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Jochem O Klompmaker - One of the best experts on this subject based on the ideXlab platform.
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comparison of associations between mortality and air pollution Exposure estimated with a hybrid a land use regression and a dispersion model
Environment International, 2021Co-Authors: Jochem O Klompmaker, Nicole A H Janssen, Zorana Jovanovic Andersen, Richard Atkinson, Mariska Bauwelinck, Jie Chen, Kees De Hoogh, Danny Houthuijs, Klea Katsouyanni, Marten MarraAbstract:Abstract Introduction To characterize air pollution Exposure at a fine spatial scale, different Exposure assessment methods have been applied. Comparison of associations with health from different Exposure methods are scarce. The aim of this study was to evaluate associations of air pollution based on hybrid, land-use regression (LUR) and dispersion Models with natural cause and cause-specific mortality. Methods We followed a Dutch national cohort of approximately 10.5 million adults aged 29+ years from 2008 until 2012. We used Cox proportional hazard Models with age as underlying time scale and adjusted for several potential individual and area-level socio-economic status confounders to evaluate associations of annual average residential NO2, PM2.5 and BC Exposure estimates based on two stochastic Models (Dutch LUR, European-wide hybrid) and deterministic Dutch dispersion Models. Results Spatial variability of PM2.5 and BC Exposure was smaller for LUR compared to hybrid and dispersion Models. NO2 Exposure variability was similar for the three methods. Pearson correlations between hybrid, LUR and dispersion modeled NO2 and BC ranged from 0.72 to 0.83; correlations for PM2.5 were slightly lower (0.61–0.72). In general, all three Models showed stronger associations of air pollutants with respiratory disease and lung cancer mortality than with natural cause and cardiovascular disease mortality. The strength of the associations differed between the three Exposure Models. Associations of air pollutants estimated by LUR were generally weaker compared to associations of air pollutants estimated by hybrid and dispersion Models. For natural cause mortality, we found a hazard ratio (HR) of 1.030 (95% confidence interval (CI): 1.019, 1.041) per 10 µg/m3 for hybrid modeled NO2, a HR of 1.003 (95% CI: 0.993, 1.013) per 10 µg/m3 for LUR modeled NO2 and a HR of 1.015 (95% CI: 1.005, 1.024) per 10 µg/m3 for dispersion modeled NO2. Conclusion Air pollution was positively associated with natural cause and cause-specific mortality, but the strength of the associations differed between the three Exposure Models. Our study documents that the selected Exposure model may contribute to heterogeneity in effect estimates of associations between air pollution and health.
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residential surrounding green air pollution traffic noise and self perceived general health
Environmental Research, 2019Co-Authors: Bert Brunekreef, Jochem O Klompmaker, Lizan D Bloemsma, Alet H Wijga, Ulrike Gehring, Nicole A H Janssen, Erik Lebret, Carolien Van Den Brink, Gerard HoekAbstract:Self-perceived general health (SGH) is one of the most inclusive and widely used measures of health status and a powerful predictor of mortality. However, only a limited number of studies evaluated associations of combined environmental Exposures on SGH. Our aim was to evaluate associations of combined residential Exposure to surrounding green, air pollution and traffic noise with poor SGH in the Netherlands. We linked data on long-term residential Exposure to surrounding green based on the Normalized Difference Vegetation Index (NDVI) and a land-use database (TOP10NL), air pollutant concentrations (including particulate matter (PM10, PM2.5), and nitrogen dioxide (NO2)) and road- and rail-traffic noise with a Dutch national health survey, resulting in a study population of 354,827 adults. We analyzed associations of single and combined Exposures with poor SGH. In single-Exposure Models, NDVI within 300 m was inversely associated with poor SGH [odds ratio (OR) = 0.91, 95% CI: 0.89, 0.94 per IQR increase], while NO2 was positively associated with poor SGH (OR = 1.07, 95% CI: 1.04, 1.11 per IQR increase). In multi-Exposure Models, associations with surrounding green and air pollution generally remained, but attenuated. Joint odds ratios (JOR) of combined Exposure to air pollution, rail-traffic noise and decreased surrounding green were higher than the odds ratios of single-Exposure Models. Studies including only one of these correlated Exposures may overestimate the risk of poor SGH attributed to the studied Exposure, while underestimating the risk of combined Exposures.
