IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i16p5890-d398701.html
   My bibliography  Save this article

Spatial and Temporal Impacts of Socioeconomic and Environmental Factors on Healthcare Resources: A County-Level Bayesian Local Spatiotemporal Regression Modeling Study of Hospital Beds in Southwest China

Author

Listed:
  • Chao Song

    (State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, School of Geoscience and Technology, Southwest Petroleum University, Chengdu 610500, China
    West China School of Public Health (West China Fourth Hospital), Sichuan University, Chengdu 610041, China
    State Key Laboratory of Resources and Environmental Information System (LREIS), Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    These authors contributed equally to this paper.)

  • Yaode Wang

    (State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, School of Geoscience and Technology, Southwest Petroleum University, Chengdu 610500, China
    These authors contributed equally to this paper.)

  • Xiu Yang

    (China Science and Technology Exchange Center, Division of Policy Study, Beijing 100045, China)

  • Yili Yang

    (West China Research Center for Rural Health Development, Sichuan University, Chengdu 610041, China)

  • Zhangying Tang

    (State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, School of Geoscience and Technology, Southwest Petroleum University, Chengdu 610500, China)

  • Xiuli Wang

    (West China School of Public Health (West China Fourth Hospital), Sichuan University, Chengdu 610041, China
    West China Research Center for Rural Health Development, Sichuan University, Chengdu 610041, China)

  • Jay Pan

    (West China School of Public Health (West China Fourth Hospital), Sichuan University, Chengdu 610041, China
    West China Research Center for Rural Health Development, Sichuan University, Chengdu 610041, China)

Abstract

Comprehensive investigation on understanding geographical inequalities of healthcare resources and their influencing factors in China remains scarce. This study aimed to explore both spatial and temporal heterogeneous impacts of various socioeconomic and environmental factors on healthcare resource inequalities at a fine-scale administrative county level. We collected data on county-level hospital beds per ten thousand people to represent healthcare resources, as well as data on 32 candidate socioeconomic and environmental covariates in southwest China from 2002 to 2011. We innovatively employed a cutting-edge local spatiotemporal regression, namely, a Bayesian spatiotemporally varying coefficients (STVC) model, to simultaneously detect spatial and temporal autocorrelated nonstationarity in healthcare-covariate relationships via estimating posterior space-coefficients (SC) within each county, as well as time-coefficients (TC) over ten years. Our findings reported that in addition to socioeconomic factors, environmental factors also had significant impacts on healthcare resources inequalities at both global and local space–time scales. Globally, the personal economy was identified as the most significant explanatory factor. However, the temporal impacts of personal economy demonstrated a gradual decline, while the impacts of the regional economy and government investment showed a constant growth from 2002 to 2011. Spatially, geographical clustered regions for both hospital bed distributions and various hospital bed-covariates relationships were detected. Finally, the first spatiotemporal series of complete county-level hospital bed inequality maps in southwest China was produced. This work is expected to provide evidence-based implications for future policy making procedures to improve healthcare equalities from a spatiotemporal perspective. The employed Bayesian STVC model provides frontier insights into investigating spatiotemporal heterogeneous variables relationships embedded in broader areas such as public health, environment, and earth sciences.

