Snowden Jonathan M, Reid Colleen E, Tager Ira B
From the aDepartment of Obstetrics and Gynecology, Oregon Health & Science University, Portland, OR; bDepartment of Public Health and Preventive Medicine, Oregon Health & Science University, Portland, OR; cDivision of Environmental Health Sciences, University of California, Berkeley, CA; and dDivision of Epidemiology, University of California, Berkeley, CA.
Epidemiology. 2015 Mar;26(2):271-9. doi: 10.1097/EDE.0000000000000236.
Air pollution epidemiology continues moving toward the study of mixtures and multipollutant 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 multipollutant 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 2 air pollutants (nitrogen dioxide and particulate matter with aerodynamic diameter ≤ 2.5 μm) related to one another within 2 seasons (summer and winter), within 3 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 1 IQR, reflecting the notion that the IQR is an appropriate measure of variability between consecutive days. Condition 2 specified that the copollutant had to remain relatively constant. We found that in some city seasons, there were very few instances in which these conditions were satisfied (eg, 1 day in Pittsburgh during summer). We discuss the practical implications of IQR scaling and suggest alternative approaches to presenting multipollutant effects that are supported by empirical data.
空气污染流行病学正不断朝着混合物研究和多污染物建模的方向发展。与此同时，流行病学领域也在进行一项行动，即估算与政策相关的健康影响，这些影响可参照特定干预措施来理解。通过四分位距（IQR）对回归模型的回归系数进行缩放，是呈现多污染物健康影响估算值的一种常用方法。我们并不清楚有关如何将这些影响估算值解读为一种干预措施的指南。为了说明IQR缩放后的空气污染健康影响的可解释性问题，我们分析了2种空气污染物（二氧化氮和空气动力学直径≤2.5μm的颗粒物）在2个季节（夏季和冬季）以及3个具有不同空气污染特征的城市（加利福尼亚州伯班克市；得克萨斯州休斯敦市；宾夕法尼亚州匹兹堡市）内每日浓度变化之间的相互关系。在每个城市的季节中，我们研究了多污染物滞后1时间序列研究中的IQR缩放如何现实地反映出一种基于观测数据可能实现的假设干预措施。我们提出了2个因果条件，以便将IQR缩放后的影响与明确界定的假设干预措施明确联系起来。条件1规定，指数污染物的每日浓度变化必须大于1个IQR，这反映出IQR是衡量连续两天之间变异性的适当指标这一概念。条件2规定，共存污染物必须保持相对恒定。我们发现，在某些城市的季节中，很少有满足这些条件的情况（例如，匹兹堡市夏季有1天）。我们讨论了IQR缩放的实际意义，并提出了由经验数据支持的呈现多污染物影响的替代方法。
