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1.
Environ Int ; 133(Pt A): 105176, 2019 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-31654985

RESUMO

BACKGROUND: Despite the relevance for occupational safety policies, the health effects of temperature on occupational injuries have been scarcely investigated. A nationwide epidemiological study was carried out to estimate the risk of injuries for workers exposed to extreme temperature and identify economic sectors and jobs most at risk. MATERIALS AND METHODS: The daily time series of work-related injuries in the industrial and services sector from the Italian national workers' compensation authority (INAIL) were collected for each of the 8090 Italian municipalities in the period 2006-2010. Daily air temperatures with a 1 × 1 km resolution derived from satellite land surface temperature data using mixed regression models were included. Distributed lag non-linear models (DLNM) were used to estimate the association between daily mean air temperature and injuries at municipal level. A meta-analysis was then carried out to retrieve national estimates. The relative risk (RR) and attributable cases of work-related injuries for an increase in mean temperature above the 75th percentile (heat) and for a decrease below the 25th percentile (cold) were estimated. Effect modification by gender, age, firm size, economic sector and job type were also assessed. RESULTS: The study considered 2,277,432 occupational injuries occurred in Italy in the period 2006-2010. There were significant effects for both heat and cold temperatures. The overall relative risks (RR) of occupational injury for heat and cold were 1.17 (95% CI: 1.14-1.21) and 1.23 (95% CI: 1.17-1.30), respectively. The number of occupational injuries attributable to temperatures above and below the thresholds was estimated to be 5211 per year. A higher risk of injury on hot days was found among males and young (age 15-34) workers occupied in small-medium size firms, while the opposite was observed on cold days. Construction workers showed the highest risk of injuries on hot days while fishing, transport, electricity, gas and water distribution workers did it on cold days. CONCLUSIONS: Prevention of the occupational exposure to extreme temperatures is a concern for occupational health and safety policies, and will become a critical issue in future years considering climate change. Epidemiological studies may help identify vulnerable jobs, activities and workers in order to define prevention plans and training to reduce occupational exposure to extreme temperature and the risk of work-related injuries.

2.
Environ Int ; 132: 105030, 2019 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-31398654

RESUMO

BACKGROUND: A large steel plant close to the urban area of Taranto (Italy) has been operating since the sixties. Several studies conducted in the past reported an excess of mortality and morbidity from various diseases at the town level, possibly due to air pollution from the plant. However, the relationship between air pollutants emitted from the industry and adverse health outcomes has been controversial. We applied a variant of the "difference-in-differences" (DID) approach to examine the relationship between temporal changes in exposure to industrial PM10 from the plant and changes in cause-specific mortality rates at area unit level. METHODS: We examined a dynamic cohort of all subjects (321,356 individuals) resident in the Taranto area in 1998-2010 and followed them up for mortality till 2014. In this work, we included only deaths occurring on 2008-2014. We observed a total of 15,303 natural deaths in the cohort and age-specific annual death rates were computed for each area unit (11 areas in total). PM10 and NO2 concentrations measured at air quality monitoring stations and the results of a dispersion model were used to estimate annual average population weighted exposures to PM10 of industrial origin for each year, area unit and age class. Changes in exposures and in mortality were analyzed using Poisson regression. RESULTS: We estimated an increased risk in natural mortality (1.86%, 95% confidence interval [CI]: -0.06, 3.83%) per 1 µg/m3 annual change of industrial PM10, mainly driven by respiratory causes (8.74%, 95% CI: 1.50, 16.51%). The associations were statistically significant only in the elderly (65+ years). CONCLUSIONS: The DID approach is intuitively simple and reduces confounding by design. Under the multiple assumptions of this approach, the study indicates an effect of industrial PM10 on natural mortality, especially in the elderly population.

