Nonlinear relationship between extreme temperature and mortality in different temperature zones: A systematic study of 122 communities across the mainland of China

The effect modification of PM2.5 and ozone on the short-term associations between temperature and mortality across the urban areas of Japan

BACKGROUND

The acute effects of temperature and air pollution on mortality are well-known environmental factors that have been receiving more recognition lately. However, the health effects resulting from the interaction of air pollution and temperature remain uncertain, particularly in cities with low levels of pollution. This study aims to examine the modification effects of particulate matter with a diameter of 2.5 µm or less (PM2.5) and ozone (O3) on the association between temperature and mortality.

METHODS

We collected the daily number of all-cause, cardiovascular, and respiratory mortality from 20 major cities in Japan from 2012-2018. We obtained meteorological data from the Japan Meteorological Agency and air pollution data from the National Institute for Environmental Studies. We conducted analyses using a quasi-Poisson regression model with a distributed lag non-linear model for temperature in each city and subsequently performed a random-effects meta-analysis to derive average estimates.

RESULTS

We found that high levels of O3 might positively modify the mortality risk of heat exposure, especially for cardiovascular diseases. Subgroups such as the elderly and females were susceptible. We did not observe consistent evidence of effect modification by PM2.5, including effect modification on cold by both pollutants.

CONCLUSION

PM2.5 and O3 may positively modify the short-term association between heat and mortality in the urban areas of Japan. These results highlight the need for public health policies and interventions to address the collective impacts of both temperature and air pollution.

Environmental greenspace, subjective well-being, and all-cause mortality in elderly Chinese: Association and mediation study in a prospective cohort

BACKGROUND

Subjective well-being (SWB) measures mental health and happiness. Greenspace can have a positive impact on mental health, and higher SWB is associated with lower all-cause mortality. We conducted a mediation analysis on greenspace and all-cause mortality through improving SWB, in a prospective cohort of Chinese older adults.

METHODS

We included older adults over 65 from the 2008-2014 Chinese Longitudinal Healthy Longevity Survey (CLHLS). We used satellite-derived normalized difference vegetation index (NDVI) to measure greenspaces and calculated SWB measured by eight items. Three main statistical approaches were used. First, we used generalized estimating equations (GEE) and Cox proportional hazard models to examine NDVI-SWB and SWB-mortality relationships. Second, we conducted a causal mediation analysis to investigate the mediating effect of greenspace on all-cause mortality through SWB. Third, we conducted subgroup analyses to discover effect modification.

FINDINGS

Among 13,133 participants, the mean SWB score and NDVI in 2008 were 28·9 (SD 4·34) and 0·41 (SD 0·14), respectively. We found SWB partially mediated the relationship between residential greenspace and mortality in the adjusted model (average causal mediation effect = 0·11, p = 0·04; average direct effect = 1·96, p < 0·001; total effect = 2·07, p < 0·001) with varying proportions in subgroups. The protective influence was more evident for people with impaired cognitive function, living in rural areas and towns, and with lower income.

INTERPRETATION

We found a positive association between greenspace, SWB, and mortality. Greenness in the living environment confers better mental health and promotes longevity in the elderly population.

Improving interventional causal predictions in regulatory risk assessment

In 2022, the US EPA published an important risk assessment concluding that "Compared to the current annual standard, meeting a revised annual standard with a lower level is estimated to reduce PM2.5-associated health risks in the 30 annually-controlled study areas by about 7-9% for a level of 11.0 µg/m3… and 30-37% for a level of 8.0 µg/m3." These are interventional causal predictions: they predict percentage reductions in mortality risks caused by different counterfactual reductions in fine particulate (PM2.5) levels. Valid causal predictions are possible if: (1) Study designs are used that can support valid causal inferences about the effects of interventions (e.g., quasi-experiments with appropriate control groups); (2) Appropriate causal models and methods are used to analyze the data; (3) Model assumptions are satisfied (at least approximately); and (4) Non-causal sources of exposure-response associations such as confounding, measurement error, and model misspecification are appropriately modeled and adjusted for. This paper examines two long-term mortality studies selected by the EPA to predict reductions in PM2.5-associated risk. Both papers use Cox proportional hazards (PH) models. For these models, none of these four conditions is satisfied, making it difficult to interpret or validate their causal predictions. Scientists, reviewers, regulators, and members of the public can benefit from more trustworthy and credible risk assessments and causal predictions by insisting that risk assessments supporting interventional causal conclusions be based on study designs, methods, and models that are appropriate for predicting effects caused by interventions.

Extreme temperatures and circulatory mortality in a temperate continental monsoon climate city in Northeast China

Epidemiological studies have proven that extreme temperatures have a significant threat to public health. This study aimed to investigate the association between extreme temperatures and circulatory mortality from January 1, 2014, to December 31, 2016, in Harbin, a city with a cold climate in Northeast China. We set a maximum lag of 27 days to evaluate the hysteresis effects of different temperatures on circulatory mortality using a distributed lag nonlinear model (DLNM). Results indicated that daily mean temperature and circulatory mortality presented approximately an L-shaped, and the cumulative relative risks (RRs) decreased continuously as the temperature increased in both low and high temperatures. Extremely low temperature showed a hysteresis and durability on circulatory mortality, with the largest RR of 1.023 (95%CI: 1.001-1.046) at lag 26, and RR of the cumulative cold effect of 0-27 days was 1.302 (95%CI: 1.160-1.462). The effect of extremely high temperatures presented more acute and intense, with the largest RR of 1.033 (95%CI: 1.004-1.063) at lag 0. RR of the cumulative hot effect of 0-3 days was 1.056 (1.008-1.106). In addition, females were more susceptible to extremely low temperatures, while males were more vulnerable to extremely high temperatures. This study demonstrated that extremely low temperatures have a stronger effect on circulatory mortality than extremely high temperatures in Harbin.

Effects of ambient temperature on mortality among elderly residents of Chengdu city in Southwest China, 2016-2020: a distributed-lag non-linear time series analysis

BACKGROUND

With complex changes in the global climate, it is critical to understand how ambient temperature affects health, especially in China. We aimed to assess the effects of temperature on daily mortality, including total non-accidental, cardiovascular disease (CVD), respiratory disease, cerebrovascular disease, and ischemic heart disease (IHD) mortality between 2016 and 2020 in Chengdu, China.

METHODS

We obtained daily temperature and mortality data for the period 2016-2020. A Poisson regression model combined with a distributed-lag nonlinear model was used to examine the association between temperature and daily mortality. We investigated the effects of individual characteristics by sex, age, education level, and marital status.

