Open AccessHands-on Tutorial on a Modeling Framework for Projections of Climate Change Impacts on Health
Author(s) -
Ana M. VicedoCabrera,
Francesco Sera,
Antonio Gasparrini
Publication year - 2019
Publication title -
epidemiology
Language(s) - English
Resource type - Journals
eISSN - 1531-5487
pISSN - 1044-3983
DOI - 10.1097/ede.0000000000000982
Subject(s) - multidisciplinary approach , computer science , climate change , data science , scripting language , adaptation (eye) , environmental epidemiology , risk analysis (engineering) , management science , environmental health , engineering , psychology , medicine , ecology , social science , neuroscience , sociology , biology , operating system
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.