Hands-on Tutorial on a Modeling Framework for Projections of Climate Change Impacts on Health

Epidemiology - Tập 30 Số 3 - Trang 321-329 - 2019
Ana M. Vicedo‐Cabrera1,2,3,4,5, Francesco Sera1,2,3,4,5, Antonio Gasparrini6,1,2,3,4,5
1Department of Public Health, Environments and Society, London School of Hygiene and Tropical Medicine, London, United Kingdom
2Submitted July 18, 2018
3Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (www.epidem.com).
4We 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.
5accepted January 28, 2019.
6Centre for Statistical Methodology, London School of Hygiene and Tropical Medicine, London, United Kingdom.

Tóm tắt

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.

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Epidemiology

Tập 30 Số 3

321-329

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