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Projecting health impacts of climate extremes: A methodological overview
CHAPTER 10
Projecting health impacts of climate extremes: A methodological overview Ana M. Vicedo-Cabreraa, Francesco Seraa, Antonio Gasparrinia,b a Department of Public Health, Environments and Society, London School of Hygiene and Tropical Medicine, London, United Kingdom b Centre for Statistical Methodology, London School of Hygiene and Tropical Medicine, London, United Kingdom
It is well recognized that climate change is one of the most important environmental threats to human health, and it is expected that impacts will be larger in the future as warming progresses (Gasparrini et al., 2017; IPCC, 2013). It can affect human health through multiple pathways, including indirect (i.e., spread of disease vectors, increase in food insecurity, and migration and conflicts) and direct effects associated to the increase in frequency and intensity of extreme weather events or climate extremes such as floods, droughts, hurricanes, and heat waves (McMichael et al., 2012; Patz et al., 2005). Regarding the latter, it has been shown that a substantial part of the health risk is likely to occur due to the direct exposure to such extreme events, in particular, associated to extreme temperatures or heat waves (IPCC, 2013). There is a growing interest in assessing what may be the impact of climate extremes on health in the future under different warming scenarios based on specific adaptation and mitigation strategies. Evidence provided by health impact projection studies are extremely valuable to policy makers and stakeholders for the implementation of effective policies against climate change. In particular, numerous projection studies have been performed so far estimating future mortality impacts associated to the direct exposure to temperature in different locations worldwide (e.g., Gasparrini et al., 2017; Petkova et al., 2017; Weinberger et al., 2017). Other studies projected health impacts associated with specific extreme events, such as heat waves, hurricanes, cyclones, wildfires, floods, etc. using several climate change scenarios (Arnell et al., 2018; Dasgupta et al., 2009; Gao et al., 2016) and Climate Extremes and Their Implications for Impact and Risk Assessment https://doi.org/10.1016/B978-0-12-814895-2.00010-0
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other e nvironmental stressors related to climate change, such as air pollution (Chen et al., 2018). Providing reliable health impact projections under climate change scenarios is not an easy task (see, e.g., Scenario chapter for a more in-depth introduction). Although projection studies generally share a common methodological scheme, even if applied over different environmental factors and health outcomes, forecasting future health impacts entails important methodological challenges (Huang et al., 2011). This basic framework consists of applying known risk functions on simulated future distributions of extreme events that are generated by climate change models under specific emissions scenarios. It would seem a priori a straightforward approach, however, specific methodological issues should be handled with caution. For instance, the patterns of risks between environmental stressors and human health are complex and sometimes heterogeneous across populations. Likewise, the inherent uncertainty of potential future climate change processes and how population would evolve in the coming decades pose important analytical challenges and hinder the appropriate interpretation of the results (Huang et al., 2011). During the past decades, there have been exceptional efforts from the research community in terms of development of advanced study designs, tools, and statistical methods to properly address these challenges. This chapter provides a comprehensive overview of the more recent methodological approaches applied in health impacts projections under climate change scenarios. This describes the state of the art of the current methods used in each analytical step of a projections study and discusses the potential alternatives, assumptions, and limitations. It specifically focuses on the cutting-edge statistical methodologies recently developed in time- series analysis in environmental epidemiology, which have shown a great potential in climate change projections. The description and discussion are coupled with an example of analysis on projected temperature-related mortality in London. The statistical analysis has been translated into R scripts which are available in the personal website and GitHub repository of the last author. The statistical framework illustrated in the example has been previously used in recent publications (Gasparrini et al., 2017; Vicedo-Cabrera et al., 2018) and described in detail in a methodological contribution (Vicedo-Cabrera et al., 2019). In the latter, the R code reproducing the described example of analysis and the corresponding data are also provided as supplementary material. The chapter is structured in seven sections, each one addressing specific analytical steps of a projection study. The first two sections describe the two
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essential elements needed: the exposure-response function representing the association of interest, and the modeled data on future climate scenarios over which this will be applied. Sections 3–6 cover the main methodological steps, briefly illustrating procedures for the downscaling and calibration of the exposure data, and the estimation of the projected impacts and uncertainty. The final Section 7 discusses more advanced issues, such as dealing with changes in vulnerability and population structure in the future.
1 Estimation of exposure-response associations The first key element needed in projection studies is a measure of the association between the environmental stressor (i.e., climate extremes) and the risk of a health outcome. This, usually represented by an exposure- response function, will be combined with the modeled data on future climate changes scenarios to estimate the projected impacts. The risk function should offer a reliable approximation of the expected future association in a study population, based on a given scenario and set of specific assumptions. These will be further discussed in Sections 4 and 6. Exposure-response functions can be obtained through two different approaches.The simplest method is to rely on functions, risk, or association estimates reported in the existing literature estimated on the same or a similar geographical area (Heaviside et al., 2016; Kendrovski et al., 2017; Knowlton et al., 2009). The latter is the only viable option when actual data on the exposure and the outcome is not available. However, it should be noted that this approach can have limited applicability for some study settings. In fact, it has been observed that associations can vary widely across populations for several environmental factors such as temperature, and therefore strong assumptions on a common shape of the association across different populations should be made. The most desirable option is to directly estimate the association in our study population using available historical data on the exposure and outcome. There are several analytical approaches, including specific epidemiological study designs and related statistical methods, to study different aspects of health associations with environmental stressors. The choice depends on various aspects, such as the timing (short- vs. long-term effects), or the type of data, with different levels of temporal or spatial aggregation. In the specific setting of climate change studies and, in particular, on direct impacts of climate extremes, time-series analysis using aggregated data has been shown to be an ideal method (Bhaskaran et al., 2013). This is mainly
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due to the fact that impacts usually occur in short-term time spans, for example, temperature-related mortality.The framework illustrated in this chapter is mostly focused in this study design, although it can be easily adapted to other types of studies available (i.e., case-crossover) to model short-term associations (Armstrong, 2006; Dominici et al., 2003; Peters et al., 2006). Other epidemiological designs such as cohort studies would be more appropriate when assessing long-term effects on health, including those associated to air pollution. In time-series analysis, data on the environmental stressor and health outcome are collected at regular spans of time (i.e., daily) along a specific study period and over a defined population. For example, routinely collected data from electronic health records or demographics (i.e., mortality, hospital admission) and environmental data registered by monitors can be used in this type of study. In the example of the London study, these are represented by historical series of daily mean temperature and total death counts in the period 1990–2012 in London. This data is part of the multicitymulticountry (MCC) network (http://mccstudy.lshtm.ac.uk/), and has been previously used as example in already published manuscripts (e.g., Gasparrini et al., 2015). The dataset is available in the personal website and GitHub repository of the last author of the chapter, and more detailed information on the data is available in Vicedo-Cabrera et al. (2019). Regarding the modeling approach, several types of functions can be used to assess the exposure-response association of climate extremes or any environmental stressor with a specific health outcome. For example, extreme events can be modeled through an indicator variable identifying the days when the event (e.g., heat wave, flood) occurred. This would imply the identification of such events based on a a priori definition mostly using extreme percentile cutoffs obtained from observed distributions. Another option is to consider exposure as a continuous variable (e.g., temperature, precipitation), and model the association along the whole range or for specific extreme intervals. In this case, it is possible to apply simple threshold or linear functions, as usually applied in air pollution studies. However, exposure-response associations can show more complex risk patterns following nonlinear shapes. Likewise, it is often observed that the effects of an environmental stressor can last beyond the day of the exposure. This is typically found in temperature-mortality studies, in which heat effects can persist 3 or 4 days after the day of exposure and even for longer periods of time up to 2 weeks for cold effects. Thus, when modeling the exposure- response association, an additional temporal dimension should be considered that accounts for these lagged dependencies.
