OBJECTIVETo project the future impacts of climate change on heat-related mortality in shanghai.

METHODSThe statistical downscaling techniques were applied to simulate the daily mean temperatures of Shanghai in the middle and farther future under the changing climate. Based on the published exposure-reaction relationship of temperature and mortality in Shanghai, we projected the heat-related mortality in the middle and farther future under the circumstance of high speed increase of carbon e mission (A2) and low speed increase of carbon emission (B2). The data of 1961 to 1990 was used to establish the model, and the data of 1991 - 2001 was used to testify the model, and then the daily mean temperature from 2030 to 2059 and from 2070 to 2099 were simulated and the heat-related mortality was projected. The data resources were from U.S. National Climatic Data Center (NCDC), U.S. National Centers for Environmental Prediction Reanalysis Data in SDSM Website and UK Hadley Centre Coupled Model Data in SDSM Website.

RESULTSThe explained variance and the standard error of the established model was separately 98.1% and 1.24°C. The R(2) value of the simulated trend line equaled to 0.978 in Shanghai, as testified by the model. Therefore, the temperature prediction model simulated daily mean temperatures well. Under A2 scenario, the daily mean temperature in 2030 - 2059 and 2070 - 2099 were projected to be 17.9°C and 20.4°C, respectively, increasing by 1.1°C and 3.6°C when compared to baseline period (16.8°C). Under B2 scenario, the daily mean temperature in 2030 - 2059 and 2070 - 2099 were projected to be 17.8°C and 19.1°C, respectively, increasing by 1.0°C and 2.3°C when compared to baseline period (16.8°C). Under A2 scenario, annual average heat-related mortality were projected to be 516 cases and 1191 cases in 2030 - 2059 and 2070 - 2099, respectively, increasing 53.6% and 254.5% when compared with baseline period (336 cases). Under B2 scenario, annual average heat-related mortality were projected to be 498 cases and 832 cases in 2030 - 2059 and 2070 - 2099, respectively, increasing 48.2% and 147.6% when compared with baseline period (336 cases).

CONCLUSIONUnder the changing climate, heat-related mortality is projected to increase in the future;and the increase will be more obvious in year 2070 - 2099 than in year 2030 - 2059.

China ; Climate Change ; Greenhouse Effect ; Humans ; Models, Theoretical ; Mortality ; Risk Assessment

OBJECTIVETo explore the relationship between temperature and non-accidental mortality in Guangzhou, Changsha and Kunming;to evaluate the temperature-related risk of mortality; and thereby to provide scientific evidence for enacting the policy to tackle climate changes.

METHODDaily meteorology data and mortality data were collected in 2006-2009 in Guangzhou, Changsha and Kunming. Distributed lag non-linear model (DLNM) was established and applied in a case-crossover design, which controlled the secular trend of time, to estimate the specified effects of temperature on non-accidental mortality at conditions of lag 0-2, lag 0-18 and lag 0-27 days, respectively.

RESULTAn obvious seasonal periodicity was found in non-accidental mortality in Guangzhou, Changsha and Kunming during 2006-2009. The mortality number was comparatively high in the winters, and some high temperature days in summer; but was comparatively low in springs and autumn. An L-shaped relationship was found between temperature and mortality in Guangzhou and Kunming and a U-shaped relationship was found in Changsha. When daily mean temperature exceeded 28.2 °C, 24.5°C and 23.2°C, as average temperature increase 1°C, non-accidental mortality increased 4.56% (95%CI:2.74%-6.63%), 5.66% (95%CI:0.22%-12.65%) , -3.94% (95%CI:-32.77%-39.01%) , respectively; when daily mean temperature below 24.8°C, 20.0°C and 17.3°C, as average temperature decrease 1°C, the corresponding increase in non-accidental mortality were 3.28% (95%CI:2.41%-4.10%) (lag 0-18 days), 1.35% (95%CI:0.31%-1.77%) (lag 0-2 days) and 2.42% (95%CI:1.08%-3.27%) (lag 0-27 days) , respectively. The effects of hot weather were acute and short term; while the effects of cold weather had a several days delay, but a longer persistence.

CONCLUSIONSExtreme cold and hot temperature could increase the risk of non-accidental mortality in Guangzhou, Changsha and Kunming. The effects of cold weather had a several days delay, but a longer persistence.

