IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i18p9488-d631717.html
   My bibliography  Save this article

Spatial Modeling of COVID-19 Vaccine Hesitancy in the United States

Author

Listed:
  • Abolfazl Mollalo

    (Department of Public Health and Prevention Science, School of Health Sciences, Baldwin Wallace University, Berea, OH 44017, USA)

  • Moosa Tatar

    (Matheson Center for Health Care Studies, University of Utah, Salt Lake City, UT 84108, USA)

Abstract

Vaccine hesitancy refers to delay in acceptance or refusal of vaccines despite the availability of vaccine services. Despite the efforts of United States healthcare providers to vaccinate the bulk of its population, vaccine hesitancy is still a severe challenge that has led to the resurgence of COVID-19 cases to over 100,000 people during early August 2021. To our knowledge, there are limited nationwide studies that examined the spatial distribution of vaccination rates, mainly based on the social vulnerability index (SVI). In this study, we compiled a database of the percentage of fully vaccinated people at the county scale across the continental United States as of 29 July 2021, along with SVI data as potential significant covariates. We further employed multiscale geographically weighted regression to model spatial nonstationarity of vaccination rates. Our findings indicated that the model could explain over 79% of the variance of vaccination rate based on Per capita income and Minority (%) (with positive impacts), and Age 17 and younger (%), Mobile homes (%), and Uninsured people (%) (with negative effects). However, the impact of each covariate varied for different counties due to using separate optimal bandwidths. This timely study can serve as a geospatial reference to support public health decision-makers in forming region-specific policies in monitoring vaccination programs from a geographic perspective.

Suggested Citation

  • Abolfazl Mollalo & Moosa Tatar, 2021. "Spatial Modeling of COVID-19 Vaccine Hesitancy in the United States," IJERPH, MDPI, vol. 18(18), pages 1-14, September.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:18:p:9488-:d:631717
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/18/9488/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/18/18/9488/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jacobs, D.E., 2011. "Environmental health disparities in housing," American Journal of Public Health, American Public Health Association, vol. 101(SUPPL. 1), pages 115-122.
    2. Oshan, Taylor M. & Smith, Jordan & Fotheringham, Alexander Stewart, 2020. "Targeting the spatial context of obesity determinants via multiscale geographically weighted regression," OSF Preprints u7j29, Center for Open Science.
    3. Juhn, Young J. & Sauver, Jennifer St. & Katusic, Slavica & Vargas, Delfino & Weaver, Amy & Yunginger, John, 2005. "The influence of neighborhood environment on the incidence of childhood asthma: a multilevel approach," Social Science & Medicine, Elsevier, vol. 60(11), pages 2453-2464, June.
