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Rue, H., Riebler, A., Sørbye, S.H., Illian, J.B., Simpson, D.P. and Lindgren, F.K. (2017) Bayesian Computing with INLA: A Review. Annual Review of Statistics and Its Application, 4, 395-421.
https://doi.org/10.1146/annurev-statistics-060116-054045

has been cited by the following article:

• TITLE: Spatiotemporal Distributions of Mortality Rates

  AUTHORS: Getachew A. Dagne

  KEYWORDS: Clustering, Geographical Mapping, Mortality, Poisson

  JOURNAL NAME: Journal of Biosciences and Medicines, Vol.13 No.1, January 27, 2025

  ABSTRACT: Geographical variations in all-cause mortality rates may be influenced by residents’ place of residence and the time period under study. Understanding these variations is essential for designing effective public health interventions and optimizing resource allocation. This study aimed to identify small area level factors associated with all-cause mortality and to map hotspots of excess deaths across a region. The analysis produced relative mortality rates and exceedance probabilities to pinpoint areas with an excess burden of death. Results showed that all-cause mortality was particularly concentrated in the upper central and northern regions of the region, where many rural counties are located. Key factors associated with higher mortality rates included lower median income, younger median age, and a lower percentage of Hispanic population in the counties studied. These findings highlight the importance of addressing income disparity in high-mortality areas, particularly in rural regions, to guide resource allocation and develop targeted interventions that can most effectively reduce mortality rates where they are needed most.