Background
Associations between long-term exposure to ambient particulate matter (PM) and cardiovascular (CVD) mortality have been widely recognized. However, health effects of long-term exposure to constituents of PM on total CVD mortality have been explored in a single study only.
Aims
The aim of this study was to examine the association of PM composition with cardiovascular mortality.
Methods
We used data from 19 European ongoing cohorts within the framework of the ESCAPE (European Study of Cohorts for Air Pollution Effects) and TRANSPHORM (Transport related Air Pollution and Health impacts — Integrated Methodologies for Assessing Particulate Matter) projects. Residential annual average exposure to elemental constituents within particle matter smaller than 2.5 and 10 μm (PM2.5 and PM10) was estimated using Land Use Regression models. Eight elements representing major sources were selected a priori (copper, iron, potassium, nickel, sulfur, silicon, vanadium and zinc). Cohort-specific analyses were conducted using Cox proportional hazards models with a standardized protocol. Random-effects meta-analysis was used to calculate combined effect estimates.
Results
The total population consisted of 322,291 participants, with 9545 CVD deaths. We found no statistically significant associations between any of the elemental constituents in PM2.5 or PM10 and CVD mortality in the pooled analysis. Most of the hazard ratios (HRs) were close to unity, e.g. for PM10 Fe the combined HR was 0.96 (0.84–1.09). Elevated combined HRs were found for PM2.5 Si (1.17, 95% CI: 0.93–1.47), and S in PM2.5 (1.08, 95% CI: 0.95–1.22) and PM10 (1.09, 95% CI: 0.90–1.32).
Conclusion
In a joint analysis of 19 European cohorts, we found no statistically significant association between long-term exposure to 8 elemental constituents of particles and total cardiovascular mortality.
Long-term exposure to air pollution and cardiovascular mortality: an analysis of 22 European cohorts
(2014)
Background
Land-use regression (LUR) and dispersion models (DM) are commonly used for estimating individual air pollution exposure in population studies. Few comparisons have however been made of the performance of these methods.
Objectives
Within the European Study of Cohorts for Air Pollution Effects (ESCAPE) we explored the differences between LUR and DM estimates for NO2, PM10 and PM2.5.
Methods
The ESCAPE study developed LUR models for outdoor air pollution levels based on a harmonised monitoring campaign. In thirteen ESCAPE study areas we further applied dispersion models. We compared LUR and DM estimates at the residential addresses of participants in 13 cohorts for NO2; 7 for PM10 and 4 for PM2.5. Additionally, we compared the DM estimates with measured concentrations at the 20–40 ESCAPE monitoring sites in each area.
Results
The median Pearson R (range) correlation coefficients between LUR and DM estimates for the annual average concentrations of NO2, PM10 and PM2.5 were 0.75 (0.19–0.89), 0.39 (0.23–0.66) and 0.29 (0.22–0.81) for 112,971 (13 study areas), 69,591 (7) and 28,519 (4) addresses respectively. The median Pearson R correlation coefficients (range) between DM estimates and ESCAPE measurements were of 0.74 (0.09–0.86) for NO2; 0.58 (0.36–0.88) for PM10 and 0.58 (0.39–0.66) for PM2.5.
Conclusions
LUR and dispersion model estimates correlated on average well for NO2 but only moderately for PM10 and PM2.5, with large variability across areas. DM predicted a moderate to large proportion of the measured variation for NO2 but less for PM10 and PM2.5.
Background: Long-term exposure to particulate matter (PM) has been associated with increased cardiovascular morbidity and mortality but little is known about the role of the chemical composition of PM. This study examined the association of residential long-term exposure to PM components with incident coronary events.
Methods: Eleven cohorts from Finland, Sweden, Denmark, Germany, and Italy participated in this analysis. 5,157 incident coronary events were identified within 100,166 persons followed on average for 11.5 years. Long-term residential concentrations of PM < 10 μm (PM10), PM < 2.5 μm (PM2.5), and a priori selected constituents (copper, iron, nickel, potassium, silicon, sulfur, vanadium, and zinc) were estimated with land-use regression models. We used Cox proportional hazard models adjusted for a common set of confounders to estimate cohort-specific component effects with and without including PM mass, and random effects meta-analyses to pool cohort-specific results.
Results: A 100 ng/m³ increase in PM10 K and a 50 ng/m³ increase in PM2.5 K were associated with a 6% (hazard ratio and 95% confidence interval: 1.06 [1.01, 1.12]) and 18% (1.18 [1.06, 1.32]) increase in coronary events. Estimates for PM10 Si and PM2.5 Fe were also elevated. All other PM constituents indicated a positive association with coronary events. When additionally adjusting for PM mass, the estimates decreased except for K.
Conclusions: This multicenter study of 11 European cohorts pointed to an association between long-term exposure to PM constituents and coronary events, especially for indicators of road dust.