China ; epidemiology ; Climate Change ; Cross-Over Studies ; Humans ; Mortality ; Seasons ; Temperature

The health effects of climatic changes constitute an important research area, yet few researchers have reported city- or region-specific projections of temperature-related deaths based on assumptions about mitigation and adaptation. Herein, we provide quantitative projections for the number of additional deaths expected in the future, owing to the cold and heat in the city of Nanjing, China, based on 31 global circulation models (GCMs), two representative concentration pathways (RCPs) (RCP4.5 and RCP8.5), and three population scenarios [a constant scenario and two shared socioeconomic pathways (SSPs) (SSP2 and SSP5)], for the periods of 2010-2039, 2040-2069, and 2070-2099. The results show that for the period 2070-2099, the net number of temperature-related deaths can be comparable in the cases of RCP4.5/SSP2 and RCP8.5/SSP5 owing to the offsetting effects attributed to the increase of heat related deaths and the decrease of cold-related deaths. In consideration of this adaptation, we suggest that RCP4.5/SSP2 is a better future development pathway/scenario.
China ; epidemiology ; Cities ; epidemiology ; Climate Change ; Humans ; Linear Models ; Mortality ; trends ; Temperature

OBJECTIVETo study the relation between temperature and mortality by estimating the temperature-related mortality in Beijing, Shanghai, and Guangzhou.

METHODSData of daily mortality, weather and air pollution in the three cities were collected. A distributed lag nonlinear model was established and used in analyzing the effects of temperature on mortality. Current and future net temperature-related mortality was estimated.

RESULTSThe association between temperature and mortality was J-shaped, with an increased death risk of both hot and cold temperature in these cities. The effects of cold temperature on health lasted longer than those of hot temperature. The projected temperature-related mortality increased with the decreased cold-related mortality. The mortality was higher in Guangzhou than in Beijing and Shanghai.

CONCLUSIONThe impact of temperature on health varies in the 3 cities of China, which may have implications for climate policy making in China.

China ; Cities ; Climate Change ; Environmental Monitoring ; statistics & numerical data ; Humans ; Mortality ; Temperature ; Urban Population ; statistics & numerical data

BACKGROUND: Ambient temperature may contribute to seasonality of mortality; in particular, a warming climate is likely to influence the seasonality of mortality. However, few studies have investigated seasonality of mortality under a warming climate. METHODS: Daily mean temperature, daily counts for all-cause, circulatory, and respiratory mortality, and annual data on prefecture-specific characteristics were collected for 47 prefectures in Japan between 1972 and 2015. A quasi-Poisson regression model was used to assess the seasonal variation of mortality with a focus on its amplitude, which was quantified as the ratio of mortality estimates between the peak and trough days (peak-to-trough ratio (PTR)). We quantified the contribution of temperature to seasonality by comparing PTR before and after temperature adjustment. Associations between annual mean temperature and annual estimates of the temperature-unadjusted PTR were examined using multilevel multivariate meta-regression models controlling for prefecture-specific characteristics. RESULTS: The temperature-unadjusted PTRs for all-cause, circulatory, and respiratory mortality were 1.28 (95% confidence interval (CI): 1.27-1.30), 1.53 (95% CI: 1.50-1.55), and 1.46 (95% CI: 1.44-1.48), respectively; adjusting for temperature reduced these PTRs to 1.08 (95% CI: 1.08-1.10), 1.10 (95% CI: 1.08-1.11), and 1.35 (95% CI: 1.32-1.39), respectively. During the period of rising temperature (1.3 °C on average), decreases in the temperature-unadjusted PTRs were observed for all mortality causes except circulatory mortality. For each 1 °C increase in annual mean temperature, the temperature-unadjusted PTR for all-cause, circulatory, and respiratory mortality decreased by 0.98% (95% CI: 0.54-1.42), 1.39% (95% CI: 0.82-1.97), and 0.13% (95% CI: - 1.24 to 1.48), respectively. CONCLUSION Seasonality of mortality is driven partly by temperature, and its amplitude may be decreasing under a warming climate.
Cardiovascular Diseases/mortality* ; Cause of Death ; Climate Change/mortality* ; Cold Temperature/adverse effects* ; Hot Temperature/adverse effects* ; Humans ; Japan/epidemiology* ; Mortality/trends* ; Regression Analysis ; Respiratory Tract Diseases/mortality* ; Seasons ; Time