Impact of neighborhood and workforce deprivation on diabetes outcomes in Veterans: a spatio-temporal analysis

社区和劳动力匮乏对退伍军人糖尿病结局的影响：时空分析

• 批准号: 9503218
• 负责人: KELLY J HUNT
• 金额: --
• 依托单位: RALPH H JOHNSON VA MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-05-01 至 2021-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9503218
• 关键词: Address; Adult; Algorithms; Ambulatory Care; Amputation; Area; Attention; Blood Pressure; Caring; Cause of Death; Characteristics; Communication; Communities; Community Healthcare; Data Base Management; Data Set; Databases; Diabetes Mellitus; Dimensions; Disease; Disease Outcome; Education; Engineering; Ethnic Origin; Geographic Information Systems; Geographic Locations; Geography; Goals; Health; Health Care Costs; Health Expenditures; Health Personnel; Health Services Accessibility; Healthcare; Healthcare Systems; Heart Diseases; High Prevalence; Improve Access; Income; Individual; Information Technology; Intervention; Kidney Failure; Knowledge; Lead; Link; Lipids; Measures; Metabolic Control; Methodology; Methods; Modeling; Neighborhoods; Non-Insulin-Dependent Diabetes Mellitus; Not Hispanic or Latino; Outcome; Patient Care; Patients; Pattern; Personal Satisfaction; Physicians; Policies; Policy Maker; Population; Prevalence; Productivity; Provider; Race; Research; Resources; Self Efficacy; Statistical Methods; Stroke; System; Time trend; Trust; United States; Variant; Veterans; burden of illness; cohort; cost; deprivation; disorder risk; flexibility; health care availability; health care model; health disparity; health literacy; high risk; improved; improved outcome; indexing; information system analysis; innovation; inpatient service; mortality; novel; pharmacy benefit; population health; racial disparity; racial minority; spatial temporal variation; spatiotemporal; statistics; therapy development; trend

Anticipated Impacts on Veterans Health Care: 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. Background: 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. Objectives: 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. Methods: 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.

对退伍军人医疗保健的预期影响：该项目将提出全面的地理空间 框架，以实现 VA 卓越蓝图战略 3：利用信息技术， 分析和医疗保健模型，以优化个人福祉和人口健康结果。 通过创建包含健康信息、劳动力生产力的空间参考数据集， 邻里剥夺，我们将开发一个综合数据库来检查邻里剥夺的多个维度 通过使用先进的地理信息系统和时空统计，我们将确定高发热点。 疾病风险、贫困的社区资源以及退伍军人事务部劳动力能力低下这些信息将得到改善。 通过帮助退伍军人事务部政策制定者更好地将资源与结果不佳的地区相匹配，获得护理服务。 针对医疗支出过高的领域，退伍军人事务部可以制定降低成本的措施，以改善 退伍军人的健康，同时控制成本。 背景：糖尿病是美国第七大死因，可导致严重的 并发症，并与增加的医疗费用有关 退伍军人的患病率估计显示 疾病负担不成比例，估计接近 25%，而美国一般情况为 8% 证据一致表明，少数族裔的糖尿病患病率更高，甚至更严重。 与非西班牙裔白人相比，其并发症风险更高，死亡率更高。 在控制了患者层面的因素（例如教育、收入、知识、健康状况）后，差异仍然存在 素养和自我效能；提供者层面的因素，例如偏见、沟通和信任以及系统层面； 诸如获得护理的机会等因素很少受到关注，而这些差异可能是由地区造成的。 患者级资源、社区级资源和卫生人力资源的差异。 目标：本研究旨在识别和解释健康结果的空间和时间变化， 社区资源、VA 劳动力能力以及 2 型糖尿病患者的健康差异。 1 将检查糖尿病结果的时空趋势，包括代谢控制、成本和死亡率。 目标 2 将开发一个新的时空邻里剥夺指数，并检验其与 目标 3 将开发和验证新型的地理劳动力。 剥夺指数来检查其与糖尿病结果和种族差异的关系。 方法：我们将构建一组患有 2 型糖尿病的退伍军人，接受住院或门诊治疗 通过链接来自 VA 的多个患者和管理文件，可以了解 2000 年至 2015 年期间 VA 的护理情况 VHA 国家患者护理和药房福利管理数据库，使用之前经过验证的 VA 使用先进的地理信息系统和空间统计方法，我们将检查时空趋势。 患有 2 型糖尿病的退伍军人的糖尿病结局 在目标 1 中，我们将开发灵活的贝叶斯模型。 用于识别糖尿病相关结果高患病率热点的时空模型 在目标 2 和 3 中， 我们将使用时空潜在因素模型来开发新的社区和劳动力剥夺 指数，使我们能够调查社区资源可用性和 VA 劳动力的演变模式 完成这些目标将使 VA 能够识别个人、社区和机构因素。 与不良的糖尿病结局相关，并针对社区和系统层面的努力来改善健康状况 资源匮乏地区。