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This paper studies the impact of land use and land cover change (LULCC) on the climate around 2500 years ago (2.5 ka), a period of rapid transitions across the European landscape. One global climate model was used to force two regional climate models (RCMs). The RCMs used two land cover descriptions. The first was from a dynamical vegetation model representing potential land cover, and the second was from a land cover description reconstructed from pollen data by statistical interpolation. The two different land covers enable us to study the impact of land cover on climate conditions. Since the difference in landscape openness between potential and reconstructed land cover is mostly due to LULCC, this can be taken as a measure of early anthropogenic effects on climate. Since the sensitivity to LULCC is dependent on the choice of climate model, we also use two RCMs. The results show that the simulated 2.5 ka climate was warmer than the simulated pre-industrial (PI, 1850 CE) climate. The largest differences are seen in northern Europe, where the 2.5 ka climate is 2–4 ∘ C warmer than the PI period. In summer, the difference between the simulated 2.5 ka and PI climates is smaller (0–3 ∘ C), with the smallest differences in southern Europe. Differences in seasonal precipitation are mostly within ± 10 %. In parts of northern Europe, the 2.5 ka climate is up to 30 % wetter in winter than that of the PI climate. In summer there is a tendency for the 2.5 ka climate to be drier than the PI climate in the Mediterranean region. The results also suggest that LULCC at 2.5 ka impacted the climate in parts of Europe. Simulations including reconstructed LULCC (i.e. those using pollen-derived land cover descriptions) give up to 1 ∘ C higher temperature in parts of northern Europe in winter and up to 1.5 ∘ C warmer in southern Europe in summer than simulations with potential land cover. Although the results are model dependent, the relatively strong response implies that anthropogenic land cover changes that had occurred during the Neolithic and Bronze Age could have affected the European climate by 2.5 ka. [ABSTRACT FROM AUTHOR]

Particulate matter air pollution and diesel engine exhaust have been classified as carcinogenic for lung cancer, yet few studies have explored associations with liver cancer. We used six European adult cohorts which were recruited between 1985 and 2005, pooled within the "Effects of low‐level air pollution: A study in Europe" (ELAPSE) project, and followed for the incidence of liver cancer until 2011 to 2015. The annual average exposure to nitrogen dioxide (NO2), particulate matter with diameter <2.5 μm (PM2.5), black carbon (BC), warm‐season ozone (O3), and eight elemental components of PM2.5 (copper, iron, zinc, sulfur, nickel, vanadium, silicon, and potassium) were estimated by European‐wide hybrid land‐use regression models at participants' residential addresses. We analyzed the association between air pollution and liver cancer incidence by Cox proportional hazards models adjusting for potential confounders. Of 330 064 cancer‐free adults at baseline, 512 developed liver cancer during a mean follow‐up of 18.1 years. We observed positive linear associations between NO2 (hazard ratio, 95% confidence interval: 1.17, 1.02‐1.35 per 10 μg/m3), PM2.5 (1.12, 0.92‐1.36 per 5 μg/m3), and BC (1.15, 1.00‐1.33 per 0.5 10−5/m) and liver cancer incidence. Associations with NO2 and BC persisted in two‐pollutant models with PM2.5. Most components of PM2.5 were associated with the risk of liver cancer, with the strongest associations for sulfur and vanadium, which were robust to adjustment for PM2.5 or NO2. Our study suggests that ambient air pollution may increase the risk of liver cancer, even at concentrations below current EU standards. What's new? Air pollution contains a number of known carcinogens. While air pollution is classified as carcinogenic and is a known risk factor for lung cancer, the evidence for cancers in other organs is limited. In this large European study, the authors detected associations between air pollution and liver cancer incidence, even at levels that are below current EU standards. These results corroborate findings from several earlier, substantially smaller studies, and suggest that ambient air pollution may increase the risk of liver cancer. [ABSTRACT FROM AUTHOR]

Most studies investigating the health effects of long-term exposure to air pollution used traditional regression models, although causal inference approaches have been proposed as alternative. However, few studies have applied causal models and comparisons with traditional methods are sparse. We therefore compared the associations between natural-cause mortality and exposure to fine particulate matter (PM 2.5) and nitrogen dioxide (NO 2) using traditional Cox and causal models in a large multicenter cohort setting. We analysed data from eight well-characterized cohorts (pooled cohort) and seven administrative cohorts from eleven European countries. Annual mean PM 2.5 and NO 2 from Europe-wide models were assigned to baseline residential addresses and dichotomized at selected cut-off values (PM 2.5 : 10, 12, 15 μg/m³; NO 2 : 20, 40 μg/m³). For each pollutant, we estimated the propensity score as the conditional likelihood of exposure given available covariates, and derived corresponding inverse-probability weights (IPW). We applied Cox proportional hazards models i) adjusting for all covariates ("traditional Cox") and ii) weighting by IPW ("causal model"). Of 325,367 and 28,063,809 participants in the pooled and administrative cohorts, 47,131 and 3,580,264 died from natural causes, respectively. For PM 2.5 above vs. below 12 μg/m³, the hazard ratios (HRs) of natural-cause mortality were 1.17 (95% CI 1.13–1.21) and 1.15 (1.11–1.19) for the traditional and causal models in the pooled cohort, and 1.03 (1.01–1.06) and 1.02 (0.97–1.09) in the administrative cohorts. For NO 2 above vs below 20 μg/m³, the HRs were 1.12 (1.09–1.14) and 1.07 (1.05–1.09) for the pooled and 1.06 (95% CI 1.03–1.08) and 1.05 (1.02–1.07) for the administrative cohorts. In conclusion, we observed mostly consistent associations between long-term air pollution exposure and natural-cause mortality with both approaches, though estimates partly differed in individual cohorts with no systematic pattern. The application of multiple modelling methods might help to improve causal inference. 299 of 300 words. [Display omitted] • We assessed associations between long-term PM 2.5 and NO 2 and natural-cause mortality. • We compared traditional Cox and causal inference models across large European cohorts. • We observed mostly consistent associations with both approaches. • Estimates partly differed in individual cohorts with no systematic pattern. • Application of multiple modelling methods is favorable to improve causal inference. [ABSTRACT FROM AUTHOR]

