Abstract
BackgroundIn time-series studies of the health effects of urban air pollutants, decisions must be made about how to characterize pollutant levels within the airshed.MethodsEmergency department visits for pediatric asthma exacerbations were collected from Atlanta hospitals. Concentrations of carbon monoxide, nitrogen dioxide, ozone, sulfur dioxide, particulate matter less than 10 microns in diameter (PM10), particulate matter less than 2.5 microns in diameter (PM2.5), and the PM2.5 components elemental carbon, organic carbon, and sulfate were obtained from networks of ambient air quality monitors. For each pollutant we created three different daily metrics. For one metric we used the measurements from a centrally-located monitor; for the second we averaged measurements across the network of monitors; and for the third we estimated the population-weighted average concentration using an isotropic spatial model. Rate ratios for each of the metrics were estimated from time-series models.ResultsFor pollutants with relatively homogeneous spatial distributions we observed only small differences in the rate ratio across the three metrics. Conversely, for spatially heterogeneous pollutants we observed larger differences in the rate ratios. For a given pollutant, the strength of evidence for an association (i.e., chi-square statistics) tended to be similar across metrics.ConclusionsGiven that the chi-square statistics were similar across the metrics, the differences in the rate ratios for the spatially heterogeneous pollutants may seem like a relatively small issue. However, these differences are important for health benefits analyses, where results from epidemiological studies on the health effects of pollutants (per unit change in concentration) are used to predict the health impacts of a reduction in pollutant concentrations. We discuss the relative merits of the different metrics as they pertain to time-series studies and health benefits analyses.
Highlights
In time-series studies of the health effects of urban air pollutants, decisions must be made about how to characterize pollutant levels within the airshed
Even though the means and interquartile range (IQR) for a given pollutant may have differed across metrics, the metrics were all well-correlated over time
Because the spatial distribution of pollutant concentrations was similar from one day to the the three metrics were well-correlated over time; the chi-square statistics from the time-series models were similar across metrics
Summary
In time-series studies of the health effects of urban air pollutants, decisions must be made about how to characterize pollutant levels within the airshed. Results from epidemiological studies on the health effects of ambient air pollutant concentrations figure prominently in these processes. One fundamental issue for time-series studies is how to characterize measurements of ambient air pollutant levels within the airshed; even in our ongoing Study of Particles and Health in Atlanta (SOPHIA), in which we have been investigating the short-term effects of ambient air pollutant concentrations on a broad range of health outcomes, we have at times characterized pollutant concentrations using measurements from a centrally-located monitoring station [3,4,5], and at other times have averaged concentrations across monitors using populationweighting [6,7]. The time series that result from these approaches may be well-correlated, the distributions of pollutant concentrations may differ, and these differences can affect the concentration-response estimates from the epidemiologic models. Rescaling the estimates in this manner has the disadvantage of tying the interpretation of the effect estimate to the distribution of the pollutant metric used in that study
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