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Little is known about the impact of environmental exposures on mortality risk after a myocardial infarction (MI). The goal of this study was to evaluate associations of long-term temperature, air pollution and greenness exposures with mortality among survivors of an MI. We used data from the US-based Nurses' Health Study to construct an open cohort of survivors of a nonfatal MI 1990–2017. Participants entered the cohort when they had a nonfatal MI, and were followed until death, loss to follow-up, end of follow-up, or they reached 80 years old, whichever came earliest. We assessed residential 12-month moving average fine particulate matter (PM 2.5) and nitrogen dioxide (NO 2), satellite-based annual average greenness (in a circular 1230 m buffer), summer average temperature and winter average temperature. We used Cox proportional hazard models adjusted for potential confounders to assess hazard ratios (HR and 95% confidence intervals). We also assessed potential effect modification. Among 2262 survivors of a nonfatal MI, we observed 892 deaths during 19,216 person years of follow-up. In single-exposure models, we observed a HR (95%CI) of 1.20 (1.04, 1.37) per 10 ppb NO 2 increase and suggestive positive associations were observed for PM 2.5 , lower greenness, warmer summer average temperature and colder winter average temperature. In multi-exposure models, associations of summer and winter average temperature remained stable, while associations of NO 2 , PM 2.5 and greenness attenuated. The strength of some associations was modified by other exposures. For example, associations of greenness (HR = 0.88 (0.78, 0.98) per 0.1) were more pronounced for participants in areas with a lower winter average temperature. We observed associations of air pollution, greenness and temperature with mortality among MI survivors. Some associations were confounded or modified by other exposures, indicating that it is important to explore the combined impact of environmental exposures. [Display omitted] • Environmental exposures were associated with mortality among MI survivors. • Associations of air pollution and greenness attenuated in multi-exposure models. • The strength of some associations was modified by other exposures. [ABSTRACT FROM AUTHOR]

Protective associations of greenspace with Parkinson's disease (PD) have been observed in some studies. Visual exposure to greenspace seems to be important for some of the proposed pathways underlying these associations. However, most studies use overhead-view measures (e.g., satellite imagery, land-classification data) that do not capture street-view greenspace and cannot distinguish between specific greenspace types. We aimed to evaluate associations of street-view greenspace measures with hospitalizations with a PD diagnosis code (PD-involved hospitalization). We created an open cohort of about 45.6 million Medicare fee-for-service beneficiaries aged 65 + years living in core based statistical areas (i.e. non-rural areas) in the contiguous US (2007–2016). We obtained 350 million Google Street View images across the US and applied deep learning algorithms to identify percentages of specific greenspace features in each image, including trees, grass, and other green features (i.e., plants, flowers, fields). We assessed yearly average street-view greenspace features for each ZIP code. A Cox-equivalent re-parameterized Poisson model adjusted for potential confounders (i.e. age, race/ethnicity, socioeconomic status) was used to evaluate associations with first PD-involved hospitalization. There were 506,899 first PD-involved hospitalizations over 254,917,192 person-years of follow-up. We found a hazard ratio (95% confidence interval) of 0.96 (0.95, 0.96) per interquartile range (IQR) increase for trees and a HR of 0.97 (0.96, 0.97) per IQR increase for other green features. In contrast, we found a HR of 1.06 (1.04, 1.07) per IQR increase for grass. Associations of trees were generally stronger for low-income (i.e. Medicaid eligible) individuals, Black individuals, and in areas with a lower median household income and a higher population density. Increasing exposure to trees and other green features may reduce PD-involved hospitalizations, while increasing exposure to grass may increase hospitalizations. The protective associations may be stronger for marginalized individuals and individuals living in densely populated areas. [ABSTRACT FROM AUTHOR]

