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. 2014 Apr;122(4):397-403.
doi: 10.1289/ehp.1307049. Epub 2014 Feb 11.

An integrated risk function for estimating the global burden of disease attributable to ambient fine particulate matter exposure

Affiliations

An integrated risk function for estimating the global burden of disease attributable to ambient fine particulate matter exposure

Richard T Burnett et al. Environ Health Perspect. 2014 Apr.

Erratum in

  • Environ Health Perspect. 2014 Sep;122(9):A235

Abstract

Background: Estimating the burden of disease attributable to long-term exposure to fine particulate matter (PM2.5) in ambient air requires knowledge of both the shape and magnitude of the relative risk (RR) function. However, adequate direct evidence to identify the shape of the mortality RR functions at the high ambient concentrations observed in many places in the world is lacking.

Objective: We developed RR functions over the entire global exposure range for causes of mortality in adults: ischemic heart disease (IHD), cerebrovascular disease (stroke), chronic obstructive pulmonary disease (COPD), and lung cancer (LC). We also developed RR functions for the incidence of acute lower respiratory infection (ALRI) that can be used to estimate mortality and lost-years of healthy life in children < 5 years of age.

Methods: We fit an integrated exposure-response (IER) model by integrating available RR information from studies of ambient air pollution (AAP), second hand tobacco smoke, household solid cooking fuel, and active smoking (AS). AS exposures were converted to estimated annual PM2.5 exposure equivalents using inhaled doses of particle mass. We derived population attributable fractions (PAFs) for every country based on estimated worldwide ambient PM2.5 concentrations.

Results: The IER model was a superior predictor of RR compared with seven other forms previously used in burden assessments. The percent PAF attributable to AAP exposure varied among countries from 2 to 41 for IHD, 1 to 43 for stroke, < 1 to 21 for COPD, < 1 to 25 for LC, and < 1 to 38 for ALRI.

Conclusions: We developed a fine particulate mass-based RR model that covered the global range of exposure by integrating RR information from different combustion types that generate emissions of particulate matter. The model can be updated as new RR information becomes available.

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Conflict of interest statement

A.C. is employed by the Health Effects Institute (HEI). The HEI receives about half of its core funds from the U.S. Environmental Protection Agency and half from the worldwide motor vehicle industry, although other public and private organizations periodically support special projects or certain research programs. The views expressed in this article do not represent those of the HEI or its sponsors. A.P.-U. is a staff member of the World Health Organization (WHO), and the views expressed in this article do not necessarily represent the views, decisions, or policies of the WHO. This article should not be reproduced for use in association with the promotion of commercial products, services, or any legal entity. The WHO does not endorse any specific organization or products. Any reproduction of this article cannot include the use of the WHO logo.

The authors declare they have no actual or potential competing financial interests.

Figures

Figure 1
Figure 1
Predicted values of IER model (solid line) and 95% CIs (dashed line) and type-specific RRs (points) and 95% CIs (error bars) for IHD (A), stroke (B), COPD (C), and LC (D) mortality. Shaded boxes for COPD and LC mortality represent uncertainty (height) and exposure contrast (width) of RR HAP estimates for males (smaller boxes) and females (larger boxes) separately.
Figure 2
Figure 2
Predicted values of IER model (solid line) and 95% CIs (dashed line) and type-specific RRs (points) and 95% CIs (error bars) for ALRI in infants.
Figure 3
Figure 3
Density plots of country-specific, population-weighted PM2.5 concentrations (μg/m3) (A) and PAFs (B) by risk model and health outcome. Dashed lines represent smooth fit of density function.

Comment in

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