Associations between combined urban and lifestyle factors and respiratory health in European children.

INTRODUCTION: Previous studies identified some environmental and lifestyle factors independently associated with children respiratory health, but few focused on exposure mixture effects. This study aimed at identifying, in pregnancy and in childhood, combined urban and lifestyle environment profiles associated with respiratory health in children. METHODS: This study is based on the European Human Early-Life Exposome (HELIX) project, combining six birth cohorts. Associations between profiles of pregnancy (38 exposures) and childhood (84 exposures) urban and lifestyle factors, identified by clustering analysis, and respiratory health were estimated by regression models adjusted for confounders. RESULTS: Among the 1033 included children (mean ± standard-deviation (SD) age: 8.2 ± 1.6 years old, 47% girls) the mean ± SD forced expiratory volume in 1s (FEV1) and forced vital capacity (FVC) were 99 ± 13% and 101 ± 14%, respectively, and 12%, 12% and 24% reported ever-asthma, wheezing and rhinitis, respectively. Four profiles of pregnancy exposures and four profiles of childhood exposures were identified. Compared to the reference childhood exposure profile (low exposures), two exposure profiles were associated with lower levels of FEV1. One profile was characterized by few natural spaces in the surroundings and high exposure to the built environment and road traffic. The second profile was characterized by high exposure to meteorological factors and low levels of all other exposures and was also associated with an increased risk of ever-asthma and wheezing. A pregnancy exposure profile characterized by high exposure levels to all risk factors, but a healthy maternal lifestyle, was associated with a lower risk of wheezing and rhinitis in children, compared to the reference pregnancy profile (low exposures). CONCLUSION: This comprehensive approach revealed pregnancy and childhood profiles of urban and lifestyle exposures associated with lung function and/or respiratory conditions in children. Our findings highlight the need to pursue the study of combined exposures to improve prevention strategies for multifactorial diseases such as asthma.

Childhood exposure to outdoor air pollution in different microenvironments and cognitive and fine motor function in children from six European cohorts.

BACKGROUND: Exposure to air pollution during childhood has been linked with adverse effects on cognitive development and motor function. However, limited research has been done on the associations of air pollution exposure in different microenvironments such as home, school, or while commuting with these outcomes. OBJECTIVE: To analyze the association between childhood air pollution exposure in different microenvironments and cognitive and fine motor function from six European birth cohorts. METHODS: We included 1301 children from six European birth cohorts aged 6-11 years from the HELIX project. Average outdoor air pollutants concentrations (NO2, PM2.5) were estimated using land use regression models for different microenvironments (home, school, and commute), for 1-year before the outcome assessment. Attentional function, cognitive flexibility, non-verbal intelligence, and fine motor function were assessed using the Attention Network Test, Trail Making Test A and B, Raven Colored Progressive Matrices test, and the Finger Tapping test, respectively. Adjusted linear regressions models were run to determine the association between each air pollutant from each microenvironment on each outcome. RESULTS: In pooled analysis we observed high correlation (rs = 0.9) between air pollution exposures levels at home and school. However, the cohort-by-cohort analysis revealed correlations ranging from low to moderate. Air pollution exposure levels while commuting were higher than at home or school. Exposure to air pollution in the different microenvironments was not associated with working memory, attentional function, non-verbal intelligence, and fine motor function. Results remained consistently null in random-effects meta-analysis. CONCLUSIONS: No association was observed between outdoor air pollution exposure in different microenvironments (home, school, commute) and cognitive and fine motor function in children from six European birth cohorts. Future research should include a more detailed exposure assessment, considering personal measurements and time spent in different microenvironments.

Long-term exposure to elemental components of fine particulate matter and all-natural and cause-specific mortality in a Danish nationwide administrative cohort study.

BACKGROUND: Fine particulate matter (PM2.5) is a well-recognized risk factor for premature death. However, evidence on which PM2.5 components are most relevant is unclear. METHODS: We evaluated the associations between mortality and long-term exposure to eight PM2.5 elemental components [copper (Cu), iron (Fe), zinc (Zn), sulfur (S), nickel (Ni), vanadium (V), silicon (Si), and potassium (K)]. Studied outcomes included death from diabetes, chronic kidney disease (CKD), dementia, and psychiatric disorders as well as all-natural causes, cardiovascular disease (CVD), respiratory diseases (RD), and lung cancer. We followed all residents in Denmark (aged ≥30 years) from January 1, 2000 to December 31, 2017. We used European-wide land-use regression models at a 100 × 100 m scale to estimate the residential annual mean levels of exposure to PM2.5 components. The models were developed with supervised linear regression (SLR) and random forest (RF). The associations were evaluated by Cox proportional hazard models adjusting for individual- and area-level socioeconomic factors and total PM2.5 mass. RESULTS: Of 3,081,244 individuals, we observed 803,373 death from natural causes during follow-up. We found significant positive associations between all-natural mortality with Si and K from both exposure modeling approaches (hazard ratios; 95% confidence intervals per interquartile range increase): SLR-Si (1.04; 1.03-1.05), RF-Si (1.01; 1.00-1.02), SLR-K (1.03; 1.02-1.04), and RF-K (1.06; 1.05-1.07). Strong associations of K and Si were detected with most causes of mortality except CKD and K, and diabetes and Si (the strongest associations for psychiatric disorders mortality). In addition, Fe was relevant for mortality from RD, lung cancer, CKD, and psychiatric disorders; Zn with mortality from CKD, RD, and lung cancer, and; Ni and V with lung cancer mortality. CONCLUSIONS: We present novel results of the relevance of different PM2.5 components for different causes of death, with K and Si seeming to be most consistently associated with mortality in Denmark.

