Public Health Guidance, Neighborhoods, and SARS-CoV-2

公共卫生指南、社区和 SARS-CoV-2

• 批准号: 2242177
• 负责人: Joseph Galaskiewicz
• 金额: $ 44.08万
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2242177&HistoricalAwards=false
• 关键词: Public; Health; Guidance; Neighborhoods; SARS

This research examines one type of government non-pharmaceutical intervention (NPI) in the COVID-19 pandemic: government mandates that closed or placed limitations on business establishments and community organizations. Pandemics demand that governments respond to the public health crisis to curb the spread of the virus and prevent excess death. The aim of this project is to see if these closures affected people’s visits to establishments, and in turn, whether their visits slowed or accelerated the spread of the SARS-CoV-2 virus, cause of COVID-19 disease. To study this, cell-phone data that recorded visits to a variety of neighborhood establishments from February 1, 2020 to February 28, 2022 are used. First examined is whether local governments’ closures of businesses affected people’s visiting patterns and if this was contingent on the political atmosphere, built environment, and composition of the metropolitan area and neighborhoods. Second examined is whether visits to establishments both within and outside the neighborhood relate to case rates within the neighborhood over time. The results of this research will inform public health departments and local governments on the effectiveness of this kind of NPI and the role that these visits to establishments play in the spread of an infectious disease like SARS-CoV-2. This study examines twelve urban areas with different pandemic profiles, i.e., those that had few and multiple NPIs and high and low infections during the study period. Data examined are case rates at the zip code level and NPIs from various web sources that describe cases and initiatives at the zip, city, county, and state levels. These data are paired with data from the Census Bureau’s American Community Survey (ACS) on neighborhood demographics and weekly SafeGraph data on visits to neighborhood establishments and travel patterns across neighborhoods. The latter are based on the presence of cell phones at the sites of specific establishments within an urban zip code and also tell how long patrons visited these establishments. The SafeGraph data also have information on the ‘home’ block group of those who visited establishments. This enables knowledge of where residents of a given zip code traveled during the pandemic and if their visiting habits subsequently affected case rates in their home zip code. Weekly data on 29 different types of establishments are included. Analysis is through application of a fixed-slope multilevel model with spatial dependence error term. This research permits elucidation of how different kinds of public health decisions, especially with regard to business closures, relate to health outcomes across areas. Since closures are so impactful for the economy and personal well-being, understanding which types of organizations contributed more to the spread of SARS-CoV-2 throughout the pandemic will help inform data driven public health decisions that limit the activity of different businesses and organizations.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

本研究考察了COVID-19大流行中的一种政府非药物干预（NPI）：政府强制关闭或限制商业机构和社区组织。 大流行病要求政府应对公共卫生危机，以遏制病毒的传播，防止过度死亡。 该项目的目的是了解这些关闭是否影响了人们对机构的访问，反过来，他们的访问是否减缓或加速了导致COVID-19疾病的SARS-CoV-2病毒的传播。 为了研究这一点，我们使用了从2020年2月1日到2022年2月28日访问各种社区机构的手机数据。 首先考察了地方政府关闭企业是否会影响人们的访问模式，以及这是否取决于政治气氛、建筑环境以及大都市区和社区的构成。 第二个检查是，随着时间的推移，对社区内外机构的访问是否与社区内的病例率有关。 这项研究的结果将使公共卫生部门和地方政府了解这种NPI的有效性以及这些访问机构在SARS-CoV-2等传染病传播中的作用。 这项研究调查了12个具有不同流行特征的城市地区，即，在研究期间，那些有很少和多个NPI和高和低感染的人。 检查的数据是在邮政编码级别的案件率和NPI从各种网络来源，描述案件和举措在邮政编码，城市，县和州一级。 这些数据与人口普查局的美国社区调查（ACS）关于社区人口统计的数据以及每周关于社区设施访问和社区旅行模式的安全图数据相结合。 后者是根据在城市邮政编码内的特定机构的地点是否有手机，并说明顾客访问这些机构的时间。 SafeGraph数据还包含访问过机构的人的“家庭”街区组的信息。 这使得人们能够了解特定邮政编码的居民在大流行期间去过哪里，以及他们的访问习惯是否随后影响了他们家乡邮政编码的病例率。 包括29个不同类型机构的每周数据。 分析是通过应用一个固定斜率的多层次模型与空间依赖性误差项。 这项研究有助于阐明不同类型的公共卫生决定，特别是关于企业关闭的决定，如何与各地区的健康结果相关。 由于关闭对经济和个人福祉的影响如此之大，了解哪些类型的组织对SARS-CoV的传播贡献更大-该奖项反映了NSF的法定使命，并通过利用基金会的知识价值和更广泛的知识价值进行评估，被认为值得支持。影响审查标准。