RESULTS

We found significant non-linear effects of temperature on total non-accidental, CVD, respiratory, cerebrovascular, and IHD mortality. Heat effects were immediate and lasted for 0-3 days, whereas cold effects persisted for 7-10 days. The relative risks associated with extreme high temperatures (99th percentile of temperature, 28 °C) over lags of 0-3 days were 1.22 (95% confidence interval [CI]: 1.17, 1.28) for total non-accidental mortality, 1.40 (95% CI: 1.30, 1.50) for CVD morality, 1.34 (95% CI: 1.24, 1.46) for respiratory morality, 1.33 (95% CI: 1.20, 1.47) for cerebrovascular mortality, and 1.38 (95% CI: 1.20, 1.58) for IHD mortality. The relative risks associated with extreme cold temperature (1st percentile of temperature, 3.0 °C) over lags of 0-14 days were 1.32 (95% CI: 1.19, 1.46) for total mortality, 1.45 (95% CI: 1.24, 1.68) for CVD morality, 1.28 (95% CI: 1.09, 1.50) for respiratory morality, 1.36 (95% CI: 1.09, 1.70) for cerebrovascular mortality, and 1.26 (95% CI: 0.95, 1.68) for IHD morality. We found that hot and cold affects were greater in those over 85 years of age, and that women, individuals with low education levels, and those who were widowed, divorced, or never married, were more vulnerable.

CONCLUSIONS

This study showed that exposure to hot and cold temperatures in Chengdu was associated with increased mortality, with people over 85 years old, women, those with low education levels, and unmarried individuals being more affected by hot and cold temperatures.

Associations of ambient temperature with mortality for ischemic and hemorrhagic stroke and the modification effects of greenness in Shandong Province, China

BACKGROUND

Evidence is scant on the relative and attributable contributions of ambient temperature on stroke subtypes mortality. Few studies have examined modification effects of multiple greenness indicators on such contributions, especially in China. We quantified the associations between ambient temperature and overall, ischemic, and hemorrhagic stroke mortality; further examined whether the associations were modified by greenness.

METHODS

We conducted a multicenter time-series analysis from January 1, 2013 to December 31, 2019. we adopted a distributed lag non-linear model to evaluate county-specific temperature-stroke mortality associations. We then applied a random-effects meta-analysis to pool county-specific effects. Attributable mortality was calculated for cold and heat, defined as temperatures below and above the minimum mortality temperature (MMT). Finally, We conducted a multivariate meta-regression to determine associations between greenness and stroke mortality risks for cold and heat, using normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), and enhanced vegetation index (EVI) as quantitative indicators of greenness exposure.

RESULTS

In the study period, 138,749 deaths from total stroke were reported: 86,873 ischemic and 51,876 hemorrhagic stroke. We observed significant W-shaped relationships between temperature and stroke mortality, with substantial differences among counties and regions. With MMT as the temperature threshold, 17.16 % (95 % empirical CI, 13.38 %-19.75 %) of overall, 20.05 % (95 % eCI, 16.46 %-22.70 %) of ischemic, and 12.55 % (95 % eCI, 5.59 %-16.24 %) of hemorrhagic stroke mortality were attributable to non-optimum temperature (combining cold and heat), more mortality was caused by cold (14.94 %; 95 % eCI, 11.57 %-17.34 %) than by heat (2.22 %; 95 % eCI, 1.54 %-2.72 %). Higher levels of NDVI, SAVI and EVI were related to mitigated effects of non-optimum temperatures-especially heat.

CONCLUSIONS

Exposure to non-optimum temperatures aggravated stroke mortality risks; increasing greenness could alleviate that risks. This evidence has important implications for local communities in developing adaptive strategies to minimize the health consequences of adverse temperatures.

How Has Climate Change Driven the Evolution of Rice Distribution in China?

Estimating the impact of climate change risks on rice distribution is one of the most important elements of climate risk management. This paper is based on the GEE (Google Earth Engine) platform and multi-source remote sensing data; the authors quantitatively extracted rice production distribution data in China from 1990 to 2019, analysed the evolution pattern of rice distribution and clusters and explored the driving effects between climatic and environmental conditions on the evolution of rice production distribution using the non-parametric quantile regression model. The results show that: The spatial variation of rice distribution is significant, mainly concentrated in the northeast, south and southwest regions of China; the distribution of rice in the northeast is expanding, while the distribution of rice in the south is extending northward, showing a spatial evolution trend of "north rising and south retreating". The positive effect of precipitation on the spatial distribution of rice has a significant threshold. This shows that when precipitation is greater than 800 mm, there is a significant positive effect on the spatial distribution of rice production, and this effect will increase with precipitation increases. Climate change may lead to a continuous northward shift in the extent of rice production, especially extending to the northwest of China. This paper's results will help implement more spatially targeted climate change adaptation measures for rice to cope with the changes in food production distribution caused by climate change.

Asthma mortality attributable to ambient temperatures: A case-crossover study in China

BACKGROUND

Whether ambient temperature exposure contributes to death from asthma remains unknown to date. We therefore conducted a case-crossover study in China to quantitatively evaluate the association and burden of ambient temperature exposure on asthma mortality.

METHODS

Using data from the National Mortality Surveillance System in China, we conducted a time-stratified case-crossover study of 15 888 individuals who lived in Hubei and Jiangsu province, China and died from asthma as the underlying cause in 2015-2019. Individual-level exposures to air temperature and apparent temperature on the date of death and 21 days prior were assessed based on each subject's residential address. Distributed lag nonlinear models based on conditional logistic regression were used to quantify exposure-response associations and calculate fraction and number of deaths attributable to non-optimum ambient temperatures.

RESULTS

We observed a reverse J-shaped association between air temperature and risk of asthma mortality, with a minimum mortality temperature of 21.3 °C. Non-optimum ambient temperature is responsible for substantial excess mortality from asthma. In total, 26.3% of asthma mortality were attributable to non-optimum temperatures, with moderate cold, moderate hot, extreme cold and extreme hot responsible for 21.7%, 2.4%, 2.1% and 0.9% of asthma mortality, respectively. The total attributable fraction and number was significantly higher among adults aged less than 80 years in hot temperature.

CONCLUSIONS

Exposure to non-optimum ambient temperature, especially moderate cold temperature, was responsible for substantial excess mortality from asthma. These findings have important implications for planning of public-health interventions to minimize the adverse respiratory damage from non-optimum ambient temperature.

