancer in smokers and non-smokers. 3)Calculate the relative risk; interpret it in words. 4)Calculate the risk difference; interpret it in words. 5)Calculate the attributable proportion; interpret it in words.

91. A prospective cohort study was used to compare lung cancer mortality in smokers and non-smokers.  Among 20,000 non smokers there were 20 deaths from lung cancer during 5 years of study.  Among 5,000 smokers there were 100 deaths from lung cancer during the 5 year study period. 1)Organize this information in a 2x2 table. 2)Calculate the cumulative incidence of death (per 1,000) due to lung cancer in smokers and non-smokers. 3)Calculate the relative risk; interpret it in words. 4)Calculate the risk difference; interpret it in words. 5)Calculate the attributable proportion; interpret it in words. 1004900 2019980 5000 20000 100/5,000=0.02=20/1,000 over 5 yrs 20/20,000=0.001=1/1,000 over 5 yrs RR-20/1 RD=19/1,000 over 5 yrs AR% = 19/20 x 100 = 95%

92. Measuring Association in a Case-Control Study

93. Cohort Type Studies X X X X Time passes Case-Control Studies X X XX X X X X Assess prior exposures Is disease more likely in exposed persons? Are diseased persons more likely to have been exposed?

94. Yes No Hepatitis 1 29 18 7 Yes No 19 36 Ate at Deli A case-control study comparing odds of exposure. The Odds Ratio 18/1 7/29 Odds Ratio = 18/1 7/29 = 75 Odds of exposure : Hepatitis cases were 75 times more likely to have eaten at the Deli. Case Control

95. To calculate incidence, you need to take a group of disease-free people and measure the occurrence of disease over time. Odds of having the risk factor prior to disease? controls cases (Already have disease) (No disease) But in a case-control study we find diseased and non-diseased people and we measure and compare the prevalence of prior exposures.

96. Yes No Wound Infection 1 78 79 7 124 131 Had Incidental Appendectomy Cumulative Incidence 5.3% 1.3% How many exposed people did it take to generate the 7 cases in the 1 st cell? Yes No A retrospective cohort study….

97. Yes No Hepatitis 1 29 18 7 Yes No Ate at Deli 18/1 7/29 Odds of exposure Case Control How many exposed people did it take to generate the 18 cases in the 1 st cell?

98. Yes No Hepatitis 1 29 18 7 Yes No Ate at Deli Case Control In cases =18/1 In controls =7/29 = 75 Odds of Exposure Deli =18/7 No Deli =1/29 = 75 Odds of Disease

99. Odds Ratios Are Interpreted Like Risk Ratios Example: “Individuals who ate at the Deli had 75 times the risk of hepatitis A compared to those who did not eat at the Deli.” An odds ratio is a good estimate of the risk ratio when the outcome is relatively uncommon. BUT The odds ratio exaggerates relative risk when the outcome is more common.

100. You can always calculate an odds ratio, but… In cohort studies and clinical trials you can calculate incidence, so you can calculate either a relative risk or an odds ratio. In a case-control study, you can only calculate an odds ratio.

101. Yes No Got Giardiasis 14 341 355 16 108 124 Yes No Exposed to Kiddy Pool Cohort Design: Calculate RR or OR I can compute either RR or OR here. Why?

102. Compare frequency of Giardia Kid pool Not vs. Yes No Giardia 16108 14341 Relative Risk = 3.3 Relative Risk = 3.3 16 / (16+108) = 12.9% 14 / (14+341) = 3.9% Incidence Viewed as a Retrospective Cohort Study By Comparing: Those were in kiddy pool vs. Those who were not. Not

103. Compare frequency of kiddy pool use. Giardia No Giardia Those who got Giardia to those who didn’t. GiardiaNo Giardia 16108 14341 Kid pool Not Odds of having been in kiddy pool 16 / 14 108 / 341 Odds = 16/14 Ratio 108/341 = 3.6 Odds = 16/14 Ratio 108/341 = 3.6 Viewed as a Case-Control Study By Comparing: vs.

104. Compare frequency of kiddy pool use. Giardia No Giardia Those who got Giardia to those who didn’t. GiardiaNo Giardia 16108 14341 Kid pool Not Odds = 16/14 Ratio 108/341 = 3.6 Odds = 16/14 Ratio 108/341 = 3.6 Cross Product: Another Way to Calculate the Odds Ratio vs. a/c b/d a b c d a x d b x c = Odds = 16x341 Ratio 108x14 = 3.6 Odds = 16x341 Ratio 108x14 = 3.6

105. With a Common Outcome OR Exaggerates RR Yes No Outcome 45 341 386 unexposed 60 108 168 exposed Yes No Risk Factor I e = 60 168 I 0 = 45 386 60 / ( 60 + 108 ) 45 / ( 45 + 341 ) 60 / ( 60 + 108 ) 45 / ( 45 + 341 ) RR = RR= 3.06 OR = 4.21 60 / 45 108 / 341 60 / 45 108 / 341 OR =

106. Let’s see if you’ve been paying attention.

107. What does one measure and compare in a case-control study? 1. Cumulative incidence 2. Incidence rate 3. Risk of disease 4. Frequency of past exposures 5. Risk difference

