Administrative Core

我们中心的主要目标是了解和减少环境健康 差异（EHDs）进行三个完全整合的研究项目，应用新的 流行病学、暴露科学和累积风险评估方法， 整个中心的社区参与。该中心强调多种健康结果 在整个生命过程中，EHD的证据（出生结果，儿童生长率， 心血管疾病死亡率），在马萨诸塞州和两个低收入的多数-少数 社区（切尔西和多切斯特）。住房和邻里的影响 环境对多重暴露和健康结果的影响， 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一个 广泛和高分辨率的化学和非化学应激源暴露，包括 项目1中生成的空间分辨空气污染和温度数据。在这份补充中， 我们将利用我们的项目3地理空间数据库，包括许多社会，住房，人口， 马萨诸塞州的种族和环境暴露，以评估 COVID-19病例、住院和死亡。我们的地理空间脆弱性数据将与 使用具有地址级地理编码和每日时间分辨率的个人级COVID-19数据， 由马萨诸塞州公共卫生部提供。我们将识别脆弱性 与各城市COVID-19感染发病率和严重程度差异相关的因素 和马萨诸塞州的城镇，每10，000人病例发病率的建模预测因子， 按城市/城镇分列的每10 000人住院率。我们还将采用新的方法， 表征时空聚类，使我们能够确定时空差异 COVID-19在城市/城镇内部和之间传播的模式，包括作为 个性特征最后，我们将研究城市/城镇具体政策的差异， 国家政策的实施，以及居民对公共卫生建议的看法， 确定观察到的模式是否可以部分地用城市间的差异来解释。与这些 分析我们将确定马萨诸塞州的COVID-19热点以及病例如何在内部传播 社区之间，包括受打击最严重的少数民族社区，我们将 确定最能解释这些暴露和健康结果的脆弱性因素 差距。

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.