    4. Luc Anselin & Daniel Arribas-Bel, 2013. "Spatial fixed effects and spatial dependence in a single cross-section," Papers in Regional Science, Wiley Blackwell, vol. 92(1), pages 3-17, March.
    5. Jinting Zhang & Xiu Wu & T. Edwin Chow, 2021. "Space-Time Cluster’s Detection and Geographical Weighted Regression Analysis of COVID-19 Mortality on Texas Counties," IJERPH, MDPI, vol. 18(11), pages 1-21, May.
    6. A. Stewart Fotheringham & Wenbai Yang & Wei Kang, 2017. "Multiscale Geographically Weighted Regression (MGWR)," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 107(6), pages 1247-1265, November.
    7. Abolfazl Mollalo & Kiara M. Rivera & Behzad Vahedi, 2020. "Artificial Neural Network Modeling of Novel Coronavirus (COVID-19) Incidence Rates across the Continental United States," IJERPH, MDPI, vol. 17(12), pages 1-13, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhong, Yan & Sang, Huiyan & Cook, Scott J. & Kellstedt, Paul M., 2023. "Sparse spatially clustered coefficient model via adaptive regularization," Computational Statistics & Data Analysis, Elsevier, vol. 177(C).
    2. Petrovici, Norbert & Belbe, Stefana (Ștefana) & Mare, Codruta (Codruța) & Cotoi, Calin (Călin), 2023. "Hybrid health regimes: Access to primary care physicians and COVID-19 vaccine uptake across municipalities in Romania," Social Science & Medicine, Elsevier, vol. 337(C).
    3. June L. Gin & Michelle D. Balut & Aram Dobalian, 2022. "COVID-19 Vaccine Hesitancy among U.S. Veterans Experiencing Homelessness in Transitional Housing," IJERPH, MDPI, vol. 19(23), pages 1-12, November.
    4. Abolfazl Mollalo & Alireza Mohammadi & Sara Mavaddati & Behzad Kiani, 2021. "Spatial Analysis of COVID-19 Vaccination: A Scoping Review," IJERPH, MDPI, vol. 18(22), pages 1-14, November.
    5. Daniel Badell & Jesica de Armas & Albert Julià, 2022. "Impact of Socioeconomic Environment on Home Social Care Service Demand and Dependent Users," IJERPH, MDPI, vol. 19(4), pages 1-21, February.
    6. Basim Aljohani & Randolph Hall, 2024. "Optimizing the Selection of Mass Vaccination Sites: Access and Equity Consideration," IJERPH, MDPI, vol. 21(4), pages 1-19, April.
    7. Saša Ranđelović & Svetozar Tanasković, 2024. "Socioeconomic determinants of COVID-19 vaccine acceptance," International Journal of Health Economics and Management, Springer, vol. 24(4), pages 537-553, December.
    8. Aloyce R. Kaliba & Donald R. Andrews, 2023. "The Impact of Meso-Level Factors on SARS-CoV-2 Vaccine Early Hesitancy in the United States," IJERPH, MDPI, vol. 20(13), pages 1-27, July.
    9. Cory Anderson & Shuai Zhou & Guangqing Chi, 2023. "Population-Wide Vaccination Hesitancy among the Amish: A County-Level Study of COVID-19 Vaccine Adoption and Implications for Public Health Policy and Practice," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 42(4), pages 1-24, August.
    10. Giuseppe Pangan & Victoria Woodard, 2024. "A Study Examining the Impact of County-Level Demographic, Socioeconomic, and Political Affiliation Characteristics on COVID-19 Vaccination Patterns in Indiana," IJERPH, MDPI, vol. 21(7), pages 1-19, July.

