John Snow - San Jose State University

Epidemiology Kept Simple Sections 11.111.3 Ecological & Cross-Sectional Studies Gerstman Ecological and Cross-Sectional 1 Basic Design Ecological and cross-sectional studies involve no followup 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 1 Random sample of population divided into exposure groups Prevalence or rate, group 2 : Compare prevalence or

rates : Prevalence or rate, group k Gerstman Ecological and Cross-Sectional 2 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 Gerstman

Ecological and Cross-Sectional 3 Illustrative Example #1 (cont.) Regional Cigarette Consumption & Lung Cancer Per capita cigarette consumption and lung cancer mortality are highly correlated, r = 0.74 Gerstman Ecological and Cross-Sectional 4 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. 1945) Gerstman

Ecological and Cross-Sectional 5 Illustrative Example #3 Demonstration of Confounding Gerstman 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 6 Illustrative Example #4 Psychosis, 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) Prevalence of psychosis and neurosis by social class, per 100,000 (Hollingshead & Redlich, 1964) Social class Psychosis Neurosis High 188 349 Moderate

291 250 Low 518 114 1505 97 Very low Gerstman Ecological and Cross-Sectional 7 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 casecontrol methods (see next set of slides) Gerstman Ecological and Cross-Sectional 8 The remaining slides in this presentation are optional Gerstman Ecological and Cross-Sectional 9 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 Gerstman Ecological and Cross-Sectional 10 Logic of the Ecological Renewed interest in ecological measures Studies that mix aggregate observations and individual-level observations are called multilevel designs Multi-level analysis useful in elucidating : causal webs interdependence between upstream factors and downstream factors Gerstman

Ecological and Cross-Sectional 11 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) Gerstman Ecological and Cross-Sectional 12 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) Gerstman Ecological and Cross-Sectional 13 Goldbergers (1918) Field Study of Food Intake (Average Calories by Food Group) pp. 200 - 201 Nonpellagrous Households Pellagrous Households Groups of Foods With Two or More Cases (Mostly Low Income Households) With Highest Income With Lowest Income

Meats (exclusive of salt pork), eggs, milk, butter, cheese 762 639 338 270 Dried and canned peas and beans (exclusive of canned string beans) 126 113 115 123 Wheaten flour, bread, cakes and crackers, cornmeal,

grits, canned corn, rice 2162 2082 1752 1840 Salt pork, lard and lard substitutes 741 673 748 745 Green and canned vegetables (exclusive of corn), green and canned string beans, fruits of all kinds

131 71 60 69 55 53 53 46 250 205 222 217 4267 3836 3288

3310 Irish and sweet potatoes Sugar, syrup, jellies and jams All foods . . . . . Gerstman With Lowest Income and One or More Cases Ecological and Cross-Sectional 14

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