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associations of combined Exposures to surrounding green air pollution and traffic noise on mental health
Environment International, 2019Co-Authors: Jochem O Klompmaker, Bert Brunekreef, Gerard Hoek, Lizan D Bloemsma, Alet H Wijga, Carolien Van Den Brink, Ulrike Gehring, Erik Lebret, Nicole A H JanssenAbstract:Abstract Background Evidence is emerging that poor mental health is associated with the environmental Exposures of surrounding green, air pollution and traffic noise. Most studies have evaluated only associations of single Exposures with poor mental health. Objectives To evaluate associations of combined Exposure to surrounding green, air pollution and traffic noise with poor mental health. Methods In this cross-sectional study, we linked data from a Dutch national health survey among 387,195 adults including questions about psychological distress, based on the Kessler 10 scale, to an external database on registered prescriptions of anxiolytics, hypnotics & sedatives and antidepressants. We added data on residential surrounding green in a 300 m and a 1000 m buffer based on the Normalized Difference Vegetation Index (NDVI) and a land-use database (TOP10NL), modeled annual average air pollutant concentrations (including particulate matter (PM10, PM2.5), and nitrogen dioxide (NO2)) and modeled road- and rail-traffic noise (Lden and Lnight) to the survey. We used logistic regression to analyze associations of surrounding green, air pollution and traffic noise Exposure with poor mental health. Results In single Exposure Models, surrounding green was inversely associated with poor mental health. Air pollution was positively associated with poor mental health. Road-traffic noise was only positively associated with prescription of anxiolytics, while rail-traffic noise was only positively associated with psychological distress. For prescription of anxiolytics, we found an odds ratio [OR] of 0.88 (95% CI: 0.85, 0.92) per interquartile range [IQR] increase in NDVI within 300 m, an OR of 1.14 (95% CI: 1.10, 1.19) per IQR increase in NO2 and an OR of 1.07 (95% CI: 1.03, 1.11) per IQR increase in road-traffic noise. In multi Exposure analyses, associations with surrounding green and air pollution generally remained but attenuated. Joint odds ratios [JOR], based on the Cumulative Risk Index (CRI) method, of combined Exposure to air pollution, traffic noise and decreased surrounding green were higher than the ORs of single Exposure Models. Associations of environmental Exposures with poor mental health differed somewhat by age. Conclusions Studies including only one of these three correlated Exposures may overestimate the influence of poor mental health attributed to the studied Exposure, while underestimating the influence of combined environmental Exposures.
Mingyi Tsai - One of the best experts on this subject based on the ideXlab platform.
