Intermediate methods in observational epidemiology 2008 Instructor: Moyses Szklo

1. Intermediate methods in observational epidemiology 2008Instructor: Moyses Szklo Study Designs in Observational Epidemiology

2. Epidemiologic reasoning • To determine whether a statistical association exists between a presumed risk factor and disease • To derive inferences regarding a possible causal relationship from the patterns of the statistical associations

3. To determine whether a statistical association exists between a presumed risk factor and a disease • Studies using populations or groups of individuals as units of observation • Descriptive studies (prevalence, incidence, trends) • Analysis of birth cohorts (cohort, age, period effects) • Ecological studies • Studies using individuals as units of observation • Randomized clinical trials • Cohort studies • Case-control studies • Cross-sectional studies • Other (nested case-control, case-crossover study)

4. Studies using groups as units of observation • ECOLOGIC STUDIES • To assess the correlation between a presumed risk factor and an outcome, mean values of the outcome (e.g., rate, mean) are plotted against mean values of the factor (e.g., average per capita fat intake), using groups as units of observation • Groups could be defined by place (geographical comparisons) or time (temporal trends).

5. A plot of the population of Oldenburg at the end of each year against the number of storks observed in that year, 1930-1936. Ornitholigische Monatsberichte 1936;44(2)

7. Relation between Anopheles inoculation and incidence of Plasmodium Falciparum parasitemia in cohorts of children in Western Kenya McElroy et al: Am J Epidemiol 1997;145:945-56.

8. Ecological fallacy “The bias that may occur because an association observed between variables on aggregate levels does no necessarily represent the association that exists at the individual level.” Last: Dictionary of Epidemiology, 1995

9. Population A Population C Population B $10.5K $28.7K $12.5K $32.5K $30.2K $34.5K $13.5K $24.3K $28.5K $23.5K $10.0K $12.2K $14.3K $45.6K $10.8K $17.5K $22.7K $38.0K $20.5K $19.8K $26.4K Mean income: $21,410 Mean income: $23,940 Mean income: $22,430 Traffic injuries: 3/7=43% Traffic injuries: 2/7=29% Example of ecological bias* Traffic injuries: 4/7=47% *Based on: Diez-Roux, Am J Public Health 1998;88:216.

10. Ecologic analysis Higher income is associated with higher injury rate

11. Population A Population C Population B $10.5K $28.7K $12.5K $32.5K $30.2K $34.5K $13.5K $24.3K $28.5K $23.5K $10.0K $12.2K $14.3K $45.6K $10.8K $17.5K $22.7K $38.0K $20.5K $19.8K $26.4K Mean income: $21,410 Mean income: $23,940 Mean income: $22,430 Traffic injuries: 3/7=43% Traffic injuries: 2/7=29% Example of ecological bias* Traffic injuries: 4/7=47% *Based on: Diez-Roux, Am J Public Health 1998;88:216.

12. Ecologic analysis Individual-based analysis Higher income is associated with higher injury rate Injury cases have lower mean income than non cases

13. Which of the two levels of inference is wrong? • Concluding that high income is a risk factor for injuries (based on the ecologic data) is subject to ecologic fallacy. • BUT … concluding that, because injury cases tend to have lower income, communities with higher average income should have lower injury rates is also wrong! • The real problem is cross-level reference* • Using ecologic data to make inference at the individual level (ecologic fallacy). • Or using the individual data to make inferences at the group (population level). • When used to make inferences at the proper level, both approaches might be right: • Individuals with a lower income are more likely to be injured. • In communities with higher average incomes, there is a greater number of cars, thus exposing lower income individuals to injuries. *Morgenstern: Ann Rev Public Health 1995;16:61-81.

