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Minimum mortality temperature (MMT) increases with global warming due to climate adaptation, which is crucial for the precise assessment of mortality burden attributed to climate change. Nevertheless, forecasting future MMT poses a challenge given the unavailability of future mortality data. Here, we attempted to develop a novel approach to project future MMT. First, we estimated the MMT of 334 locations in China using a distributed lag nonlinear model. Then, meta regression models were applied to investigate the associations between MMT and several temperature variables(Most Frequent Temperature(MFT), average daily mean temperature, average daily minimum temperature, average daily maximum temperature and percentiles of temperature from 1st to 100th). A generalized linear regression model was employed to investigate whether significant differences existed in the relationships between MMT and temperature from the 1st to the 100th percentile. Finally, an optional indicator of MMT for projecting future values was identified. Our results indicated that temperatures in the 85th to 89th percentiles were closely associated with MMT, with the 88th percentile temperature serving as the most effective indicator, as confirmed by meta-regression models. Using the 88th percentile of temperature as alternative indicator of MMT, compared with the period of 2006–2015, the projected MMT in most districts and counties in China tended to rise under three representative concentration pathways (RCPs) in the 2030 s (2030–2039), 2060 s (2060–2069), and 2090 s (2090–2099). Our findings provide some insight to project future MMT for assessing mortality burden related to temperature change driven by global warming.

Background: Although the health impacts of ambient ozone (O3) have been widely assessed, studies simultaneously investigating the acute and chronic effects of O3 on mortality from respiratory diseases (RESP) are scarce. Methods: We extracted personal information of all recorded deaths from RESP throughout 2013–2018 in six provinces in China. The daily, seasonal, and annual mean air pollutant concentrations at the township/subdistrict level were estimated by a random forest model. The acute association between the maximum daily average 8h ozone (MDA8 O3) and RESP mortality was examined by a time-stratified case-crossover study design, and the chronic association was estimated by a difference-in-differences (DID) analysis approach. We also calculated the attributable fraction (AF) of RESP mortality attributable to MDA8 O3. Results: There were 1,034,226 RESP deaths included in this study. The excess risks (ERs) of overall RESP mortality for each 10 μg/m3 increase in short-term (lag03 days) and long-term (one-year average) exposure to MDA8 O3 were 0.38 % (95%CI: 0.26 %, 0.50 %) and 4.37 % (3.91 %, 4.84 %), respectively. The AFs of overall RESP mortality ascribed to short- and long-term MDA8 O3 exposures were 3.00 % (2.03 %, 3.95 %) and 29.45 % (26.86 %, 31.95 %), respectively. The average annual number of RESP deaths attributable to short-term MDA8 O3 exposure was 30,790 and 302,254 were attributable to long-term MDA8 O3 exposure across China during 2013–2018. Conclusion: Both short- and long-term exposure to ambient MDA8 O3 were positively associated with RESP mortality, and long-term exposure to MDA8 O3 may lead to a greater RESP mortality burden than short-term exposure to MDA8 O3.

This study investigates the spatial patterns of residential migration among older adults in the city center of Yancheng and the influencing factors using data on the home purchases of individuals aged 65 and older from 2016 to 2018, along with peripheral point of interest (POI) data, analyzed with ArcGIS and a decision tree model. The results indicated that persons aged 60–65 accounted for 42.8% of the total sample and primarily chose to migrate in the early stages of retirement. The intra-city migration of older adults exhibits both centripetal and centrifugal patterns, with a greater tendency toward the city center. House prices, floor levels, and commercial facilities significantly impact their choice of migration destinations. Among these, house prices were the most critical determinant, with the majority of older adults migrating to neighborhoods with lower house prices. This study contributes by integrating residential migration and location choice research and constructing an analytical framework based on facility accessibility. The findings provide insights into the key determinants of location choice for intra-city residential migration among older adults and the construction of livable neighborhoods for them.

The crux of building and planning standards for age-friendly built environment in all countries resides in the regulation of age-friendly built environment practices, yet there exist variations in the representation of content dimensions. The UK is distinguished by its discretionary approach to guidance, whereas China exhibits a highly controlled disposition. Control and guidance may appear to be antithetical, it is essential to recognize that the building and planning standards for age-friendly built environment in both countries never deviate from the legal constraints while providing guidance in achieving age-friendly environments, thus striking a delicate balance between control and guidance. The study examines the standard systems of national standards, local standards and organizational standards, as well as the three dimensions of foundation standards, generic standards and specialized standards. The analysis of building and planning standards for age-friendly built environment in the UK and China scrutinizes the disparities between control and guidance, identifying similarities and differences in the building and planning standard system and content dimensions of the two countries. This analysis serves as a valuable reference for the development of building and planning standards for age-friendly built environment in China.

