195 research outputs found

    Long-term exposure to low ambient air pollution concentrations and mortality among 28 million people: results from seven large European cohorts within the ELAPSE project

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    Background Long-term exposure to ambient air pollution has been associated with premature mortality, but associations at concentrations lower than current annual limit values are uncertain. We analysed associations between low-level air pollution and mortality within the multicentre study Effects of Low-Level Air Pollution: A Study in Europe (ELAPSE). Methods In this multicentre longitudinal study, we analysed seven population-based cohorts of adults (age ≥30 years) within ELAPSE, from Belgium, Denmark, England, the Netherlands, Norway, Rome (Italy), and Switzerland (enrolled in 2000-11; follow-up until 2011-17). Mortality registries were used to extract the underlying cause of death for deceased individuals. Annual average concentrations of fine particulate matter (PM2·5), nitrogen dioxide (NO2), black carbon, and tropospheric warm-season ozone (O3) from Europe-wide land use regression models at 100 m spatial resolution were assigned to baseline residential addresses. We applied cohort-specific Cox proportional hazard models with adjustment for area-level and individual-level covariates to evaluate associations with non-accidental mortality, as the main outcome, and with cardiovascular, non-malignant respiratory, and lung cancer mortality. Subset analyses of participants living at low pollutant concentrations (as per predefined values) and natural splines were used to investigate the concentration-response function. Cohort-specific effect estimates were pooled in a random-effects meta-analysis. Findings We analysed 28 153 138 participants contributing 257 859 621 person-years of observation, during which 3 593 741 deaths from non-accidental causes occurred. We found significant positive associations between non-accidental mortality and PM2·5, NO2, and black carbon, with a hazard ratio (HR) of 1·053 (95% CI 1·021-1·085) per 5 μg/m3 increment in PM2·5, 1·044 (1·019-1·069) per 10 μg/m3 NO2, and 1·039 (1·018-1·059) per 0·5 × 10−5/m black carbon. Associations with PM2·5, NO2, and black carbon were slightly weaker for cardiovascular mortality, similar for non-malignant respiratory mortality, and stronger for lung cancer mortality. Warm-season O3 was negatively associated with both non-accidental and cause-specific mortality. Associations were stronger at low concentrations: HRs for non-accidental mortality at concentrations lower than the WHO 2005 air quality guideline values for PM2·5 (10 μg/m3) and NO2 (40 μg/m3) were 1·078 (1·046-1·111) per 5 μg/m3 PM2·5 and 1·049 (1·024-1·075) per 10 μg/m3 NO2. Similarly, the association between black carbon and non-accidental mortality was highest at low concentrations, with a HR of 1·061 (1·032-1·092) for exposure lower than 1·5× 10−5/m, and 1·081 (0·966-1·210) for exposure lower than 1·0× 10−5/m. Interpretation Long-term exposure to concentrations of PM2·5 and NO2 lower than current annual limit values was associated with non-accidental, cardiovascular, non-malignant respiratory, and lung cancer mortality in seven large European cohorts. Continuing research on the effects of low concentrations of air pollutants is expected to further inform the process of setting air quality standards in Europe and other global regions

    Особливості державного регулювання інвестиційно-інноваційної діяльності, в сфері екології

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    BACKGROUND: Particulate matter (PM) air pollution is a human lung carcinogen; however, the components responsible have not been identified. We assessed the associations between PM components and lung cancer incidence. METHODS: We used data from 14 cohort studies in eight European countries. We geocoded baseline addresses and assessed air pollution with land-use regression models for eight elements (Cu, Fe, K, Ni, S, Si, V and Zn) in size fractions of PM2.5 and PM10. We used Cox regression models with adjustment for potential confounders for cohort-specific analyses and random effect models for meta-analysis. RESULTS: The 245,782 cohort members contributed 3,229,220 person-years at risk. During follow-up (mean, 13.1 years), 1878 incident cases of lung cancer were diagnosed. In the meta-analyses, elevated hazard ratios (HRs) for lung cancer were associated with all elements except V; none was statistically significant. In analyses restricted to participants who did not change residence during follow-up, statistically significant associations were found for PM2.5 Cu (HR, 1.25; 95% CI, 1.01-1.53 per 5 ng/m(3)), PM10 Zn (1.28; 1.02-1.59 per 20 ng/m(3)), PM10 S (1.58; 1.03-2.44 per 200 ng/m(3)), PM10 Ni (1.59; 1.12-2.26 per 2 ng/m(3)) and PM10 K (1.17; 1.02-1.33 per 100 ng/m(3)). In two-pollutant models, associations between PM10 and PM2.5 and lung cancer were largely explained by PM2.5 S. CONCLUSIONS: This study indicates that the association between PM in air pollution and lung cancer can be attributed to various PM components and sources. PM containing S and Ni might be particularly important

