Applied Epidemiologic Analysis

1. Applied Epidemiologic Analysis Patricia Cohen, Ph.D. Henian Chen, M.D., Ph. D. Teaching Assistants Julie Kranick Sylvia Taylor Chelsea Morroni Judith Weissman

2. Lecture 4 Examining the process of data analysis for epidemiological studies using multiple regression analysis as a fundamental tool Goal: To review how recently published analyses were used to show relationship between an exposure or other predictor of a disease indicator and to take potential confounders or other covariates into account.

3. Objectives • To see how current epidemiological studies have used multiple regression when disease measures are scales. • To understand how stratifying variables and potential confounders are selected and used in these studies.

4. First Study : Longitudinal trends in the severity of acute myocardial infarction Reference:Hellermann JP, Reeder GS, Jacobsen SJ, Weston SA, Killian JM, Roger VL. American Journal of Epidemiology, 156, No 3., p 246-253, 2002.

5. The problem: Myocardial infarction (MI) study There has been a decline in age-adjusted mortality due to coronary heart disease not fully accounted for by a decline in the incidence of hospitalized myocardial infarction cases. Is it due to more effective hospital care or to a decline in severity of cases?

6. Population studied, study design, and sample size : MI study • Olmstead Minnesota county is served by the Mayo Clinic Hospitals and one other hospital. Detailed data on all admissions is kept in accordance with protocols maintained by an epidemiology program for that site. • A cohort of 1300 cases presenting at one of the two hospitals for incident myocardial infarction are studied for changes in indicators of severity over a 12 year period. Cases were sampled differentially on ICD codes and weighted to reflect these sampling fractions.

7. Measurement issues: MI study Created values of tests taken on admission: 1) For average of 2.6 EKGs: Segment elevation and Q wave pattern 2) For average of 4.2 enzyme draws Peak values of indicators of problem 3) Status indicator on admission 4 ordinal levels (highest, in cardiac shock) • No variables had more than 3% missing data except 1: does not indicate how they managed missing data.

8. Measurement issues: MI study Indicators of risk: • Prior congestive heart failure • Cardiovascular risks (hypertension, diabetes, smoking, hyperlipidemia) • Comorbidity index (count of risks) • Indicators of treatment: • Time to first EKG • Use of repurfusion therapy (thrombolytic therapy or acute coronary angioplasty within 24 hours)

9. The effect being estimated: Trend over time in indicators of MI severity Basic analysis to answer study questions: Multiple linear and multiple logistic regression analyses: • Dependent variables: 4 cardiac status indicators: Killip class on admission, • Hypothesized Predictor: Year of intake (question: why not use exact date?) • Covariates: Time to first EKG (step 2); step 3 added reperfusion therapy for Q wave and peak creatine kinase indicators of severity. • Note: although test of interactions with age and sex were carried out they are not explicitly reported: findings for two indicators are reported separately by sex.

10. Hellerman, et al

11. Selection and inclusion of confounders in the analysis • Note that Table 1 shows a significant increase in risk/comorbidity (except smoking) and a nearly significant (p = .081 for trend across 4 year periods) but text says AOver time, no change in the age distribution and degree of comorbidity was observed.@ • What is the role of risk factors as potential confounders? Of treatment?

12. TABLE 2. Distributions of ST-segment elevation and severity indicators, Olmsted County, Minnesota, 1983–1994 Hellerman, et al

13. TABLE 3. Change in severity indicators among 1,295 hospitalized myocardial infarction patients, Olmsted County, Minnesota, 1983–1994 Hellerman, et al

14. Conclusion: MI study • Severity of cases of incident MI to these hospitals declined over this 12 year period in spite of greater survival prior to arrival. • Question: why did they use only incident MI?

