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A Systematic Review of Joint Spatial and Spatiotemporal Models in Health Research

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  • Getayeneh Antehunegn Tesema

    (School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
    Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar 196, Ethiopia)

  • Zemenu Tadesse Tessema

    (School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
    Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar 196, Ethiopia)

  • Stephane Heritier

    (School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia)

  • Rob G. Stirling

    (Department of Respiratory Medicine, Alfred Health, Melbourne, VIC 3004, Australia
    Faculty of Medicine, Nursing and Health Sciences, Central Clinical School, Monash University, Melbourne, VIC 3004, Australia)

  • Arul Earnest

    (School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia)

Abstract

With the advancement of spatial analysis approaches, methodological research addressing the technical and statistical issues related to joint spatial and spatiotemporal models has increased. Despite the benefits of spatial modelling of several interrelated outcomes simultaneously, there has been no published systematic review on this topic, specifically when such models would be useful. This systematic review therefore aimed at reviewing health research published using joint spatial and spatiotemporal models. A systematic search of published studies that applied joint spatial and spatiotemporal models was performed using six electronic databases without geographic restriction. A search with the developed search terms yielded 4077 studies, from which 43 studies were included for the systematic review, including 15 studies focused on infectious diseases and 11 on cancer. Most of the studies (81.40%) were performed based on the Bayesian framework. Different joint spatial and spatiotemporal models were applied based on the nature of the data, population size, the incidence of outcomes, and assumptions. This review found that when the outcome is rare or the population is small, joint spatial and spatiotemporal models provide better performance by borrowing strength from related health outcomes which have a higher prevalence. A framework for the design, analysis, and reporting of such studies is also needed.

Suggested Citation

  • Getayeneh Antehunegn Tesema & Zemenu Tadesse Tessema & Stephane Heritier & Rob G. Stirling & Arul Earnest, 2023. "A Systematic Review of Joint Spatial and Spatiotemporal Models in Health Research," IJERPH, MDPI, vol. 20(7), pages 1-24, March.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:7:p:5295-:d:1109745
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