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These complex associations can be flexibly modeled in time-series analysis using a widely used methodological framework called distributed lag nonlinear models (DLNMs). In brief, this methodology can simultaneously model potential nonlinearities, along with other simple functions (e.g., l inear and threshold), of the association and its distribution across time, through the so-called exposure-lag-response bidimensional risk surfaces (Gasparrini, 2011, 2014; Gasparrini and Armstrong, 2013). The top-left panel of Fig. 1 shows the three-dimensional plot that represents exposure-lag-response association between temperature and mortality in London along 21 days
Fig. 1 Temperature-related mortality in London (1990–2012). (Top-left) three- dimensional plot showing the exposure-lag-response association. (Top-right) Overall cumulative mortality risk (and 95% confidence interval) up to 21 days of lag. (Bottomleft) Lag-specific mortality risks for heat and cold, corresponding to the maximum and minimum temperature value. (Bottom-right) Comparison between the exposure- response shapes estimated using three alternative modeling approaches.
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of lag. This has been obtained through a usual time-series model, that is, a generalized linear model with Poisson regression accounting for overdispersion. Temperature-mortality association was modeled through two nonlinear functions defined in both dimensions (i.e., exposure-response and lag-response dimensions). In particular, as further explained in Section 4, we used natural cubic splines that allows the log-linear extrapolation of the function beyond the boundaries of the observed series, a step needed to project the risk using the modeled temperature. The top-right panel of Fig. 1 represents the overall cumulative exposure-response association over 21 days of lag.The relationship is reported in terms of relative risk (RR), using as reference the temperature of minimum mortality (MMT) estimated from the overall curve. As expected, temperature-mortality risk in this study follows clearly a nonlinear shape, with increases above and below the MMT, corresponding to heat and cold associations. At the same time, as shown in the bottom-left panel of Fig. 1, these are distributed differently across time, suggesting an immediate increase in mortality risk after an extremely hot day (at the maximum temperature value), in contrast an initially negative and then more delayed and sustained risk persisting for some weeks after a cold spell (at the minimum temperature value). The choice of the appropriate model for estimating health risks is paramount. For comparison, the bottom-right panel of Fig. 1 illustrates the resulting exposure-response shapes when applying simpler functions, as examples of other modeling approaches usually used (Carmona et al., 2016; Díaz et al., 2018). These include the use of an indicator term for the extreme temperature days (when values above or below the fifth and 95th percentile) (dashed blue line—gray in print version) or threshold function with linear relationships for extreme ranges above or below these percentile temperature values (dotted-dashed orange line—light gray in print version), and a linear function (dotted green—dark gray in print version). As it can be observed, these choices seem less ideal for modeling the mortality risk of nonoptimal temperature, highlighting the importance of the selection of suitable functions to represent the association of interest, and the potential bias of inappropriate simplifications.
2 Future climatic and population scenarios The second essential element needed in projection studies on climate extremes is the data on future climatic and population scenarios over which the exposure-response function will be applied to quantify health impacts.
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Data on future distribution of environmental stressors or climate extremes (as described in Chapter 2) are commonly based on specific scenarios that account for changes in multiple and often interrelated factors. For instance, socioeconomic and technological changes, population growth, and land-use changes can affect pathways of greenhouse gas emissions or atmospheric concentrations of other pollutants, which in turn will determine trends in global warming and potential levels of specific environmental exposures (IPCC, 2013). Based on specific and simplified assumptions, these trends can be generated from general circulation models (GCMs) under a defined climate change scenario. To have a better representation of future trends, the usual approach is to combine impact estimates obtained either using more than one model per scenario [e.g., Coupled Model Intercomparison Project (Eyring et al., 2016)] or using ensemble members output from multiple runs of the same climate model, but with different initial conditions (Huang et al., 2011; Sanderson et al., 2017). The first approach is the one selected in the proposed methodological framework applied in the example of the London study. As shown in Vicedo-Cabrera et al. (2019), projected temperature data is derived as an average across five different GCMs for two climate change scenarios (further details on the projected temperature series in VicedoCabrera et al., 2019). Specifically, these are defined as representative concentration pathways 4.5 and 8.5 (RCP4.5 and RCP8.5) within the Coupled Model Intercomparison Project Phase 5 of IPCC (Taylor et al., 2011). As discussed later in Section 6, the availability of exposure trends from multiple models can be used to determine the related uncertainty of the projected health impacts. Likewise, health impact projections due to climate extremes also depend on future representations of the population at risk. These are determined by hypothetical demographic structures and baseline mortality and morbidity rates in the future. Data on these population scenarios can be built following different approaches based on the adopted assumptions. Several studies applied the simplest procedure consisting in assuming that populations and outcome rates will remain constant in the future, thus isolating the climate effect from other important trends (Baccini et al., 2011; Doherty et al., 2009; Doyon et al., 2008). However, other studies relied on population projections derived from predictive models under assumptions on future levels of fertility, mortality, and migration (Ostro et al., 2012; Peng et al., 2011;Vardoulakis et al., 2014). For example, some studies used age-specific risks and population trends to take into account the progressive population
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growth and aging (Hajat et al., 2014; Heaviside et al., 2016). In this case, assumptions on changes in demographic structure of future populations should be also adopted, which would entail the introduction of additional sources of uncertainty. This topic on demographic changes will be further discussed in Section 7. In the case of the example for London, projected series of total mortality counts are computed as the average for each day of the year from daily observed deaths, then repeated along the same projection period of the modeled temperature series. By doing so, the seasonal structure of the observed mortality series is kept. Projected mortality is then built under the assumption of “stable populations” with no changes in age distribution and overall mortality rates.
