Recent studies have reported a variety of health consequences of climate change. However, the vulnerability of individuals and cities to climate change remains to be evaluated. We project the excess ...cause-, age-, region-, and education-specific mortality attributable to future high temperatures in 161 Chinese districts/counties using 28 global climate models (GCMs) under two representative concentration pathways (RCPs). To assess the influence of population ageing on the projection of future heat-related mortality, we further project the age-specific effect estimates under five shared socioeconomic pathways (SSPs). Heat-related excess mortality is projected to increase from 1.9% (95% eCI: 0.2-3.3%) in the 2010s to 2.4% (0.4-4.1%) in the 2030 s and 5.5% (0.5-9.9%) in the 2090 s under RCP8.5, with corresponding relative changes of 0.5% (0.0-1.2%) and 3.6% (-0.5-7.5%). The projected slopes are steeper in southern, eastern, central and northern China. People with cardiorespiratory diseases, females, the elderly and those with low educational attainment could be more affected. Population ageing amplifies future heat-related excess deaths 2.3- to 5.8-fold under different SSPs, particularly for the northeast region. Our findings can help guide public health responses to ameliorate the risk of climate change.
Since December 2019, when coronavirus disease 2019 (Covid-19) emerged in Wuhan city and rapidly spread throughout China, data have been needed on the clinical characteristics of the affected ...patients.
We extracted data regarding 1099 patients with laboratory-confirmed Covid-19 from 552 hospitals in 30 provinces, autonomous regions, and municipalities in mainland China through January 29, 2020. The primary composite end point was admission to an intensive care unit (ICU), the use of mechanical ventilation, or death.
The median age of the patients was 47 years; 41.9% of the patients were female. The primary composite end point occurred in 67 patients (6.1%), including 5.0% who were admitted to the ICU, 2.3% who underwent invasive mechanical ventilation, and 1.4% who died. Only 1.9% of the patients had a history of direct contact with wildlife. Among nonresidents of Wuhan, 72.3% had contact with residents of Wuhan, including 31.3% who had visited the city. The most common symptoms were fever (43.8% on admission and 88.7% during hospitalization) and cough (67.8%). Diarrhea was uncommon (3.8%). The median incubation period was 4 days (interquartile range, 2 to 7). On admission, ground-glass opacity was the most common radiologic finding on chest computed tomography (CT) (56.4%). No radiographic or CT abnormality was found in 157 of 877 patients (17.9%) with nonsevere disease and in 5 of 173 patients (2.9%) with severe disease. Lymphocytopenia was present in 83.2% of the patients on admission.
During the first 2 months of the current outbreak, Covid-19 spread rapidly throughout China and caused varying degrees of illness. Patients often presented without fever, and many did not have abnormal radiologic findings. (Funded by the National Health Commission of China and others.).
Drought is a major natural disaster that causes severe social and economic losses. The prediction of regional droughts may provide important information for drought preparedness and farm irrigation. ...The existing drought prediction models are mainly based on a single weather station. Efforts need to be taken to develop a new multistation-based prediction model.
This study optimizes the predictor selection process and develops a new model to predict droughts using past drought index, meteorological measures and climate signals from 32 stations during 1961 to 2016 in Shaanxi province, China.
We applied and compared two methods, including a cross-correlation function and a distributed lag nonlinear model (DLNM), in selecting the optimal predictors and specifying their lag time. Then, we built a DLNM, an artificial neural network model and an XGBoost model and compared their validations for predicting the Standardized Precipitation Evapotranspiration Index (SPEI) 1–6 months in advance.
The DLNM was better than the cross-correlation function in predictor selection and lag effect determination. The XGBoost model more accurately predicted SPEI with a lead time of 1–6 months than the DLNM and the artificial neural network, with cross-validation R2 values of 0.68–0.82, 0.72–0.89, 0.81–0.92, and 0.84–0.95 at 3-, 6-, 9- and 12-month time scales, respectively. Moreover, the XGBoost model had the highest prediction accuracy for overall droughts (89%–97%) and for three specific drought categories (i.e., moderate, severe, and extreme) (76%–94%).
This study offers a new modeling strategy for drought predictions based on multistation data. The incorporation of nonlinear and lag effects of predictors into the XGBoost method can significantly improve prediction accuracy of SPEI and drought.
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•A new modeling strategy was developed to predict SPEI and droughts at 32 stations in Shaanxi province, China.•The distributed lag non-linear model outperformed CCF in selecting the optimal predictors and specifying their lag time.•XGBoost had better prediction accuracy for droughts with a lead time of 1-6 months (89%~97%) than ANN.