3.
N Engl J Med ; 381(8): 705-715, 2019 08 22.
Artigo em Inglês | MEDLINE | ID: mdl-31433918

RESUMO

BACKGROUND: The systematic evaluation of the results of time-series studies of air pollution is challenged by differences in model specification and publication bias. METHODS: We evaluated the associations of inhalable particulate matter (PM) with an aerodynamic diameter of 10 µm or less (PM10) and fine PM with an aerodynamic diameter of 2.5 µm or less (PM2.5) with daily all-cause, cardiovascular, and respiratory mortality across multiple countries or regions. Daily data on mortality and air pollution were collected from 652 cities in 24 countries or regions. We used overdispersed generalized additive models with random-effects meta-analysis to investigate the associations. Two-pollutant models were fitted to test the robustness of the associations. Concentration-response curves from each city were pooled to allow global estimates to be derived. RESULTS: On average, an increase of 10 µg per cubic meter in the 2-day moving average of PM10 concentration, which represents the average over the current and previous day, was associated with increases of 0.44% (95% confidence interval [CI], 0.39 to 0.50) in daily all-cause mortality, 0.36% (95% CI, 0.30 to 0.43) in daily cardiovascular mortality, and 0.47% (95% CI, 0.35 to 0.58) in daily respiratory mortality. The corresponding increases in daily mortality for the same change in PM2.5 concentration were 0.68% (95% CI, 0.59 to 0.77), 0.55% (95% CI, 0.45 to 0.66), and 0.74% (95% CI, 0.53 to 0.95). These associations remained significant after adjustment for gaseous pollutants. Associations were stronger in locations with lower annual mean PM concentrations and higher annual mean temperatures. The pooled concentration-response curves showed a consistent increase in daily mortality with increasing PM concentration, with steeper slopes at lower PM concentrations. CONCLUSIONS: Our data show independent associations between short-term exposure to PM10 and PM2.5 and daily all-cause, cardiovascular, and respiratory mortality in more than 600 cities across the globe. These data reinforce the evidence of a link between mortality and PM concentration established in regional and local studies. (Funded by the National Natural Science Foundation of China and others.).


Assuntos
Poluição do Ar/efeitos adversos , Exposição Ambiental/análise , Mortalidade , Material Particulado/efeitos adversos , Poluição do Ar/análise , Doenças Cardiovasculares/mortalidade , Causas de Morte , Exposição Ambiental/efeitos adversos , Exposição Ambiental/legislação & jurisprudência , Saúde Global , Humanos , Tamanho da Partícula , Material Particulado/análise , Doenças Respiratórias/mortalidade , Risco
4.
Environ Sci Technol ; 53(17): 10279-10287, 2019 Sep 03.
Artigo em Inglês | MEDLINE | ID: mdl-31415154

RESUMO

Nitrogen dioxide (NO2) remains an important traffic-related pollutant associated with both short- and long-term health effects. We aim to model daily average NO2 concentrations in Switzerland in a multistage framework with mixed-effect and random forest models to respectively downscale satellite measurements and incorporate local sources. Spatial and temporal predictor variables include data from the Ozone Monitoring Instrument, Copernicus Atmosphere Monitoring Service, land use, and meteorological variables. We derived robust models explaining ∼58% (R2 range, 0.56-0.64) of the variation in measured NO2 concentrations using mixed-effect models at a 1 × 1 km resolution. The random forest models explained ∼73% (R2 range, 0.70-0.75) of the overall variation in the residuals at a 100 × 100 m resolution. This is one of the first studies showing the potential of using earth observation data to develop robust models with fine-scale spatial (100 × 100 m) and temporal (daily) variation of NO2 across Switzerland from 2005 to 2016. The novelty of this study is in demonstrating that methods originally developed for particulate matter can also successfully be applied to NO2. The predicted NO2 concentrations will be made available to facilitate health research in Switzerland.

5.
Environ Int ; 130: 104934, 2019 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-31229871

RESUMO

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.

