2026-04-14T02:28:00Zhttps://recercat.cat/oai/request

oai:recercat.cat:10230/712182025-09-19T23:37:16Zcom_2072_6col_2072_452952

00925njm 22002777a 4500 dc Chen, Xuan author Gehring, Ulrike author Dyer, Georgia M. C. author de Hoogh, Kees author Khomenko, Sasha author Khreis, Haneen author Mueller, Natalie, 1988- author Vermeulen, Roel author Williams, Harry author Zapata-Diomedi, Belén author Nieuwenhuijsen, Mark J. author Hoek, Gerard author 2025-09-18T06:17:47Z 2025-09-18T06:17:47Z 2025 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. This project has received funding from the European Union's, Horizon Europe Framework Programme (HORIZON) under GA No 101094639 – THE URBAN BURDEN OF DISEASE ESTIMATION FOR POLICY MAKING (UBDPolicy). We also acknowledge support from the Spanish Ministry of Science, Innovation and Universities and State Research Agency through the grant CEX2018-000806-S funded by MCIN/AEI/10.13039/501100011033; support from the Generalitat de Catalunya through the Centres de Recerca de Catalunya (CERCA) programme; support from Centro de Investigacioń Bioḿedica en red Epidemiología y Salud Pública (CIBERESP). http://hdl.handle.net/10230/71218 Air pollution Exposure–response functions Green space Health impact assessment Noise Exposure-response functions of the correlated environmental exposures green space, noise, and air pollution for quantifying mortality burden in health impact assessment