1. GerstmanEcological and Cross-Sectional1 Epidemiology Kept Simple Sections 11.1–11.3 Ecological & Cross-Sectional Studies

2. GerstmanEcological and Cross-Sectional2 Basic Design Ecological and cross-sectional studies involve no follow- up of individuals, so are often grouped together In addition, these studies depend on a full accounting or random cross-section of the population This design is capable of measuring prevalences and open population incidence rates: Prevalence or rate, group k Compare prevalence or rates Random sample of population divided into exposure groups Prevalence or rate, group 1 Prevalence or rate, group 2 ::::

3. GerstmanEcological and Cross-Sectional3 Illustrative Example #1 Regional Cigarette Consumption & Lung Cancer Each line of data represents a geographic aggregate → this is an ecological design The variables name cig1930 refers to “cigarette consumption per capital in 1930.” The variable mortalit represents “lung cancer mortality per 100,000 person- years in 1950”

4. GerstmanEcological and Cross-Sectional4 Illustrative Example #1 (cont.) Regional Cigarette Consumption & Lung Cancer Per capita cigarette consumption and lung cancer mortality are highly correlated, r = 0.74

5. GerstmanEcological and Cross-Sectional5 Illustrative Example #2 % calories from fat & heart disease Studies in the 1950s showed an ecological correlation between high fat diet and cardiovascular disease mortality (see pp. 194–5)

6. GerstmanEcological and Cross-Sectional6 Illustrative Example #3 Demonstration of Confounding Confounding = bias due to an extraneous variable This historical study by Farr (1852) reveals how ecological studies are susceptible to confounding. Explanatory variable = elevation above sea level by neighborhood Outcome variable = cholera mortality This strong correlation was used to support the erroneous miasma theory (see Chapter 1!) In fact, elevation plays no part in cholera transmission Confounding variable = proximity to Thames River.

7. GerstmanEcological and Cross-Sectional7 Illustrative Example #4 Psychosis, Neurosis, & Social Class Prevalence of psychosis and neurosis by social class, per 100,000 (Hollingshead & Redlich, 1964 ) Social classPsychosisNeurosis High188349 Moderate291250 Low518114 Very low150597  Here are data from a 1964 field study of mental disorders  Note the negative correlation between high SES and psychosis  Note the positive correlation between high SES and neurosis  Can you predict biases in this study? (see next slide)

8. GerstmanEcological and Cross-Sectional8 Illustrative example #4 (cont.) Psychosis, Neurosis, & Social Class Detection bias: Different diagnostic practices create artificial differences in incidence or prevalence –e.g., Poor people labeled psychotic; rich people labeled neurotic Reverse-causality bias: “Disease” causes the “exposure” –e.g., Psychosis causes low SES Prevalence-incidence bias: Difference in prevalence but not incidence –wealthy people no more likely to be diagnosed with neurosis but more persistent diagnoses (due to different type of health care) During later half of 20 th century, epidemiologists became increasingly aware of the limitations of cross-sectional surveys, prompting development of cohort and case- control methods (see next set of slides…)

9. GerstmanEcological and Cross-Sectional9 The remaining slides in this presentation are optional

10. GerstmanEcological and Cross-Sectional10 The Ecological Fallacy (aggregation bias) The ecological fallacy occurs when an association seen in aggregate does not hold for individuals Illustrative example: There is a negative ecological association between high foreign birth and illiteracy rate (r = −0.62) –When data are disaggregated, there is a positive association high foreign birth and literacy (as one would expect) –Reason: high immigration states had better public education

11. GerstmanEcological and Cross-Sectional11 “Logic of the Ecological” Renewed interest in ecological measures Studies that mix aggregate observations and individual-level observations are called multi- level designs Multi-level analysis useful in elucidating : –causal webs –interdependence between upstream factors and downstream factors

12. GerstmanEcological and Cross-Sectional12 Types of aggregate-level risk factors (Susser, 1994) Integral variables – factors that effect all community members (e.g., the local economy) Contextual variables – summary of individual attributes (e.g., % of calories from fat) Contagion variables – a property that involves a group outcome (e.g., prevalence of HIV effects risk of exposure)

13. GerstmanEcological and Cross-Sectional13 Illustrative Example Goldberger on Pellagra Pellagra epidemics of early 1900s initially thought to be of infectious origin Joseph Goldberger used epidemiologic studies to demonstrate nutritional basis of pellagra (niacin deficiency)

14. GerstmanEcological and Cross-Sectional14 Goldberger’s (1918) Field Study of Food Intake (Average Calories by Food Group) pp. 200 - 201