Cancer and mortality in relation to traffic-related air pollution among coronary patients: Using an ensemble of exposure estimates to identify high-risk individuals.

BACKGROUND

Moderate correlations were previously observed between individual estimates of traffic-related air pollution (TRAP) produced by different exposure modeling approaches. This induces exposure misclassification for a substantial fraction of subjects.

AIM: We used an ensemble of well-established modeling approaches to increase certainty of exposure classification and reevaluated the association with cancers previously linked to TRAP (lung, breast and prostate), other cancers, and all-cause mortality in a cohort of coronary patients.

METHODS

Patients undergoing percutaneous coronary interventions in a major Israeli medical center from 2004 to 2014 (n = 10,627) were followed for cancer (through 2015) and mortality (through 2017) via national registries. Residential exposure to nitrogen oxides (NOx) -a proxy for TRAP- was estimated by optimized dispersion model (ODM) and land use regression (LUR) (rPearson = 0.50). Mutually exclusive groups of subjects classified as exposed by none of the methods (high-certainty low-exposed), ODM alone, LUR alone, or both methods (high-certainty high-exposed) were created. Associations were examined using Cox regression models.

RESULTS

During follow-up, 741 incident cancer cases were diagnosed and 3051 deaths occurred. Using a ≥25 ppb cutoff, compared with high-certainty low exposed, the multivariable-adjusted hazard ratios (95% confidence intervals) for lung, breast and prostate cancer were 1.56 (1.13-2.15) in high-certainty exposed, 1.27 (0.86-1.86) in LUR-exposed alone, and 1.13 (0.77-1.65) in ODM-exposed alone. The association of the former category was strengthened using more extreme NOx cutoffs. A similar pattern, albeit less strong, was observed for mortality, whereas no association was shown for cancers not previously linked to TRAP.

CONCLUSIONS

Use of an ensemble of TRAP exposure estimates may improve classification, resulting in a stronger association with outcomes.