IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i19p12304-d927458.html
   My bibliography  Save this article

Effects of Social Vulnerability and Spatial Accessibility on COVID-19 Vaccination Coverage: A Census-Tract Level Study in Milwaukee County, USA

Author

Listed:
  • Zengwang Xu

    (Department of Geography, University of Wisconsin-Milwaukee, Milwaukee, WI 53201, USA)

  • Bin Jiang

    (Faculty of Engineering and Sustainable Development, Division of GIScience, University of Gävle, 801 76 Gävle, Sweden)

Abstract

COVID-19 vaccination coverage was studied by race/ethnicity, up-to-date doses, and by how it was affected by social vulnerability and spatial accessibility at the census-tract level in Milwaukee County, WI, USA. Social vulnerability was quantified at the census-tract level by an aggregate index and its sub-components calculated using the principal components analysis method. The spatial accessibility was assessed by clinic-to-population ratio and travel impedance. Ordinary least squares (OLS) and spatial regression models were employed to examine how social vulnerability and spatial accessibility relate to the vaccination rates of different doses. We found great disparities in vaccination rates by race and between areas of low and high social vulnerability. Comparing to non-Hispanic Blacks, the vaccination rate of non-Hispanic Whites in the county is 23% higher (60% vs. 37%) in overall rate (one or more doses), and 20% higher (29% vs. 9%) in booster rate (three or more doses). We also found that the overall social-vulnerability index does not show a statistically significant relationship with the overall vaccination rate when it is defined as the rate of people who have received one or more doses of vaccines. However, after the vaccination rate is stratified by up-to-date doses, social vulnerability has positive effects on one-dose and two-dose rates, but negative effects on booster rate, and the effects of social vulnerability become increasingly stronger and turn to negative for multi-dose vaccination rates, indicating the increasing challenges of high social vulnerability areas to multi-dose vaccination. The large negative effects of socio-economic status on the booster rate suggests the importance of improving general socio-economic conditions to promote multi-dose vaccination rates.