Association between exposure to multiple air pollutants, transportation noise and cause-specific mortality in adults in Switzerland.

BACKGROUND: Long-term exposure to air pollution and noise is detrimental to health; but studies that evaluated both remain limited. This study explores associations with natural and cause-specific mortality for a range of air pollutants and transportation noise. METHODS: Over 4 million adults in Switzerland were followed from 2000 to 2014. Exposure to PM2.5, PM2.5 components (Cu, Fe, S and Zn), NO2, black carbon (BC) and ozone (O3) from European models, and transportation noise from source-specific Swiss models, were assigned at baseline home addresses. Cox proportional hazards models, adjusted for individual and area-level covariates, were used to evaluate associations with each exposure and death from natural, cardiovascular (CVD) or non-malignant respiratory disease. Analyses included single and two exposure models, and subset analysis to study lower exposure ranges. RESULTS: During follow-up, 661,534 individuals died of natural causes (36.6% CVD, 6.6% respiratory). All exposures including the PM2.5 components were associated with natural mortality, with hazard ratios (95% confidence intervals) of 1.026 (1.015, 1.038) per 5 µg/m3 PM2.5, 1.050 (1.041, 1.059) per 10 µg/m3 NO2, 1.057 (1.048, 1.067) per 0.5 × 10-5/m BC and 1.045 (1.040, 1.049) per 10 dB Lden total transportation noise. NO2, BC, Cu, Fe and noise were consistently associated with CVD and respiratory mortality, whereas PM2.5 was only associated with CVD mortality. Natural mortality associations persisted < 20 µg/m3 for PM2.5 and NO2, < 1.5 10-5/m BC and < 53 dB Lden total transportation noise. The O3 association was inverse for all outcomes. Including noise attenuated all outcome associations, though many remained significant. Across outcomes, noise was robust to adjustment to air pollutants (e.g. natural mortality 1.037 (1.033, 1.042) per 10 dB Lden total transportation noise, after including BC). CONCLUSION: Long-term exposure to air pollution and transportation noise in Switzerland contribute to premature mortality. Considering co-exposures revealed the importance of local traffic-related pollutants such as NO2, BC and transportation noise.

Long-term exposure to low-level air pollution and incidence of asthma: the ELAPSE project.

BACKGROUND: Long-term exposure to ambient air pollution has been linked to childhood-onset asthma, although evidence is still insufficient. Within the multicentre project Effects of Low-Level Air Pollution: A Study in Europe (ELAPSE), we examined the associations of long-term exposures to particulate matter with a diameter <2.5 µm (PM2.5), nitrogen dioxide (NO2) and black carbon (BC) with asthma incidence in adults. METHODS: We pooled data from three cohorts in Denmark and Sweden with information on asthma hospital diagnoses. The average concentrations of air pollutants in 2010 were modelled by hybrid land-use regression models at participants' baseline residential addresses. Associations of air pollution exposures with asthma incidence were explored with Cox proportional hazard models, adjusting for potential confounders. RESULTS: Of 98 326 participants, 1965 developed asthma during a mean follow-up of 16.6 years. We observed associations in fully adjusted models with hazard ratios of 1.22 (95% CI 1.04-1.43) per 5 µg·m-3 for PM2.5, 1.17 (95% CI 1.10-1.25) per 10 µg·m-3 for NO2 and 1.15 (95% CI 1.08-1.23) per 0.5×10-5 m-1 for BC. Hazard ratios were larger in cohort subsets with exposure levels below the European Union and US limit values and possibly World Health Organization guidelines for PM2.5 and NO2. NO2 and BC estimates remained unchanged in two-pollutant models with PM2.5, whereas PM2.5 estimates were attenuated to unity. The concentration-response curves showed no evidence of a threshold. CONCLUSIONS: Long-term exposure to air pollution, especially from fossil fuel combustion sources such as motorised traffic, was associated with adult-onset asthma, even at levels below the current limit values.

Long-term exposure to fine particle elemental components and lung cancer incidence in the ELAPSE pooled cohort.

BACKGROUND: An association between long-term exposure to fine particulate matter (PM2.5) and lung cancer has been established in previous studies. PM2.5 is a complex mixture of chemical components from various sources and little is known about whether certain components contribute specifically to the associated lung cancer risk. The present study builds on recent findings from the "Effects of Low-level Air Pollution: A Study in Europe" (ELAPSE) collaboration and addresses the potential association between specific elemental components of PM2.5 and lung cancer incidence. METHODS: We pooled seven cohorts from across Europe and assigned exposure estimates for eight components of PM2.5 representing non-tail pipe emissions (copper (Cu), iron (Fe), and zinc (Zn)), long-range transport (sulfur (S)), oil burning/industry emissions (nickel (Ni), vanadium (V)), crustal material (silicon (Si)), and biomass burning (potassium (K)) to cohort participants' baseline residential address based on 100 m by 100 m grids from newly developed hybrid models combining air pollution monitoring, land use data, satellite observations, and dispersion model estimates. We applied stratified Cox proportional hazards models, adjusting for potential confounders (age, sex, calendar year, marital status, smoking, body mass index, employment status, and neighborhood-level socio-economic status). RESULTS: The pooled study population comprised 306,550 individuals with 3916 incident lung cancer events during 5,541,672 person-years of follow-up. We observed a positive association between exposure to all eight components and lung cancer incidence, with adjusted HRs of 1.10 (95% CI 1.05, 1.16) per 50 ng/m3 PM2.5 K, 1.09 (95% CI 1.02, 1.15) per 1 ng/m3 PM2.5 Ni, 1.22 (95% CI 1.11, 1.35) per 200 ng/m3 PM2.5 S, and 1.07 (95% CI 1.02, 1.12) per 200 ng/m3 PM2.5 V. Effect estimates were largely unaffected by adjustment for nitrogen dioxide (NO2). After adjustment for PM2.5 mass, effect estimates of K, Ni, S, and V were slightly attenuated, whereas effect estimates of Cu, Si, Fe, and Zn became null or negative. CONCLUSIONS: Our results point towards an increased risk of lung cancer in connection with sources of combustion particles from oil and biomass burning and secondary inorganic aerosols rather than non-exhaust traffic emissions. Specific limit values or guidelines targeting these specific PM2.5 components may prove helpful in future lung cancer prevention strategies.

