Measures of Comparison

1. Measures of Comparison Chapter 3

2. Purpose • Summarize relationship between exposure and disease by comparing at least two measures of disease frequency • Overall rate of disease in an exposed group says nothing about whether exposure is a risk factor for or causes a disease. This can only be evaluated by comparing disease occurrence in an exposed group to another group that is usually not exposed. The latter group is usually called the comparison or reference group. • Comparison is the essence of epidemiology.

3. Two Main Options for Comparison Can you name two mathematical ways we can compare disease rates across multiple populations?

4. Two Main Options for Comparison 1. Calculate ratio of two measures of disease frequency ( a measure in exposed group and a measure in unexposed comparison group) 2. Calculate difference between two measures of disease frequency (a measure in exposed group and a measure in unexposed comparison group)

5. Data Set Up: Two by Two Table For cumulative incidence and prevalence Disease Exposure What makes the most sense to calculate from this data? What would you compare?

6. For Incidence Rates Disease Exposure What makes the most sense to calculate from this data? What would you compare?

7. Rate/Risk Ratio (also called Relative Risk) Comparing disease occurrence among exposed with disease occurrence among unexposed in a ratio measure. • RR= Rate in exposed group / Rate in unexposed group = Risk in exposed group / Risk in unexposed group = Rexp/Runexp • For CI: CIexp/ CIunexp = a / (a+b) / c / (c+d) • For IR: IRexp/ IRunexp = a / PTexp / c / PTunexp • Purpose: Gives information on the relative effect of the exposure on the disease. Tells you how many times higher or lower the disease risk is among the exposed as compared to the unexposed. Is commonly used in etiologic research.

8. Rate/Risk Ratio • RR=1.0 • no association between exposure and disease • RR=2.0 • two times the risk of disease in the exposed compared to the unexposed • RR=1.6 • 1.6 times the risk of disease in the exposed compared to the unexposed • or 60% increased risk of disease in the exposed (1.6 - 1.0 = .60 = 60%) • RR = 0.5 • 0.5 times or ½ the risk of disease in exposed compared to unexposed

9. Example: Cohort study of hypertension and cardiovascular morbidity and mortality(Nurses Health Study) Non Fatal Heart Attack Hypertension Calculate the Risk Ratio.

10. Example: Cohort study of hypertension and cardiovascular morbidity and mortality(Nurses Health Study) Non Fatal Heart Attack Hypertension RR = CIexp/ CIunexp = 117/13,422 = .00872 = 7.5 125/106,541 .00117 Interpretation: Women with hypertension have 7.5 times the risk of having a non-fatal heart attack compared to women without hypertension.

11. Example of an R x C Table in a Study of Magnetic Field Exposure and Leukemia Leukemia Magnetic Field Exposure Note: Low exposure group is the comparison group for both high and medium exposures.

12. Example of an R x C Table in a Study of Magnetic Field Exposure and Leukemia Leukemia Magnetic Field Exposure Note: Low exposure group is the comparison group for both high and medium exposures.

13. Example of an R x C Table in a Study of Magnetic Field Exposure and Leukemia Leukemia Magnetic Field Exposure Note: Low exposure group is the comparison group for both high and medium exposures. Interpretation: Children exposed to medium magnetic field levels have a 23% increased risk of leukemia as compared to children exposed to low magnetic field levels.Children exposed to high magnetic field levels have a 33% increased risk of leukemia as compared to children exposed to low magnetic field levels.

14. Difference Measures Comparing disease occurrence among the exposed with the disease occurrence among the unexposed comparison group by subtracting one from the other.

15. Risk/Rate Difference -- also called Attributable Risk/Rate (in the Exposed) RD = • Rate in exposed – Rate in unexposed • Risk in exposed – Risk in unexposed • Rexp– Runexp • For CI: CIexp - CIunexp = a / (a+b) - c / (c+d) • For IR: IRexp - IRunexp = a / PTexp - c / PTunexp • RD = 0 when there is no association

16. Data Set Up: Two by Two Table Disease Exposure • For CI: CIexp - CIunexp = a / (a+b) - c / (c+d)

17. For Incidence Rates Disease Exposure For IR: IRexp - IRunexp = a / PTexp - c / PTunexp

18. Risk/Rate Difference (continued) • Purpose: Gives information on • the absolute effect of exposure on disease occurrence. • the excess disease risk in the exposed group compared to the unexposed group. • the public health impact of an exposure, that is, how much disease would be prevented if the exposure were removed among those exposed. This assumes that the exposure causes the disease.

