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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 ?m) 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.

SUBMITTER: Snowden JM

PROVIDER: S-EPMC4374891 | biostudies-literature | 2015 Mar

REPOSITORIES: biostudies-literature

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Framing air pollution epidemiology in terms of population interventions, with applications to multipollutant modeling.

Snowden Jonathan M JM Reid Colleen E CE Tager Ira B IB

Epidemiology (Cambridge, Mass.) 20150301 2

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 ...[more]

PMID: 25643106

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Project description:Air pollutants including particulate matter (PM) and chemicals adsorbed onto PM pose a serious threat to human health. In this study, we analyzed the ability of PM to induce diverse gene expression profile modulation after chronic exposure in subjects living in two regions of the Czech Republic differing in levels and sources of the air pollution. We also considered impact of different seasonal conditions on concentrations and compositions of PM. Blood samples of 312 subjects from polluted Ostrava city and 154 controls from Prague city were collected in winter 2009, summer 2009 and winter 2010. The highest concentrations of air pollutants were detected in winter 2010 when the subjects were exposed to: PM of aerodynamic diameter < 2.5 M-BM-5m (70 vs. 44.9 M-BM-5g/m3); benzo[a]pyrene (9.02 vs. 2.56 ng/m3) and benzene (10.2 vs. 5.5 M-BM-5g/m3) in Ostrava and Prague, respectively. Global gene expression analysis of total RNA extracted from leukocytes was performed using whole genome microarrays (Illumina). The expression of selected genes was verified by quantitative real-time PCR (qRT-PCR). Despite lower concentrations of air pollutants we found a higher number of differentially expressed genes and affected KEGG pathways in subjects from Prague. In both locations we observed differences between seasons. The qRT-PCR analysis showed a significant decrease in expression of APEX, ATM, FAS, GSTM1, IL1B and RAD21 in subjects from Ostrava, in a comparison of winter 2010 and summer 2009. In Prague, an increase in gene expression was observed for GADD45A and PTGS2. In conclusion, high concentrations of pollutants in Ostrava do not increase the number of differentially expressed genes. This may be explained by adaption of humans to chronic exposure to air pollution. Total RNA was extracted from leukocytes of total of 154 control subjects and 312 subjects exposed to heavy air pollution. The samples were collected in three seasons (winter 2009, summer 2009, winter 2010) with different levels of air pollution. Most of the subjects were sampled repeatedly; however, some of them joined the study in summer 2009 or winter 2010.

Dataset Information

Framing air pollution epidemiology in terms of population interventions, with applications to multipollutant modeling.

Publications

Framing air pollution epidemiology in terms of population interventions, with applications to multipollutant modeling.

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