Mortality and Morbidity Effects of Long-Term Exposure to Low-Level PM2.5, BC, NO2, and O3: An Analysis of European Cohorts in the ELAPSE Project.

INTRODUCTION: Epidemiological cohort studies have consistently found associations between long-term exposure to outdoor air pollution and a range of morbidity and mortality endpoints. Recent evaluations by the World Health Organization and the Global Burden of Disease study have suggested that these associations may be nonlinear and may persist at very low concentrations. Studies conducted in North America in particular have suggested that associations with mortality persisted at concentrations of particulate matter with an aerodynamic diameter of less than 2.5 µm (PM2.5) well below current air quality standards and guidelines. The uncertainty about the shape of the concentration-response function at the low end of the concentration distribution, related to the scarcity of observations in the lowest range, was the basis of the current project. Previous studies have focused on PM2.5, but increasingly associations with nitrogen dioxide (NO2) are being reported, particularly in studies that accounted for the fine spatial scale variation of NO2. Very few studies have evaluated the effects of long-term exposure to low concentrations of ozone (O3). Health effects of black carbon (BC), representing primary combustion particles, have not been studied in most large cohort studies of PM2.5. Cohort studies assessing health effects of particle composition, including elements from nontailpipe traffic emissions (iron, copper, and zinc) and secondary aerosol (sulfur) have been few in number and reported inconsistent results. The overall objective of our study was to investigate the shape of the relationship between long-term exposure to four pollutants (PM2.5, NO2, BC, and O3) and four broad health effect categories using a number of different methods to characterize the concentration-response function (i.e., linear, nonlinear, or threshold). The four health effect categories were (1) natural- and cause-specific mortality including cardiovascular and nonmalignant as well as malignant respiratory and diabetes mortality; and morbidity measured as (2) coronary and cerebrovascular events; (3) lung cancer incidence; and (4) asthma and chronic obstructive pulmonary disease (COPD) incidence. We additionally assessed health effects of PM2.5 composition, specifically the copper, iron, zinc, and sulfur content of PM2,5. METHODS: We focused on analyses of health effects of air pollutants at low concentrations, defined as less than current European Union (EU) Limit Values, U.S. Environmental Protection Agency (U.S. EPA), National Ambient Air Quality Standards (NAAQS), and/or World Health Organization (WHO) Air Quality Guideline values for PM2.5, NO2, and O3. We address the health effects at low air pollution levels by performing new analyses within selected cohorts of the ESCAPE study (European Study of Cohorts for Air Pollution Effects; Beelen et al. 2014a) and within seven very large European administrative cohorts. By combining well-characterized ESCAPE cohorts and large administrative cohorts in one study the strengths and weaknesses of each approach can be addressed. The large administrative cohorts are more representative of national or citywide populations, have higher statistical power, and can efficiently control for area-level confounders, but have fewer possibilities to control for individual-level confounders. The ESCAPE cohorts have detailed information on individual confounders, as well as country-specific information on area-level confounding. The data from the seven included ESCAPE cohorts and one additional non-ESCAPE cohort have been pooled and analyzed centrally. More than 300,000 adults were included in the pooled cohort from existing cohorts in Sweden, Denmark, Germany, the Netherlands, Austria, France, and Italy. Data from the administrative cohorts have been analyzed locally, without transfer to a central database. Privacy regulations prevented transfer of data from administrative cohorts to a central database. More than 28 million adults were included from national administrative cohorts in Belgium, Denmark, England, the Netherlands, Norway, and Switzerland as well as an administrative cohort in Rome, Italy. We developed central exposure assessment using Europewide hybrid land use regression (LUR) models, which incorporated European routine monitoring data for PM2.5, NO2, and O3, and ESCAPE monitoring data for BC and PM2.5 composition, land use, and traffic data supplemented with satellite observations and chemical transport model estimates. For all pollutants, we assessed exposure at a fine spatial scale, 100 × 100 m grids. These models have been applied to individual addresses of all cohorts including the administrative cohorts. In sensitivity analyses, we applied the PM2.5 models developed within the companion HEI-funded Canadian MAPLE study (Brauer et al. 2019) and O3 exposures on a larger spatial scale for comparison with previous studies. Identification of outcomes included linkage with mortality, cancer incidence, hospital discharge registries, and physician-based adjudication of cases. We analyzed natural-cause, cardiovascular, ischemic heart disease, stroke, diabetes, cardiometabolic, respiratory, and COPD mortality. We also analyzed lung cancer incidence, incidence of coronary and cerebrovascular events, and incidence of asthma and COPD (pooled cohort only). We applied the Cox proportional hazard model with increasing control for individual- and area-level covariates to analyze the associations between air pollution and mortality and/or morbidity for both the pooled cohort and the individual administrative cohorts. Age was used as the timescale because of evidence that this results in better adjustment for potential confounding by age. Censoring occurred at the time of the event of interest, death from other causes, emigration, loss to follow-up for other reasons, or at the end of follow-up, whichever came first. A priori we specified three confounder models, following the modeling methods of the ESCAPE study. Model 1 included only age (time axis), sex (as strata), and calendar year of enrollment. Model 2 added individual-level variables that were consistently available in the cohorts contributing to the pooled cohort or all variables available in the administrative cohorts, respectively. Model 3 further added area-level socioeconomic status (SES) variables. A priori model 3 was selected as the main model. All analyses in the pooled cohort were stratified by subcohort. All analyses in the administrative cohorts accounted for clustering of the data in neighborhoods by adjusting the variance of the effect estimates. The main exposure variable we analyzed was derived from the Europewide hybrid models based on 2010 monitoring data. Sensitivity analyses were conducted using earlier time periods, time-varying exposure analyses, local exposure models, and the PM2.5 models from the Canadian MAPLE project. We first specified linear single-pollutant models. Two-pollutant models were specified for all combinations of the four main pollutants. Two-pollutant models for particle composition were analyzed with PM2.5 and NO2 as the second pollutant. We then investigated the shape of the concentration-response function