6.
Environ Health Perspect ; 127(6): 67004, 2019 06.
Artigo em Inglês | MEDLINE | ID: mdl-31166133

RESUMO

BACKGROUND: The link between particulate matter (PM) exposure and adverse health outcomes has been widely evaluated using large cohort studies. However, the possibility of residual confounding and lack of information about the health effects of PM in rural and suburban areas are unsolved issues. OBJECTIVE: Our aim was to estimate the effect of annual PM≤10µg (PM10) exposure on cause-specific mortality in the Latium region (central Italy, of which Rome is the main city) during 2006-2012 using a difference-in-differences approach. METHODS: We estimated daily PM10 concentrations for each 1 km2 of the region from 2006 to 2012 by use of satellite data, land-use predictors, and meteorological parameters. For each of the 378 regional municipalities and each year, we averaged daily PM10 values to obtain annual mean PM10 exposures. We applied a variant of the difference-in-differences approach to estimate the association between PM10 and cause-specific mortality by focusing on within-municipality fluctuations of mortality rates and annual PM exposures around municipality means, therefore controlling by design for confounding from all spatial and temporal potential confounders. Analyses were also stratified by population size of the municipalities to obtain effect estimates in rural and suburban areas of the region. RESULTS: In the period 2006-2012, we observed deaths due to three causes: 347,699 nonaccidental; 92,787 cardiovascular; and 16,509 respiratory causes. The annual average (standard deviation, SD) PM10 concentration was 21.9 (±4.9) µg/km3 in Latium. For each 1-µg/m3 increase in annual PM10 we estimated increases of 0.8% (95% confidence intervals (CIs): 0.2%, 1.3%), 0.9% (0.0%, 1.8%), and 1.4% (-0.4%, 3.3%) in nonaccidental, cardiovascular, and respiratory mortality, respectively. Similar results were found when we excluded the metropolitan area of Rome from the analysis. Higher effects were estimated in the smaller municipalities, e.g., those with population < 5,000 inhabitants. CONCLUSION: Our study suggests a significant association of annual PM10 exposure with nonaccidental and cardiorespiratory mortality in the Latium region, even outside Rome and in suburban and rural areas. https://doi.org/10.1289/EHP3759.

7.
Environ Int ; 124: 170-179, 2019 03.
Artigo em Inglês | MEDLINE | ID: mdl-30654325

RESUMO

Particulate matter (PM) air pollution is one of the major causes of death worldwide, with demonstrated adverse effects from both short-term and long-term exposure. Most of the epidemiological studies have been conducted in cities because of the lack of reliable spatiotemporal estimates of particles exposure in nonurban settings. The objective of this study is to estimate daily PM10 (PM < 10 µm), fine (PM < 2.5 µm, PM2.5) and coarse particles (PM between 2.5 and 10 µm, PM2.5-10) at 1-km2 grid for 2013-2015 using a machine learning approach, the Random Forest (RF). Separate RF models were defined to: predict PM2.5 and PM2.5-10 concentrations in monitors where only PM10 data were available (stage 1); impute missing satellite Aerosol Optical Depth (AOD) data using estimates from atmospheric ensemble models (stage 2); establish a relationship between measured PM and satellite, land use and meteorological parameters (stage 3); predict stage 3 model over each 1-km2 grid cell of Italy (stage 4); and improve stage 3 predictions by using small-scale predictors computed at the monitor locations or within a small buffer (stage 5). Our models were able to capture most of PM variability, with mean cross-validation (CV) R2 of 0.75 and 0.80 (stage 3) and 0.84 and 0.86 (stage 5) for PM10 and PM2.5, respectively. Model fitting was less optimal for PM2.5-10, in summer months and in southern Italy. Finally, predictions were equally good in capturing annual and daily PM variability, therefore they can be used as reliable exposure estimates for investigating long-term and short-term health effects.


Assuntos
Poluentes Atmosféricos/análise , Monitoramento Ambiental , Material Particulado/análise , Aerossóis/análise , Florestas , Itália , Aprendizado de Máquina , Modelos de Interação Espacial , Estações do Ano
8.
Environ Health ; 17(1): 86, 2018 Dec 05.
Artigo em Inglês | MEDLINE | ID: mdl-30518403