3 Downscaling and calibration In most projection studies, the future series on the environmental stressor needs to be processed before the estimation of the health impact. As explained in Chapter 4, the main reason is that climate simulations usually show systematic deviations from the observed real-world climate. This can be due to the different geographical resolution (gridded vs. point-source), or to biases due to poor performance of climate models (i.e., areas with sparse information from meteorological stations). These deviations should be carefully considered in climate change projections studies, as the predicted impacts will depend on the alignment of observed and modeled series (Hewitson et al., 2014; Maraun, 2016). Corrections of biases related to these two aspects have been defined separately as downscaling and calibration, although in most cases they rely on similar analytic procedures. Downscaling refers to the process of obtaining location-specific climate information from global or regional models that provide data at a higher geographical resolution. Conversely, calibration is a more general concept of realigning two series of data, in this case observed and modeled series. Several bias correction methods have been proposed using different techniques with varying degree of complexity (Maraun, 2016) (see also Chapter 5 in this book). However, there exists limited evidence about the potential impact of the choice of method on the estimated projections. In the example of the London study, the model outputs from the GCMs are firstly downscaled through bilinear interpolation at a 0.5° × 0.5° spatial resolution and linear interpolated by day of the year.The resulting series are then calibrated with the observed data using the bias-correction method
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developed by Hempel et al. (2013). This ensures that the trend and variability of the original data are preserved by adjusting the cumulative distribution of the simulated data to the observed one. Specifically, the monthly variability and mean are corrected only using a constant offset or multiplicative correction factor that corrects for long-term differences between the simulated and observed monthly mean data in the historical period (Hempel et al., 2013).
4 Extrapolation of exposure-response curve It is important to keep in mind that the risk estimates obtained over historical periods do not automatically apply to future scenarios. It is possible that the estimated exposure-response association will be different in the future, due to, for example, adaptation or changes in vulnerability of the population, as it will be further discussed in Section 7. However, even if assuming no changes in risk, the estimated exposure-response curve may not be appropriate to obtain projections in future periods. This is mainly due to the fact that the distribution of a specific environmental stressors in the future is likely to be different from that observed in the present days. Thus, we need to perform an additional step consisting in the extrapolation of the exposure-response beyond the observed boundaries. This, however, implies the adoption of additional assumptions on the hypothetical shape of the association over the unobserved range. A viable method is based on a log-linear extrapolation of the curve beyond the observed boundaries. We assume in this case that the risk will change linearly in the log-scale beyond the observed boundaries. To do so, in the example of the temperature-mortality study in London, the exposure-response function is modeled through a natural cubic spline function which ensures this log-linear extrapolation. Fig. 2 (top panel) shows the estimated exposure-response function along the observed temperature values (solid line) and the extrapolated part of the curve (dashed line) covering the warmer range of the projected temperature series. For the sake of the example, we use the projected temperature obtained from one of the five GCM output, in this case the IPSL-CM5A-LR model, (Mignot and Bony, 2013), under RCP8.5 scenario. However, this log-linear extra polation entails a series of strong assumptions on the future risk associated to environmental factors. Apart from assuming that the exposure-response association estimated will not change in the future, this approach assumes that the extrapolation represents appropriately the risk over the unobserved
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Fig. 2 Temperature and excess mortality in London for present and future periods. The top panel shows the exposure-response curve represented as mortality relative risk (RR) across the temperature (°C) range, with 95% empirical confidence intervals (gray area). The dotted vertical corresponds to the minimum mortality temperature used as reference, defining the two portions of the curve for cold and heat (blue and red, respectively—dark gray and gray in print version). The dashed part of the curve represent the extrapolation beyond the maximum temperature observed in 2010–19 (dashed vertical line). The mid-panel displays the distribution of modeled temperature for the current (2010–19, light yellow area—light gray in print version) and at the end of the century (2090–99, orange area—gray in print version), projected using a specific climate model (IPSL-CM5A-LR) and scenario (RCP8.5). The bottom panel represents the related distribution of excess mortality for the current (light green—light gray in print version) and future period (dark green—gray in print version) (estimated using the project series from this specific GCM under RCP8.5), expressed as the fraction of additional deaths (%) attributed to nonoptimal temperature compared with the temperature of minimum mortality.
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range. This assumption can be wrong if the log-risk increases exponentially beyond the range already experienced, for instance, because of physiological cascades related to the loss of acclimatization capacity. While a functional model would be more appropriate than the empirical approach adopted here, the lack of knowledge on the pathophysiology of temperature-related risks makes the former difficult to define.
5 Projection and quantification of the impact Once the two key components (association estimates and projected series) are properly processed, we can proceed with the main step in this analysis that is the estimation of the impact projections. As a general definition, it consists in applying the exposure-response association estimates over the future or projected series of the specific environmental stressor and outcome to derive the corresponding health impacts estimates. Several measures of impact have been used so far, for instance, in terms of percent changes in the rate of the outcome, excess mortality or morbidity, or attributable fractions (Gasparrini et al., 2017; Hajat et al., 2014; Wu et al., 2014). Here we illustrate a procedure to estimate the impacts in terms of attributable fractions within the DLNM framework using a statistical method previously developed (Gasparrini and Leone, 2014). Although for the specific example of the London study, we estimate the attributable number of deaths due to nonoptimal temperatures, the same method can be applied over other outcomes and environmental stressors. The general procedure consists in computing for each day of the series the number of cases attributed to a specific environmental stressor (e.g., temperature, air pollution) based on the estimated risk and the level of exposure in that day. In the specific setting of projecting impacts due to climate extremes, this would correspond to the number of cases occurring on an event day defined by the estimated risk. Then daily attributable numbers are aggregated by defined intervals of time in the future period and/or further divided into specific exposure ranges or by types of events (e.g., extreme events of variable magnitude such as heat waves of different intensity). In the example of London study, impacts are divided into heat and cold components, by summing the subsets corresponding to days with temperatures higher or lower than the MMT (Gasparrini et al., 2015). Heat and cold impacts can be further divided between moderate and extreme ranges according to whether temperature is above or below a defined extreme threshold (e.g., 2.5th and 97.5th percentiles) (Gasparrini et al., 2015). Impacts can be also
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be expressed in terms of attributable fraction computed as the ratio between the estimated impact and the corresponding total number of cases. Fig. 2 (mid and bottom panels) shows the distributions of temperatures and estimated attributable mortality, respectively, for the historic and future period in London using the IPSL-CM5A-LR model under RCP8.5 scenario. It can be observed that the mortality burden due to cold temperatures is currently much larger than for heat, especially across the moderate cold temperatures. However, if we compare the GCM-ensemble estimates between the periods (2010–19 vs. 2090–99), we found that heat-attributable mortality will substantially increase in the future by 4.0% [95% empirical confidence interval (eCI): 0.7–6.8], while mortality due to cold will be reduced by 3.3% (95% eCI: 4.3–1.9). This is due to the shift in the temperature distribution, thus, the changes between present and future periods reflect the interrelation between changes in occurrences of given temperatures and related risks.