using natural splines with two, three, and four degrees of freedom; penalized splines with the degrees of freedom determined by the algorithm and shape-constrained health impact functions (SCHIF) using confounder model 3. Additionally, we specified linear models in subsets of the concentration range, defined by removing concentrations above a certain value from the analysis, such as for PM2.5 25 µg/m3 (EU limit value), 20, 15, 12 µg/m3 (U.S. EPA National Ambient Air Quality Standard), and 10 µg/m3 (WHO Air Quality Guideline value). Finally, threshold models were evaluated to investigate whether the associations persisted below specific concentration values. For PM2.5, we evaluated 10, 7.5, and 5 µg/m3 as potential thresholds. Performance of threshold models versus the corresponding no-threshold linear model were evaluated using the Akaike information criterion (AIC). RESULTS: In the pooled cohort, virtually all subjects in 2010 had PM2.5 and NO2 annual average exposures below the EU limit values (25 µg/m3 and 40 µg/m3, respectively). More than 50,000 had a residential PM2.5 exposure below the U.S. EPA NAAQS (12 µg/m3). More than 25,000 subjects had a residential PM2.5 exposure below the WHO guideline (10 µg/m3). We found significant positive associations between PM2.5, NO2, and BC and natural-cause, respiratory, cardiovascular, and diabetes mortality. In our main model, the hazard ratios (HRs) (95% [confidence interval] CI) were 1.13 (CI = 1.11, 1.16) for an increase of 5 µg/m3 PM2.5, 1.09 (CI = 1.07, 1.10) for an increase of 10 µg/m3 NO2, and 1.08 (CI = 1.06, 1.10) for an increase of 0.5 × 10-5/m BC for natural-cause mortality. The highest HRs were found for diabetes mortality. Associations with O3 were negative, both in the fine spatial scale of the main ELAPSE model and in large spatial scale exposure models. For PM2.5, NO2, and BC, we generally observed a supralinear association with steeper slopes at low exposures and no evidence of a concentration below which no association was found. Subset analyses further confirmed that these associations remained at low levels: below 10 µg/m3 for PM2.5 and 20 µg/m3 for NO2. HRs were similar to the full cohort HRs for subjects with exposures below the EU limit values for PM2.5 and NO2, the U.S. NAAQS values for PM2.5, and the WHO guidelines for PM2.5 and NO2. The mortality associations were robust to alternative specifications of exposure, including different time periods, PM2.5 from the MAPLE project, and estimates from the local ESCAPE model. Time-varying exposure natural spline analyses confirmed associations at low pollution levels. HRs in two-pollutant models were attenuated but remained elevated and statistically significant forPM2.5 and NO2. In two-pollutant models of PM2.5 and NO2 HRs for natural-cause mortality were 1.08 (CI = 1.05, 1.11) for PM2.5 and 1.05 (CI = 1.03, 1.07) for NO2. Associations with O3 were attenuated but remained negative in two-pollutant models with NO2, BC, and PM2.5. We found significant positive associations between PM2.5, NO2, and BC and incidence of stroke and asthma and COPD hospital admissions. Furthermore, NO2 was significantly related to acute coronary heart disease and PM2.5 was significantly related to lung cancer incidence. We generally observed linear to supralinear associations with no evidence of a threshold, with the exception of the association between NO2 and acute coronary heart disease, which was sublinear. Subset analyses documented that associations remained even with PM2.5 below 20 µg/m3 and possibly 12 µg/m3. Associations remained even when NO2 was below 30 µg/m3 and in some cases 20 µg/m3. In two-pollutant models, NO2 was most consistently associated with acute coronary heart disease, stroke, asthma, and COPD hospital admissions. PM2.5 was not associated with these outcomes in two-pollutant models with NO2. PM2.5 was the only pollutant that was associated with lung cancer incidence in two-pollutant models. Associations with O3 were negative though generally not statistically significant. In the administrative cohorts, virtually all subjects in 2010 had PM2.5 and NO2 annual average exposures below the EU limit values. More than 3.9 million subjects had a residential PM2.5 exposure below the U.S. EPA NAAQS (12 µg/m3) and more than 1.9 million had residential PM2.5 exposures below the WHO guideline (10 µg/m3). We found significant positive associations between PM2.5, NO2, and BC and natural-cause, respiratory, cardiovascular, and lung cancer mortality, with moderate to high heterogeneity between cohorts. We found positive but statistically nonsignificant associations with diabetes mortality. In our main model meta-analysis, the HRs (95% CI) for natural-cause mortality were 1.05 (CI = 1.02, 1.09) for an increase of 5 µg/m3 PM2.5, 1.04 (CI = 1.02, 1.07) for an increase of 10 µg/m3 NO2, and 1.04 (CI = 1.02, 1.06) for an increase of 0.5 × 10-5/m BC, and 0.95 (CI = 0.93, 0.98) for an increase of 10 µg/m3 O3. The shape of the concentration-response functions differed between cohorts, though the associations were generally linear to supralinear, with no indication of a level below which no associations were found. Subset analyses documented that these associations remained at low levels: below 10 µg/m3 for PM2.5 and 20 µg/m3 for NO2. BC and NO2 remained significantly associated with mortality in two-pollutant models with PM2.5 and O3. The PM2.5 HR attenuated to unity in a two-pollutant model with NO2. The negative O3 association was attenuated to unity and became nonsignificant. The mortality associations were robust to alternative specifications of exposure, including time-varying exposure analyses. Time-varying exposure natural spline analyses confirmed associations at low pollution levels. Effect estimates in the youngest participants (<65 years at baseline) were much larger than in the elderly (>65 years at baseline). Effect estimates obtained with the ELAPSE PM2.5 model did not differ from the MAPLE PM2.5 model on average, but in individual cohorts, substantial differences were found. CONCLUSIONS: Long-term exposure to PM2.5, NO2, and BC was positively associated with natural-cause and cause-specific mortality in the pooled cohort and the administrative cohorts. Associations were found well below current limit values and guidelines for PM2.5 and NO2. Associations tended to be supralinear, with steeper slopes at low exposures with no indication of a threshold. Two-pollutant models documented the importance of characterizing the ambient mixture with both NO2 and PM2.5. We mostly found negative associations with O3. In two-pollutant models with NO2, the negative associations with O3 were attenuated to essentially unity in the mortality analysis of the administrative cohorts and the incidence analyses in the pooled cohort. In the mortality analysis of the pooled cohort, significant negative associations with O3 remained in two-pollutant models. Long-term exposure to PM2.5, NO2, and BC was also positively associated with morbidity outcomes in the pooled cohort. For stroke, asthma, and COPD, positive associations were found for PM2.5, NO2, and BC. For acute coronary heart disease, an increased HR was observed for NO2. For lung cancer, an increased HR was found only for PM2.5. Associations mostly showed steeper slopes at low exposures with no indication of a threshold.

