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Small-area racial disparity in stroke mortality: an application of bayesian spatial hierarchical modeling.

ABSTRACT: In the United States, excess burden of stroke mortality has persisted among African Americans compared with whites despite declines in stroke mortality for both groups. New insights may be gleaned by examining local, small-area patterns in racial disparities in stroke.The study population includes all non-Hispanic African Americans and non-Hispanic whites aged 35 to 64 in the southeastern United States during 1999 to 2002. We assessed county-level numbers of stroke deaths and population estimates in a Bayesian spatial hierarchical modeling framework allowing for inclusion of potential covariates (poverty and rurality), and generating county-specific model-based estimates of both absolute and relative racial disparity. The resulting estimates of race-specific stroke death rates, relative racial disparity, and absolute racial disparity were expressed in maps.After adjustment for age, poverty, and rurality, county-level estimates of relative racial disparity ranged from 2.3 to 3.3 and estimates of absolute racial disparity ranged from 19 to 45 excess deaths per 100,000. For both racial groups, stroke death rates were higher in rural areas and with increasing poverty. High relative racial disparity was concentrated primarily in the eastern portion of the region and large absolute racial disparity was concentrated primarily in the western portion.The results highlight the pervasiveness and magnitude of substantial local racial disparities in stroke mortality in the southeast.

SUBMITTER: Tassone EC

PROVIDER: S-EPMC4030758 | biostudies-literature | 2009 Mar

REPOSITORIES: biostudies-literature

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Small-area racial disparity in stroke mortality: an application of bayesian spatial hierarchical modeling.

Tassone Eric C EC Waller Lance A LA Casper Michele L ML

Epidemiology (Cambridge, Mass.) 20090301 2

<h4>Background</h4>In the United States, excess burden of stroke mortality has persisted among African Americans compared with whites despite declines in stroke mortality for both groups. New insights may be gleaned by examining local, small-area patterns in racial disparities in stroke.<h4>Methods</h4>The study population includes all non-Hispanic African Americans and non-Hispanic whites aged 35 to 64 in the southeastern United States during 1999 to 2002. We assessed county-level numbers of st ...[more]

PMID: 19142164

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Project description:BackgroundOpioid-related overdose mortality has remained at crisis levels across the United States, increasing 5-fold and worsened during the COVID-19 pandemic. The ability to provide forecasts of opioid-related mortality at granular geographical and temporal scales may help guide preemptive public health responses. Current forecasting models focus on prediction on a large geographical scale, such as states or counties, lacking the spatial granularity that local public health officials desire to guide policy decisions and resource allocation.ObjectiveThe overarching objective of our study was to develop Bayesian spatiotemporal dynamic models to predict opioid-related mortality counts and rates at temporally and geographically granular scales (ie, ZIP Code Tabulation Areas [ZCTAs]) for Massachusetts.MethodsWe obtained decedent data from the Massachusetts Registry of Vital Records and Statistics for 2005 through 2019. We developed Bayesian spatiotemporal dynamic models to predict opioid-related mortality across Massachusetts' 537 ZCTAs. We evaluated the prediction performance of our models using the one-year ahead approach. We investigated the potential improvement of prediction accuracy by incorporating ZCTA-level demographic and socioeconomic determinants. We identified ZCTAs with the highest predicted opioid-related mortality in terms of rates and counts and stratified them by rural and urban areas.ResultsBayesian dynamic models with the full spatial and temporal dependency performed best. Inclusion of the ZCTA-level demographic and socioeconomic variables as predictors improved the prediction accuracy, but only in the model that did not account for the neighborhood-level spatial dependency of the ZCTAs. Predictions were better for urban areas than for rural areas, which were more sparsely populated. Using the best performing model and the Massachusetts opioid-related mortality data from 2005 through 2019, our models suggested a stabilizing pattern in opioid-related overdose mortality in 2020 and 2021 if there were no disruptive changes to the trends observed for 2005-2019.ConclusionsOur Bayesian spatiotemporal models focused on opioid-related overdose mortality data facilitated prediction approaches that can inform preemptive public health decision-making and resource allocation. While sparse data from rural and less populated locales typically pose special challenges in small area predictions, our dynamic Bayesian models, which maximized information borrowing across geographic areas and time points, were used to provide more accurate predictions for small areas. Such approaches can be replicated in other jurisdictions and at varying temporal and geographical levels. We encourage the formation of a modeling consortium for fatal opioid-related overdose predictions, where different modeling techniques could be ensembled to inform public health policy.

Dataset Information

Small-area racial disparity in stroke mortality: an application of bayesian spatial hierarchical modeling.

Publications

Small-area racial disparity in stroke mortality: an application of bayesian spatial hierarchical modeling.

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