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BMC Public Health

, 11:644

Biostatistics and methods


BackgroundStroke and myocardial infarction MI are serious public health burdens in the US. These burdens vary by geographic location with the highest mortality risks reported in the southeastern US. While these disparities have been investigated at state and county levels, little is known regarding disparities in risk at lower levels of geography, such as neighborhoods. Therefore, the objective of this study was to investigate spatial patterns of stroke and MI mortality risks in the East Tennessee Appalachian Region so as to identify neighborhoods with the highest risks.

MethodsStroke and MI mortality data for the period 1999-2007, obtained free of charge upon request from the Tennessee Department of Health, were aggregated to the census tract neighborhood level. Mortality risks were age-standardized by the direct method. To adjust for spatial autocorrelation, population heterogeneity, and variance instability, standardized risks were smoothed using Spatial Empirical Bayesian technique. Spatial clusters of high risks were identified using spatial scan statistics, with a discrete Poisson model adjusted for age and using a 5% scanning window. Significance testing was performed using 999 Monte Carlo permutations. Logistic models were used to investigate neighborhood level socioeconomic and demographic predictors of the identified spatial clusters.

ResultsThere were 3,824 stroke deaths and 5,018 MI deaths. Neighborhoods with significantly high mortality risks were identified. Annual stroke mortality risks ranged from 0 to 182 per 100,000 population median: 55.6, while annual MI mortality risks ranged from 0 to 243 per 100,000 population median: 65.5. Stroke and MI mortality risks exceeded the state risks of 67.5 and 85.5 in 28% and 32% of the neighborhoods, respectively. Six and ten significant p < 0.001 spatial clusters of high risk of stroke and MI mortality were identified, respectively. Neighborhoods belonging to high risk clusters of stroke and MI mortality tended to have high proportions of the population with low education attainment.

ConclusionsThese methods for identifying disparities in mortality risks across neighborhoods are useful for identifying high risk communities and for guiding population health programs aimed at addressing health disparities and improving population health.

List of Abbreviations UsedCDCCenters for Disease Control and Prevention

CTcensus tracts

ICDInternational classification of diseases

GISgeographic information systems

MImyocardial infarction

SEBspatial empirical Bayes


USUnited States.

Electronic supplementary materialThe online version of this article doi:10.1186-1471-2458-11-644 contains supplementary material, which is available to authorized users.

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Autor: Ashley Pedigo - Tim Aldrich - Agricola Odoi


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