An association between long-term exposure to fine particulate matter (PM 2.5) and lung cancer has been established in previous studies. PM 2.5 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 PM 2.5 and lung cancer incidence. We pooled seven cohorts from across Europe and assigned exposure estimates for eight components of PM 2.5 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). 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 PM 2.5 K, 1.09 (95% CI 1.02, 1.15) per 1 ng/m3 PM 2.5 Ni, 1.22 (95% CI 1.11, 1.35) per 200 ng/m3 PM 2.5 S, and 1.07 (95% CI 1.02, 1.12) per 200 ng/m3 PM 2.5 V. Effect estimates were largely unaffected by adjustment for nitrogen dioxide (NO 2). After adjustment for PM 2.5 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. 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 PM 2.5 components may prove helpful in future lung cancer prevention strategies. • Exposure to PM 2.5 is associated with a higher risk of lung cancer. • PM 2.5 is a complex mixture of components from various sources. • We observed positive associations between all components and lung cancer. • Combustion particles and secondary inorganic aerosols may be of special importance. [ABSTRACT FROM AUTHOR]

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 (PM 2.5) and nitrogen dioxide (NO 2) 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 PM 2.5 and 2399 sites for NO 2), 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 (PM 2.5) and 1396 sites (NO 2) from the ESCAPE study. We further assessed the correlations between predictions by each pair of algorithms at the ESCAPE sites. For PM 2.5 , 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 PM 2.5 model predictions at ESCAPE sites were highly correlated (R2 > 0.85, with the exception of predictions from the artificial neural network). For NO 2 , 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 PM 2.5 models whilst dispersion model estimates and traffic variables were most important for NO 2 models in all algorithms that allow assessment of the importance of variables. Different statistical algorithms performed similarly when modelling spatial variation in annual average air pollution concentrations using a large number of training sites. • Multiple statistical algorithms with very different assumptions were compared. • Despite the difference in modeling frameworks, predictions among the models exhibit generally good agreement. • The use of an external evaluation dataset strengthens evaluation by cross-validation. [ABSTRACT FROM AUTHOR]

Background : Particulate matter (PM) is classified as a group 1 human carcinogen. Previous experimental studies suggest that particles in diesel exhaust induce oxidative stress, inflammation and DNA damage in kidney cells, but the evidence from population studies linking air pollution to kidney cancer is limited. Methods : We pooled six European cohorts (N = 302,493) to assess the association of residential exposure to fine particles (PM 2.5), nitrogen dioxide (NO 2), black carbon (BC), warm season ozone (O 3) and eight elemental components of PM 2.5 (copper, iron, potassium, nickel, sulfur, silicon, vanadium, and zinc) with cancer of the kidney parenchyma. The main exposure model was developed for year 2010. We defined kidney parenchyma cancer according to the International Classification of Diseases 9th and 10th Revision codes 189.0 and C64. We applied Cox proportional hazards models adjusting for potential confounders at the individual and area-level. Results : The participants were followed from baseline (1985–2005) to 2011–2015. A total of 847 cases occurred during 5,497,514 person-years of follow-up (average 18.2 years). Median (5–95%) exposure levels of NO 2 , PM 2.5 , BC and O 3 were 24.1 μg/m3 (12.8–39.2), 15.3 μg/m3 (8.6–19.2), 1.6 10−5 m−1 (0.7–2.1), and 87.0 μg/m3 (70.3–97.4), respectively. The results of the fully adjusted linear analyses showed a hazard ratio (HR) of 1.03 (95% confidence interval [CI]: 0.92, 1.15) per 10 μg/m³ NO 2 , 1.04 (95% CI: 0.88, 1.21) per 5 μg/m³ PM 2.5 , 0.99 (95% CI: 0.89, 1.11) per 0.5 10−5 m−1 BCE, and 0.88 (95% CI: 0.76, 1.02) per 10 μg/m³ O 3. We did not find associations between any of the elemental components of PM 2.5 and cancer of the kidney parenchyma. Conclusion : We did not observe an association between long-term ambient air pollution exposure and incidence of kidney parenchyma cancer. •A large sample size was obtained by pooling six cohorts with harmonized covariates. •A newly developed Europe-wide spatial land-use regression model was applied. •Results did not indicate a role of air pollutants in development of kidney cancer. [ABSTRACT FROM AUTHOR]

Global hydrological models (GHMs) have been developed to calculate the water balance on global or continental scales which is essential for water resources management. The hydrological processes are complex, heterogeneous, and hardly characterized by laboratory and field measurements, especially for the evapotranspiration (ET) rate because of the difficulties of directly measuring atmospheric vapor flux. As a result, practical theories of potential ET (PET) and their impact on runoff generation remain elusive. PET/ET methods in GHM can behave differently because of different assumptions and input requirements. In this study, we assess the variations of the water balance components in Europe based on six PET methods, i.e., Penman-Monteith (PM), radiation-based Priestley-Taylor (PT), radiation-based Turc, temperature-based Hargreaves, an empirical temperature-based (empirical-T) method, and a PET product of Global Land Evaporation Amsterdam Model (GLEAM). Besides, a global Macro-scale Water and Snow Balance Modeling System (WASMOD-M) is calibrated at daily time step based on 66 gauged watersheds in Europe and applied with two regionalization methods (i.e., the Global mean method and the Koppen method) at the 0.5°×0.5° grid scale for the 1997-2008 period. Results show that (1) the Turc and empirical-T methods derive similar results of water balance with that of GLEAM while largely different from that of PT and PM; (2) the empirical-T and Turc methods show a slight decrease, while others show a slightly increase trend in Europe on average; as for the seasonal PET, all methods show a slight increase in spring and autumn and a slight decrease in winter; (3) all methods indicate that the increase of PET and Actual ET (AET) in central Europe is more obvious than other parts of Europe; and (4) different methods derive a similar spatial distribution of PET and runoff in Europe although they vary considerably in the quantity. Further study is in progress to characterize surface processes and to investigate the differences of water balance component regarding the assumptions of PET methods. [ABSTRACT FROM AUTHOR]

