Current Methods and Challenges for Epidemiological Studies of the Associations Between Chemical Constituents of Particulate Matter and Health

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Abstract

<p class="first" id="P1">Epidemiological studies have been critical for estimating associations between exposure to ambient particulate matter (PM) air pollution and adverse health outcomes. Because total PM mass is a temporally and spatially varying mixture of constituents with different physical and chemical properties, recent epidemiological studies have focused on PM constituents. Most studies have estimated associations between PM constituents and health using the same statistical methods as in studies of PM mass. However, these approaches may not be sufficient to address challenges specific to studies of PM constituents, namely assigning exposure, disentangling health effects, and handling measurement error. We reviewed large, population-based epidemiological studies of PM constituents and health and describe the statistical methods typically applied to address these challenges. Development of statistical methods that simultaneously address multiple challenges, for example, both disentangling health effects and handling measurement error, could improve estimation of associations between PM constituents and adverse health outcomes. </p>

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Exposure measurement error in time-series studies of air pollution: concepts and consequences.

S Zeger, Owen D Thomas, F Dominici … (2000)

Misclassification of exposure is a well-recognized inherent limitation of epidemiologic studies of disease and the environment. For many agents of interest, exposures take place over time and in multiple locations; accurately estimating the relevant exposures for an individual participant in epidemiologic studies is often daunting, particularly within the limits set by feasibility, participant burden, and cost. Researchers have taken steps to deal with the consequences of measurement error by limiting the degree of error through a study's design, estimating the degree of error using a nested validation study, and by adjusting for measurement error in statistical analyses. In this paper, we address measurement error in observational studies of air pollution and health. Because measurement error may have substantial implications for interpreting epidemiologic studies on air pollution, particularly the time-series analyses, we developed a systematic conceptual formulation of the problem of measurement error in epidemiologic studies of air pollution and then considered the consequences within this formulation. When possible, we used available relevant data to make simple estimates of measurement error effects. This paper provides an overview of measurement errors in linear regression, distinguishing two extremes of a continuum-Berkson from classical type errors, and the univariate from the multivariate predictor case. We then propose one conceptual framework for the evaluation of measurement errors in the log-linear regression used for time-series studies of particulate air pollution and mortality and identify three main components of error. We present new simple analyses of data on exposures of particulate matter < 10 microm in aerodynamic diameter from the Particle Total Exposure Assessment Methodology Study. Finally, we summarize open questions regarding measurement error and suggest the kind of additional data necessary to address them. Images Figure 1 Figure 2 Figure 3

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Spatial and Temporal Variation in PM2.5 Chemical Composition in the United States for Health Effects Studies

Michelle L. Bell, Francesca Dominici, Keita Ebisu … (2007)

Background Although numerous studies have demonstrated links between particulate matter (PM) and adverse health effects, the chemical components of the PM mixture that cause injury are unknown. Objectives This work characterizes spatial and temporal variability of PM2.5 (PM with aerodynamic diameter < 2.5 μm) components in the United States; our objective is to identify components for assessment in epidemiologic studies. Methods We constructed a database of 52 PM2.5 component concentrations for 187 U.S. counties for 2000–2005. First, we describe the challenges inherent to analysis of a national PM2.5 chemical composition database. Second, we identify components that contribute substantially to and/or co-vary with PM2.5 total mass. Third, we characterize the seasonal and regional variability of targeted components. Results Strong seasonal and geographic variations in PM2.5 chemical composition are identified. Only seven of the 52 components contributed ≥ 1% to total mass for yearly or seasonal averages [ammonium (NH4 +), elemental carbon (EC), organic carbon matter (OCM), nitrate (NO3 −), silicon, sodium (Na+), and sulfate (SO4 2−)]. Strongest correlations with PM2.5 total mass were with NH4 + (yearly), OCM (especially winter), NO3 − (winter), and SO4 2− (yearly, spring, autumn, and summer), with particularly strong correlations for NH4 + and SO4 2− in summer. Components that co-varied with PM2.5 total mass, based on daily detrended data, were NH4 +, SO4 2− , OCM, NO3 2−, bromine, and EC. Conclusions The subset of identified PM2.5 components should be investigated further to determine whether their daily variation is associated with daily variation of health indicators, and whether their seasonal and regional patterns can explain the seasonal and regional heterogeneity in PM10 (PM with aerodynamic diameter < 10 μm) and PM2.5 health risks.

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The Effect of Fine and Coarse Particulate Air Pollution on Mortality: A National Analysis

Antonella Zanobetti, Joel Schwartz (2009)

Background Although many studies have examined the effects of air pollution on mortality, data limitations have resulted in fewer studies of both particulate matter with an aerodynamic diameter of ≤ 2.5 μm (PM2.5; fine particles) and of coarse particles (particles with an aerodynamic diameter > 2.5 and < 10 μm; PM coarse). We conducted a national, multicity time-series study of the acute effect of PM2.5 and PM coarse on the increased risk of death for all causes, cardiovascular disease (CVD), myocardial infarction (MI), stroke, and respiratory mortality for the years 1999–2005. Method We applied a city- and season-specific Poisson regression in 112 U.S. cities to examine the association of mean (day of death and previous day) PM2.5 and PM coarse with daily deaths. We combined the city-specific estimates using a random effects approach, in total, by season and by region. Results We found a 0.98% increase [95% confidence interval (CI), 0.75–1.22] in total mortality, a 0.85% increase (95% CI, 0.46–1.24) in CVD, a 1.18% increase (95% CI, 0.48–1.89) in MI, a 1.78% increase (95% CI, 0.96–2.62) in stroke, and a 1.68% increase (95% CI, 1.04–2.33) in respiratory deaths for a 10-μg/m3 increase in 2-day averaged PM2.5. The effects were higher in spring. For PM coarse, we found significant but smaller increases for all causes analyzed. Conclusions We conclude that our analysis showed an increased risk of mortality for all and specific causes associated with PM2.5, and the risks are higher than what was previously observed for PM10. In addition, coarse particles are also associated with more deaths.