15. Second Study : Physical inactivity is associated with lower forced expiratory volume Reference:Jakes RW, Day NE, Patel B, Khaw K-T, Oakes S, Luben R, Welch A, Bingham S, Wareham NJ. American Journal of Epidemiology, 156, No 2, p 1389-147, 2002

16. The problem: Forced expiratory volume (FEV) • Although low forced expiratory volume has been shown to be a risk for cardiovascular disease, stroke, lung cancer, and all-cause mortality, its relationship to physical activity has not been extensively studied. • Is there a relationship that is independent of other correlates such as weight?

17. Population studied, study design, and sample size : FEV study • UK general practice prospective cohort of 25,000 persons ages 45-74 who had an initial health assessment and a second assessment 3.7 years later on average. • Exclusions: history or current respiratory disease

18. Measurement issues: FEV study Created values of tests taken on admission: • Complex self-report of activity levels at home, work, and recreationally • Standard FEV per 1 second test • Height, weight, plasma vitamin C, smoking • Stratifier: By sex in initial analyses, sex included as a covariate in final analyses

19. The effect being estimated: Independent association between FEV and activity level Basic analysis to answer study questions: Multiple linear regression analyses: Dependent variables: • FEV, average FEV, and % change in FEV Basic independent variables (Aexposure@): • Level of activity or average level of activity, • Daily hours of TV viewing

20. Selection and inclusion of confounders in the analysis • Height , Vitamin C, smoking status • Sex, age (also examined obesity)

21. Jakes, et al. TABLE 1. Baseline characteristics 5,467 men and 6,816women

22. Jakes, et al. Television viewing in hours per day (proportion) FIGURE 1. Adjusted mean forced expiratory volume in 1 second (FEV1) (liters) by categories of television viewing (hours per day) Adjusted for age (continuous), height (continuous), plasma vitamin C (continuous), sex, and smoking status (never, former, and current).

23. Jakes, et al. Stair climbing in flights per day (proportion) FIGURE 2. Adjusted mean forced expiratory volume in 1 second (FEV1) (liters) by categories of stair climbing (flights per day) Adjusted for age (continuous), height (continuous), plasma vitamin C (continuous), sex, and smoking status (never, former, and current).

24. Conclusions: FEV study Higher FEV is related to level of physical activity and low duration of TV watching independently of other predictors and thus potentially causally.

25. Third Study : Dietary soy isoflavones and bone mineral densityResults from the Study of Women=s Health Across the Nation Reference:Greendale GA, FitzGerald G, Huang M-H, Sternfeld B, Gold E, Seeman T, Sherman S, Sowers MF. American Journal of Epidemiology, 155, No. 8, p746-754. 2002

26. The problem: Bone density study • Japanese and Chinese women are at about 2 the risk of hip fracture as Caucasian women. • May this be due to a high-soy diet?

27. Population studied, study design, and sample size : Bone density study • Multi-site longitudinal study of Chinese-American and Japanese-American women ages 42-52 recruited from clinical sites, pre-menopausal without a history of uterus or ovary removal or hormone therapy. N = 200 and 227 respectively. Caucasian and African American women had too low consumption of relevant nutrients to be included in analysis. • Excluded: eating disorder, hypercalcemia, and those who took certain relevant medications.

28. Measurement issues: Bone density study • Estimation of isoflavone phytoestrogen from food frequency questionnaires. Quality control exclusions were identified (basically too much or too little intake) • Note that data were missing from some sites on certain bone density measures: Measurement of bone density subjected to standard data quality criteria and reviewed for specific flagging criteria. • Missing data are noted for entire sample but not separated for the sample actually comprising the major analysis. • Because consumption of most nutrients is positively related to total energy intake, it is necessary to adjust for total energy. Therefore, our relational analyses used energy-adjusted genistein as the primary exposure variable.@ (p.748) • Stratifiers: Ethnicity, menstrual status

29. Selection and inclusion of confounders in the analysis No clear justification: selected on basis of statistical significance

30. The effect being estimated: The relationship between isoflavone phytoestrogen consumption and indicators of bone density Basic analysis to answer study questions: A. Created separate residuals from the prediction of genistein as indicated by codes of food frequency reports from estimated total energy intake for Japanese- and Chinese-American women to predict indicators of BMD. B. Analysis including age, smoking, activity level, dietary calcium, alcohol use, protein, height, weight, menopause status, duration in the US. Interaction of genistein with menopause status was tested in each model. Menopausal strata were pre-menopausal or early peri-menopausal.

