Heat-related mortality risk model for climate change impact projection

Abstract

Objectives We previously developed a model for projection of heat-related mortality attributable to climate change. The objective of this paper is to improve the fit and precision of and examine the robustness of the model. Methods We obtained daily data for number of deaths and maximum temperature from respective governmental organizations of Japan, Korea, Taiwan, the USA, and European countries. For future projection, we used the Bergen climate model 2 (BCM2) general circulation model, the Special Report on Emissions Scenarios (SRES) A1B socioeconomic scenario, and the mortality projection for the 65?-year-old age group developed by the World Health Organization (WHO). The heat-related excess mortality was defined as follows: The temperature-mortality relation forms a V-shaped curve, and the temperature at which mortality becomes lowest is called the optimum temperature (OT). The difference in mortality between the OT and a temperature beyond the OT is the excess mortality. To develop the model for projection, we used Japanese 47-prefecture data from 1972 to 2008. Using a distributed lag nonlinear model (two-dimensional nonparametric regression of temperature and its lag effect), we included the lag effect of temperature up to 15 days, and created a risk function curve on which the projection is based. As an example, we perform a future projection using the above-mentioned risk function. In the projection, we used 1961-1990 temperature as the baseline, and temperatures in the 2030s and 2050s were projected using the BCM2 global circulation model, SRES A1B scenario, and WHO-provided annual mortality. Here, we used the ''counterfactual method'' to evaluate the climate change 123Environ Health Prev Med (2014) 19:56-63 DOI 10.1007 impact; For example, baseline temperature and 2030 mortality were used to determine the baseline excess, and compared with the 2030 excess, for which we used 2030 temperature and 2030 mortality. In terms of adaptation to warmer climate, we assumed 0 % adaptation when the OT as of the current climate is used and 100 % adaptation when the OT as of the future climate is used. The midpoint of the OTs of the two types of adaptation was set to be the OT for 50 % adaptation. Results We calculated heat-related excess mortality for 2030 and 2050. Conclusions Our new model is considered to be better fit, and more precise and robust compared with the previous model.

Aging Will Amplify the Heat-related Mortality Risk under a Changing Climate: Projection for the Elderly in Beijing, China

“…The minimum mortality occurred at approximately the 70 th percentile across the range of temperatures, consistent with previous studies, which reported MMTs in the 60–80 th percentile range443444546. As previously mentioned, heat effects were strongest at short time lags, whereas cold effects extended over multiple lag days.…”

Emergency Cardiovascular Hospitalization Risk Attributable to Cold Temperatures in Hong Kong

“…[4][5][6][7][8] Although most studies have quantified the association with ambient temperature in terms of relative risk (RR), only a few studies have assessed the disease burden attributed to cold/hot temperatures. [9][10][11][12][13] The attributable fraction (AF) representing the fraction of cases or deaths from a specific disease that would be avoided in the absence of exposure to extreme weather either in the exposed population or the population as a whole. AFs multiplied by the total number of cases of a given disease would obtain the absolute number (AN) of preventable cases because of extreme weather.…”

“…[11][12][13] Until recently, Gasparrini et al 9,10 introduced an updated approach to estimate the attributable risk (AR) based on the distributed lag nonlinear model (DLNM) framework with consideration of the complex pattern of potentially nonlinear and delayed associations described through exposure-lagresponse associations for time series study. Furthermore, no study has examined the AR of temperature on the morbidity end points, such as emergency hospitalizations, which was considered to better catch the effect of temperature change than mortality did.…”

A Region at Risk:

“…Models such as the one applied by WHO mostly assume that the basic form of the established statistical relationships will be constant in the future. However, acclimatization to generally higher temperatures in the future is at least partly included by raising the comfort temperature inherent in the model (Honda et al 2014).…”