Academic journal article Environmental Health Perspectives

A National Multicity Analysis of the Causal Effect of Local Pollution, N[O.Sub.2], and P[M.Sub.2.5] on Mortality

Academic journal article Environmental Health Perspectives

A National Multicity Analysis of the Causal Effect of Local Pollution, N[O.Sub.2], and P[M.Sub.2.5] on Mortality

Article excerpt

Introduction

Hundreds of studies have reported associations between short-term exposure to air pollution and daily deaths (Baccini et al. 2006; Bell et al. 2004; Braga et al. 2001; Chen et al. 2014; Jhun et al. 2014; Katsouyanni et al. 1997; Katsouyanni et al. 2009; Levy et al. 2012; Peng et al. 2005; Peng et al. 2013; Samet et al. 2000; Schwartz 1991; Tao et al. 2012; Zanobetti et al. 2002; Zanobetti and Schwartz 2008, 2009). The most common findings are that associations with particulate air pollution and ozone exist and that these two exposures do not confound each other. Many toxicology and controlled human-exposure studies showing associations of these pollutants with changes in intermediary outcomes (blood pressure, inflammation, autonomic function, endothelial function, thrombosis, etc.) support those findings. (Bartoli et al. 2009b; Calderon-Garciduenas et al. 2008a; Fakhri et al. 2009; Langrish et al. 2009; Lundback et al. 2009; Matsumoto et al. 2010; O'Toole et al. 2010; Peretzetal. 2008).

More recently, studies have reported associations with nitrogen dioxide (N[O.sub.2]) with daily deaths, often while controlling for other pollutants (Mills et al. 2015). N[O.sub.2] is a byproduct of combustion and local traffic, particularly Diesel traffic, is a major source. This information has raised questions as to whether the N[O.sub.2] findings represent health effects of N[O.sub.2] itself, or if it acts as a surrogate for some other pollutant from traffic. To date, there has been less toxicological investigation as to how N[O.sub.2] might influence the processes that rapidly generate respiratory and cardiovascular deaths. However, N[O.sub.2] clearly deserves more attention than it has received.

Few time-series studies of acute effects of air pollution applied modern causal-modeling techniques. Causal modeling seeks to analyze observational data in a way that simulates conducting a randomized experiment. Randomization makes exposure independent of all potential confounders, and causal methods seek to replicate that situation, rather than to control for the confounders in the outcome regressions, as conventional analysis does. Under specified assumptions, including ones that are untestable in the data and rely on external knowledge, causal methods yield causal estimates of the effects of exposure. Often, they provide marginal estimates of the effects of exposure, that is, ones that are not conditional on the distribution of covariates and are therefore more generalizable.

Marginal structural models are the best known causal models in epidemiology, estimating the marginal effects of exposure by using inverse probability weights of time-varying exposures to render the exposure independent of the measured covariates. If the exposure is independent of covariates, its effect on the outcome cannot be confounded by them and resulting estimates do not depend on the distributions of confounders. If all important covariates are measured, these models provide causal estimates of the marginal effects of exposure.

Recently, we used an instrumental variable analysis to estimate the causal effects of locally generated air pollution in Boston (Schwartz et al. 2016). The analysis used an instrument for the part of the daily fluctuations in air pollution caused by changes in the mixing height and wind speed, which modify the build-up of locally generated pollution but do not have other plausible connections to daily changes in mortality except through air pollution. If that assumption is true, then the instrument represents variations in local pollutants that are randomized with respect to confounders, measured or unmeasured, and therefore provides a causal estimate of the effect of local air pollution concentrations. However, a lower mixing height increases the concentration of all locally emitted pollutants. Thus, these models do not provide much guidance on the relative importance of those pollutants. …

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