Green space may influence health through several pathways, for example, increased physical activity, enhanced social cohesion, reduced stress, and improved air quality. For green space to increase ...physical activity and social cohesion, spending time in green spaces is likely to be important.
We examined whether adolescents visit green spaces and for what purposes. Furthermore, we assessed the predictors of green space visits.
In this cross-sectional study, data for 1911 participants of the Dutch PIAMA (Prevention and Incidence of Asthma and Mite Allergy) birth cohort were analyzed. At age 17, adolescents reported how often they visited green spaces for physical activities, social activities, relaxation, and to experience nature and quietness. We assessed the predictors of green space visits altogether and for different purposes by log-binomial regression.
Fifty-three percent of the adolescents visited green spaces at least once a week in summer, mostly for physical and social activities. Adolescents reporting that a green environment was (very) important to them visited green spaces most frequently {adjusted prevalence ratio (PR) 95% confidence interval (CI) very vs. not important: 6.84 (5.10, 9.17) for physical activities and 4.76 (3.72, 6.09) for social activities}. Boys and adolescents with highly educated fathers visited green spaces more often for physical and social activities. Adolescents who own a dog visited green spaces more often to experience nature and quietness. Green space visits were not associated with the objectively measured quantity of residential green space, i.e., the average normalized difference vegetation index (NDVI) and percentages of urban, agricultural, and natural green space in circular buffers around the adolescents' homes.
Subjective variables are stronger predictors of green space visits in adolescents than the objectively measured quantity of residential green space. https://doi.org/10.1289/EHP2429.
Celotno besedilo
Dostopno za:
CEKLJ, DOBA, IZUM, KILJ, NUK, OILJ, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK, VSZLJ
Empirical spatial air pollution models have been applied extensively to assess exposure in epidemiological studies with increasingly sophisticated and complex statistical algorithms beyond ordinary ...linear regression. However, different algorithms have rarely been compared in terms of their predictive ability.
This study compared 16 algorithms to predict annual average fine particle (PM2.5) and nitrogen dioxide (NO2) concentrations across Europe. The evaluated algorithms included linear stepwise regression, regularization techniques and machine learning methods. Air pollution models were developed based on the 2010 routine monitoring data from the AIRBASE dataset maintained by the European Environmental Agency (543 sites for PM2.5 and 2399 sites for NO2), using satellite observations, dispersion model estimates and land use variables as predictors. We compared the models by performing five-fold cross-validation (CV) and by external validation (EV) using annual average concentrations measured at 416 (PM2.5) and 1396 sites (NO2) from the ESCAPE study. We further assessed the correlations between predictions by each pair of algorithms at the ESCAPE sites.
For PM2.5, the models performed similarly across algorithms with a mean CV R2 of 0.59 and a mean EV R2 of 0.53. Generalized boosted machine, random forest and bagging performed best (CV R2~0.63; EV R2 0.58–0.61), while backward stepwise linear regression, support vector regression and artificial neural network performed less well (CV R2 0.48–0.57; EV R2 0.39–0.46). Most of the PM2.5 model predictions at ESCAPE sites were highly correlated (R2 > 0.85, with the exception of predictions from the artificial neural network). For NO2, the models performed even more similarly across different algorithms, with CV R2s ranging from 0.57 to 0.62, and EV R2s ranging from 0.49 to 0.51. The predicted concentrations from all algorithms at ESCAPE sites were highly correlated (R2 > 0.9). For both pollutants, biases were low for all models except the artificial neural network. Dispersion model estimates and satellite observations were two of the most important predictors for PM2.5 models whilst dispersion model estimates and traffic variables were most important for NO2 models in all algorithms that allow assessment of the importance of variables.
Different statistical algorithms performed similarly when modelling spatial variation in annual average air pollution concentrations using a large number of training sites.
•Multiple statistical algorithms with very different assumptions were compared.•Despite the difference in modeling frameworks, predictions among the models exhibit generally good agreement.•The use of an external evaluation dataset strengthens evaluation by cross-validation.
Exposure to ambient particulate matter (PM) has been associated with adverse cardiovascular effects in epidemiological studies. Current knowledge of independent effects of individual PM ...characteristics remains limited.
Using a semi-experimental design we investigated which PM characteristics were consistently associated with blood biomarkers believed to be predictive of the risk of cardiovascular events. We exposed healthy adult volunteers at 5 different locations chosen to provide PM exposure contrasts with reduced correlations among PM characteristics. Each of the 31 volunteers was exposed for 5 h, exercising intermittently, 3-7 times at different sites from March to October 2009. Extensive on-site exposure characterization included measurements of PM mass and number concentration, elemental- (EC) and organic carbon (OC), trace metals, sulfate, nitrate, and PM oxidative potential (OP). Before and 2 h and 18 h after exposure we measured acute vascular blood biomarkers - C-reactive protein, fibrinogen, platelet counts, von Willebrand Factor, and tissue plasminogen activator/plasminogen activator inhibitor-1 complex. We used two-pollutant models to assess which PM characteristics were most consistently associated with the measured biomarkers.
