Administrative Core

行政核心

• 批准号: 10266212
• 负责人: Jaydutt V. Vadgama
• 金额: $ 17.94万
• 依托单位: CHARLES R. DREW UNIVERSITY OF MED & SCI
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-28 至 2024-02-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10266212
• 关键词: Address; Area; COVID-19; Chronic Disease; Communities; Coronavirus; County; Data; Data Sources; Development; Disease; Disease model; Environment; Epidemic; Epidemiologic Methods; Future; Health; Impaired cognition; Incidence; Knowledge; Manuscripts; Minority; Modeling; Neighborhoods; Outcome; Outcomes Research; Pattern; Physical Function; Policy Maker; Population Group; Publishing; Recommendation; Science; Stress; Structure; Testing; Translational Research; United States; authority; base; disability; metropolitan; mortality; pandemic disease; social determinants; social disparities; social factors; social health determinants; transmission process

Abstract This project will use national and local incidence, mortality and testing data, with GIS driven agent-based disease modelling to identify the “neighborhood effects” that drive the uneven patterns of transmission and serious outcomes from COVID-19. Neighborhood environments have been associated with chronic disease mortality, disability, cumulative stress, cognitive decline, loss of physical functioning. These same neighborhood effects are implicit in exacerbating disparities in the spread and health outcomes of COVID-19, however the exact mechanisms and the magnitude of the impact of entrenched social disparities specifically on COVID-19 outcomes are not yet known. The key hypothesis is that agent based disease spread modeling of the existing retrospective COVID-19 data sources at the national and local levels with geospatial data input and spatial-temporal analysis will provide powerful knowledge on the structural factors that influenced the pandemic’s spread in local areas and will facilitate the development of valuable recommendations on how to mitigate current and future disparities in impacts of COVID-19 and other future infectious epidemics and pandemics. We will test our key hypothesis and accomplish our objectives via the following Specific Aims. Determine what Counties factors related to the social determinants of health have influenced the spread of coronavirus in the United States. 1) Determine, within the identified Counties, which social determinants have had significant impact on either spread or inhibit spread of CV-19. 2) Determine which populations groups have been impacted more severely in these local contexts and why? 3) Determine what specific recommendations can be made from these findings? The expected outcomes of this research are published manuscripts and scholarly presentations that provide science- based recommendations on the how health authorities and policy makers can proactively address social factors that amplify disease and poor health outcomes in certain communities.

抽象的 该项目将使用GIS Drive使用国家和地方事件，死亡率和测试数据 基于代理的疾病建模，以识别驱动不均衡的“邻居效应” COVID-19的传播和严重结果的模式。邻里环境 与慢性疾病死亡率，残疾，累积压力，认知有关 下降，身体功能丧失。这些相同的邻居效应是隐含的 加剧了Covid-19的差异和健康成果的差异，但是极端 根深蒂固的社会差异的机制和影响 Covid-19结果尚不清楚。关键假设是基于药物的疾病蔓延 在国家和地方一级的现有回顾性COVID-19数据源建模 借助地理空间数据输入和时空分析将为有关 影响大流行在当地传播的结构性因素，并将促进 开发有关如何减轻当前和未来分布的宝贵建议 COVID-19和其他未来感染性流行病和流行病的影响。我们将测试我们的钥匙 假设并通过以下特定目的实现我们的目标。 确定哪些县因素与健康的社会决定者有关的因素影响了 冠状病毒在美国的传播。 1）确定在确定的县内，社会决定者拥有重要的 对CV-19的扩散或抑制扩散的影响。 2）确定这些局部人群对哪些人群受到更严重的影响 上下文，为什么？ 3）确定这些发现可以提出哪些具体建议？ 这项研究的预期结果是发表的手稿和科学演讲 这提供了有关卫生当局和政策制定者如何的基于科学的建议 可以主动解决一些在某些地方扩大疾病和健康结果不佳的社会因素 社区。