6 Ensemble estimates and quantification of uncertainty As introduced at the beginning of the chapter, one of the key methodological issues in projection studies is to properly identify and deal with the different sources of uncertainty in the estimated impacts. These include those related to purely statistical aspects, such as the imprecision of the estimated exposure-response function and its extrapolation, and the inherent uncertainty of the exposure simulations obtained from the climate and circulation models. Other sources of uncertainty can derive from the adopted assumptions on potential future scenarios, such as variation in the exposure-response association due to changes in vulnerability (adaptation), different population structure, etc. (Huang et al., 2011). However, the latter components are more complex to quantify. In our example of the London analysis, there are two main sources of uncertainly: the estimation of the exposure-response function and climate projections. Regarding the former, the unobserved temperature range over which the curve is extrapolated contributes in a larger extent to the total uncertainty.These can be represented by the covariance matrix of the model coefficients defining the exposure-response function, and the variability of the modeled series generated in each GCM, respectively. This uncertainty is quantified by generating 1000 samples of the coefficients through Monte Carlo simulations, assuming a multivariate normal distribution for the estimated spline model coefficients, and then generating results for each of
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the five GCMs (Gasparrini and Leone, 2014). Results are reported as point estimates, using the average across climate models (GCM-ensemble) obtained by the estimated coefficients, and as eCI, defined as the 2.5th and 97.5th percentiles of the empirical distribution of the attributable mortality across coefficients samples and GCMs. These eCI account for both sources of uncertainty. By assuming constant vulnerability and population structure, the introduction of additional sources of uncertainty is prevented, which however, can be important in more complex projection models, as discussed in the following section.
7 More complex scenarios: Demographic changes and adaptation As introduced before, it is expected that population in the future will be different from today’s both in terms of size and population structure. These demographic changes will modify the vulnerability of the population to climate extremes by, for example, increasing the population at risk and by shifting it toward older people, usually more susceptible to environmental stressors such as heat waves. Thus, future health impacts will strongly depend on the nature of the population scenarios considered under specific assumptions on the hypothetical demographic structure in the future. Some studies have accounted for changes in population by using age- specific exposure-response functions and baseline mortality projections (Hajat et al., 2014; Heaviside et al., 2016; Jackson et al., 2010; Ostro et al., 2012). In contrast, others projected temperature-related impacts relying on scenarios of no demographic changes (Doherty et al., 2009; Doyon et al., 2008; Gosling et al., 2009).The latter approach, built on simplistic assumptions, presents important limitations as it cannot provide a realistic representation of future excess burdens. However, it offers a more straightforward interpretation as it separates the impact of global warming from other changes, such as those related to demographic variations, that would occur anyway even in a stable climate. In addition, the second method is also applicable when projections of age-specific risks and baseline populations are not available. Another important aspect to be considered in health projection studies is the potential acclimatization and/or adaptation of the population to climate extremes or reduction in vulnerability to specific environmental stressors. For example, evidence obtained so far indicates that populations have adapted to heat stress in the last decades, with an attenuation of the related risks (Arbuthnott et al., 2016). Under this framework, exposure- response associations obtained on historical data would not be representative
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of future risks. Several methods have been proposed to account for these changes in vulnerability. These include the analog city approach (Hayhoe et al., 2004; Knowlton et al., 2007), which makes use of exposure-response estimates from a location with a climate similar to that projected in the future. However, biases would arise if important differences in terms of social, economic, and demographic characteristics exist between the target and the analog city or region (Huang et al., 2011). Other methods consist in modifying the estimated exposure-response association.This could be done, for example, by shifting the threshold values defining a climate extreme or decreasing the slope of the exposure-response function (Gosling et al., 2009; Huynen and Martens, 2015). Although this approach seems more reasonable, it often implies simplistic assumptions on the form of the future exposure-response shape and its changes. In addition, while few studies have used empirical evidence from historical data (Honda et al., 2007, 2014), most of them have used an arbitrary set of parameters to model the extent and timing of adaptation mechanisms (Huynen and Martens, 2015). Gosling et al. (2017) discussed problems and limitations of existing methods for modeling adaptation in temperature studies, and also showed how the choice greatly influences the estimated health impacts. More sophisticated methodologies should be developed to empirically estimate future exposure-response associations based on the available evidence on the potential adaptation drivers and under specific population scenarios. The example of analysis assumes the simplest scenario of no adaptation and stable populations. That is, the results answer to the question: “What will the temperature-related impact be in the future if the current population would be exposed to warmer temperatures projected in the future?” Although it should be noted that this relies on a substantial simplification, this method does not imply the adoption of any assumption on these two aspects and reduces considerably the level of uncertainty of the estimates.
8 Final remarks This chapter is meant to serve as a guidance for researchers with different backgrounds and level of knowledge to perform health projection studies under climate change scenarios. It provides an overview on the key methodological issues presenting the available methodological choices, along with their potential limitations and advantages. It also provides a more in-depth description of a methodological framework recently developed and applied in health impact projection studies (Gasparrini et al., 2017;
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Vicedo-Cabrera et al., 2018, 2019). This incorporates cutting-edge advanced methods to estimate risks and apply them over future temperature projections under simple but consistent assumptions. The method is illustrated through a practical example of an applied analysis, complemented with real data and corresponding R code. Further details on the developed methodology can be found in Vicedo-Cabrera et al. (2019). It should be noted that the analytical approaches described in the example are tailored to the specific study settings. These should be modified based on the researcher criteria which could potentially involve the extension of the proposed modeling framework to apply other modeling choices using other environmental stressors, outcomes, study settings, and more complex climate change scenarios.
