IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v22y2025i3p326-d1597181.html
   My bibliography  Save this article

Unveiling Community Vulnerability to COVID-19 Incidence: A Population-Based Spatial Analysis in Clark County, Nevada

Author

Listed:
  • Lung-Chang Chien

    (Department of Epidemiology and Biostatistics, University of Nevada, Las Vegas, NV 89154, USA)

  • L.-W. Antony Chen

    (Department of Environmental and Occupational Health, University of Nevada, Las Vegas, NV 89154, USA)

  • Chad L. Cross

    (Department of Epidemiology and Biostatistics, University of Nevada, Las Vegas, NV 89154, USA)

  • Edom Gelaw

    (Department of Epidemiology and Biostatistics, University of Nevada, Las Vegas, NV 89154, USA)

  • Cheryl Collins

    (Desert Research Institute, Las Vegas, NV 89119, USA)

  • Lei Zhang

    (Southern Nevada Health District, Las Vegas, NV 89107, USA)

  • Anil T. Mangla

    (Southern Nevada Health District, Las Vegas, NV 89107, USA)

  • Cassius Lockett

    (Southern Nevada Health District, Las Vegas, NV 89107, USA)

Abstract

Community vulnerability is influenced by various determinants beyond socioeconomic status and plays a crucial role in COVID-19 disparities. This study aimed to develop and evaluate a novel community vulnerability index (CVI) related to temporal variations in COVID-19 incidence to provide insights into spatial disparities and inform targeted public health interventions in Clark County, Nevada. Utilizing data from the American Community Survey and other sources, 23 community measures were identified at the census tract level. The CVI was constructed using a lagged weighted quantile sum (LWQS) regression linking these measures to the monthly COVID-19 incidence from March 2020 to November 2021. The Besag–York–Mollié model subsequently evaluated the spatial association between the CVI and COVID-19 incidence, controlling for temporal and spatial autocorrelations. This study identified minority status, housing inadequacy, and inactive commuting as primary contributors to the CVI that consistently influenced COVID-19 vulnerability over time. The CVI demonstrated significant spatial disparities, with higher values found in northern Clark County and the northeastern Las Vegas metropolitan area. Spatial analyses revealed varying associations between COVID-19 incidence and the CVI across census tracts, with significant associations clustered in the northern and eastern regions of the Las Vegas metropolitan area. These findings advance our understanding of the complex interplay between community conditions and COVID-19. The CVI framework may be applied to other COVID-19 outcomes such as testing, vaccination, and hospitalization, offering a valuable tool for assessing and addressing community vulnerability.