    Cancer risks in populations living near landfill sites in Great Britain

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    Previous studies have raised concerns about possible excess risks of bladder, brain and hepatobiliary cancers and leukaemias near landfill sites. Several cancers have been implicated, but no consistent pattern has emerged. We present a large nationwide analysis of selected cancers near landfill sites in Great Britain. The base population comprised people living within 2 km of 9565 (from a total of 19 196) landfill sites that were operational at some time from 1982 to 1997, with populations living more than 2 km from a landfill as reference. Risks of cancers at the above sites were computed with adjustment for age, sex, year of diagnosis, region and deprivation. National post-coded registers provided a total of 341 856 640 person–years for the adult cancer analyses and 113 631 443 person–years for childhood leukaemia. There were 89 786 cases of bladder cancer, 36 802 cases of brain cancer, 21 773 cases of hepatobiliary cancer, 37 812 cases of adult leukaemia and 3973 cases of childhood leukaemia. In spite of the very large scale of this national study, we found no excess risks of cancers of the bladder and brain, hepatobiliary cancer or leukaemia, in populations living within 2 km of landfill sites. The results were similar if the analysis were restricted to landfill sites licensed to carry special (hazardous) waste. Our results do not support suggestions of excess risks of cancer associated with landfill sites reported in other studies

    A review of exposure assessment methods for epidemiological studies of health effects related to industrially contaminated sites

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    BACKGROUND: this paper is based upon work from COST Action ICSHNet. Health risks related to living close to industrially contaminated sites (ICSs) are a public concern. Toxicology-based risk assessment of single contaminants is the main approach to assess health risks, but epidemiological studies which investigate the relationships between exposure and health directly in the affected population have contributed important evidence. Limitations in exposure assessment have substantially contributed to uncertainty about associations found in epidemiological studies. OBJECTIVES: to examine exposure assessment methods that have been used in epidemiological studies on ICSs and to provide recommendations for improved exposure assessment in epidemiological studies by comparing exposure assessment methods in epidemiological studies and risk assessments. METHODS: after defining the multi-media framework of exposure related to ICSs, we discussed selected multi-media models applied in Europe. We provided an overview of exposure assessment in 54 epidemiological studies from a systematic review of hazardous waste sites; a systematic review of 41 epidemiological studies on incinerators and 52 additional studies on ICSs and health identified for this review. RESULTS: we identified 10 multi-media models used in Europe primarily for risk assessment. Recent models incorporated estimation of internal biomarker levels. Predictions of the models differ particularly for the routes ‘indoor air inhalation’ and ‘vegetable consumption’. Virtually all of the 54 hazardous waste studies used proximity indicators of exposure, based on municipality or zip code of residence (28 studies) or distance to a contaminated site (25 studies). One study used human biomonitoring. In virtually all epidemiological studies, actual land use was ignored. In the 52 additional studies on contaminated sites, proximity indicators were applied in 39 studies, air pollution dispersion modelling in 6 studies, and human biomonitoring in 9 studies. Exposure assessment in epidemiological studies on incinerators included indicators (presence of source in municipality and distance to the incinerator) and air dispersion modelling. Environmental multi-media modelling methods were not applied in any of the three groups of studies. CONCLUSIONS: recommendations for refined exposure assessment in epidemiological studies included the use of more sophisticated exposure metrics instead of simple proximity indicators where feasible, as distance from a source results in misclassification of exposure as it ignores key determinants of environmental fate and transport, source characteristics, land use, and human consumption behaviour. More validation studies using personal exposure or human biomonitoring are needed to assess misclassification of exposure. Exposure assessment should take more advantage of the detailed multi-media exposure assessment procedures developed for risk assessment. The use of indicators can be substantially improved by linking definition of zones of exposure to existing knowledge of extent of dispersion. Studies should incorporate more often land use and individual behaviour