Vulnerable windows in child development in utero and after birth are critical time points for uncovering the links between environment and health. Particular attention is paid to the first 1000 days of life from conception to the second year of life. The ELSPAC (European Longitudinal Study of Pregnancy and Childhood) birth cohort, launched in the early 1990s, is a rich source of longitudinal data about health and life events, based mainly in Brno, Czechia. There are currently no air quality concentration maps that can be used to assess exposure to air pollutants for this period of the 1990s in Central Europe. Simply transferring current models to the 1990's is burdened with the error introduced by the temporal change in emission sources and land use of the area. Therefore, Czech air quality monitoring data were used to develop monthly land use regression (LUR) models, which combine collected spatial variables with monitoring data to predict the variation in exposures to pollutants. Monthly pollutant concentrations were regressed against the GIS-based potential predictor variables to develop LUR models, following a supervised forward linear regression, with several predefined constraints. We constructed 180 LUR monthly models for sulphur dioxide (SO 2), nitrogen oxides (NOx) and suspended particulate matter (SPM) for 1990–1994, that completely cover the first 1000 days for all ELSPAC study participants. The final models showed, on average reasonably good performance (adjusted R2 = 0.59 with hold-out validation (HOV) R2 = 0.40 for SO 2 ; adjusted R2 = 0.75 with HOV R2 = 0.35 for NOx; and adjusted R2 = 0.61 with HOV R2 = 0.31 for SPM; with a mean number of stations of 74, 38 and 41, respectively). For these models, roads and greenness were predominantly selected as the best predictors. The modelled exposures will serve in many subsequent ELSPAC epidemiological studies, but our models may be also used in other Czech and possibly other Central European cities in that period. [Display omitted] • Monthly historical LUR models were developed for air pollutants in Central Europe. • Model performance was moderate. • Exposures assigned to ELSPAC participants showed temporal and spatial variability. [ABSTRACT FROM AUTHOR]

• We study the association of long-term air pollution exposure with Parkinson's Disease (PD) • Our study among 271,720 ELAPSE cohort participants noted 381 deaths from PD. • We find that long-term exposure to PM 2.5 , NO 2 and BC are associated with risk of PD death. • Associations persist at low levels of pollutant concentration, below current EU standards. • Our study adds strong evidence in support of an association between air pollution and PD. The link between exposure to ambient air pollution and mortality from cardiorespiratory diseases is well established, while evidence on neurodegenerative disorders including Parkinson's Disease (PD) remains limited. We examined the association between long-term exposure to ambient air pollution and PD mortality in seven European cohorts. Within the project 'Effects of Low-Level Air Pollution: A Study in Europe' (ELAPSE), we pooled data from seven cohorts among six European countries. Annual mean residential concentrations of fine particulate matter (PM 2.5), nitrogen dioxide (NO 2), black carbon (BC), and ozone (O 3), as well as 8 PM 2.5 components (copper, iron, potassium, nickel, sulphur, silicon, vanadium, zinc), for 2010 were estimated using Europe-wide hybrid land use regression models. PD mortality was defined as underlying cause of death being either PD, secondary Parkinsonism, or dementia in PD. We applied Cox proportional hazard models to investigate the associations between air pollution and PD mortality, adjusting for potential confounders. Of 271,720 cohort participants, 381 died from PD during 19.7 years of follow-up. In single-pollutant analyses, we observed positive associations between PD mortality and PM 2.5 (hazard ratio per 5 µg/m3: 1.25; 95% confidence interval: 1.01–1.55), NO 2 (1.13; 0.95–1.34 per 10 µg/m3), and BC (1.12; 0.94–1.34 per 0.5 × 10-5m-1), and a negative association with O 3 (0.74; 0.58–0.94 per 10 µg/m3). Associations of PM 2.5 , NO 2 , and BC with PD mortality were linear without apparent lower thresholds. In two-pollutant models, associations with PM 2.5 remained robust when adjusted for NO 2 (1.24; 0.95–1.62) or BC (1.28; 0.96–1.71), whereas associations with NO 2 or BC attenuated to null. O 3 associations remained negative, but no longer statistically significant in models with PM 2.5. We detected suggestive positive associations with the potassium component of PM 2.5. Long-term exposure to PM 2.5 , at levels well below current EU air pollution limit values, may contribute to PD mortality. [ABSTRACT FROM AUTHOR]

[Display omitted] • Air pollution was not associated with dementia mortality in seven European cohorts. • PM 2.5 , NO 2 , and BC were positively associated with psychiatric disorders mortality and suicide. • O 3 was not associated with dementia, psychiatric disorders, and suicide mortality. Ambient air pollution is an established risk factor for premature mortality from chronic cardiovascular, respiratory and metabolic diseases, while evidence on neurodegenerative diseases and psychiatric disorders remains limited. We examined the association between long-term exposure to air pollution and mortality from dementia, psychiatric disorders, and suicide in seven European cohorts. Within the multicenter project 'Effects of Low-Level Air Pollution: A Study in Europe' (ELAPSE), we pooled data from seven European cohorts from six countries. Based on the residential addresses, annual mean levels of fine particulate matter (PM 2.5), nitrogen dioxide (NO 2), black carbon (BC), ozone (O 3), and 8 PM 2.5 components were estimated using Europe-wide hybrid land-use regression models. We applied stratified Cox proportional hazard models to investigate the associations between air pollution and mortality from dementia, psychiatric disorders, and suicide. Of 271,720 participants, 900 died from dementia, 241 from psychiatric disorders, and 164 from suicide, during a mean follow-up of 19.7 years. In fully adjusted models, we observed positive associations of NO 2 (hazard ratio [HR] = 1.38; 95 % confidence interval [CI]: 1.13, 1.70 per 10 µg/m3), PM 2.5 (HR = 1.29; 95 % CI: 0.98, 1.71 per 5 µg/m3), and BC (HR = 1.37; 95 % CI: 1.11, 1.69 per 0.5 × 10−5/m) with psychiatric disorders mortality, as well as with suicide (NO 2 : HR = 1.13 [95 % CI: 0.92, 1.38]; PM 2.5 : HR = 1.19 [95 % CI: 0.76, 1.87]; BC: HR = 1.08 [95 % CI: 0.87, 1.35]), and no association with dementia mortality. We did not detect any positive associations of O 3 and 8 PM 2.5 components with any of the three mortality outcomes. Long-term exposure to NO 2 , PM 2.5 , and BC may lead to premature mortality from psychiatric disorders and suicide. [ABSTRACT FROM AUTHOR]

