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Health effects of multi-pollutant profiles.

ABSTRACT: BACKGROUND:The association between exposure to particle mass and mortality is well established; however, there are still uncertainties as to whether certain chemical components are more harmful than others. Moreover, understanding the health effects associated with exposure to pollutant mixtures may lead to new regulatory strategies. OBJECTIVES:Recently we have introduced a new approach that uses cluster analysis to identify distinct air pollutant mixtures by classifying days into groups based on their pollutant concentration profiles. In Boston during the years 1999-2009, we examined whether the effect of PM2.5 on total mortality differed by distinct pollution mixtures. METHODS:We applied a time series analysis to examine the association of PM2.5 with daily deaths. Subsequently, we included an interaction term between PM2.5 and the pollution mixture clusters. RESULTS:We found a 1.1% increase (95% CI: 0.0, 2.2) and 2.3% increase (95% CI: 0.9-3.7) in total mortality for a 10 ?g/m(3) increase in the same day and the two-day average of PM2.5 respectively. The association is larger in a cluster characterized by high concentrations of the elements related to primary traffic pollution and oil combustion emissions with a 3.7% increase (95% CI: 0.4, 7.1) in total mortality, per 10 ?g/m(3) increase in the same day average of PM2.5. CONCLUSIONS:Our study shows a higher association of PM2.5 on total mortality during days with a strong contribution of traffic emissions, and fuel oil combustion. Our proposed method to create multi-pollutant profiles is robust, and provides a promising tool to identify multi-pollutant mixtures which can be linked to the health effects.

SUBMITTER: Zanobetti A

PROVIDER: S-EPMC4383187 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature

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Health effects of multi-pollutant profiles.

Zanobetti Antonella A Austin Elena E Coull Brent A BA Schwartz Joel J Koutrakis Petros P

Environment international 20140617

<h4>Background</h4>The association between exposure to particle mass and mortality is well established; however, there are still uncertainties as to whether certain chemical components are more harmful than others. Moreover, understanding the health effects associated with exposure to pollutant mixtures may lead to new regulatory strategies.<h4>Objectives</h4>Recently we have introduced a new approach that uses cluster analysis to identify distinct air pollutant mixtures by classifying days into ...[more]

PMID: 24950160

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Project description:BackgroundMost epidemiological studies estimate associations without considering exposure measurement error. While some studies have estimated the impact of error in single-exposure models we aimed to quantify the effect of measurement error in multi-exposure models, specifically in time-series analysis of PM2.5, NO2, and mortality using simulations, under various plausible scenarios for exposure errors. Measurement error in multi-exposure models can lead to effect transfer where the effect estimate is overestimated for the pollutant estimated with more error to the one estimated with less error. This complicates interpretation of the independent effects of different pollutants and thus the relative importance of reducing their concentrations in air pollution policy.MethodsMeasurement error was defined as the difference between ambient concentrations and personal exposure from outdoor sources. Simulation inputs for error magnitude and variability were informed by the literature. Error-free exposures with their consequent health outcome and error-prone exposures of various error types (classical/Berkson) were generated. Bias was quantified as the relative difference in effect estimates of the error-free and error-prone exposures.ResultsMortality effect estimates were generally underestimated with greater bias observed when low ratios of the true exposure variance over the error variance were assumed (27.4% underestimation for NO2). Higher ratios resulted in smaller, but still substantial bias (up to 19% for both pollutants). Effect transfer was observed indicating that less precise measurements for one pollutant (NO2) yield more bias, while the co-pollutant (PM2.5) associations were found closer to the true. Interestingly, the sum of single-pollutant model effect estimates was found closer to the summed true associations than those from multi-pollutant models, due to cancelling out of confounding and measurement error bias.ConclusionsOur simulation study indicated an underestimation of true independent health effects of multiple exposures due to measurement error. Using error parameter information in future epidemiological studies should provide more accurate concentration-response functions.

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Health effects of multi-pollutant profiles.

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Health effects of multi-pollutant profiles.

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