气候变化领域集成服务门户(CCPortal): Exposure density and neighborhood disparities in COVID-19 infection risk

CCPortal

DOI	10.1073/pnas.2021258118
	Exposure density and neighborhood disparities in COVID-19 infection risk
	Hong B.; Bonczak B.J.; Gupta A.; Thorpe L.E.; Kontokosta C.E.
发表日期	2021
ISSN	00278424
卷号	118 期号:13
英文摘要	Although there is increasing awareness of disparities in COVID-19 infection risk among vulnerable communities, the effect of behavioral interventions at the scale of individual neighborhoods has not been fully studied. We develop a method to quantify neighborhood activity behaviors at high spatial and temporal resolutions and test whether, and to what extent, behavioral responses to social-distancing policies vary with socioeconomic and demographic characteristics. We define exposure density (Exρ) as a measure of both the localized volume of activity in a defined area and the proportion of activity occurring in distinct land-use types. Using detailed neighborhood data for New York City, we quantify neighborhood exposure density using anonymized smartphone geolocation data over a 3-mo period covering more than 12 million unique devices and rasterize granular land-use information to contextualize observed activity. Next, we analyze disparities in community social distancing by estimating variations in neighborhood activity by land-use type before and after a mandated stay-at-home order. Finally, we evaluate the effects of localized demographic, socioeconomic, and built-environment density characteristics on infection rates and deaths in order to identify disparities in health outcomes related to exposure risk. Our findings demonstrate distinct behavioral patterns across neighborhoods after the stay-at-home order and that these variations in exposure density had a direct and measurable impact on the risk of infection. Notably, we find that an additional 10% reduction in exposure density citywide could have saved between 1,849 and 4,068 lives during the study period, predominantly in lower-income and minority communities. © 2021 National Academy of Sciences. All rights reserved.
英文关键词	Computational modeling; COVID-19; Geolocation data; Mobility behavior; Neighborhood disparities
语种	英语
scopus关键词	Article; computer model; coronavirus disease 2019; exposure; health disparity; human; infection rate; infection risk; land use; lowest income group; mortality; neighborhood; New York; patient mobility; priority journal; quarantine; social distancing; demography; epidemiology; geographic information system; health disparity; prevention and control; risk factor; socioeconomics; spatiotemporal analysis; Built Environment; COVID-19; Geographic Information Systems; Health Status Disparities; Humans; New York City; Physical Distancing; Residence Characteristics; Risk Factors; SARS-CoV-2; Socioeconomic Factors; Spatio-Temporal Analysis
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/180094
作者单位	Marron Institute of Urban Management, New York University, New York, NY 10011, United States; Stern School of Business, New York University, New York, NY 10012, United States; Department of Population Health, New York University School of Medicine, New York, NY 10016, United States; Center for Urban Science and Progress, New York University, Brooklyn, NY 11201, United States
推荐引用方式 GB/T 7714	Hong B.,Bonczak B.J.,Gupta A.,et al. Exposure density and neighborhood disparities in COVID-19 infection risk[J],2021,118(13).
APA	Hong B.,Bonczak B.J.,Gupta A.,Thorpe L.E.,&Kontokosta C.E..(2021).Exposure density and neighborhood disparities in COVID-19 infection risk.Proceedings of the National Academy of Sciences of the United States of America,118(13).
MLA	Hong B.,et al."Exposure density and neighborhood disparities in COVID-19 infection risk".Proceedings of the National Academy of Sciences of the United States of America 118.13(2021).