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.
From the aDepartment of Public Health, Environments and Society, London School of Hygiene and Tropical Medicine, London, United Kingdom
bCentre for Statistical Methodology, London School of Hygiene and Tropical Medicine, London, United Kingdom.
Submitted July 18, 2018; accepted January 28, 2019.
This work was primarily supported by the Medical Research Council-UK (MR/M022625/1).
The authors report no conflicts of interest.
Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (www.epidem.com).
We provide the data and R code as supplementary digital content that illustrates the analytical steps described in this contribution and reproduces the figures shown in the main manuscript.
Correspondence: Ana M. Vicedo-Cabrera, Department of Public Health, Environments and Society, London School of Hygiene and Tropical Medicine, 15-17 Tavistock Place, London WC1H 9SH, United Kingdom. E-mail: email@example.com.
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