A short introduction to epidemiology Chapter 4: More complex study designs

1. A short introduction to epidemiologyChapter 4: More complex study designs Neil Pearce Centre for Public Health Research Massey University Wellington, New Zealand

2. Birth End of Follow up Death other death lost to follow up “non-diseased” symptoms severe disease

3. Study Design Options • All epidemiological studies are (or should be) based on a particular population (the source population) followed over a particular period of time (the risk period) • The different study design options differ only in how the source population is defined and how information is drawn from this population and time period

4. Incidence and Prevalence • Incidence is the number of new cases of the condition over a specified period of time • Prevalence is the number of cases of the condition at a particular point in time

5. Study Design Options

6. Chapter 4More complex study designs • Other axes of classification • Continuous outcome measures • Ecologic and multi-level studies

7. Other axes of classification • Continuous exposure data • Timing of collection of exposure data (retrospective, prospective) • Sources of exposure information (interviews, routine records, biomarkers) • Level of measurement of exposure (individual, population)

8. Chapter 4More complex study designs • Other axes of classification • Continuous outcome measures • Ecologic and multi-level studies

9. Continuous Outcome Measures • Lung function in a cross-sectional study (a prevalence study is a cross-sectional study with a dichotomous outcome measure) • Changes in lung function over time in a longitudinal study (an incidence study is a longitudinal study with a dichotomous outcome measure)

10. Continuous Outcome Measures • Tager et al (1983), longitudinal study of pulmonary function in children aged 5-9 years, followed for 7 years • Exposures: maternal smoking • Outcomes: annual increase in FEV1 (this was 28mL lower in children exposed to maternal smoking)

11. Continuous Outcome Measures • Roemer et al (1993), time series study of winter air pollution and respiratory health of children aged 6-12 years • Exposures: daily air pollution measures • Outcomes: asthma symptoms, medication use (e.g. wheeze was more common on days when particulate concentrations were high

12. Cross-Sectional Studies Particularly valuable for: • Non-fatal diseases • Degenerative diseases with no clear point of onset (e.G. Chronic bronchitis) • Examining effects on physiologic variables (e.G. Liver enzyme levels, blood pressure, lung function)

13. Cross-Sectional Studies: Examples • General household surveys (e.g. England and Wales, Spain, New Zealand) • National Health and Nutrition Examination Survey (USA) • International surveys (e.g. European Community Respiratory Health Survey (ECRHS), International Study of Asthma and Allergies in Childhood (ISAAC) • Pre-employment surveys • Studies in specific populations (e.g. occupational health research)

14. Cross-Sectional studies • Disease is measured at one point in time • Exposure may be measured at the same time and/or historical exposure information may be available • May be difficult to know the temporal relationship between exposure and disease • This problem is avoided in repeated cross-sectional studies

15. Study Design Options • Case series • Incidence studies • Incidence case-control studies • Prevalence studies • Prevalence case-control studies • Cross-sectional studies (with continuous outcome measure) • Longitudinal studies (with continuous outcome measure)

16. Chapter 4More complex study designs • Other axes of classification • Continuous outcome measures • Ecologic and multi-level studies

17. Ecologic Studies An ecologic study is a study in which one or more exposures (or confounders) is measured at the population level rather than the individual level

18. Reasons for Ecologic Studies • Low cost and convenience • Measurement limitations of individual-level studies (e.g. diet, air pollution) • Design limitations of individual-level studies (e.g. lack of exposure variation) • Interest in ecologic effects • Simplicity of analysis and presentation

19. Levels of Measurement • Individual measures, e.g. smoking status, income • Aggregate measures, e.g. % smokers, median family income • Environmental measures, e.g. air pollution levels • Global measures, e.g. smoking legislation, income inequality, GNP, type of health care system, population density

20. Levels of Analysis • Individual level, e.g. average level of air pollution is “assigned” to each individual, and individual age, gender, ethnicity, smoking status are known • Partially ecologic analysis: e.g. some variables known for individuals (age, gender, air pollution) but others for the population (%smokers) • Fully ecologic analysis: all information on exposure and disease only known for the population

21. Levels of Analysis Multilevel analysis • First level: individual level analysis within each group (population) • Second level: regression parameters from first level are modelled as a function of ecologic variables • e.g. Humphreys and Carr-Hill (1991) used multilevel modeling to estimate the contextual effect of living in a poor area, controlling for individual income and other risk factors

22. Levels of Inference • Individual (e.g. fat intake and breast cancer) • Contextual (e.g. living in a poor neighbourhood) • Ecologic (e.g. GNP, income inequality) • The major problem is with cross-level inferences, e.g. using ecologic data to estimate the individual risk from fat intake

23. 12 Month Period Prevalence of Asthma Symptoms in 13-14 Yr Old Children

24. The Ecologic Fallacy in ISAAC:Indoor Humidity and Asthma

25. The Ecologic Fallacy in ISAAC:Indoor Humidity and Asthma

26. Example of ecologic data

27. Example of ecologic data

28. Example With No Confounding by Group

29. Example With Confounding by Group

30. Example With Effect Modification by Group

31. Problems of Ecologic Studies Ecologic bias, in estimating effects at the individual level may result from: • Within group bias: if there is confounding, selection bias or misclassification within each group then the ecologic estimate may also be biased • Confounding by group: the “background” disease rate varies across groups • Effect modification by group: the “excess rate” due to exposure varies across groups

32. Problems of Ecologic Studies • The major problems of “ecologic bias” arise from attempts at cross-level inference, e.g. in studies where the intention is to make inferences at the individual level • Nevertheless, ecologic studies have played a major role in the development, and to some extent in the testing, of epidemiological hypotheses • Furthermore, some important risk factors can only be studied at the population level.

33. A short introduction to epidemiologyChapter 4: More complex study designs Neil Pearce Centre for Public Health Research Massey University Wellington, New Zealand