108. A study of smoking and lung cancer was conducted in a small island population. There were a total of 1,000 people in the study, and at the beginning of the study none had lung cancer. Four hundred were smokers and 600 were not. Subjects were followed for 10 years. Of the smokers, fifty developed lung cancer. Of the non-smokers, 10 developed lung cancer. What kind of study was this? 1. A case series 2. A case-control study 3. A retrospective cohort study 4. A prospective cohort study

109. In the previous study examining the association between smoking and lung cancer, suppose the RELATIVE RISK = 17. How would you interpret this relative risk in words? 1. There were 17 more cases of lung cancer in the smokers. 2. Smokers had 17 times the risk of lung cancer compared to non-smokers. 3. Smokers had 17% more lung cancers compared to non- smokers. 4. 17% of the lung cancers in smokers were due to smoking.

110. In a cohort study one may measure the degree of association between an exposure and an outcome by calculating either a relative risk or an odds ratio? 1. True 2. False 3. I’m not sure

111. In a case-control study one may measure the degree of association between an exposure and an outcome by calculating either a relative risk or an odds ratio. 1. True 2. False

112. When is an odds ratio a legitimate estimate of relative risk? 1. Whenever one is conducting a case-control study. 2. When the exposure is relatively uncommon. 3. When the outcome is relatively uncommon. 4. When the sample size is large.

113. Disparities in Health Care Based on Race, Ethnicity, or Gender - Audience Response for Opinion & Shifts in Opinion

114. Do you believe there are significant disparities in health care based on race or ethnicity? 1. Yes 2. No 3. I don’t have an opinion

115. Do you believe there are significant disparities in health care based on gender? 1. Yes 2. No 3. I don’t have an opinion.

116. Do you believe that physicians are an important source of race or gender-based disparities in health care? 1. Yes 2. No 3. I don’t have an opinion.

117. Nightline Report Many studies have described disparities in health care based on race and gender, but the etiology is not always clear. Socioeconomic differences contribute to disparities between blacks and whites, but are other factors also responsible? Do conscious or subconscious attitudes of physicians contribute to these disparities? Shulman et al. sought to address this question in a study that was reported in the N. Engl. J. Med. (Feb. 1999). The study sparked much discussion. Racial Disparities in Health Care (Go to 5:21)

118. N. Engl. J. Med. Abstract N. Engl. J. Med. Abstract ? Racial Disparities in Health Care

119. Do you believe there are significant disparities in health care based on race or ethnicity? 1. Yes 2. No 3. I don’t have an opinion

120. Do you believe there are significant disparities in health care based on gender? 1. Yes 2. No 3. I don’t have an opinion.

121. Do you believe that physicians are an important source of race or gender-based disparities in health care? 1. Yes 2. No 3. I don’t have an opinion.

122. Yes No Referral for Cardiac Cath 326 34 360 305 55 360 Black White Incidence What was the probability that a black patient would be referred for catheterization? What was the probability that a white patient would be referred for catheterization? (Calculate and compare your answer with your neighbor’s.)

123. Yes No 326 34 360 326/360=90.5% 305 55 360 305/360=84.7% Black White Incidence What was the probability that a black patient would be referred for catheterization? What was the probability that a white patient would be referred for catheterization? (Calculate and compare your answer with your neighbor’s.) Referral for Cardiac Cath.

124. Yes No 326 34 360 90.5% 305 55 360 84.7% Black White Incidence What was the “relative risk” of referral? (Calculate and compare your answer with your neighbors.) Referral for Cardiac Cath.

125. Yes No BlackWhite Incidence = 0.94 305/326 55/34 OR = = 0.58 RR = 326 34 360 90.5% 305 55 360 84.7% 84.7% 90.5% The OR suggests a larger difference than the RR. Why? Referral for Cardiac Cath.

126. Yes No Black 163 17 180 90.5% Referral for Cath. White Incidence Yes No Black 163 17 180 90.5% 142 38 180 78.9% Referral for Cath. White Incidence What does the stratified analysis suggest? MalesFemales Stratified by Gender

127. Do you believe there are significant disparities in health care based on race or ethnicity? 1. Yes 2. No 3. I don’t have an opinion

128. Do you believe there are significant disparities in health care based on gender? 1. Yes 2. No 3. I don’t have an opinion.

129. Do you believe that physicians are an important source of race or gender-based disparities in health care? 1. Yes 2. No 3. I don’t have an opinion. :10

130. The conclusion that was widely circulated in the press was that blacks and women were 40% less likely than white men to be referred for cardiac catheterization. ? If you were writing the abstract for the Shulman study, how would you report the major findings? (Small group and open discussion)

131. How would you rate the presidency of George W. Bush? 1. Excellent 2. Very good 3. Good 4. Fair 5. Poor 6. Worst U.S. President ever :10

132. How would you rate the presidency of Barack Obama? 1. Excellent 2. Very good 3. Good 4. Fair 5. Poor 6. Worst U.S. President ever :10

133. Active Learning Outside the Classroom Homework “Stat Tools”Stat Tools On-line quizzes Data sets for analysis (Framingham Heart Study)Framingham Heart Study Team projects (semester long prospective cohort study) Online, interactive case-based modules (hepatitis outbreak)