RESUMO

BACKGROUND: Due to the complex interplay among different urban-related exposures, a comprehensive approach is advisable to estimate the health effects. We simultaneously assessed the effect of "green", "grey" and air pollution exposure on respiratory/allergic conditions and general symptoms in schoolchildren. METHODS: This study involved 219 schoolchildren (8-10 years) of the Municipality of Palermo, Italy. Data were collected through questionnaires self-administered by parents and children. Exposures to greenness and greyness at the home addresses were measured using the normalized difference vegetation index (NDVI), residential surrounding greyness (RSG) and the CORINE land-cover classes (CLC). RSG was defined as the percentage of buffer covered by either industrial, commercial and transport units, or dump and construction sites, or urban fabric related features. Two specific categories of CLC, namely "discontinuous urban fabric - DUF" - and "continuous urban fabric - CUF" - areas were found. Exposure to traffic-related nitrogen dioxide (NO2) was assessed using a Land-Use Regression model. A symptom score ranging from 0 to 22 was built by summing affirmative answers to twenty-two questions on symptoms. To avoid multicollinearity, multiple Logistic and Poisson ridge regression models were applied to assess the relationships between environmental factors and self-reported symptoms. RESULTS: A very low exposure to NDVI ≤0.15 (1st quartile) had a higher odds of nasal symptoms (OR = 1.47, 95% CI [1.07-2.03]). Children living in CUF areas had higher odds of ocular symptoms (OR = 1.49, 95% CI [1.10-2.03]) and general symptoms (OR = 1.18, 95% CI [1.00-1.48]) than children living in DUF areas. Children living in proximity (≤200 m) to High Traffic Roads (HTRs) had increased odds of ocular (OR = 1.68, 95% CI [1.31-2.17]) and nasal symptoms (OR = 1.49, 95% CI [1.12-1.98]). A very high exposure to NO2 ≥ 60 µg/m3 (4th quartile) was associated with a higher odds of general symptoms (OR = 1.28, 95% CI [1.10-1.48]). No associations were found with RGS. A Poisson ridge regression model on the symptom score showed that children living in proximity to HTRs (≤200 m) had a higher symptoms score (RR = 1.09, 95% CI [1.02-1.17]) than children living > 200 m from HTRs. Children living in CUF areas had a higher symptoms score (RR = 1.11, 95% CI [1.03-1.19]) than children living in DUF areas. CONCLUSIONS: Multiple exposures related to greenness, greyness (measured by CORINE) and air pollution within the urban environment are associated with respiratory/allergic and general symptoms in schoolchildren. No associations were found when considering the individual exposure to greyness measured using the RSG indicator.

9.
Environ Int ; 120: 81-92, 2018 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-30075373

RESUMO

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 (PM2.5, NO2, BC, O3) across Europe. METHODS: We developed West-European land use regression models (LUR) for 2010 estimating annual mean PM2.5, NO2, BC and O3 concentrations (including cold and warm season estimates for O3). The models were based on AirBase routine monitoring data (PM2.5, NO2 and O3) 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 PM2.5 and NO2 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 (NO2 and O3: 2000 and 2005; PM2.5: 2013). RESULTS: The PM2.5, BC, NO2, O3 annual, O3 warm season and O3 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 PM2.5, O3 warm and O3 cold models. Explained variance at fully independent sites vs the internal hold-out validation was slightly lower for PM2.5 (65% vs 66%) and lower for NO2 (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 PM2.5, NO2, O3 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.

10.
Environ Int ; 120: 163-171, 2018 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-30096610

RESUMO

INTRODUCTION: Previous analysis from the large European multicentre ESCAPE study showed an association of ambient particulate matter <2.5 µm (PM2.5) air pollution exposure at residence with the incidence of gastric cancer. It is unclear which components of PM are most relevant for gastric and also upper aerodigestive tract (UADT) cancer and some of them may not be strongly correlated with PM mass. We evaluated the association between long-term exposure to elemental components of PM2.5 and PM10 and gastric and UADT cancer incidence in European adults. METHODS: Baseline addresses of individuals were geocoded and exposure was assessed by land-use regression models for copper (Cu), iron (Fe) and zinc (Zn) representing non-tailpipe traffic emissions; sulphur (S) indicating long-range transport; nickel (Ni) and vanadium (V) for mixed oil-burning and industry; silicon (Si) for crustal material and potassium (K) for biomass burning. Cox regression models with adjustment for potential confounders were used for cohort-specific analyses. Combined estimates were determined with random effects meta-analyses. RESULTS: Ten cohorts in six countries contributed data on 227,044 individuals with an average follow-up of 14.9 years with 633 incident cases of gastric cancer and 763 of UADT cancer. The combined hazard ratio (HR) for an increase of 200 ng/m3 of PM2.5_S was 1.92 (95%-confidence interval (95%-CI) 1.13;3.27) for gastric cancer, with no indication of heterogeneity between cohorts (I2 = 0%), and 1.63 (95%-CI 0.88;3.01) for PM2.5_Zn (I2 = 70%). For the other elements in PM2.5 and all elements in PM10 including PM10_S, non-significant HRs between 0.78 and 1.21 with mostly wide CIs were seen. No association was found between any of the elements and UADT cancer. The HR for PM2.5_S and gastric cancer was robust to adjustment for additional factors, including diet, and restriction to study participants with stable addresses over follow-up resulted in slightly higher effect estimates with a decrease in precision. In a two-pollutant model, the effect estimate for total PM2.5 decreased whereas that for PM2.5_S was robust. CONCLUSION: This large multicentre cohort study shows a robust association between gastric cancer and long-term exposure to PM2.5_S but not PM10_S, suggesting that S in PM2.5 or correlated air pollutants may contribute to the risk of gastric cancer.