Long-term trends of atmospheric hot-and-polluted episodes (HPE) and the public health implications in the Pearl River Delta region of China

Air pollution and extreme heat have been responsible for more than a million deaths in China every year, especially in densely urbanized regions. While previous studies intensively evaluated air pollution episodes and extreme heat events, a limited number of studies comprehensively assessed atmospheric hot-and-polluted-episodes (HPE) - an episode with simultaneously high levels of air pollution and temperature - which have potential adverse synergic impacts on human health. This study focused on the Pearl River Delta (PRD) region of China due to its high temperature in summer and poor air quality throughout a year. We employed geostatistical downscaling to model meteorology at a spatial resolution of 1 km, and applied a machine learning algorithm (XGBoost) to estimate a high-resolution (1 km) daily concentration of particulate matter with an aerodynamic diameter ≤2.5 μm (PM_2.5) and ozone (O₃) for June to October over 20 years (2000-2019). Our results indicate an increasing trend (∼50%) in the frequency of HPE occurrence in the first decade (2000-2010). Conversely, the annual frequency of HPE occurrence reduced (16.7%), but its intensity increased during the second decade (2010-2019). The northern cities in the PRD region had higher levels of PM_2.5 and O₃ than their southern counterparts. During HPEs, regional daily PM_2.5 exceeded the World Health Organization (WHO) and Chinese guideline levels by 75% and 25%, respectively, while the O₃ exceeded the WHO O₃ standard by up to 69%. Overall, 567,063 (95% confidence interval (CI): 510,357-623,770) and 52,231 (95%CI: 26,116-78,346) excessive deaths were respectively attributable to exposure to PM_2.5 and O₃ in the PRD region. Our findings imply the necessity and urgency to formulate co-benefit policies to mitigate the region's air pollution and heat problems.

Association between ambient temperature and age-specific mortality from the elderly: Epidemiological evidence from the Chinese prefecture with most serious aging

Older people are main susceptible group affected by non-optimal temperature. The aim of the study was to determine how mortality of older people with different ages are affected by temperatures. For this study, we collected data of all-cause death of 256,037 people aged between 65 and 104 years of age from a prefecture located in the north subtropical area with most serious aging rate in 2000, 2010 and 2020 in China. A distributed lag nonlinear model under different age groups was used to estimate non-optimal temperature associations to mortality. The results revealed: (1) With increasing age, older people were more likely to die during moderate low temperature, the proportion of attributable fraction of moderate low temperature in all temperature gradually increased with age. (2) Moderate low temperature could be divided into two parts, the lower part caused most death at age 65-79 and the higher part was not so dangerous, while for age 80+, preventive actions should be taken for both parts. (3) A leveling-off and deceleration phenomenon was observed at age 95-99 for low temperature, but not 100-104, it may be virtually a consequence of "harvesting effect" in that susceptible and common people have died before age 95, it was coincidence with mortality deceleration at extreme old ages found by demographic scholars over the past 200 years. (4) Heat wave had much higher relative risk than cold spell compared with moderate high and low temperature because of steeper slope of relative risk at the period of moderate-extreme conversion of high temperature, the older people should pay more attention to weather with moderate-extreme conversion of high temperature. Furthermore, our findings could help improve the understanding of non-optimal temperature on health of older people and support the development of response strategies for different seasons at different ages.

Relationship between diurnal temperature range and emergency ambulance dispatches due to stroke in Guangzhou, China

BACKGROUND

The evidence between diurnal temperature range (DTR) and stroke remains controversial and sparse. We aimed to assess the relationship between DTR and emergency ambulance dispatches (EADs) due to stroke, and to explore whether there were effect modifications to the relationship.

METHODS

A Quasi-Poisson generalized linear regression combined with a distributed lag non-linear model was used to examine the relationship between DTR and EADs for stroke between January 1st 2011 and June 30th 2018 in Guangzhou, China. We estimated the effects of the low DTR and high DTR (defined as DTR below and above 10 °C respectively) on EADs. The effects of minimum, maximum, 5th, 25th, 50th, 75th, and 95th percentiles of DTR compared with the DTR of 10 °C were also analyzed.

RESULTS

A total of 20,275 EADs for stroke were included for analyses, among which 17,556 EADs were used in the model further adjusted for age and sex. A quasi-U-shaped relationship between DTR and EADs over lag0-2 days was observed. For the low DTR, per 1 °C decrease in DTR was significantly associated with an increase of 2.64% (RR = 1.03, 95% CI: 1.01-1.04) for EADs, while per 1 °C increase for the high DTR was non-significantly related with an increased risk of EADs (RR = 1.01, 95% CI: 0.90-1.13). Significant effects of the 5th and 25th percentiles of DTR on EADs were found when compared with the DTR of 10 °C. No significant effect modifications by age, sex or season were found to the association between DTR and EADs.

CONCLUSIONS

We found a quasi-U-shaped relationship between DTR and EADs due to stroke in this study, while age, sex or season did not significantly modify the association between DTR and EADs. More high-quality evidence is needed to further explore and validate the relationship between DTR and stroke.

Modeling the Impact of High Temperature on Mortality in Pakistan

The objective of this paper is to model and study the impact of high temperature on mortality in Pakistan. For this purpose, we have used mortality and climate data consisting of maximum temperature, variation in monthly temperature, average rainfall, humidity, dewpoint, as well as average air pressure in the country over the period from 2000 to 2019. We have used the Generalized Linear Model with Quasi-Poisson link function to model the number of deaths in the country and to assess the impact of maximum temperature on mortality. We have found that the maximum temperature in the country has a significant impact on mortality. The number of deaths in Pakistan increases as the maximum temperature increases. We found that, as the maximum temperature increase beyond 30 °C, mortality increases significantly. Our results indicate that mortality increases by 27% when the maximum temperature in the country increases from medium category to a very high level. Similarly, the number of deaths in the country increases by 11% when the temperature increases from medium temperature to high level. Furthermore, our study found that when the maximum temperature in the country decreases from a medium level to a low level, the number of deaths in the country decreases by 23%. This study does not consider the impact of other factors on mortality, such as age, medical conditions, gender, geographical location, as well as variability of temperature across the country.

Disproportionate exposure to urban heat island intensity across major US cities

Urban heat stress poses a major risk to public health. Case studies of individual cities suggest that heat exposure, like other environmental stressors, may be unequally distributed across income groups. There is little evidence, however, as to whether such disparities are pervasive. We combine surface urban heat island (SUHI) data, a proxy for isolating the urban contribution to additional heat exposure in built environments, with census tract-level demographic data to answer these questions for summer days, when heat exposure is likely to be at a maximum. We find that the average person of color lives in a census tract with higher SUHI intensity than non-Hispanic whites in all but 6 of the 175 largest urbanized areas in the continental United States. A similar pattern emerges for people living in households below the poverty line relative to those at more than two times the poverty line.

The association between extreme temperature and pulmonary tuberculosis in Shandong Province, China, 2005-2016: a mixed method evaluation

BACKGROUND

The effects of extreme temperature on infectious diseases are complex and far-reaching. There are few studies to access the relationship of pulmonary tuberculosis (PTB) with extreme temperature. The study aimed to identify whether there was association between extreme temperature and the reported morbidity of PTB in Shandong Province, China, from 2005 to 2016.