19. Risk/Rate Difference Non Fatal Heart Attack Hypertension Calculate the Risk Difference.

20. Risk/Rate Difference Non Fatal Heart Attack Hypertension RD = CIexp - CIunexp = 117 / 13,422 - 125 / 106,541 = .00872 - .00117 = .00755 or 755 / 100,000 Interpretation: The excess occurrence of non-fatal heart attack among these hypertensive women was 755 per 100,000. Or, if hypertension causes non-fatal heart attacks then 755 cases of non-fatal heart attack per 100,000 women could be eliminated if the hypertension were prevented or treated.

21. Comparison of RR and RD Annual Mortality Rate Per 100,000 Calculate the RR and RD for smoking and lung cancer as well as smoking and coronary heart disease.

22. Comparison of RR and RD Annual Mortality Rate Per 100,000 Which disease is smoking a stronger risk factor for? Which disease does smoking have a greater impact on?

23. Comparison of RR and RD Annual Mortality Rate Per 100,000 Conclusion: Cigarette smoking is a much stronger risk factor for lung cancer but (assuming smoking is causally related to both diseases) the elimination of cigarettes would prevent far more deaths from coronary heart disease.Why is this so?

24. Population Risk/Rate Difference (PRD) • Purpose: Measures excess disease occurrence among the total population that is associated with the exposure. Helps to evaluate which exposures are most relevant to the health of a target population. • Two formulas for PRD: • PRD = (RD) (Pexp) where Pexp = proportion of population that is exposed, and RD is the risk or rate difference • PRD = Rtotal - Runexp where Rtotal = risk/rate in total population and Runexp = risk/rate among unexposed

25. Population Risk Difference Non-fatal Heart Attack (outcome) Hypertension (exposure) Calculate the Population Risk Difference.

26. Population Risk Difference Non-fatal Heart Attack Hypertension PRD = (RD) (Pex) = [(117/13,422) - (125/106,541)] x (13,422/119,963) = .00755 x .112 = .00085 PRD = Rtotal - Runexp = 242/119,963 - 125/106,541 = .00202 - .00117 = .00085 or 85/100,000 Interpretation: Hypertension results in an excess incidence of 8.5/10,000 non-fatal heart attacks in the total study population. Or, if hypertension were eliminated, 8.5/10,000 cases of non-fatal heart attacks could be eliminated among the total study population.(Assumes that hypertension causes heart attacks.)

27. Population Risk/Rate Difference Note the dependence of PRD on prevalence of exposure. What would the excess of non-fatal heart attack due to hypertension be if the prevalence of hypertension were 1% rather than 11.2%? A relatively weak risk factor (in terms of relative risk) that is quite prevalent could account for more of disease incidence in a population than a stronger risk factor that is rarely present.

28. Population Risk Difference Non-fatal Heart Attack Hypertension Calculate the Population Risk Difference.

29. Population Risk Difference Non-fatal Heart Attack Hypertension PRD = (RD) (Pex) = [(12/1342) - (125/118621)] x (1342/119,963) = 0.007888x 0.011187= .000088 or 8.8/100,000 Interpretation: Hypertension results in an excess incidence of 8.8/100,000 non-fatal heart attacks in the total study population. Or, if hypertension were eliminated, 8.8/100,000 cases of non-fatal heart attacks could be eliminated among the total study population.(Assumes that hypertension causes heart attacks.)

30. Calculating Measures of Comparison for Cigarette Smoking and Lung Cancer* Simple Rates • Death rate from lung cancer in smokers 0.96 / 1,000 / year • Death rate from lung cancer in non-smokers: 0.07 / 1,000 / year • Prevalence of smoking in population: 56% Compare Rates • Rate Ratio: 0.96/1,000/year / 0.07/1,000/year = 13.7 • Rate Difference: 0.96/1,000/year – 0.07/1,000/year = 0.89/1,000/year=89/100,000 person-year • Population Rate Difference: 0.89/1,000/year x 0.56 =0.50/1,000/year=50/100,000 person-year * Estimated data from Doll and Hill. Br J Med 1:1399-1410, 1964.

