Disparities in Exposure and Health Effects of Multiple Environmental Stressors Across the Life Course

项目总结 我们中心的主要目标是了解和减少环境健康 差异(EHD)，通过实施三个应用新技术的完全集成的研究项目 在流行病学、暴露科学和累积风险评估方面的方法，具有强大的 整个中心的社区参与度。该中心强调多种健康结果 在整个生命过程中都有EHD的证据(出生结果、儿童生长速度和 心血管死亡率)，在马萨诸塞州和在两个低收入多数族裔 社区(切尔西和多切斯特)。住房和邻里关系的影响 多次暴露的环境和健康结果在整个 中心。在项目3中，我们使用新的地理空间数据和模拟技术来提供 广泛和高度可分辨的化学和非化学应激源暴露，包括 项目1中产生的空间分辨的空气污染和温度数据。在本补充资料中， 我们将利用我们的Project 3地理空间数据库， 和马萨诸塞州的环境暴露来评估种族/民族差异 新冠肺炎病例、住院和死亡。我们的地理空间漏洞数据将与 使用具有地址级地理编码和每日时间分辨率的个人级新冠肺炎数据， 由马萨诸塞州公共卫生部提供。我们将确定漏洞 不同城市新冠肺炎感染发生率和严重程度差异的相关因素 和马萨诸塞州的城镇，对每10,000人的病例发病率和 按城市/城镇分列的每10,000人的住院次数。我们还将应用新方法来 描述时空集群，使我们能够确定时空上的差异 新冠肺炎在城镇内部和城镇之间的传播模式，包括作为 个人特点。最后，我们将检查城市/城镇特定政策的差异， 执行国家政策和居民对公共卫生建议的感知，以 确定观察到的模式是否可以部分地由城市之间的差异来解释。有了这些 分析我们将确定马萨诸塞州的新冠肺炎热点以及病例如何在 以及社区之间，包括受灾最严重的少数族裔社区，我们将 确定最能解释这些暴露和健康后果的易损性因素 差距。

PROJECT SUMMARY The primary objective of our Center is to understand and reduce environmental health disparities (EHDs) by conducting three fully-integrated research projects applying novel methods in epidemiology, exposure science, and cumulative risk assessment, with strong community engagement across the Center. The Center emphasizes multiple health outcomes across the life course with evidence for EHDs (birth outcomes, childhood growth rates, and cardiovascular mortality), in Massachusetts and within two low-income majority-minority communities (Chelsea and Dorchester). The influence of housing and the neighborhood environment on multiple exposures and health outcomes are emphasized throughout the Center. Within Project 3, we use novel geospatial data and simulation techniques to provide an extensive and highly resolved set of chemical and non-chemical stressor exposures, including spatially-resolved air pollution and temperature data generated in Project 1. In this supplement, we will leverage our Project 3 geospatial database of numerous social, housing, demographic, and environmental exposures across Massachusetts to evaluate racial/ethnic disparities in COVID-19 cases, hospitalizations, and deaths. Our geospatial vulnerability data will be linked with individual-level COVID-19 data with address-level geocodes and daily temporal resolution, provided by the Massachusetts Department of Public Health. We will identify vulnerability factors associated with disparities in incidence and severity of COVID-19 infection across cities and towns in Massachusetts, modeling predictors of case incidence per 10,000 persons and hospitalizations per 10,000 persons by city/town over time. We will also apply novel methods to characterize spatiotemporal clustering, allowing us to determine differences in spatiotemporal patterns of COVID-19 spread within and between cities/towns, including as a function of individual characteristics. Finally, we will examine differences in city/town-specific policies, implementation of state policy, and resident perception of public health recommendations, to determine if observed patterns can be explained in part by between-city differences. With these analyses we will identify COVID-19 hot spots in Massachusetts and how cases spread within and between communities, including the hardest-hit majority-minority communities, and we will determine the vulnerability factors that best explain these exposure and health outcome disparities.