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Risk Assessment and Mapping of Hand, Foot, and Mouth Disease at the County Level in Mainland China Using Spatiotemporal Zero-Inflated Bayesian Hierarchical Models

Author

Listed:
  • Chao Song

    (State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
    School of Geoscience and Technology, Southwest Petroleum University, Sichuan 610500, China)

  • Yaqian He

    (Department of Geology and Geography, West Virginia University, Morgantown, WV 26505, USA)

  • Yanchen Bo

    (State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China)

  • Jinfeng Wang

    (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)

  • Zhoupeng Ren

    (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)

  • Huibin Yang

    (State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China)

Abstract

Hand, foot, and mouth disease (HFMD) is a worldwide infectious disease, prominent in China. China’s HFMD data are sparse with a large number of observed zeros across locations and over time. However, no previous studies have considered such a zero-inflated problem on HFMD’s spatiotemporal risk analysis and mapping, not to mention for the entire Mainland China at county level. Monthly county-level HFMD cases data combined with related climate and socioeconomic variables were collected. We developed four models, including spatiotemporal Poisson, negative binomial, zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB) models under the Bayesian hierarchical modeling framework to explore disease spatiotemporal patterns. The results showed that the spatiotemporal ZINB model performed best. Both climate and socioeconomic variables were identified as significant risk factors for increasing HFMD incidence. The relative risk ( RR ) of HFMD at the local scale showed nonlinear temporal trends and was considerably spatially clustered in Mainland China. The first complete county-level spatiotemporal relative risk maps of HFMD were generated by this study. The new findings provide great potential for national county-level HFMD prevention and control, and the improved spatiotemporal zero-inflated model offers new insights for epidemic data with the zero-inflated problem in environmental epidemiology and public health.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:7:p:1476-:d:157660
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    References listed on IDEAS

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    1. Leonhard Knorr-Held & Günter Raßer, 2000. "Bayesian Detection of Clusters and Discontinuities in Disease Maps," Biometrics, The International Biometric Society, vol. 56(1), pages 13-21, March.
    2. Lindgren, Finn & Rue, Håvard, 2015. "Bayesian Spatial Modelling with R-INLA," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i19).
    3. Enrique Gracia & Antonio López-Quílez & Miriam Marco & Silvia Lladosa & Marisol Lila, 2014. "Exploring Neighborhood Influences on Small-Area Variations in Intimate Partner Violence Risk: A Bayesian Random-Effects Modeling Approach," IJERPH, MDPI, vol. 11(1), pages 1-17, January.
    4. Chengdong Xu, 2017. "Spatio-Temporal Pattern and Risk Factor Analysis of Hand, Foot and Mouth Disease Associated with Under-Five Morbidity in the Beijing–Tianjin–Hebei Region of China," IJERPH, MDPI, vol. 14(4), pages 1-13, April.
    5. Yang Yang & Liwen Luo & Chao Song & Hao Yin & Jintao Yang, 2018. "Spatiotemporal Assessment of PM 2.5 -Related Economic Losses from Health Impacts during 2014–2016 in China," IJERPH, MDPI, vol. 15(6), pages 1-16, June.
    6. 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.
    7. Jixia Huang & Jinfeng Wang & Yanchen Bo & Chengdong Xu & Maogui Hu & Dacang Huang, 2014. "Identification of Health Risks of Hand, Foot and Mouth Disease in China Using the Geographical Detector Technique," IJERPH, MDPI, vol. 11(3), pages 1-17, March.
    8. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    9. Yong Li & Jinhui Zhang & Xinan Zhang, 2014. "Modeling and Preventive Measures of Hand, Foot and Mouth Disease (HFMD) in China," IJERPH, MDPI, vol. 11(3), pages 1-10, March.
    10. Ali Arab, 2015. "Spatial and Spatio-Temporal Models for Modeling Epidemiological Data with Excess Zeros," IJERPH, MDPI, vol. 12(9), pages 1-13, August.
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    Cited by:

    1. Antonio López-Quílez, 2019. "Spatio-Temporal Analysis of Infectious Diseases," IJERPH, MDPI, vol. 16(4), pages 1-2, February.
    2. 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.
    3. Suyan Yi & Hongwei Wang & Shengtian Yang & Ling Xie & Yibo Gao & Chen Ma, 2021. "Spatial and Temporal Characteristics of Hand-Foot-and-Mouth Disease and Its Response to Climate Factors in the Ili River Valley Region of China," IJERPH, MDPI, vol. 18(4), pages 1-13, February.
    4. Yibo Gao & Hongwei Wang & Suyan Yi & Deping Wang & Chen Ma & Bo Tan & Yiming Wei, 2021. "Spatial and Temporal Characteristics of Hand-Foot-and-Mouth Disease and Their Influencing Factors in Urumqi, China," IJERPH, MDPI, vol. 18(9), pages 1-17, May.

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