BackgroundAir pollution is a major public health concern. Air Quality Health Index (AQHI) is a very important air quality risk communication tool. However, AQHI is usually constructed by single-pollutant model, which has obvious disadvantages.ObjectiveTo construct an AQHI based on the joint effects of multiple air pollutants (J-AQHI), and to provide a scientific tool for health risk warning and risk communication of air pollution.MethodsData on non-accidental deaths in Yunnan, Guangdong, Hunan, Zhejiang, and Jilin provinces from January 1, 2013 to December 31, 2018 were obtained from the corresponding provincial disease surveillance points systems (DSPS), including date of death, age, gender, and cause of death. Daily meteorological (temperature and relative humidity) and air pollution data (SO2, NO2, CO, PM2.5, PM10, and maximum 8 h O3 concentrations) at the same period were respectively derived from China Meteorological Data Sharing Service System and National Urban Air Quality Real-time Publishing Platform. Lasso regression was first applied to select air pollutants, then a time-stratified case-crossover design was applied. Each case was matched to 3 or 4 control days which were selected on the same days of the week in the same calendar month. Then a distributed lag nonlinear model (DLNM) was used to estimate the exposure-response relationship between selected air pollutants and mortality, which was used to construct the AQHI. Finally, AQHI was classified into four levels according to the air pollutant guidance limit values from World Health Organization Global Air Quality Guidelines (AQG 2021), and the excess risks (ERs) were calculated to compare the AQHI based on single-pollutant model and the J-AQHI based on multi-pollutant model.ResultsPM2.5, NO2, SO2, and O3 were selected by Lasso regression to establish DLNM model. The ERs for an interquartile range (IQR) increase and 95% confidence intervals (CI) for PM2.5, NO2, SO2 and O3 were 0.71% (0.34%–1.09%), 2.46% (1.78%–3.15%), 1.25% (0.9%–1.6%), and 0.27% (−0.11%–0.65%) respectively. The distribution of J-AQHI was right-skewed, and it was divided into four levels, with ranges of 0-1 for low risk, 2-3 for moderate risk, 4-5 for high health risk, and ≥6 for severe risk, and the corresponding proportions were 11.25%, 64.61%, 19.33%, and 4.81%, respectively. The ER (95%CI) of mortality risk increased by 3.61% (2.93–4.29) for each IQR increase of the multi-pollutant based J-AQHI , while it was 3.39% (2.68–4.11) for the single-pollutant based AQHI .ConclusionThe J-AQHI generated by multi-pollutant model demonstrates the actual exposure health risk of air pollution in the population and provides new ideas for further improvement of AQHI calculation methods.

Injury poses heavy burden on public health, but little evidence on the potential role of climate change on injury exists. Here, the authors collect data during 2013-2019 in six provinces of China to estimate the associations between temperature and injury mortality, and to project future mortality burden attributable to temperature change driven by climate change.

Although studies have estimated the associations of PM2.5 with total mortality or cardiopulmonary mortality, few have comprehensively examined cause-specific mortality risk and burden caused by ambient PM2.5. Thus, this study investigated the association of short-term exposure to PM2.5 with cause-specific mortality using a death-spectrum wide association study (DWAS). Individual information of 5,450,764 deaths during 2013–2018 were collected from six provinces in China. Daily PM2.5 concentration in the case and control days were estimated by a random forest model. A time-stratified case-crossover study design was applied to estimate the associations (access risk, ER) of PM2.5 with cause-specific mortality, which was then used to calculate the population-attributable fraction (PAF) of mortality and the corresponding mortality burden caused by PM2.5. Each 10 μg/m3 increase in PM2.5 concentration (lag03) was associated with a 0.80 % [95 % confidence interval (CI): 0.73 %, 0.86 %] rise in total mortality. We found greater mortality effect at PM2.5 concentrations

