Liang Mingming, Min Min, Ye Pengpeng, Duan Leilei, Sun Yehuan
Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, No. 81 Meishan Road, Hefei, 230032, Anhui, China.
Anhui Institute of Medical Information (Anhui Medical Association), Hefei, 230061, Anhui, China.
Environ Sci Pollut Res Int. 2023 Jan;30(3):6818-6827. doi: 10.1007/s11356-022-22662-0. Epub 2022 Aug 26.
Exposure to air pollutants is considered to be associated with mental disorders (MD). Few studies have addressed joint effect of multiple air pollutants and meteorological factors on admissions of MD. We examined the association between multiple air pollutants (PM, PM, O, SO, and NO), meteorological factors (temperature, precipitation, relative humidity, and sunshine time), and MD risk in Yancheng, China. Associations were estimated by a generalized linear regression model (GLM) adjusting for time trend, day of the week, and patients' average age. Empirical weights of environmental exposures were judged by a weighted quantile sum (WQS) model. A machine learning approach, Bayesian kernel machine regression (BKMR), was used to assess the overall effect of mixed exposures. We calculated excess risk (ER) and 95% confidence interval (CI) for each exposure. According to the effect of temperature on MD, we divided the exposure of all factors into different temperature groups. In the high temperature group, GLM found that for every 10 μg/m increase in O, PM and PM exposure, the ERs were 1.926 (95%CI 0.345, 3.531), 1.038 (95%CI 0.024, 2.062), and 0.780 (95% CI 0.052, 1.512) after adjusting for covariates. Temperature, relative humidity, and sunshine time also reported significant results. The WQS identified O and temperature (above the threshold) had the highest weights among air pollutants and meteorological factors. BKMR found a significant positive association between mixed exposure and MD risks. In the low temperature group, only O and temperature (below the threshold) showed significant results. These findings provide policymakers and practitioners with important scientific evidence for possible interventions. The association between different exposures and MD risk warrants further study.
暴露于空气污染物被认为与精神障碍（MD）有关。很少有研究探讨多种空气污染物和气象因素对MD住院率的联合影响。我们研究了中国盐城多种空气污染物（PM、PM、O、SO和NO）、气象因素（温度、降水、相对湿度和日照时间）与MD风险之间的关联。通过广义线性回归模型（GLM）估计关联，并对时间趋势、星期几和患者平均年龄进行了调整。通过加权分位数和（WQS）模型判断环境暴露的经验权重。采用机器学习方法贝叶斯核机器回归（BKMR）来评估混合暴露的总体影响。我们计算了每种暴露的超额风险（ER）和95%置信区间（CI）。根据温度对MD的影响，我们将所有因素的暴露分为不同的温度组。在高温组中，GLM发现，在调整协变量后，O、PM和PM暴露每增加10μg/m，ER分别为1.926（95%CI 0.345，3.531）、1.038（95%CI 0.024，2.062）和0.780（95%CI 0.052，1.512）。温度、相对湿度和日照时间也呈现出显著结果。WQS确定O和温度（高于阈值）在空气污染物和气象因素中权重最高。BKMR发现混合暴露与MD风险之间存在显著正相关。在低温组中，只有O和温度（低于阈值）显示出显著结果。这些发现为政策制定者和从业者提供了可能干预措施的重要科学依据。不同暴露与MD风险之间的关联值得进一步研究。