11.
Environ Int ; 120: 472-479, 2018 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-30145311

RESUMO

BACKGROUND: Increased PM10 concentrations are commonly observed during Saharan dust advections. Limited epidemiological evidence suggests that PM10 from anthropogenic and desert sources increase mortality. We aimed to evaluate the association between source-specific PM10 (non-desert and desert) and cause-specific mortality in Sicily during 2006-2012 period. METHODS: Daily PM10 concentrations at 1-km2 were estimated in Sicily using satellite-based data, fixed monitors and land use variables. We identified Saharan dust episodes using meteorological models, back-trajectories, aerosol maps, and satellite images. For each dust day, we estimated desert and non-desert PM10 concentrations. We applied a time-series approach on 390 municipalities of Sicily to estimate the association between PM10 (non-desert and desert) and daily cause-specific mortality. RESULTS: 33% of all days were affected by Saharan dust advections. PM10 concentrations were 8 µg/m3 higher during dust days compared to other days. We found positive associations of both non-desert and desert PM10 with cause-specific mortality. We estimated percent increases of risk (IR%) of non-accidental mortality equal to 2.3% (95% Confidence Interval [CI]: 1.4, 3.1) and 3.8% (3.2, 4.4), per 10 µg/m3 increases in non-desert and desert PM10 at lag 0-5, respectively. We also observed significant associations with cardiovascular (2.4% [1.3, 3.4] and 4.5% [3.8, 5.3]) and respiratory mortality (8.1% [6.8, 9.5], and 6.3% [5.4, 7.2]). We estimated higher effects during April-September, with IR% = 4.4% (3.2, 5.7) and 6.3% (5.4, 7.2) for non-desert and desert PM10, respectively. CONCLUSIONS: Our results confirm previous evidence of harmful effects of desert PM10 on non-accidental and cardio-respiratory mortality, especially during the warm season.

12.
Artigo em Inglês | MEDLINE | ID: mdl-30126130

RESUMO

Evidence on the health effects of extreme temperatures and air pollution is copious. However few studies focused on their interaction. The aim of this study is to evaluate daily PM10 and ozone as potential effect modifiers of the relationship between temperature and natural mortality in 25 Italian cities. Time-series analysis was run for each city. To evaluate interaction, a tensor product between mean air temperature (lag 0⁻3) and either PM10 or ozone (both lag 0⁻5) was defined and temperature estimates were extrapolated at low, medium, and high levels of pollutants. Heat effects were estimated as percent change in mortality for increases in temperature between 75th and 99th percentiles. Results were pooled by geographical area. Differential temperature-mortality risks by air pollutants were found. For PM10, estimates ranged from 3.9% (low PM10) to 14.1% (high PM10) in the North, from 3.6% to 24.4% in the Center, and from 7.5% to 21.6% in the South. Temperature-related mortality was similarly modified by ozone in northern and central Italy, while no effect modification was observed in the South. This study underlines the synergistic effects of heat and air pollution on mortality. Considering the predicted increase in heat waves and stagnation events in the Mediterranean countries such as Italy, it is time to enclose air pollution within public health heat prevention plans.

13.
Int J Epidemiol ; 47(4): 1343-1354, 2018 Aug 01.
Artigo em Inglês | MEDLINE | ID: mdl-29939274

RESUMO

Multicentre studies are common in epidemiological research aiming at identifying disease risk factors. A major advantage of multicentre over single-centre studies is that, by including a larger number of participants, they allow consideration of rare outcomes and exposures. Their multicentric nature introduces some complexities at the step of data analysis, in particular when it comes to controlling for confounding by centre, which is the focus of this tutorial. Commonly, epidemiologists use one of the following options: pooling all centre-specific data and adjusting for centre using fixed effects; adjusting for centre using random effects; or fitting centre-specific models and combining the results in a meta-analysis. Here, we illustrate the similarities of and differences between these three modelling approaches, explain the reasons why they may provide different conclusions and offer advice on which model to choose depending on the characteristics of the study. Two key issues to examine during the analyses are to distinguish within-centre from between-centre associations, and the possible heterogeneity of the effects (of exposure and/or confounders) by centre. A real epidemiological study is used to illustrate a situation in which these various options yield different results. A synthetic dataset and R and Stata codes are provided to reproduce the results.