Numerous studies have linked the dispersion and deposition of atmospheric pollutants to meteorology. However, the lag structure of the effects lacks investigation. A two-stage analysis was used to ...assess the effects of meteorological factors on daily levels of particulate matter with an atmospheric diameter of less than 2.5 μg (PM2.5) and ozone (O3) in 284 major Chinese cities during 2015–2018. A quantile regression model combined with a distributed lag nonlinear model was first used to estimate the city-specific nonlinear and delayed effects of meteorology on air pollutants. Then, a multivariate meta-analysis was utilized to pool the city-specific effect estimates across China. In general, the meteorological effects were nonlinear. The wind speed, temperature, and rainfall were observed to be the primary meteorological factors influencing PM2.5 concentration, while temperature, relative humidity, and sunshine duration played crucial roles in influencing O3 concentration. Additionally, diverse meteorological lag pattern effects were also noted. For PM2.5, the effects of rainfall and wind were delayed and lasted for 2–4 d, while the effects of relative humidity, temperature, and sunshine duration peaked in real time and then quickly became negative or vanished after 1 d. For O3, the effects of relative humidity and sunshine duration were limited to 5 d, and rainfall and temperature only exerted significant impacts on the current day. This large-scale study thoroughly investigated the delayed and nonlinear association between meteorology and air pollution, and it presented important implications for the development of air pollution forecasts and control strategies from the meteorological perspective.
•A national study of 284 Chinese major cities was conducted during 2015–2018.•Developing a two-stage analysis of nonlinear and lagged effects of meteorology.•PM2.5 was primarily affected by wind, temperature, and rainfall.•O3 was mostly influenced by temperature, relative humidity, and sunshine duration.•There were diverse meteorological lag pattern effects on PM2.5 and O3.
To examine cardiovascular disease (CVD) mortality burden attributable to ambient temperature; to estimate effect modification of this burden by gender, age and education level.
We obtained daily data ...on temperature and CVD mortality from 15 Chinese megacities during 2007-2013, including 1,936,116 CVD deaths. A quasi-Poisson regression combined with a distributed lag non-linear model was used to estimate the temperature-mortality association for each city. Then, a multivariate meta-analysis was used to derive the overall effect estimates of temperature at the national level. Attributable fraction of deaths were calculated for cold and heat (ie, temperature below and above minimum-mortality temperatures, MMTs), respectively. The MMT was defined as the specific temperature associated to the lowest mortality risk.
The MMT varied from the 70th percentile to the 99th percentile of temperature in 15 cities, centring at 78 at the national level. In total, 17.1% (95% empirical CI 14.4% to 19.1%) of CVD mortality (330,352 deaths) was attributable to ambient temperature, with substantial differences among cities, from 10.1% in Shanghai to 23.7% in Guangzhou. Most of the attributable deaths were due to cold, with a fraction of 15.8% (13.1% to 17.9%) corresponding to 305,902 deaths, compared with 1.3% (1.0% to 1.6%) and 24,450 deaths for heat.
This study emphasises how cold weather is responsible for most part of the temperature-related CVD death burden. Our results may have important implications for the development of policies to reduce CVD mortality from extreme temperatures.
In the context of global warming, most researches have been conducted on health influences of heat waves, with limited understanding of health impacts of cold spells, especially for developing ...countries.
We collected daily mortality and meteorological data for 31 capital cities across China during the maximum period of 2007–2013. A quasi-Poisson regression model combined with a distributed lag non-linear model was used to estimate the short-term effects of cold spells on mortality in cold seasons (November to March). 19 definitions of cold spell were clearly compared, including three definitions from the China Meteorological Administration (CMA) and 16 definitions by combining two temperature indicators (daily minimum and mean temperature), two temperature thresholds (3rd and 5th percentile) and four durations of at least 2–5 days. Then, a random effect meta-analysis was applied to pool the effect estimates at national level. Furthermore, a stratified analysis was constructed to identify the vulnerable subpopulations to cold spells.
The definition, in which daily mean temperature falls below 5th percentile for at least two consecutive days, produced the optimum model fit performance. Generally, the mortality risk increased to the maximum after 3–6 days' exposure to cold spell and then leveled off in the next 3 weeks. The pooled relative risks (RR) of non-accidental mortality for cold spells were 1.03 (95% CI: 1.01–1.05), 1.27 (1.19–1.35) and 1.55 (1.40–1.70) at lag 0, lag 0–14 and lag 0–27 days, respectively. The greatest effect estimates of cold spells were found among total respiratory diseases and COPD, with RR of 1.88 (1.65–2.11) and 1.88 (1.58–2.19), respectively. The elderly, less-educated individuals and residents in southern China were more vulnerable to cold spells.
There are remarkable mortality effects of cold spells, with effect estimates varying with the definition of cold spell and subpopulations. Using the official definition of cold spells may fail to capture the mortality risk associated with cold spells. These findings may facilitate the development of cold alert warning system and preventive actions to the vulnerable populations.
•This is the first multicity study to comprehensively examine health-related cold spell definitions in China.•Using the official cold spell definition failed to capture its mortality risk around China.•The elderly, respiratory patients, less-educated persons and residents in southern China were more vulnerable to cold spells.