METHODS

A generalized additive model (GAM) was firstly conducted to evaluate the relationship between daily reported incidence rate of PTB and extreme temperature events in the prefecture-level cities. Then, the effect estimates were pooled using meta-analysis at the provincial level. The fixed-effect model or random-effect model was selected based on the result of heterogeneity test.

RESULTS

Among the 446,016 PTB reported cases, the majority of reported cases occurred in spring. The higher reported incidence rate areas were located in Liaocheng, Taian, Linyi and Heze. Extreme low temperature had an impact on the reported incidence of PTB in only one prefecture-level city, i.e., Binzhou (RR = 0.903, 95% CI: 0.817-0.999). While, extreme high temperature was found to have a positive effect on reported morbidity of PTB in Binzhou (RR = 0.924, 95% CI: 0.856-0.997) and Weihai (RR = 0.910, 95% CI: 0.843-0.982). Meta-analysis showed that extreme high temperature was associated with a decreased risk of PTB (RR = 0.982, 95% CI: 0.966-0.998). However, extreme low temperature was no relationship with the reported incidence of PTB.

CONCLUSION

Our findings are suggested that extreme high temperature has significantly decreased the risk of PTB at the provincial levels. The findings have implications for developing strategies to response to climate change.

Effect of ambient temperatures on category C notifiable infectious diarrhea in China: An analysis of national surveillance data

BACKGROUND

Many studies have explored the association between meteorological factors and infectious diarrhea (ID) transmission but with inconsistent results, in particular the roles from temperatures. We aimed to explore the effects of temperatures on the transmission of category C ID, to identify its potential heterogeneity in different climate zones of China, and to provide scientific evidence to health authorities and local communities for necessary public health actions.

METHODS

Daily category C ID counts and meteorological variables were collected from 270 cities in China over the period of 2014-16. Distributed lag non-linear models (DLNMs) were applied in each city to obtain the city-specific temperature-disease associations, then a multivariate meta-analysis was implemented to pool the city-specific effects. Multivariate meta-regression was conducted to explore the potential effect modifiers. Attributable fraction was calculated for both low and high temperatures, defined as temperatures below the 5th percentile of temperature or above the 95th percentile of temperature.

RESULTS

A total of 2,715,544 category C ID cases were reported during the study period. Overall, a M-shaped curve relationship was observed between temperature and category C ID, with a peak at the 81st percentile of temperatures (RR = 1.723, 95% CI: 1.579-1.881) compared to 50th percentile of temperatures. The pooled associations were generally stronger at high temperatures compared to low ambient temperatures, and the attributable fraction due to heat was higher than cold. Latitude was identified as a possible effect modifier.

CONCLUSIONS

The overall positive pooled associations between temperature and category C ID in China suggest the increasing temperature could bring about more category C infectious diarrhea cases, which warrants further public health measurements.

Monitoring responses of vegetation phenology and productivity to extreme climatic conditions using remote sensing across different sub-regions of China

Drought is a major natural disaster that significantly impacts the susceptibility and flexibility of the ecosystem by changing vegetation phenology and productivity. This study aimed to investigate the impact of extreme climatic variation on vegetation phenology and productivity over the four sub-regions of China from 2000 to 2017. Daily rain gauge precipitation and air temperature datasets were used to estimate the trends, and to compute the standardized precipitation-evapotranspiration index (SPEI). Remote sensing-based Enhanced Vegetation Index (EVI) data from a moderate resolution imaging spectroradiometer (MODIS) was used to characterize vegetation phenology. The results revealed that (1) air temperature had significant increasing trends (P < 0.05) in all sub-regions. Precipitation showed a non-significant increasing trend in Northwest China (NWC) and insignificant decreasing trends in North China (NC), Qinghai Tibet area (QTA), and South China (SC). (2) Integrated enhanced vegetation index (iEVI) and SPEI variations depicted that 2011 and 2016 were the extremely driest and wettest years during 2000-2017. (3) Rapid changes were observed in the vegetation phenology and productivity between 2011 and 2016. In 2011, changes in the vegetation phenology with the length of the growing season (ΔLGS) = was - 14 ± 36 days. In 2016, the overall net effect changed at the onset and end of the growing season with ΔLGS of 34 ± 71 days. The change in iEVI per SPEI increased rapidly with a changing rate of 0.16 from arid (NWC, and QTA) to semi-arid (NWC, QTA and NC) and declined with a rate of - 0.04 from semi-humid (QTA, NC, and SC) to humid (SC) region. A higher association was observed between iEVI and SPEI as compared to iEVI and precipitation. Our finding exposed that north China is more sensitive to climatic variation.

The effect of cold waves on daily mortality in districts in Madrid considering sociodemographic variables

While there is much research that focuses on the association between cold waves and their impacts on daily mortality at the city level, few analyze the impact related to social context and demographic variables at levels lower than the municipal. The objective of this study was to determine the role of the percentage of people over age 65, income level and percentage of homes without heating in the analysis of the impact of cold waves on daily mortality between January 1, 2010 and December 31, 2013 in different districts of the municipality of Madrid. We calculated Relative Risks (RR) and Attributable Risks (RA) for each of 17 districts to determine correlations between the effect of cold waves and daily mortality due to natural causes (CIEX: A00-R99) using Generalized Linear Models (GLM) of the Poisson family (link log). The pattern of risks obtained by district was analyzed using binomial family models (link logit), considering socioeconomic and demographic variables. In terms of results, an impact of cold on mortality was detected in 9 of the 17 districts analyzed. The analysis of risk patterns revealed that the probability of detecting an impact in a district increases in a statistically significant way (p-value <0.05) with a higher percentage of homes without heating systems and a higher percentage of population over age 65. The results obtained identify the factors that should be considered in public health policies that target the district level to reduce the impact of cold waves.

The Influence of Apparent Temperature on Mortality in the Kintampo Health and Demographic Surveillance Area in the Middle Belt of Ghana: A Retrospective Time-Series Analysis

Globally, studies have shown that diurnal changes in weather conditions and extreme weather events have a profound effect on mortality. Here, we assessed the effect of apparent temperature on all-cause mortality and the modifying effect of sex on the apparent temperature-mortality relationship using mortality and weather data archived over an eleven-year period. An overdispersed Poisson regression and distributed lag nonlinear models were used for this analysis. With these models, we analysed the relative risk of mortality at different temperature values over a 10-day lag period. By and large, we observed a nonlinear association between mean daily apparent temperature and all-cause mortality. An assessment of different temperature values over a 10-day lag period showed an increased risk of death at the lowest apparent temperature (18°C) from lag 2 to 4 with the highest relative risk of mortality (RR = 1.61, 95% CI: 1.2, 2.15, p value = 0.001) occurring three days after exposure. The relative risk of death also varied between males (RR = 0.31, 95% CI: 0.10, 0.94) and females (RR = 4.88, 95% CI: 1.40, 16.99) by apparent temperature and lag. On the whole, males are sensitive to both temperature extremes whilst females are more vulnerable to low temperature-related mortality. Accordingly, our findings could inform efforts at reducing temperature-related mortality in this context and other settings with similar environmental and demographic characteristics.

