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

Getayeneh Antehunegn Tesema (), Zemenu Tadesse Tessema, Stephane Heritier, Rob G. Stirling and Arul Earnest
Additional contact information
Getayeneh Antehunegn Tesema: School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
Zemenu Tadesse Tessema: School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
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
Arul Earnest: School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia

IJERPH, 2023, vol. 20, issue 7, 1-24

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.

Keywords: spatial analysis; joint spatiotemporal analysis; systematic review; public health; geographic information system; disease mapping; shared component models (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1660-4601/20/7/5295/pdf (application/pdf)
https://www.mdpi.com/1660-4601/20/7/5295/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:20:y:2023:i:7:p:5295-:d:1109745

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().