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

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    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 (PM2.5), nitrogen dioxide (NO2), black carbon (BC), and ozone (O3), as well as 8 PM2.5 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 PM2.5 (hazard ratio per 5 µg/m3: 1.25; 95% confidence interval: 1.01-1.55), NO2 (1.13; 0.95-1.34 per 10 µg/m3), and BC (1.12; 0.94-1.34 per 0.5 × 10-5m-1), and a negative association with O3 (0.74; 0.58-0.94 per 10 µg/m3). Associations of PM2.5, NO2, and BC with PD mortality were linear without apparent lower thresholds. In two-pollutant models, associations with PM2.5 remained robust when adjusted for NO2 (1.24; 0.95-1.62) or BC (1.28; 0.96-1.71), whereas associations with NO2 or BC attenuated to null. O3 associations remained negative, but no longer statistically significant in models with PM2.5. We detected suggestive positive associations with the potassium component of PM2.5. CONCLUSION: Long-term exposure to PM2.5, at levels well below current EU air pollution limit values, may contribute to PD mortality

    Air quality and urban sustainable development: the application of machine learning tools

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    [EN] Air quality has an efect on a population¿s quality of life. As a dimension of sustainable urban development, governments have been concerned about this indicator. This is refected in the references consulted that have demonstrated progress in forecasting pollution events to issue early warnings using conventional tools which, as a result of the new era of big data, are becoming obsolete. There are a limited number of studies with applications of machine learning tools to characterize and forecast behavior of the environmental, social and economic dimensions of sustainable development as they pertain to air quality. This article presents an analysis of studies that developed machine learning models to forecast sustainable development and air quality. Additionally, this paper sets out to present research that studied the relationship between air quality and urban sustainable development to identify the reliability and possible applications in diferent urban contexts of these machine learning tools. To that end, a systematic review was carried out, revealing that machine learning tools have been primarily used for clustering and classifying variables and indicators according to the problem analyzed, while tools such as artifcial neural networks and support vector machines are the most widely used to predict diferent types of events. The nonlinear nature and synergy of the dimensions of sustainable development are of great interest for the application of machine learning tools.Molina-Gómez, NI.; Díaz-Arévalo, JL.; López Jiménez, PA. (2021). Air quality and urban sustainable development: the application of machine learning tools. 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    Residential greenspace and lung function decline over 20 years in a prospective cohort: the ECRHS study

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    Background The few studies that have examined associations between greenspace and lung function in adulthood have yielded conflicting results and none have examined whether the rate of lung function decline is affected. Objective We explored the association between residential greenspace and change in lung function over 20 years in 5559 adults from 22 centers in 11 countries participating in the population-based, international European Community Respiratory Health Survey. Methods Forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC) were measured by spirometry when participants were approximately 35 (1990–1994), 44 (1999–2003), and 55 (2010–2014) years old. Greenness was assessed as the mean Normalized Difference Vegetation Index (NDVI) in 500 m, 300 m, and 100 m circular buffers around the residential addresses at the time of lung function measurement. Green spaces were defined as the presence of agricultural, natural, or urban green spaces in a circular 300 m buffer. Associations of these greenspace parameters with the rate of lung function change were assessed using adjusted linear mixed effects regression models with random intercepts for subjects nested within centers. Sensitivity analyses considered air pollution exposures. Results A 0.2-increase (average interquartile range) in NDVI in the 500 m buffer was consistently associated with a faster decline in FVC (−1.25 mL/year [95% confidence interval: −2.18 to −0.33]). These associations were especially pronounced in females and those living in areas with low PM10 levels. We found no consistent associations with FEV1 and the FEV1/FVC ratio. Residing near forests or urban green spaces was associated with a faster decline in FEV1, while agricultural land and forests were related to a greater decline in FVC. Conclusions More residential greenspace was not associated with better lung function in middle-aged European adults. Instead, we observed slight but consistent declines in lung function parameters. The potentially detrimental association requires verification in future studies

    Study protocol of the European Urban Burden of Disease Project: a health impact assessment study