BackgroundDrowning is a serious public health problem worldwide. Previous epidemiological studies on the association between meteorological factors and drowning mainly focused on individual weather factors, and the combined effect of mixed exposure to multiple meteorological factors on drowning is unclear. ObjectiveWe aimed to investigate the combined effects of multiple meteorological factors on unintentional drowning mortality in China and to identify the important meteorological factors contributing to drowning mortality. MethodsUnintentional drowning death data (based on International Classification of Diseases, 10th Edition, codes W65-74) from January 1, 2013, to December 31, 2018, were collected from the Disease Surveillance Points System for Guangdong, Hunan, Zhejiang, Yunnan, and Jilin Provinces, China. Daily meteorological data, including daily mean temperature, relative humidity, sunlight duration, and rainfall in the same period were obtained from the Chinese Academy of Meteorological Science Data Center. We constructed a time-stratified case-crossover design and applied a generalized additive model to examine the effect of individual weather factors on drowning mortality, and then used quantile g-computation to estimate the joint effect of the mixed exposure to meteorological factors. ResultsA total of 46,179 drowning deaths were reported in the 5 provinces in China from 2013 to 2018. In an effect analysis of individual exposure, we observed a positive effect for sunlight duration, a negative effect for relative humidity, and U-shaped associations for temperature and rainfall with drowning mortality. In a joint effect analysis of the above 4 meteorological factors, a 2.99% (95% CI 0.26%-5.80%) increase in drowning mortality was observed per quartile rise in exposure mixture. For the total population, sunlight duration was the most important weather factor for drowning mortality, with a 93.1% positive contribution to the overall effects, while rainfall was mainly a negative factor for drowning deaths (90.5%) and temperature and relative humidity contributed 6.9% and –9.5% to the overall effects, respectively. ConclusionsThis study found that mixed exposure to temperature, relative humidity, sunlight duration, and rainfall was positively associated with drowning mortality and that sunlight duration, rather than temperature, may be the most important meteorological factor for drowning mortality. These findings imply that it is necessary to incorporate sunshine hours and temperature into early warning systems for drowning prevention in the future.

Background: Although many studies have reported the mortality effect of temperature, there were few studies on the mortality risk of humidity, let alone the joint effect of temperature and humidity. This study aimed to investigate the joint and interaction effect of high temperature and relative humidity on mortality in China, which will deepen understanding the health risk of mixture climate exposure. Methods: The mortality and meteorological data were collected from 353 locations in China (2013–2017 in Jilin, Hunan, Guangdong and Yunnan provinces, 2009–2017 in Zhejiang province, and 2006–2011 in other Provinces). We defined location-specific daily mean temperature ≥ 75th percentile of distribution as high temperature, while minimum mortality relative humidity as the threshold of high relative humidity. A time-series model with a distributed lag non-linear model was first employed to estimate the location-specific associations between humid-hot events and mortality, then we conducted meta-analysis to pool the mortality effect of humid-hot events. Finally, an additive interaction model was used to examine the interactive effect between high temperature and relative humidity. Results: The excess rate (ER) of non-accidental mortality attributed to dry-hot events was 10.18% (95% confidence interval (CI): 8.93%, 11.45%), which was higher than that of wet-hot events (ER = 3.21%, 95% CI: 0.59%, 5.89%). The attributable fraction (AF) of mortality attributed to dry-hot events was 10.00% (95% CI: 9.50%, 10.72%) with higher burden for females, older people, central China, cardiovascular diseases and urban city. While for wet-hot events, AF was much lower (3.31%, 95% CI: 2.60%, 4.30%). We also found that high temperature and low relative humidity had synergistic additive interaction on mortality risk. Conclusion: Dry-hot events may have a higher risk of mortality than wet-hot events, and the joint effect of high temperature and low relative humidity may be greater than the sum of their individual effects.

Abstract Background Several studies have investigated the associations between ambient temperature and years of life lost (YLLs), but few focused on the difference of life loss attributable to temperature among different socioeconomic development levels. Objectives We investigated the disparity in temperature-YLL rate relationships and life loss per death attributable to nonoptimal temperature in regions with various development levels. Methods Three hundred sixty-four Chinese counties or districts were classified into 92 high-development regions (HDRs) and 272 low-development regions (LDRs) according to socioeconomic factors of each location using K-means clustering approach. We used distributed lag non-linear models (DLNM) and multivariate meta-analysis to estimate the temperature-YLL rate relationships. We calculated attributable fraction (AF) of YLL and temperature-related average life loss per death to compare mortality burden of temperature between HDRs and LDRs. Stratified analyses were conducted by region, age, sex and cause of death. Results We found that non-optimal temperatures increased YLL rates in both HDRs and LDRs, but all subgroups in LDRs were more vulnerable. The disparity of cold effects between HDRs and LDRs was significant, while the difference in heat effect was insignificant. The overall AF of non-optimal temperature in LDRs [AF = 12.2, 95% empirical confidence interval (eCI):11.0–13.5%] was higher than that in HDRs (AF = 8.9, 95% eCI: 8.3–9.5%). Subgroups analyses found that most groups in LDRs had greater AFs than that in HDRs. The average life loss per death due to non-optimal temperature in LDRs (1.91 years, 95% eCI: 1.72–2.10) was also higher than that in HDRs (1.32 years, 95% eCI: 1.23–1.41). Most of AFs and life loss per death were caused by moderate cold in both HDRs and LDRs. Conclusions Mortality burden caused by temperature was more significant in LDRs than that in HDRs, which means that more attention should be paid to vulnerable populations in LDRs in planning adaptive strategies.

