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Risk estimation and boundary detection in Bayesian disease mapping

  • Xueqing Yin EMAIL logo , Craig Anderson , Duncan Lee und Gary Napier
Veröffentlicht/Copyright: 22. Mai 2025
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The International Journal of Biostatistics
Aus der Zeitschrift The International Journal of Biostatistics Band 21 Heft 1

Abstract

Bayesian hierarchical models with a spatially smooth conditional autoregressive prior distribution are commonly used to estimate the spatio-temporal pattern in disease risk from areal unit data. However, most of the modeling approaches do not take possible boundaries of step changes in disease risk between geographically neighbouring areas into consideration, which may lead to oversmoothing of the risk surfaces, prevent the detection of high-risk areas and yield biased estimation of disease risk. In this paper, we propose a two-stage method to jointly estimate the disease risk in small areas over time and detect the locations of boundaries that separate pairs of neighbouring areas exhibiting vastly different risks. In the first stage, we use a graph-based optimisation algorithm to construct a set of candidate neighbourhood matrices that represent a range of possible boundary structures for the disease data. In the second stage, a Bayesian hierarchical spatio-temporal model that takes the boundaries into account is fitted to the data. The performance of the methodology is evidenced by simulation, before being applied to a study of respiratory disease risk in Greater Glasgow, Scotland.

Keywords: Bayesian hierarchical model; boundary detection; conditional autoregressive models; disease mapping; risk smoothing; spatio-temporal modelling
Corresponding author: Xueqing Yin, School of Mathematics and Statistics, Liaoning University, Shenyang, Liaoning, China, E-mail:

Acknowledgment

The authors would like to thank the editors and anonymous reviewers, whose valuable comments have greatly improved the paper.

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: The raw data can be obtained from the corresponding author on reasonable request.

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Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/ijb-2023-0138).

Received: 2023-06-06
Accepted: 2025-03-18
Published Online: 2025-05-22

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/ijb-2023-0138
Schlagwörter für diesen Artikel
Bayesian hierarchical model; boundary detection; conditional autoregressive models; disease mapping; risk smoothing; spatio-temporal modelling

Artikel in diesem Heft

  1. Frontmatter
  2. Research Articles
  3. Prognostic adjustment with efficient estimators to unbiasedly leverage historical data in randomized trials
  4. Homogeneity test and sample size of response rates for AC 1 in a stratified evaluation design
  5. A review of survival stacking: a method to cast survival regression analysis as a classification problem
  6. DsubCox: a fast subsampling algorithm for Cox model with distributed and massive survival data
  7. A hybrid hazard-based model using two-piece distributions
  8. Regression analysis of clustered current status data with informative cluster size under a transformed survival model
  9. Bayesian covariance regression in functional data analysis with applications to functional brain imaging
  10. Risk estimation and boundary detection in Bayesian disease mapping
  11. An improved estimator of the logarithmic odds ratio for small sample sizes using a Bayesian approach
  12. Short Communication
  13. A multivariate Bayesian learning approach for improved detection of doping in athletes using urinary steroid profiles
  14. Research Articles
  15. Guidance on individualized treatment rule estimation in high dimensions
  16. Weighted Euclidean balancing for a matrix exposure in estimating causal effect
  17. Penalized regression splines in Mixture Density Networks
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