ZIP-code-based versus tract-based income measures as long-term risk-adjusted mortality predictors

Am J Epidemiol. 2006 Sep 15;164(6):586-90. doi: 10.1093/aje/kwj234. Epub 2006 Aug 7.

Abstract

There is a well-established, strong association between socioeconomic position and mortality. Public health mortality analyses thus routinely consider the confounding effect of socioeconomic position when possible. Particularly in the absence of personally reported data, researchers often use area-based measures to estimate the effects of socioeconomic position. Data are limited regarding the relative merits of measures based on US Census tract versus ZIP code (postal code). ZIP-code measures have more within-unit variation but are also more easily obtained. The current study reports on 293,138 middle-aged men screened in 14 states in 1973-1975 for the Multiple Risk Factor Intervention Trial and having 25-year mortality follow-up. In risk-adjusted proportional hazards models containing either ZIP-code-based or tract-based median household income, all-cause mortality hazard ratios were 1.16 (95% confidence interval: 1.14, 1.17) per Dollars 10,000 less ZIP-code-based income and 1.15 (95% confidence interval: 1.13, 1.16) per Dollars 10,000 less tract-based income; adding either income variable to a risk-adjusted model improved model fit substantially. Both were significant independent predictors in a combined model; tract-based income was a slightly stronger mortality predictor (hazard ratios = 1.05 and 1.11 for ZIP-code-based and tract-based income, respectively). These patterns held across various causes of death, for both Blacks and non-Blacks, and with or without adjustment for ZIP-code-based income diversity or tract-based proportion below poverty.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Analysis of Variance
  • Censuses
  • Chi-Square Distribution
  • Continental Population Groups / statistics & numerical data
  • Humans
  • Income*
  • Longitudinal Studies
  • Male
  • Mortality / trends*
  • Proportional Hazards Models
  • Regression Analysis
  • Residence Characteristics*
  • Risk Factors
  • Social Class*
  • United States / epidemiology