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. To evaluate associations of combined exposure to surrounding green, air pollution and traffic noise with poor mental health. 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 (PM 10 , PM 2.5), and nitrogen dioxide (NO 2)) 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. 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 NO 2 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. 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. • Surrounding green was inversely associated with poor mental health. • Air pollution and to a limited extent traffic noise were positively associated with poor mental health. • In multi exposure models, associations with surrounding green and air pollution attenuated, but remained significant. • The most consistent associations were observed with prescription of anxiolytics and prescription of hypnotics & sedatives. • Joint odds ratios of combined exposure were higher than the ORs of single exposure models. [ABSTRACT FROM AUTHOR]

Most climate-health studies focus on temperature; however, less is known about health effects of exposure to atmospheric moisture. Humid air limits sweat evaporation from the body and can in turn exert strain on the cardiovascular system. We evaluated associations of long-term exposure to summer specific humidity with cardiovascular disease (CVD), coronary heart disease (CHD) and cerebrovascular disease (CBV) hospitalization. We built an open cohort consisting of ∼63 million fee-for-service Medicare beneficiaries, aged ≥65, living in the contiguous US (2000–2016). We assessed zip code level summer average specific humidity and specific humidity variability, based on daily estimates from the Gridded Surface Meteorological dataset (∼4km spatial resolution). To estimate associations of summer specific humidity with first CVD, CHD, and CBV hospitalization, we used Cox-equivalent Poisson models adjusted for individual and area-level socioeconomic status indicators, temperature, and winter specific humidity. Higher summer average specific humidity was associated with an increased risk of CVD, CHD, and CBV hospitalization. We found hazard ratios (HRs) of 1.07 (95%CI: 1.07, 1.08) for CVD hospitalization, 1.08 (95%CI: 1.08, 1.09) for CHD hospitalization, and 1.07 (95%CI: 1.07, 1.08) for CBV hospitalization per IQR increase (4.0 g of water vapor/kg of dry air) in summer average specific humidity. Associations of summer average specific humidity were strongest for beneficiaries eligible for Medicaid and for beneficiaries with an unknown or other race. Higher summer specific humidity variability was also associated with increased risk of CVD, CHD, and CBV hospitalization. Associations were not affected by adjustment for temperature and regions of the US, as well as exclusion of potentially prevalent cases. Long-term exposure to higher summer average specific humidity and specific humidity variability were positively associated with CVD hospitalization. As global warming could increase humidity levels, our findings are important to assess potential health impacts of climate change. [ABSTRACT FROM AUTHOR]

Natural environments have been linked to decreased risk of cardiovascular disease (CVD) and respiratory disease (RSD) mortality. However, few cohort studies have looked at associations of natural environments with CVD or RSD hospitalization. The aim of this study was to evaluate these associations in a cohort of U.S. Medicare beneficiaries (∼63 million individuals). Our open cohort included all fee-for-service Medicare beneficiaries (2000–2016), aged ≥65, living in the contiguous U.S. We assessed zip code-level park cover based on the United States Geological Survey Protected Areas Database, average greenness (Normalized Difference Vegetation Index, NDVI), and percent blue space cover based on Landsat satellite images. Cox-equivalent Poisson models were used to estimate associations of the exposures with first CVD and RSD hospitalization in the full cohort and among those living in urban zip codes (≥1000 persons/mile2). NDVI was weakly negatively correlated with percent park cover (Spearman ρ = −0.23) and not correlated with percent blue space (Spearman ρ = 0.00). After adjustment for potential confounders, percent park cover was not associated with CVD or RSD hospitalization in the full or urban population. An IQR (0.27) increase in NDVI was negatively associated with CVD (HR: 0.97, 95%CI: 0.96, 0.97), but not with RSD hospitalization (HR: 0.99, 95%CI: 0.98, 1.00). In urban zip codes, an IQR increase in NDVI was positively associated with RSD hospitalization (HR: 1.02, 95%CI: 1.00, 1.03). In stratified analyses, percent park cover was negatively associated with CVD and RSD hospitalization for Medicaid eligible individuals and individuals living in low socioeconomic status neighborhoods in the urban population. We observed no associations of percent blue space cover with CVD or RSD hospitalization. This study suggests that natural environments may benefit cardiorespiratory health; however, benefits may be limited to certain contexts and certain health outcomes. [Display omitted] • Greenness and public park cover were weakly negatively correlated with each other. • Public parks were negatively associated with CVD hospitalization in low SES areas. • Greenness was negatively associated with CVD, but not RSD, hospitalizations. • Blue space cover was not associated with CVD and RSD hospitalization. [ABSTRACT FROM AUTHOR]

