Neighborhood disparities in stroke and myocardial infarction mortality: a GIS and spatial scan statistics approach

Abstract

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.

Geographic clustering of emergency department presentations for acute coronary syndromes and heart failure in Alberta: a population-based study

“…It is a popular method for identifying clusters. [11][12][13][14][15] We performed calculations in SaTScan Age-group-and sex-adjusted directly standardized rates per 1000 (and 95% confidence intervals) for heart failure by area. P denotes entire province.…”

Neighborhood socioeconomic disadvantage and mortality after stroke

“…[2][3][4][5][6][7][8] Recent evidence suggests that the association between neighborhood socioeconomic status (SES) and incident stroke is mediated by biologic risk factors, such as control of blood pressure, blood sugars, and lipids. 4 Fewer studies have explored whether neighborhood factors influence poststroke mortality, 2,9,10 and although socioeconomic features of neighborhoods, such as area-level deprivation 2,10 and neighborhood social cohesion, 9 have been implicated in poststroke mortality, the mechanisms remain poorly understood. To examine the relationship between neighborhood SES (NSES) and mortality after stroke and whether these associations are mediated by traditional behavioral and biologic risk factors, we analyzed data from the Cardiovascular Health Study (CHS), a large population-based, longitudinal study of coronary heart disease and stroke in adults 65 years of age and older.…”

The Neighborhood Where You Live Is a Risk Factor for Stroke

“…Some investigators have implicated neighborhoods in the occurrence of stroke deaths, [10][11][12][13][14] with those living in socioeconomically disadvantaged neighborhoods having a higher risk. Census tracts were used as proxies for neighborhoods in these studies.…”