We found OC, nitrate and sulfate to be most consistently associated with different biomarkers of acute cardiovascular risk. Associations with PM mass concentrations and OP were less consistent, whereas other measured components of the air pollution mixture, including PNC, EC, trace metals and NO2, were not associated with the biomarkers after adjusting for other pollutants.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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 (PM2.5) at age 12. The association of PM2.5 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 PM2.5).
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.
Display omitted
•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.
Relatively high concentrations of ultrafine particles (UFPs) have been observed around airports, in which aviation and road traffic emissions are the major sources. This raises concerns about the ...potential health impacts of airport UFPs, particularly in comparison to those emitted by road traffic. UFPs mainly derived from aviation or road traffic emissions were collected from a location near a major international airport, Amsterdam-Schiphol airport (AMS), depending on the wind direction, along with UFPs from an aircraft turbine engine at low and full thrust. Human bronchial epithelial cells (Calu-3) model in combination with an air-liquid interface (ALI) cloud system was used for the in vitro exposure to UFPs at low doses ranging from 0.09 to 2.07 μg/cm2. Particle size distribution was measured. Cell viability, cytotoxicity and inflammatory potential (interleukin (IL) 6 and 8 secretion) on Calu-3 cells were assessed after exposure for 24 h. The biological measurements on Calu-3 cells confirm that pro-inflammatory responses still can be activated at the high cell viability (> 80%) and low cytotoxicity. By the Benchmark Dose (BMD) analysis, Airport and Non-Airport (road traffic) UFPs as well as UFPs samples from a turbine engine have similar toxic properties. Our results suggest that UFPs from aviation and road traffic in airport surroundings may have similar adverse effects on public health.
•Airport and road traffic UFPs can activate inflammation in Calu-3 cells.•Airport UFPs exert similar toxicity compared to UFPs from road traffic emission.•ALI condition promotes cellular responses to particles at low exposed dose.
We developed Europe-wide models of long-term exposure to eight elements (copper, iron, potassium, nickel, sulfur, silicon, vanadium, and zinc) in particulate matter with diameter <2.5 μm (PM2.5) ...using standardized measurements for one-year periods between October 2008 and April 2011 in 19 study areas across Europe, with supervised linear regression (SLR) and random forest (RF) algorithms. Potential predictor variables were obtained from satellites, chemical transport models, land-use, traffic, and industrial point source databases to represent different sources. Overall model performance across Europe was moderate to good for all elements with hold-out-validation R-squared ranging from 0.41 to 0.90. RF consistently outperformed SLR. Models explained within-area variation much less than the overall variation, with similar performance for RF and SLR. Maps proved a useful additional model evaluation tool. Models differed substantially between elements regarding major predictor variables, broadly reflecting known sources. Agreement between the two algorithm predictions was generally high at the overall European level and varied substantially at the national level. Applying the two models in epidemiological studies could lead to different associations with health. If both between- and within-area exposure variability are exploited, RF may be preferred. If only within-area variability is used, both methods should be interpreted equally.
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 (PM10 and PM2.5), nitrogen dioxide, PM2.5 absorbance and the oxidative potential of PM2.5) and road traffic and railway noise (Lden) 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.
•Surrounding green, air pollution and traffic noise were moderately correlated.•Surrounding green was not significantly associated with non-accidental mortality.•Analyses restricted to non-movers did ...not change the findings.•Air pollution was not significantly associated with non-accidental mortality.
Most previous studies that investigated associations of surrounding green, air pollution or traffic noise with mortality focused on single exposures.
The aim of this study was to evaluate combined associations of long-term residential exposure to surrounding green, air pollution and traffic noise with total non-accidental and cause-specific mortality.
We linked a national health survey (Public Health Monitor, PHM) conducted in 2012 to the Dutch longitudinal mortality database. Subjects of the survey who were 30 years or older on 1 January 2013 (n = 339,633) were followed from 1 January 2013 till 31 December 2017. We used Cox proportional hazard models to evaluate associations of residential surrounding green (including the average Normalized Difference Vegetation Index (NDVI) in buffers of 300 m and 1000 m), annual average air pollutant concentrations (including particulate matter (PM10, PM2.5), nitrogen dioxide (NO2)) and traffic noise with non-accidental, circulatory disease, respiratory disease, lung cancer and neurodegenerative disease mortality.