Suggested Citation

  • Zengwang Xu & Bin Jiang, 2022. "Effects of Social Vulnerability and Spatial Accessibility on COVID-19 Vaccination Coverage: A Census-Tract Level Study in Milwaukee County, USA," IJERPH, MDPI, vol. 19(19), pages 1-13, September.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:19:p:12304-:d:927458
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/19/12304/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/19/19/12304/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lixin Lin & Yanji Zhao & Boqiang Chen & Daihai He, 2022. "Multiple COVID-19 Waves and Vaccination Effectiveness in the United States," IJERPH, MDPI, vol. 19(4), pages 1-12, February.
    2. Magdalena Sycinska-Dziarnowska & Iwona Paradowska-Stankiewicz & Krzysztof Woźniak, 2021. "The Global Interest in Vaccines and Its Prediction and Perspectives in the Era of COVID-19. Real-Time Surveillance Using Google Trends," IJERPH, MDPI, vol. 18(15), pages 1-11, July.
    3. Yun Li & Moming Li & Megan Rice & Yanfang Su & Chaowei Yang, 2021. "Phased Implementation of COVID-19 Vaccination: Rapid Assessment of Policy Adoption, Reach and Effectiveness to Protect the Most Vulnerable in the US," IJERPH, MDPI, vol. 18(14), pages 1-14, July.
    4. Eunha Shim, 2021. "Projecting the Impact of SARS-CoV-2 Variants and the Vaccination Program on the Fourth Wave of the COVID-19 Pandemic in South Korea," IJERPH, MDPI, vol. 18(14), pages 1-11, July.
    5. Chen Dong & Qian Liang & Tanao Ji & Jun Gu & Jian Feng & Min Shuai & Xiaoming Zhang & Rui Zhao & Zhifeng Gu, 2021. "Determinants of Vaccine Acceptance against COVID-19 in China: Perspectives on Knowledge and DrVac-COVID19S Scale," IJERPH, MDPI, vol. 18(21), pages 1-13, October.
    6. Guangqing Chi & Jun Zhu, 2008. "Spatial Regression Models for Demographic Analysis," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 27(1), pages 17-42, February.
    7. Bin Jiang, 2019. "A Recursive Definition of Goodness of Space for Bridging the Concepts of Space and Place for Sustainability," Sustainability, MDPI, vol. 11(15), pages 1-13, July.
    8. Susan L. Cutter & Bryan J. Boruff & W. Lynn Shirley, 2003. "Social Vulnerability to Environmental Hazards," Social Science Quarterly, Southwestern Social Science Association, vol. 84(2), pages 242-261, June.
    9. J. Elhorst, 2010. "Applied Spatial Econometrics: Raising the Bar," Spatial Economic Analysis, Taylor & Francis Journals, vol. 5(1), pages 9-28.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Agnese Vitali & Arnstein Aassve & Trude Lappegård, 2015. "Diffusion of Childbearing Within Cohabitation," Demography, Springer;Population Association of America (PAA), vol. 52(2), pages 355-377, April.
    2. Kelsea Best & Siobhan Kerr & Allison Reilly & Anand Patwardhan & Deb Niemeier & Seth Guikema, 2023. "Spatial regression identifies socioeconomic inequality in multi-stage power outage recovery after Hurricane Isaac," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 117(1), pages 851-873, May.
    3. Lixin Lin & Yanji Zhao & Boqiang Chen & Daihai He, 2022. "Multiple COVID-19 Waves and Vaccination Effectiveness in the United States," IJERPH, MDPI, vol. 19(4), pages 1-12, February.
    4. Kiziltan, Mustafa, 2021. "Water-energy nexus of Turkey’s municipalities: Evidence from spatial panel data analysis," Energy, Elsevier, vol. 226(C).
    5. Zhao, Mingxuan & Lv, Lianhong & Wu, Jing & Wang, Shen & Zhang, Nan & Bai, Zihan & Luo, Hong, 2022. "Total factor productivity of high coal-consuming industries and provincial coal consumption: Based on the dynamic spatial Durbin model," Energy, Elsevier, vol. 251(C).
    6. Yi Peng, 2015. "Regional earthquake vulnerability assessment using a combination of MCDM methods," Annals of Operations Research, Springer, vol. 234(1), pages 95-110, November.
    7. Meryl Jagarnath & Tirusha Thambiran & Michael Gebreslasie, 2020. "Heat stress risk and vulnerability under climate change in Durban metropolitan, South Africa—identifying urban planning priorities for adaptation," Climatic Change, Springer, vol. 163(2), pages 807-829, November.
    8. Ashley C. Freeman & Walker S. Ashley, 2017. "Changes in the US hurricane disaster landscape: the relationship between risk and exposure," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 88(2), pages 659-682, September.
    9. Anna M. Ferragina & Giulia Nunziante, 2018. "Are Italian firms performances influenced by innovation of domestic and foreign firms nearby in space and sectors?," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 45(3), pages 335-360, September.
    10. Yongdeng Lei & Jing’ai Wang & Yaojie Yue & Hongjian Zhou & Weixia Yin, 2014. "Rethinking the relationships of vulnerability, resilience, and adaptation from a disaster risk perspective," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 70(1), pages 609-627, January.
    11. Yingcheng Li & Kai Zhu, 2017. "Spatial dependence and heterogeneity in the location processes of new high-tech firms in Nanjing, China," Papers in Regional Science, Wiley Blackwell, vol. 96(3), pages 519-535, August.
    12. Pujun Liang & Wei Xu & Yunjia Ma & Xiujuan Zhao & Lianjie Qin, 2017. "Increase of Elderly Population in the Rainstorm Hazard Areas of China," IJERPH, MDPI, vol. 14(9), pages 1-17, August.
    13. Deslatte, Aaron & Szmigiel-Rawska, Katarzyna & Tavares, António F. & Ślawska, Justyna & Karsznia, Izabela & Łukomska, Julita, 2022. "Land use institutions and social-ecological systems: A spatial analysis of local landscape changes in Poland," Land Use Policy, Elsevier, vol. 114(C).
    14. Kamaldeen Mohammed & Evans Batung & Moses Kansanga & Hanson Nyantakyi-Frimpong & Isaac Luginaah, 2021. "Livelihood diversification strategies and resilience to climate change in semi-arid northern Ghana," Climatic Change, Springer, vol. 164(3), pages 1-23, February.
    15. Vicente Rios Ibañez, 2014. "What drives regional unemployment convergence?," ERSA conference papers ersa14p924, European Regional Science Association.
    16. Tomasz Kijek & Anna Matras-Bolibok, 2020. "Knowledge-intensive Specialisation and Total Factor Productivity (TFP) in the EU Regional Scope," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 68(1), pages 181-188.
    17. R. Bryson Touchstone & Kathleen Sherman-Morris, 2016. "Vulnerability to prolonged cold: a case study of the Zeravshan Valley of Tajikistan," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 83(2), pages 1279-1300, September.
    18. Burhan Can Karahasan & Firat Bilgel, 2018. "Economic Geography, Growth Dynamics and Human Capital Accumulation in Turkey: Evidence from Regional and Micro Data," Working Papers 1233, Economic Research Forum, revised 10 Oct 2018.
    19. Eric Tate, 2012. "Social vulnerability indices: a comparative assessment using uncertainty and sensitivity analysis," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 63(2), pages 325-347, September.
    20. Parent, Olivier & LeSage, James P., 2011. "A space-time filter for panel data models containing random effects," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 475-490, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:19:p:12304-:d:927458. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.