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A Comparison of Spatio-Temporal Disease Mapping Approaches Including an Application to Ischaemic Heart Disease in New South Wales, Australia

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  • Craig Anderson

    (School of Mathematical and Physical Sciences, University of Technology Sydney, 15 Broadway, Ultimo, NSW 2007, Australia
    ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS), Parkville, VIC 3010, Australia)

  • Louise M. Ryan

    (School of Mathematical and Physical Sciences, University of Technology Sydney, 15 Broadway, Ultimo, NSW 2007, Australia
    ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS), Parkville, VIC 3010, Australia)

Abstract

The field of spatio-temporal modelling has witnessed a recent surge as a result of developments in computational power and increased data collection. These developments allow analysts to model the evolution of health outcomes in both space and time simultaneously. This paper models the trends in ischaemic heart disease (IHD) in New South Wales, Australia over an eight-year period between 2006 and 2013. A number of spatio-temporal models are considered, and we propose a novel method for determining the goodness-of-fit for these models by outlining a spatio-temporal extension of the Moran’s I statistic. We identify an overall decrease in the rates of IHD, but note that the extent of this health improvement varies across the state. In particular, we identified a number of remote areas in the north and west of the state where the risk stayed constant or even increased slightly.

Suggested Citation

  • Craig Anderson & Louise M. Ryan, 2017. "A Comparison of Spatio-Temporal Disease Mapping Approaches Including an Application to Ischaemic Heart Disease in New South Wales, Australia," IJERPH, MDPI, vol. 14(2), pages 1-16, February.
    • Handle: RePEc:gam:jijerp:v:14:y:2017:i:2:p:146-:d:89312
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    References listed on IDEAS

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    1. Ying C. MacNab & C. B. Dean, 2001. "Autoregressive Spatial Smoothing and Temporal Spline Smoothing for Mapping Rates," Biometrics, The International Biometric Society, vol. 57(3), pages 949-956, September.
    2. Duncan Lee & Gavin Shaddick, 2010. "Spatial Modeling of Air Pollution in Studies of Its Short-Term Health Effects," Biometrics, The International Biometric Society, vol. 66(4), pages 1238-1246, December.
    3. Chib, Siddhartha, 1993. "Bayes regression with autoregressive errors : A Gibbs sampling approach," Journal of Econometrics, Elsevier, vol. 58(3), pages 275-294, August.
    4. P. Congdon & H. Southall, 2005. "Trends in inequality in infant mortality in the north of England, 1921–1973, and their association with urban and social structure," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(4), pages 679-700, November.
    5. Mahmoud Torabi & Rhonda J. Rosychuk, 2011. "Spatio-temporal modelling using B-spline for disease mapping: analysis of childhood cancer trends," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(9), pages 1769-1781, October.
    6. 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.
    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.
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    Cited by:

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    3. Samuel O. M Manda & Nada Abdelatif, 2017. "Smoothed Temporal Atlases of Age-Gender All-Cause Mortality in South Africa," IJERPH, MDPI, vol. 14(9), pages 1-18, September.

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