• Exposure to PM 2.5 was associated with higher risk of lung cancer. • Elevated risks persisted even at levels lower than the EU limit value of 25 µg/m3. • No association between NO 2 , BC or O 3 and lung cancer incidence was observed. Ambient air pollution has been associated with lung cancer, but the shape of the exposure-response function - especially at low exposure levels - is not well described. The aim of this study was to address the relationship between long-term low-level air pollution exposure and lung cancer incidence. The "Effects of Low-level Air Pollution: a Study in Europe" (ELAPSE) collaboration pools seven cohorts from across Europe. We developed hybrid models combining air pollution monitoring, land use data, satellite observations, and dispersion model estimates for nitrogen dioxide (NO 2), fine particulate matter (PM 2.5), black carbon (BC), and ozone (O 3) to assign exposure to cohort participants' residential addresses in 100 m by 100 m grids. 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). We fitted linear models, linear models in subsets, Shape-Constrained Health Impact Functions (SCHIF), and natural cubic spline models to assess the shape of the association between air pollution and lung cancer at concentrations below existing standards and guidelines. The analyses included 307,550 cohort participants. During a mean follow-up of 18.1 years, 3956 incident lung cancer cases occurred. Median (Q1, Q3) annual (2010) exposure levels of NO 2 , PM 2.5 , BC and O 3 (warm season) were 24.2 µg/m3 (19.5, 29.7), 15.4 µg/m3 (12.8, 17.3), 1.6 10−5m−1 (1.3, 1.8), and 86.6 µg/m3 (78.5, 92.9), respectively. We observed a higher risk for lung cancer with higher exposure to PM 2.5 (HR: 1.13, 95% CI: 1.05, 1.23 per 5 µg/m3). This association was robust to adjustment for other pollutants. The SCHIF, spline and subset analyses suggested a linear or supra-linear association with no evidence of a threshold. In subset analyses, risk estimates were clearly elevated for the subset of subjects with exposure below the EU limit value of 25 µg/m3. We did not observe associations between NO 2 , BC or O 3 and lung cancer incidence. Long-term ambient PM 2.5 exposure is associated with lung cancer incidence even at concentrations below current EU limit values and possibly WHO Air Quality Guidelines. [ABSTRACT FROM AUTHOR]

Background The clinicopathologic-radiologic and prognostic characteristics of intratumoral fat in hepatocellular carcinoma (HCC) are critical for personalized treatment but remain understudied. Purpose To investigate the clinicopathologic-radiologic associations and prognostic implications of MRI-assessed intratumoral fat in HCCs. Materials and Methods This retrospective cohort study included consecutive adult patients who underwent resection for solitary HCCs and preoperative contrast-enhanced MRI from two tertiary-care hospitals in East Asia (March 2011 to December 2021) and Western Europe (September 2012 to December 2019). MRI scans were independently evaluated by three radiologists at each hospital. Based on Liver Imaging Reporting and Data System (LI-RADS) version 2018, intratumoral fat was defined as "fat in mass more than adjacent liver," and the homogeneous subtype was defined as intratumoral fat "in absence of mosaic and nodule-in-nodule architecture." Recurrence-free survival (RFS) and overall survival (OS) were estimated using the Kaplan-Meier method and compared using the log-rank test. Cox regression analyses were conducted to identify factors associated with RFS and OS. Results A total of 933 patients were included in the Asian ( n = 736; median age, 53 years [IQR, 45-62 years]; 626 male) and European ( n = 207; median age, 64 years [IQR, 55-70 years]; 161 male) cohorts. MRI-assessed intratumoral fat was detected in 30% (215 of 726) and 31% (64 of 207) of patients in the Asian and European cohorts, respectively ( P = .72). In both cohorts, the steatohepatitic subtype, nonperipheral washout, enhancing capsule, and mosaic architecture were more frequent in tumors with intratumoral fat ( P value range, <.001 to .04). Intratumoral fat in general was not associated with RFS or OS in either cohort ( P value range, .48-.97). However, in the Asian cohort, homogeneous intratumoral fat was associated with longer RFS (hazard ratio [HR], 0.60; P = .009) and OS (HR, 0.33; P = .008) in multivariable Cox regression analyses. Conclusion MRI-assessed intratumoral fat was more frequent in steatohepatitic HCCs and associated with nonperipheral washout, enhancing capsule, and mosaic architecture. Although intratumoral fat was generally nonprognostic, homogeneous intratumoral fat was associated with longer RFS and OS in the Asian cohort. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Harmath in this issue.