Development of Europe-Wide Models for Particle Elemental Composition Using Supervised Linear Regression and Random Forest.

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.

Agreement between self-reported and registry-based use of sleep medications and tranquilizers.

PURPOSE: The purpose of the present study was to assess the agreement between self-reported use of sleep medications and tranquilizers and dispensed hypnotics and anxiolytics. METHODS: Self-reported medication use was obtained from the population-based survey Health and Environment in Oslo (HELMILO) (2009-2010) (n = 13 019). Data on dispensed hypnotics and anxiolytics were obtained from the Norwegian Prescription Database (NorPD). As measures of validity, we calculated sensitivity and specificity using both self-reports and prescription records as the reference standard. Furthermore, we calculated Cohen's kappa. Current self-reported medication use was compared with prescription data in time windows of both 100 and 200 days preceding questionnaire completion. RESULTS: The highest sensitivity was observed for current sleep medication use in the 100-day time window (sensitivity = 0.76, 95% confidence interval [CI]: 0.74, 0.79) when using prescription records as the reference standard. Sensitivity was generally lower for tranquilizers compared with sleep medications. Cohen's kappa showed the highest agreement for the 200-day time window with substantial agreement for sleep medications (kappa = 0.64; 95% CI: 0.62, 0.67) and moderate agreement for tranquilizers (kappa = 0.45; 95% CI: 0.41, 0.48). CONCLUSIONS: The present study suggests moderate to substantial agreement between self-reported use of sleep medications and tranquilizers and dispensed drugs in a general adult population. The magnitude of agreement varied according to drug category and time window. Since self-reported and registry-based use of these drug classes does not match each other accurately, limitations of each data source should be considered when such medications are applied as the exposure or outcome in epidemiologic studies.

Air pollution and incidence of cancers of the stomach and the upper aerodigestive tract in the European Study of Cohorts for Air Pollution Effects (ESCAPE).

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.

A Longitudinal Study of Road Traffic Noise and Body Mass Index Trajectories from Birth to 8 Years.

