C J Mansfield1, J L Wilson, E J Kobrinski, J Mitchell. 1. Center for Health Services Research and Development, East Carolina University, Greenville, NC 27858, USA. mansfield@brody.med.ecu.edu
Abstract
OBJECTIVES: This study examined premature mortality by county in the United States and assessed its association with metro/urban/rural geographic location, socioeconomic status, household type, and availability of medical care. METHODS: Age-adjusted years of potential life lost before 75 years of age were calculated and mapped by county. Predictors of premature mortality were determined by multiple regression analysis. RESULTS: Premature mortality was greatest in rural counties in the Southeast and Southwest. In a model predicting 55% of variation across counties, community structure factors explained more than availability of medical care. The proportions of female-headed households and Black populations were the strongest predictors, followed by variables measuring low education, American Indian population, and chronic unemployment. Greater availability of generalist physicians predicted fewer years of life lost in metropolitan counties but more in rural counties. CONCLUSIONS: Community structure factors statistically explain much of the variation in premature mortality. The degree to which premature mortality is predicted by percentage of female-headed households is important for policy-making and delivery of medical care. The relationships described argue strongly for broadening the biomedical model.
OBJECTIVES: This study examined premature mortality by county in the United States and assessed its association with metro/urban/rural geographic location, socioeconomic status, household type, and availability of medical care. METHODS: Age-adjusted years of potential life lost before 75 years of age were calculated and mapped by county. Predictors of premature mortality were determined by multiple regression analysis. RESULTS: Premature mortality was greatest in rural counties in the Southeast and Southwest. In a model predicting 55% of variation across counties, community structure factors explained more than availability of medical care. The proportions of female-headed households and Black populations were the strongest predictors, followed by variables measuring low education, American Indian population, and chronic unemployment. Greater availability of generalist physicians predicted fewer years of life lost in metropolitan counties but more in rural counties. CONCLUSIONS: Community structure factors statistically explain much of the variation in premature mortality. The degree to which premature mortality is predicted by percentage of female-headed households is important for policy-making and delivery of medical care. The relationships described argue strongly for broadening the biomedical model.
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