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具有连续曲线和暴露误差校正的因果浓度-反应模型:医疗保险队列中的发病率和死亡率

Causal Concentration-Response Modeling with Continuous Curves and Exposure Error Correction: and Mortality in the Medicare Cohort.

作者信息

Schwartz Joel, Feng Yijing, Castro Edgar, Wei Yaguang

机构信息

Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.

Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.

出版信息

Environ Health Perspect. 2025 Jun;133(6):67007. doi: 10.1289/EHP15238. Epub 2025 Jun 10.

Abstract

BACKGROUND

Many studies have reported associations of fine particulate matter with aerodynamic diameter ()with mortality but fewer at low concentrations and even fewer using causal modeling or correcting for exposure error bias. None have corrected for the nonrepresentativeness of monitoring locations.

OBJECTIVES

We examined the association of with all-cause mortality in the Medicare cohort using a combination of causal modeling, flexible concentration-response modeling, and bias correction for exposure error, while controlling for and as well as standard confounders.

METHODS

Using monitors not used to fit our model, we fitted 72 regression calibration models stratified by season, region, and elevation in the US. We fitted a B-spline with 4 degrees of freedom to the calibrated and fitted separate generalized propensity score models for each spline component using gradient boosting. We also used inverse probability weights to account for the nonrepresentativeness of monitoring locations. Using the generalized propensity scores and the B-splines, we fitted quasi-Poisson models to counts of deaths in each ZIP code-year stratified by race, Medicaid status, and gender. Separate models were fit for participants identifying as black and as white and for ZIP codes with higher and lower poverty rates. We fit a model using the original exposure to estimate the extent of exposure error bias.

RESULTS

The propensity score analysis achieved good balance for all covariates. Controlling for the propensity scores, we found a concentration-response curve with no evidence of a threshold and whose confidence interval did not include the null from and upward. There were 223,666,531 person-years of follow-up between the current US Environmental Protection Agency (EPA) standard of and the World Health Organization (WHO) guideline of , and the rate ratio between them was 1.088 [95% confidence interval (CI): 1.064, 1.113]. Using the original exposure, the rate ratio was 1.076 (95% CI: 1.070, 1.083). Hence, effects continue below the EPA standard, and calibrated estimates of effect were 16% higher. Effects were larger from among participants identifying as black.

DISCUSSION

The concentration-response curve between air pollution and mortality remains after adjustment for exposure error and using causal models and continues to concentrations below current US EPA and EU standards and even below WHO guidelines. Exposure error in the original exposure resulted in noticeable downward bias at low concentrations. Persons identifying as black are more susceptible. https://doi.org/10.1289/EHP15238.

摘要

背景

许多研究报告了空气动力学直径小于等于2.5微米的细颗粒物(PM2.5)与死亡率之间的关联,但在低浓度下的相关研究较少,而使用因果模型或校正暴露误差偏差的研究更少。尚无研究校正监测地点的非代表性问题。

目的

我们使用因果模型、灵活的浓度-反应模型以及暴露误差偏差校正相结合的方法,在医疗保险队列中研究了PM2.5与全因死亡率之间的关联,同时控制了PM10以及标准混杂因素。

方法

我们使用未用于拟合PM2.5模型的监测器,在美国按季节、地区和海拔分层拟合了72个回归校准模型。我们对校准后的PM2.5拟合了一个具有4个自由度的B样条,并使用梯度提升为每个样条成分拟合单独的广义倾向得分模型。我们还使用逆概率权重来校正监测地点的非代表性问题。利用广义倾向得分和B样条,我们对按种族、医疗补助状态和性别分层的每个邮政编码年份的死亡人数拟合了准泊松模型。分别为自我认定为黑人及白人的参与者以及贫困率较高和较低的邮政编码区域拟合了单独的模型。我们使用原始暴露拟合了一个模型,以估计暴露误差偏差的程度。

结果

倾向得分分析在所有协变量上实现了良好的平衡。在控制倾向得分后,我们发现了一条浓度-反应曲线,没有阈值证据,其置信区间在10微克/立方米及以上不包括零值。在美国环境保护局(EPA)当前标准的10微克/立方米与世界卫生组织(WHO)指南的5微克/立方米之间有223,666,531人年的随访,二者之间的率比为1.088[95%置信区间(CI):1.064, 1.113]。使用原始暴露时,率比为1.076(95% CI:1.070, 1.083)。因此,在EPA标准以下效应仍持续存在,校正后的效应估计值高16%。在自我认定为黑人的参与者中,10微克/立方米及以上时效应更大。

讨论

在校正暴露误差并使用因果模型后,空气污染与死亡率之间的浓度-反应曲线仍然存在,并且在低于当前美国EPA和欧盟标准甚至低于WHO指南的浓度下仍持续存在。原始暴露中的暴露误差在低浓度时导致了明显的向下偏差。自我认定为黑人的人更易受影响。https://doi.org/10.1289/EHP15238

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a5c/12151321/07a499b787d2/ehp15238_f1.jpg

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