Exposure-response functions of the correlated environmental exposures green space, noise, and air pollution for quantifying mortality burden in health impact assessment

OBJECTIVE: Environmental health impact assessments (HIA)on green space, air pollution (fine particulate matter (PM2.5) or nitrogen dioxide (NO2)), and noise use exposure-response functions (ERF) based on single-exposure models from epidemiological studies, not accounting for potential confounding by other commonly correlated exposures. We assessed differences in ERFs between single- and multi-exposure models for calculation of joint health impacts in HIA.

METHODS: We systematically searched cohort studies that reported both single- and multi-exposure models for associations of long-term exposure to any combination of the following exposures green space, PM2.5, NO2, and noise, with all-cause mortality. For each exposure, pooled hazard ratios (HRs) were calculated by meta-analyses and compared between single- and two-exposure models. The joint effects of two exposures in each exposure pair were expressed as joint HRs calculated by multiplying the individual HRs. Coefficient differences were calculated, and population attributable fractions (PAF) were used to estimate joint health impacts.

RESULTS: Eleven studies were identified, examining associations between multiple exposures and mortality in the general population. The studies show substantial variability in exposure levels and correlations between exposures. For most exposure pairs, adjusting for a second exposure resulted in moderately attenuated HRs compared to single-exposure models. The mortality PAFs estimated from joint single-exposure model HRs were higher than those from two-exposure models, indicating an overestimation of mortality burden when not accounting for other co-exposures. For example, when adjusted for green space or noise, the mortality HRs for PM2.5 were attenuated from 1.071 to 1.061 and 1.072 to 1.055, respectively. As for PAFs, for the green space-PM2.5 pair, the single-exposure model PAF (0.090) was 18.4% higher than the two-exposure model (0.076). For all exposure pairs, the joint PAFs of two-exposure models were higher than the PAFs from the single-exposure models for each exposure individually.

CONCLUSION: The pooled coefficient differences from this study can be used to adjust single-exposure ERFs from meta-analyses and allow the calculation of combined impacts from multiple environmental exposures, making HIA estimates more robust and realistic.