OBJECTIVES: (1) To examine the association of socioeconomic characteristics (SES) with hospitalization by age group, and when using measures of SES at the community as opposed to the individual level. (2) Thus, to support the inference that socioeconomic factors are important in the analysis of small area utilization data and address potential criticisms of this conclusion. DATA SOURCES: The 1989 Michigan Inpatient Database (MIDB), the 1990 U.S. Census, the 1989 Area Resource File (ARF), and the 1990 National Health Interview Survey (NHIS). STUDY DESIGN: A qualitative comparison of socioeconomic predictors of hospitalization in two cross-sectional analyses when using community as opposed to individual socioeconomic characteristics was done. DATA EXTRACTION. Hospitalizations (excluding delivery) were extracted by county from the MIDB and by individual from the NHIS. SES variables were extracted from the U.S. Census for communities and from the NHIS for individuals. Measures of employment for communities were from the ARF and information on health insurance and health status of individuals from the NHIS. PRINCIPAL FINDINGS: Both analyses show similar age-specific patterns for income and education. The effects were greatest in young adults, and diminished with increasing age. Accounting for multiple admissions did not change these conclusions. In the individual-level data the addition of variables representing health and insurance status substantially diminished the size of the coefficients for the socioeconomic variables. CONCLUSIONS: By comparison to parallel individual-level analyses, small area analyses with community-level SES characteristics appear to represent the effect of individual-level characteristics. They are also not substantially affected by the inability to track individuals with multiple readmissions across hospitals. We conclude that the impact of SES characteristics on hospitalization rates is consistent when measured by individual or community-level measures and varies substantially by age. These variables should be included in analyses of small area variation.
OBJECTIVES: (1) To examine the association of socioeconomic characteristics (SES) with hospitalization by age group, and when using measures of SES at the community as opposed to the individual level. (2) Thus, to support the inference that socioeconomic factors are important in the analysis of small area utilization data and address potential criticisms of this conclusion. DATA SOURCES: The 1989 Michigan Inpatient Database (MIDB), the 1990 U.S. Census, the 1989 Area Resource File (ARF), and the 1990 National Health Interview Survey (NHIS). STUDY DESIGN: A qualitative comparison of socioeconomic predictors of hospitalization in two cross-sectional analyses when using community as opposed to individual socioeconomic characteristics was done. DATA EXTRACTION. Hospitalizations (excluding delivery) were extracted by county from the MIDB and by individual from the NHIS. SES variables were extracted from the U.S. Census for communities and from the NHIS for individuals. Measures of employment for communities were from the ARF and information on health insurance and health status of individuals from the NHIS. PRINCIPAL FINDINGS: Both analyses show similar age-specific patterns for income and education. The effects were greatest in young adults, and diminished with increasing age. Accounting for multiple admissions did not change these conclusions. In the individual-level data the addition of variables representing health and insurance status substantially diminished the size of the coefficients for the socioeconomic variables. CONCLUSIONS: By comparison to parallel individual-level analyses, small area analyses with community-level SES characteristics appear to represent the effect of individual-level characteristics. They are also not substantially affected by the inability to track individuals with multiple readmissions across hospitals. We conclude that the impact of SES characteristics on hospitalization rates is consistent when measured by individual or community-level measures and varies substantially by age. These variables should be included in analyses of small area variation.
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