title: Area characteristics and individual-level socioeconomic position indicators in three population-based epidemiologic studies.
abstract: OBJECTIVE
  There is growing interest in incorporating area indicators into epidemiologic analyses. Using data from the 1990 U.S. Census linked to individual-level data from three epidemiologic studies, we investigated how different area indicators are interrelated, how measures for different sized areas compare, and the relation between area and individual-level social position indicators.
  
  
  METHODS
  The interrelations between 13 area indicators of wealth/income, education, occupation, and other socioenvironmental characteristics were investigated using correlation coefficients and factor analyses. The extent to which block-group measures provide information distinct from census tract measures was investigated using intraclass correlation coefficients. Loglinear models were used to investigate associations between area and individual-level indicators.
  
  
  RESULTS
  Correlations between area measures were generally in the 0.5--0.8 range. In factor analyses, six indicators of income/wealth, education, and occupation loaded on one factor in most geographic sites. Correlations between block-group and census tract measures were high (correlation coefficients 0.85--0.96). Most of the variability in block-group indicators was between census tracts (intraclass correlation coefficients 0.72--0.92). Although individual-level and area indicators were associated, there was evidence of important heterogeneity in area of residence within individual-level income or education categories. The strength of the association between individual and area measures was similar in the three studies and in whites and blacks, but blacks were much more likely to live in more disadvantaged areas than whites.
  
  
  CONCLUSIONS
  Area measures of wealth/income, education, and occupation are moderately to highly correlated. Differences between using census tract or block-group measures in contextual investigations are likely to be relatively small. Area and individual-level indicators are far from perfectly correlated and provide complementary information on living circumstances. Differences in the residential environments of blacks and whites may need to be taken into account in interpreting race differences in epidemiologic studies.
authors.affiliation: Division of General Medicine, Columbia College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA. ad290@columbia.edu
authors.forename: A V
authors.initials: AV
authors.lastname: Diez-Roux
authors.forename: C I
authors.initials: CI
authors.lastname: Kiefe
authors.forename: D R
authors.initials: DR
authors.lastname: Jacobs
authors.forename: M
authors.initials: M
authors.lastname: Haan
authors.forename: S A
authors.initials: SA
authors.lastname: Jackson
authors.forename: F J
authors.initials: FJ
authors.lastname: Nieto
authors.forename: C C
authors.initials: CC
authors.lastname: Paton
authors.forename: R
authors.initials: R
authors.lastname: Schulz
authors.forename: A V
authors.initials: AV
authors.lastname: Roux
mesh_terms: D000328:Adult; D001741:Black or African American / Q000706:statistics & numerical data; D002318:Cardiovascular Diseases / Q000453:epidemiology* / Q000209:etiology; D003710:Demography; D004522:Educational Status; D005163:Factor Analysis, Statistical; D006801:Humans; D007182:Income / Q000706:statistics & numerical data; D016014:Linear Models; D009790:Occupations / Q000706:statistics & numerical data; D012307:Risk Factors; D012923:Social Class; D012931:Social Environment; D012959:Socioeconomic Factors; D018709:Statistics, Nonparametric; D014481:United States / Q000453:epidemiology; D044465:White People / Q000706:statistics & numerical data