Dataset Information

Air pollution and mortality in a large, representative U.S. cohort: multiple-pollutant analyses, and spatial and temporal decompositions.

ABSTRACT:

Background

Cohort studies have documented associations between fine particulate matter air pollution (PM_2.5) and mortality risk. However, there remains uncertainty regarding the contribution of co-pollutants and the stability of pollution-mortality associations in models that include multiple air pollutants. Furthermore, it is unclear whether the PM_2.5-mortality relationship varies spatially, when exposures are decomposed according to scale of spatial variability, or temporally, when effect estimates are allowed to change between years.

Methods

A cohort of 635,539 individuals was compiled using public National Health Interview Survey (NHIS) data from 1987 to 2014 and linked with mortality follow-up through 2015. Modelled air pollution exposure estimates for PM_2.5, other criteria air pollutants, and spatial decompositions (< 1 km, 1-10 km, 10-100 km, > 100 km) of PM_2.5 were assigned at the census-tract level. The NHIS samples were also divided into yearly cohorts for temporally-decomposed analyses. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) in regression models that included up to six criteria pollutants; four spatial decompositions of PM_2.5; and two- and five-year lagged mean PM_2.5 exposures in the temporally-decomposed cohorts. Meta-analytic fixed-effect estimates were calculated using results from temporally-decomposed analyses and compared with time-independent results using 17- and 28-year exposure windows.

Results

In multiple-pollutant analyses, PM_2.5 demonstrated the most robust pollutant-mortality association. Coarse fraction particulate matter (PM_2.5-10) and sulfur dioxide (SO₂) were also associated with excess mortality risk. The PM_2.5-mortality association was observed across all four spatial scales of PM_2.5, with higher but less precisely estimated HRs observed for local (< 1 km) and neighborhood (1-10 km) variations. In temporally-decomposed analyses, the PM_2.5-mortality HRs were stable across yearly cohorts. The meta-analytic HR using two-year lagged PM_2.5 equaled 1.10 (95% CI 1.07, 1.13) per 10 μg/m³. Comparable results were observed in time-independent analyses using a 17-year (HR 1.13, CI 1.09, 1.16) or 28-year (HR 1.09, CI 1.07, 1.12) exposure window.

Conclusions

Long-term exposures to PM_2.5, PM_2.5-10, and SO₂ were associated with increased risk of all-cause and cardiopulmonary mortality. Each spatial decomposition of PM_2.5 was associated with mortality risk, and PM_2.5-mortality associations were consistent over time.

SUBMITTER: Lefler JS

PROVIDER: S-EPMC6873509 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Publications

Air pollution and mortality in a large, representative U.S. cohort: multiple-pollutant analyses, and spatial and temporal decompositions.

Lefler Jacob S JS Higbee Joshua D JD Burnett Richard T RT Ezzati Majid M Coleman Nathan C NC Mann Dalton D DD Marshall Julian D JD Bechle Matthew M Wang Yuzhou Y Robinson Allen L AL Arden Pope C C

Environmental health : a global access science source 20191121 1

<h4>Background</h4>Cohort studies have documented associations between fine particulate matter air pollution (PM<sub>2.5</sub>) and mortality risk. However, there remains uncertainty regarding the contribution of co-pollutants and the stability of pollution-mortality associations in models that include multiple air pollutants. Furthermore, it is unclear whether the PM<sub>2.5</sub>-mortality relationship varies spatially, when exposures are decomposed according to scale of spatial variability, o ...[more]

PMID: 31752939

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Dataset Information

Air pollution and mortality in a large, representative U.S. cohort: multiple-pollutant analyses, and spatial and temporal decompositions.

Background

Methods

Results

Conclusions

Publications

Air pollution and mortality in a large, representative U.S. cohort: multiple-pollutant analyses, and spatial and temporal decompositions.

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