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Projecting health impacts of climate extremes: A methodological overview Ana M. Vicedo-Cabreraa, Francesco Seraa, Antonio Gasparrinia,b a Department of Public Health, Environments and Society, London School of Hygiene and Tropical Medicine, London, United Kingdom b Centre for Statistical Methodology, London School of Hygiene and Tropical Medicine, London, United Kingdom
It is well recognized that climate change is one of the most important environmental threats to human health, and it is expected that impacts will be larger in the future as warming progresses (Gasparrini et al., 2017; IPCC, 2013). It can affect human health through multiple pathways, including indirect (i.e., spread of disease vectors, increase in food insecurity, and migration and conflicts) and direct effects associated to the increase in frequency and intensity of extreme weather events or climate extremes such as floods, droughts, hurricanes, and heat waves (McMichael et al., 2012; Patz et al., 2005). Regarding the latter, it has been shown that a substantial part of the health risk is likely to occur due to the direct exposure to such extreme events, in particular, associated to extreme temperatures or heat waves (IPCC, 2013). There is a growing interest in assessing what may be the impact of climate extremes on health in the future under different warming scenarios based on specific adaptation and mitigation strategies. Evidence provided by health impact projection studies are extremely valuable to policy makers and stakeholders for the implementation of effective policies against climate change. In particular, numerous projection studies have been performed so far estimating future mortality impacts associated to the direct exposure to temperature in different locations worldwide (e.g., Gasparrini et al., 2017; Petkova et al., 2017; Weinberger et al., 2017). Other studies projected health impacts associated with specific extreme events, such as heat waves, hurricanes, cyclones, wildfires, floods, etc. using several climate change scenarios (Arnell et al., 2018; Dasgupta et al., 2009; Gao et al., 2016) and Climate Extremes and Their Implications for Impact and Risk Assessment https://doi.org/10.1016/B978-0-12-814895-2.00010-0
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other e nvironmental stressors related to climate change, such as air pollution (Chen et al., 2018). Providing reliable health impact projections under climate change scenarios is not an easy task (see, e.g., Scenario chapter for a more in-depth introduction). Although projection studies generally share a common methodological scheme, even if applied over different environmental factors and health outcomes, forecasting future health impacts entails important methodological challenges (Huang et al., 2011). This basic framework consists of applying known risk functions on simulated future distributions of extreme events that are generated by climate change models under specific emissions scenarios. It would seem a priori a straightforward approach, however, specific methodological issues should be handled with caution. For instance, the patterns of risks between environmental stressors and human health are complex and sometimes heterogeneous across populations. Likewise, the inherent uncertainty of potential future climate change processes and how population would evolve in the coming decades pose important analytical challenges and hinder the appropriate interpretation of the results (Huang et al., 2011). During the past decades, there have been exceptional efforts from the research community in terms of development of advanced study designs, tools, and statistical methods to properly address these challenges. This chapter provides a comprehensive overview of the more recent methodological approaches applied in health impacts projections under climate change scenarios. This describes the state of the art of the current methods used in each analytical step of a projections study and discusses the potential alternatives, assumptions, and limitations. It specifically focuses on the cutting-edge statistical methodologies recently developed in time- series analysis in environmental epidemiology, which have shown a great potential in climate change projections. The description and discussion are coupled with an example of analysis on projected temperature-related mortality in London. The statistical analysis has been translated into R scripts which are available in the personal website and GitHub repository of the last author. The statistical framework illustrated in the example has been previously used in recent publications (Gasparrini et al., 2017; Vicedo-Cabrera et al., 2018) and described in detail in a methodological contribution (Vicedo-Cabrera et al., 2019). In the latter, the R code reproducing the described example of analysis and the corresponding data are also provided as supplementary material. The chapter is structured in seven sections, each one addressing specific analytical steps of a projection study. The first two sections describe the two
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essential elements needed: the exposure-response function representing the association of interest, and the modeled data on future climate scenarios over which this will be applied. Sections 3–6 cover the main methodological steps, briefly illustrating procedures for the downscaling and calibration of the exposure data, and the estimation of the projected impacts and uncertainty. The final Section 7 discusses more advanced issues, such as dealing with changes in vulnerability and population structure in the future.
1 Estimation of exposure-response associations The first key element needed in projection studies is a measure of the association between the environmental stressor (i.e., climate extremes) and the risk of a health outcome. This, usually represented by an exposure- response function, will be combined with the modeled data on future climate changes scenarios to estimate the projected impacts. The risk function should offer a reliable approximation of the expected future association in a study population, based on a given scenario and set of specific assumptions. These will be further discussed in Sections 4 and 6. Exposure-response functions can be obtained through two different approaches.The simplest method is to rely on functions, risk, or association estimates reported in the existing literature estimated on the same or a similar geographical area (Heaviside et al., 2016; Kendrovski et al., 2017; Knowlton et al., 2009). The latter is the only viable option when actual data on the exposure and the outcome is not available. However, it should be noted that this approach can have limited applicability for some study settings. In fact, it has been observed that associations can vary widely across populations for several environmental factors such as temperature, and therefore strong assumptions on a common shape of the association across different populations should be made. The most desirable option is to directly estimate the association in our study population using available historical data on the exposure and outcome. There are several analytical approaches, including specific epidemiological study designs and related statistical methods, to study different aspects of health associations with environmental stressors. The choice depends on various aspects, such as the timing (short- vs. long-term effects), or the type of data, with different levels of temporal or spatial aggregation. In the specific setting of climate change studies and, in particular, on direct impacts of climate extremes, time-series analysis using aggregated data has been shown to be an ideal method (Bhaskaran et al., 2013). This is mainly
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due to the fact that impacts usually occur in short-term time spans, for example, temperature-related mortality.The framework illustrated in this chapter is mostly focused in this study design, although it can be easily adapted to other types of studies available (i.e., case-crossover) to model short-term associations (Armstrong, 2006; Dominici et al., 2003; Peters et al., 2006). Other epidemiological designs such as cohort studies would be more appropriate when assessing long-term effects on health, including those associated to air pollution. In time-series analysis, data on the environmental stressor and health outcome are collected at regular spans of time (i.e., daily) along a specific study period and over a defined population. For example, routinely collected data from electronic health records or demographics (i.e., mortality, hospital admission) and environmental data registered by monitors can be used in this type of study. In the example of the London study, these are represented by historical series of daily mean temperature and total death counts in the period 1990–2012 in London. This data is part of the multicitymulticountry (MCC) network (http://mccstudy.lshtm.ac.uk/), and has been previously used as example in already published manuscripts (e.g., Gasparrini et al., 2015). The dataset is available in the personal website and GitHub repository of the last author of the chapter, and more detailed information on the data is available in Vicedo-Cabrera et al. (2019). Regarding the modeling approach, several types of functions can be used to assess the exposure-response association of climate extremes or any environmental stressor with a specific health outcome. For example, extreme events can be modeled through an indicator variable identifying the days when the event (e.g., heat wave, flood) occurred. This would imply the identification of such events based on a a priori definition mostly using extreme percentile cutoffs obtained from observed distributions. Another option is to consider exposure as a continuous variable (e.g., temperature, precipitation), and model the association along the whole range or for specific extreme intervals. In this case, it is possible to apply simple threshold or linear functions, as usually applied in air pollution studies. However, exposure-response associations can show more complex risk patterns following nonlinear shapes. Likewise, it is often observed that the effects of an environmental stressor can last beyond the day of the exposure. This is typically found in temperature-mortality studies, in which heat effects can persist 3 or 4 days after the day of exposure and even for longer periods of time up to 2 weeks for cold effects. Thus, when modeling the exposure- response association, an additional temporal dimension should be considered that accounts for these lagged dependencies.