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long term Exposure Models for traffic related no2 across geographically diverse areas over separate years
Atmospheric Environment, 2012Co-Authors: Armin Gemperli, Alex Ineichen, Lucy Bayeroglesby, Dirk Keidel, Mingyi Tsai, Thierry RochatAbstract:Although recent air pollution epidemiologic studies have embraced land-use regression Models for estimating outdoor traffic Exposure, few have examined the spatio-temporal variability of traffic related pollution over a long term period and the optimal methods to take these factors into account for Exposure estimates. We used home outdoor NO(2) measurements taken from eight geographically diverse areas to examine spatio-temporal variations, construct, and evaluate Models that could best predict the within-city contrasts in observations. Passive NO(2) measurements were taken outside of up to 100 residences per area over three seasons in 1993 and 2003 as part of the Swiss cohort study on air pollution and lung and heart disease in adults (SAPALDIA). The spatio-temporal variation of NO(2) differed by area and year. Regression Models constructed using the annual NO(2) means from central monitoring stations and geographic parameters predicted home outdoor NO(2) levels better than a dispersion model. However, both the regression and dispersion Models underestimated the within-city contrasts of NO(2) levels. Our results indicated that the best Models should be constructed for individual areas and years, and would use the dispersion estimates as the urban background, geographic information system (GIS) parameters to enhance local characteristics, and temporal and meteorological variables to capture changing local dynamics. Such Models would be powerful tools for assessing health effects from long-term Exposure to air pollution in a large cohort. (C) 2011 Elsevier Ltd. All rights reserved
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long term Exposure Models for traffic related no2 across geographically diverse areas over separate years
Atmospheric Environment, 2012Co-Authors: Armin Gemperli, Dirk Keidel, Mingyi Tsai, L Sally J Liu, Alex IneichenAbstract:Abstract Although recent air pollution epidemiologic studies have embraced land-use regression Models for estimating outdoor traffic Exposure, few have examined the spatio-temporal variability of traffic related pollution over a long term period and the optimal methods to take these factors into account for Exposure estimates. We used home outdoor NO2 measurements taken from eight geographically diverse areas to examine spatio-temporal variations, construct, and evaluate Models that could best predict the within-city contrasts in observations. Passive NO2 measurements were taken outside of up to 100 residences per area over three seasons in 1993 and 2003 as part of the Swiss cohort study on air pollution and lung and heart disease in adults (SAPALDIA). The spatio-temporal variation of NO2 differed by area and year. Regression Models constructed using the annual NO2 means from central monitoring stations and geographic parameters predicted home outdoor NO2 levels better than a dispersion model. However, both the regression and dispersion Models underestimated the within-city contrasts of NO2 levels. Our results indicated that the best Models should be constructed for individual areas and years, and would use the dispersion estimates as the urban background, geographic information system (GIS) parameters to enhance local characteristics, and temporal and meteorological variables to capture changing local dynamics. Such Models would be powerful tools for assessing health effects from long-term Exposure to air pollution in a large cohort.
Nicole A H Janssen - One of the best experts on this subject based on the ideXlab platform.
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comparison of associations between mortality and air pollution Exposure estimated with a hybrid a land use regression and a dispersion model
Environment International, 2021Co-Authors: Jochem O Klompmaker, Nicole A H Janssen, Zorana Jovanovic Andersen, Richard Atkinson, Mariska Bauwelinck, Jie Chen, Kees De Hoogh, Danny Houthuijs, Klea Katsouyanni, Marten MarraAbstract:Abstract Introduction To characterize air pollution Exposure at a fine spatial scale, different Exposure assessment methods have been applied. Comparison of associations with health from different Exposure methods are scarce. The aim of this study was to evaluate associations of air pollution based on hybrid, land-use regression (LUR) and dispersion Models with natural cause and cause-specific mortality. Methods We followed a Dutch national cohort of approximately 10.5 million adults aged 29+ years from 2008 until 2012. We used Cox proportional hazard Models with age as underlying time scale and adjusted for several potential individual and area-level socio-economic status confounders to evaluate associations of annual average residential NO2, PM2.5 and BC Exposure estimates based on two stochastic Models (Dutch LUR, European-wide hybrid) and deterministic Dutch dispersion Models. Results Spatial variability of PM2.5 and BC Exposure was smaller for LUR compared to hybrid and dispersion Models. NO2 Exposure variability was similar for the three methods. Pearson correlations between hybrid, LUR and dispersion modeled NO2 and BC ranged from 0.72 to 0.83; correlations for PM2.5 were slightly lower (0.61–0.72). In general, all three Models showed stronger associations of air pollutants with respiratory disease and lung cancer mortality than with natural cause and cardiovascular disease mortality. The strength of the associations differed between the three Exposure Models. Associations of air pollutants estimated by LUR were generally weaker compared to associations of air pollutants estimated by hybrid and dispersion Models. For natural cause mortality, we found a hazard ratio (HR) of 1.030 (95% confidence interval (CI): 1.019, 1.041) per 10 µg/m3 for hybrid modeled NO2, a HR of 1.003 (95% CI: 0.993, 1.013) per 10 µg/m3 for LUR modeled NO2 and a HR of 1.015 (95% CI: 1.005, 1.024) per 10 µg/m3 for dispersion modeled NO2. Conclusion Air pollution was positively associated with natural cause and cause-specific mortality, but the strength of the associations differed between the three Exposure Models. Our study documents that the selected Exposure model may contribute to heterogeneity in effect estimates of associations between air pollution and health.