14. Types of ecologic variables • Analogs of individual-level characteristics • Aggregate measures (proportion, mean) • Prevalence of disease • Mean saturated fat intake • Percentage with less than high school education • Environmental measures • Air pollution • Global measures • Health care system • Gun control law • Herd immunity

15. Ecologic studies are the design of choice in certain situations: • When the level of inference of interest is at the population level • Food availability (e.g., Goldberger et al: Public Health Rep 1916;35:2673-714). • Effects of tax hikes in cigarette sales • When the variability of exposure within the population is limited • Salt intake and hypertension (Elliot, 1992) • Fat intake and breast cancer (Wynder et al, 1997)

16. Strong positive (linear) association Hypothetical data on individuals from a World-wide population Systolic blood pressure (mm Hg) Usual daily salt intake

17. Individuals from country A SBP (mm Hg) Usual daily salt intake No association Hypothetical data on individuals from a World-wide population Systolic blood pressure (mm Hg) Usual daily salt intake

18. Country A Country B Country C Country D Country E Country F Country G Hypothetical data on individuals from a World-wide population Systolic blood pressure (mm Hg) Usual daily salt intake

19. Country A Country B Country C Country D Country E Country F Strong positive (linear) association Country G Hypothetical ecologic data from 7 countries Mean systolic blood pressure (mm Hg) Mean usual daily salt intake

20. Relation between sodium (Na) excretion and age increase in systolic blood pressure (SBP) in centers in the INTERSALT cohort* *Elliot, in Marmot and Elliot (eds.): Coronary Heart Disease Epidemiology, Oxford, 1992, pp.166-78.

21. Experimental (Randomized clinical trial) Study Population Random allocation Intervention Control Follow-up Outcome Outcome Studies based on individuals: Prospective Studies

22. Non-experimental (observational*) Study Population Non-random allocation Intervention Control Outcome Outcome Studies based on individuals: Prospective Studies Experimental (Randomized clinical trial) Study Population Random allocation Intervention Control Follow-up Follow-up Outcome Outcome *Cohort Study

23. Non-experimental (observational*) Study Population Non-random allocation Intervention Control Outcome Outcome Studies based on individuals: Prospective Studies Experimental (Randomized clinical trial) Study Population Random allocation Intervention Control Follow-up Follow-up Outcome Outcome *Cohort Study

24. Cohort Outcome Death Disease Recurrence Recovery Suspected Exposure Time Studies based on individuals1.- Cohort studies

25. Diseased Non diseased Exposed Ince RR Non Exposed Incē Time Studies based on individuals1.- Cohort studies

26. Cohort study time Initial pop Losses to follow-up Events Final pop

27. Losses to follow-up Events EXPOSED INCIDENCEEXP Final pop = RR Losses to follow-up Events time time UNEXPOSED INCIDENCEUNEXP Initial pop Initial pop Final pop Cohort study

28. Cohort StudiesStrengths • Allows calculation of incidence • Time sequence is clear (exposure →outcome) Reduces potential for bias • Allows calculation of all measures of association • Multiple outcomes can be assessed • Multiple exposures can be assessed • New hypothesis can be tested as time goes by • Efficient ways to evaluate associations Stored specimens can be analyzed later for new analytes / risk factors

29. Cohort StudiesAdditional Advantages • Can incorporate changes in exposures and confounders over time: As participants age As exposure accumulates • Exposures and outcomes do not (necessarily) have to be identified a priori • New endpoints can be assessed – e.g., cancer • Examination of baseline associations Cross-sectional bias less likely with subclinical outcomes • The cohort as an epidemiologic laboratory Ancillary studies can be done

30. Cohort StudiesAdditional Advantages • Can incorporate changes in exposures and confounders over time: As participants age As exposure accumulates • Exposures and outcomes do not (necessarily) have to be identified a priori • New endpoints can be assessed – e.g., cancer • Examination of baseline associations Cross-sectional bias less likely with subclinical outcomes • The cohort as an epidemiologic laboratory Ancillary studies can be done