BACKGROUND: Being overweight constitutes a health risk, and the proportion of overweight and obese children is increasing. It has been argued that road traffic noise could be linked to adiposity through its influence on sleep and stress. Few studies, to our knowledge, have investigated whether noise and adiposity are associated. Most of them were on adults, and we are not aware of any longitudinal study using repeated measures. OBJECTIVES: The present longitudinal study investigated whether road traffic noise exposures in pregnancy (N = 6,963; obs = 22,975) or childhood (N = 6,403; obs = 14,585) were associated with body mass index (BMI) trajectories in children. METHODS: We obtained information on BMI and covariates from questionnaires used in the Norwegian Mother and Child Cohort Study, Statistics Norway, and Medical Birth Registry of Norway. We modeled road traffic noise for the most exposed façade of children's present and historical addresses at 6 time points from pregnancy to age 8. We investigated effects on BMI trajectories using repeated measures and linear mixed models. RESULTS: The results indicated that BMI curves depended on road traffic noise exposure during pregnancy, but not on exposure during childhood. Children in the highest decile of traffic noise exposure had increased BMI, with 0.35 kg/m more than children in the lowest decile, from birth to age 8 years. CONCLUSIONS: The results indicate that exposure to road traffic noise during pregnancy may be associated with children's BMI trajectories. Future studies should investigate this further, using anthropometric measures such as waist-hip ratio and skinfold thickness, in addition to BMI.

Early life exposure to air pollution particulate matter (PM) as risk factor for attention deficit/hyperactivity disorder (ADHD): Need for novel strategies for mechanisms and causalities.

Epidemiological studies have demonstrated that air pollution particulate matter (PM) and adsorbed toxicants (organic compounds and trace metals) may affect child development already in utero. Recent studies have also indicated that PM may be a risk factor for neurodevelopmental disorders (NDDs). A pattern of increasing prevalence of attention deficit/hyperactivity disorder (ADHD) has been suggested to partly be linked to environmental pollutants exposure, including PM. Epidemiological studies suggest associations between pre- or postnatal exposure to air pollution components and ADHD symptoms. However, many studies are cross-sectional without possibility to reveal causality. Cohort studies are often small with poor exposure characterization, and confounded by traffic noise and socioeconomic factors, possibly overestimating the study associations. Furthermore, the mechanistic knowledge how exposure to PM during early brain development may contribute to increased risk of ADHD symptoms or cognitive deficits is limited. The closure of this knowledge gap requires the combined use of well-designed longitudinal cohort studies, supported by mechanistic in vitro studies. As ADHD has profound consequences for the children affected and their families, the identification of preventable risk factors such as air pollution exposure should be of high priority.

Long-term exposure to ambient air pollution and traffic noise and incident hypertension in seven cohorts of the European study of cohorts for air pollution effects (ESCAPE).

Aims: We investigated whether traffic-related air pollution and noise are associated with incident hypertension in European cohorts. Methods and results: We included seven cohorts of the European study of cohorts for air pollution effects (ESCAPE). We modelled concentrations of particulate matter with aerodynamic diameter ≤2.5 µm (PM2.5), ≤10 µm (PM10), >2.5, and ≤10 µm (PMcoarse), soot (PM2.5 absorbance), and nitrogen oxides at the addresses of participants with land use regression. Residential exposure to traffic noise was modelled at the facade according to the EU Directive 2002/49/EC. We assessed hypertension as (i) self-reported and (ii) measured (systolic BP ≥ 140 mmHg or diastolic BP ≥ 90 mmHg or intake of BP lowering medication (BPLM). We used Poisson regression with robust variance estimation to analyse associations of traffic-related exposures with incidence of hypertension, controlling for relevant confounders, and combined the results from individual studies with random-effects meta-analysis. Among 41 072 participants free of self-reported hypertension at baseline, 6207 (15.1%) incident cases occurred within 5-9 years of follow-up. Incidence of self-reported hypertension was positively associated with PM2.5 (relative risk (RR) 1.22 [95%-confidence interval (CI):1.08; 1.37] per 5 µg/m³) and PM2.5 absorbance (RR 1.13 [95% CI:1.02; 1.24] per 10 - 5m - 1). These estimates decreased slightly upon adjustment for road traffic noise. Road traffic noise was weakly positively associated with the incidence of self-reported hypertension. Among 10 896 participants at risk, 3549 new cases of measured hypertension occurred. We found no clear associations with measured hypertension. Conclusion: Long-term residential exposures to air pollution and noise are associated with increased incidence of self-reported hypertension.

Outdoor air pollution and risk for kidney parenchyma cancer in 14 European cohorts.

Several studies have indicated weakly increased risk for kidney cancer among occupational groups exposed to gasoline vapors, engine exhaust, polycyclic aromatic hydrocarbons and other air pollutants, although not consistently. It was the aim to investigate possible associations between outdoor air pollution at the residence and the incidence of kidney parenchyma cancer in the general population. We used data from 14 European cohorts from the ESCAPE study. We geocoded and assessed air pollution concentrations at baseline addresses by land-use regression models for particulate matter (PM10 , PM2.5 , PMcoarse , PM2.5 absorbance (soot)) and nitrogen oxides (NO2 , NOx ), and collected data on traffic. We used Cox regression models with adjustment for potential confounders for cohort-specific analyses and random effects models for meta-analyses to calculate summary hazard ratios (HRs). The 289,002 cohort members contributed 4,111,908 person-years at risk. During follow-up (mean 14.2 years) 697 incident cancers of the kidney parenchyma were diagnosed. The meta-analyses showed higher HRs in association with higher PM concentration, e.g. HR = 1.57 (95%CI: 0.81-3.01) per 5 µg/m3 PM2.5 and HR = 1.36 (95%CI: 0.84-2.19) per 10-5 m-1 PM2.5 absorbance, albeit never statistically significant. The HRs in association with nitrogen oxides and traffic density on the nearest street were slightly above one. Sensitivity analyses among participants who did not change residence during follow-up showed stronger associations, but none were statistically significant. Our study provides suggestive evidence that exposure to outdoor PM at the residence may be associated with higher risk for kidney parenchyma cancer; the results should be interpreted cautiously as associations may be due to chance.

