Neighbourhood social and physical environment and general practitioner assessed morbidity.

The aim of our study was to investigate the association between health enhancing and threatening, and social and physical aspects of the neighbourhood environment and general practitioner (GP) assessed morbidity of the people living there, in order to find out whether the effects of environmental characteristics add up or modify each other. We combined GP electronic health records with environmental data on neighbourhoods in the Netherlands. Cross-classified logistic multilevel models show the importance of taking into account several environmental characteristics and confounders, as social capital effects on the prevalence of morbidity disappear when other area characteristics are taken into account. Stratification by area socio-economic status, shows that the association between environmental characteristics and the prevalence of morbidity is stronger for people living in low SES areas. In low SES areas, green space seems to alleviate effects of air pollution on the prevalence of high blood pressure and diabetes, while the effects of green space and social capital reinforce each other

Associations between lifestyle and air pollution exposure: Potential for confounding in large administrative data cohorts.

Cohorts based on administrative data have size advantages over individual cohorts in investigating air pollution risks, but often lack in-depth information on individual risk factors related to lifestyle. If there is a correlation between lifestyle and air pollution, omitted lifestyle variables may result in biased air pollution risk estimates. Correlations between lifestyle and air pollution can be induced by socio-economic status affecting both lifestyle and air pollution exposure

Long-term exposure to particulate matter, NO2 and the oxidative potential of particulates and diabetes prevalence in a large national health survey.

The evidence from observational epidemiological studies of a link between long-term air pollution exposure and diabetes prevalence and incidence is currently mixed. Some studies found the strongest associations of diabetes with fine particles, other studies with nitrogen dioxide and some studies found no associations

Long-term air pollution exposure and Parkinson's disease mortality in a large pooled European cohort: An ELAPSE study

BACKGROUND: The link between exposure to ambient air pollution and mortality from cardiorespiratory diseases is well established, while evidence on neurodegenerative disorders including Parkinson's Disease (PD) remains limited. OBJECTIVE: We examined the association between long-term exposure to ambient air pollution and PD mortality in seven European cohorts. METHODS: Within the project 'Effects of Low-Level Air Pollution: A Study in Europe' (ELAPSE), we pooled data from seven cohorts among six European countries. Annual mean residential concentrations of fine particulate matter (PM 2.5), nitrogen dioxide (NO 2), black carbon (BC), and ozone (O 3), as well as 8 PM 2.5 components (copper, iron, potassium, nickel, sulphur, silicon, vanadium, zinc), for 2010 were estimated using Europe-wide hybrid land use regression models. PD mortality was defined as underlying cause of death being either PD, secondary Parkinsonism, or dementia in PD. We applied Cox proportional hazard models to investigate the associations between air pollution and PD mortality, adjusting for potential confounders. RESULTS: Of 271,720 cohort participants, 381 died from PD during 19.7 years of follow-up. In single-pollutant analyses, we observed positive associations between PD mortality and PM 2.5 (hazard ratio per 5 µg/m 3: 1.25; 95% confidence interval: 1.01-1.55), NO 2 (1.13; 0.95-1.34 per 10 µg/m 3), and BC (1.12; 0.94-1.34 per 0.5 × 10 -5m -1), and a negative association with O 3 (0.74; 0.58-0.94 per 10 µg/m 3). Associations of PM 2.5, NO 2, and BC with PD mortality were linear without apparent lower thresholds. In two-pollutant models, associations with PM 2.5 remained robust when adjusted for NO 2 (1.24; 0.95-1.62) or BC (1.28; 0.96-1.71), whereas associations with NO 2 or BC attenuated to null. O 3 associations remained negative, but no longer statistically significant in models with PM 2.5. We detected suggestive positive associations with the potassium component of PM 2.5. CONCLUSION: Long-term exposure to PM 2.5, at levels well below current EU air pollution limit values, may contribute to PD mortality

Long-term exposure to low-level air pollution and incidence of chronic obstructive pulmonary disease: The ELAPSE project.

BACKGROUND: Air pollution has been suggested as a risk factor for chronic obstructive pulmonary disease (COPD), but evidence is sparse and inconsistent. OBJECTIVES: We examined the association between long-term exposure to low-level air pollution and COPD incidence. METHODS: Within the 'Effects of Low-Level Air Pollution: A Study in Europe' (ELAPSE) study, we pooled data from three cohorts, from Denmark and Sweden, with information on COPD hospital discharge diagnoses. Hybrid land use regression models were used to estimate annual mean concentrations of particulate matter with a diameter < 2.5 µm (PM2.5), nitrogen dioxide (NO2), and black carbon (BC) in 2010 at participants' baseline residential addresses, which were analysed in relation to COPD incidence using Cox proportional hazards models. RESULTS: Of 98,058 participants, 4,928 developed COPD during 16.6 years mean follow-up. The adjusted hazard ratios (HRs) and 95% confidence intervals for associations with COPD incidence were 1.17 (1.06, 1.29) per 5 µg/m3 for PM2.5, 1.11 (1.06, 1.16) per 10 µg/m3 for NO2, and 1.11 (1.06, 1.15) per 0.5 10-5m-1 for BC. Associations persisted in subset participants with PM2.5 or NO2 levels below current EU and US limit values and WHO guidelines, with no evidence for a threshold. HRs for NO2 and BC remained unchanged in two-pollutant models with PM2.5, whereas the HR for PM2.5 was attenuated to unity with NO2 or BC. CONCLUSIONS: Long-term exposure to low-level air pollution is associated with the development of COPD, even below current EU and US limit values and possibly WHO guidelines. Traffic-related pollutants NO2 and BC may be the most relevant