14.
Environ Int ; 118: 17-25, 2018 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-29787898

RESUMO

BACKGROUND: Ankle-brachial index (ABI) has been linked to the risk of cardiovascular events. However, the association between long-term exposure to air pollution and abnormal ABI has not been fully investigated. METHODS: This cross-sectional study involved 4544 participants from the KORA Study (2004-2008) in the region of Augsburg, Germany. Participants' residential annual mean concentrations of particulate matter (PM) and nitrogen dioxide (NO2) were predicted with land-use regression models, and the traffic information was collected from geographic information systems. We applied multinomial logistic regression models to assess the effects of air pollution on the prevalence of low and high ABI, and quantile regression models to explore the non-monotonic relationship between air pollution and ABI. We also examined effect modification by individual characteristics. RESULTS: Long-term exposure to PM with an aerodynamic diameter ≤ 10 µm (PM10) and ≤ 2.5 µm (PM2.5) was significantly associated with a higher prevalence of low ABI, with the respective odds ratios (ORs) of 1.82 (95%CI: 1.11-2.97) and 1.59 (95%CI: 1.01-2.51) for a 5th to 95th percentile increment in pollutants. Positive associations with the prevalence of high ABI were observed for PM (e.g., PM10: OR = 1.63, 95%CI: 1.07-2.50) and NO2 (OR = 1.84, 95%CI: 1.15-2.94). Quantile regression analyses revealed similar non-monotonic results. The effects of air pollution on having abnormal ABI were stronger in physically inactive, hypertensive, or non-diabetic participants. CONCLUSIONS: Long-term exposure to PM and NO2 was associated with a higher prevalence of both low and high ABI, indicating the adverse effects of air pollution on atherosclerosis and arterial stiffness in the lower extremities.

15.
Int J Cancer ; 2018 Apr 26.
Artigo em Inglês | MEDLINE | ID: mdl-29696642

RESUMO

Air pollution has been classified as carcinogenic to humans. However, to date little is known about the relevance for cancers of the stomach and upper aerodigestive tract (UADT). We investigated the association of long-term exposure to ambient air pollution with incidence of gastric and UADT cancer in 11 European cohorts. Air pollution exposure was assigned by land-use regression models for particulate matter (PM) below 10 µm (PM10 ), below 2.5 µm (PM2.5 ), between 2.5 and 10 µm (PMcoarse ), PM2.5 absorbance and nitrogen oxides (NO2 and NOX ) as well as approximated by traffic indicators. Cox regression models with adjustment for potential confounders were used for cohort-specific analyses. Combined estimates were determined with random effects meta-analyses. During average follow-up of 14.1 years of 305,551 individuals, 744 incident cases of gastric cancer and 933 of UADT cancer occurred. The hazard ratio for an increase of 5 µg/m3 of PM2.5 was 1.38 (95% CI 0.99; 1.92) for gastric and 1.05 (95% CI 0.62; 1.77) for UADT cancers. No associations were found for any of the other exposures considered. Adjustment for additional confounders and restriction to study participants with stable addresses did not influence markedly the effect estimate for PM2.5 and gastric cancer. Higher estimated risks of gastric cancer associated with PM2.5 was found in men (HR 1.98 [1.30; 3.01]) as compared to women (HR 0.85 [0.5; 1.45]). This large multicentre cohort study shows an association between long-term exposure to PM2.5 and gastric cancer, but not UADT cancers, suggesting that air pollution may contribute to gastric cancer risk.

