Martha Arrieta1, Harvey L White, Errol D Crook. 1. Center for Healthy Communities, University of South Alabama College of Medicine, Mastin, 5th Floor, Suite 524, 2451 Fillingim Street, Mobile, AL 36617, USA. marrieta@usouthal.edu
Abstract
OBJECTIVE: Having local health data is critical to combat health disparities, and zip code-level data are an underutilized source of such information. We sought to use zip code-level mortality data to determine where health disparities existed in our local area. METHODS: The most heavily populated zip codes were aggregated into 2 groups: (1) study area population (SAP) where >or=20% of individuals lived in poverty in 1999; (2) comparison area population, <20% in poverty. Disease-specific mortality rates (per 100,000 population) were calculated and compared. The relative risk (RR) of death from specific causes was the ratio of the mortality rate experienced by the SAP compared with that experienced by the comparison area population. RESULTS: The SAP had higher percentages of African Americans and women and much lower levels of income, employment, and education. Some zip codes in SAP had over 40% living below the poverty level. The RR of death from all but 4 of the 22 causes examined was >or=40% higher in the SAP. Major disparities (RR >2.5) were seen for human immunodeficiency virus, homicide, hypertensive heart and renal disease, and kidney disease. Actual death rates were highest for major cardiovascular disease and cancer, and the level of disparities here (RR approximately 1.4) make them important areas for concentration. CONCLUSION: Using zip code-level data provides an accurate foundation from which to design local interventions to address health disparities.
OBJECTIVE: Having local health data is critical to combat health disparities, and zip code-level data are an underutilized source of such information. We sought to use zip code-level mortality data to determine where health disparities existed in our local area. METHODS: The most heavily populated zip codes were aggregated into 2 groups: (1) study area population (SAP) where >or=20% of individuals lived in poverty in 1999; (2) comparison area population, <20% in poverty. Disease-specific mortality rates (per 100,000 population) were calculated and compared. The relative risk (RR) of death from specific causes was the ratio of the mortality rate experienced by the SAP compared with that experienced by the comparison area population. RESULTS: The SAP had higher percentages of African Americans and women and much lower levels of income, employment, and education. Some zip codes in SAP had over 40% living below the poverty level. The RR of death from all but 4 of the 22 causes examined was >or=40% higher in the SAP. Major disparities (RR >2.5) were seen for human immunodeficiency virus, homicide, hypertensive heart and renal disease, and kidney disease. Actual death rates were highest for major cardiovascular disease and cancer, and the level of disparities here (RR approximately 1.4) make them important areas for concentration. CONCLUSION: Using zip code-level data provides an accurate foundation from which to design local interventions to address health disparities.
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