Despite the known link between air pollution and cause-specific mortality, its relation to chronic kidney disease (CKD)-associated mortality is understudied. Therefore, we investigated the association between long-term exposure to air pollution and CKD-related mortality in a large multicentre population-based European cohort. Cohort data were linked to local mortality registry data. CKD-death was defined as ICD10 codes N18-N19 or corresponding ICD9 codes. Mean annual exposure at participant's home address was determined with fine spatial resolution exposure models for nitrogen dioxide (NO 2 ), black carbon (BC), ozone (O 3 ), particulate matter ≤2.5 μm (PM 2.5 ) and several elemental constituents of PM 2.5 . Cox regression models were adjusted for age, sex, cohort, calendar year of recruitment, smoking status, marital status, employment status and neighbourhood mean income. Over a mean follow-up time of 20.4 years, 313 of 289,564 persons died from CKD. Associations were positive for PM 2.5 (hazard ratio (HR) with 95% confidence interval (CI) of 1.31 (1.03-1.66) per 5 μg/m 3 , BC (1.26 (1.03-1.53) per 0.5 × 10 - 5 /m), NO 2 (1.13 (0.93-1.38) per 10 μg/m 3 ) and inverse for O 3 (0.71 (0.54-0.93) per 10 μg/m 3 ). Results were robust to further covariate adjustment. Exclusion of the largest sub-cohort contributing 226 cases, led to null associations. Among the elemental constituents, Cu, Fe, K, Ni, S and Zn, representing different sources including traffic, biomass and oil burning and secondary pollutants, were associated with CKD-related mortality. In conclusion, our results suggest an association between air pollution from different sources and CKD-related mortality., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Aim: To assess the long-term safety of hyaluronidase-facilitated subcutaneous immunoglobulin (fSCIG) 10% in European routine clinical practice. Materials & methods: This prospective, noninterventional, open-label, post-authorization safety study (EUPAS5812) sourced data on adverse events, immunogenicity, treatment regimens and product administration for 106 adult patients prescribed fSCIG 10% across 17 sites in six European countries from July 2014 to February 2020. Results: In total, 1171 treatment-emergent adverse events were reported in 94 patients (88.7%); 25.5% of these events were considered related to fSCIG 10%. Positive binding antibody titers developed in three patients; no neutralizing antibodies to recombinant human hyaluronidase were detected. Conclusion: This real-world study of fSCIG 10% is the longest to date and confirms its long-term safety and tolerability in adults with antibody deficiency diseases.

Background: Studies across the globe generally reported increased mortality risks associated with particulate matter with aerodynamic diameter ≤ 2.5 μ m ( PM 2.5 ) exposure with large heterogeneity in the magnitude of reported associations and the shape of concentration-response functions (CRFs). We aimed to evaluate the impact of key study design factors (including confounders, applied exposure model, population age, and outcome definition) on PM 2.5 effect estimates by harmonizing analyses on three previously published large studies in Canada [Mortality-Air Pollution Associations in Low Exposure Environments (MAPLE), 1991-2016], the United States (Medicare, 2000-2016), and Europe [Effects of Low-Level Air Pollution: A Study in Europe (ELAPSE), 2000-2016] as much as possible., Methods: We harmonized the study populations to individuals 65 + years of age, applied the same satellite-derived PM 2.5 exposure estimates, and selected the same sets of potential confounders and the same outcome. We evaluated whether differences in previously published effect estimates across cohorts were reduced after harmonization among these factors. Additional analyses were conducted to assess the influence of key design features on estimated risks, including adjusted covariates and exposure assessment method. A combined CRF was assessed with meta-analysis based on the extended shape-constrained health impact function (eSCHIF)., Results: More than 81 million participants were included, contributing 692 million person-years of follow-up. Hazard ratios and 95% confidence intervals (CIs) for all-cause mortality associated with a 5 - μ g / m 3 increase in PM 2.5 were 1.039 (1.032, 1.046) in MAPLE, 1.025 (1.021, 1.029) in Medicare, and 1.041 (1.014, 1.069) in ELAPSE. Applying a harmonized analytical approach marginally reduced difference in the observed associations across the three studies. Magnitude of the association was affected by the adjusted covariates, exposure assessment methodology, age of the population, and marginally by outcome definition. Shape of the CRFs differed across cohorts but generally showed associations down to the lowest observed PM 2.5 levels. A common CRF suggested a monotonically increased risk down to the lowest exposure level. https://doi.org/10.1289/EHP12141.

