Department of Environmental Health Sciences, Yale School of Public Health, New Haven, Connecticut.
Yale Center on Climate Change and Health, Yale School of Public Health, New Haven, Connecticut.
JAMA Netw Open. 2024 Mar 4;7(3):e2354607. doi: 10.1001/jamanetworkopen.2023.54607.
The association between short-term exposure to air pollution and mortality has been widely documented worldwide; however, few studies have applied causal modeling approaches to account for unmeasured confounders that vary across time and space.
To estimate the association between short-term changes in fine particulate matter (PM2.5) and nitrogen dioxide (NO2) concentrations and changes in daily all-cause mortality rates using a causal modeling approach.
DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study used air pollution and mortality data from Jiangsu, China; California; central-southern Italy; and Germany with interactive fixed-effects models to control for both measured and unmeasured spatiotemporal confounders. A total of 8 963 352 deaths in these 4 regions from January 1, 2015, to December 31, 2019, were included in the study. Data were analyzed from June 1, 2021, to October 30, 2023.
Day-to-day changes in county- or municipality-level mean PM2.5 and NO2 concentrations.
Day-to-day changes in county- or municipality-level all-cause mortality rates.
Among the 8 963 352 deaths in the 4 study regions, a 10-μg/m3 increase in daily PM2.5 concentration was associated with an increase in daily all-cause deaths per 100 000 people of 0.01 (95% CI, 0.001-0.01) in Jiangsu, 0.03 (95% CI, 0.004-0.05) in California, 0.10 (95% CI, 0.07-0.14) in central-southern Italy, and 0.04 (95% CI, 0.02- 0.05) in Germany. The corresponding increases in mortality rates for a 10-μg/m3 increase in NO2 concentration were 0.04 (95% CI, 0.03-0.05) in Jiangsu, 0.03 (95% CI, 0.01-0.04) in California, 0.10 (95% CI, 0.05-0.15) in central-southern Italy, and 0.05 (95% CI, 0.04-0.06) in Germany. Significant effect modifications by age were observed in all regions, by sex in Germany (eg, 0.05 [95% CI, 0.03-0.06] for females in the single-pollutant model of PM2.5), and by urbanicity in Jiangsu (0.07 [95% CI, 0.04-0.10] for rural counties in the 2-pollutant model of NO2).
The findings of this cross-sectional study contribute to the growing body of evidence that increases in short-term exposures to PM2.5 and NO2 may be associated with increases in all-cause mortality rates. The interactive fixed-effects model, which controls for unmeasured spatial and temporal confounders, including unmeasured time-varying confounders in different spatial units, can be used to estimate associations between changes in short-term exposure to air pollution and changes in health outcomes.
短期暴露于空气污染与死亡率之间的关联已在全球范围内得到广泛证实;然而,很少有研究应用因果建模方法来解释随时间和空间变化的未测量混杂因素。
使用因果建模方法估计短期细颗粒物(PM2.5)和二氧化氮(NO2)浓度变化与每日全因死亡率变化之间的关联。
设计、地点和参与者:本横断面研究使用了来自中国江苏、加利福尼亚、意大利中南部和德国的空气污染和死亡率数据,使用交互固定效应模型来控制测量和未测量的时空混杂因素。共有来自这 4 个地区的 8963352 人于 2015 年 1 月 1 日至 2019 年 12 月 31 日死亡,纳入了这项研究。数据分析于 2021 年 6 月 1 日至 2023 年 10 月 30 日进行。
县或市一级每日 PM2.5 和 NO2 浓度的变化。
县或市一级每日全因死亡率的变化。
在这 4 个研究地区的 8963352 例死亡中,每日 PM2.5 浓度增加 10μg/m3,与每 10 万人每日全因死亡人数增加 0.01(95%置信区间,0.001-0.01)相关,在江苏,0.03(95%置信区间,0.004-0.05)在加利福尼亚,0.10(95%置信区间,0.07-0.14)在意大利中南部,0.04(95%置信区间,0.02-0.05)在德国。NO2 浓度每增加 10μg/m3,死亡率相应增加 0.04(95%置信区间,0.03-0.05)在江苏,0.03(95%置信区间,0.01-0.04)在加利福尼亚,0.10(95%置信区间,0.05-0.15)在意大利中南部,0.05(95%置信区间,0.04-0.06)在德国。在所有地区都观察到了年龄的显著效应修饰,在德国还观察到了性别的显著效应修饰(例如,PM2.5 的单污染物模型中女性为 0.05[95%置信区间,0.03-0.06]),在江苏还观察到了城市的显著效应修饰(NO2 的双污染物模型中农村县为 0.07[95%置信区间,0.04-0.10])。
本横断面研究的结果有助于增加短期暴露于 PM2.5 和 NO2 可能与全因死亡率增加相关的证据。交互固定效应模型可控制未测量的空间和时间混杂因素,包括不同空间单位中未测量的时变混杂因素,可用于估计短期空气污染暴露变化与健康结果变化之间的关联。