Individual-level studies with adjustment for important COVID-19 risk factors suggest positive associations of long-term air pollution exposure (particulate matter and nitrogen dioxide) with COVID-19 infection, hospitalisations and mortality. The evidence, however, remains limited and mechanisms unclear. We aimed to investigate these associations within UK Biobank, and to examine the role of underlying chronic disease as a potential mechanism. UK Biobank COVID-19 positive laboratory test results were ascertained via Public Health England and general practitioner record linkage, COVID-19 hospitalisations via Hospital Episode Statistics, and COVID-19 mortality via Office for National Statistics mortality records from March–December 2020. We used annual average outdoor air pollution modelled at 2010 residential addresses of UK Biobank participants who resided in England (n = 424,721). We obtained important COVID-19 risk factors from baseline UK Biobank questionnaire responses (2006–2010) and general practitioner record linkage. We used logistic regression models to assess associations of air pollution with COVID-19 outcomes, adjusted for relevant confounders, and conducted sensitivity analyses. We found positive associations of fine particulate matter (PM 2.5) and nitrogen dioxide (NO 2) with COVID-19 positive test result after adjustment for confounders and COVID-19 risk factors, with odds ratios of 1.05 (95% confidence intervals (CI) = 1.02, 1.08), and 1.05 (95% CI = 1.01, 1.08), respectively. PM 2.5 and NO 2 were positively associated with COVID-19 hospitalisations and deaths in minimally adjusted models, but not in fully adjusted models. No associations for PM 10 were found. In analyses with additional adjustment for pre-existing chronic disease, effect estimates were not substantially attenuated, indicating that underlying chronic disease may not fully explain associations. We found some evidence that long-term exposure to PM 2.5 and NO 2 was associated with a COVID-19 positive test result in UK Biobank, though not with COVID-19 hospitalisations or deaths. [Display omitted] • We assessed the associations of air pollution with COVID-19 outcomes in UK Biobank. • Long-term exposure to PM 2.5 and NO 2 was associated with COVID-19 positive test result. • Pre-existing chronic diseases did not fully explain associations. • Air pollution was not associated with COVID-19 hospitalisations or deaths. [ABSTRACT FROM AUTHOR]

• PM 2.5 and NO 2 were positively associated with CVD hospitalization. • Ozone was only positively associated with CVD hospitalization at low exposure levels. • Associations of PM 2.5 were stronger with higher greenness. • Associations of PM 2.5 were stronger with lower temperature, specific humidity and O x. Studies have observed associations between long-term air pollution and cardiovascular disease hospitalization. Little is known, however, about effect modification of these associations by greenness, temperature and humidity. We constructed an open cohort consisting of all fee-for-service Medicare beneficiaries, aged ≥ 65, living in the contiguous US from 2000 through 2016 (~63 million individuals). We assigned annual average PM 2.5 , NO 2 and ozone zip code concentrations. Cox-equivalent Poisson models were used to estimate associations with first cardiovascular disease (CVD), coronary heart disease (CHD) and cerebrovascular disease (CBV) hospitalization. PM 2.5 and NO 2 were both positively associated with CVD, CHD and CBV hospitalization, after adjustment for potential confounders. Associations were substantially stronger at the lower end of the exposure distributions. For CVD hospitalization, the hazard ratio (HR) of PM 2.5 was 1.041 (1.038, 1.045) per IQR increase (4.0 µg/m3) in the full study population and 1.327 (1.305, 1.350) per IQR increase for a subgroup with annual exposures always below 10 µg/m3 PM 2.5. Ozone was only positively associated with CVD, CHD and CBV hospitalization for the low-exposure subgroup (<40 ppb). Associations of PM 2.5 were stronger in areas with higher greenness, lower ozone and O x , lower summer and winter temperature and lower summer and winter specific humidity. PM 2.5 and NO 2 were positively associated with CVD, CHD and CBV hospitalization. Associations were more pronounced at low exposure levels. Associations of PM 2.5 were stronger with higher greenness, lower ozone and O x , lower temperature and lower specific humidity. [ABSTRACT FROM AUTHOR]

