A Systematic Review of Areal Units and Adjacency Used in Bayesian Spatial and Spatio-Temporal Conditional Autoregressive Models in Health Research

Zemenu Tadesse Tessema (), Getayeneh Antehunegn Tesema, Susannah Ahern and Arul Earnest
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Zemenu Tadesse Tessema: School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
Getayeneh Antehunegn Tesema: School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
Susannah Ahern: School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
Arul Earnest: School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia

IJERPH, 2023, vol. 20, issue 13, 1-24

Abstract: Advancements in Bayesian spatial and spatio-temporal modelling have been observed in recent years. Despite this, there are unresolved issues about the choice of appropriate spatial unit and adjacency matrix in disease mapping. There is limited systematic review evidence on this topic. This review aimed to address these problems. We searched seven databases to find published articles on this topic. A modified quality assessment tool was used to assess the quality of studies. A total of 52 studies were included, of which 26 (50.0%) were on infectious diseases, 10 (19.2%) on chronic diseases, 8 (15.5%) on maternal and child health, and 8 (15.5%) on other health-related outcomes. Only 6 studies reported the reasons for using the specified spatial unit, 8 (15.3%) studies conducted sensitivity analysis for prior selection, and 39 (75%) of the studies used Queen contiguity adjacency. This review highlights existing variation and limitations in the specification of Bayesian spatial and spatio-temporal models used in health research. We found that majority of the studies failed to report the rationale for the choice of spatial units, perform sensitivity analyses on the priors, or evaluate the choice of neighbourhood adjacency, all of which can potentially affect findings in their studies.

Keywords: Bayesian; spatio-temporal; adjacency matrices; conditional autoregressive model (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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