通过数据驱动模拟在减轻全球高温相关疾病负担方面进行优化决策

IF 8.8 3区 医学 Q1 Medicine Infectious Disease Modelling Pub Date : 2024-03-19 DOI:10.1016/j.idm.2024.03.001
Xin-Chen Li , Hao-Ran Qian , Yan-Yan Zhang , Qi-Yu Zhang , Jing-Shu Liu , Hong-Yu Lai , Wei-Guo Zheng , Jian Sun , Bo Fu , Xiao-Nong Zhou , Xiao-Xi Zhang
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引用次数: 0

摘要

全球变暖速度加快,导致高温相关疾病(HTDs)的负担加重,突出表明需要先进的循证管理策略。我们以 "一个健康 "理念为基础,制定了一个旨在减轻全球高温相关疾病负担的概念框架。该框架完善了影响途径,并建立了系统的数据驱动模型,为采用循证决策提供信息,适合不同的环境。我们从权威的公共数据库中收集了 2010-2019 年的大量国家级数据。心血管疾病、传染性呼吸系统疾病、伤害、代谢性疾病和非传染性呼吸系统疾病这五类疾病的负担被指定为中间结果变量。这五类疾病的累积负担被称为 HTD 总负担,是最终结果变量。我们评估了八个模型的预测性能,随后引入了十二项干预措施,从而探索出最佳决策策略并评估其相应的贡献。我们的模型选择结果表明,图形神经网络(GNN)模型在各种指标上都表现出色。利用图形神经网络模型驱动的模拟,我们确定了一套专门针对七个主要地区的减轻疾病负担的最佳干预策略:这些地区包括:东亚和太平洋地区、欧洲和中亚地区、拉丁美洲和加勒比地区、中东和北非地区、北美地区、南亚地区以及撒哈拉以南非洲地区。针对不同地区和疾病的部门减缓和适应措施表现尤为突出,这些措施与我们的 "基础设施与营地"、"社区"、"生态系统复原力 "和 "卫生系统能力 "等类别相关。十二项干预措施中有七项被纳入各地区的最佳干预一揽子方案,包括提高低碳能源使用率、增加能源强度、改善牲畜饲料、扩大基本医疗服务覆盖面、加强医疗融资、解决空气污染问题以及改善道路基础设施。这项研究的成果是一个全球性的决策工具,为政策制定者提供了一个系统的方法论,以制定有针对性的干预战略,应对全球变暖背景下日益严峻的高温热害疾病挑战。
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Optimal decision-making in relieving global high temperature-related disease burden by data-driven simulation

The rapid acceleration of global warming has led to an increased burden of high temperature-related diseases (HTDs), highlighting the need for advanced evidence-based management strategies. We have developed a conceptual framework aimed at alleviating the global burden of HTDs, grounded in the One Health concept. This framework refines the impact pathway and establishes systematic data-driven models to inform the adoption of evidence-based decision-making, tailored to distinct contexts. We collected extensive national-level data from authoritative public databases for the years 2010–2019. The burdens of five categories of disease causes – cardiovascular diseases, infectious respiratory diseases, injuries, metabolic diseases, and non-infectious respiratory diseases – were designated as intermediate outcome variables. The cumulative burden of these five categories, referred to as the total HTD burden, was the final outcome variable. We evaluated the predictive performance of eight models and subsequently introduced twelve intervention measures, allowing us to explore optimal decision-making strategies and assess their corresponding contributions. Our model selection results demonstrated the superior performance of the Graph Neural Network (GNN) model across various metrics. Utilizing simulations driven by the GNN model, we identified a set of optimal intervention strategies for reducing disease burden, specifically tailored to the seven major regions: East Asia and Pacific, Europe and Central Asia, Latin America and the Caribbean, Middle East and North Africa, North America, South Asia, and Sub-Saharan Africa. Sectoral mitigation and adaptation measures, acting upon our categories of Infrastructure & Community, Ecosystem Resilience, and Health System Capacity, exhibited particularly strong performance for various regions and diseases. Seven out of twelve interventions were included in the optimal intervention package for each region, including raising low-carbon energy use, increasing energy intensity, improving livestock feed, expanding basic health care delivery coverage, enhancing health financing, addressing air pollution, and improving road infrastructure. The outcome of this study is a global decision-making tool, offering a systematic methodology for policymakers to develop targeted intervention strategies to address the increasingly severe challenge of HTDs in the context of global warming.

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来源期刊
Infectious Disease Modelling
Infectious Disease Modelling Mathematics-Applied Mathematics
CiteScore
17.00
自引率
3.40%
发文量
73
审稿时长
17 weeks
期刊介绍: Infectious Disease Modelling is an open access journal that undergoes peer-review. Its main objective is to facilitate research that combines mathematical modelling, retrieval and analysis of infection disease data, and public health decision support. The journal actively encourages original research that improves this interface, as well as review articles that highlight innovative methodologies relevant to data collection, informatics, and policy making in the field of public health.
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