Bias in the estimated association between all-cause mortality and long-term exposure to a specific chemical component of fine particulate matter: The example of black carbon.

INTRODUCTION

Long-term exposure to fine particulate matter (PM2.5) has been linked to many adverse health outcomes, which can vary significantly depending on the chemical profile of the PM2.5. However, many meta-analyses of the health effects of a specific component of PM2.5 have ignored the effects of other components, leading to omitted variable bias (OVB). This study developed a new method to address this problem and conducted a simulation using black carbon (BC) as an example.

METHOD: We used data from two published meta-analyses as input for our model, with supplementary information obtained from a reanalysis product of PM2.5 components. Based on the classical OVB formula, we developed a post hoc adjusted model and verified its performance via a simulation study. We obtained pooled estimates of the effect of BC on all-cause mortality, with adjustment for the effect of non-black carbon (NBC) components. Finally, based on the estimated effects of BC and NBC, we investigated global patterns in PM2.5 toxicity (i.e., the per-unit effect of PM2.5) and the degree of OVB associated with ignoring the differential effects of BC and NBC.

RESULTS

The post hoc adjusted model included 46 individual estimates of the effects of BC or NBC on all-cause mortality. Results from the model indicate that a 10 μg/m³ increase in BC and NBC was associated with a 49 % (95 % confidence interval [CI]: 26 - 76 %) and 6 % (95 % CI: 3 - 10 %) increase in mortality risk, respectively. Based on global average total PM2.5 mass composition values (6.1 % and 93.9 % for BC and NBC, respectively), we estimated that the relative risk of all-cause mortality increased by 1.09 (95 % CI: 1.06 - 1.12) per 10 μg/m3 increment in long-term PM2.5 exposure. Estimation of the effects of BC on mortality based on observations obtained within one city yielded a median OVB of 147 % (95 % CI: -151 - 700) when using a single-pollutant model.

CONCLUSION

In meta-analyses on the health impacts of PM2.5 components, ignoring the differential effects of BC and NBC causes significant biases in estimating associations with all-cause mortality. Our study presents a novel method to adjust for OVB in meta-analyses, and we find that BC more harmful than NBC components of PM2.5 by using the novel method.