    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. Xiang Li & Qipeng Yan & Yafeng Ma & Chen Luo, 2023. "Spatially Varying Impacts of Built Environment on Transfer Ridership of Metro and Bus Systems," Sustainability, MDPI, vol. 15(10), pages 1-24, May.
    2. Sisman, S. & Aydinoglu, A.C., 2022. "A modelling approach with geographically weighted regression methods for determining geographic variation and influencing factors in housing price: A case in Istanbul," Land Use Policy, Elsevier, vol. 119(C).
    3. Rémy Le Boennec & Julie Bulteau & Thierry Feuillet, 2022. "The role of commuter rail accessibility in the formation of residential land values: exploring spatial heterogeneity in peri-urban and remote areas," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 69(1), pages 163-186, August.
    4. Huxiao Zhu & Xiangjun Ou & Zhen Yang & Yiwen Yang & Hongxin Ren & Le Tang, 2022. "Spatiotemporal Dynamics and Driving Forces of Land Urbanization in the Yangtze River Delta Urban Agglomeration," Land, MDPI, vol. 11(8), pages 1-21, August.
    5. Liang, Fachao & Zhu, Runmiao & Lin, Sheng-Hau, 2023. "Exploring spatial relationship between landscape configuration and ecosystem services: A case study of Xiamen–Zhangzhou–Quanzhou in China," Ecological Modelling, Elsevier, vol. 486(C).
    6. Reda, Abel Kebede & Tavasszy, Lori & Gebresenbet, Girma & Ljungberg, David, 2023. "Modelling the effect of spatial determinants on freight (trip) attraction: A spatially autoregressive geographically weighted regression approach," Research in Transportation Economics, Elsevier, vol. 99(C).
    7. Yanxia Hu & Changqing Wang & Xingxiu Yu & Shengzhou Yin, 2021. "Evaluating Trends of Land Productivity Change and Their Causes in the Han River Basin, China: In Support of SDG Indicator 15.3.1," Sustainability, MDPI, vol. 13(24), pages 1-20, December.
    8. Cynthia Sin Tian Ho & Mats Wilhelmsson, 2022. "Geographical accessibility to bank branches and its relationship to new firm formation in Sweden via multiscale geographically weighted regression," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 42(2), pages 191-218, August.
    9. Luc Anselin & Pedro Amaral, 2024. "Endogenous spatial regimes," Journal of Geographical Systems, Springer, vol. 26(2), pages 209-234, April.
    10. Weipeng Yuan & Hui Sun & Yu Chen & Xuechao Xia, 2021. "Spatio-Temporal Evolution and Spatial Heterogeneity of Influencing Factors of SO 2 Emissions in Chinese Cities: Fresh Evidence from MGWR," Sustainability, MDPI, vol. 13(21), pages 1-26, November.
    11. Yanzhao Wang & Jianfei Cao, 2023. "Examining the Effects of Socioeconomic Development on Fine Particulate Matter (PM2.5) in China’s Cities Based on Spatial Autocorrelation Analysis and MGWR Model," IJERPH, MDPI, vol. 20(4), pages 1-23, February.
    12. Wang, Xiaoxi & Zhang, Yaojun & Yu, Danlin & Qi, Jinghan & Li, Shujing, 2022. "Investigating the spatiotemporal pattern of urban vibrancy and its determinants: Spatial big data analyses in Beijing, China," Land Use Policy, Elsevier, vol. 119(C).
    13. Ilir Nase & Jim Berry & Alastair Adair, 2016. "Impact of quality-led design on real estate value: a spatiotemporal analysis of city centre apartments," Journal of Property Research, Taylor & Francis Journals, vol. 33(4), pages 309-331, October.
    14. Agarwal, Sumit & Satyanarain, Rengarajan & Sing, Tien Foo & Vollmer, Derek, 2016. "Effects of construction activities on residential electricity consumption: Evidence from Singapore's public housing estates," Energy Economics, Elsevier, vol. 55(C), pages 101-111.
    15. Hengyu Gu & Hanchen Yu & Mehak Sachdeva & Ye Liu, 2021. "Analyzing the distribution of researchers in China: An approach using multiscale geographically weighted regression," Growth and Change, Wiley Blackwell, vol. 52(1), pages 443-459, March.
    16. Shichao Lu & Zhihua Zhang & M. James C. Crabbe & Prin Suntichaikul, 2024. "Effects of Urban Land-Use Planning on Housing Prices in Chiang Mai, Thailand," Land, MDPI, vol. 13(8), pages 1-13, July.
    17. Jin, Peizhen & Mangla, Sachin Kumar & Song, Malin, 2021. "Moving towards a sustainable and innovative city: Internal urban traffic accessibility and high-level innovation based on platform monitoring data," International Journal of Production Economics, Elsevier, vol. 235(C).
    18. Chunfang Zhao & Yingliang Wu & Yunfeng Chen & Guohua Chen, 2023. "Multiscale Effects of Hedonic Attributes on Airbnb Listing Prices Based on MGWR: A Case Study of Beijing, China," Sustainability, MDPI, vol. 15(2), pages 1-21, January.
    19. Collins, Timothy W. & Nadybal, Shawna & Grineski, Sara E., 2020. "Sonic injustice: Disparate residential exposures to transport noise from road and aviation sources in the continental United States," Journal of Transport Geography, Elsevier, vol. 82(C).
    20. Li Gao & Mingjing Huang & Wuping Zhang & Lei Qiao & Guofang Wang & Xumeng Zhang, 2021. "Comparative Study on Spatial Digital Mapping Methods of Soil Nutrients Based on Different Geospatial Technologies," Sustainability, MDPI, vol. 13(6), pages 1-19, March.

    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:18:y:2021:i:18:p:9488-:d:631717. 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.