Associations between long-term exposure to chemical constituents of fine particulate matter (PM2.5) and mortality in Medicare enrollees in the eastern United States

Abstract

Background: Several epidemiological studies have reported that long-term exposure to fine particulate matter (PM2.5) is associated with higher mortality. Evidence regarding contributions of PM2.5 constituents is inconclusive.

Objectives: We assembled a data set of 12.5 million Medicare enrollees (≥ 65 years of age) to determine which PM2.5 constituents are a) associated with mortality controlling for previous-year PM2.5 total mass (main effect); and b) elevated in locations exhibiting stronger associations between previous-year PM2.5 and mortality (effect modification).

Methods: For 518 PM2.5 monitoring locations (eastern United States, 2000-2006), we calculated monthly mortality rates, monthly long-term (previous 1-year average) PM2.5, and 7-year averages (2000-2006) of major PM2.5 constituents [elemental carbon (EC), organic carbon matter (OCM), sulfate (SO42-), silicon (Si), nitrate (NO3-), and sodium (Na)] and community-level variables. We applied a Bayesian hierarchical model to estimate location-specific mortality rates associated with previous-year PM2.5 (model level 1) and identify constituents that contributed to the spatial variability of mortality, and constituents that modified associations between previous-year PM2.5 and mortality (model level 2), controlling for community-level confounders.

Results: One-standard deviation (SD) increases in 7-year average EC, Si, and NO3- concentrations were associated with 1.3% [95% posterior interval (PI): 0.3, 2.2], 1.4% (95% PI: 0.6, 2.4), and 1.2% (95% PI: 0.4, 2.1) increases in monthly mortality, controlling for previous-year PM2.5. Associations between previous-year PM2.5 and mortality were stronger in combination with 1-SD increases in SO42- and Na.

Conclusions: Long-term exposures to PM2.5 and several constituents were associated with mortality in the elderly population of the eastern United States. Moreover, some constituents increased the association between long-term exposure to PM2.5 and mortality. These results provide new evidence that chemical composition can partly explain the differential toxicity of PM2.5.

Figure 1

Map of 518 PM_2.5 monitor locations and 174 PM_2.5 chemical constituent monitor locations in the eastern region of the United States.

Figure 2

(A) Map of 7-year (2000–2006) averages of monthly long-term (previous 1-year average) PM_2.5 exposure (μg/m³) for all PM_2.5 monitor locations (n = 518). (B) Map of 7-year (2000–2006) averages of monthly mortality rate (deaths/month/1,000 persons) for all PM_2.5 monitor locations (n = 518).

Figure 3

Maps of 7-year (2000–2006) averages of each of the six PM_2.5 chemical constituents (μg/m³) for 241 monitor locations with available data.

Figure 4

(A) Maps of the estimates (posterior means) of the SV intercept from the complete-case data analysis (n = 241, left) and the all-sites data analysis (n = 518, right). The values represent the monthly mortality rate (deaths/month/1,000 persons) when previous-year PM_2.5 is at location-specific average. (B) Maps of the estimates (posterior means) of the SV slope from the complete-case data analysis (n = 241, left) and the all-sites data analysis (n = 518, right). The values represent the percent increase in the monthly mortality rate associated with a 1-μg/m³ increase in previous-year PM_2.5.

Figure 5

(A) Posterior estimates with 95% posterior intervals for the β_k regression coefficients in the second-level SV intercept model. Solid error bars are for the complete-case data (n = 241) and dashed error bars are for the all-sites data (n = 518). Values correspond to the estimated percentage increase in monthly mortality rate associated with a 1-SD increase in each constituent, adjusted for previous-year average of PM_2.5 total mass and for community-level covariates. (B) Posterior estimates with 95% posterior intervals for the γ_k regression coefficients in the second-level SV slope model. Solid error bars are for the complete-case data (n = 241) and dashed error bars are for the all-sites data (n = 518). Values correspond to the estimated percentage increase in the association between previous-year average of PM_2.5 and mortality when combined with a 1-SD increase in each constituent, adjusted for community level covariates.