16.
Environ Int ; 116: 186-196, 2018 07.
Artigo em Inglês | MEDLINE | ID: mdl-29689465

RESUMO

BACKGROUND: Although epidemiological studies have reported associations between mortality and both ambient air pollution and air temperature, it remains uncertain whether the mortality effects of air pollution are modified by temperature and vice versa. Moreover, little is known on the interactions between ultrafine particles (diameter ≤ 100 nm, UFP) and temperature. OBJECTIVE: We investigated whether the short-term associations of particle number concentration (PNC in the ultrafine range (≤100 nm) or total PNC ≤ 3000 nm, as a proxy for UFP), particulate matter ≤ 2.5 µm (PM2.5) and ≤ 10 µm (PM10), and ozone with daily total natural and cardiovascular mortality were modified by air temperature and whether air pollution levels affected the temperature-mortality associations in eight European urban areas during 1999-2013. METHODS: We first analyzed air temperature-stratified associations between air pollution and total natural (nonaccidental) and cardiovascular mortality as well as air pollution-stratified temperature-mortality associations using city-specific over-dispersed Poisson additive models with a distributed lag nonlinear temperature term in each city. All models were adjusted for long-term and seasonal trend, day of the week, influenza epidemics, and population dynamics due to summer vacation and holidays. City-specific effect estimates were then pooled using random-effects meta-analysis. RESULTS: Pooled associations between air pollutants and total and cardiovascular mortality were overall positive and generally stronger at high relatively compared to low air temperatures. For example, on days with high air temperatures (>75th percentile), an increase of 10,000 particles/cm3 in PNC corresponded to a 2.51% (95% CI: 0.39%, 4.67%) increase in cardiovascular mortality, which was significantly higher than that on days with low air temperatures (<25th percentile) [-0.18% (95% CI: -0.97%, 0.62%)]. On days with high air pollution (>50th percentile), both heat- and cold-related mortality risks increased. CONCLUSION: Our findings showed that high temperature could modify the effects of air pollution on daily mortality and high air pollution might enhance the air temperature effects.

17.
Epidemiol Prev ; 42(1): 46-59, 2018 Jan-Feb.
Artigo em Italiano | MEDLINE | ID: mdl-29506361

RESUMO

OBJECTIVES: to define a national geographic domain, with high spatial (1 km²) and temporal (daily) resolution, and to build a list of georeferenced environmental and temporal indicators useful for environmental epidemiology applications at national level. DESIGN: geographic study. SETTING AND PARTICIPANTS: study domain: Italian territory divided into 307,635 1-km² grid cells; study period: 2006-2012, divided into 2,557 daily time windows. MAIN OUTCOME MEASURES: for each grid cell and day, an extensive number of indicators has been computed. These indicators include spatial (administrative layers, resident population, presence of water bodies, climatic zones, land use variables, impervious surfaces, orography, viability, point and areal emissions of air pollutants) and spatio-temporal predictors (particulate matter data from monitoring stations, meteorological parameters, desert dust advection episodes, aerosol optical depth, normalized difference vegetation index, planetary boundary layer) potentially useful to characterize population environmental exposures and to estimate their health effects, at national level. RESULTS AND CONCLUSIONS: this study represents the first example of relational big data in environmental epidemiology at national level, where multiple sources of data (satellite, environmental, meteorology, land use, population) have been linked on a common spatial and temporal domain, aimed at promoting environmental epidemiology applications at national and local level.

18.
Accid Anal Prev ; 115: 25-33, 2018 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-29544134

RESUMO

Use of mobile phones while driving is known to cause crashes with possible fatalities. Different habits of mobile phone use might be distracting forces and display differential impacts on accident risk; the assessment of the relative importance is relevant to implement prevention, mitigation, and control measures. This study aimed to assess the relationship between the use of mobile phones at population level and road crash fatalities in large urban areas. Data on road crashes with fatalities were collected from seven Italian metropolitan areas and matched in time and space with high resolution mobile phone traffic volume data about calls, texts, Internet connections and upload/download data. A case-crossover study design was applied to estimate the relative risks of road accident for increases in each type of mobile phone traffic volumes in underlying population present in the small areas where accidents occurred. Effect modification was evaluated by weekday/weekend, hour of the day, meteorological conditions, and street densities. Positive associations between road crashes rates and the number of calls, texts, and Internet connections were found, with incremental risks of 17.2% (95% Confidence Interval [CI] 7.7, 27.6), 8.4% (CI 0.7, 16.8), and 54.6% (CI 34.0, 78.5) per increases (at 15 min intervals) of 5 calls/100 people, 3 text/100 people, and 40 connections/100 people, respectively. Small differences across cities were detected. Working days, nighttime and morning hours were associated with greater phone use and more road accidents. The relationship between mobile phone use and road fatalities at population level is strong. Strict controls on cellular phone in the vehicle may results in a large health benefit.


