Cargando…

Racial Segregation, Testing Site Access, and COVID-19 Incidence Rate in Massachusetts, USA

The U.S. has merely 4% of the world population, but contains 25% of the world’s COVID-19 cases. Since the COVID-19 outbreak in the U.S., Massachusetts has been leading other states in the total number of COVID-19 cases. Racial residential segregation is a fundamental cause of racial disparities in h...

Descripción completa

Detalles Bibliográficos
Autores principales: Hu, Tao, Yue, Han, Wang, Changzhen, She, Bing, Ye, Xinyue, Liu, Regina, Zhu, Xinyan, Guan, Weihe Wendy, Bao, Shuming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7766428/
https://www.ncbi.nlm.nih.gov/pubmed/33352650
http://dx.doi.org/10.3390/ijerph17249528
_version_ 1783628716330450944
author Hu, Tao
Yue, Han
Wang, Changzhen
She, Bing
Ye, Xinyue
Liu, Regina
Zhu, Xinyan
Guan, Weihe Wendy
Bao, Shuming
author_facet Hu, Tao
Yue, Han
Wang, Changzhen
She, Bing
Ye, Xinyue
Liu, Regina
Zhu, Xinyan
Guan, Weihe Wendy
Bao, Shuming
author_sort Hu, Tao
collection PubMed
description The U.S. has merely 4% of the world population, but contains 25% of the world’s COVID-19 cases. Since the COVID-19 outbreak in the U.S., Massachusetts has been leading other states in the total number of COVID-19 cases. Racial residential segregation is a fundamental cause of racial disparities in health. Moreover, disparities of access to health care have a large impact on COVID-19 cases. Thus, this study estimates racial segregation and disparities in testing site access and employs economic, demographic, and transportation variables at the city/town level in Massachusetts. Spatial regression models are applied to evaluate the relationships between COVID-19 incidence rate and related variables. This is the first study to apply spatial analysis methods across neighborhoods in the U.S. to examine the COVID-19 incidence rate. The findings are: (1) Residential segregations of Hispanic and Non-Hispanic Black/African Americans have a significantly positive association with COVID-19 incidence rate, indicating the higher susceptibility of COVID-19 infections among minority groups. (2) Non-Hispanic Black/African Americans have the shortest drive time to testing sites, followed by Hispanic, Non-Hispanic Asians, and Non-Hispanic Whites. The drive time to testing sites is significantly negatively associated with the COVID-19 incidence rate, implying the importance of the accessibility of testing sites by all populations. (3) Poverty rate and road density are significant explanatory variables. Importantly, overcrowding represented by more than one person per room is a significant variable found to be positively associated with COVID-19 incidence rate, suggesting the effectiveness of social distancing for reducing infection. (4) Different from the findings of previous studies, the elderly population rate is not statistically significantly correlated with the incidence rate because the elderly population in Massachusetts is less distributed in the hotspot regions of COVID-19 infections. The findings in this study provide useful insights for policymakers to propose new strategies to contain the COVID-19 transmissions in Massachusetts.
format Online
Article
Text
id pubmed-7766428
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-77664282020-12-28 Racial Segregation, Testing Site Access, and COVID-19 Incidence Rate in Massachusetts, USA Hu, Tao Yue, Han Wang, Changzhen She, Bing Ye, Xinyue Liu, Regina Zhu, Xinyan Guan, Weihe Wendy Bao, Shuming Int J Environ Res Public Health Article The U.S. has merely 4% of the world population, but contains 25% of the world’s COVID-19 cases. Since the COVID-19 outbreak in the U.S., Massachusetts has been leading other states in the total number of COVID-19 cases. Racial residential segregation is a fundamental cause of racial disparities in health. Moreover, disparities of access to health care have a large impact on COVID-19 cases. Thus, this study estimates racial segregation and disparities in testing site access and employs economic, demographic, and transportation variables at the city/town level in Massachusetts. Spatial regression models are applied to evaluate the relationships between COVID-19 incidence rate and related variables. This is the first study to apply spatial analysis methods across neighborhoods in the U.S. to examine the COVID-19 incidence rate. The findings are: (1) Residential segregations of Hispanic and Non-Hispanic Black/African Americans have a significantly positive association with COVID-19 incidence rate, indicating the higher susceptibility of COVID-19 infections among minority groups. (2) Non-Hispanic Black/African Americans have the shortest drive time to testing sites, followed by Hispanic, Non-Hispanic Asians, and Non-Hispanic Whites. The drive time to testing sites is significantly negatively associated with the COVID-19 incidence rate, implying the importance of the accessibility of testing sites by all populations. (3) Poverty rate and road density are significant explanatory variables. Importantly, overcrowding represented by more than one person per room is a significant variable found to be positively associated with COVID-19 incidence rate, suggesting the effectiveness of social distancing for reducing infection. (4) Different from the findings of previous studies, the elderly population rate is not statistically significantly correlated with the incidence rate because the elderly population in Massachusetts is less distributed in the hotspot regions of COVID-19 infections. The findings in this study provide useful insights for policymakers to propose new strategies to contain the COVID-19 transmissions in Massachusetts. MDPI 2020-12-19 2020-12 /pmc/articles/PMC7766428/ /pubmed/33352650 http://dx.doi.org/10.3390/ijerph17249528 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hu, Tao
Yue, Han
Wang, Changzhen
She, Bing
Ye, Xinyue
Liu, Regina
Zhu, Xinyan
Guan, Weihe Wendy
Bao, Shuming
Racial Segregation, Testing Site Access, and COVID-19 Incidence Rate in Massachusetts, USA
title Racial Segregation, Testing Site Access, and COVID-19 Incidence Rate in Massachusetts, USA
title_full Racial Segregation, Testing Site Access, and COVID-19 Incidence Rate in Massachusetts, USA
title_fullStr Racial Segregation, Testing Site Access, and COVID-19 Incidence Rate in Massachusetts, USA
title_full_unstemmed Racial Segregation, Testing Site Access, and COVID-19 Incidence Rate in Massachusetts, USA
title_short Racial Segregation, Testing Site Access, and COVID-19 Incidence Rate in Massachusetts, USA
title_sort racial segregation, testing site access, and covid-19 incidence rate in massachusetts, usa
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7766428/
https://www.ncbi.nlm.nih.gov/pubmed/33352650
http://dx.doi.org/10.3390/ijerph17249528
work_keys_str_mv AT hutao racialsegregationtestingsiteaccessandcovid19incidencerateinmassachusettsusa
AT yuehan racialsegregationtestingsiteaccessandcovid19incidencerateinmassachusettsusa
AT wangchangzhen racialsegregationtestingsiteaccessandcovid19incidencerateinmassachusettsusa
AT shebing racialsegregationtestingsiteaccessandcovid19incidencerateinmassachusettsusa
AT yexinyue racialsegregationtestingsiteaccessandcovid19incidencerateinmassachusettsusa
AT liuregina racialsegregationtestingsiteaccessandcovid19incidencerateinmassachusettsusa
AT zhuxinyan racialsegregationtestingsiteaccessandcovid19incidencerateinmassachusettsusa
AT guanweihewendy racialsegregationtestingsiteaccessandcovid19incidencerateinmassachusettsusa
AT baoshuming racialsegregationtestingsiteaccessandcovid19incidencerateinmassachusettsusa