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These complex associations can be flexibly modeled in time-series analysis using a widely used methodological framework called distributed lag nonlinear models (DLNMs). In brief, this methodology can simultaneously model potential nonlinearities, along with other simple functions (e.g., l inear and threshold), of the association and its distribution across time, through the so-called exposure-lag-response bidimensional risk surfaces (Gasparrini, 2011, 2014; Gasparrini and Armstrong, 2013). The top-left panel of Fig. 1 shows the three-dimensional plot that represents exposure-lag-response association between temperature and mortality in London along 21 days
Fig. 1 Temperature-related mortality in London (1990–2012). (Top-left) three- dimensional plot showing the exposure-lag-response association. (Top-right) Overall cumulative mortality risk (and 95% confidence interval) up to 21 days of lag. (Bottomleft) Lag-specific mortality risks for heat and cold, corresponding to the maximum and minimum temperature value. (Bottom-right) Comparison between the exposure- response shapes estimated using three alternative modeling approaches.
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of lag. This has been obtained through a usual time-series model, that is, a generalized linear model with Poisson regression accounting for overdispersion. Temperature-mortality association was modeled through two nonlinear functions defined in both dimensions (i.e., exposure-response and lag-response dimensions). In particular, as further explained in Section 4, we used natural cubic splines that allows the log-linear extrapolation of the function beyond the boundaries of the observed series, a step needed to project the risk using the modeled temperature. The top-right panel of Fig. 1 represents the overall cumulative exposure-response association over 21 days of lag.The relationship is reported in terms of relative risk (RR), using as reference the temperature of minimum mortality (MMT) estimated from the overall curve. As expected, temperature-mortality risk in this study follows clearly a nonlinear shape, with increases above and below the MMT, corresponding to heat and cold associations. At the same time, as shown in the bottom-left panel of Fig. 1, these are distributed differently across time, suggesting an immediate increase in mortality risk after an extremely hot day (at the maximum temperature value), in contrast an initially negative and then more delayed and sustained risk persisting for some weeks after a cold spell (at the minimum temperature value). The choice of the appropriate model for estimating health risks is paramount. For comparison, the bottom-right panel of Fig. 1 illustrates the resulting exposure-response shapes when applying simpler functions, as examples of other modeling approaches usually used (Carmona et al., 2016; Díaz et al., 2018). These include the use of an indicator term for the extreme temperature days (when values above or below the fifth and 95th percentile) (dashed blue line—gray in print version) or threshold function with linear relationships for extreme ranges above or below these percentile temperature values (dotted-dashed orange line—light gray in print version), and a linear function (dotted green—dark gray in print version). As it can be observed, these choices seem less ideal for modeling the mortality risk of nonoptimal temperature, highlighting the importance of the selection of suitable functions to represent the association of interest, and the potential bias of inappropriate simplifications.
2 Future climatic and population scenarios The second essential element needed in projection studies on climate extremes is the data on future climatic and population scenarios over which the exposure-response function will be applied to quantify health impacts.
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Data on future distribution of environmental stressors or climate extremes (as described in Chapter 2) are commonly based on specific scenarios that account for changes in multiple and often interrelated factors. For instance, socioeconomic and technological changes, population growth, and land-use changes can affect pathways of greenhouse gas emissions or atmospheric concentrations of other pollutants, which in turn will determine trends in global warming and potential levels of specific environmental exposures (IPCC, 2013). Based on specific and simplified assumptions, these trends can be generated from general circulation models (GCMs) under a defined climate change scenario. To have a better representation of future trends, the usual approach is to combine impact estimates obtained either using more than one model per scenario [e.g., Coupled Model Intercomparison Project (Eyring et al., 2016)] or using ensemble members output from multiple runs of the same climate model, but with different initial conditions (Huang et al., 2011; Sanderson et al., 2017). The first approach is the one selected in the proposed methodological framework applied in the example of the London study. As shown in Vicedo-Cabrera et al. (2019), projected temperature data is derived as an average across five different GCMs for two climate change scenarios (further details on the projected temperature series in VicedoCabrera et al., 2019). Specifically, these are defined as representative concentration pathways 4.5 and 8.5 (RCP4.5 and RCP8.5) within the Coupled Model Intercomparison Project Phase 5 of IPCC (Taylor et al., 2011). As discussed later in Section 6, the availability of exposure trends from multiple models can be used to determine the related uncertainty of the projected health impacts. Likewise, health impact projections due to climate extremes also depend on future representations of the population at risk. These are determined by hypothetical demographic structures and baseline mortality and morbidity rates in the future. Data on these population scenarios can be built following different approaches based on the adopted assumptions. Several studies applied the simplest procedure consisting in assuming that populations and outcome rates will remain constant in the future, thus isolating the climate effect from other important trends (Baccini et al., 2011; Doherty et al., 2009; Doyon et al., 2008). However, other studies relied on population projections derived from predictive models under assumptions on future levels of fertility, mortality, and migration (Ostro et al., 2012; Peng et al., 2011;Vardoulakis et al., 2014). For example, some studies used age-specific risks and population trends to take into account the progressive population
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growth and aging (Hajat et al., 2014; Heaviside et al., 2016). In this case, assumptions on changes in demographic structure of future populations should be also adopted, which would entail the introduction of additional sources of uncertainty. This topic on demographic changes will be further discussed in Section 7. In the case of the example for London, projected series of total mortality counts are computed as the average for each day of the year from daily observed deaths, then repeated along the same projection period of the modeled temperature series. By doing so, the seasonal structure of the observed mortality series is kept. Projected mortality is then built under the assumption of “stable populations” with no changes in age distribution and overall mortality rates.
