Short-term exposure to PM2.5 increases the risk for hospital admission for cardiovascular and respiratory diseases.

No abstract

The widely used generalized additive models (GAM) method is a flexible and effective technique for conducting nonlinear regression analysis in time-series studies of the health effects of air pollution. When the data to which the GAM are being applied have two characteristics--1) the estimated regression coefficients are small and 2) there exist confounding factors that are modeled using at least two nonparametric smooth functions--the default settings in the gam function of the S-Plus software package (version 3.4) do not assure convergence of its iterative estimation procedure and can provide biased estimates of regression coefficients and standard errors. This phenomenon has occurred in time-series analyses of contemporary data on air pollution and mortality. To evaluate the impact of default implementation of the gam software on published analyses, the authors reanalyzed data from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) using three different methods: 1) Poisson regression with parametric nonlinear adjustments for confounding factors; 2) GAM with default convergence parameters; and 3) GAM with more stringent convergence parameters than the default settings. The authors found that pooled NMMAPS estimates were very similar under the first and third methods but were biased upward under the second method.

BackgroundPopulation-based studies have estimated health risks of short-term exposure to fine particles using mass of PM2.5 (particulate matter ≤ 2.5 μm in aerodynamic diameter) as the indicator. Evidence regarding the toxicity of the chemical components of the PM2.5 mixture is limited.ObjectiveIn this study we investigated the association between hospital admission for cardiovascular disease (CVD) and respiratory disease and the chemical components of PM2.5 in the United States.MethodsWe used a national database comprising daily data for 2000–2006 on emergency hospital admissions for cardiovascular and respiratory outcomes, ambient levels of major PM2.5 chemical components [sulfate, nitrate, silicon, elemental carbon (EC), organic carbon matter (OCM), and sodium and ammonium ions], and weather. Using Bayesian hierarchical statistical models, we estimated the associations between daily levels of PM2.5 components and risk of hospital admissions in 119 U.S. urban communities for 12 million Medicare enrollees (≥ 65 years of age).ResultsIn multiple-pollutant models that adjust for the levels of other pollutants, an interquartile range (IQR) increase in EC was associated with a 0.80% [95% posterior interval (PI), 0.34–1.27%] increase in risk of same-day cardiovascular admissions, and an IQR increase in OCM was associated with a 1.01% (95% PI, 0.04–1.98%) increase in risk of respiratory admissions on the same day. Other components were not associated with cardiovascular or respiratory hospital admissions in multiple-pollutant models.ConclusionsAmbient levels of EC and OCM, which are generated primarily from vehicle emissions, diesel, and wood burning, were associated with the largest risks of emergency hospitalization across the major chemical constituents of PM2.5.

EGULATORY CONTROL OF AIRborne particulate matter is hindered by an uncertain understanding of the toxicity of the particulate matter mixture. The National Research Council's Committee on Research Priorities for Airborne Particulate Matter identified the limited information on the health effects of particulate matter characteristics, including size, as a key area for research. 1 Numerous epidemiological studies have been published on risks associated with particulate matter that is 10 µm or less in diameter (PM 10 ). 2 More recent work has focused on particulate matter that is 2.5 µm or less in diameter (PM 2.5 ), for which strong evidence of an association with mortality and morbidity has been found. 3,4 Research on the health effects of coarse thoracic particles in the size range of greater than 2.5 µm and 10 µm or less in diameter (PM 10-2.5 ) is limited and findings have been mixed. 5 The chemical composition of particulate matter differs by size with more crustal materials in PM 10-2.5 and more combustionrelated constituents in PM 2.5 . [6][7][8] The health effects associated with ambient exposure to PM 10-2.5 could differ from those of PM 2.5 given differences in the sites of deposition in the respiratory tract and the sources and chemical composition for these 2 different-sized fractions.Coarse particles, which are produced primarily by processes such as mechanical grinding, windblown dust, and agricultural activities, deposit pref-Author Affiliations: Departments of Biostatistics