Suggested Citation

  • Chao Song & Yaode Wang & Xiu Yang & Yili Yang & Zhangying Tang & Xiuli Wang & Jay Pan, 2020. "Spatial and Temporal Impacts of Socioeconomic and Environmental Factors on Healthcare Resources: A County-Level Bayesian Local Spatiotemporal Regression Modeling Study of Hospital Beds in Southwest Ch," IJERPH, MDPI, vol. 17(16), pages 1-23, August.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:16:p:5890-:d:398701
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/16/5890/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/17/16/5890/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yingru Li & Yehua Dennis Wei, 2014. "Multidimensional Inequalities in Health Care Distribution in Provincial China: A Case Study of Henan Province," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 105(1), pages 91-106, February.
    2. Pan, Jay & Zhao, Hanqing & Wang, Xiuli & Shi, Xun, 2016. "Assessing spatial access to public and private hospitals in Sichuan, China: The influence of the private sector on the healthcare geography in China," Social Science & Medicine, Elsevier, vol. 170(C), pages 35-45.
    3. Chao Song & Yaqian He & Yanchen Bo & Jinfeng Wang & Zhoupeng Ren & Huibin Yang, 2018. "Risk Assessment and Mapping of Hand, Foot, and Mouth Disease at the County Level in Mainland China Using Spatiotemporal Zero-Inflated Bayesian Hierarchical Models," IJERPH, MDPI, vol. 15(7), pages 1-16, July.
    4. Jay Pan & Gordon G. Liu, 2012. "The Determinants Of Chinese Provincial Government Health Expenditures: Evidence From 2002–2006 Data," Health Economics, John Wiley & Sons, Ltd., vol. 21(7), pages 757-777, July.
    5. 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.
    6. Stanley Lazic, 2019. "Bayesian Regression Modeling with INLA," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(3), pages 1115-1115, June.
    7. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    8. Xueqian Song & Yongping Wei & Wei Deng & Shaoyao Zhang & Peng Zhou & Ying Liu & Jiangjun Wan, 2019. "Spatio-Temporal Distribution, Spillover Effects and Influences of China’s Two Levels of Public Healthcare Resources," IJERPH, MDPI, vol. 16(4), pages 1-18, February.
    9. Qingbin Guo & Kang Luo, 2019. "Concentration of Healthcare Resources in China: The Spatial–Temporal Evolution and Its Spatial Drivers," IJERPH, MDPI, vol. 16(23), pages 1-14, November.
    10. Xinyu Zhang & Lin Zhao & Zhuang Cui & Yaogang Wang, 2015. "Study on Equity and Efficiency of Health Resources and Services Based on Key Indicators in China," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-15, December.
    11. Daniel Adyro Martínez-Bello & Antonio López-Quílez & Alexander Torres Prieto, 2018. "Spatio-Temporal Modeling of Zika and Dengue Infections within Colombia," IJERPH, MDPI, vol. 15(7), pages 1-18, June.
    12. Daniel P. McMillen, 2004. "Geographically Weighted Regression: The Analysis of Spatially Varying Relationships," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(2), pages 554-556.
    13. Lindgren, Finn & Rue, Håvard, 2015. "Bayesian Spatial Modelling with R-INLA," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i19).
    14. Gelfand A.E. & Kim H-J. & Sirmans C.F. & Banerjee S., 2003. "Spatial Modeling With Spatially Varying Coefficient Processes," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 387-396, January.
    15. Qin, Xuezheng & Hsieh, Chee-Ruey, 2014. "Economic growth and the geographic maldistribution of health care resources: Evidence from China, 1949-2010," China Economic Review, Elsevier, vol. 31(C), pages 228-246.
    16. Alfonso Ceccherini‐Nelli & Stefan Priebe, 2007. "Economic factors and psychiatric hospital beds – an analysis of historical trends," International Journal of Social Economics, Emerald Group Publishing Limited, vol. 34(11), pages 788-810, October.
    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. Jiansheng Wu & Jiayi Fu & Hongliang Wang & Yuhao Zhao & Tengyun Yi, 2022. "Identifying Spatial Matching between the Supply and Demand of Medical Resource and Accessing Carrying Capacity: A Case Study of Shenzhen, China," IJERPH, MDPI, vol. 19(4), pages 1-22, February.

