Hands-on Tutorial on a Modeling Framework for Projections of Climate Change Impacts on Health
Hands-on Tutorial on a Modeling Framework for Projections of Climate Change Impacts on Health
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DOI：
10.1097/ede.0000000000000982
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发表时间：
2019-05-01
期刊：
影响因子：
5.4
通讯作者：
Gasparrini, Antonio
中科院分区：
文献类型：
--
作者：
Vicedo-Cabrera, Ana M.;Sera, Francesco;Gasparrini, Antonio
需要对气候变化今后对健康的影响作出可靠的估计，以便为制定有效的适应和减缓战略提供信息和作出贡献。然而，预测与特定环境压力因素相关的健康负担是一项具有挑战性的任务，因为风险模式复杂，未来气候情景具有内在的不确定性。这些评估涉及多学科知识，需要流行病学、统计学和气候科学等方面的专门知识。在这里，我们提出了一个方法框架，以估计未来的健康影响气候变化情景的基础上定义的一套假设和先进的统计技术，在时间序列分析环境流行病学。通过一个分步实践教程来说明拟议的方法，该教程由定义明确的部分组成，涵盖了主要方法步骤和基本要素。每一节都对每一步进行了详尽的描述，沿着讨论了现有的分析备选方案以及拟议框架中所作选择的理由。该插图补充了一个实际的研究实例，使用真实世界的数据和一系列R脚本作为补充数字内容; http：//links.lww.com/EDE/B504，这有助于其复制和扩展到其他环境压力，结果，研究设置和预测情景。用户应该批判性地评估潜在的建模替代方案，并修改框架和R代码，使其适应健康影响预测的研究。
Reliable estimates of future health impacts due to climate change are needed to inform and contribute to the design of efficient adaptation and mitigation strategies. However, projecting health burdens associated to specific environmental stressors is a challenging task because of the complex risk patterns and inherent uncertainty of future climate scenarios. These assessments involve multidisciplinary knowledge, requiring expertise in epidemiology, statistics, and climate science, among other subjects. Here, we present a methodologic framework to estimate future health impacts under climate change scenarios based on a defined set of assumptions and advanced statistical techniques developed in time-series analysis in environmental epidemiology. The proposed methodology is illustrated through a step-by-step hands-on tutorial structured in well-defined sections that cover the main methodological steps and essential elements. Each section provides a thorough description of each step, along with a discussion on available analytical options and the rationale on the choices made in the proposed framework. The illustration is complemented with a practical example of study using real-world data and a series of R scripts included as Supplementary Digital Content; http://links.lww.com/EDE/B504, which facilitates its replication and extension on other environmental stressors, outcomes, study settings, and projection scenarios. Users should critically assess the potential modeling alternatives and modify the framework and R code to adapt them to their research on health impact projections.