31. TABLE 2. Means and frequencies of selected characteristics of Japanese and Chinese women study participants,* Study of Women’s Health Across the Nation, United States, 1996–1997 Greendale, et al.

32. TABLE 3. Tertile distributions of dietary genistein intake among Japanese and Chinese women,* Study of Women’s Health Across the Nation, United States, 1996–1997 Greendale, et al.

33. TABLE 4. Adjusted* mean values of spine and femoral neck bone mineral densities, by tertile of genistein intake, among Japanese premenopausal, Japanese perimenopausal, and Chinese women, Study of Women’s Health Across the Nation, United States, 1996–1997 Greendale, et al.

34. Conclusions: Bone density study ABecause the estimated genistein consumption by Japanese women was greater than that of Chinese women, it was important to discern whether the apparent ethnic difference in the effect of genistein on BMD was due to variation in the dose.. or to an ethnic difference in physiologic response. Results of the combined model including both Chinese and Japanese women supported the latter conclusion.@ Possible mechanisms discussed, including both biological differences and differences in the form in which isoflavones appear in the culturally different foods due to more fermented forms of soy in the Japanese diet.

35. Fourth Study : Associations of blood lead, dimercaptosuccinic acid-chelatable lead, and tibia lead with neurobehavioral test scores in South Korean lead workers Reference:Schwartz BS, Lee B-K, Lee G-S, Stewart WF, Lee S-S, Hwang K-Y, Ahn K-D, Kim Y-B, Bolla KI, Simon D, Parsons PJ, Todd AC. American Journal of Epidemiology 153, No 5, 453-464, 2001.

36. The problem: Lead study What are the associations between alternative measures of lead exposure and neurobehavioral and peripheral nervous system function?

37. Population studied, study design, and sample size : Lead study 803 South Korean lead-exposed and 135 control workers studied cross-sectionally.

38. Measurement issues: Lead study • Standardized all neurobehavioral measures such that a higher test score always indicated better performance. • Stratifiers: (none used as such)

39. The effect being estimated: The associations of lead exposure measures with neurobehavioral test scores Basic analysis to answer study questions: Multiple linear regression analyses: • Dependent variables: 19 measures of neurobehavioral and peripheral nervous system function. • Basic IV (Aexposure@): • blood lead level • tibia lead level • job duration

40. Selection and inclusion of confounders in the analysis Age, gender, and education

41. TABLE 5. Linear regression modeling† of relations of neurobehavioral and peripheral nervous system measures with blood lead levels, Republic of Korea, 1997–1999 Schwartz, et al.

42. Footnotes to previous table * p < 0.10; ** p < 0.05; *** p < 0.01. † All models controlled for age, gender, and education. Models of the peripheral nervous system sensory measures also included height; models of the peripheral nervous system strength measures also included body mass index. All outcomes have been standardized, so a negative ?coefficient indicates that performance is worse with increasing tibia lead. The tabulated ?coefficients are for the tibia lead term and are expressed in units of test score per g Pb/g bone mineral. ‡ Included tibia lead alone. § Included tibia lead and blood lead. ¶ Included tibia lead and job duration. # Included tibia lead, blood lead, and job duration. †† SE, standard error; SD, standard deviation; MSD, mean square deviation; CES-D, Center for Epidemiologic Studies Depression Scale. ‡‡ Vibration unit = 1/2 (amplitude ())2.

43. FIGURE 2. Associations of blood lead, tibia lead, job duration, and age with scores (number of correct responses) on the Pursuit Aiming Test (correct responses) (PATR) among 803 lead-exposed workers in South Korea

44. Conclusions: Bone density study • Blood levels of lead are much better indicators than tibia lead. • Test scores tended to improve with increasing job durations, suggesting a Asurvivor bias.@