METHODS: We collected daily all-cause, respiratory and cardiovascular mortality data in 347 cities across 15 countries and regions worldwide based on the Multi-City Multi-Country collaborative research network. The station-observed PM2.5 data were obtained from official monitoring stations. The model-estimated global PM2.5 product was developed using a machine-learning approach. The associations between daily exposure to PM2.5 and mortality were evaluated using a two-stage analytical approach.
RESULTS: We included 15.8 million all-cause, 1.5 million respiratory and 4.5 million cardiovascular deaths from 2000 to 2018. Short-term exposure to PM2.5 was associated with a relative risk increase (RRI) of mortality from both station-observed and model-estimated exposures. Every 10-μg/m3 increase in the 2-day moving average PM2.5 was associated with overall RRIs of 0.67% (95% CI: 0.49 to 0.85), 0.68% (95% CI: -0.03 to 1.39) and 0.45% (95% CI: 0.08 to 0.82) for all-cause, respiratory, and cardiovascular mortality based on station-observed PM2.5 and RRIs of 0.87% (95% CI: 0.68 to 1.06), 0.81% (95% CI: 0.08 to 1.55) and 0.71% (95% CI: 0.32 to 1.09) based on model-estimated exposure, respectively.
CONCLUSIONS: Mortality risks associated with daily PM2.5 exposure were consistent for both station-observed and model-estimated exposures, suggesting the reliability and potential applicability of the global PM2.5 product in epidemiological studies.
方法:我们每天收集所有原因,基于多城市多国家合作研究网络,全球15个国家和地区的347个城市的呼吸和心血管死亡率数据。站观测的PM2.5数据是从官方监测站获得的。模型估计的全球PM2.5产品是使用机器学习方法开发的。使用两阶段分析方法评估了每日PM2.5暴露与死亡率之间的关系。
结果:我们包括1580万所有原因,从2000年到2018年,有150万例呼吸死亡和450万例心血管死亡。短期暴露于PM2.5与站观测和模型估计暴露的死亡率相对风险增加(RRI)相关。2天移动平均PM2.5每增加10μg/m3,总RRI为0.67%(95%CI:0.49至0.85),所有原因为0.68%(95%CI:-0.03至1.39)和0.45%(95%CI:0.08至0.82),呼吸,基于站点观测的PM2.5和RRI的心血管死亡率为0.87%(95%CI:0.68至1.06),基于模型估计的暴露量,为0.81%(95%CI:0.08至1.55)和0.71%(95%CI:0.32至1.09),分别。
结论:与每日PM2.5暴露相关的死亡率风险对于站点观测和模型估计的暴露是一致的,表明全球PM2.5产品在流行病学研究中的可靠性和潜在适用性。