Assessing the coordination between economic growth and urban climate change in China from 2000 to 2015

The balance between economic growth and environmental protection has been a critical concern for sustainable urban development. However, among the multiple research efforts exploring the coordination between the two aspects, the widespread urban climate change has rarely been considered. This study encompasses urban climate change into the cross-system coupling analysis framework to assess its coordination with economic growth using the Coupling Coordination Degree (CCD) model. The two aspects are respectively represented using indicators of Surface Urban Heat Island Intensity (SUHII) and Gross Domestic Product (GDP). Specifically, China is selected as case study, and a total of 259 cities from the 2000-2015 period are analyzed. The spatio-temporal patterns of CCD are investigated through time series clustering to uncover its performance under diversified economic and climatic contexts. The regional inequality and spatial agglomeration effects are also examined. Results reveal significant spatio-temporal heterogeneity of CCD. Geographically, CCD varies from uncoordinated to high-level coordination. Wealthier cities in the eastern coastal region are significantly better coordinated than their inland counterparts. Temporally, the uptrend of CCD is not significant for most cities due to the relatively insufficient emphasis on urban heat island (UHI) mitigation in previous efforts. Evident spatial inequality and agglomeration patterns are also observed with slight downtrends. The spatio-temporal patterns of CCD revealed in this study indicate great necessity for the central government to develop policies suiting cities' special characteristics and contexts, and more efforts should be targeted on reducing regional imbalance. Hence, a nation-city-community policy skeleton is last outlined to enhance the pursuit of a more climate-friendly urban environment under rapid economic development. Overall, this study advances the understanding of economy-urban climate interactions and facilitates the future pursuit of better sustainable cities. The proposed workflow can be utilized for other countries with diversified urbanization processes and potentially used for comparison among different countries.

Geospatial context of social and environmental factors associated with health risk during temperature extremes: Review and discussion

This study reviews forty-six publications between 2008 and 2017 dealing with socio-environmental impacts on adverse health effects of temperature extremes, in a geospatial context. The review showed that most studies focus on extremely hot weather but lack analysis of how spatial heterogeneity across a region can influence cold mortality/morbidity. There are limitations regarding the use of temperature datasets for spatial analyses. Only a few studies have applied air temperature datasets with high spatial resolution to health studies, but none of these studies have used anthropogenic heat as a factor for analysis of health risk. In addition, the elderly is generally recognized as a vulnerable group in most studies, but the interaction between old age and temperature risk varies by location. Other socio-demographic factors such as low income, low education and accessibility to community shelters may also need to be considered in the future. There are only a few studies which investigate the interaction between temperature and air pollution in a geospatial context, despite the fact that this is a known interaction that can influence health risk under extreme weather. In conclusions, although investigation of temperature effects on health risk is already at the "mature stage", studies of socio-environmental influences on human health under extreme weather in a geospatial context is still being investigated. A comprehensive assessment is required to analyse how the spatial aspects of the geophysical and social environments can influence human health under extreme weather, in order to develop a better community plan and health protocols for disaster preparedness.

The association between ambient temperature and clinical visits for inflammation-related diseases in rural areas in China

BACKGROUND

The association between temperature and mortality has been widely reported. However, it remains largely unclear whether inflammation-related diseases, caused by excessive or inappropriate inflammatory reaction, may be affected by ambient temperature, particularly in low-income areas.

OBJECTIVES

To explore the association between ambient temperature and clinical visits for inflammation-related diseases in rural villages in the Ningxia Hui Autonomous Region, China, during 2012─2015.

METHODS

Daily data on inflammation-related diseases and weather conditions were collected from 258 villages in Haiyuan (161 villages) and Yanchi (97 villages) counties during 2012─2015. A Quasi-Poisson regression with distributed lag non-linear model was used to examine the association between temperature and clinical visits for inflammation-related diseases. Stratified analyses were performed by types of diseases including arthritis, gastroenteritis, and gynecological inflammations.

RESULTS

During the study period, there were 724,788 and 288,965 clinical visits for inflammation-related diseases in Haiyuan and Yanchi, respectively. Both exposure to low (RR: 2.045, 95% CI: 1.690, 2.474) and high temperatures (RR: 1.244, 95% CI: 1.107, 1.399) were associated with increased risk of total inflammation-related visits in Haiyuan county. Low temperatures were associated with increased risks of all types of inflammation-related diseases in Yanchi county (RR: 4.344, 95% CI: 2.887, 6.535), while high temperatures only affected gastroenteritis (RR: 1.274, 95% CI: 1.040, 1.561). Moderate temperatures explained approximately 26% and 33% of clinical visits due to inflammation-related diseases in Haiyuan and Yanchi, respectively, with the burden attributable to cold exposure higher than hot exposure. The reference temperature values ranged from 17 to 19 in Haiyuan, and 12 to 14 in Yanchi for all types of clinical visits.

CONCLUSIONS

Our findings add additional evidence for the adverse effect of suboptimal ambient temperature and provide useful information for public health programs targeting people living in rural villages.

Performance of heat-health warning systems in Shanghai evaluated by using local heat-related illness data

In response to more frequent heatwaves, various regional or national heat-health warning systems (HHWSs) have been developed recently as adaptation measures. A wide range of methodologies have been utilized to issue warnings, as there is no universal definition of "heat event" or "heatwave", nor are there quantified thresholds of human-health tolerance to extreme weather. The performance of these warning systems has rarely been evaluated with actual heat-health data, especially the morbidity data, in regions with severe impact. In this study, we assessed the performance of the Shanghai HHWS based on heat-related illness data collected by the Chinese Center for Disease Control and Prevention (China CDC) and then conducted a comparative analysis among the Shanghai HHWS, the China Meteorological Administration HHWS, the Chinese national standard for heatwave indexes, the heat index adopted by the USA's National Weather Service and the definition suggested by the World Meteorological Organization to understand their potential performance for application in Shanghai and to evaluate the temperature thresholds and different meteorological indices employed. The results show that: 1) during the research period, 50% of heat-related illnesses and 58.2% of heat-related deaths in Shanghai occurred on dates that had no heat warnings; 2) for the current threshold (35 °C), the single metric of temperature outperformed the temperature-duration two-parameter method; 3) different from existing studies, while infants and seniors are deemed as vulnerable population groups to heat, young and middle-aged males were found to suffer more heat-related illnesses in hot weather. More detailed analyses reveal that the performance of heat-health warning systems needs to be evaluated and revised periodically, and warning thresholds utilized must be localized to reflect public tolerance to heat and to address the vulnerability of target population groups. Temperature is the dominant threshold in heat-related morbidity and mortality analysis. While a decrease in the temperature threshold would definitely increase the warning frequency and socioeconomic costs, it might also cause warning fatigue. The trade-off between these two aspects is essential for decision-makers and other stakeholders in HHWS design and improvement.

