Search

Background: 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., Methods: 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., Results: HTV calculated in a relatively long-time window like 18 d (HTV 0-17 ) could capture the impact of HTV adequately. Per 1 °C raise of HTV 0-17 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., Conclusion: 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., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Due to the COVID-19 outbreak, the Chinese government implemented nationwide traffic restrictions and self-quarantine measures from January 23 to April 8 (in Wuhan), 2020. We estimated how these measures impacted ambient air pollution and the subsequent consequences on health and the health-related economy in 367 Chinese cities. A random forests modeling was used to predict the business-as-usual air pollution concentrations in 2020, after adjusting for the impact of long-term trend and weather conditions. We calculated changes in mortality attributable to reductions in air pollution in early 2020 and health-related economic benefits based on the value of statistical life (VSL). Compared with the business-as-usual scenario, we estimated 1239 (95% CI: 844-1578) PM 2.5 -related deaths were avoided, as were 2777 (95% CI: 1565-3995) PM 10 -related deaths, 1587 (95% CI: 98-3104) CO-related deaths, 4711 (95% CI: 3649-5781) NO 2 -related deaths, 215 (95% CI: 116-314) O 3 -related deaths, and 1088 (95% CI: 774-1421) SO 2 -related deaths. Based on the reduction in deaths, economic benefits for in PM 2.5 , PM 10 , CO, NO 2 , O 3 , and SO 2 were 1.22, 2.60, 1.36, 4.05, 0.20, and 0.95 billion USD, respectively. Our findings demonstrate the substantial benefits in human health and health-related costs due to improved urban air quality during the COVID lockdown period in China in early 2020., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: 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., Methods: 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., Results: 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., Conclusion: 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., (Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.)