Evidence for the association between long-term exposure to ambient particulate matter components and mortality from natural causes is sparse and inconsistent. We evaluated this association in six large administrative cohorts in the framework of the Effects of Low-Level Air Pollution: A Study in Europe (ELAPSE) project. We analyzed data from country-wide administrative cohorts in Norway, Denmark, the Netherlands, Belgium, Switzerland and in Rome (Italy). Annual 2010 mean concentrations of copper (Cu), iron (Fe), potassium (K), nickel (Ni), sulfur (S), silicon (Si), vanadium (V) and zinc (Zn) in fine particulate matter (PM 2.5 ) were estimated using 100 × 100 m Europe-wide hybrid land use regression models assigned to the participants' residential addresses. We applied cohort-specific Cox proportional hazard models controlling for area- and individual-level covariates to evaluate associations with natural mortality. Two pollutant models adjusting for PM 2.5 total mass or nitrogen dioxide (NO 2 ) were also applied. We pooled cohort-specific estimates using a random effects meta-analysis. We included almost 27 million participants contributing more than 240 million person-years. All components except Zn were significantly associated with natural mortality [pooled Hazard Ratios (HRs) (95% CI): 1.037 (1.014, 1.060) per 5 ng/m 3 Cu; 1.069 (1.031, 1.108) per 100 ng/m 3 Fe; 1.039 (1.018, 1.062) per 50 ng/m 3 K; 1.024 (1.006, 1.043) per 1 ng/m 3 Ni; 1.036 (1.016, 1.057) per 200 ng/m 3 S; 1.152 (1.048, 1.266) per 100 ng/m 3 Si; 1.020 (1.006, 1.034) per 2 ng/m 3 V]. Only K and Si were robust to PM 2.5 or NO 2 adjustment [pooled HRs (95% CI) per 50 ng/m 3 in K: 1.025 (1.008, 1.044), 1.020 (0.999, 1.042) and per 100 ng/m 3 in Si: 1.121 (1.039, 1.209), 1.068 (1.022, 1.117) adjusted for PM 2.5 and NO 2 correspondingly]. Our findings indicate an association of natural mortality with most components, which was reduced after adjustment for PM 2.5 and especially NO 2 ., Competing Interests: Declaration of competing interest None declared., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Background: Long-term exposure to ambient air pollution has been associated with premature mortality, but associations at concentrations lower than current annual limit values are uncertain. We analysed associations between low-level air pollution and mortality within the multicentre study Effects of Low-Level Air Pollution: A Study in Europe (ELAPSE)., Methods: In this multicentre longitudinal study, we analysed seven population-based cohorts of adults (age ≥30 years) within ELAPSE, from Belgium, Denmark, England, the Netherlands, Norway, Rome (Italy), and Switzerland (enrolled in 2000-11; follow-up until 2011-17). Mortality registries were used to extract the underlying cause of death for deceased individuals. Annual average concentrations of fine particulate matter (PM 2·5 ), nitrogen dioxide (NO 2 ), black carbon, and tropospheric warm-season ozone (O 3 ) from Europe-wide land use regression models at 100 m spatial resolution were assigned to baseline residential addresses. We applied cohort-specific Cox proportional hazard models with adjustment for area-level and individual-level covariates to evaluate associations with non-accidental mortality, as the main outcome, and with cardiovascular, non-malignant respiratory, and lung cancer mortality. Subset analyses of participants living at low pollutant concentrations (as per predefined values) and natural splines were used to investigate the concentration-response function. Cohort-specific effect estimates were pooled in a random-effects meta-analysis., Findings: We analysed 28 153 138 participants contributing 257 859 621 person-years of observation, during which 3 593 741 deaths from non-accidental causes occurred. We found significant positive associations between non-accidental mortality and PM 2·5 , NO 2 , and black carbon, with a hazard ratio (HR) of 1·053 (95% CI 1·021-1·085) per 5 μg/m 3 increment in PM 2·5 , 1·044 (1·019-1·069) per 10 μg/m 3 NO 2 , and 1·039 (1·018-1·059) per 0·5 × 10 -5 /m black carbon. Associations with PM 2·5 , NO 2 , and black carbon were slightly weaker for cardiovascular mortality, similar for non-malignant respiratory mortality, and stronger for lung cancer mortality. Warm-season O 3 was negatively associated with both non-accidental and cause-specific mortality. Associations were stronger at low concentrations: HRs for non-accidental mortality at concentrations lower than the WHO 2005 air quality guideline values for PM 2·5 (10 μg/m 3 ) and NO 2 (40 μg/m 3 ) were 1·078 (1·046-1·111) per 5 μg/m 3 PM 2·5 and 1·049 (1·024-1·075) per 10 μg/m 3 NO 2 . Similarly, the association between black carbon and non-accidental mortality was highest at low concentrations, with a HR of 1·061 (1·032-1·092) for exposure lower than 1·5× 10 -5 /m, and 1·081 (0·966-1·210) for exposure lower than 1·0× 10 -5 /m., Interpretation: Long-term exposure to concentrations of PM 2·5 and NO 2 lower than current annual limit values was associated with non-accidental, cardiovascular, non-malignant respiratory, and lung cancer mortality in seven large European cohorts. Continuing research on the effects of low concentrations of air pollutants is expected to further inform the process of setting air quality standards in Europe and other global regions., Funding: Health Effects Institute., Competing Interests: Declaration of interests We declare no competing interests., (Copyright © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

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 (PM 2.5 ), nitrogen dioxide (NO 2 ) 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 PM 2.5 , 1.17 (95% CI 1.10-1.25) per 10 µg·m -3 for NO 2 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 PM 2.5 and NO 2 . NO 2 and BC estimates remained unchanged in two-pollutant models with PM 2.5 , whereas PM 2.5 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., Competing Interests: Conflict of interest: S. Liu has nothing to disclose. Conflict of interest: J.T. Jørgensen has nothing to disclose. Conflict of interest: P. Ljungman has nothing to disclose. Conflict of interest: G. Pershagen has nothing to disclose. Conflict of interest: T. Bellander has nothing to disclose. Conflict of interest: K. Leander has nothing to disclose. Conflict of interest: P.K.E. Magnusson has nothing to disclose. Conflict of interest: D. Rizzuto has nothing to disclose. Conflict of interest: U.A. Hvidtfeldt has nothing to disclose. Conflict of interest: O. Raaschou-Nielsen has nothing to disclose. Conflict of interest: K. Wolf has nothing to disclose. Conflict of interest: B. Hoffmann has nothing to disclose. Conflict of interest: B. Brunekreef has nothing to disclose. Conflict of interest: M. Strak has nothing to disclose. Conflict of interest: J. Chen has nothing to disclose. Conflict of interest: A. Mehta has nothing to disclose. Conflict of interest: R.W. Atkinson has nothing to disclose. Conflict of interest: M. Bauwelinck has nothing to disclose. Conflict of interest: R. Varraso has nothing to disclose. Conflict of interest: M-C. Boutron-Ruault has nothing to disclose. Conflict of interest: J. Brandt has nothing to disclose. Conflict of interest: G. Cesaroni has nothing to disclose. Conflict of interest: F. Forastiere has nothing to disclose. Conflict of interest: D. Fecht has nothing to disclose. Conflict of interest: J. Gulliver has nothing to disclose. Conflict of interest: O. Hertel has nothing to disclose. Conflict of interest: K. de Hoogh has nothing to disclose. Conflict of interest: N.A.H. Janssen has nothing to disclose. Conflict of interest: K. Katsouyanni has nothing to disclose. Conflict of interest: M. Ketzel has nothing to disclose. Conflict of interest: J.O. Klompmaker has nothing to disclose. Conflict of interest: G. Nagel has nothing to disclose. Conflict of interest: B. Oftedal has nothing to disclose. Conflict of interest: A. Peters has nothing to disclose. Conflict of interest: A. Tjønneland has nothing to disclose. Conflict of interest: S.P. Rodopoulou has nothing to disclose. Conflict of interest: E Samoli has nothing to disclose. Conflict of interest: D.T. Kristoffersen has nothing to disclose. Conflict of interest: T. Sigsgaard has nothing to disclose. Conflict of interest: M. Stafoggia has nothing to disclose. Conflict of interest: D. Vienneau has nothing to disclose. Conflict of interest: G. Weinmayr has nothing to disclose. Conflict of interest: G. Hoek has nothing to disclose. Conflict of interest: Z.J. Andersen has nothing to disclose., (Copyright ©ERS 2021.)

