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

检查 COVID-19 大流行期间中断的护理与慢性病结果之间的联系：VAMC 级别时空分析

• 批准号: 10641136
• 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: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10641136
• 关键词: 2019-nCoV; Accounting; Address; American; Amputation; Area; Atherosclerosis; Black race; Businesses; COVID-19 pandemic; Cardiology; Cardiovascular system; Caring; Censuses; Cessation of life; Chronic; Chronic Disease; Clinic; Community Healthcare; Complex; Data; Data Display; Decision Making; Diabetes Mellitus; Disease; Disease Outcome; Disparity; Drug Prescriptions; Economics; Emergency department visit; Equity; Ethnic Population; Evaluation; Face; Future; Geographic Information Systems; Health; Health Care Costs; Healthcare; Heart Diseases; Hispanic; Hospitalization; Hospitals; Hypertension; Individual; Inpatients; Intelligence; Interruption; Intervention; Kidney Failure; Knowledge; Leadership; Learning; Lower Extremity; Measures; Medical center; Methodology; Modeling; Monitor; Nature; Not Hispanic or Latino; Outcome; Patient Care; Patients; Persons; Pharmaceutical Preparations; Phase; Policies; Population; Positioning Attribute; Prevalence; Primary Care; Provider; Race; Recording of previous events; Research; Retrospective cohort; Risk; Risk Factors; Rural; Social Conditions; Social Distance; Social Work; South Carolina; Statistical Methods; Statistical Models; Stroke; Suspensions; System; Telephone; Testing; Transportation; Veterans; Visit; Visual; Workplace; adverse outcome; cardiovascular disorder risk; cardiovascular risk factor; care delivery; cohort; college; dashboard; disparity reduction; follow-up; future pandemic; geographic disparity; health care delivery; health care service; health disparity; high risk; improved; individual patient; insight; medical appointment; model design; mortality; operation; outcome disparities; pandemic disease; pandemic impact; post-pandemic; pre-pandemic; primary care visit; racial population; residence; response; social determinants; social vulnerability; socioeconomic disparity; spatiotemporal; statistics; tool; trend; urban residence

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.

摘要 背景：SARS-CoV-2引起的全球大流行深刻影响了健康和护理， 退伍军人，他们通常比美国整体人口年龄更大，病情更重，经济上更脆弱。 退伍军人可能面临与护理中断有关的持久风险。了解这些问题的长期影响 VA医疗中心（VAMC）的中断和不同的响应对于理解（1）主要 护理需求向前发展，（2）识别高风险患者进行有针对性的干预，以及（3）减少 护理中断加剧了差异。 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护理 管理并导致高医疗成本。大约有四分之一的退伍军人在退伍军人事务部接受护理， 糖尿病，超过三分之一的人患有高血压。糖尿病和高血压与高血压有关。 心血管风险和导致严重并发症，包括中风，心脏病，肾衰竭，截肢 与死疾病患病率和进展方面的种族、社会经济和地理差异 因此，我们必须了解大流行病的影响，特别是 “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 结果在全国范围内的病人和VAMC水平;（2）确定退伍军人在高心血管风险 由于护理中断，并决定在多大程度上不平等的种族-族裔群体， 在大流行期间，城乡居住和社会脆弱性扩大;（3）在我们的投入下， 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：我们将创建两个回顾性队列的退伍军人接受初级保健从2017年到 2022年：糖尿病和高血压队列。将在普查范围内指定社会脆弱性衡量标准 根据退伍军人住宅的水平。我们的模型旨在研究个体之间的关联， 使用复杂GIS的人口普查区域和VAMC级因素、卫生保健提供指标和健康结果 联系和先进的时空统计方法。护理指标的提供包括 在大流行病早期监测心血管风险因素及其水平（监测时）。成果 包括动脉粥样硬化性心血管疾病（ASCVD）患病率、CVD风险水平、住院治疗， mortality.我们的工作与正在进行的项目不同的方面是：（1）我们包含完整数据的能力 当分析仅限于南卡罗来纳州时，对住院患者和急诊科患者进行随访，（2） 先进的统计建模使我们能够在多个层次上考虑多个因素（即，请耐心等候， 人口普查区，VAMC）;和（3）的时空方面的建议，这是至关重要的，因为空间- 大流行病的暂时性 下一步/实施：我们计划在Power BI中创建一个仪表板，这是一个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。我们的仪表板 将为每个VAMC提供个性化信息，说明从2017年到2019年的调整后季度水平。 2022用于（1）中断的护理指标（即，CVD风险监测，按模式进行的初级保健访视），（2）ASCVD水平， (3)ACC ASCVD风险水平（无ASCVD的患者）;（4）死亡率。我们使用区域数据， 我们将重点放在对虚拟资产管理中心的分析上，这将为决策层的决策提供信息， 流行病关于护理提供变化、VAMC水平适应性和 慢性疾病的结果将告知整个VA的大流行后护理。