• Air pollution was associated with natural cause and cause-specific mortality. • Associations differed between the hybrid, LUR and dispersion models. • Associations of air pollutants estimated by LUR were generally weaker. 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. 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 NO 2 , PM 2.5 and BC exposure estimates based on two stochastic models (Dutch LUR, European-wide hybrid) and deterministic Dutch dispersion models. Spatial variability of PM 2.5 and BC exposure was smaller for LUR compared to hybrid and dispersion models. NO 2 exposure variability was similar for the three methods. Pearson correlations between hybrid, LUR and dispersion modeled NO 2 and BC ranged from 0.72 to 0.83; correlations for PM 2.5 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 NO 2 , a HR of 1.003 (95% CI: 0.993, 1.013) per 10 µg/m3 for LUR modeled NO 2 and a HR of 1.015 (95% CI: 1.005, 1.024) per 10 µg/m3 for dispersion modeled NO 2. 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. [ABSTRACT FROM AUTHOR]

[Display omitted] Green space, air pollution and traffic noise exposure may be associated with mental health in adolescents. We assessed the associations of long-term exposure to residential green space, ambient air pollution and traffic noise with mental wellbeing from age 11 to 20 years. We included 3059 participants of the Dutch PIAMA birth cohort who completed the five-item Mental Health Inventory (MHI-5) at ages 11, 14, 17 and/or 20 years. We estimated exposure to green space (the average Normalized Difference Vegetation Index (NDVI) and percentages of green space in circular buffers of 300 m, 1000 m and 3000 m), ambient air pollution (particulate matter (PM 10 and PM 2.5), nitrogen dioxide, PM 2.5 absorbance and the oxidative potential of PM 2.5) and road traffic and railway noise (L den) at the adolescents' home addresses at the times of completing the MHI-5. Associations with poor mental wellbeing (MHI-5 score ≤ 60) were assessed by generalized linear mixed models with a logit link, adjusting for covariates. The odds of poor mental wellbeing at age 11 to 20 years decreased with increasing exposure to green space in a 3000 m buffer (adjusted odds ratio (OR) 0.78 [95% CI 0.68–0.88] per IQR increase in the average NDVI; adjusted OR 0.77 [95% CI 0.67–0.88] per IQR increase in the total percentage of green space). These associations persisted after adjustment for air pollution and road traffic noise. Relationships between mental wellbeing and green space in buffers of 300 m and 1000 m were less consistent. Higher air pollution exposure was associated with higher odds of poor mental wellbeing, but these associations were strongly attenuated after adjustment for green space in a buffer of 3000 m, traffic noise and degree of urbanization. Traffic noise was not related to mental wellbeing throughout adolescence. Residential exposure to green space may be associated with a better mental wellbeing in adolescents. [ABSTRACT FROM AUTHOR]

