BackgroundAlthough the association between exposure to particulate matter and health is well established, there remains uncertainty as to whether certain chemical components are more harmful than others. We explored whether the association between cause-specific hospital admissions and PM2.5 was modified by PM2.5 chemical composition.MethodsWe estimated the association between daily PM2.5 and emergency hospital admissions for cardiac causes (CVD), myocardial infarction (MI), congestive heart failure (CHF), respiratory disease, and diabetes in 26 US communities, for the years 2000-2003. Using meta-regression, we examined how this association was modified by season- and community-specific PM2.5 composition, controlling for seasonal temperature as a surrogate for ventilation.ResultsFor a 10 μg/m3 increase in 2-day averaged PM2.5 concentration we found an increase of 1.89% (95% CI: 1.34- 2.45) in CVD, 2.25% (95% CI: 1.10- 3.42) in MI, 1.85% (95% CI: 1.19- 2.51) in CHF, 2.74% (95% CI: 1.30- 4.2) in diabetes, and 2.07% (95% CI: 1.20- 2.95) in respiratory admissions. The association between PM2.5 and CVD admissions was significantly modified when the mass was high in Br, Cr, Ni, and Na+, while mass high in As, Cr, Mn, OC, Ni, and Na+ modified MI, and mass high in As, OC, and SO42- modified diabetes admissions. For these species, an interquartile range increase in their relative proportion was associated with a 1-2% additional increase in daily admissions per 10 μg/m3 increase in mass.ConclusionsWe found that PM2.5 mass higher in Ni, As, and Cr, as well as Br and OC significantly increased its effect on hospital admissions. This result suggests that particles from industrial combustion sources and traffic may, on average, have greater toxicity.

While fine mode particulate matter (PM 2.5 ) forms the basis for regulating particles in the US and other countries, there is a serious paucity of large population-based studies of its acute effect on mortality. To address this issue, we examined the association between PM 2.5 and both all-cause and specificcause mortality using over 1.3 million deaths in 27 US communities between 1997 and 2002. A two-stage approach was used. First, the association between PM 2.5 and mortality in each community was quantified using a case-crossover design. Second, meta-analysis was used to estimate a summary effect over all 27 communities. Effect modification of age and gender was examined using interaction terms in the case-crossover model, while effect modification of community-specific characteristics including geographic location, annual PM 2.5 concentration above 15 mg/m 3 and central air conditioning prevalence was examined using meta-regression. We observed a 1.21% (95% CI 0.29, 2.14%) increase in all-cause mortality, a 1.78% (95% CI 0.20, 3.36%) increase in respiratory related mortality and a 1.03% (95% CI 0.02, 2.04%) increase in stroke related mortality with a 10 mg/m 3 increase in previous day's PM 2.5 . The magnitude of these associations is more than triple that recently reported for PM 10 , suggesting that combustion and traffic related particles are more toxic than larger sized particles. Effect modification occurred in all-cause and specific-cause deaths with greater effects in subjects Z75 years of age. There was suggestive evidence that women may be more susceptible to PM 2.5 effects than men, and that effects were larger in the East than in the West. Increased prevalence of central air conditioning was associated with a decreased effect of PM 2.5 . Our findings describe the magnitude of the effect on all-cause and specific-cause mortality, the modifiers of this association, and suggest that PM 2.5 may pose a public health risk even at or below current ambient levels.

Background Although the association between exposure to particulate matter (PM) mass and mortality is well established, there remains uncertainty about which chemical components of PM are most harmful to human health. Methods A hierarchical approach was used to determine how the association between daily PM2.5 mass and mortality was modified by PM2.5 composition in 25 US communities. First, the association between daily PM2.5 and mortality was determined for each community and season using Poisson regression. Second, we used meta-regression to examine how the pooled association was modified by community and season-specific particle composition. Results There was a 0.74% (95% confidence interval = 0.41%–1.07%) increase in nonaccidental deaths associated with a 10 μg/m3 increase in 2-day averaged PM2.5 mass concentration. This association was smaller in the west (0.51% [0.10%– 0.92%]) than in the east (0.92% [0.23%–1.36%]), and was highest in spring (1.88% [0.23%–1.36%]). It was increased when PM2.5 mass contained a higher proportion of aluminum (interquartile range = 0.58%), arsenic (0.55%), sulfate (0.51%), silicon (0.41%), and nickel (0.37%). The combination of aluminum, sulfate, and nickel also modified the effect. These species proportions explained residual variability between the community-specific PM2.5 mass effect estimates. Conclusions This study shows that certain chemical species modify the association between PM2.5 and mortality and illustrates that mass alone is not a sufficient metric when evaluating health effects of PM exposure.

BackgroundLog-binomial and robust (modified) Poisson regression models are popular approaches to estimate risk ratios for binary response variables. Previous studies have shown that comparatively they produce similar point estimates and standard errors. However, their performance under model misspecification is poorly understood.MethodsIn this simulation study, the statistical performance of the two models was compared when the log link function was misspecified or the response depended on predictors through a non-linear relationship (i.e. truncated response).ResultsPoint estimates from log-binomial models were biased when the link function was misspecified or when the probability distribution of the response variable was truncated at the right tail. The percentage of truncated observations was positively associated with the presence of bias, and the bias was larger if the observations came from a population with a lower response rate given that the other parameters being examined were fixed. In contrast, point estimates from the robust Poisson models were unbiased.ConclusionUnder model misspecification, the robust Poisson model was generally preferable because it provided unbiased estimates of risk ratios.Electronic supplementary materialThe online version of this article (10.1186/s12874-018-0519-5) contains supplementary material, which is available to authorized users.

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