Examining linkages between disrupted care and chronic disease outcomes during the COVID-19 pandemic: a VAMC level spatio-temporal analysis

摘要 背景：SARS-CoV-2引发的全球大流行深刻影响了人们的健康和护理 退伍军人，他们通常比美国总人口年龄更大、病情更重、经济上更脆弱。 退伍军人可能面临与护理中断相关的持久风险。了解这些项目的长期影响 退伍军人医疗中心(VAMC)的中断和不同的响应对于了解(1)主要 护理需要向前推进，(2)确定高危患者进行有针对性的干预，以及(3)减少 护理中断加剧了不平等。 意义：糖尿病和高血压是慢性疾病，需要大量的提供者和患者的护理 管理并导致高昂的医疗成本。大致来说，在退伍军人管理局接受护理的退伍军人中，有四分之一的人 糖尿病和远远超过三分之一的人患有高血压。糖尿病和高血压与高血糖有关 心血管风险，并导致严重并发症，包括中风、心脏病、肾衰竭、截肢 和死亡。疾病流行和进展方面的种族、社会经济和地理差异很大 记录在案；因此，至关重要的是，我们必须了解大流行的影响，特别注重 “吸取的教训”和扩大的健康差距。 具体目标：我们的目标是：(1)确定中断护理对慢性病的长期影响 在患者和VAMC层面上在全国范围内的结果；(2)确定具有高心血管风险的退伍军人 作为护理中断的结果，并确定在种族-族裔群体方面的差异程度， 在大流行期间，城乡居民和社会脆弱性有所扩大；以及(3)由于我们的 咨询小组，创建一个心血管监测和风险的Power BI仪表板，以传播我们的结果。 方法：我们将建立两个接受初级保健的退伍军人回顾队列，从2017年到 2022年：糖尿病和高血压队列。将在人口普查区域分配社会脆弱性衡量标准 以退伍军人住宅为基础的级别。我们的模型旨在研究个体之间的联系-- 使用复杂的地理信息系统的人口普查区域和VAMC级别的因素、医疗保健提供指标和健康结果 联系和先进的时空统计方法。提供护理的指标包括以下程度 在大流行早期监测心血管危险因素及其水平(在监测时)。结果 包括动脉粥样硬化性心血管疾病(ASCVD)患病率、心血管疾病风险水平、住院时间和 死亡率。我们工作的不同于正在进行的项目的方面是：(1)我们能够包括完整的数据 当分析仅限于南卡罗来纳州时，住院就诊和急诊科就诊情况，(2) 高级统计建模使我们能够在多个层次上考虑多个因素(即，患者， 人口普查区域，VAMC)；和(3)该提案的时空方面，这是至关重要的，因为空间- 大流行的时间性质 下一步/实施：我们计划在Power BI中创建仪表板，这是VA支持的商业智能 该工具允许用户以可视化格式显示数据，从而为战略决策提供信息。我们的控制面板 将为每个VAMC提供个性化信息，说明2017年至 2022用于(1)中断的护理指标(即，心血管疾病风险监测、按模式的初级护理访问)，(2)ASCVD的水平， (3)ACC ASCVD风险水平(无ASCVD者)；及(4)死亡率。我们使用区域级别的数据和 我们对VAMC级别分析的关注将在政策级别决策期间和之后提供信息 大流行。关于护理提供的变化、VAMC水平的适应性和 慢性病的结果将为整个退伍军人事务部的大流行后护理提供信息。

ABSTRACT Background: The global pandemic brought on by SARS-CoV-2 has profoundly impacted health and care for veterans, who are generally older, sicker and more economically vulnerable than the overall U.S. population. Veterans are likely to face lasting risks related to care disruptions. Understanding the long-term impact of these disruptions and varied responses across VA Medical Centers (VAMC) is critical to understanding (1) primary care needs moving forward, (2) identifying high risk patients for targeted interventions, and (3) reducing disparities exacerbated by care disruptions. Significance: Diabetes and hypertension are chronic conditions requiring substantial provider and patient care to manage and result in high healthcare cost. Roughly, a quarter of all veterans receiving care at the VA have diabetes and well over a third have hypertension. Diabetes and hypertension are associated with high cardiovascular risk and lead to serious complications, including stroke, heart disease, kidney failure, amputation and death. Racial, socioeconomic and geographic disparities in disease prevalence and progression are well documented; hence, it is critical that we understand the impact of the pandemic with a particular focus on “lessons learned” and health disparities that have widened. Specific Aims: Our aims are (1) To determine the long-term impact of disrupted care on chronic disease outcomes across the nation at the patient and VAMC level; (2) to identify veterans at high cardiovascular risk as a result of disrupted care and determine the extent to which disparities with respect to race-ethnic group, rural-urban residence and social vulnerability have widened during the pandemic; and (3) with input from our advisory panel, create a Power BI dashboard of cardiovascular monitoring and risk to disseminate our results. Methodology: We will create two retrospective cohorts of Veterans receiving primary care from 2017 through 2022: a diabetes and a hypertension cohort. Social vulnerability measures will be assigned at the census-tract level based on a veterans’ residence. Our models are designed to investigate associations between individual-, census tract- and VAMC- level factors, health care delivery metrics, and health outcomes using complex GIS linkages and advanced spatio-temporal statistical methods. Delivery of care metrics include the extent to which cardiovascular risk factors are monitored and their levels (when monitored) early in the pandemic. Outcomes include prevalence of atherosclerotic cardiovascular disease (ASCVD), CVD risk levels, hospitalization, and mortality. Aspects of our work that set it apart from ongoing projects are (1) our ability to include complete data on inpatient hospital visits and emergency department visits when analyses are limited to South Carolina, (2) the advanced statistical modeling that enables us to account for multiple factors at multiple levels (i.e., patient, census tract, VAMC); and (3) the spatio-temporal aspects of the proposal which are critical given the spatio- temporal nature of the pandemic Next Steps/Implementation: We plan to create a dashboard in Power BI, a VA supported business intelligence tool, that allows users to display data in visual format allowing data to inform strategic decisions. Our dashboard will provide individualized information for each VAMC illustrating adjusted quarterly levels from 2017 through 2022 for (1) disrupted care metrics (i.e., CVD risk monitoring, primary care visits by mode), (2) levels of ASCVD, (3) ACC ASCVD risk levels (in those without ASCVD); and (4) mortality rates. Our use of area-level data and our focus on the VAMC level analyses will inform policy-level decision making during and following the pandemic. Lessons learned on the relationship between changes in care delivery, VAMC-level adaptability and chronic disease outcomes will inform post-pandemic care throughout the VA.