National Health and Environmental Effects Research Laboratory, Office of Research and Development, Environmental Protection Agency, Research Triangle Park, NC, 27711, USA.
National Exposure Research Laboratory, Office of Research and Development, Environmental Protection Agency, Research Triangle Park, NC, 27711, USA.
J Expo Sci Environ Epidemiol. 2019 Jun;29(4):557-567. doi: 10.1038/s41370-018-0080-7. Epub 2018 Oct 11.
Multi-city population-based epidemiological studies of short-term fine particulate matter (PM) exposures and mortality have observed heterogeneity in risk estimates between cities. Factors affecting exposures, such as pollutant infiltration, which are not captured by central-site monitoring data, can differ between communities potentially explaining some of this heterogeneity. This analysis evaluates exposure factors as potential determinants of the heterogeneity in 312 core-based statistical areas (CBSA)-specific associations between PM and mortality using inverse variance weighted linear regression. Exposure factor variables were created based on data on housing characteristics, commuting patterns, heating fuel usage, and climatic factors from national surveys. When survey data were not available, air conditioning (AC) prevalence was predicted utilizing machine learning techniques. Across all CBSAs, there was a 0.95% (Interquartile range (IQR) of 2.25) increase in non-accidental mortality per 10 µg/m increase in PM and significant heterogeneity between CBSAs. CBSAs with larger homes, more heating degree days, a higher percentage of home heating with oil had significantly (p < 0.05) higher health effect estimates, while cities with more gas heating had significantly lower health effect estimates. While univariate models did not explain much of heterogeneity in health effect estimates (R < 1%), multivariate models began to explain some of the observed heterogeneity (R = 13%).
多城市基于人群的短期细颗粒物（PM）暴露与死亡率的流行病学研究表明，不同城市之间的风险估计存在异质性。影响暴露的因素（如污染物渗透），这些因素无法通过中心站点监测数据捕捉到，可能因社区而异，这可能解释了部分异质性。本分析使用方差倒数加权线性回归，评估了暴露因素作为 PM 与死亡率之间 312 个核心统计区（CBSA）特异性关联的异质性的潜在决定因素。暴露因素变量是基于住房特征、通勤模式、取暖燃料使用和全国调查中的气候因素数据创建的。当调查数据不可用时，利用机器学习技术预测空调（AC）的普及率。在所有 CBSA 中，PM 每增加 10 µg/m，非意外死亡率增加 0.95%（IQR 为 2.25），且 CBSA 之间存在显著异质性。住房面积较大、取暖度日数较多、家庭取暖中使用石油比例较高的 CBSA，健康效应估计值显著（p<0.05）较高，而燃气取暖较多的城市健康效应估计值显著较低。虽然单变量模型不能解释健康效应估计值的大部分异质性（R<1%），但多变量模型开始解释部分观察到的异质性（R=13%）。