3 Downscaling and calibration In most projection studies, the future series on the environmental stressor needs to be processed before the estimation of the health impact. As explained in Chapter 4, the main reason is that climate simulations usually show systematic deviations from the observed real-world climate. This can be due to the different geographical resolution (gridded vs. point-source), or to biases due to poor performance of climate models (i.e., areas with sparse information from meteorological stations). These deviations should be carefully considered in climate change projections studies, as the predicted impacts will depend on the alignment of observed and modeled series (Hewitson et al., 2014; Maraun, 2016). Corrections of biases related to these two aspects have been defined separately as downscaling and calibration, although in most cases they rely on similar analytic procedures. Downscaling refers to the process of obtaining location-specific climate information from global or regional models that provide data at a higher geographical resolution. Conversely, calibration is a more general concept of realigning two series of data, in this case observed and modeled series. Several bias correction methods have been proposed using different techniques with varying degree of complexity (Maraun, 2016) (see also Chapter 5 in this book). However, there exists limited evidence about the potential impact of the choice of method on the estimated projections. In the example of the London study, the model outputs from the GCMs are firstly downscaled through bilinear interpolation at a 0.5° × 0.5° spatial resolution and linear interpolated by day of the year.The resulting series are then calibrated with the observed data using the bias-correction method
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developed by Hempel et al. (2013). This ensures that the trend and variability of the original data are preserved by adjusting the cumulative distribution of the simulated data to the observed one. Specifically, the monthly variability and mean are corrected only using a constant offset or multiplicative correction factor that corrects for long-term differences between the simulated and observed monthly mean data in the historical period (Hempel et al., 2013).
4 Extrapolation of exposure-response curve It is important to keep in mind that the risk estimates obtained over historical periods do not automatically apply to future scenarios. It is possible that the estimated exposure-response association will be different in the future, due to, for example, adaptation or changes in vulnerability of the population, as it will be further discussed in Section 7. However, even if assuming no changes in risk, the estimated exposure-response curve may not be appropriate to obtain projections in future periods. This is mainly due to the fact that the distribution of a specific environmental stressors in the future is likely to be different from that observed in the present days. Thus, we need to perform an additional step consisting in the extrapolation of the exposure-response beyond the observed boundaries. This, however, implies the adoption of additional assumptions on the hypothetical shape of the association over the unobserved range. A viable method is based on a log-linear extrapolation of the curve beyond the observed boundaries. We assume in this case that the risk will change linearly in the log-scale beyond the observed boundaries. To do so, in the example of the temperature-mortality study in London, the exposure-response function is modeled through a natural cubic spline function which ensures this log-linear extrapolation. Fig. 2 (top panel) shows the estimated exposure-response function along the observed temperature values (solid line) and the extrapolated part of the curve (dashed line) covering the warmer range of the projected temperature series. For the sake of the example, we use the projected temperature obtained from one of the five GCM output, in this case the IPSL-CM5A-LR model, (Mignot and Bony, 2013), under RCP8.5 scenario. However, this log-linear extra polation entails a series of strong assumptions on the future risk associated to environmental factors. Apart from assuming that the exposure-response association estimated will not change in the future, this approach assumes that the extrapolation represents appropriately the risk over the unobserved
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Fig. 2 Temperature and excess mortality in London for present and future periods. The top panel shows the exposure-response curve represented as mortality relative risk (RR) across the temperature (°C) range, with 95% empirical confidence intervals (gray area). The dotted vertical corresponds to the minimum mortality temperature used as reference, defining the two portions of the curve for cold and heat (blue and red, respectively—dark gray and gray in print version). The dashed part of the curve represent the extrapolation beyond the maximum temperature observed in 2010–19 (dashed vertical line). The mid-panel displays the distribution of modeled temperature for the current (2010–19, light yellow area—light gray in print version) and at the end of the century (2090–99, orange area—gray in print version), projected using a specific climate model (IPSL-CM5A-LR) and scenario (RCP8.5). The bottom panel represents the related distribution of excess mortality for the current (light green—light gray in print version) and future period (dark green—gray in print version) (estimated using the project series from this specific GCM under RCP8.5), expressed as the fraction of additional deaths (%) attributed to nonoptimal temperature compared with the temperature of minimum mortality.
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range. This assumption can be wrong if the log-risk increases exponentially beyond the range already experienced, for instance, because of physiological cascades related to the loss of acclimatization capacity. While a functional model would be more appropriate than the empirical approach adopted here, the lack of knowledge on the pathophysiology of temperature-related risks makes the former difficult to define.
5 Projection and quantification of the impact Once the two key components (association estimates and projected series) are properly processed, we can proceed with the main step in this analysis that is the estimation of the impact projections. As a general definition, it consists in applying the exposure-response association estimates over the future or projected series of the specific environmental stressor and outcome to derive the corresponding health impacts estimates. Several measures of impact have been used so far, for instance, in terms of percent changes in the rate of the outcome, excess mortality or morbidity, or attributable fractions (Gasparrini et al., 2017; Hajat et al., 2014; Wu et al., 2014). Here we illustrate a procedure to estimate the impacts in terms of attributable fractions within the DLNM framework using a statistical method previously developed (Gasparrini and Leone, 2014). Although for the specific example of the London study, we estimate the attributable number of deaths due to nonoptimal temperatures, the same method can be applied over other outcomes and environmental stressors. The general procedure consists in computing for each day of the series the number of cases attributed to a specific environmental stressor (e.g., temperature, air pollution) based on the estimated risk and the level of exposure in that day. In the specific setting of projecting impacts due to climate extremes, this would correspond to the number of cases occurring on an event day defined by the estimated risk. Then daily attributable numbers are aggregated by defined intervals of time in the future period and/or further divided into specific exposure ranges or by types of events (e.g., extreme events of variable magnitude such as heat waves of different intensity). In the example of London study, impacts are divided into heat and cold components, by summing the subsets corresponding to days with temperatures higher or lower than the MMT (Gasparrini et al., 2015). Heat and cold impacts can be further divided between moderate and extreme ranges according to whether temperature is above or below a defined extreme threshold (e.g., 2.5th and 97.5th percentiles) (Gasparrini et al., 2015). Impacts can be also
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be expressed in terms of attributable fraction computed as the ratio between the estimated impact and the corresponding total number of cases. Fig. 2 (mid and bottom panels) shows the distributions of temperatures and estimated attributable mortality, respectively, for the historic and future period in London using the IPSL-CM5A-LR model under RCP8.5 scenario. It can be observed that the mortality burden due to cold temperatures is currently much larger than for heat, especially across the moderate cold temperatures. However, if we compare the GCM-ensemble estimates between the periods (2010–19 vs. 2090–99), we found that heat-attributable mortality will substantially increase in the future by 4.0% [95% empirical confidence interval (eCI): 0.7–6.8], while mortality due to cold will be reduced by 3.3% (95% eCI: 4.3–1.9). This is due to the shift in the temperature distribution, thus, the changes between present and future periods reflect the interrelation between changes in occurrences of given temperatures and related risks.
