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

对退伍军人医疗保健的预期影响：该项目将提出一个全面的地理空间 解决退伍军人管理局卓越蓝图战略3的框架：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、劳动力生产率 邻里剥夺，我们将开发一个全面的数据库来检查多个维度的 通过使用先进的地理信息系统和时空统计，我们将确定高海拔地区的热点 疾病风险高，社区资源贫乏，退伍军人事务部劳动力能力低。这一信息将会改善 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来获得医疗服务。最后，通过 针对医疗费用过高的领域，退伍军人管理局可以制定降低成本的措施，以改善 背景：糖尿病是美国第七大死因，可导致严重 并发症，并与增加的医疗成本有关。退伍军人患病率估计显示 疾病负担不成比例，估计接近25%，而美国一般人的这一比例为8% 人口。不断有证据表明，少数民族患糖尿病的几率更高，情况更糟 结果，与非西班牙裔白人相比，并发症风险更高，死亡率也更高。这 在控制了患者层面的因素，如教育、收入、知识、健康后，差距仍然存在 识字和自我效能感；提供者层面的因素，如偏见、沟通和信任；以及系统层面 因素，如获得护理的机会。很少有人注意到可能由地区差异来解释的差异 患者级资源、社区级资源和卫生人力资源的差异。 目的：这项研究试图确定和解释健康结果的空间和时间差异， 社区资源、退伍军人劳动力能力和2型糖尿病患者之间的健康差距。目标 1将研究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域剥夺指数，并考察其与 AIM 3将开发和验证一种新的地理劳动力 剥夺指数，以检查其与糖尿病结局和种族差异的关系。 方法：我们将建立一个2型糖尿病退伍军人队列，接受住院或门诊治疗。 从2000年到2015年，通过链接来自 VHA全国患者护理和药房福利管理数据库，使用先前验证的VA 算法。利用先进的地理信息系统和空间统计方法，我们将研究 患有2型糖尿病的退伍军人的糖尿病结局。在目标1中，我们将开发一种灵活的贝叶斯 时空模型，以确定糖尿病相关结果高发的热点。在目标2和目标3中， 我们将使用时空潜在因素模型来开发新的邻居和劳动力剥夺 指数，使我们能够调查社区资源可用性和退伍军人队伍的演变模式 容量。完成这些目标将使退伍军人管理局能够确定个人、社区和机构因素 与糟糕的糖尿病结局相关，并以社区和系统一级的努力为目标，以改善#年的健康

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.