title: Stroke disparities in older Americans: is wealth a more powerful indicator of risk than income and education?
abstract: OBJECTIVE
  This study examines the independent effect of wealth, income, and education on stroke and how these disparities evolve throughout middle and old age in a representative cohort of older Americans.
  
  
  METHODS
  Stroke-free participants in the Health and Retirement Study (n=19,565) were followed for an average of 8.5 years. Total wealth, income, and education assessed at baseline were used in Cox proportional hazards models to predict time to stroke. Separate models were estimated for 3 age-strata (50 to 64, 65 to 74, and >or=75), and incorporating risk factor measures (smoking, physical activity, body mass index, hypertension, diabetes, and heart disease).
  
  
  RESULTS
  1542 subjects developed incident stroke. Higher education predicted reduced stroke risk at ages 50 to 64, but not after adjustment for wealth and income. Wealth and income were independent risk factors for stroke at ages 50 to 64. Adjusted hazard ratios comparing the lowest decile with the 75th-90th percentiles were 2.3 (95% CI 1.6, 3.4) for wealth and 1.8 (95% CI 1.3, 2.6) for income. Risk factor adjustment attenuated these effects by 30% to 50%, but coefficients for both wealth (HR=1.7, 95% CI 1.2, 2.5) and income (HR=1.6, 95% CI 1.2, 2.3) remained significant. Wealth, income, and education did not consistently predict stroke beyond age 65.
  
  
  CONCLUSIONS
  Wealth and income are independent predictors of stroke at ages 50 to 64 but do not predict stroke among the elderly. This age patterning might reflect buffering of the negative effect of low socioeconomic status by improved access to social and health care programs at old ages, but may also be an artifact of selective survival.
authors.affiliation: Department of Public Health, Erasmus MC, University Medical Center Rotterdam, PO Box 2040, 3000 CA Rotterdam, The Netherlands. m.avendanopabon@erasmusmc.nl
authors.forename: Mauricio
authors.initials: M
authors.lastname: Avendano
authors.forename: M Maria
authors.initials: MM
authors.lastname: Glymour
mesh_terms: D017677:Age Distribution; D000367:Age Factors; D000368:Aged; D015331:Cohort Studies; D004522:Educational Status; D005260:Female; D006801:Humans; D015994:Incidence; D007182:Income / Q000706:statistics & numerical data; D008019:Life Style; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D016016:Proportional Hazards Models; D012307:Risk Factors; D012959:Socioeconomic Factors; D020521:Stroke / Q000191:economics* / Q000453:epidemiology*; D014481:United States / Q000453:epidemiology