We observed 26,886 non-accidental deaths over 1.627.365 person-years of follow-up. Surrounding green, air pollution and traffic noise exposure were not significantly associated with non-accidental or cause-specific mortality. For non-accidental mortality, we found a hazard ratio (HR) of 0.99 (0.98, 1.01) per IQR increase in NDVI 300 m, a HR of 0.99 (95% CI: 0.97, 1.01) per IQR increase in NO2, a HR of 0.98 (0.97, 1.00) per IQR increase in PM2.5 and a HR of 0.99 (95% CI: 0.97, 1.01) per IQR increase in road-traffic noise. Analyses restricted to non-movers or excluding subjects aged 85+ years did not change the findings.
We found no evidence for associations of long-term residential exposures to surrounding green, air pollution and traffic noise with non-accidental or cause-specific mortality in a large population based survey in the Netherlands, possibly related to the relatively short follow-up period.
Both social and physical neighbourhood factors may affect residents' health, but few studies have considered the combination of several exposures in relation to individual health status.
To assess a ...range of different potentially relevant physical and social environmental characteristics in a sample of small neighbourhoods in the Netherlands, to study their mutual correlations and to explore associations with morbidity of residents using routinely collected general practitioners' (GPs') data.
For 135 neighbourhoods in 43 Dutch municipalities, we could assess area-level social cohesion and collective efficacy using external questionnaire data, urbanisation, amount of greenspace and water areas, land use diversity, air pollution (particulate matter (PM) with a diameter <10 μm (PM10), PM <2.5 μm (PM2.5) and nitrogen dioxide (NO2), and noise (from road traffic and from railways). Health data of the year 2013 from GPs were available for 4450 residents living in these 135 neighbourhoods, that were representative for the entire country. Morbidity of 10 relevant physical or mental health groupings was considered. Individual-level socio-economic information was obtained from Statistics Netherlands. Associations between neighbourhood exposures and individual morbidity were quantified using multilevel mixed effects logistic regression analyses, adjusted for sex, age (continuous), household income and socio-economic status (individual level) and municipality and neighbourhood (group level).
Most physical exposures were strongly correlated with degree of urbanisation. Social cohesion and collective efficacy tended to be higher in less urbanised municipalities. Degree of urbanisation was associated with higher morbidity of all disease groupings. A higher social cohesion at the municipal level coincided with a lower prevalence of depression, migraine/severe headache and Medically Unexplained Physical Symptoms (MUPS). An increase in both natural and agricultural greenspace in the neighbourhood was weakly associated with less morbidity for all conditions. A high land use diversity was consistently associated with lower morbidities, in particular among non-occupationally active individuals.
A high diversity in land use of neighbourhoods may be beneficial for physical and mental health of the inhabitants. If confirmed, this may be incorporated into urban planning, in particular regarding the diversity of greenspace.
•Less crowded neighbourhoods have both social and environmental benefits.•Greenspace near the home may be beneficial for physical and mental health.•A diverse land use in the neighbourhood may have health benefits for the residents.
Fatigue is a common side effect of tyrosine kinase inhibitor (TKI) therapy in chronic myeloid leukemia (CML) patients. However, the prevalence of TKI-induced fatigue remains uncertain and little is ...known about predictors of fatigue and its relationship with physical activity. In this study, 220 CML patients receiving TKI therapy and 110 gender- and age-matched controls completed an online questionnaire to assess fatigue severity and fatigue predictors (Part 1). In addition, physical activity levels were objectively assessed for 7 consecutive days in 138 severely fatigued and non-fatigued CML patients using an activity monitor (Part 2). We demonstrated that the prevalence of severe fatigue was 55.5% in CML patients and 10.9% in controls (P<0.001). We identified five predictors of fatigue in our CML population: age (OR 0.96, 95% CI 0.93-0.99), female gender (OR 1.76, 95% CI 0.92-3.34), Charlson Comorbidity Index (OR 1.91, 95% CI 1.16-3.13), the use of comedication known to cause fatigue (OR 3.43, 95% CI 1.58-7.44), and physical inactivity (OR of moderately active, vigorously active and very vigorously active compared to inactivity 0.43 (95% CI 0.12-1.52), 0.22 (95% CI 0.06-0.74), and 0.08 (95% CI 0.02-0.26), respectively). Objective monitoring of activity patterns confirmed that fatigued CML patients performed less physical activity on both light (P=0.017) and moderate to vigorous intensity (P=0.009). In fact, compared to the non-fatigued patients, fatigued CML patients performed 1 hour less of physical activity per day and took 2000 fewer steps per day. Our findings facilitate the identification of patients at risk of severe fatigue and highlight the importance to set the reduction of fatigue as a treatment goal in CML care. This study was registered at The Netherlands Trial Registry, NTR7308 (Part 1) and NTR7309 (Part 2).
PDF
