title: Framing air pollution epidemiology in terms of population interventions, with applications to multipollutant modeling.
abstract: 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.
authors: 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.
authors: Jonathan M
authors: JM
authors: Snowden
authors: Colleen E
authors: CE
authors: Reid
authors: Ira B
authors: IB
authors: Tager
mesh_terms: D000393:Air Pollutants / Q000009:adverse effects / Q000032:analysis*; D000397:Air Pollution / Q000009:adverse effects / Q000032:analysis* / Q000706:statistics & numerical data; D002140:California / Q000453:epidemiology; D004781:Environmental Exposure / Q000009:adverse effects / Q000032:analysis* / Q000706:statistics & numerical data; D006801:Humans; D015233:Models, Statistical; D009585:Nitrogen Dioxide / Q000009:adverse effects / Q000032:analysis*; D052638:Particulate Matter / Q000009:adverse effects / Q000032:analysis*; D010414:Pennsylvania / Q000453:epidemiology; D012621:Seasons; D013781:Texas / Q000453:epidemiology; D014504:Urban Health
chemical_list: D000393:Air Pollutants; D052638:Particulate Matter; D009585:Nitrogen Dioxide
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