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Bayesian Spatial Survival Models for Hospitalisation of Dengue: A Case Study of Wahidin Hospital in Makassar, Indonesia

Author

Listed:
  • Aswi Aswi

    (ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, 2 George St, Brisbane, Queensland 4001, Australia)

  • Susanna Cramb

    (ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, 2 George St, Brisbane, Queensland 4001, Australia
    School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Victoria Park Road, Kelvin Grove, Queensland 4059, Australia)

  • Earl Duncan

    (ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, 2 George St, Brisbane, Queensland 4001, Australia)

  • Wenbiao Hu

    (School of Public Health and Social Work, Institute of Health and Biomedical Innovation, Queensland University of Technology, Victoria Park Road, Kelvin Grove, Queensland 4059, Australia)

  • Gentry White

    (ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, 2 George St, Brisbane, Queensland 4001, Australia)

  • Kerrie Mengersen

    (ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, 2 George St, Brisbane, Queensland 4001, Australia)

Abstract

Spatial models are becoming more popular in time-to-event data analysis. Commonly, the intrinsic conditional autoregressive prior is placed on an area level frailty term to allow for correlation between areas. We considered a range of Bayesian Weibull and Cox semiparametric spatial models to describe a dataset on hospitalisation of dengue. This paper aimed to extend these two models, to evaluate the suitability of these models for estimation and prediction of the length of stay, compare different spatial priors, and determine factors that significantly affect the duration of hospital stay for dengue fever patients in the case study location, namely Wahidin hospital in Makassar, Indonesia. We compared two different models with three different spatial priors with respect to goodness of fit and generalisability. For all models considered, the Leroux prior was preferred over the intrinsic conditional autoregressive and independent priors, but Cox and Weibull versions had similar predictive performance, model fit, and results. Age and platelet count were negatively associated with the length of stay, while red blood cell count was positively associated with the length of stay of dengue patients at this hospital. Using appropriate Bayesian spatial survival models enables identification of factors that substantively affect the length of stay.

Suggested Citation

  • Aswi Aswi & Susanna Cramb & Earl Duncan & Wenbiao Hu & Gentry White & Kerrie Mengersen, 2020. "Bayesian Spatial Survival Models for Hospitalisation of Dengue: A Case Study of Wahidin Hospital in Makassar, Indonesia," IJERPH, MDPI, vol. 17(3), pages 1-12, January.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:3:p:878-:d:314643
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    References listed on IDEAS

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    1. Yoon Ling Cheong & Katrin Burkart & Pedro J. Leitão & Tobia Lakes, 2013. "Assessing Weather Effects on Dengue Disease in Malaysia," IJERPH, MDPI, vol. 10(12), pages 1-16, November.
    2. 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.
    3. 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.
    4. David Darmofal, 2009. "Bayesian Spatial Survival Models for Political Event Processes," American Journal of Political Science, John Wiley & Sons, vol. 53(1), pages 241-257, January.
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