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Modelling the Relationship between Rainfall and Mental Health Using Different Spatial and Temporal Units

Author

Listed:
  • Matthew Yap

    (Medical School, University of Western Australia, Crawley 6009, Australia)

  • Matthew Tuson

    (Medical School, University of Western Australia, Crawley 6009, Australia
    Department of Mathematics and Statistics, University of Western Australia, Crawley 6009, Australia)

  • Berwin Turlach

    (Department of Mathematics and Statistics, University of Western Australia, Crawley 6009, Australia)

  • Bryan Boruff

    (Department of Geography, University of Western Australia, Crawley 6009, Australia
    UWA School of Agriculture and Environment, University of Western Australia, Crawley 6009, Australia)

  • David Whyatt

    (Medical School, University of Western Australia, Crawley 6009, Australia)

Abstract

Drought is thought to impact upon the mental health of agricultural communities, but studies of this relationship have reported inconsistent results. A source of inconsistency could be the aggregation of data by a single spatiotemporal unit of analysis, which induces the modifiable areal and temporal unit problems. To investigate this, mental health-related emergency department (MHED) presentations among residents of the Wheat Belt region of Western Australia, between 2002 and 2017, were examined. Average daily rainfall was used as a measure of drought. Associations between MHED presentations and rainfall were estimated based on various spatial aggregations of underlying data, at multiple temporal windows. Wide variation amongst results was observed. Despite this, two key features were found: Associations between MHED presentations and rainfall were generally positive when rainfall was measured in summer months (rate ratios up to 1.05 per 0.5 mm of daily rainfall) and generally negative when rainfall was measured in winter months (rate ratios as low as 0.96 per 0.5 mm of daily rainfall). These results demonstrate that the association between drought and mental health is quantifiable; however, the effect size is small and varies depending on the spatial and temporal arrangement of the underlying data. To improve understanding of this association, more studies should be undertaken with longer time spans and examining specific mental health outcomes, using a wide variety of spatiotemporal units.

Suggested Citation

  • Matthew Yap & Matthew Tuson & Berwin Turlach & Bryan Boruff & David Whyatt, 2021. "Modelling the Relationship between Rainfall and Mental Health Using Different Spatial and Temporal Units," IJERPH, MDPI, vol. 18(3), pages 1-15, February.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:3:p:1312-:d:491260
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    References listed on IDEAS

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    1. Holly Vins & Jesse Bell & Shubhayu Saha & Jeremy J. Hess, 2015. "The Mental Health Outcomes of Drought: A Systematic Review and Causal Process Diagram," IJERPH, MDPI, vol. 12(10), pages 1-25, October.
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    3. 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.
    4. Martins, Thiago G. & Simpson, Daniel & Lindgren, Finn & Rue, Håvard, 2013. "Bayesian computing with INLA: New features," Computational Statistics & Data Analysis, Elsevier, vol. 67(C), pages 68-83.
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