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

Abstract

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.

FIGURE 1.

Temperature-related mortality in London (1990–2012). A, three-dimensional plot showing the estimated exposure–lag–response association between temperature and mortality. B, Overall cumulative mortality risk (relative risk (RR) and 95% confidence interval). C, Comparison between the exposure–response shapes estimated using three modeling approaches.

FIGURE 2.

Temporal trends in projected temperature in London (1971–2099). Solid lines correspond to the mean annual temperature estimated across the five GCM-specific modeled series. The shaded area shows its variability, corresponding to the range for each year. The two horizontal bars in the right correspond to the average annual maximum and minimum for each modeled temperature series. Representative concentration pathway (RCP).

FIGURE 3.

Seasonal mortality trends in London. Gray dots correspond to the observed daily mortality counts registered in each day of the year between 1990 and 2012. The blue line depicts the mean number of deaths per day of the year.

FIGURE 4.

Bias correction of the modeled temperature series. Comparison between the distribution (left panel) and cumulative distribution (right panel) of the raw and bias-corrected modeled temperature, and the observed temperature series . GCM indicates general circulation model.

FIGURE 5.

Temperature and excess mortality in London for present and future periods. Top panel: exposure–response curve represented as mortality RR across the temperature (°C) range, with 95% empirical confidence intervals (gray area). The dotted vertical line corresponds to the minimum mortality temperature () used as reference, which defines the two portions of the curve related to cold and heat (blue and red, respectively). The dashed part of the curve represents the extrapolation beyond the maximum temperature observed in 2010–2019 (dashed vertical line). Mid panel: distribution of for the current (2010–2019, gray area) and at the end of the century (2090–2099, green area), projected using a specific climate model (NorESM1−M) and scenario (RCP8.5). Bottom panel: the related distribution of excess mortality, expressed as the fraction of additional deaths (%) attributed to nonoptimal temperature compared with .

FIGURE 6.

Accounting for complex scenarios accounting for sociodemographic changes and adaptation. A, Age-specific exposure–response curves, applicable to project health impact separately for each age category, thus potentially accounting for demographic changes by using differential baseline mortality trends. B, Comparison between the exposure–response curves under scenarios of no adaptation (continuous line) and adaptation (dashed line), the latter under the (simplistic) assumption of a hypothetical attenuation of 30% in risk associated with heat. Relative risk (RR).