6 Ensemble estimates and quantification of uncertainty As introduced at the beginning of the chapter, one of the key methodological issues in projection studies is to properly identify and deal with the different sources of uncertainty in the estimated impacts. These include those related to purely statistical aspects, such as the imprecision of the estimated exposure-response function and its extrapolation, and the inherent uncertainty of the exposure simulations obtained from the climate and circulation models. Other sources of uncertainty can derive from the adopted assumptions on potential future scenarios, such as variation in the exposure-response association due to changes in vulnerability (adaptation), different population structure, etc. (Huang et al., 2011). However, the latter components are more complex to quantify. In our example of the London analysis, there are two main sources of uncertainly: the estimation of the exposure-response function and climate projections. Regarding the former, the unobserved temperature range over which the curve is extrapolated contributes in a larger extent to the total uncertainty.These can be represented by the covariance matrix of the model coefficients defining the exposure-response function, and the variability of the modeled series generated in each GCM, respectively. This uncertainty is quantified by generating 1000 samples of the coefficients through Monte Carlo simulations, assuming a multivariate normal distribution for the estimated spline model coefficients, and then generating results for each of
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the five GCMs (Gasparrini and Leone, 2014). Results are reported as point estimates, using the average across climate models (GCM-ensemble) obtained by the estimated coefficients, and as eCI, defined as the 2.5th and 97.5th percentiles of the empirical distribution of the attributable mortality across coefficients samples and GCMs. These eCI account for both sources of uncertainty. By assuming constant vulnerability and population structure, the introduction of additional sources of uncertainty is prevented, which however, can be important in more complex projection models, as discussed in the following section.
7 More complex scenarios: Demographic changes and adaptation As introduced before, it is expected that population in the future will be different from today’s both in terms of size and population structure. These demographic changes will modify the vulnerability of the population to climate extremes by, for example, increasing the population at risk and by shifting it toward older people, usually more susceptible to environmental stressors such as heat waves. Thus, future health impacts will strongly depend on the nature of the population scenarios considered under specific assumptions on the hypothetical demographic structure in the future. Some studies have accounted for changes in population by using age- specific exposure-response functions and baseline mortality projections (Hajat et al., 2014; Heaviside et al., 2016; Jackson et al., 2010; Ostro et al., 2012). In contrast, others projected temperature-related impacts relying on scenarios of no demographic changes (Doherty et al., 2009; Doyon et al., 2008; Gosling et al., 2009).The latter approach, built on simplistic assumptions, presents important limitations as it cannot provide a realistic representation of future excess burdens. However, it offers a more straightforward interpretation as it separates the impact of global warming from other changes, such as those related to demographic variations, that would occur anyway even in a stable climate. In addition, the second method is also applicable when projections of age-specific risks and baseline populations are not available. Another important aspect to be considered in health projection studies is the potential acclimatization and/or adaptation of the population to climate extremes or reduction in vulnerability to specific environmental stressors. For example, evidence obtained so far indicates that populations have adapted to heat stress in the last decades, with an attenuation of the related risks (Arbuthnott et al., 2016). Under this framework, exposure- response associations obtained on historical data would not be representative
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of future risks. Several methods have been proposed to account for these changes in vulnerability. These include the analog city approach (Hayhoe et al., 2004; Knowlton et al., 2007), which makes use of exposure-response estimates from a location with a climate similar to that projected in the future. However, biases would arise if important differences in terms of social, economic, and demographic characteristics exist between the target and the analog city or region (Huang et al., 2011). Other methods consist in modifying the estimated exposure-response association.This could be done, for example, by shifting the threshold values defining a climate extreme or decreasing the slope of the exposure-response function (Gosling et al., 2009; Huynen and Martens, 2015). Although this approach seems more reasonable, it often implies simplistic assumptions on the form of the future exposure-response shape and its changes. In addition, while few studies have used empirical evidence from historical data (Honda et al., 2007, 2014), most of them have used an arbitrary set of parameters to model the extent and timing of adaptation mechanisms (Huynen and Martens, 2015). Gosling et al. (2017) discussed problems and limitations of existing methods for modeling adaptation in temperature studies, and also showed how the choice greatly influences the estimated health impacts. More sophisticated methodologies should be developed to empirically estimate future exposure-response associations based on the available evidence on the potential adaptation drivers and under specific population scenarios. The example of analysis assumes the simplest scenario of no adaptation and stable populations. That is, the results answer to the question: “What will the temperature-related impact be in the future if the current population would be exposed to warmer temperatures projected in the future?” Although it should be noted that this relies on a substantial simplification, this method does not imply the adoption of any assumption on these two aspects and reduces considerably the level of uncertainty of the estimates.
8 Final remarks This chapter is meant to serve as a guidance for researchers with different backgrounds and level of knowledge to perform health projection studies under climate change scenarios. It provides an overview on the key methodological issues presenting the available methodological choices, along with their potential limitations and advantages. It also provides a more in-depth description of a methodological framework recently developed and applied in health impact projection studies (Gasparrini et al., 2017;
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Vicedo-Cabrera et al., 2018, 2019). This incorporates cutting-edge advanced methods to estimate risks and apply them over future temperature projections under simple but consistent assumptions. The method is illustrated through a practical example of an applied analysis, complemented with real data and corresponding R code. Further details on the developed methodology can be found in Vicedo-Cabrera et al. (2019). It should be noted that the analytical approaches described in the example are tailored to the specific study settings. These should be modified based on the researcher criteria which could potentially involve the extension of the proposed modeling framework to apply other modeling choices using other environmental stressors, outcomes, study settings, and more complex climate change scenarios.
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