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

Tao Hu, Han Yue, Changzhen Wang, Bing She, Xinyue Ye, Regina Liu, Xinyan Zhu, Weihe Wendy Guan and Shuming Bao
Additional contact information
Tao Hu: Center for Geographic Analysis, Harvard University, Cambridge, MA 02138, USA
Han Yue: Center of GeoInformatics for Public Security, School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China
Changzhen Wang: Department of Geography and Anthropology, Louisiana State University, Baton Rouge, LA 70803, USA
Bing She: Institute for Social Research, University of Michigan, Ann Arbor, MI 48106, USA
Xinyue Ye: Department of Landscape Architecture and Urban Planning, Texas A&M University, College Station, TX 77840, USA
Regina Liu: Department of Biology, Mercer University, Macon, GA 31207, USA
Xinyan Zhu: State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Weihe Wendy Guan: Center for Geographic Analysis, Harvard University, Cambridge, MA 02138, USA
Shuming Bao: China Data Institute, Ann Arbor, MI 48108, USA

IJERPH, 2020, vol. 17, issue 24, 1-18

Abstract: 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.

Keywords: COVID-19 incidence rate; racial segregation; access to testing site; spatial regression (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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