31. Cohort StudiesAdditional Advantages • Can incorporate changes in exposures and confounders over time: As participants age As exposure accumulates • Exposures and outcomes do not (necessarily) have to be identified a priori • New endpoints can be assessed – e.g., cancer • Examination of baseline associations Cross-sectional bias less likely with subclinical outcomes • The cohort as an epidemiologic laboratory Ancillary studies can be done

32. Cohort StudiesAdditional Advantages • Can incorporate changes in exposures and confounders over time: As participants age As exposure accumulates • Exposures and outcomes do not (necessarily) have to be identified a priori • New endpoints can be assessed – e.g., cancer • Examination of baseline associations Cross-sectional bias less likely with subclinical outcomes • The cohort as an epidemiologic laboratory Ancillary studies can be done

33. Cohort StudiesAdditional Advantages • Can incorporate changes in exposures and confounders over time: As participants age As exposure accumulates • Exposures and outcomes do not (necessarily) have to be identified a priori • New endpoints can be assessed – e.g., cancer • Examination of baseline associations Cross-sectional bias less likely with subclinical outcomes • The cohort as an epidemiologic laboratory Ancillary studies can be done Rich database for analyses

34. Non diseased Diseased Exposed Non Exposed - Odds expD Odds expD OR Studies based on individuals2.- Case-control studies

35. Case-control study Cases Controls time Losses Hypothetical pop

36. Case-control study Cases Controls Hypothetical pop time Losses Recruiting only cases with longest survival (Prevalent cases) Risk of duration (incidence-prevalence) bias

37. Incidence-Prevalence Bias or Duration bias or Survival bias or Selection bias Relative Risk INCIDENCE-PREVALENCE BIAS

38. CASE-CONTROL STUDY INCLUDING ALL INCIDENT CASES AND NON-CASES* Losses Cases EXPOSED (n= 100) Controls = ´ ¸ ´ = OR ( 4 96 ) ( 96 4 ) 1 . 0 Hypothetical pop time Losses Cases UNEXPOSED (n= 100) Controls Hypothetical pop time Assumption: All non-cases survive through the end of the follow-up

39. CASE-CONTROL STUDY INCLUDING ALL INCIDENT CASES AND NON-CASES* Losses Cases EXPOSED (n= 100) Controls = ´ ¸ ´ = OR ( 4 96 ) ( 96 4 ) 1 . 0 Hypothetical pop time Losses Cases UNEXPOSED (n= 100) Controls Hypothetical pop = ´ ¸ ´ = OR ( 1 96 ) ( 96 4 ) 0 . 25 time CASE-CONTROL STUDY INCLUDING ONLY POINT PREVALENT CASES, BUT ALL NON-CASES SELECTION/SURVIVAL BIAS (ALSO KNOWN AS PREVALENCE-INCIDENCE BIAS) Assumption: All non-cases survive through the end of the follow-up

40. Results from cross-sectional surveys can be analyzed in a prospective or case-control mode

41. CASE-CONTROL STUDY INCLUDING ALL INCIDENT CASES AND NON-CASES* Losses Cases EXPOSED (n= 100) Controls = ´ ¸ ´ = OR ( 4 96 ) ( 96 4 ) 1 . 0 Hypothetical pop time Losses Cases UNEXPOSED (n= 100) Controls Hypothetical pop time PREVALENCE OF DISEASE BY EXPOSURE PR= 1/97 ÷ 4/100= 0.26 SELECTION/SURVIVAL BIAS (ALSO KNOWN AS PREVALENCE-INCIDENCE BIAS) Assumption: All non-cases survive through the end of the follow-up

42. Cross-Sectional Vs. “Retrospective” Case-Control Studies Key concept: How “caseness” and exposure are ascertained ANOTHER TYPE OF CROSS-SECTIONAL BIAS: REVERSE CAUSALITY Cross-Sectional Exposure Assessment: Association of Low Serum Carotenoids with Age-Related Macular Degeneration Odds Ratio= 1.5 IT IS NOT POSSIBLE TO DETERMINE WHAT CAME FIRST (EXPOSURE OR OUTCOME). THUS, INDIVIDUALS WITH AGE-RELATED MACULAR DEGENERATION MAY CHANGE THEIR DIETS, WHICH IN TURN MAY RESULT IN LOW CONCENTRATIONS OF TOTAL CAROTENOIDS  ‘REVERSE CAUSALITY’

