Framing air pollution epidemiology in terms of population interventions, with applications to multipollutant modeling.
Framing air pollution epidemiology in terms of population interventions, with applications to multipollutant modeling.
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DOI：
10.1097/ede.0000000000000236
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发表时间：
2015-03
期刊：
影响因子：
--
通讯作者：
Tager IB
中科院分区：
文献类型：
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作者：
Snowden JM;Reid CE;Tager IB
空气污染流行病学继续向混合物和多污染物模型研究方向发展。同时，流行病学中出现了一种趋势，即估计与政策相关的健康影响，这可以参照具体的干预措施来理解。通过四分位数范围(IQR)对回归模型中的回归系数进行缩放是提出多污染物健康影响评估的一种常见方法。我们不知道如何将这些影响估计解读为干预。为了说明IQR尺度空气污染健康影响的可解释性问题，我们分析了两种空气污染物(二氧化氮和PM2.5；二氧化氮和空气动力学直径为≤2.5μm的颗粒物)在两个季节(夏季和冬季)内的日浓度变化如何相互关联，三个城市(加利福尼亚州伯班克、得克萨斯州休斯顿和宾夕法尼亚州匹兹堡)的空气污染状况截然不同。在每个城市季节，我们检查了多污染物LAG-1时间序列研究中的IQR标度如何现实地反映了给定观察数据可能的假设干预。我们提出了两个因果条件，以明确地将IQR尺度的影响与明确定义的假设干预联系起来。条件1规定，指数污染物的每日浓度变化必须大于一个IQR，这反映了IQR是连续几天之间变化性的适当衡量标准。条件2规定，共同污染物必须保持相对恒定。我们发现，在某些城市季节，满足这些条件的情况很少(例如，夏季匹兹堡有一天)。我们讨论了IQR比例的实际影响，并提出了替代方法来呈现经验数据支持的多污染物影响。
Air pollution epidemiology continues moving toward the study of mixtures and multi-pollutant modeling. Simultaneously, there is a movement in epidemiology to estimate policy-relevant health effects that can be understood in reference to specific interventions. Scaling regression coefficients from a regression model by an interquartile range (IQR) is one common approach to presenting multi-pollutant health effect estimates. We are unaware of guidance on how to interpret these effect estimates as an intervention. To illustrate the issues of interpretability of IQR-scaled air pollution health effects, we analyzed how daily concentration changes in two air pollutants (NO2 and PM2.5; nitrogen dioxide and particulate matter with aerodynamic diameter ≤ 2.5μm) related to one another within two seasons (summer and winter), within three cities with distinct air pollution profiles (Burbank, California; Houston, Texas; and Pittsburgh, Pennsylvania). In each city-season, we examined how realistically IQR-scaling in multipollutant lag-1 time-series studies reflects a hypothetical intervention that is possible given the observed data. We proposed 2 causal conditions to explicitly link IQR-scaled effects to a clearly defined hypothetical intervention. Condition 1 specified that the index pollutant had to experience a daily concentration change of greater than one IQR, reflecting the notion that the IQR is an appropriate measure of variability between consecutive days. Condition 2 specified that the co-pollutant had to remain relatively constant. We found that in some city-seasons, there were very few instances in which these conditions were satisfied (e.g., 1 day in Pittsburgh during summer). We discuss the practical implications of IQR scaling and suggest alternative approaches to presenting multi-pollutant effects that are supported by empirical data.