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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

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  • 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
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    References listed on IDEAS

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    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.
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