Wang Veronica A, Delaney Scott, Flynn Lauren E, Racette Brad A, Miller Gary W, Braun Danielle, Zanobetti Antonella, Mork Daniel
Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Division of Pulmonary Medicine, Boston Children's Hospital, Boston, MA, USA.
NPJ Parkinsons Dis. 2024 Oct 24;10(1):196. doi: 10.1038/s41531-024-00815-x.
We examined the effect of annual exposure to fine particulate matter (PM), nitrogen dioxide (NO), and ozone (O), on the rate of first hospitalization with a PD-related diagnosis (hospitalization with PD) among Medicare Fee-for-Service beneficiaries (2001-2016). Machine learning-derived annual air pollution concentrations were linked to residential ZIP codes. For each exposure, we fitted four models: 1) traditional outcome stratification, 2) marginal structural, 3) doubly robust, and 4) generalized propensity score matching Poisson regression models, adjusted for sociodemographic and meteorological confounders and long-term trends. Among 49,121,026 beneficiaries, incidence rate ratios of 1.08 (95% CI: 1.07, 1.10), 1.07 (95% CI: 1.05, 1.08), and 1.03 (95% CI: 1.02, 1.05) for an interquartile range increase in PM (3.72 µg/m), NO (13.84 ppb), and O (10.09 ppb), respectively, were estimated from doubly robust models. Results were similar across modeling approaches. In this nationwide study, higher air pollution exposure increased the rate of hospitalizations with PD.
我们研究了每年暴露于细颗粒物(PM)、二氧化氮(NO)和臭氧(O)对医疗保险按服务收费受益人(2001 - 2016年)中首次因帕金森病相关诊断住院(帕金森病住院)率的影响。通过机器学习得出的年度空气污染浓度与居民邮政编码相关联。对于每种暴露因素,我们拟合了四种模型:1)传统结局分层模型,2)边际结构模型,3)双重稳健模型,以及4)广义倾向得分匹配泊松回归模型,并对社会人口统计学和气象混杂因素以及长期趋势进行了调整。在49,121,026名受益人中,双重稳健模型估计,PM(3.72 µg/m³)、NO(13.84 ppb)和O(10.09 ppb)每增加一个四分位间距,发病率比值分别为1.08(95%置信区间:1.07,1.10)、1.07(95%置信区间:1.05,1.08)和1.03(95%置信区间:1.02,1.05)。不同建模方法的结果相似。在这项全国性研究中,更高的空气污染暴露增加了帕金森病住院率。