Road traffic noise and children's inattention.

BACKGROUND: An increasing number of children are exposed to road traffic noise levels that may lead to adverse effects on health and daily functioning. Childhood is a period of intense growth and brain maturation, and children may therefore be especially vulnerable to road traffic noise. The objective of the present study was to examine whether road traffic noise was associated with reported inattention symptoms in children, and whether this association was mediated by sleep duration. METHODS: This study was based on the Norwegian Mother and Child Cohort Study conducted by the Norwegian Institute of Public Health. Parental reports of children's inattention at age 8 were linked to modelled levels of residential road traffic noise. We investigated the association between inattention and noise exposure during pregnancy (n = 1934), noise exposure averaged over 5 years (age 3 to 8 years; n = 1384) and noise exposure at age 8 years (n = 1384), using fractional logit response models. The participants were children from Oslo, Norway. RESULTS: An association with inattention at age 8 years was found for road traffic noise exposure at age 8 years (coef = .0083, CI = [.0012, .0154]; 1.2% point increase in inattention score per 10 dB increase in noise level), road traffic noise exposure average for the last 5 years (coef = .0090, CI = [.0016, .0164]; 1.3% point increase/10 dB), and for pregnancy road traffic noise exposure for boys (coef = .0091, CI = [.0010, .0171]), but not girls (coef = -.0021, CI = [-.0094, .0053]). Criteria for doing mediation analyses were not fulfilled. CONCLUSION: Results indicate that road traffic noise has a negative impact on children's inattention. We found no mediation by sleep duration.

Road traffic noise and registry based use of sleep medication.

BACKGROUND: Road traffic noise has been associated with adverse health effects including sleep disturbances. Use of sleep medication as an indicator of sleeping problems has rarely been explored in studies of the effects of traffic noise. Furthermore, using registry data on sleep medications provides an opportunity to study the effects of noise on sleep where attribution of sleep problems to noise is not possible. METHODS: We used questionnaire data from the population-based study Health and Environment in Oslo (HELMILO) (2009-10) (n = 13,019). Individual data on sleep medications was obtained from the Norwegian Prescription Database (NorPD). Noise levels (L night) were modeled for the most exposed façade of the building at each participant's home address. Logistic regression models adjusted for potential confounders were used to analyze the association between traffic noise and sleep medication use both for one whole year and for the summer season. The results were reported as changes in the effect estimate per 5 decibel (dB) increase in noise level. RESULTS: We observed no association between traffic noise and sleep medication use during one year [odds ratio (OR) = 1.00; 95% confidence interval (CI): 0.96, 1.04]. For sleep medication use in the summer season, there was a positive, however non-significant association (OR = 1.04; 95% CI: 0.99, 1.10). Among individuals sleeping with the bedroom window open, the association increased slightly and was borderline statistically significant (OR = 1.06; 95% CI: 1.00, 1.12). CONCLUSIONS: We found no evidence of an association between traffic noise and sleep medication use during one year. However, for the summer season, there was some suggestive evidence of an association. These findings indicate that season may play a role in the association between traffic noise and sleep, possibly because indoor traffic noise levels are likely to be higher during summer due to more frequent window opening. More studies are, however, necessary in order to confirm this.

Effects of long-term exposure to air pollution on natural-cause mortality: an analysis of 22 European cohorts within the multicentre ESCAPE project.

BACKGROUND: Few studies on long-term exposure to air pollution and mortality have been reported from Europe. Within the multicentre European Study of Cohorts for Air Pollution Effects (ESCAPE), we aimed to investigate the association between natural-cause mortality and long-term exposure to several air pollutants. METHODS: We used data from 22 European cohort studies, which created a total study population of 367,251 participants. All cohorts were general population samples, although some were restricted to one sex only. With a strictly standardised protocol, we assessed residential exposure to air pollutants as annual average concentrations of particulate matter (PM) with diameters of less than 2.5 µm (PM2.5), less than 10 µm (PM10), and between 10 µm and 2.5 µm (PMcoarse), PM2.5 absorbance, and annual average concentrations of nitrogen oxides (NO2 and NOx), with land use regression models. We also investigated two traffic intensity variables-traffic intensity on the nearest road (vehicles per day) and total traffic load on all major roads within a 100 m buffer. We did cohort-specific statistical analyses using confounder models with increasing adjustment for confounder variables, and Cox proportional hazards models with a common protocol. We obtained pooled effect estimates through a random-effects meta-analysis. FINDINGS: The total study population consisted of 367,251 participants who contributed 5,118,039 person-years at risk (average follow-up 13.9 years), of whom 29,076 died from a natural cause during follow-up. A significantly increased hazard ratio (HR) for PM2.5 of 1.07 (95% CI 1.02-1.13) per 5 µg/m(3) was recorded. No heterogeneity was noted between individual cohort effect estimates (I(2) p value=0.95). HRs for PM2.5 remained significantly raised even when we included only participants exposed to pollutant concentrations lower than the European annual mean limit value of 25 µg/m(3) (HR 1.06, 95% CI 1.00-1.12) or below 20 µg/m(3) (1.07, 1.01-1.13). INTERPRETATION: Long-term exposure to fine particulate air pollution was associated with natural-cause mortality, even within concentration ranges well below the present European annual mean limit value. FUNDING: European Community's Seventh Framework Program (FP7/2007-2011).

