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. 2020 Jul 17;6(29):eaba5692.
doi: 10.1126/sciadv.aba5692. eCollection 2020 Jul.

Evaluating the impact of long-term exposure to fine particulate matter on mortality among the elderly

X Wu  1 , D Braun  1   2 , J Schwartz  3 , M A Kioumourtzoglou  4 , F Dominici  1
Affiliations

Evaluating the impact of long-term exposure to fine particulate matter on mortality among the elderly

X Wu et al. Sci Adv. .

Abstract

Many studies link long-term fine particle (PM2.5) exposure to mortality, even at levels below current U.S. air quality standards (12 micrograms per cubic meter). These findings have been disputed with claims that the use of traditional statistical approaches does not guarantee causality. Leveraging 16 years of data-68.5 million Medicare enrollees-we provide strong evidence of the causal link between long-term PM2.5 exposure and mortality under a set of causal inference assumptions. Using five distinct approaches, we found that a decrease in PM2.5 (by 10 micrograms per cubic meter) leads to a statistically significant 6 to 7% decrease in mortality risk. Based on these models, lowering the air quality standard to 10 micrograms per cubic meter would save 143,257 lives (95% confidence interval, 115,581 to 170,645) in one decade. Our study provides the most comprehensive evidence to date of the link between long-term PM2.5 exposure and mortality, even at levels below current standards.

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Figures

Fig. 1
Fig. 1. Annual average PM2.5 concentrations in the continental United States for 2000 and 2016.
Fig. 2
Fig. 2. Mean AC for unadjusted, weighted, and matched populations.
Mean AC was smaller than 0.1 using causal inference GPS methods (matching and weighting). AC values of <0.1 indicate good covariate balance, strengthening the interpretability and validity of our analyses as providing evidence of causality. BMI, body mass index.
Fig. 3
Fig. 3. HR and 95% CIs.
The estimated HRs were obtained under five different statistical approaches (two traditional approaches and three causal inference approaches). HRs were adjusted by 10 potential confounders, four meteorological variables, geographic region, and year.
See this image and copyright information in PMC

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