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IIR 16-274 – HSR&D Study

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IIR 16-274
Impact of neighborhood and workforce deprivation on diabetes outcomes in Veterans: a spatio-temporal analysis
Brian H. Neelon PhD MS BA
Ralph H. Johnson VA Medical Center, Charleston, SC
Charleston, SC
Funding Period: May 2018 - April 2021

Diabetes is the seventh leading cause of death in the United States, can lead to serious complications, and is associated with increased healthcare costs. Prevalence estimates for Veterans show a disproportionate burden of disease, with estimates close to 25%, as compared to 8% of the general US population. Evidence consistently shows racial minorities have a higher prevalence of diabetes, worse outcomes, higher risk of complications, and higher mortality rate compared to non-Hispanic whites. This disparity persists after controlling for patient-level factors such as education, income, knowledge, health literacy, and self-efficacy; provider-level factors, such as bias, communication, and trust; and system-level factors, such as access to care. Little attention has been given to differences that may be explained by regional variation in patient-level resources, community-level resources, and health workforce resources.

This study seeks to identify and explain spatial and temporal variation in health outcomes, community resources, VA workforce capacity, and health disparities among patients with type 2 diabetes. Aim 1 will examine spatiotemporal trends in diabetes outcomes, including metabolic control, cost, and mortality. Aim 2 will develop a new spatiotemporal neighborhood deprivation index and examine its association with diabetes outcomes and racial disparities. Aim 3 will develop and validate a novel geographic workforce deprivation index to examine its association with diabetes outcomes and racial disparities.

We will construct a cohort of veterans with type 2 diabetes receiving either inpatient or outpatient care at the VA during the years 2000 through 2015 by linking multiple patient and administrative files from the VHA National Patient Care and Pharmacy Benefits Management databases, using a previously validated VA algorithm. Using advanced GIS and spatial statistical methods, we will examine spatiotemporal trends in diabetes outcomes among Veterans with type 2 diabetes. In Aim 1, we will develop a flexible Bayesian spatiotemporal model to identify hotspots of high prevalence of diabetes-related outcomes. In Aims 2 and 3, we will use spatiotemporal latent factor models to develop novel neighborhood and workforce deprivation indices, allowing us to investigate evolving patterns in community resource availability and VA workforce capacity. Completion of these aims will enable the VA to identify individual, community, and institutional factors associated with poor diabetes outcomes and to target community and system-level efforts to improve health in low-resource areas.

No findings to date, project was just awarded May 1st 2018.

This project will put forth a comprehensive geospatial framework to address the VA Blueprint for Excellence Strategy 3: Leverage information technologies, analytics, and models of healthcare to optimize individual well-being and population health outcomes. By creating a spatially referenced dataset incorporating health information, workforce productivity, neighborhood deprivation, we will develop a comprehensive database to examine multiple dimensions of diabetes care. Through the use of advanced GIS and spatiotemporal statistics, we will identify hotspots of high disease risk, poor neighborhood resources, and low VA workforce capacity. This information will improve access to care by helping VA policy makers better match resources to areas with poor outcomes. Finally, by pinpointing areas with excessive health expenditures, the VA can develop cost-reduction measures to improve Veterans' health while containing costs.

None at this time.

DRA: Diabetes and Related Disorders
DRE: Epidemiology, TRL - Applied/Translational
Keywords: Data Visualization, Diabetes, Disparities, Research Tools, Socioeconomic Factors
MeSH Terms: none