Mitigating effects of telehealth uptake on disparities in maternal care access, quality, outcomes, and expenditures during the COVID-19 pandemic

项目摘要 冠状病毒病2019年(新冠肺炎)大流行已造成重大的社会、医疗和经济 美国(美国)的破坏，可能加剧农村面临的孕产妇健康差距 女人和有色人种女人。远程保健是减少孕产妇收入差距的一个有希望的机会 卫生机会、质量和成果，考虑到其广泛的机会，包括视听 患者和提供者之间的同步和异步会诊、远程患者监控、 促进循证实践的可视化交流，并支持临床决策。然而， 多层次的障碍可能会阻碍一些未得到充分服务的妇女充分受益于远程保健。已扩展 通过冠状病毒援助、救济和经济安全覆盖联邦和州一级的远程医疗 (CARE)法案和国家政策可能通过以下方式减轻这一史无前例的大流行的有害影响 缩小在获得远程保健和优质产妇保健方面的差距。尽管自我报告的数据显示 在大流行期间远程保健摄取量的增加，可获得的关于 围产期远程保健对大流行的影响以及国家一级政策在远程保健适应中的作用 在新冠肺炎期间。这项纵向的、真实世界的数据研究将使用重复出现的国家电子健康记录 (EHR)数据[国家COVID队列协作(N3C)]和全州范围内基于人口的综合数据 南卡罗来纳州和佛罗里达州，补充了城市人口在N3C中的过多代表。使用 使用通用数据建模的多种创新方法，对遗漏进行多层次归因，以及 贝叶斯统计模拟方法，本研究的目的是：1)调查新冠肺炎疫情的影响 按产妇种族/族裔和城乡分列的孕产妇保健机会、质量以及孕产妇和生育结果 居住；2)检查围产期远程保健的普及是否减轻了大流行对 按产妇种族/族裔和城乡居住地分列的孕产妇保健机会、质量和结果；以及3) 评估州级远程医疗政策(对始发站点的放宽、对商店和 转发服务、远程患者监控和提供商扩展)-通过以下方式与围产期远程健康相关 种族--族裔和农村/城市居住。我们还将开发一个随机模拟模型来预测 产妇保健服务的获得、质量、结果和支出方面的长期变化 没有各自的州远程医疗政策。我们的首要目标是促进对 新冠肺炎大流行、国家远程保健政策和围产期远程保健普及之间的三向交叉 关于弱势产妇群体--农村和种族/少数民族妇女--面临的健康差距。我们 已经组成了一个多学科的调查团队，包括孕产妇健康服务研究人员、健康 政策分析员、远程保健专家、高级临床研究人员、围产期流行病学家、农村健康倡导者、 健康心理学家、大数据分析师和机器学习专家进行了这项研究。

Project Summary The Coronavirus disease 2019 (COVID-19) pandemic has caused significant social, healthcare, and economic devastation in the United States (US), potentially exacerbating the maternal health disparities facing rural women and women of color. Telehealth represents a promising opportunity to reducing disparities in maternal health access, quality, and outcomes, given its substantial range of opportunities, including audiovisual synchronous and asynchronous encounters between patients and providers, remote patient monitoring, facilitating visual communication of evidence-based practice, and supporting clinical decision-making. Yet, multilevel barriers might hinder some underserved women from fully benefiting from telehealth. Expanded federal and state-level telehealth coverage through the Coronavirus Aid, Relief, and Economic Security (CARES) Act and state policies may be mitigating the detrimental effects of this unprecedented pandemic by reducing gaps in access to telehealth and quality maternity care. Although self-reported data indicated abrupt increases in telehealth uptake during the pandemic, limited real-world data are available regarding the role of perinatal telehealth uptake on the pandemic’s effects and the role of state level policy in telehealth adaptation during COVID-19. This longitudinal, real-world data study will use recurring national electronic health records (EHR) data [National COVID Cohort Collaborative (N3C)] and integrated statewide population-based data in South Carolina and Florida which complement the overrepresentation of urban populations in N3C. With multiple innovative approaches using common data modeling, multi-level imputation for missingness, and Bayesian statistics simulation methods, this study aims to: 1) investigate the impact of the COVID-19 pandemic on maternal care access, quality, and maternal and birth outcomes by maternal race/ethnicity and rural/urban residence; 2) examine whether perinatal telehealth uptake mitigates the pandemic's effects on disparities in maternal care access, quality, and outcomes by maternal race/ethnicity and rural/urban residence; and 3) assess how state-level telehealth policies (relaxation for originating sites, reimbursements for store-and- forward services, remote patient monitoring, and provider expansion) – relate to perinatal telehealth uptake by race-ethnicity and rural/urban residence. We will also 4) develop a stochastic simulation model to predict long-term changes in maternal care access, quality, outcomes, and expenditures of maternity care, with and without each respective state telehealth policy. Our overarching goal is to advance the understanding of the three-way intersections among the COVID-19 pandemic, state telehealth policy, and perinatal telehealth uptake on health disparities facing vulnerable maternal populations – rural and racial/ethnic minority women. We have formed a multidisciplinary team of investigators, including maternal health services researchers, health policy analysts, telehealth experts, senior clinical researchers, perinatal epidemiologists, rural health advocates, health psychologist, big data analysts, and machine learning experts to conduct this study.