High temperatures and emergency department visits in 18 sites with different climatic characteristics in China: Risk assessment and attributable fraction identification

BACKGROUND

Health impacts of high temperatures on hospital emergency department visits (EDVs) have been less reported, especially from developing countries.

OBJECTIVES

To investigate high temperature-EDVs relationship in various regions with different climatic characteristics, to explore the regional differences, to identify vulnerable populations, and to provide scientific evidence for climate change adaptation strategies in China.

METHODS

Daily data on weather, air pollution and EDVs were collected from 18 sites in China from June to August during 2014-2017. A quasi-Poisson generalized additive regression model was applied to examine the high temperature-EDVs relationship in each site. Site-specific risks of EDVs were pooled using a random effect meta-analysis model. Stratified analyses were performed by gender, age-groups, cause-specific EDVs and regions. Attributable fractions of EDVs due to high temperatures were calculated in different regions.

RESULTS

1 °C increase in daily mean temperature was associated with 1.07% (95% CI, 0.46-1.67%) increase in EDVs across all study regions. The negative health effects from high temperatures were worse for the people living in southern China, in subtropical monsoon climate zone or in counties, with percentage change of 1.96% (95% CI, 0.92-3.02%), 1.35% (95% CI, 0.95-1.76%) and 1.41% (95% CI, 0.48-2.34%), respectively. People under 18 were more vulnerable to high temperatures. Exposure to high temperatures increased EDVs risks from endocrine, respiratory, and digestive diseases and injury. The attributable fraction due to high temperatures was 8.64% for overall EDVs, 11.70% for the people living in southern China, 10.80% for people living in subtropical monsoon climate zone and 12.65% for the county population.

CONCLUSIONS

Exposure to high temperatures resulted in extra burden to China's already overloaded hospital emergency departments. More resources are needed to meet increasing demands and effective preventative measurements are warranted to tackle such a challenge. Further studies should pay more attention to both heat and cold-related EDVs risks and socioeconomic cost for better climate change adaptation.

Synoptic approach to evaluate the effect of temperature on pediatric respiratory disease-related hospitalization in Seoul, Korea

BACKGROUND

The spatial synoptic classification (SSC) scheme is a daily weather-type classification scheme that is widely used in climatological and epidemiological investigations. We investigated the modification effect of temperature on respiratory disease-related hospital admissions (RAs) in children based on the SSC type.

METHODS

Data on RAs among children aged ≤19 years were collected from the National Inpatient Sample (NIS) database between 2011 and 2015 in Seoul, Korea. In this time-series study, we used Poisson generalized linear models allowing for over-dispersion, with stratification by season and each of the seven distinctive SSC types (Dry moderate [DM], Dry polar [DP], Dry tropical [DT], Moist moderate [MM], Moist polar [MP], Moist tropical [MT], and Transition [T]). We investigated the modification effects of temperature by SSC type through seasonal stratification, adjusting for other meteorological conditions, air pollution, and time trends, and considered the delayed effect for up to four consecutive days. We reported the relative risk percentage change with 95% confidence intervals (CIs) of RAs for every 1 °C increase in ambient temperature, by season and SSC type.

RESULTS

Of the 8346 RAs overall investigated between 2011 and 2015, there were 5271 [63.2%] RAs in children aged 0-4 years. Overall, on majority of the days, the SSC types were DM (665 days [36.4%]), and DP (371 days [20.3%]). With regard to seasons, the different SSC types were distributed as follows: spring, DM (193 days [42.0%]); summer, MT (193 days [42.0%]); fall, DM (241 days [53.0%]); and winter, DP (228 days [50.6%]). A stronger association between RAs and temperature was observed overall (1.28% [95% CI; 0.04%, 2.53%]), and in spring (2.10% [0.62%, 3.60%]). According to SSC type, increased associations between RAs and temperature were detected with DM (2.20% [0.47%, 3.97%]) condition during spring and DT (2.41% [0.22%, 4.64%]) during fall. In addition, on re-categorizing the SSC types from a temperature or humidity standpoint, a modified association was observed especially in children aged ≤4 years and 5-9 years during spring.

CONCLUSIONS

Using temperature variability to distinguish seasonal characteristics, we found that the relationship between temperature and RAs in children varied by SSC type. Application of the SSC scheme as an integrative approach may assist in gaining an understanding of seasonal characteristics and health effects due to temperature change.

Mortality Related to Cold Temperatures in Two Capitals of the Baltics: Tallinn and Riga

Background and objectives: Despite global warming, the climate in Northern Europe is generally cold, and the large number of deaths due to non-optimal temperatures is likely due to cold temperatures. The aim of the current study is to investigate the association between cold temperatures and all-cause mortality, as well as cause-specific mortality, in Tallinn and Riga in North-Eastern Europe. Materials and Methods: We used daily information on deaths from state death registries and minimum temperatures from November to March over the period 1997-2015 in Tallinn and 2009-2015 in Riga. The relationship between the daily minimum temperature and mortality was investigated using the Poisson regression, combined with a distributed lag non-linear model considering lag times of up to 21 days. Results: We found significantly higher all-cause mortality owing to cold temperatures both in Tallinn (Relative Risk (RR) = 1.28, 95% Confidence Interval (CI) 1.01-1.62) and in Riga (RR = 1.41, 95% CI 1.11-1.79). In addition, significantly increased mortality due to cold temperatures was observed in the 75+ age group (RR = 1.64, 95% CI 1.17-2.31) and in cardiovascular mortality (RR = 1.83, 95% CI 1.31-2.55) in Tallinn and in the under 75 age group in Riga (RR = 1.58, 95% CI 1.12-2.22). In this study, we found no statistically significant relationship between mortality due to respiratory or external causes and cold days. The cold-related attributable fraction (AF) was 7.4% (95% CI -3.7-17.5) in Tallinn and 8.3% (95% CI -0.5-16.3) in Riga. This indicates that a relatively large proportion of deaths in cold periods can be related to cold in North-Eastern Europe, where winters are relatively harsh.

Impact of summer heat on mortality and years of life lost: Application of a novel indicator of daily excess hourly heat

BACKGROUND

Previous studies have widely assessed heat-mortality relationships across global regions, while the epidemiological evidence regarding the heat effect on years of life lost (YLL) is relatively sparse. Current investigations using daily mean data cannot take hourly temperature variation into consideration and may underestimate heat effects. We developed a novel indicator, daily excess hourly heat (DEHH), to precisely evaluate the potential heat effects on mortality and YLL.

