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A systematic review of Bayesian spatial–temporal models on cancer incidence and mortality

  • Review
  • Published:
International Journal of Public Health

Abstract

Objectives

This study aimed to review the types and applications of fully Bayesian (FB) spatial–temporal models and covariates used to study cancer incidence and mortality.

Methods

This systematic review searched articles published within Medline, Embase, Web-of-Science and Google Scholar between 2014 and 2018.

Results

A total of 38 studies were included in our study. All studies applied Bayesian spatial–temporal models to explore spatial patterns over time, and over half assessed the association with risk factors. Studies used different modelling approaches and prior distributions for spatial, temporal and spatial–temporal interaction effects depending on the nature of data, outcomes and applications. The most common Bayesian spatial–temporal model was a generalized linear mixed model. These models adjusted for covariates at the patient, area or temporal level, and through standardization.

Conclusions

Few studies (4) modelled patient-level clinical characteristics (11%), and the applications of an FB approach in the forecasting of spatial–temporally aligned cancer data were limited. This review highlighted the need for Bayesian spatial-temporal models to incorporate patient-level prognostic characteristics through the multi-level framework and forecast future cancer incidence and outcomes for cancer prevention and control strategies.

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Abbreviations

PRISMA:

Preferred reporting items for systematic review and meta-analysis

PQL:

Penalized quasi-likelihood

EB:

Empirical Bayes

FB:

Fully Bayesian

MCMC:

Markov chain Monte Carlo

INLA:

Integrated nested Laplace approximations

IQR:

Interquartile range

GLMM:

Generalized linear mixed models

CAR:

Conditional autoregressive

BYM:

Besag, York and Mollie

APC:

Age–period cohort

AFT:

Accelerated failure time

ANOVA:

Analysis of variance

SLA:

Second-level area

ESM:

Electronic supplementary material

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This research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia

    Win Wah, Susannah Ahern & Arul Earnest

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  1. Win Wah
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  2. Susannah Ahern
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Correspondence to Win Wah.

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Wah, W., Ahern, S. & Earnest, A. A systematic review of Bayesian spatial–temporal models on cancer incidence and mortality. Int J Public Health 65, 673–682 (2020). https://doi.org/10.1007/s00038-020-01384-5

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  • DOI: https://doi.org/10.1007/s00038-020-01384-5

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