Improving the spatial and temporal resolution of burden of disease measures with Bayesian models

被引：0

作者：

Hogg, James ^{[1
]}

Staples, Kerry ^{[2
]}

Davis, Alisha ^{[2
]}

Cramb, Susanna ^{[1
,3
]}

Patterson, Candice ^{[2
]}

Kirkland, Laura ^{[2
]}

Gourley, Michelle ^{[4
]}

Xiao, Jianguo ^{[2
]}

Sun, Wendy ^{[2
]}

机构：

[1] Queensland Univ Technol QUT, Ctr Data Sci, Sch Math Sci, 2 George St, Brisbane 4000, Australia

[2] Western Australia Dept Hlth WADOH, Epidemiol Directorate, 189 Royal St, East Perth 6004, Australia

[3] QUT, Australian Ctr Hlth Serv Innovat, Sch Publ Hlth & Social Work, Brisbane, Australia

[4] Australian Govt, Australian Inst Hlth & Welf AIHW, 1 Thynne St, Bruce 2617, Australia

来源：

SPATIAL AND SPATIO-TEMPORAL EPIDEMIOLOGY | 2024年 / 49卷

关键词：

Bayesian inference; Spatio-temporal modeling; Burden of disease; Disease mapping; Small area estimation; CANCER INCIDENCE;

D O I：

10.1016/j.sste.2024.100663

中图分类号：

R1 [预防医学、卫生学];

学科分类号：

1004 ; 120402 ;

摘要：

This paper contributes to the field by addressing the critical issue of enhancing the spatial and temporal resolution of health data. Although Bayesian methods are frequently employed to address this challenge in various disciplines, the application of Bayesian spatio-temporal models to burden of disease (BOD) studies remains limited. Our novelty lies in the exploration of two existing Bayesian models that we show to be applicable to a wide range of BOD data, including mortality and prevalence, thereby providing evidence to support the adoption of Bayesian modeling in full BOD studies in the future. We illustrate the benefits of Bayesian modeling with an Australian case study involving asthma and coronary heart disease. Our results showcase the effectiveness of Bayesian approaches in increasing the number of small areas for which results are available and improving the reliability and stability of the results compared to using data directly from surveys or administrative sources.

引用

页数：14

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