BackgroundThe Spring Festival is one of the most important traditional festivals in China. This study aimed to estimate the mortality risk attributable to the Spring Festival.MethodsBetween 2013 and 2017, daily meteorological, air pollution, and mortality data were collected from 285 locations in China. The Spring Festival was divided into three periods: pre-Spring Festival (16 days before Lunar New Year's Eve), mid-Spring Festival (16 days from Lunar New Year's Eve to Lantern Festival), and post-Spring Festival (16 days after Lantern Festival). The mortality risk attributed to the Spring Festival in each location was first evaluated using a distributed lag nonlinear model (DLNM), and then it was pooled using a meta-analysis model.ResultsWe observed a dip/rise mortality pattern during the Spring Festival. Pre-Spring Festival was significantly associated with decreased mortality risk (ER: −1.58%, 95%CI: −3.09% to −0.05%), and mid-Spring Festival was unrelated to mortality risks, while post-Spring Festival was significantly associated with increased mortality risk (ER: 3.63%, 95%CI: 2.15–5.12%). Overall, a 48-day Spring Festival period was associated with a 2.11% (95%CI: 0.91–3.33%) increased mortality. We also found that the elderly aged over 64 years old, women, people with cardiovascular disease (CVD), and people living in urban areas were more vulnerable to the Spring Festival.ConclusionOur study found that the Spring Festival significantly increased the mortality risk in China. These findings suggest that it is necessary to develop clinical and public health policies to alleviate the mortality burden associated with the Spring Festival.

Although studies have investigated the associations between PM2.5 and mortality risk, evidence from rural areas is scarce. We aimed to compare the PM2.5-mortality associations between urban cities and rural areas in China. Daily mortality and air pollution data were collected from 215 locations during 2014–2017 in China. A two-stage approach was employed to estimate the location-specific and combined cumulative associations between short-term exposure to PM2.5 (lag 0–3 days) and mortality risks. The excess risks (ER) of all-cause, respiratory disease (RESP), cardiovascular disease (CVD), and cerebrovascular disease (CED) mortality for each 10 μg/m3 increment in PM2.5 across all locations were 0.54% (95% confidence interval [CI]: 0.38%, 0.70%), 0.51% (0.10%, 0.93%), 0.74% (0.50%, 0.97%), and 0.52% (0.20%, 0.83%), respectively. Slightly stronger associations for CVD (0.80% versus 0.60%) and CED (0.61% versus 0.26%) mortality were observed in urban cities than in rural areas, and slightly greater associations for RESP mortality (0.51% versus 0.43%) were found in rural areas than in urban cities. A mean of 2.11% (attributable fraction [AF], 95% CI: 1.48%, 2.76%) of all-cause mortality was attributable to PM2.5 exposure in China, with a larger AF in urban cities (2.89% [2.12%, 3.67%]) than in rural areas (0.61% [−0.60%, 1.84%]). Disparities in PM2.5-mortality associations between urban cities and rural areas were also found in some subgroups classified by sex and age. This study provided robust evidence on the associations of PM2.5 with mortality risks in China and demonstrated urban-rural disparities of PM2.5-mortality associations for various causes of death.