Green space has been hypothesized to improve cardiometabolic health of adolescents, whereas air pollution and traffic noise may negatively impact cardiometabolic health. To examine the associations of green space, air pollution and traffic noise with cardiometabolic health in adolescents aged 12 and 16 years. Waist circumference, blood pressure, cholesterol and glycated hemoglobin (HbA1c) were measured in subsets of participants of the Dutch PIAMA birth cohort, who participated in medical examinations at ages 12 (n = 1505) and/or 16 years (n = 797). We calculated a combined cardiometabolic risk score for each participant, with a higher score indicating a higher cardiometabolic risk. We estimated exposure to green space (i.e. the average Normalized Difference Vegetation Index (NDVI) and percentages of green space in circular buffers of 300 m and 3000 m), air pollution (by land-use regression models) and traffic noise (using the Standard Model Instrumentation for Noise Assessments (STAMINA) model) at the adolescents' home addresses at the time of the medical examinations. We assessed associations of these exposures with cardiometabolic health outcomes at ages 12 and 16 by multiple linear regression, adjusting for potential confounders. We did not observe consistent patterns of associations of green space, air pollution and traffic noise with the cardiometabolic risk score, blood pressure, total cholesterol levels, the total/HDL cholesterol ratio and HbA1c. We found inverse associations of air pollution with waist circumference at both age 12 and 16. These associations weakened after adjustment for region, except for particulate matter with a diameter of <2.5 μm (PM 2.5) at age 12. The association of PM 2.5 with waist circumference at age 12 remained after adjustment for green space and road traffic noise (adjusted difference − 1.42 cm [95% CI −2.50, −0.35 cm] per 1.16 μg/m3 increase in PM 2.5). This study does not provide evidence for beneficial effects of green space or adverse effects of air pollution and traffic noise on cardiometabolic health in adolescents. Unlabelled Image • Green space, air pollution and traffic noise may influence children's health. • We studied associations of these exposures with cardiometabolic health in adolescents. • We found no associations with cardiometabolic health at either age 12 or 16 years. [ABSTRACT FROM AUTHOR]

Abstract Background In order to investigate associations between air pollution and adverse health effects consistent fine spatial air pollution surfaces are needed across large areas to provide cohorts with comparable exposures. The aim of this paper is to develop and evaluate fine spatial scale land use regression models for four major health relevant air pollutants (PM 2.5 , NO 2 , BC, O 3) across Europe. Methods We developed West-European land use regression models (LUR) for 2010 estimating annual mean PM 2.5 , NO 2 , BC and O 3 concentrations (including cold and warm season estimates for O 3). The models were based on AirBase routine monitoring data (PM 2.5 , NO 2 and O 3) and ESCAPE monitoring data (BC), and incorporated satellite observations, dispersion model estimates, land use and traffic data. Kriging was performed on the residual spatial variation from the LUR models and added to the exposure estimates. One model was developed using all sites (100%). Robustness of the models was evaluated by performing a five-fold hold-out validation and for PM 2.5 and NO 2 additionally with independent comparison at ESCAPE measurements. To evaluate the stability of each model's spatial structure over time, separate models were developed for different years (NO 2 and O 3 : 2000 and 2005; PM 2.5 : 2013). Results The PM 2.5 , BC, NO 2 , O 3 annual, O 3 warm season and O 3 cold season models explained respectively 72%, 54%, 59%, 65%, 69% and 83% of spatial variation in the measured concentrations. Kriging proved an efficient technique to explain a part of residual spatial variation for the pollutants with a strong regional component explaining respectively 10%, 24% and 16% of the R2 in the PM 2.5 , O 3 warm and O 3 cold models. Explained variance at fully independent sites vs the internal hold-out validation was slightly lower for PM 2.5 (65% vs 66%) and lower for NO 2 (49% vs 57%). Predictions from the 2010 model correlated highly with models developed in other years at the overall European scale. Conclusions We developed robust PM 2.5 , NO 2 , O 3 and BC hybrid LUR models. At the West-European scale models were robust in time, becoming less robust at smaller spatial scales. Models were applied to 100 × 100 m surfaces across Western Europe to allow for exposure assignment for 35 million participants from 18 European cohorts participating in the ELAPSE study. Highlights • Robust PM 2.5 , NO 2 , BC and O 3 hybrid LUR models at a 100x100 m resolution for Western Europe were developed • Models included large scale SAT and CTM estimates and fine scale traffic and land use and were further improved with kriging • Models were robust in time at European scale, becoming less robust at smaller spatial scales. [ABSTRACT FROM AUTHOR]