Exposure to traffic noise and markers of obesity.

OBJECTIVES: Limited evidence suggests adverse effects of traffic noise exposure on the metabolic system. This study investigates the association between road traffic noise and obesity markers as well as the role of combined exposure to multiple sources of traffic noise. METHODS: In a cross-sectional study performed in 2002-2006, we assessed exposure to noise from road traffic, railways and aircraft at the residences of 5075 Swedish men and women, primarily from suburban and semirural areas of Stockholm County. A detailed questionnaire and medical examination provided information on markers of obesity and potential confounders. Multiple linear and logistic regression models were used to assess associations between traffic noise and body mass index (BMI), waist circumference and waist-hip ratio using WHO definitions of obesity. RESULTS: Road traffic noise was significantly related to waist circumference with a 0.21 cm (95% CI 0.01 to 0.41) increase per 5 dB(A) rise in L(den). The OR for central obesity among those exposed to road traffic noise ≥ 45 dB(A) was 1.18 (95% CI 1.03 to 1.34) in comparison to those exposed below this level. Similar results were seen for waist-hip ratio (OR 1.29; 95% CI 1.14 to 1.45) but not for BMI (OR 0.89; 95% CI 0.76 to 1.04). Central obesity was also associated with exposure to railway and aircraft noise, and a particularly high risk was seen for combined exposure to all three sources of traffic noise (OR 1.95; 95% CI 1.24 to 3.05). CONCLUSIONS: Our results suggest that traffic noise exposure can increase the risk of central obesity. Combined exposure to different sources of traffic noise may convey a particularly high risk.

Road traffic noise and markers of obesity - a population-based study.

BACKGROUND: Noise has been found to be associated with endocrine changes and cardiovascular disease. Increased cortisol levels and chronic sleep problems due to noise may increase the risk of obesity. OBJECTIVES: We investigated the relationship between road traffic noise and obesity markers. Furthermore, we explored the modifying role of noise sensitivity, noise annoyance, and sleep disturbances. METHODS: We used data from a population-based study, HUBRO (N=15,085), and its follow-up study HELMILO (N=8410) conducted in Oslo, Norway. Measurements were used to define body mass index (BMI), waist circumference (WC), waist-hip ratio (WHR), and these binary outcomes: BMI≥30kg/m(2), WC≥102cm (men)/88cm (women), and WHR≥0.90 (men)/0.85 (women). Modelled levels of road traffic noise (Lden) were assigned to each participant's home address. Linear and logistic regression models were used to examine the associations. RESULTS: The results indicated no significant associations between road traffic noise and obesity markers in the total populations. However, in highly noise sensitive women (n=1106) a 10dB increase in noise level was associated with a slope (=beta) of 1.02 (95% confidence interval (CI): 1.01, 1.03) for BMI, 1.01 (CI: 1.00, 1.02) for WC, and an odds ratio (OR) of 1.24 (CI: 1.01, 1.53) for WHR ≥0.85. The associations appeared weaker in highly noise sensitive men. We found no effect modification of noise annoyance or sleep disturbances. In a sub-population with bedroom facing a road, the associations increased in men (e.g. an OR of 1.25 (CI: 0.88, 1.78) for BMI ≥30kg/m(2)), but not in women. Among long-term residents the associations increased for BMI ≥30kg/m(2) (OR of 1.07 (CI: 0.93, 1.24) in men and 1.10 (CI: 0.97, 1.26) in women), but not for the other outcomes. CONCLUSION: In an adult urban Scandinavian population, road traffic noise was positively associated with obesity markers among highly noise sensitive women. The associations appeared stronger among men with bedroom facing a street, representing a population with more accurately assigned exposure.

Air pollution and nonmalignant respiratory mortality in 16 cohorts within the ESCAPE project.

RATIONALE: Prospective cohort studies have shown that chronic exposure to particulate matter and traffic-related air pollution is associated with reduced survival. However, the effects on nonmalignant respiratory mortality are less studied, and the data reported are less consistent. OBJECTIVES: We have investigated the relationship of long-term exposure to air pollution and nonmalignant respiratory mortality in 16 cohorts with individual level data within the multicenter European Study of Cohorts for Air Pollution Effects (ESCAPE). METHODS: Data from 16 ongoing cohort studies from Europe were used. The total number of subjects was 307,553. There were 1,559 respiratory deaths during follow-up. MEASUREMENTS AND MAIN RESULTS: Air pollution exposure was estimated by land use regression models at the baseline residential addresses of study participants and traffic-proximity variables were derived from geographical databases following a standardized procedure within the ESCAPE study. Cohort-specific hazard ratios obtained by Cox proportional hazard models from standardized individual cohort analyses were combined using metaanalyses. We found no significant associations between air pollution exposure and nonmalignant respiratory mortality. Most hazard ratios were slightly below unity, with the exception of the traffic-proximity indicators. CONCLUSIONS: In this study of 16 cohorts, there was no association between air pollution exposure and nonmalignant respiratory mortality.