Centre for Data Science, School of Mathematical Sciences, Queensland University of Technology (QUT), 2 George Street, Brisbane City, 4000, Australia.
Epidemiology Directorate, Western Australia Department of Health (WADOH), 189 Royal Street, East Perth, 6004, Australia.
Spat Spatiotemporal Epidemiol. 2024 Jun;49:100663. doi: 10.1016/j.sste.2024.100663. Epub 2024 May 31.
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.
本文通过解决提高健康数据的空间和时间分辨率的关键问题为该领域做出了贡献。虽然贝叶斯方法经常被用于解决各个学科中的这一挑战，但贝叶斯时空模型在疾病负担（BOD）研究中的应用仍然有限。我们的创新之处在于探索了两种现有的贝叶斯模型，我们证明它们适用于广泛的 BOD 数据，包括死亡率和患病率，从而为未来在全面的 BOD 研究中采用贝叶斯建模提供了证据支持。我们通过涉及哮喘和冠心病的澳大利亚案例研究说明了贝叶斯建模的好处。我们的结果展示了贝叶斯方法在增加可获得结果的小区域数量以及提高结果的可靠性和稳定性方面的有效性，与直接使用调查或行政来源的数据相比。