Summary: Although numerous studies have investigated premature deaths attributable to temperature, effects of temperature on years of life lost (YLL) remain unclear. We estimated the relationship between temperatures and YLL, and quantified the YLL per death caused by temperature in China. We collected daily meteorological and mortality data, and calculated the daily YLL values for 364 locations (2013–2017 in Yunnan, Guangdong, Hunan, Zhejiang, and Jilin provinces, and 2006–2011 in other locations) in China. A time-series design with a distributed lag nonlinear model was first employed to estimate the location-specific associations between temperature and YLL rates (YLL/100,000 population), and a multivariate meta-analysis model was used to pool location-specific associations. Then, YLL per death caused by temperatures was calculated. The temperature and YLL rates consistently showed U-shaped associations. A mean of 1.02 (95% confidence interval: 0.67, 1.37) YLL per death was attributable to temperature. Cold temperature caused 0.98 YLL per death with most from moderate cold (0.84). The mean YLL per death was higher in those with cardiovascular diseases (1.14), males (1.15), younger age categories (1.31 in people aged 65–74 years), and in central China (1.34) than in those with respiratory diseases (0.47), females (0.87), older people (0.85 in people ≥75 years old), and northern China (0.64) or southern China (1.19). The mortality burden was modified by annual temperature and temperature variability, relative humidity, latitude, longitude, altitude, education attainment, and central heating use. Temperatures caused substantial YLL per death in China, which was modified by demographic and regional characteristics.

Many studies have linked temperature with respiratory deaths, but epidemiological evidence of temperature-attributable years of life lost (YLL) from respiratory diseases is limited. Daily respiratory YLL rates were calculated using mortality data from 364 locations of China during 2006–2017, and meteorological data were collected for the same period. First, the distributed lag non-linear model (DLNM) was applied to estimate specific temperature-respiratory YLL rate associations in each location. Then multivariable meta-analysis was conducted to pool the location-specific estimates. Finally, we calculated the average life loss per death (LLD) to quantify the respiratory mortality burden of non-optimal temperature. Subgroup analyses were conducted by gender, age, region and cause of death. Inversely J-shaped association was observed between non-optimal temperature and respiratory YLL rate in China. The minimum YLL-rate temperature was 26.9 °C nationwide. An average of 1.37 years (95% CI: 1.06–1.65) LLD was attributable to non-optimal temperatures with 2.06 years (95% CI: 1.57–2.60) for pneumonia, 2.03 years (95% CI: 1.76–2.31) for chronic lower respiratory infections (LRTI), 0.88 years (95% CI: 0.65–1.09) for chronic obstructive pulmonary disease (COPD), most of which was attributed to moderate cold (0.73 years, 95% CI: 0.65–0.80). LLD caused by non-optimal temperature was higher in males, the young, and north China. Exposure to non-optimal temperature increases respiratory YLL rate in China, most of which were attributed to moderate cold. People with respiratory diseases including pneumonia, chronic LRTI and COPD are vulnerable to non-optimal temperature exposure. The result of this study provides useful information to reduce temperature-related respiratory disease burden.

The evidence is limited for the mortality burden of temperature variability between neighboring days. This study developed a novel indicator to measure temperature variability between neighboring days and quantify its mortality burden. Daily mortality and meteorological data during 2006–2017 from 364 locations across China were collected. We first employed a distributed lag non-linear model and multivariable meta-analysis to investigate the association between the diurnal temperature range (DTR) with the years of life lost (YLL) rate and the association between the nocturnal temperature range (NTR) with the YLL rate. Then, we calculated the weight temperature variability between neighboring days (weight-TVN) based on the attributable YLL rate of the DTR and NTR. The relationship between the weight-TVN and YLL rate was analyzed, and the attributable fraction (AF) of the YLL and weight-TVN related life loss per death was calculated to quantify the mortality burden. Stratified analyses were conducted by region, season, gender, age group and cause of death. The DTR-YLL rate curve and NTR-YLL rate curve were both J-shaped and a higher YLL rate attributable to DTR was observed than NTR. There was a significant association between the weight-TVN and YLL rate. An estimated AF of the weight-TVN was 6.02% (95%CI: 3.71%–8.33%). The average life loss per death due to weight-TVN was 0.93 year (95%CI: 0.57–1.29). Stratification analyses showed that the AFs of weight-TVN were relatively larger in southern China, in the cold season, in the elderly, females and patients with respiratory illnesses. Although the AF of weight-TVN among the young group (AF = 4.74%, 95%CI: 1.79%–7.69%) was lower than for the elderly (AF = 6.06%, 95%CI: 3.72%–8.41%), weight-TVN related life loss per death among the young population (1.51, 95%CI: 0.57–2.45) was much higher than in the elderly (0.59, 95%CI: 0.36–0.82). A novel indicator to measure temperature variability between neighboring days was developed, and temperature fluctuation between adjacent days significantly increased the mortality burden. Our results indicate that more attention should be paid to short-term temperature fluctuation.