METHODS

Hourly data on temperature and daily information, including concentrations of air pollutants, relative humidity, and records of all registered deaths were obtained in Wuhan, China during the warm seasons (May-September) of 2009-2012. DEHH, developed in this study, is defined as daily total hourly temperatures that exceed a specific heat threshold. By performing time series regression analyses, we assessed the changes in daily mortality and YLL per interquartile range (IQR) increase in DEHH across different lag days.

RESULTS

The heat threshold evaluated by the Akaike Information Criterion for DEHH calculation is 30 °C (92th percentile of whole-year mean temperature distribution). Daily average DEHH was 13.9 °C, with an IQR of 19.9 °C. Linear exposure-response curves were found between DEHH and two health outcomes. Generally, heat effects lasted for 2-3 days and DEHH at lag 0-1 was most strongly associated with increased mortality and YLL. The effects were especially remarkable for stroke and ischemic heart disease mortality. Most intense effect on YLL was found in non-accidental deaths (20.11, 95% confidence interval: 8.90-31.33) at lag 0-1. More DEHH-related mortality and YLL from cardiovascular deaths were observed among males. People aged 0-74 years and males suffered more from YLL burden due to high temperatures.

CONCLUSIONS

Our study demonstrated that DEHH may be an alternative indicator to precisely measure heat effects on daily mortality and YLL. Further DEHH-based evidence from large scale investigations is needed so as to better understand heat-associated health burden and improve public response to extremely high temperatures.

Impact of heat on mortality and morbidity in low and middle income countries: A review of the epidemiological evidence and considerations for future research

Heat waves and high air temperature are associated with increased morbidity and mortality. However, the majority of research conducted on this topic is focused on high income areas of the world. Although heat waves have the most severe impacts on vulnerable populations, relatively few studies have studied their impacts in low and middle income countries (LMICs). The aim of this paper is to review the existing evidence in the literature on the impact of heat on human health in LMICs. We identified peer-reviewed epidemiologic studies published in English between January 1980 and August 2018 investigating potential associations between high ambient temperature or heat waves and mortality or morbidity. We selected studies according to the following criteria: quantitative studies that used primary and/or secondary data and report effect estimates where ambient temperature or heat waves are the main exposure of interest in relation to human morbidity or mortality within LMICs. Of the total 146 studies selected, eighty-two were conducted in China, nine in other countries of East Asia and the Pacific, twelve in South Asia, ten in Sub-Saharan Africa, eight in the Middle East and North Africa, and seven in each of Latin America and Europe. The majority of studies (92.9%) found positive associations between heat and human morbidity/mortality. Additionally, while outcome variables and study design differed greatly, most utilized a time-series study design and examined overall heath related morbidity/mortality impacts in an entire population, although it is notable that the selected studies generally found that the elderly, women, and individuals within the low socioeconomic brackets were the most vulnerable to the effects of high temperature. By highlighting the existing evidence on the impact of extreme heat on health in LMICs, we hope to determine data needs and help direct future studies in addressing this knowledge gap. The focus on LMICs is justified by the lack of studies and data studying the health burden of higher temperatures in these regions even though LMICs have a lower capacity to adapt to high temperatures and thus an increased risk.

Impacts of heat, cold, and temperature variability on mortality in Australia, 2000-2009

OBJECTIVES

Evidence is limited on the relative contribution of different temperature exposures (i.e., heat, cold and significant temperature variability) to mortality. This study aims to examine mortality risk and associated mortality burden from heat, cold, and temperature variability in Australia.

METHODS

We collected daily time-series data on all-cause deaths and weather variables for the five most populous Australian cities (Sydney, Melbourne, Brisbane, Adelaide, and Perth), from 2000 to 2009. Temperature variability was calculated from the standard deviation of hourly temperatures between two adjacent days. Three-stage analysis was used. We firstly used quasi-Poisson regression models to model the associations of mortality with heat (mean temperature) during the warm season, with cold (mean temperature) during the cold season, and with temperature variability all year round, while controlling for long-term trend and seasonality, day of week, and population change over time. We then estimated the effects of different non-optimum temperatures using the simplified log-linear regression model. Finally, we computed and compared the fraction (%) of deaths attributable to different non-optimum temperatures.

RESULTS

The greatest percentage increase in mortality was for cold (2.0%, 95% confidence interval (CI): 1.4%, 2.6%), followed by heat (1.2%, 95% CI: 0.7%, 1.7%), and temperature variability (0.5%, 95% CI: 0.3%, 0.7%). There was no clear temporal pattern in mortality risk associated with any temperature exposure in Australia. Heat, cold and temperature variability together resulted in 42,414 deaths during the study period, accounting for about 6.0% of all deaths. Most of attributable deaths were due to cold (61.4%), and noticeably, contribution from temperature variability (28.0%) was greater than that from heat (10.6%).

CONCLUSIONS

Exposure to either cold or heat or a large variation in temperature was associated with increased mortality risk in Australia, but population adaptation appeared to have not occurred in most cities studied. Most of the temperature-induced deaths were attributable to cold, and contributions from temperature variability were greater than that from heat. Our findings highlight that, in addition to heat and cold, temperature variability needs to be considered in assessing and projecting the health impacts of climate change.

Burden of non-accidental mortality attributable to ambient temperatures: a time series study in a high plateau area of southwest China

OBJECTIVE

To examine the total non-accidental mortality burden attributable to ambient temperatures and assess the effect modification of the burden by specific causes of death and individual characteristics in a high plateau area in southwest China.

METHODS

Using daily mortality and meteorological data from 2009 to 2016, we applied a quasi-Poisson model combined with a distributed lag non-linear model to estimate the temperature-mortality association with the assessment of attributable fraction and number. We calculated attributable fractions and deaths with 95% empirical CIs (eCIs), that were due to cold and heat, defined as temperatures below and above the median temperature, and for mild and extreme temperatures, defined by cut-offs at the 2.5th and 97.5th temperature percentiles.

RESULTS

We analysed 89 467 non-accidental deaths; 4131 were attributable to overall temperatures, with an attributable fraction of 4.75% (95% eCI 2.33% to 6.79%). Most of the mortality burden was caused by cold (4.08%; 0.86% to 7.12%), whereas the burden due to heat was low and non-significant (0.67%; -2.44% to 3.64%). Extreme cold (1.17%; 0.58% to 1.69%) was responsible for 24.6% (ie, 1.17% divided by 4.75%) of the total death burden. In the stratification analyses, attributable risk due to cold was higher for cardiovascular than respiratory disease (6.18% vs 3.50%). We found a trend of risk of increased death due to ambient temperatures with increasing age, with attributable fractions of 1.83%, 2.27% and 6.87% for age ≤64, 65-74 and ≥75 years old, respectively. The cold-related burden was slightly greater for females, farmers, ethnic minorities and non-married individuals than their corresponding categories.

