A Brief Introduction to Epidemiology - X (Epidemiologic Research Designs: Cohort Studies)

1. A Brief Introduction to Epidemiology - X(Epidemiologic Research Designs:Cohort Studies) Betty C. Jung, RN, MPH, CHES

2. Learning/Performance Objectives • To develop an understanding of: • What cohort studies are • The value of such studies • The basic methodology • Pros and Cons of such studies

3. Introduction Epidemiology studies the distribution of disease in a number of ways. The two major categories of epidemiological studies are: Observational and experimental studies. Most epidemiological studies are observational.

4. Epidemiological Study Designs • Observational Studies - examine associations between risk factors and outcomes (Analytical - determinants and risk of disease, and descriptive - patterns and frequency of disease) • Intervention Studies - explore the association between interventions and outcomes. (Experimental studies or clinical trials)

5. Research Designs in Analytic Epidemiology • Ecologic Designs Cross-Sectional Study • Case-Control Study • Cohort Study

6. Cohort Studies • “Motion Picture Studies” (Paffenbarger, 1988) • Forward looking. The most powerful of observational studies • Follow groups of individuals free from disease through a period of time • Quantified with relative risk/incidence rates/attributable risk

7. Examples of Cohort Studies • Framingham Heart Study • Body fat distribution and 5-year risk of death in older women (1993) - 15-unit increase in waist/hip circumference was associated with 60% greater relative risk of death. Waist/hip circumference ratio as a better marker than body mass index of risk of death in older women. • Vasectomy and Prostate Cancer - those who had a vasectomy and those who did not. Increase relative risk of those with vasectomy - increase risk of prostate cancer.

8. Historical Cohort Studies • Cohort formed in the past with period of follow-up ending also in the past • Used in occupational settings were population registers (payroll records) are available • Example: Atomic bomb blast survivors

9. Value • Gold standard for studying the association between a risk factor and outcome • Useful for studying incidence, risk factors, natural history or prognosis • Useful for studying multiple outcomes • Useful for looking at multiple exposures and their interactions

10. Cohort Study Design Time Direction of Inquiry Exposed Disease People Without Disease Population No Disease Not Exposed Disease No Disease

11. Cohort Study Design Concurrent 1995 Retrospect 1975 Define Population Non-randomizing 2005 1985 Exposed Non-Exposed 2015 1995 Disease Disease No Disease No Disease

12. Methodology • Start with persons having the presumed cause (antecedent or exposure). BUT free from the effect (disease), and then wait for them to develop the effect • Comparison group - also free from disease, but who, also DO NOT have the presumed cause

13. Methodology • Cohort - group or aggregate of persons who have presume antecedent characteristics in common and observe the development or non-development of a given health outcome • Compare to those free of the disease or health outcome under study. Issue being at risk of repeated episode (i.e., Stroke, antecedents may different between prestroke 1 and prestroke 2

14. Cohort Study Measures • Cumulative Incidence - # new cases/at risk population • Incidence Density - # new cases/at risk person-time • Measures of association • Relative Risk • Odds Ratio

15. Strength of Association Relative Risk;(Prevalence); Odds Ratio Strength of Association 0.83-1.00 1.0-1.2 None 0.67-0.83 1.2-1.5 Weak 0.33-0.67 1.5-3.0 Moderate 0.10-0.33 3.0-10.00 Strong <0.01 >10.0 Approaching Infinity Source: Handler,A, Rosenberg,D., Monahan, C., Kennelly, J. (1998) Analytic Methods in Maternal and Child Health. p. 69.

16. Pros • Can study situations where randomization is not possible • Time sequence strengthens the inference about cause (temporal relationship between exposure and outcome) • Only way to establish population-based incidence

17. Pros • Direct measure of incidence (risk) and prognosis (natural history) • Incidence rate is not influenced by the presence of the effect (outcome/disease) at the beginning of the study • Magnitude of a risk factor’s effect can be quantified • Can estimate the relative contribution of different (multiple) causes to the occurrence of the effect (disease or outcome)

18. Pros • Can count the number of prevalent cases, and new cases, as well as the number and proportion of cases that can be prevented • Information bias is decreased (i.e., selective recall/memory) • Can better measure the impact of confounding

19. Pros (Historical Cohort Studies) • Easier to create the cohort • Baseline measurements available • Follow-up has already occurred • Less costly and time consuming

20. Cons • Expensive • Not good for low-incidence (rare) diseases • Not good for chronic diseases with long latency • Time needed to conduct these studies • Unexpected changes to the environment can influence the association of disease and possible cause over time

21. Cons • Non-response/Migration bias – “loss to follow-up” • Selection bias – zero time not defined (lead-time bias) • Sampling bias • Ascertainment/Assessment bias of outcome (can be reduced by blinding/masking)

22. Cons • Information bias – data are different (i.e., different hospitals) – must to be comparable for exposed and unexposed • Confounding bias • Measurement bias - misclassification • Analytic/Observer bias – how data are analyzed and interpreted

23. Cons (Historical Cohort Studies) • Incomplete data sets • No control over the quality of the measurements that are available • Incomplete control of confounding

24. References • For Internet Resources on the topics covered in this lecture, check out my Web site: • http://www.bettycjung.net/