31. Exercise to Practice Measures of Comparison The 58th annual convention of the American Legion was held in Philadelphia from July 21 until July 24, 1976. People at the convention included American Legion delegates, their families, and other legionnaires who were not official delegates. Between July 20th and August 30th, some of those who were or had been present became ill with a type of pneumonia subsequently named Legionnaire's Disease. No one attending the convention developed the disease after august 30th. Following are the numbers of delegates and non-delegates who developed Legionnaire's Disease during the period July 20 to August 30 (41 day period).

32. Exercise to Practice Measures of Comparison Developed Legionnaire’s Disease Convention Status

33. Exercise to Practice Measures of Comparison 1. Compute the "risk" of Legionnaires' Disease among the delegates and non-delegates. What type of measure of disease frequency is this “risk"? 2. Calculate the "risk" ratio of Legionnaires' Disease among delegates compared to non-delegates. State in words the meaning of this “risk” ratio. 3. Calculate the "risk" difference of Legionnaires' Disease for delegates. State in words the meaning of this “risk” difference.

34. Exercise to Practice Measures of Comparison 1. Compute the "risk" of Legionnaires' Disease among the delegates and non-delegates. What type of measure of disease frequency is this "risk"? 2. Calculate the "risk" ratio of Legionnaires' Disease among delegates compared to non-delegates. State in words the meaning of this “risk” ratio. 3. Calculate the “risk" difference of Legionnaires' Disease for delegates. State in words the meaning of this “risk” difference. CI Del 125/1849 = .068 in 41 Days CI No 3/762 = .004 in 41 Days RR .068 / .004 = 17 AR = .068 - .004 = .064 or 64/1000

35. Further Analysis of Convention Delegates Developed Legionnaire’s Disease Hotel of Residence

36. Further Analysis of Convention Delegates Developed Legionnaire’s Disease Hotel of Residence Cumulative incidence among Hotel A residents: 62 / 690 = 9.0/100 or 9.0% Cumulative incidence among other hotel residents: 63 / 1,161 = 5.4/100 or 5.4% RR= 9.0 / 5.4 = 1.7 RD = 9.0 %- 5.4% = 3.4%

37. Epidemiology In the News

38. Epidemiology In the News • What is the exposure under study? • How is it defined? • What are the diseases under study? • Find the measures of disease frequency. • Find the measures of association. • What did the investigators do to ensure that the comparisons were “fair?”

39. Epidemiology In the News -- Alcohol • NEW YORK TIME (2018): How Much Alcohol Is Safe to Drink? None, Say These Researchers • A large study of drinking habits in 195 countries contradicts widely shared advice on healthy drinking. • Just one alcoholic drink a day slightly increases an individual’s risk for health problems, according to a large new study. • No level of alcohol consumption conferred any health benefits, the authors also concluded — a finding that runs contrary to much previous research and public health guidelines in many countries. • The analysis, involving 195 countries and territories from 1990 to 2016, relied on 694 sources of data and analyzed 592 studies to determine the health risks of alcohol use. While the study is among the largest of its kind, it was also observational, linking population-wide consumption to population-wide trends. • The methods left many experts unconvinced. • Online in Medium, David Spiegelhalter, a statistician at Cambridge University in England, wrote of the study’s conclusion: “Claiming there is no ‘safe’ level does not seem an argument for abstention. There is no safe level of driving, but governments do not recommend that people avoid driving.”

40. Epidemiology In the News -- Alcohol • The researchers relied on sales data and surveys to estimate the prevalence of drinking in each country and calculated alcohol consumption in standard drinks daily, defined as 10 grams, or about one-third of an ounce, of pure ethyl alcohol — the equivalent of 3.4 ounces of red wine at 13 percent alcohol, 12 ounces of beer at 3.5 percent alcohol or one ounce of 80-proof whiskey. • They also devised a method for distinguishing alcohol consumption among tourists from that of resident populations, and linked consumption data to 23 health outcomes, ranging from car accidents, suicides and tuberculosis, to liver cirrhosis, cardiovascular disease and cancers. • In 2016, 25 percent of women and 39 percent of men were currently drinkers — about 2.4 billion people worldwide. Women consumed an average of 0.73 drinks a day, while men had 1.7 drinks. • Rates of alcohol consumption vary widely by country, but in general the higher a country’s income level, the higher the prevalence of drinking. • The study, published in the Lancet, concluded that alcohol consumption is involved in 2.8 million deaths annually worldwide, making it the seventh leading risk factor for death and disability.

