Centre for Data Science, Queensland University of Technology, Australia; Children's Health and Environment Program, Child Health Research Centre, The University of Queensland, Australia; School of Public Health and Social Work, Queensland University of Technology, Australia.
Centre for Data Science, Queensland University of Technology, Australia.
Sci Total Environ. 2024 Nov 1;949:174989. doi: 10.1016/j.scitotenv.2024.174989. Epub 2024 Jul 23.
Queensland is the main coal mining state in Australia where populations in coal mining areas have been historically exposed to coal mining emissions. Although a higher risk of chronic circulatory and respiratory diseases has been associated with coal mining globally, few studies have investigated these associations in the Queensland general population. This study estimates the association of coal production with hospitalisations for chronic circulatory and respiratory diseases in Queensland considering spatial and temporal variations during 1997-2014. An ecological analysis used a Bayesian hierarchical spatiotemporal model to estimate the association of coal production with standardised rates of each, chronic circulatory and respiratory diseases, adjusting for sociodemographic factors and considering the spatial structure of Queensland's statistical areas (SA2) in the 18-year period. Two specifications; with and without a space-time interaction effect were compared using the integrated nested Laplace approximation -INLA approach. The posterior mean of the best fit model was used to map the spatial, temporal and spatiotemporal trends of risk. The analysis considered 2,831,121 hospitalisation records. Coal mining was associated with a 4 % (2.4-5.5) higher risk of hospitalisation for chronic respiratory diseases in the model with a space-time interaction effect which had the best fit. An emerging higher risk of either chronic circulatory and respiratory diseases was identified in eastern areas and some coal-mining areas in central and southeast Queensland. There were important disparities in the spatiotemporal trend of risk between coal -and non-coal mining areas for each, chronic circulatory and respiratory diseases. Coal mining is associated with an increased risk of chronic respiratory diseases in the Queensland general population. Bayesian spatiotemporal analyses are robust methods to identify environmental determinants of morbidity in exposed populations. This methodology helps identifying at-risk populations which can be useful to support decision-making in health. Future research is required to investigate the causality links between coal mining and these diseases.
昆士兰州是澳大利亚主要的采煤州，历史上，该州采煤区的居民一直受到采煤排放物的影响。虽然全球范围内，采煤与慢性循环和呼吸系统疾病的风险增加有关，但很少有研究调查过昆士兰州普通人群中的这些关联。本研究考虑到 1997 年至 2014 年期间的空间和时间变化，估计了煤炭生产与昆士兰州慢性循环和呼吸系统疾病住院治疗之间的关联。生态分析使用贝叶斯层次时空模型，根据社会人口因素调整了每种慢性循环和呼吸系统疾病的标准化发病率，并考虑了昆士兰州统计区（SA2）的空间结构，以估计煤炭生产与这些疾病之间的关联。使用集成嵌套 Laplace 近似（INLA）方法比较了两种规格；具有和不具有时空相互作用效应的规格。使用最佳拟合模型的后验平均值来绘制风险的空间、时间和时空趋势图。该分析考虑了 2831121 例住院记录。在具有时空相互作用效应的模型中，煤炭开采与慢性呼吸系统疾病住院率增加 4%（2.4-5.5）相关，该模型具有最佳拟合度。在昆士兰州东部地区和中部以及东南部的一些采煤区，发现慢性循环和呼吸系统疾病的风险呈上升趋势。对于每种慢性循环和呼吸系统疾病，煤矿区和非煤矿区的风险时空趋势存在重要差异。在昆士兰州普通人群中，煤炭开采与慢性呼吸系统疾病的风险增加有关。贝叶斯时空分析是识别暴露人群发病的环境决定因素的稳健方法。这种方法有助于识别高风险人群，这对于支持健康决策非常有用。需要进一步研究以调查煤炭开采与这些疾病之间的因果关系。
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