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.

摘要 该项目将使用国家和地方的发病率、死亡率和检测数据，并由地理信息系统驱动 基于代理的疾病建模，以确定驱动不均衡的“邻里效应” COVID-19的传播模式和严重后果。邻里环境 与慢性病死亡率、残疾、累积压力、认知能力 身体机能衰退同样的邻里效应也隐含在 加剧了COVID-19传播和健康结果的差异，但确切的 社会不平等根深蒂固的影响，特别是 COVID-19的结果尚不清楚。关键的假设是基于病原体的疾病传播 在国家和地方层面对现有的回顾性COVID-19数据源进行建模 地理空间数据输入和时空分析将提供关于 结构性因素影响了大流行病在当地的传播，并将促进 就如何减少目前和今后的差距提出宝贵建议， COVID-19和其他未来传染性流行病和大流行病的影响。我们将测试我们的钥匙 假设并通过以下具体目标实现我们的目标。 确定与健康的社会决定因素相关的县因素影响了 冠状病毒在美国的传播。 1)在确定的县内，确定哪些社会决定因素对 影响CV-19扩散或抑制CV-19扩散。 2)确定哪些人群在这些地方受到的影响更严重 背景和为什么？ 3)确定从这些发现中可以提出哪些具体建议？ 这项研究的预期成果是出版的手稿和学术报告 为卫生当局和政策制定者提供科学建议 可以积极主动地解决在某些地区放大疾病和不良健康结果的社会因素， 社区.