The impact of particle filtration on indoor air quality in a classroom near a highway

A pilot study was performed to investigate whether the application of a new mechanical ventilation system with a fine F8 (MERV14) filter could improve indoor air quality in a high school near the Amsterdam ring road. PM10, PM2.5 and black carbon (BC) concentrations were measured continuously inside an occupied intervention classroom and outside the school during three sampling periods in the winter of 2013/2014. Initially, three weeks of baseline measurements were performed, with the existing ventilation system and normal ventilation habits. Next, an intervention study was performed. A new ventilation system was installed in the classroom, and measurements were performed during 8 school weeks, in alternating 2-week periods with and without the filter in the ventilation system under otherwise identical ventilation conditions. Indoor/outdoor ratios measured during the weeks with filter were compared with those measured without filter to evaluate the ability of the F8 filter to improve indoor air quality. During teaching hours, the filter reduced BC exposure by, on average, 36%. For PM10 and PM2.5, a reduction of 34% and 30% was found, respectively. This implies that application of a fine filter can reduce the exposure of schoolchildren to traffic exhaust at hot spot locations by about one-third. This article is protected by copyright. All rights reserved

The impact of particle filtration on indoor air quality in a classroom near a highway

A pilot study was performed to investigate whether the application of a new mechanical ventilation system with a fine F8 (MERV14) filter could improve indoor air quality in a high school near the Amsterdam ring road. PM10, PM2.5 and black carbon (BC) concentrations were measured continuously inside an occupied intervention classroom and outside the school during three sampling periods in the winter of 2013/2014. Initially, three weeks of baseline measurements were performed, with the existing ventilation system and normal ventilation habits. Next, an intervention study was performed. A new ventilation system was installed in the classroom, and measurements were performed during 8 school weeks, in alternating 2-week periods with and without the filter in the ventilation system under otherwise identical ventilation conditions. Indoor/outdoor ratios measured during the weeks with filter were compared with those measured without filter to evaluate the ability of the F8 filter to improve indoor air quality. During teaching hours, the filter reduced BC exposure by, on average, 36%. For PM10 and PM2.5, a reduction of 34% and 30% was found, respectively. This implies that application of a fine filter can reduce the exposure of schoolchildren to traffic exhaust at hot spot locations by about one-third. This article is protected by copyright. All rights reserved

High resolution annual average air pollution concentration maps for the Netherlands

Long-term exposure to air pollution is considered a major public health concern and has been related to overall mortality and various diseases such as respiratory and cardiovascular disease. Due to the spatial variability of air pollution concentrations, assessment of individual exposure to air pollution requires spatial datasets at high resolution. Combining detailed air pollution maps with personal mobility and activity patterns allows for an improved exposure assessment. We present high-resolution datasets for the Netherlands providing average ambient air pollution concentration values for the year 2009 for NO 2 ,NO x , PM 2.5 ,PM 2.5absorbance and PM 10. The raster datasets on 5 × 5 m grid cover the entire Netherlands and were calculated using the land use regression models originating from the European Study of Cohorts for Air Pollution Effects (ESCAPE) project. Additional datasets with nationwide and regional measurements were used to evaluate the generated concentration maps. The presented datasets allow for spatial aggregations on different scales, nationwide individual exposure assessment, and the integration of activity patterns in the exposure estimation of individuals

Associations between the fast-food environment and diabetes prevalence in the Netherlands: a cross-sectional study

Background: Diabetes is a major health concern and is influenced by lifestyle, which can be affected by the neighbourhood environment. Specifically, a fast-food environment can influence eating behaviours and thus diabetes prevalence. Therefore, our aim was to assess the relationship between fast-food environment and diabetes prevalence for urban and rural environments in the Netherlands, using multiple indicators and buffer sizes. Methods: In this cross-sectional study, data on a nationwide sample of adults older than 19 years in the Netherlands were taken from the 2012 Dutch national health survey (from Public Health Monitor), in which participants were surveyed on topics related to health and lifestyle behaviour. Fast-food outlet exposures were determined within street-network buffers of 100 m, 400 m, 1000 m, and 1500 m around residential addresses. For each of these buffers, three indicators were calculated: presence (yes or no) of fast-food outlets, fast-food outlet density, and ratio. Logistic regression analyses were carried out to assess associations of these indicators with diabetes, adjusting for potential confounders and stratifying into urban and rural areas. Findings: 387 195 adults were surveyed, 284 793 of whom were included in the study. 22 951 (8%) reported having diabetes. Fast-food outlet exposures were positively associated with diabetes prevalence. We did not observe large differences between urban and rural areas. The effect estimates were small for all indicators. For example, in the 400 m buffer in the urban environment, the odds ratio (OR) for having diabetes among people with a fast-food outlet present compared with those without, was 1·006 (95% CI 1·003–1·009) using the presence indicator. The presence indicator showed higher effect estimates and the most consistent results across buffer sizes (ranging from OR 1·005 [95% CI 1·000–1·010] with the 1000 m buffer to 1·016 [1·005–1·028] with the 1500 m buffer in urban areas and from 1·002 [0·998–1·005] with the 1500 m buffer to 1·009 [1·006–1·018] with the 100 m buffer in rural areas) compared with the density and ratio indicators. Interpretation: The results confirm the evidence that the fast-food outlet environment is a diabetes risk factor. All data included were at the individual level and the variability was ensured by the spatial distribution and number of participants. In this study, we only accounted for residential exposure because we were unable to account for exposure outside the residential environment. The findings of this study encourage local governments to consider the potential adverse effects of fast-food exposures and aim at minimising unhealthy food access. Funding: Global Geo Health Data Centre, Utrecht University, Netherlands