Academic journal article Bulletin of the World Health Organization

Death Rate Variation in US Subpopulations. (Research)

Academic journal article Bulletin of the World Health Organization

Death Rate Variation in US Subpopulations. (Research)

Article excerpt

Voir page 14 le resume en francais. En la pagina 14 figura un resumen en espanol.


It is well known that death rates differ by geographical location in the United States (1-3). For example, a recent study of US counties for 1990 found significant variation in death rates for males aged 61-76.2 years and for females aged 70-82.6 years (4). However, the reasons for such differences remain unclear. Current concepts in population health regard mortality to be the product of multiple determinants, such as medical care, genetics, the physical environment, the socioeconomic environment, and individual biology and behaviour (5). However, it is not known if almost any combination of determinants can produce optimal health, or if a smaller number of basic patterns dominate. These relationships need to be unravelled to guide financial incentives aimed at improving population health outcomes (6, 7).

To try to understand the basis of mortality patterns, we examined the geographical variation in age-adjusted death rates as a function of area characteristics and population composition (3, 4, 8, 9). Although this "ecological" analysis of aggregate population data cannot produce valid inferences about individual mortality risks, it can generate hypotheses that can be further tested with survival analysis at the individual level.


The study population consisted of 320 primary metropolitan statistical areas (MSA) and 46 areas that included non-metropolitan counties within state boundaries (four states had no non-metropolitan counties). Data were aggregated from the county level to the MSA level, using the definition of MSA boundaries that were in effect in 1996. All remaining non-metropolitan counties were pooled into one non-metropolitan area for each state. These geographical units are referred to as "metropolitan statistical areas or non-metropolitan balance of states" (MSA/NBS), and were chosen as units of analysis because of their intermediate size between states and counties.

The independent contribution of demographical and socioeconomic factors, as well as medical care access, to variation in age-adjusted death rates across the 366 MSA/NBS was estimated using linear regression analysis of data from the Bureau of Health Professions Area Resource File (10). The Area Resource File is composed mostly of summed county totals and population-weighted averages for adjacent counties within each of the multi-county areas. The dependent variable is the annual number of deaths per 100 000 population averaged over 1990, 1991 and 1992, and age-standardized by applying local age-specific death rates to the 1990 US population age distribution.

We chose to analyse differences in death rates using variables known to exhibit strong geographical gradients, including racial or ethnic identity (11), socioeconomic status (12), rural/urban differences (13, 14) and medical services (8). Specific independent variables were: census region; gender; racial/ethnic composition (such as Black and Hispanic, which are not mutually exclusive categories); socioeconomic composition (e.g. the proportion of adults aged 25 years or older who completed high school; per capita income; percentage unemployed; and the Gini-coefficient of inequality in household incomes in 1990 (15)); urban/rural composition (metropolitan/non-metropolitan dummy variable and percentage of labour force in agriculture, forestry, and fisheries); and medical service measures (physician-to-population ratio, Medicare payments per person over 65 years of age, and the number of hospital inpatient days per person for short-term general medical and surgical procedures). The dependent variable was age-standardized, but not the independent variables, which may produce biased regression coefficient estimates (16). An alternative method suggested by Rosen-baum and Rubin for controlling for age structure and reducing bias led to similar results and conclusions. …

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