CONCLUSIONS

Most of the burden of death was attributable to cold, and specific causes and individual characteristics might modify the mortality burden attributable to ambient temperatures. The results may help make preventive measures to confront climate change for susceptible population in this region.

Heat and cold related-mortality in 18 French cities

OBJECTIVES

Understanding the dynamics of the temperature-mortality relationship is an asset to support public health interventions. We investigated the lag structure of the mortality response to cold and warm temperatures in 18 French cities between 2000 and 2010.

METHODS

A distributed lag non-linear generalized model using a quasi-Poisson distribution and controlling for classical confounding factors was built in each city. A fitted meta-analytical model combined the city-specific models to derive the best linear unbiased prediction of the association, and a meta-regression explored the influence of background characteristics of the cities. The fraction of mortality attributable to cold and heat was estimated with reference to the minimum mortality temperature.

RESULTS

Between 2000 and 2010, 3.9% [CI 95% 3.2:4.6] of the total mortality was attributed to cold, and 1.2% [1.1:1.2] to heat. The immediate increase in mortality following high temperatures was partly compensated by a harvesting effect when temperatures were below the 99.2 percentiles of the mean temperature distributions.

DISCUSSION

Cold represents a significant public health burden, mostly driven by moderate temperatures (between percentiles 2.5 and 25). The population is better adapted to warm temperatures, up to a certain intensity when heat becomes an acute environmental health emergency (above percentile 99). The rapid increase in mortality risk at very high temperatures percentiles calls for an active adaptation in a context of climate change.

Effects of ambient temperature on ambulance emergency call-outs in the subtropical city of Shenzhen, China

The associations between meteorological factors and mortality have been well documented worldwide, but limited evidence is available for the non-fatal health impacts of ambient temperature, particularly there are few population-based investigations on the impacts of emergency ambulance dispatches in Asia. In this study, based on 809,906 ambulance emergency call-outs (AECOs) for the total population from 2010-2016 in the subtropical city of Shenzhen, China, a Poisson regression combined with a distributed lag nonlinear model was used to simultaneously assess the nonlinear and lag effects of daily mean temperature on AECOs. Stratified analyses by age and sex were performed to identify vulnerable subpopulations. A U-shaped relationship was found between temperature and AECOs. Cold effects were delayed and persisted for 3-4 weeks, with a cumulative relative risk (RR) and 95% confidence interval (CI) of 1.23 (1.10-1.38) and 1.25 (1.16-1.35) over lag 0-28 when comparing the 1st and 5th percentile of the temperature distribution to the optimal (i.e. minimum AECOs) temperature, respectively. Hot effects were immediate and diminished quickly in 5 days, with an increase of 19% (RR = 1.19, 95%CI: 1.14-1.23) and 21% (RR = 1.21, 95%CI: 1.16-1.26) in AECOs over lag 0-5 when comparing the 95th and 99th percentile of temperature to the optimal temperature. Children and the elderly were more vulnerable to cold effects. The youth and middle-aged people suffered more from high temperature. The effects of temperature were similar between males and females. In summary, significant increases were observed in the frequency of AECOs during cold and hot days, and the weather-associated increases in AECOs are different among age groups. This information has valuable implications in ambulance demand prediction and service provision planning.

Mortality effects of heat waves vary by age and area: a multi-area study in China

BACKGROUND

Many studies have reported an increased mortality risk from heat waves comparing with non-heat wave days. However, how much the mortality rate change with the heat intensity-vulnerability curve-is still unknown. Such unknown information makes the related managers impossible to assess scientifically life losses from heat waves, consequently fail in conducting suitable integrated risk management measures.

METHODS

We used the heat wave intensity index (HWII) to characterize quantitatively the heat waves, then applied a distributed lag non-linear model to explore the area-specific definition of heat wave, and developed the vulnerability models on the relationships between HWII and mortality by age and by area. Finally, Monte Carlo method was run to assess and compare the event-based probabilistic heat wave risk during the periods of 1971-2015 and 2051-2095.

RESULTS

We found a localized definition of heat wave for each corresponding area based on the minimum AIC (Akaike information criterion). Under the local heat wave events, the expected life loss during 1971-2015 does distinguish across areas, and decreases consistently in the order of WZ Chongqing, PK Nanjing and YX Guangzhou for each age group. More specifically, for the elders (≥65), the average annual loss (AAL) (and 95% confidence interval) would be 61.3 (30.6-91.9), 38 (3.8-72.2) and 18.7 (7.3-30) deaths per million people. With two stresses from warming and aging in future China, the predicted average AAL of the elders under four Representative Carbon Pathways (2.6, 4.5, 6.0, and 8.5) during 2051-2095 would be 2460, 1675, 465 deaths per million for PK Nanjing, YX Guangzhou and WZ Chongqing, respectively, approximately becoming 8~ 90 times of the AAL during 1971-2015.

CONCLUSION

This study found that the non-linear HWII-mortality relationships vary by age and area. The heat wave mortality losses are closely associated with the social-economic level. With the increasing extreme climatic events and a rapid aging trend in China, our findings can provide guidance for policy-makers to take appropriate regional adaptive measures to reduce health risks in China.

Different response of human mortality to extreme temperatures (MoET) between rural and urban areas: A multi-scale study across China

BACKGROUND

The environmental variation in mortality due to extreme temperatures has been well-documented by many studies. Mortality to extreme temperatures (MoET) was recognized to vary geographically, either by countries within a region or by areas within a country. However, so far, little attention has been paid to rural residents, with even lesser attention on the potential rural-urban differences. The aim of our study was to offer a quite comprehensive analysis on the differences in temperature-mortality relationship between rural and urban areas across China.

METHOD

A distributed lag nonlinear model was built to describe the temperature-mortality relationship, based on the mortality data and meteorological variable of 75 communities in China from 2007 to 2012. Subsequently, a meta-analysis was applied to compare the differences in the temperature-mortality relationship between rural and urban areas at various levels.

RESULTS

Distinct responses regarding MoET between rural and urban areas were observed at different spatial scales. At regional level, more U-shaped curves were observed for temperature-mortality relationships in urban areas, while more J-shaped curves were observed in rural areas. At national scale, we found that the cold effect was stronger in rural areas (RR: rural 1.69 vs. urban 1.51), while heat effect was stronger in urban areas (RR: rural 1.01 vs. urban 1.12). Moreover, the modifying influence of air pollution on temperature-mortality relationship was found to be very limited.

CONCLUSION

The difference in response of MoET between rural and urban areas was noticeable, cold effect is more significant in China both in rural and urban areas. Additionally, urban areas in southern China and rural areas in northern China suffered more from extreme temperature events. Our findings suggest that differences in rural-urban responses to MoET should be taken seriously when intervention measures for reducing the risks to residents' health were adopted.