Zhang Boya, Eum Ki-Do, Szpiro Adam A, Zhang Ning, Hernández-Ramírez Raúl U, Spiegelman Donna, Wang Molin, Suh Helen
Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Int J Environ Health Res. 2025 Apr 7:1-11. doi: 10.1080/09603123.2025.2488481.
Despite demonstrated adverse health effects of air pollution, the impact of exposure measurement error on these associations remains unexplored, especially for NO and PM components. We compiled daily personal measurements of PM, NO, and PM components - including Al, Cd, Fe, K, Ni, Pb, S, and Si - from previous studies as true exposure indicators. These were compared against ambient concentrations from the nearest monitors. We used Spearman correlation to examine relationships between monthly averages of personal exposures and ambient concentrations. Calibration coefficients were derived using linear mixed models to quantify measurement errors. Results showed strong correlations between monthly personal exposures and ambient concentrations for PM, NO, Cd, Ni, S, and Si across the US. Calibration coefficients for personal PM and NO were 0.46 (95% CI: 0.13, 0.78) and 0.97 (0.35, 1.59), respectively. Significant coefficients were also found for S (0.48; 95% CI: 0.27, 0.68), Cd (0.47; 0.17, 0.76), and Ni (0.17; 0.02, 0.32). Point estimates for calibration coefficients were all below one, indicating that using the nearest monitors as exposure surrogates would attenuate associations with health risks. The measurement error in component-wise analysis highlights the need for incorporating these calibration coefficients into future studies to adjust for such errors adequately.
尽管空气污染已被证明会对健康产生不利影响,但暴露测量误差对这些关联的影响仍未得到探索,尤其是对于一氧化氮(NO)和颗粒物(PM)成分而言。我们汇总了先前研究中对PM、NO以及PM成分(包括铝、镉、铁、钾、镍、铅、硫和硅)的每日个人测量数据,将其作为真实暴露指标。并将这些数据与距离最近的监测站的环境浓度进行比较。我们使用斯皮尔曼相关性来检验个人暴露月平均值与环境浓度之间的关系。通过线性混合模型得出校准系数,以量化测量误差。结果显示,在美国,个人暴露月平均值与PM、NO、镉、镍、硫和硅的环境浓度之间存在很强的相关性。个人PM和NO的校准系数分别为0.46(95%置信区间:0.13,0.78)和0.97(0.35,1.59)。硫(0.48;95%置信区间:0.27,0.68)、镉(0.47;0.17,0.76)和镍(0.17;0.02,0.32)也有显著系数。校准系数的点估计值均低于1,这表明使用距离最近的监测站作为暴露替代指标会削弱与健康风险的关联。成分分析中的测量误差凸显了在未来研究中纳入这些校准系数以充分校正此类误差的必要性。