A consistent and equitable global drought risk assessment for multiple regions, populations, and economic sectors at the gridded scale under future diverse climate change scenarios has been the subject of scarce research. Climate change is projected to increase the future hazard of drought and cause consequential damages to socioeconomic systems. The risk assessment of drought caused by climate change can be a bridge between impacts and adaptation. To assess the socioeconomic risk to droughts in a base period and two future periods (2016 to 2035 and 2046 to 2065), the projections of five general circulation models and population and gross domestic product (GDP), land cover, and water resources data were used to analyze the socioeconomic risk under three scenarios combining representative concentration pathways (RCPs) and shared socioeconomic pathways (SSPs). The socioeconomic risk was calculated as the product of three determinants: hazard, exposure, and vulnerability. The risk of the global population to drought was projected to be highest in 2046 to 2065 under scenario RCP8.5-SSP3, with up to 1.45 × 10 9 persons affected, a 63% increase compared with the base period. The highest risk to GDP (4.29 × 10 13 purchasing power parity $) was possibly in 2046 to 2065 under scenario RCP2.6-SSP1, with the risk increasing 5.64 times compared to the base period. Regions with high socioeconomic risk were primarily concentrated in the East and South Asia, Midwestern Europe, eastern US, and the coastal areas of South America. With climate change, the inequality in future socioeconomic risk to drought among countries is predicted to increase. The ten countries with the highest risks to population and GDP accounted for nearly 70% of the global risk., Competing Interests: Declaration of competing interest The authors declare that they have no conflict of interest., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Background: Air pollution has been suggested as a risk factor for chronic obstructive pulmonary disease (COPD), but evidence is sparse and inconsistent., Objectives: We examined the association between long-term exposure to low-level air pollution and COPD incidence., Methods: Within the 'Effects of Low-Level Air Pollution: A Study in Europe' (ELAPSE) study, we pooled data from three cohorts, from Denmark and Sweden, with information on COPD hospital discharge diagnoses. Hybrid land use regression models were used to estimate annual mean concentrations of particulate matter with a diameter < 2.5 µm (PM 2.5 ), nitrogen dioxide (NO 2 ), and black carbon (BC) in 2010 at participants' baseline residential addresses, which were analysed in relation to COPD incidence using Cox proportional hazards models., Results: Of 98,058 participants, 4,928 developed COPD during 16.6 years mean follow-up. The adjusted hazard ratios (HRs) and 95% confidence intervals for associations with COPD incidence were 1.17 (1.06, 1.29) per 5 µg/m 3 for PM 2.5 , 1.11 (1.06, 1.16) per 10 µg/m 3 for NO 2 , and 1.11 (1.06, 1.15) per 0.5 10 -5 m -1 for BC. Associations persisted in subset participants with PM 2.5 or NO 2 levels below current EU and US limit values and WHO guidelines, with no evidence for a threshold. HRs for NO 2 and BC remained unchanged in two-pollutant models with PM 2.5 , whereas the HR for PM 2.5 was attenuated to unity with NO 2 or BC., Conclusions: Long-term exposure to low-level air pollution is associated with the development of COPD, even below current EU and US limit values and possibly WHO guidelines. Traffic-related pollutants NO 2 and BC may be the most relevant., (Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.)

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 (PM 2.5 ) 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.

