The Impacts of Mitigation Strategies to Prevent COVID-19 Transmission in American Indian Communities

其他项目信息-项目概要/摘要 缓解策略对预防COVID-19在美国印第安社区传播的影响 关于当地强制性的COVID-19缓解政策的影响，如非 药物干预（NPI）对医疗和经济弱势群体的健康和福祉 居住在农村部落土地上的美国印第安人等人口。迫切需要评估 已经实施的措施对COVID-19传播的影响，这些措施的社会和经济影响 这些社区的措施，以及对非AIAN社区的潜在溢出效应。交错 在部落土地上实施新产品导入的性质和主权国家政策决定的独立性 从地方（如县级）政策提出了一个独特的准自然实验的政策时机，使我们能够 研究从部落NPI到相连的非部落地区的溢出效应。我们的研究努力旨在改善 美国印第安人健康通过理解和评估这些NPI措施对NIH指定的差距 社区R01提案有四个具体目标：（1）确定COVID-19病例和死亡是否 与美洲印第安人保留地和/或土著和非土著部落赌场的早期关闭有关 生活在保留地内外的居民(2)确定通勤模式的变化， 运动行为是NPI的结果。(3)使用及时索赔数据分析NPI的实施 疫情的发展影响了不同非COVID-19医疗保健服务的使用， 设施(4)对居住在农村地区的美国印第安人进行调查，以创建新的主要数据资源 并通过手机流量的二次数据分析，交叉验证我们得到的结果。很长的- 本建议书的长期目标是开发和验证可用于大型二次开发的分析工具， 数据集，以帮助评估和设计公共政策，以应对人工智能中的公共卫生突发事件 人口数量。我们的中心假设是，及时实施部落NPI减少了 AI社区和交通连接地区的COVID-19病例和死亡人数。然而，我们也希望， NPI的次要影响，如经济困难和非COVID相关医疗保健利用不足， 在部落的土地上会更强大我们将采用创新的双管齐下的方法，将联合收割机的实证分析 二级大数据源与主要数据收集，以提供关于当前和未来的强有力证据 这些政策的影响。该提议意义重大，因为COVID-19及相关政策对 部落社区研究不足，资金不足，也没有得到很好的理解，但不仅与人工智能有关， 部落政策和人口，但区域卫生和经济政策。我们的研究是由 C.A.R.E.由我们的团队成员制定的本土数据治理原则。创新是因为 它应用经济学、计算机科学和公共卫生技术来提供可操作的结果， 对未来政策实施提出建议。

OTHER PROJECT INFORMATION – Project Summary/Abstract The Impacts of Mitigation Strategies to Prevent COVID-19 Transmission in American Indian Communities Very little is known about the impact of locally-mandated COVID-19 mitigation policies such as Non- Pharmaceutical Interventions (NPI) on the health and wellbeing of medically and economically vulnerable populations such as American Indians (AI) living in rural tribal lands. There is an urgent need to evaluate the effect of already implemented measures on the spread of COVID-19, the social and economic impact of those measures in these communities, and the potential spillover effects on non-AIAN communities. The staggered nature of the NPI implementation in tribal lands and the independence of sovereign nations’ policy decisions from local (e.g. county-level) policies present a unique quasi-natural experiment in policy timing, allowing us to study spillover effects from tribal NPIs to connected non-tribal areas. Our research efforts seek to improve American Indian health by understanding and evaluating these NPI measures on an NIH-designated disparity community. This R01 proposal has four specific aims: (1) Identify whether COVID-19 cases and deaths are related to the early closure of American Indian reservations and/or tribal casinos among Native and non-Native populations living on and off of adjacent reservation lands. (2) Identify the change in commuter patterns and movement behaviors as a result of NPIs. (3) Use in-time claims data to analyze how the implementation of NPIs and the progression of the epidemic affected utilization of different non-COVID-19 health care services and facilities. (4) Conduct a survey of American Indians living in rural areas to create a new primary data resource and cross-validate the results we obtain through analysis of secondary data of cell phone traffic flows. The long- term goal of this proposal is to develop and validate analytical tools that could be applied with large secondary datasets to aid the evaluation and design of public policies to address public health emergencies in AI populations. Our central hypothesis is that the timely implementation of tribal NPIs reduced the incidence of COVID-19 cases and deaths in AI communities and in traffic-connected areas. However, we also expect that the secondary effects of NPIs, such as economic hardship and under-utilization of non-COVID-related health care, would be stronger on tribal lands. We will use an innovative two-pronged approach to combine empirical analysis of secondary big data sources with primary data collection to provide robust evidence on the current and future effects of these policies. This proposal is significant because the impact of COVID-19 and related policies on tribal communities is under-researched, under-funded, and not well understood, and yet relevant not only for AI tribal policies and populations but for regional health and economic policies. Our research is guided by the C.A.R.E. Principles of Indigenous Data Governance developed by our team members. It is innovative because it applies techniques from economics, computer science and public health to deliver actionable results and recommendations for future policy implementation.