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residential surrounding green air pollution traffic noise and self perceived general health
Environmental Research, 2019Co-Authors: Bert Brunekreef, Jochem O Klompmaker, Lizan D Bloemsma, Alet H Wijga, Ulrike Gehring, Nicole A H Janssen, Erik Lebret, Carolien Van Den Brink, Gerard HoekAbstract:Self-perceived general health (SGH) is one of the most inclusive and widely used measures of health status and a powerful predictor of mortality. However, only a limited number of studies evaluated associations of combined environmental Exposures on SGH. Our aim was to evaluate associations of combined residential Exposure to surrounding green, air pollution and traffic noise with poor SGH in the Netherlands. We linked data on long-term residential Exposure to surrounding green based on the Normalized Difference Vegetation Index (NDVI) and a land-use database (TOP10NL), air pollutant concentrations (including particulate matter (PM10, PM2.5), and nitrogen dioxide (NO2)) and road- and rail-traffic noise with a Dutch national health survey, resulting in a study population of 354,827 adults. We analyzed associations of single and combined Exposures with poor SGH. In single-Exposure Models, NDVI within 300 m was inversely associated with poor SGH [odds ratio (OR) = 0.91, 95% CI: 0.89, 0.94 per IQR increase], while NO2 was positively associated with poor SGH (OR = 1.07, 95% CI: 1.04, 1.11 per IQR increase). In multi-Exposure Models, associations with surrounding green and air pollution generally remained, but attenuated. Joint odds ratios (JOR) of combined Exposure to air pollution, rail-traffic noise and decreased surrounding green were higher than the odds ratios of single-Exposure Models. Studies including only one of these correlated Exposures may overestimate the risk of poor SGH attributed to the studied Exposure, while underestimating the risk of combined Exposures.
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associations of combined Exposures to surrounding green air pollution and traffic noise on mental health
Environment International, 2019Co-Authors: Jochem O Klompmaker, Bert Brunekreef, Gerard Hoek, Lizan D Bloemsma, Alet H Wijga, Carolien Van Den Brink, Ulrike Gehring, Erik Lebret, Nicole A H JanssenAbstract:Abstract Background Evidence is emerging that poor mental health is associated with the environmental Exposures of surrounding green, air pollution and traffic noise. Most studies have evaluated only associations of single Exposures with poor mental health. Objectives To evaluate associations of combined Exposure to surrounding green, air pollution and traffic noise with poor mental health. Methods In this cross-sectional study, we linked data from a Dutch national health survey among 387,195 adults including questions about psychological distress, based on the Kessler 10 scale, to an external database on registered prescriptions of anxiolytics, hypnotics & sedatives and antidepressants. We added data on residential surrounding green in a 300 m and a 1000 m buffer based on the Normalized Difference Vegetation Index (NDVI) and a land-use database (TOP10NL), modeled annual average air pollutant concentrations (including particulate matter (PM10, PM2.5), and nitrogen dioxide (NO2)) and modeled road- and rail-traffic noise (Lden and Lnight) to the survey. We used logistic regression to analyze associations of surrounding green, air pollution and traffic noise Exposure with poor mental health. Results In single Exposure Models, surrounding green was inversely associated with poor mental health. Air pollution was positively associated with poor mental health. Road-traffic noise was only positively associated with prescription of anxiolytics, while rail-traffic noise was only positively associated with psychological distress. For prescription of anxiolytics, we found an odds ratio [OR] of 0.88 (95% CI: 0.85, 0.92) per interquartile range [IQR] increase in NDVI within 300 m, an OR of 1.14 (95% CI: 1.10, 1.19) per IQR increase in NO2 and an OR of 1.07 (95% CI: 1.03, 1.11) per IQR increase in road-traffic noise. In multi Exposure analyses, associations with surrounding green and air pollution generally remained but attenuated. Joint odds ratios [JOR], based on the Cumulative Risk Index (CRI) method, of combined Exposure to air pollution, traffic noise and decreased surrounding green were higher than the ORs of single Exposure Models. Associations of environmental Exposures with poor mental health differed somewhat by age. Conclusions Studies including only one of these three correlated Exposures may overestimate the influence of poor mental health attributed to the studied Exposure, while underestimating the influence of combined environmental Exposures.