Several studies have attempted to predict ground PM2.5 concentrations using satellite aerosol optical depth (AOD) retrieval. However, over 70%–90% of aerosol retrievals are non-random missing, which ...limits and biases the estimation. To the best of our knowledge, this issue has not been well resolved to date.
The aim of this study was to develop an interpolation technique to handle the missing data retrieval problem and to estimate the daily PM2.5 for a high coverage dataset with 3-km resolution in China by fitting the complex temporal and spatial variations.
We developed a two-step interpolation method (i.e., the mixed-effect model and inverse distance weighting technology) to replace the missing values in AOD. Next, the extreme gradient boosting (XGBoost) technique that includes a non-linear exposure-lag-response model (NELRM) was proposed and validated to estimate the daily levels of PM2.5 across China during 2014–2015.
After two steps of interpolation, the missing value rate of daily AOD data was reduced from 87.91% to 13.83%. The cross-validation (CV) R-square, root mean square error (RMSE) and mean absolute percentage prediction error (MAPE) of the interpolation were 0.76, 0.10 and 21.41%, respectively. The cross-validation for the prediction of daily PM2.5 resulted in R2 = 0.86, RMSE = 14.98, and MAPE = 23.72%.
The results of this study indicate that the two-step interpolation method can largely resolve the non-random missing data problem and that the combined XGBoost methods have a good ability to estimate fine particulate matter concentrations.
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•88% of aerosol retrievals were non-random missing over mainland China.•The missing rate reduced to 14% by using the proposed interpolation method.•A new modelling strategy was developed to estimate PM2.5 using missing-filled data.•The extreme gradient boosting improved the estimation of PM2.5 levels (Cross-Validation R2 = 0.86).
Although many studies have documented health effects of ambient temperature, little evidence is available in subtropical or tropical regions, and effect modifiers remain uncertain. We examined the ...effects of daily mean temperature on mortality and effect modification in the subtropical city of Guangzhou, China.
A Poisson regression model combined with distributed lag non-linear model was applied to assess the non-linear and lag patterns of the association between daily mean temperature and mortality from 2003 to 2007 in Guangzhou. The case-only approach was used to determine whether the effect of temperature was modified by individual characteristics, including sex, age, educational attainment and occupation class.
Hot effect was immediate and limited to the first 5 days, with an overall increase of 15.46% (95% confidence interval: 10.05% to 20.87%) in mortality risk comparing the 99th and the 90th percentile temperature. Cold effect persisted for approximately 12 days, with a 20.39% (11.78% to 29.01%) increase in risk comparing the first and the 10th percentile temperature. The effects were especially remarkable for cardiovascular and respiratory mortality. The effects of both hot and cold temperatures were greater among the elderly. Females suffered more from hot-associated mortality than males. We also found significant effect modification by educational attainment and occupation class.
There are significant mortality effects of hot and cold temperatures in Guangzhou. The elderly, females and subjects with low socioeconomic status have been identified as especially vulnerable to the effect of ambient temperatures.
•Explore the impact of hourly temperature variability in 31 Chinese capital cities.•Investigate the time window of hourly temperature variability.•5.47% of non-accidental mortality could be ...attributed to temperature variability.•Female, old, or undereducated people were more affected by temperature variability.•Effect of temperature variability may be modified by season and temperature zone.
In the context of ongoing climate change, temperature variability (TV) has been considered as an important trigger of death. However, evidence of association between mortality and hourly temperature variability (HTV) is scarce at the multi-city level, and the time window of health effects of HTV is lack of investigation. This study aims at quantifying the mortality risk and burden of HTV and exploring subpopulations susceptible to HTV from a large-scale multi-city perspective.
Data on daily number of deaths and meteorology were collected for 31 Chinese major cities during 2007–2013. HTV was calculated as the standard deviation of hourly temperature within a few days. The optimal exposure period of HTV was chosen according to multiple scientific criteria. A quasi-Poisson regression combined with distributed lag nonlinear model was used to assess the city-specific HTV-mortality associations. Then, meta-analysis was further applied to pool city-specific effect estimates. Finally, we calculated the fraction of mortality attributable to HTV. Stratification analyses were conducted by individual characteristics (i.e. age, sex, and educational attainment), season, and region.
HTV calculated in a relatively long-time window like 18 d (HTV0–17) could capture the impact of HTV adequately. Per 1 °C raise of HTV0–17 associated with 1.38% (95%CI: 0.77, 1.99) increase of non-accidental mortality. During the study period, 5.47% (95%CI: 1.06, 9.64) of non-accidental mortality could be attributed to HTV. The females, the elderly, and individuals with low education level were more susceptible to HTV than their counterparts, respectively. Moreover, a stronger HTV-mortality association was observed in individuals who live in warmer season and temperature zone.
HTV is associated with a considerable mortality burden, which may be modified by season, geographic and individual-level factors. Our findings highlight the practical importance of establishing early warning systems and promoting health education to mitigate the impacts of temperature variability.
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