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    Introduction Cities have long been known to be society’s predominant engine of innovation and wealth creation, yet they are also hotspots of pollution and disease partly due to current urban and transport practices. The aim of the European Urban Burden of Disease project is to evaluate the health burden and its determinants related to current and future potential urban and transport planning practices and related exposures in European cities and make this evidence available for policy and decision making for healthy and sustainable futures. Methods and analysis Drawing on an established comparative risk assessment methodology (ie, Urban and Transport Planning Health Impact Assessment) tool), in nearly 1000 European cities we will (1) quantify the health impacts of current urban and transport planning related exposures (eg, air pollution, noise, excess heat, lack of green space) (2) and evaluate the relationship between current levels of exposure, health impacts and city characteristics (eg, size, density, design, mobility) (3) rank and compare the cities based on exposure levels and the health impacts, (4) in a number of selected cities assess in-depth the linkages between urban and transport planning, environment, physical activity and health, and model the health impacts of alternative and realistic urban and transport planning scenarios, and, finally, (5) construct a healthy city index and set up an effective knowledge translation hub to generate impact in society and policy. Ethics and dissemination All data to be used in the project are publicly available data and do not need ethics approval. We will request consent for personal data on opinions and views and create data agreements for those providing information on current and future urban and transport planning scenarios. For dissemination and to generate impact, we will create a knowledge translation hub with information tailored to various stakeholders

    Advancing global health through environmental and public health tracking

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    Challenges such as climate change, resource depletion (with its huge implications for human health and wellbeing), and persistent social inequalities in health have been identified as global public health issues with implications for both communicable and noncommunicable diseases. This contributes to pressure on healthcare systems, as well as societal systems that affect health. A novel strategy to tackle these multiple, interacting and interdependent drivers of change is required to protect the population’s health. Public health professionals have found that building strong, enduring interdisciplinary partnerships across disciplines can address environment and health complexities, and that developing Environmental and Public Health Tracking (EPHT) systems has been an effective tool. EPHT aims to merge, integrate, analyse and interpret environmental hazards, exposure and health data. In this article, we explain that public health decision-makers can use EPHT insights to drive public health actions, reduce exposure and prevent the occurrence of disease more precisely in efficient and cost-effective ways. An international network exists for practitioners and researchers to monitor and use environmental health intelligence, and to support countries and local areas toward sustainable and healthy development. A global network of EPHT programs and professionals has the potential to advance global health by implementing and sharing experience, to magnify the impact of local efforts and to pursue data knowledge improvement strategies, aiming to recognise and support best practices. EPHT can help increase the understanding of environmental public health and global health, improve comparability of risks between different areas of the world including Low and Middle-Income Countries (LMICs), enable transparency and trust among citizens, institutions and the private sector, and inform preventive decision making consistent with sustainable and healthy development. This shows how EPHT advances global health efforts by sharing recent global EPHT activities and resources with those working in this field. Experiences from the US, Europe, Asia and Australasia are outlined for operating successful tracking systems to advance global health

    Reasons for prehospital delay in acute ischemic stroke

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    Background Prehospital delay reduces the proportion of patients with stroke treated with recanalization therapies. We aimed to identify novel and modifiable risk factors for prehospital delay. Methods and Results We included patients with an ischemic stroke confirmed by diffusion-weighted magnetic resonance imaging, symptom onset within 24 hours and hospitalized in the Stroke Center of the University Hospital Basel, Switzerland. Trained study nurses interviewed patients and proxies along a standardized questionnaire. Prehospital delay was defined as >4.5 hours between stroke onset-or time point of wake-up-and admission. Overall, 336 patients were enrolled. Prehospital delay was observed in 140 patients (42%). The first healthcare professionals to be alarmed were family doctors for 29% of patients (97/336), and a quarter of these patients had a baseline National Institute of Health Stroke Scale score of 4 or higher. The main modifiable risk factor for prehospital delay was a face-to-face visit to the family doctor (adjusted odds ratio, 4.19; 95% CI, 1.85-9.46). Despite transport by emergency medical services being associated with less prehospital delay (adjusted odds ratio, 0.41; 95% CI, 0.24-0.71), a minority of patients (39%) who first called their family doctor were transported by emergency medical services to the hospital. The second risk factor was lack of awareness of stroke symptoms (adjusted odds ratio, 4.14; 95% CI, 2.36-7.24). Conclusions Almost 1 in 3 patients with a diffusion-weighted magnetic resonance imaging-confirmed ischemic stroke first called the family doctor practice. Face-to-face visits to the family doctor quadrupled the odds of prehospital delay. Efforts to reduce prehospital delay should address family doctors and their staffs as important partners in the prehospital pathway. Clinical Trial Registration URL: http://www.clinicaltrials.gov. Unique identifier: NCT02798770
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