Bert Brunekreef - One of the best experts on this subject based on the ideXlab platform.
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residential surrounding green air pollution traffic noise and self perceived general health
Environmental Research, 2019Co-Authors: Bert Brunekreef, Jochem O Klompmaker, Lizan D Bloemsma, Alet H Wijga, Ulrike Gehring, Nicole A H Janssen, Erik Lebret, Carolien Van Den Brink, Gerard HoekAbstract:Self-perceived general health (SGH) is one of the most inclusive and widely used measures of health status and a powerful predictor of mortality. However, only a limited number of studies evaluated associations of combined environmental Exposures on SGH. Our aim was to evaluate associations of combined residential Exposure to surrounding green, air pollution and traffic noise with poor SGH in the Netherlands. We linked data on long-term residential Exposure to surrounding green based on the Normalized Difference Vegetation Index (NDVI) and a land-use database (TOP10NL), air pollutant concentrations (including particulate matter (PM10, PM2.5), and nitrogen dioxide (NO2)) and road- and rail-traffic noise with a Dutch national health survey, resulting in a study population of 354,827 adults. We analyzed associations of single and combined Exposures with poor SGH. In single-Exposure Models, NDVI within 300 m was inversely associated with poor SGH [odds ratio (OR) = 0.91, 95% CI: 0.89, 0.94 per IQR increase], while NO2 was positively associated with poor SGH (OR = 1.07, 95% CI: 1.04, 1.11 per IQR increase). In multi-Exposure Models, associations with surrounding green and air pollution generally remained, but attenuated. Joint odds ratios (JOR) of combined Exposure to air pollution, rail-traffic noise and decreased surrounding green were higher than the odds ratios of single-Exposure Models. Studies including only one of these correlated Exposures may overestimate the risk of poor SGH attributed to the studied Exposure, while underestimating the risk of combined Exposures.
