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Epidemiology Kept Simple

Epidemiology Kept Simple

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Epidemiology Kept Simple

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

  2. Prevalence or rate, group 1 Prevalence or rate, group 2 Random sample of population divided into exposure groups Compare prevalence or rates : : Prevalence or rate, group k 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: Ecological and Cross-Sectional

  3. 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” Ecological and Cross-Sectional

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

  5. 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) Ecological and Cross-Sectional

  6. 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. Ecological and Cross-Sectional

  7. Illustrative Example #4Psychosis, Neurosis, & Social Class • 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) Ecological and Cross-Sectional

  8. 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 20th 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…) Ecological and Cross-Sectional

  9. The remaining slides in this presentation are optional Ecological and Cross-Sectional

  10. 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 Ecological and Cross-Sectional

  11. “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 Ecological and Cross-Sectional

  12. 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) Ecological and Cross-Sectional

  13. 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) Ecological and Cross-Sectional

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