Woodward Sophie M, Mork Daniel, Wu Xiao, Hou Zhewen, Braun Danielle, Dominici Francesca
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.
Department of Biostatistics, Columbia University, New York, New York, United States of America.
PLOS Glob Public Health. 2023 Aug 2;3(8):e0002178. doi: 10.1371/journal.pgph.0002178. eCollection 2023.
Imposing stricter regulations for PM2.5 has the potential to mitigate damaging health and climate change effects. Recent evidence establishing a link between exposure to air pollution and COVID-19 outcomes is one of many arguments for the need to reduce the National Ambient Air Quality Standards (NAAQS) for PM2.5. However, many studies reporting a relationship between COVID-19 outcomes and PM2.5 have been criticized because they are based on ecological regression analyses, where area-level counts of COVID-19 outcomes are regressed on area-level exposure to air pollution and other covariates. It is well known that regression models solely based on area-level data are subject to ecological bias, i.e., they may provide a biased estimate of the association at the individual-level, due to within-area variability of the data. In this paper, we augment county-level COVID-19 mortality data with a nationally representative sample of individual-level covariate information from the American Community Survey along with high-resolution estimates of PM2.5 concentrations obtained from a validated model and aggregated to the census tract for the contiguous United States. We apply a Bayesian hierarchical modeling approach to combine county-, census tract-, and individual-level data to ultimately draw inference about individual-level associations between long-term exposure to PM2.5 and mortality for COVID-19. By analyzing data prior to the Emergency Use Authorization for the COVID-19 vaccines we found that an increase of 1 μg/m3 in long-term PM2.5 exposure, averaged over the 17-year period 2000-2016, is associated with a 3.3% (95% credible interval, 2.8 to 3.8%) increase in an individual's odds of COVID-19 mortality. Code to reproduce our study is publicly available at https://github.com/NSAPH/PM_COVID_ecoinference. The results confirm previous evidence of an association between long-term exposure to PM2.5 and COVID-19 mortality and strengthen the case for tighter regulations on harmful air pollution and greenhouse gas emissions.
对细颗粒物(PM2.5)实施更严格的监管有可能减轻对健康的损害以及气候变化影响。近期有证据表明空气污染暴露与新冠疫情结果之间存在关联,这是诸多支持降低国家环境空气质量标准(NAAQS)中PM2.5标准的论据之一。然而,许多报告新冠疫情结果与PM2.5之间关系的研究受到了批评,因为它们基于生态回归分析,即将新冠疫情结果的区域层面计数与区域层面的空气污染暴露及其他协变量进行回归分析。众所周知,仅基于区域层面数据的回归模型容易受到生态偏差的影响,也就是说,由于数据在区域内的变异性,它们可能会对个体层面的关联提供有偏差的估计。在本文中,我们用来自美国社区调查的具有全国代表性的个体层面协变量信息样本,以及从经过验证的模型获得并汇总到美国本土普查区的PM2.5浓度高分辨率估计值,来扩充县级新冠疫情死亡率数据。我们应用贝叶斯分层建模方法来结合县级、普查区层面和个体层面的数据,最终推断长期暴露于PM2.5与新冠疫情死亡率之间的个体层面关联。通过分析新冠疫苗紧急使用授权之前的数据,我们发现,在2000 - 2016年这17年期间长期PM2.5暴露每增加1μg/m³,个体新冠疫情死亡几率就会增加3.3%(95%可信区间为2.8%至3.8%)。重现我们研究的代码可在https://github.com/NSAPH/PM_COVID_ecoinference上公开获取。研究结果证实了长期暴露于PM2.5与新冠疫情死亡率之间存在关联的先前证据,并强化了对有害空气污染和温室气体排放实施更严格监管的理由。