Epidemiology Kept Simple

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Epidemiology Kept Simple

• Sections 11.111.3
• Ecological Cross-Sectional Studies

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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

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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
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Illustrative Example 1 (cont.) Regional
Cigarette Consumption Lung Cancer
Per capita cigarette consumption and lung cancer
mortality are highly correlated, r 0.74
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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)
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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.

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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)

Prevalence of psychosis and neurosis by social class, per 100,000 (Hollingshead Redlich, 1964) Prevalence of psychosis and neurosis by social class, per 100,000 (Hollingshead Redlich, 1964) 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
Very low 1505 97
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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)

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The remaining slides in this presentation are
optional
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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

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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

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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)

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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)

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Goldbergers (1918) Field Study of Food Intake
(Average Calories by Food Group) pp. 200 - 201