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

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  • Tao Hu

    (Center for Geographic Analysis, Harvard University, Cambridge, MA 02138, USA
    Geocomputation Center for Social Science, Wuhan University, Wuhan 430079, China)

  • 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
    Collaborative Innovation Center of Geospatial Technology, 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)

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.

Suggested Citation

  • Tao Hu & Han Yue & Changzhen Wang & Bing She & Xinyue Ye & Regina Liu & Xinyan Zhu & Weihe Wendy Guan & Shuming Bao, 2020. "Racial Segregation, Testing Site Access, and COVID-19 Incidence Rate in Massachusetts, USA," IJERPH, MDPI, vol. 17(24), pages 1-18, December.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:24:p:9528-:d:464928
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    References listed on IDEAS

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    1. Siqin Wang & Yan Liu & Tao Hu, 2020. "Examining the Change of Human Mobility Adherent to Social Restriction Policies and Its Effect on COVID-19 Cases in Australia," IJERPH, MDPI, vol. 17(21), pages 1-17, October.
    2. Chang, Virginia W., 2006. "Racial residential segregation and weight status among US adults," Social Science & Medicine, Elsevier, vol. 63(5), pages 1289-1303, September.
    3. Acevedo-Garcia, D. & Lochner, K.A. & Osypuk, T.L. & Subramanian, S.V., 2003. "Future directions in residential segregation and health research: A multilevel approach," American Journal of Public Health, American Public Health Association, vol. 93(2), pages 215-221.
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    2. Yong Xu & Chunlan Guo & Jinxin Yang & Zhenjie Yuan & Hung Chak Ho, 2023. "Modelling Impact of High-Rise, High-Density Built Environment on COVID-19 Risks: Empirical Results from a Case Study of Two Chinese Cities," IJERPH, MDPI, vol. 20(2), pages 1-15, January.
    3. Jingjing Wang & Xueying Wu & Ruoyu Wang & Dongsheng He & Dongying Li & Linchuan Yang & Yiyang Yang & Yi Lu, 2021. "Review of Associations between Built Environment Characteristics and Severe Acute Respiratory Syndrome Coronavirus 2 Infection Risk," IJERPH, MDPI, vol. 18(14), pages 1-16, July.
    4. Nushrat Nazia & Zahid Ahmad Butt & Melanie Lyn Bedard & Wang-Choi Tang & Hibah Sehar & Jane Law, 2022. "Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review," IJERPH, MDPI, vol. 19(14), pages 1-28, July.

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