Global estimates of mortality associated with long-term exposure to outdoor fine particulate matter.

Exposure to ambient fine particulate matter (PM2.5) is a major global health concern. Quantitative estimates of attributable mortality are based on disease-specific hazard ratio models that incorporate risk information from multiple PM2.5 sources (outdoor and indoor air pollution from use of solid fuels and secondhand and active smoking), requiring assumptions about equivalent exposure and toxicity. We relax these contentious assumptions by constructing a PM2.5-mortality hazard ratio function based only on cohort studies of outdoor air pollution that covers the global exposure range. We modeled the shape of the association between PM2.5 and nonaccidental mortality using data from 41 cohorts from 16 countries-the Global Exposure Mortality Model (GEMM). We then constructed GEMMs for five specific causes of death examined by the global burden of disease (GBD). The GEMM predicts 8.9 million [95% confidence interval (CI): 7.5-10.3] deaths in 2015, a figure 30% larger than that predicted by the sum of deaths among the five specific causes (6.9; 95% CI: 4.9-8.5) and 120% larger than the risk function used in the GBD (4.0; 95% CI: 3.3-4.8). Differences between the GEMM and GBD risk functions are larger for a 20% reduction in concentrations, with the GEMM predicting 220% higher excess deaths. These results suggest that PM2.5 exposure may be related to additional causes of death than the five considered by the GBD and that incorporation of risk information from other, nonoutdoor, particle sources leads to underestimation of disease burden, especially at higher concentrations.

Confounding by Socioeconomic Status in Epidemiological Studies of Air Pollution and Health: Challenges and Opportunities.

BACKGROUND: Despite a vast air pollution epidemiology literature to date and the recognition that lower-socioeconomic status (SES) populations are often disproportionately exposed to pollution, there is little research identifying optimal means of adjusting for confounding by SES in air pollution epidemiology, nor is there a strong understanding of biases that may result from improper adjustment. OBJECTIVE: We aim to provide a conceptualization of SES and a review of approaches to its measurement in the U.S. context and discuss pathways by which SES may influence health and confound effects of air pollution. We explore bias related to measurement and operationalization and identify statistical approaches to reduce bias and confounding. DISCUSSION: Drawing on the social epidemiology, health geography, and economic literatures, we describe how SES, a multifaceted construct operating through myriad pathways, may be conceptualized and operationalized in air pollution epidemiology studies. SES varies across individuals within the contexts of place, time, and culture. Although no single variable or index can fully capture SES, many studies rely on only a single measure. We recommend examining multiple facets of SES appropriate to the study design. Furthermore, investigators should carefully consider the multiple mechanisms by which SES might be operating to identify those SES indicators that may be most appropriate for a given context or study design and assess the impact of improper adjustment on air pollution effect estimates. Last, exploring model contraction and expansion methods may enrich adjustment, whereas statistical approaches, such as quantitative bias analysis, may be used to evaluate residual confounding. https://doi.org/10.1289/EHP7980.

Accountability Studies on Air Pollution and Health: the HEI Experience.

PURPOSE OF REVIEW: Assessing health effects of air quality interventions is of ever-increasing interest. Given the prominent role Health Effects Institute (HEI) has played in accountability research, this review focuses on HEI's recent experiences, the challenges it has encountered, and provides possible directions for future research. RECENT FINDINGS: Most accountability studies to date have focused on effects of relatively short-term, local-scale, and sometimes temporary interventions. Only a few recent accountability studies have sought to investigate large-scale, multiyear regulatory programs. Common challenges encountered include lack of statistical power, how to account appropriately for background trends in air quality and health, and difficulties in direct attribution of changes in air pollution and health to a single intervention among many regulatory actions. New methods have been developed for accountability research that has shown promise addressing some of those challenges, including use of causal inference methods. These and other approaches that would enhance the attribution of changes in air quality and health directly to an intervention should continue to be further explored. In addition, integration of social and behavioral sciences in accountability research is warranted, and climate related co-benefits and dis-benefits may be considered.

Use of expert judgment in exposure assessment: part 2. Calibration of expert judgments about personal exposures to benzene.

The recent movement of regulatory agencies toward probabilistic analyses of human health and environmental risks has focused greater attention on the quality of the estimates of variability and uncertainty that underlie them. Of particular concern is how uncertainty--a measure of what is not known--is characterized, as uncertainty can play an influential role in analyses of the need for regulatory controls or in estimates of the economic value of additional research. This paper reports the second phase of a study, conducted as an element of the National Human Exposure Assessment Survey (NHEXAS), to obtain and calibrate exposure assessment experts judgments about uncertainty in residential ambient, residential indoor, and personal air benzene concentrations experienced by the nonsmoking, nonoccupationally exposed population in U.S. EPA's Region V. Subjective judgments (i.e., the median, interquartile range, and 90% confidence interval) about the means and 90th percentiles of each of the benzene distributions were elicited from the seven experts participating in the study. The calibration or quality of the experts' judgments was assessed by comparing them to the actual measurements from the NHEXAS Region V study using graphical techniques, a quadratic scoring rule, and surprise and interquartile indices. The results from both quantitative scoring methods suggested that, considered collectively, the experts' judgments were relatively well calibrated although on balance, underconfident. The calibration of individual expert judgments appeared variable, highlighting potential pitfalls in reliance on individual experts. In a surprising finding, the experts' judgments about the 90th percentiles of the benzene distributions were better calibrated than their predictions about the means; the experts tended to be overconfident in their ability to predict the means. This paper is also one of the first calibration studies to demonstrate the importance of taking into account intraexpert correlation on the statistical significance of the findings. When the judgments were assumed to be independent, analysis of the surprise and interquartile indices found evidence of poor calibration (P<0.05). However, when the intraexpert correlation in the study was taken into account, these findings were no longer statistically significant. The analysis further found that the experts' judgments scored better than estimates of Region V benzene concentrations simply drawn from earlier studies of ambient, indoor and personal benzene levels in other U.S. cities. These results suggest the value of careful elicitation of expert judgments in characterizing exposures in probabilistic form. Additional calibration studies need to be undertaken to corroborate and extend these findings.

Expert judgment assessment of the mortality impact of changes in ambient fine particulate matter in the U.S.

In this paper, we present findings from a multiyear expert judgment study that comprehensively characterizes uncertainty in estimates of mortality reductions associated with decreases in fine particulate matter (PM(2.5)) in the U.S. Appropriate characterization of uncertainty is critical because mortality-related benefits represent up to 90% of the monetized benefits reported in the Environmental Protection Agency's (EPA's) analyses of proposed air regulations. Numerous epidemiological and toxicological studies have evaluated the PM(2.5)-mortality association and investigated issues that may contribute to uncertainty in the concentration-response (C-R) function, such as exposure misclassification and potential confounding from other pollutant exposures. EPA's current uncertainty analysis methods rely largely on standard errors in published studies. However, no one study can capture the full suite of issues that arise in quantifying the C-R relationship. Therefore, EPA has applied state-of-the-art expert judgment elicitation techniques to develop probabilistic uncertainty distributions that reflect the broader array of uncertainties in the C-R relationship. These distributions, elicited from 12 of the world's leading experts on this issue, suggest both potentially larger central estimates of mortality reductions for decreases in long-term PM(2.5) exposure in the U.S. and a wider distribution of uncertainty than currently employed in EPA analyses.