Assuntos
Acidentes de Trânsito , Condução de Veículo , Telefone Celular/estatística & dados numéricos , Direção Distraída , Acidentes de Trânsito/mortalidade , Acidentes de Trânsito/prevenção & controle , Cidades , Estudos Cross-Over , Feminino , Humanos , Itália , Masculino , Pessoa de Meia-Idade , Fatores de Risco , População Urbana
19.
Environ Health Perspect ; 126(2): 027008, 2018 02 15.
Artigo em Inglês | MEDLINE | ID: mdl-29467106

RESUMO

BACKGROUND: Although epidemiologic studies have shown associations between particle mass and daily mortality, evidence on other particle metrics is weak. OBJECTIVES: We investigated associations of size-specific particle number concentration (PNC) and lung-deposited particle surface area concentration (PSC) with cause-specific daily mortality in contrast to PM10. METHODS: We used time-series data (March 2009-December 2014) on daily natural, cardiovascular, and respiratory mortality (NM, CVM, RM) of three adjacent cities in the Ruhr Area, Germany. Size-specific PNC (electric mobility diameter of 13.3-750 nm), PSC, and PM10 were measured at an urban background monitoring site. In single- and multipollutant Poisson regression models, we estimated percentage change (95% confidence interval) [% (95% CI)] in mortality per interquartile range (IQR) in exposure at single-day (0-7) and aggregated lags (0-1, 2-3, 4-7), accounting for time trend, temperature, humidity, day of week, holidays, period of seasonal population decrease, and influenza. RESULTS: PNC100-750 and PSC were highly correlated and had similar immediate (lag0-1) and delayed (lag4-7) associations with NM and CVM, for example, 1.12% (95% CI: 0.09, 2.33) and 1.56% (95% CI: 0.22, 2.92) higher NM with IQR increases in PNC100-750 at lag0-1 and lag4-7, respectfully, which were slightly stronger then associations with IQR increases in PM10. Positive associations between PNC and NM were strongest for accumulation mode particles (PNC 100-500 nm), and for larger UFPs (PNC 50-100 nm). Associations between NM and PNC<100 changed little after adjustment for O3 or PM10, but were more sensitive to adjustment for NO2. CONCLUSION: Size-specific PNC (50-500 nm) and lung-deposited PSC were associated with natural and cardiovascular mortality in the Ruhr Area. Although associations were similar to those estimated for an IQR increase in PM10, particle number size distributions can be linked to emission sources, and thus may be more informative for potential public health interventions. Moreover, PSC could be used as an alternative metric that integrates particle size distribution as well as deposition efficiency. https://doi.org/10.1289/EHP2054.

20.
Neuro Oncol ; 20(3): 420-432, 2018 Feb 19.
Artigo em Inglês | MEDLINE | ID: mdl-29016987

RESUMO

Background: Epidemiological evidence on the association between ambient air pollution and brain tumor risk is sparse and inconsistent. Methods: In 12 cohorts from 6 European countries, individual estimates of annual mean air pollution levels at the baseline residence were estimated by standardized land-use regression models developed within the ESCAPE and TRANSPHORM projects: particulate matter (PM) ≤2.5, ≤10, and 2.5-10 µm in diameter (PM2.5, PM10, and PMcoarse), PM2.5 absorbance, nitrogen oxides (NO2 and NOx) and elemental composition of PM. We estimated cohort-specific associations of air pollutant concentrations and traffic intensity with total, malignant, and nonmalignant brain tumor, in separate Cox regression models, adjusting for risk factors, and pooled cohort-specific estimates using random-effects meta-analyses. Results: Of 282194 subjects from 12 cohorts, 466 developed malignant brain tumors during 12 years of follow-up. Six of the cohorts also had data on nonmalignant brain tumor, where among 106786 subjects, 366 developed brain tumor: 176 nonmalignant and 190 malignant. We found a positive, statistically nonsignificant association between malignant brain tumor and PM2.5 absorbance (hazard ratio and 95% CI: 1.67; 0.89-3.14 per 10-5/m3), and weak positive or null associations with the other pollutants. Hazard ratio for PM2.5 absorbance (1.01; 0.38-2.71 per 10-5/m3) and all other pollutants were lower for nonmalignant than for malignant brain tumors. Conclusion: We found suggestive evidence of an association between long-term exposure to PM2.5 absorbance indicating traffic-related air pollution and malignant brain tumors, and no association with overall or nonmalignant brain tumors.

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