    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. Ropo E. Ogunsakin & Themba G. Ginindza, 2022. "Bayesian Spatial Modeling of Diabetes and Hypertension: Results from the South Africa General Household Survey," IJERPH, MDPI, vol. 19(15), pages 1-17, July.
    2. Zhang, Shen & Liu, Xin & Tang, Jinjun & Cheng, Shaowu & Qi, Yong & Wang, Yinhai, 2018. "Spatio-temporal modeling of destination choice behavior through the Bayesian hierarchical approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 537-551.
    3. John M. Humphreys, 2022. "Amplification in Time and Dilution in Space: Partitioning Spatiotemporal Processes to Assess the Role of Avian-Host Phylodiversity in Shaping Eastern Equine Encephalitis Virus Distribution," Geographies, MDPI, vol. 2(3), pages 1-16, July.
    4. Carlos Díaz-Avalos & Pablo Juan & Somnath Chaudhuri & Marc Sáez & Laura Serra, 2020. "Association between the New COVID-19 Cases and Air Pollution with Meteorological Elements in Nine Counties of New York State," IJERPH, MDPI, vol. 17(23), pages 1-18, December.
    5. Deslatte, Aaron & Scott, Tyler A. & Carter, David P., 2019. "Specialized governance and regional land-use outcomes: A spatial analysis of Florida community development districts," Land Use Policy, Elsevier, vol. 83(C), pages 227-239.
    6. Chien-Chou Chen & Guo-Jun Lo & Ta-Chien Chan, 2022. "Spatial Analysis on Supply and Demand of Adult Surgical Masks in Taipei Metropolitan Areas in the Early Phase of the COVID-19 Pandemic," IJERPH, MDPI, vol. 19(11), pages 1-12, May.
    7. Chiranjit Dutta & Nalini Ravishanker & Sumanta Basu, 2022. "Modeling Multivariate Positive-Valued Time Series Using R-INLA," Papers 2206.05374, arXiv.org, revised Jul 2022.
    8. Ali Arab, 2015. "Spatial and Spatio-Temporal Models for Modeling Epidemiological Data with Excess Zeros," IJERPH, MDPI, vol. 12(9), pages 1-13, August.
    9. Mabel Morales-Otero & Vicente Núñez-Antón, 2021. "Comparing Bayesian Spatial Conditional Overdispersion and the Besag–York–Mollié Models: Application to Infant Mortality Rates," Mathematics, MDPI, vol. 9(3), pages 1-33, January.
    10. Zongyuan Xia & Bo Tang & Long Qin & Huiguo Zhang & Xijian Hu, 2023. "Spatially Dependent Bayesian Modeling of Geostatistics Data and Its Application for Tuberculosis (TB) in China," Mathematics, MDPI, vol. 11(19), pages 1-15, October.
    11. Cho, Daegon & Hwang, Youngdeok & Park, Jongwon, 2018. "More buzz, more vibes: Impact of social media on concert distribution," Journal of Economic Behavior & Organization, Elsevier, vol. 156(C), pages 103-113.
    12. Andre Python & Andreas Bender & Marta Blangiardo & Janine B. Illian & Ying Lin & Baoli Liu & Tim C.D. Lucas & Siwei Tan & Yingying Wen & Davit Svanidze & Jianwei Yin, 2022. "A downscaling approach to compare COVID‐19 count data from databases aggregated at different spatial scales," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(1), pages 202-218, January.
    13. 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.
    14. David Jiménez-Hernández & Víctor González-Calatayud & Ana Torres-Soto & Asunción Martínez Mayoral & Javier Morales, 2020. "Digital Competence of Future Secondary School Teachers: Differences According to Gender, Age, and Branch of Knowledge," Sustainability, MDPI, vol. 12(22), pages 1-16, November.
    15. 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.
    16. 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.
    17. Braulio-Gonzalo, Marta & Bovea, María D. & Jorge-Ortiz, Andrea & Juan, Pablo, 2021. "Which is the best-fit response variable for modelling the energy consumption of households? An analysis based on survey data," Energy, Elsevier, vol. 231(C).
    18. I Gede Nyoman Mindra Jaya & Henk Folmer, 2024. "High-Resolution Spatiotemporal Forecasting with Missing Observations Including an Application to Daily Particulate Matter 2.5 Concentrations in Jakarta Province, Indonesia," Mathematics, MDPI, vol. 12(18), pages 1-29, September.
    19. 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.
    20. Jacqueline D. Seufert & Andre Python & Christoph Weisser & Elías Cisneros & Krisztina Kis‐Katos & Thomas Kneib, 2022. "Mapping ex ante risks of COVID‐19 in Indonesia using a Bayesian geostatistical model on airport network data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 2121-2155, October.

    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:17:y:2020:i:16:p:5890-:d:398701. 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.