41. Epidemiology In the News – Asthma • Before and after thunderstorms, Cynthia Lucking often experiences asthma symptoms, which she used to think was related to heavy humidity and steam coming off of hot roads. • Lucking, a 55-year-old outside sales representative who lives in Mount Pleasant, has both asthma and seasonal allergies. • "This past week (after a string of storms earlier this summer), I found myself using my nebulizer, much more than usual. I can feel my lungs tighten up before and particularly after a storm." • Research in recent years is showing that in some people, allergy and asthma symptoms do get worse after storms. • Thunderstorms causing allergy and asthma symptoms do not follow conventional wisdom. After all, rain washes pollen away. • The culprits may be the electricity, updrafts and downdrafts generated by the storm.

42. Epidemiology In the News – Asthma • Cause unknown • In 2012, the British medical journal, QJM, published an overview showing that thunderstorms set off asthma attacks, causing a spike in emergency room visits and ambulance calls around the world. The phenomena has been followed primarily in Australia, the United Kingdom and Italy. • Four years earlier, the British Thoracic Society's "Thorax" published an analysis of 12 years of data by Atlanta researchers that found three percent more emergency room visits for asthma attacks in the 24 hours after thunderstorms compared to days without such storms. • The study's co-author Stephanie EbeltSarnat, an assistant professor of environmental health at Emory University, said the scope of thunderstorm asthma might well be broader because the emergency room figure doesn't include individuals who might have self-medicated or seen their personal physicians in the wake of a storm. • But the exact cause or mechanism is not yet known. • "The phenomenon exists ... it's not entirely predictable," says Elizabeth Matsui, an associate professor of pediatrics, epidemiology and environmental health sciences at the Johns Hopkins Children's Center in Baltimore, in an interview with The Washington Post earlier this summer. PARTICIPANTS: 121 young, active adults (mean age 26 years) with acute ACL injury to a previously uninjured knee. One patient was lost to five year follow-up. INTERVENTION: All patients received similar structured rehabilitation. In addition to rehabilitation, 62 patients were assigned to early ACL reconstruction and 59 were assigned to the option of having a delayed ACL reconstruction if needed. MAIN OUTCOME MEASURE: The main outcome was the change from baseline to five years in the mean value of four of the five subscales of the knee injury and osteoarthritis outcome score (KOOS(4)). Other outcomes included the absolute KOOS(4) score, all five KOOS subscale scores, SF-36, Tegner activity scale, meniscal surgery, and radiographic osteoarthritis at five years.

43. Attributable Risk

44. Among the Exposed • Attributable Risk, rate/risk difference (RD) • RD= Re – Ru • Excess risk of death associated with the exposure among those exposed • the number of cases that would be eliminated if the exposure was eliminated among those exposed • Public health impact of the exposure among exposed • Attributable Proportion among the exposed, Etiologic Fraction • APe=[(Re-Ru)/Re] X 100 • The proportion of disease among the exposed that would be eliminated if the exposure were eliminated

45. Among the Total Population • Population Attributable Risk (PAR) or Population rate/risk difference (PRD) • PRD= Rt – Ru=RD X Pe • Excess risk of death associated with the exposure in the total population • the number of cases that would be eliminated if the exposure was eliminated in the total population • Public health impact of the exposure in the total population • Attributable Proportion in the total population • APt=[(Rt-Ru)/Rt] X 100 • The proportion of disease in the total population that would be eliminated if the exposure were eliminated

46. Measures of Association Risk Ratio = [a/(a + b)] / [c/(c + d)] = [prevalence or incidence of outcome for the exposed group] divided by [prevalence or incidence of the outcome for the unexposed group] Risk Difference = [a/(a + b)] - [c/(c + d)] = [prevalence or incidence of outcome for the exposed group] minus [prevalence or incidence of the outcome for the unexposed group]

47. Measures of Association Odds Ratio = (a/c) / (b/d) = (exposed divided by unexposed for those with the outcome, or cases) divided by (exposed divided by unexposed for those without the outcome, or controls)

48. Measures of Association Attributable Proportion among Exposed=Etiologic Fraction = (Re-Ru)/Re= [a/(a + b)] - [c/(c + d)] / [a/(a + b)] = Proportion of the prevalence or incidence of the outcome for the exposed group that can be attributed to the exposure. Population Attributable Proportion = Rt-Ru/Rt=[(a + c) / (a + b + c + d)] - [c/(c + d)] / [(a + c) / (a + b + c + d)] = Proportion of the total prevalence or incidence that can be attributed to the exposure.