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associations of combined Exposures to surrounding green air pollution and traffic noise on mental health
Environment International, 2019Co-Authors: Jochem O Klompmaker, Bert Brunekreef, Gerard Hoek, Lizan D Bloemsma, Alet H Wijga, Carolien Van Den Brink, Ulrike Gehring, Erik Lebret, Nicole A H JanssenAbstract:Abstract Background Evidence is emerging that poor mental health is associated with the environmental Exposures of surrounding green, air pollution and traffic noise. Most studies have evaluated only associations of single Exposures with poor mental health. Objectives To evaluate associations of combined Exposure to surrounding green, air pollution and traffic noise with poor mental health. Methods In this cross-sectional study, we linked data from a Dutch national health survey among 387,195 adults including questions about psychological distress, based on the Kessler 10 scale, to an external database on registered prescriptions of anxiolytics, hypnotics & sedatives and antidepressants. We added data on residential surrounding green in a 300 m and a 1000 m buffer based on the Normalized Difference Vegetation Index (NDVI) and a land-use database (TOP10NL), modeled annual average air pollutant concentrations (including particulate matter (PM10, PM2.5), and nitrogen dioxide (NO2)) and modeled road- and rail-traffic noise (Lden and Lnight) to the survey. We used logistic regression to analyze associations of surrounding green, air pollution and traffic noise Exposure with poor mental health. Results In single Exposure Models, surrounding green was inversely associated with poor mental health. Air pollution was positively associated with poor mental health. Road-traffic noise was only positively associated with prescription of anxiolytics, while rail-traffic noise was only positively associated with psychological distress. For prescription of anxiolytics, we found an odds ratio [OR] of 0.88 (95% CI: 0.85, 0.92) per interquartile range [IQR] increase in NDVI within 300 m, an OR of 1.14 (95% CI: 1.10, 1.19) per IQR increase in NO2 and an OR of 1.07 (95% CI: 1.03, 1.11) per IQR increase in road-traffic noise. In multi Exposure analyses, associations with surrounding green and air pollution generally remained but attenuated. Joint odds ratios [JOR], based on the Cumulative Risk Index (CRI) method, of combined Exposure to air pollution, traffic noise and decreased surrounding green were higher than the ORs of single Exposure Models. Associations of environmental Exposures with poor mental health differed somewhat by age. Conclusions Studies including only one of these three correlated Exposures may overestimate the influence of poor mental health attributed to the studied Exposure, while underestimating the influence of combined environmental Exposures.
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determinants of the spatial distributions of elemental carbon and particulate matter in eight southern californian communities
Atmospheric Environment, 2014Co-Authors: Robert Urman, James Gauderman, Edward L Avol, Bryan Penfold, Fred Lurmann, Scott Fruin, Frank D Gilliland, Meredith Franklin, Reza Hosseini, Bert BrunekreefAbstract:Emerging evidence indicates that near-roadway pollution (NRP) in ambient air has adverse health effects. However, specific components of the NRP mixture responsible for these effects have not been established. A major limitation for health studies is the lack of Exposure Models that estimate NRP components observed in epidemiological studies over fine spatial scale of tens to hundreds of meters. In this study, Exposure Models were developed for fine-scale variation in biologically relevant elemental carbon (EC). Measurements of particulate matter (PM) and EC less than 2.5 mu m in aerodynamic diameter (EC2.5) and of PM and EC of nanoscale size less than 0.2 mu m were made at up to 29 locations in each of eight Southern California Children's Health Study communities. Regression-based prediction Models were developed using a guided forward selection process to identify traffic variables and other pollutant sources, community physical characteristics and land use as predictors of PM and EC variation in each community. A combined eight-community model including only CALINE4 near-roadway dispersion-estimated vehicular emissions accounting for distance, distance-weighted traffic volume, and meteorology, explained 51% of the EC0.2 variability. Community-specific Models identified additional predictors in some communities; however, in most communities the correlation between predicted concentrations from the eight-community model and observed concentrations stratified by community was similar to those for the community-specific Models. EC2.5 could be predicted as well as EC0.2. EC2.5 estimated from CALINE4 and population density explained 53% of the within-community variation. Exposure prediction was further improved after accounting for between-community heterogeneity of CALINE4 effects associated with average distance to Pacific Ocean shoreline (to 61% for EC0.2) and for regional NOx pollution (to 57% for EC2.5). PM fine spatial scale variation was poorly predicted in both size fractions. In conclusion, Models of Exposure that include traffic measures such as C.ALINE4 can provide useful estimates for EC0.2 and EC2.5 on a spatial scale appropriate for health studies of NRP in selected Southern California communities. (C) 2013 Elsevier Ltd. All rights reserved.
Gerard Hoek - One of the best experts on this subject based on the ideXlab platform.