43. Cross-sectional StudiesNational Center for Health Statistics (NCHS) • National Health and Nutrition Examination Survey (NHANES) • 20,000+ individuals • Oversampled children, age>65, minorities • Questionnaires, physical exam, laboratory data • National Health Interview Survey (NHIS) • National Immunization Survey (NIS) • National Survey of Family Growth (NSFG) www.cdc.gov/nchs

44. Cross-sectional survey Point prevalence= Snapshot of prevalence at time of a cross-sectional survey

45. Cross-sectional StudiesWhat can we learn? • Descriptions / Distributions: • Standardized centile curves of body mass index for Japanese children and adolescents based on the 1978-1981 national survey data. • Ann Hum Biol. 2006 Jul-Aug;33(4):444-53. • Prevalence: • The prevalence of oral mucosal lesions in U.S. adults: data from the Third National Health and Nutrition Examination Survey, 1988-1994. • J Am Dent Assoc. 2004 Sep;135(9):1279-86 Trends in prevalence: Thirty-year trends in cardiovascular risk factor levels among US adults with diabetes: National Health and Nutrition Examination Surveys, 1971- 2000 Am J Epidemiol. 2004 Sep 15;160(6):531-9 • Association of exposure with prevalence of disease: • Prevalence of urinary schistosomiasis and HIV in females living in a rural community of Zimbabwe: does age matter? • Trans R Soc Trop Med Hyg. 2006 Oct 23

46. Cross-sectional Studies • Baseline examination of randomized trials • Cross-sectional study of health-related quality of life in African Americans with chronic renal insufficiency: the African American Study of Kidney Disease and Hypertension Trial. • Am J Kidney Dis. 2002 Mar;39(3):513-24. • Baseline examination of cohort studies • Association of kidney function and hemoglobin with left ventricular morphology among African Americans: the Atherosclerosis Risk in Communities (ARIC) study. • Am J Kidney Dis. 2004 May;43(5):836-45.

47. Cross-sectional StudiesStrengths and Limitations • Strengths • Primary method of estimating prevalence • Logistically efficient Relatively fast (no follow-up required) Can enroll large numbers of participants • Large surveys can be used for many exposures and diseases • Often generalizable – can oversample smaller subpopulations • Limitations • Large numbers needed for rare exposures / outcomes • No information on timing of outcome relative to exposure (temporality) • Includes only those individuals alive at the time of the study Prevalence-incidence bias

48. Case-control studies within a defined cohort • Case-Cohort Studies • Nested Case-Control Studies

49. Example of case-cohort study Association between CMV antibodies and incident coronary heart disease (CHD) in the Atherosclerosis Risk in Communities (ARIC) Study (Sorlie et al: Arch Intern Med 2000;160:2027-32) Cohort: 14,170 adult individuals (45-64 yrs at baseline) from 4 US communities (Jackson, Miss; Minneapolis, MN, Forsyth Co NC; Washington Co, MD), free of CHD at baseline. Followed-up for up to 5 years. • Cases: 221 incident CHD cases • Controls: Random sample from baseline cohort, n=515 (included 10 subsequent cases). “The population with the highest antibody levels of CMV (approximately the upper 20%) showed an increased relative risk (RR) of CHD of 1.76 (95% confidence interval, 1.00-3.11), adjusting for age, sex, and race.”

50. Case-cohort study Random sample of 515 cohort subjects 221 cases Final pop Time (5 years) N~14,000 Option 1= thaw serum samples of 14,000 persons, classify by CMV titer (+) or (-), and follow- up to calculate incidence in each group (exposed vs. unexposed) Option 2: Case-cohort study Initial pop