Li Xin-Chen, Qian Hao-Ran, Zhang Yan-Yan, Zhang Qi-Yu, Liu Jing-Shu, Lai Hong-Yu, Zheng Wei-Guo, Sun Jian, Fu Bo, Zhou Xiao-Nong, Zhang Xiao-Xi
School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.
Institute of One Health, Shanghai Jiao Tong University, Shanghai, People's Republic of China.
Infect Dis Model. 2024 Mar 19;9(2):618-633. doi: 10.1016/j.idm.2024.03.001. eCollection 2024 Jun.
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.
全球变暖的迅速加速导致了高温相关疾病(HTDs)负担的增加,凸显了先进的循证管理策略的必要性。我们基于“同一健康”概念,开发了一个旨在减轻全球HTDs负担的概念框架。该框架完善了影响路径,并建立了系统的数据驱动模型,以指导采用针对不同背景的循证决策。我们从权威公共数据库收集了2010 - 2019年广泛的国家级数据。将心血管疾病、传染性呼吸道疾病、损伤、代谢性疾病和非传染性呼吸道疾病这五类疾病病因的负担指定为中间结果变量。这五类疾病的累积负担,即总HTD负担,为最终结果变量。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,从而能够探索最优决策策略并评估其相应贡献。我们的模型选择结果表明,图神经网络(GNN)模型在各种指标上表现更优。利用GNN模型驱动的模拟,我们确定了一套针对七个主要地区(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、南亚地区和撒哈拉以南非洲地区)减轻疾病负担的最优干预策略。针对基础设施与社区、生态系统恢复力和卫生系统能力等类别采取的部门缓解和适应措施,在不同地区和疾病方面表现尤为突出。每个地区的最优干预方案中包含了十二项干预措施中的七项,包括提高低碳能源使用、增加能源强度、改善牲畜饲料、扩大基本医疗服务覆盖范围、加强卫生融资、应对空气污染以及改善道路基础设施。本研究的成果是一个全球决策工具,为政策制定者提供了一种系统方法,以制定有针对性的干预策略,应对全球变暖背景下日益严峻的HTDs挑战。