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: 9904151
• 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/9904151
• 关键词: Address; Adult; Algorithms; Ambulatory Care; Amputation; Area; Attention; Blood Pressure; Caring; Cause of Death; Characteristics; Communication; Communities; Community Health; Data Set; Database Management Systems; 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.

对退伍军人卫生保健的预期影响：该项目将提出全面的地理空间 解决VA蓝图的卓越策略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成果。 通过创建一个包含健康信息，劳动力生产率的空间引用的数据集， 邻里剥夺，我们将开发一个综合数据库，以检查 糖尿病护理。通过使用高级GIS和时空统计，我们将确定高点 疾病风险，邻里资源差和VA劳动力低。这些信息将改善 通过帮助VA政策制定者更好地将资源与成果差的领域相匹配，从而获得护理。最后，by 通过过多的健康支出来指出区域，VA可以制定降低成本措施以改进 退伍军人的健康，同时包含费用。 背景：糖尿病是美国第七大死亡原因，可能导致严重 并发症，与医疗保健成本的增加有关。退伍军人的患病率估计值显示 疾病的烧伤不成比例，估计接近25％，而美国一般的8％ 人口。证据始终显示少数民族的糖尿病患病率更高，更糟 与非西班牙裔白人相比，结果，更高的并发症风险和更高的死亡率。这 控制患者级别的因素，例如教育，收入，知识，健康之后，差异仍然存在 识字和自我效能；提供者级别的因素，例如偏见，沟通和信任；和系统级 因素，例如获得护理。很少关注区域可能解释的差异 患者级资源，社区级资源和健康劳动力资源的差异。 目标：本研究旨在识别和解释健康结果的空间和临时变化， 2型糖尿病患者的社区资源，VA劳动力能力和健康差异。目的 1将检查糖尿病结局的时空趋势，包括代谢控制，成本和死亡率。 AIM 2将开发一个新的时空社区剥夺指数，并检查其与 糖尿病结果和种族差异。 AIM 3将发展并验证一个新颖的地理劳动力 剥夺指数以检查其与糖尿病结果和种族分布的关联。 方法：我们将构建一群老兵，其中2型糖尿病接受住院或门诊病人 在2000年至2015年期间，VA的护理通过将多个患者和管理文件链接 VHA国家患者护理和药房福利管理数据库，使用先前验证的VA 算法。使用高级GIS和空间统计方法，我们将检查 2型糖尿病的退伍军人中的糖尿病结局。在AIM 1中，我们将开发灵活的贝叶斯人 时空模型，以鉴定与糖尿病相关结局的高流行率的热点。在目标2和3中 我们将使用时空潜在因素模型来发展新型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劳动力中不断发展的模式 容量。这些目标的完成将使VA能够确定个人，社区和机构因素 与不良的糖尿病结果以及针对社区和系统级别的努力相关，以改善健康 低资源地区。