Suggested Citation

  • Lung-Chang Chien & L.-W. Antony Chen & Chad L. Cross & Edom Gelaw & Cheryl Collins & Lei Zhang & Anil T. Mangla & Cassius Lockett, 2025. "Unveiling Community Vulnerability to COVID-19 Incidence: A Population-Based Spatial Analysis in Clark County, Nevada," IJERPH, MDPI, vol. 22(3), pages 1-11, February.
    • Handle: RePEc:gam:jijerp:v:22:y:2025:i:3:p:326-:d:1597181
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/22/3/326/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/22/3/326/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jina Mahmoudi & Chenfeng Xiong, 2022. "How social distancing, mobility, and preventive policies affect COVID-19 outcomes: Big data-driven evidence from the District of Columbia-Maryland-Virginia (DMV) megaregion," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-21, February.
    2. Seth E. Spielman & Joseph Tuccillo & David C. Folch & Amy Schweikert & Rebecca Davies & Nathan Wood & Eric Tate, 2020. "Evaluating social vulnerability indicators: criteria and their application to the Social Vulnerability Index," 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. 100(1), pages 417-436, January.
    3. Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
    4. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
    5. V. Martins & Delta Silva & Pedro Cabral, 2012. "Social vulnerability assessment to seismic risk using multicriteria analysis: the case study of Vila Franca do Campo (São Miguel Island, Azores, Portugal)," 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. 62(2), pages 385-404, June.
    6. David C. Wheeler & Salem Rustom & Matthew Carli & Todd P. Whitehead & Mary H. Ward & Catherine Metayer, 2021. "Assessment of Grouped Weighted Quantile Sum Regression for Modeling Chemical Mixtures and Cancer Risk," IJERPH, MDPI, vol. 18(2), pages 1-20, January.
    7. Elizabeth B. Pathak & Janelle M. Menard & Rebecca B. Garcia & Jason L. Salemi, 2022. "Joint Effects of Socioeconomic Position, Race/Ethnicity, and Gender on COVID-19 Mortality among Working-Age Adults in the United States," IJERPH, MDPI, vol. 19(9), pages 1-15, April.
    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. Shreosi Sanyal & Thierry Rochereau & Cara Nichole Maesano & Laure Com-Ruelle & Isabella Annesi-Maesano, 2018. "Long-Term Effect of Outdoor Air Pollution on Mortality and Morbidity: A 12-Year Follow-Up Study for Metropolitan France," IJERPH, MDPI, vol. 15(11), pages 1-8, November.
    2. Mayer Alvo & Jingrui Mu, 2023. "COVID-19 Data Analysis Using Bayesian Models and Nonparametric Geostatistical Models," Mathematics, MDPI, vol. 11(6), pages 1-13, March.
    3. Vanessa Santos-Sánchez & Juan Antonio Córdoba-Doña & Javier García-Pérez & Antonio Escolar-Pujolar & Lucia Pozzi & Rebeca Ramis, 2020. "Cancer Mortality and Deprivation in the Proximity of Polluting Industrial Facilities in an Industrial Region of Spain," IJERPH, MDPI, vol. 17(6), pages 1-15, March.
    4. Massimo Bilancia & Giacomo Demarinis, 2014. "Bayesian scanning of spatial disease rates with integrated nested Laplace approximation (INLA)," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(1), pages 71-94, March.
    5. Douglas R. M. Azevedo & Marcos O. Prates & Dipankar Bandyopadhyay, 2021. "MSPOCK: Alleviating Spatial Confounding in Multivariate Disease Mapping Models," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(3), pages 464-491, September.
    6. Bondo, Kristin J. & Rosenberry, Christopher S. & Stainbrook, David & Walter, W. David, 2024. "Comparing risk of chronic wasting disease occurrence using Bayesian hierarchical spatial models and different surveillance types," Ecological Modelling, Elsevier, vol. 493(C).
    7. Jonathan Wakefield & Taylor Okonek & Jon Pedersen, 2020. "Small Area Estimation for Disease Prevalence Mapping," International Statistical Review, International Statistical Institute, vol. 88(2), pages 398-418, August.
    8. Julien Riou & Anthony Hauser & Anna Fesser & Christian L. Althaus & Matthias Egger & Garyfallos Konstantinoudis, 2023. "Direct and indirect effects of the COVID-19 pandemic on mortality in Switzerland," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    9. Isabel Martínez-Pérez & Verónica González-Iglesias & Valentín Rodríguez Suárez & Ana Fernández-Somoano, 2021. "Spatial Distribution of Hospitalizations for Ischemic Heart Diseases in the Central Region of Asturias, Spain," IJERPH, MDPI, vol. 18(23), pages 1-10, November.
    10. Johnson, Blair T. & Sisti, Anthony & Bernstein, Mary & Chen, Kun & Hennessy, Emily A. & Acabchuk, Rebecca L. & Matos, Michaela, 2021. "Community-level factors and incidence of gun violence in the United States, 2014–2017," Social Science & Medicine, Elsevier, vol. 280(C).
    11. Maike Tahden & Juliane Manitz & Klaus Baumgardt & Gerhard Fell & Thomas Kneib & Guido Hegasy, 2016. "Epidemiological and Ecological Characterization of the EHEC O104:H4 Outbreak in Hamburg, Germany, 2011," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-19, October.
    12. Márcio Poletti Laurini, 2017. "A spatial error model with continuous random effects and an application to growth convergence," Journal of Geographical Systems, Springer, vol. 19(4), pages 371-398, October.
    13. Radka Jersakova & James Lomax & James Hetherington & Brieuc Lehmann & George Nicholson & Mark Briers & Chris Holmes, 2022. "Bayesian imputation of COVID‐19 positive test counts for nowcasting under reporting lag," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(4), pages 834-860, August.
    14. Exaverio Chireshe & Retius Chifurira & Knowledge Chinhamu & Jesca Mercy Batidzirai & Ayesha B. M. Kharsany, 2025. "Spatial Analysis of HIV Determinants Among Females Aged 15–34 in KwaZulu Natal, South Africa: A Bayesian Spatial Logistic Regression Model," IJERPH, MDPI, vol. 22(3), pages 1-25, March.
    15. repec:osf:osfxxx:39ke6_v1 is not listed on IDEAS
    16. Birgit Schrödle & Leonhard Held, 2011. "A primer on disease mapping and ecological regression using $${\texttt{INLA}}$$," Computational Statistics, Springer, vol. 26(2), pages 241-258, June.
    17. Darren J. Mayne & Geoffrey G. Morgan & Bin B. Jalaludin & Adrian E. Bauman, 2018. "Does Walkability Contribute to Geographic Variation in Psychosocial Distress? A Spatial Analysis of 91,142 Members of the 45 and Up Study in Sydney, Australia," IJERPH, MDPI, vol. 15(2), pages 1-24, February.
    18. Ana Carolina Carioca da Costa & Cláudia Torres Codeço & Elias Teixeira Krainski & Marcelo Ferreira da Costa Gomes & Aline Araújo Nobre, 2018. "Spatiotemporal diffusion of influenza A (H1N1): Starting point and risk factors," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-20, September.
    19. Luca Grassetti & Laura Rizzi, 2019. "The determinants of individual health care expenditures in the Italian region of Friuli Venezia Giulia: evidence from a hierarchical spatial model estimation," Empirical Economics, Springer, vol. 56(3), pages 987-1009, March.
    20. Ferreira, Marco A.R. & Porter, Erica M. & Franck, Christopher T., 2021. "Fast and scalable computations for Gaussian hierarchical models with intrinsic conditional autoregressive spatial random effects," Computational Statistics & Data Analysis, Elsevier, vol. 162(C).
    21. William Gonzalez Daza & Renata L. Muylaert & Thadeu Sobral-Souza & Victor Lemes Landeiro, 2023. "Malaria Risk Drivers in the Brazilian Amazon: Land Use—Land Cover Interactions and Biological Diversity," IJERPH, MDPI, vol. 20(15), pages 1-16, August.

    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:22:y:2025:i:3:p:326-:d:1597181. 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.