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

其他项目信息-项目摘要/摘要 缓解策略对防止新冠肺炎在美洲印第安人社区传播的影响 人们对当地强制实施的新冠肺炎缓解政策的影响知之甚少，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(AI)等人口。目前迫切需要评估 已经实施的措施对新冠肺炎传播的影响，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。摇摇晃晃的 在部落土地上实施NPI的性质和主权国家决策的独立性 来自地方(例如县级)的政策在政策时机上呈现了一种独特的准自然实验，使我们能够 研究部落NPI对相连的非部落地区的溢出效应。我们的研究努力寻求改进 通过了解和评估NIH指定的差距上的这些NPI措施来了解和评估美国印第安人的健康 社区。这份R01提案有四个具体目标：(1)确定新冠肺炎病例和死亡人数是否 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 居住在邻近保留地上和之外的人口。(2)确定通勤模式的变化，并 作为NPI的结果的运动行为。(3)使用及时索赔数据分析NPI的实施情况 疫情的发展影响了不同非新冠肺炎医疗服务的利用和 设施。(四)对生活在农村的美国印第安人进行调查，打造新的一手数据资源 并通过对手机流量二次数据的分析，对得到的结果进行交叉验证。长的- 本提案的长期目标是开发和验证可应用于大型二级市场的分析工具 帮助评估和设计公共政策以应对人工智能领域突发公共卫生事件的数据集 人口。我们的中心假设是，及时实施部落NPI降低了 人工智能社区和交通相关地区的新冠肺炎病例和死亡人数。不过，我们也预计， 非营利机构的次要影响，如经济困难和非COVID相关保健得不到充分利用， 在部落土地上会更强大。我们将采用创新的双管齐下的方法来结合实证分析 具有主要数据收集的次要大数据来源，以提供关于当前和未来的可靠证据 这些政策的效果。这一提议意义重大，因为新冠肺炎及相关政策对 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，而不是区域卫生和经济政策。我们的研究是在 C.A.R.E.由我们的团队成员开发的本地数据治理原则。它的创新之处在于 它应用经济学、计算机科学和公共卫生的技术来提供可操作的结果和 对未来政策执行的建议。

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.