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residential surrounding green air pollution traffic noise and self perceived general health
Environmental Research, 2019Co-Authors: Bert Brunekreef, Jochem O Klompmaker, Lizan D Bloemsma, Alet H Wijga, Ulrike Gehring, Nicole A H Janssen, Erik Lebret, Carolien Van Den Brink, Gerard HoekAbstract:Self-perceived general health (SGH) is one of the most inclusive and widely used measures of health status and a powerful predictor of mortality. However, only a limited number of studies evaluated associations of combined environmental Exposures on SGH. Our aim was to evaluate associations of combined residential Exposure to surrounding green, air pollution and traffic noise with poor SGH in the Netherlands. We linked data on long-term residential Exposure to surrounding green based on the Normalized Difference Vegetation Index (NDVI) and a land-use database (TOP10NL), air pollutant concentrations (including particulate matter (PM10, PM2.5), and nitrogen dioxide (NO2)) and road- and rail-traffic noise with a Dutch national health survey, resulting in a study population of 354,827 adults. We analyzed associations of single and combined Exposures with poor SGH. In single-Exposure Models, NDVI within 300 m was inversely associated with poor SGH [odds ratio (OR) = 0.91, 95% CI: 0.89, 0.94 per IQR increase], while NO2 was positively associated with poor SGH (OR = 1.07, 95% CI: 1.04, 1.11 per IQR increase). In multi-Exposure Models, associations with surrounding green and air pollution generally remained, but attenuated. Joint odds ratios (JOR) of combined Exposure to air pollution, rail-traffic noise and decreased surrounding green were higher than the odds ratios of single-Exposure Models. Studies including only one of these correlated Exposures may overestimate the risk of poor SGH attributed to the studied Exposure, while underestimating the risk of combined Exposures.
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associations of combined Exposures to surrounding green air pollution and traffic noise on mental health
Environment International, 2019Co-Authors: Jochem O Klompmaker, Bert Brunekreef, Gerard Hoek, Lizan D Bloemsma, Alet H Wijga, Carolien Van Den Brink, Ulrike Gehring, Erik Lebret, Nicole A H JanssenAbstract:Abstract Background Evidence is emerging that poor mental health is associated with the environmental Exposures of surrounding green, air pollution and traffic noise. Most studies have evaluated only associations of single Exposures with poor mental health. Objectives To evaluate associations of combined Exposure to surrounding green, air pollution and traffic noise with poor mental health. Methods In this cross-sectional study, we linked data from a Dutch national health survey among 387,195 adults including questions about psychological distress, based on the Kessler 10 scale, to an external database on registered prescriptions of anxiolytics, hypnotics & sedatives and antidepressants. We added data on residential surrounding green in a 300 m and a 1000 m buffer based on the Normalized Difference Vegetation Index (NDVI) and a land-use database (TOP10NL), modeled annual average air pollutant concentrations (including particulate matter (PM10, PM2.5), and nitrogen dioxide (NO2)) and modeled road- and rail-traffic noise (Lden and Lnight) to the survey. We used logistic regression to analyze associations of surrounding green, air pollution and traffic noise Exposure with poor mental health. Results In single Exposure Models, surrounding green was inversely associated with poor mental health. Air pollution was positively associated with poor mental health. Road-traffic noise was only positively associated with prescription of anxiolytics, while rail-traffic noise was only positively associated with psychological distress. For prescription of anxiolytics, we found an odds ratio [OR] of 0.88 (95% CI: 0.85, 0.92) per interquartile range [IQR] increase in NDVI within 300 m, an OR of 1.14 (95% CI: 1.10, 1.19) per IQR increase in NO2 and an OR of 1.07 (95% CI: 1.03, 1.11) per IQR increase in road-traffic noise. In multi Exposure analyses, associations with surrounding green and air pollution generally remained but attenuated. Joint odds ratios [JOR], based on the Cumulative Risk Index (CRI) method, of combined Exposure to air pollution, traffic noise and decreased surrounding green were higher than the ORs of single Exposure Models. Associations of environmental Exposures with poor mental health differed somewhat by age. Conclusions Studies including only one of these three correlated Exposures may overestimate the influence of poor mental health attributed to the studied Exposure, while underestimating the influence of combined environmental Exposures.
