Estimating Causal Effects of Local Air Pollution on Daily Deaths: Effect of Low Levels

Abstract

Background: Although many time-series studies have established associations of daily pollution variations with daily deaths, there are fewer at low concentrations, or focused on locally generated pollution, which is becoming more important as regulations reduce regional transport. Causal modeling approaches are also lacking.

Objective: We used causal modeling to estimate the impact of local air pollution on mortality at low concentrations.

Methods: Using an instrumental variable approach, we developed an instrument for variations in local pollution concentrations that is unlikely to be correlated with other causes of death, and examined its association with daily deaths in the Boston, Massachusetts, area. We combined height of the planetary boundary layer and wind speed, which affect concentrations of local emissions, to develop the instrument for particulate matter ≤ 2.5 μm (PM2.5), black carbon (BC), or nitrogen dioxide (NO2) variations that were independent of year, month, and temperature. We also used Granger causality to assess whether omitted variable confounding existed.

Results: We estimated that an interquartile range increase in the instrument for local PM2.5 was associated with a 0.90% increase in daily deaths (95% CI: 0.25, 1.56). A similar result was found for BC, and a weaker association with NO2. The Granger test found no evidence of omitted variable confounding for the instrument. A separate test confirmed the instrument was not associated with mortality independent of pollution. Furthermore, the association remained when all days with PM2.5 concentrations > 30 μg/m3 were excluded from the analysis (0.84% increase in daily deaths; 95% CI: 0.19, 1.50).

Conclusions: We conclude that there is a causal association of local air pollution with daily deaths at concentrations below U.S. EPA standards. The estimated attributable risk in Boston exceeded 1,800 deaths during the study period, indicating that important public health benefits can follow from further control efforts. Citation: Schwartz J, Bind MA, Koutrakis P. 2017. Estimating causal effects of local air pollution on daily deaths: effect of low levels. Environ Health Perspect 125:23-29; http://dx.doi.org/10.1289/EHP232.

Figure 1

Directed acyclic graph illustrating an instrumental variable Z. The association between Z and Y is not confounded by C. By calibrating the instrument to A, estimates of causal effects of increases in A can be obtained.

Figure 2

Directed acyclic graph for the Granger causality model. Confounder U2 is measured and controlled, but confounder U1 is not. POL_b is pollution before the outcome (O), and POL_a is pollution after the outcome. If U1 is not controlled, there is a backdoor path from O to POL_a, and an association would be expected. Hence, failure to find an association is evidence of a lack of confounding (i.e., no U1).