As new scientific evidence on health effects of air pollution is generated, air quality guidelines need to be periodically updated. The objective of this review is to support the derivation of updated guidelines by the World Health Organization (WHO) by performing a systematic review of evidence of associations between long-term exposure to particulate matter with diameter under 2.5 µm (PM 2.5 ) and particulate matter with diameter under 10 µm (PM 10 ), in relation to all-cause and cause-specific mortality. As there is especially uncertainty about the relationship at the low and high end of the exposure range, the review needed to provide an indication of the shape of the concentration-response function (CRF). We systematically searched MEDLINE and EMBASE from database inception to 9 October 2018. Articles were checked for eligibility by two reviewers. We included cohort and case-control studies on outdoor air pollution in human populations using individual level data. In addition to natural-cause mortality, we evaluated mortality from circulatory diseases (ischemic heart disease (IHD) and cerebrovascular disease (stroke) also specifically), respiratory diseases (Chronic Obstructive Pulmonary Disease (COPD) and acute lower respiratory infection (ALRI) also specifically) and lung cancer. A random-effect meta-analysis was performed when at least three studies were available for a specific exposure-outcome pair. Risk of bias was assessed for all included articles using a specifically developed tool coordinated by WHO. Additional analyses were performed to assess consistency across geographic region, explain heterogeneity and explore the shape of the CRF. An adapted GRADE (Grading of Recommendations Assessment, Development and Evaluation) assessment of the body of evidence was made using a specifically developed tool coordinated by WHO. A large number (N = 107) of predominantly cohort studies (N = 104) were included after screening more than 3000 abstracts. Studies were conducted globally with the majority of studies from North America (N = 62) and Europe (N = 25). More studies used PM 2.5 (N = 71) as the exposure metric than PM 10 (N = 42). PM 2.5 was significantly associated with all causes of death evaluated. The combined Risk Ratio (RR) for PM 2.5 and natural-cause mortality was 1.08 (95%CI 1.06, 1.09) per 10 µg/m 3 . Meta analyses of studies conducted at the low mean PM 2.5 levels (<25, 20, 15, 12, 10 µg/m 3 ) yielded RRs that were similar or higher compared to the overall RR, consistent with the finding of generally linear or supra-linear CRFs in individual studies. Pooled RRs were almost identical for studies conducted in North America, Europe and Western Pacific region. PM 10 was significantly associated with natural-cause and most but not all causes of death. Application of the risk of bias tool showed that few studies were at a high risk of bias in any domain. Application of the adapted GRADE tool resulted in an assessment of "high certainty of evidence" for PM 2.5 with all assessed endpoints except for respiratory mortality (moderate). The evidence was rated as less certain for PM 10 and cause-specific mortality ("moderate" for circulatory, IHD, COPD and "low" for stroke mortality. Compared to the previous global WHO evaluation, the evidence base has increased substantially. However, studies conducted in low- and middle- income countries (LMICs) are still limited. There is clear evidence that both PM 2.5 and PM 10 were associated with increased mortality from all causes, cardiovascular disease, respiratory disease and lung cancer. Associations remained below the current WHO guideline exposure level of 10 µg/m 3 for PM 2.5 . Systematic review registration number (PROSPERO ID): CRD42018082577., (Copyright © 2020 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: Ambient air pollution has recently been related to type 2 diabetes mellitus (T2DM), a disease that has caused an economic and health burden worldwide. Evidence of an association between air pollution and T2DM was reported in the United States and Europe. However, few studies have focused on the association with high levels of air pollutants in a developing country., Objectives: We conducted a 12-year cohort study to assess the incidence and mortality of T2DM associated with long-term exposure to PM 10 , SO 2 , and NO 2 ., Methods: A retrospective cohort with participants from four cities in northern China was conducted to assess mortality and incidence of T2DM from 1998 to 2009. Incidence of T2DM was self-reported, and incident intake of an antidiabetic drug or injection of insulin simultaneously and mortality of T2DM was obtained from a family member and double checked against death certificates provided from the local center for disease control and prevention. Individual pollution exposures were the mean concentrations of pollutants estimated from the local environmental monitoring centers over the survival years. Hazard ratios (HRs) were estimated using Cox regression models after adjusting for potential confounding factors., Results: A total of 39 054 participants were recruited into the mortality cohort, among which 59 subjects died from T2DM; 38 529 participants were analyzed in the incidence cohort, and 1213 developed new cases of T2DM. For each 10 μg/m 3 increase in PM 10 , SO 2 , and NO 2 , the adjusted HRs and 95% confidence interval (CI) for diabetic incidence were 1.831 (1.778, 1.886), 1.287 (1.256, 1.318), and 1.472 (1.419, 1.528), respectively. Similar results can be observed in the analysis of diabetic mortality with HRs (95% CI) up to 2.260 (1.732, 2.950), 1.130 (1.042, 1.225), and 1.525 (1.280, 1.816), respectively., Conclusions: Our results suggested that long-term exposure to high levels of PM 10 , SO 2 , and NO 2 increase risk of incident and mortality of T2DM in China., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Importance: There is insufficient evidence on the efficacy of masks in the general population for the prevention of coronavirus disease 2019 (COVID-19) in public areas. Therefore, it is imperative to investigate the association of mandatory mask-wearing policies with behaviors associated with the transmission of COVID-19., Objective: To assess the association of mask wearing with face-touching behavior among the general population in public areas., Design, Setting, and Participants: This cross-sectional study used videos recorded in public transportation stations, streets, and parks among the general population in China, Japan, South Korea, Western Europe (ie, England, France, Germany, Spain, and Italy), and the US to analyze mask-wearing and face-touching behavior in public areas. Videos before the COVID-19 pandemic were defined as those recorded from January 2018 to October 2019, and those during the COVID-19 pandemic were defined as those recorded during February 2020 to March 2020 in China, Japan, and South Korea and during March 2020 in Western Europe and the US. Individuals who clearly displayed their face and face-touching behavior were included, and those whose behaviors were influenced by filming or public events were excluded., Exposures: Mandatory mask-wearing policies enacted at various time points in China, Japan, South Korea, Western Europe, and the US., Main Outcomes and Measures: Proportion of individuals wearing masks and incidence of face touching., Results: This study included 4699 individuals before the COVID-19 pandemic and 2887 individuals during the pandemic. During the periods studied, mask wearing increased in all regions except the US, from 20 of 1745 individuals (1.1%) to 1090 of 1097 individuals (99.4%) in mainland China (P < .001), 44 of 1422 individuals (3.1%) to 346 of 893 individuals (38.7%) in Japan (P < .001), 6 of 717 individuals (0.8%) to 277 of 324 individuals (85.5% ) in South Korea (P < .001), 1 of 546 individuals (0.2%) to 6 of 379 individuals (1.6%) in Western Europe (P = .02), and 1 of 269 individuals (0.4%) to 4 of 194 individuals (2.1%) in the US (P = .17). Surgical masks were predominant in China (989 masks [89.1%]), and fabric masks were predominant in the other regions (Japan: 371 masks [95.1%]; South Korea: 240 masks [84.8%]; Western Europe: 6 masks [85.7%]; US: 5 masks [100%]). Face-touching behaviors decreased from before COVID-19 to during COVID-19 among individuals in China (72 incidences of 1745 observations [4.1%] to 12 incidences of 1097 observations [1.1%]; P < .001), South Korea (80 incidences of 717 observations [11.2%] to 7 incidences of 324 observations [2.2%]; P < .001), and Europe (62 incidences of 546 observations [11.4%] to 23 incidences of 379 observations [6.1%]; P = .01). Logistic regression found that mask wearing was associated with a reduction in face touching in China (odds ratio [OR], 3.91; 95% CI, 2.11-7.24) and South Korea (OR, 6.69; 95% CI, 2.69-16.69) and of touching the nose, mouth, and eyes (China: OR, 8.60; 95% CI, 2.65-27.86; South Korea: OR, 29.27; 95% CI, 1.79-478.22)., Conclusions and Relevance: The findings of this cross-sectional study suggest that mandatory mask-wearing policies were associated with increased mask wearing during the COVID-19 pandemic. Mask wearing was associated with reduced face-touching behavior, especially touching of the eyes, nose, and mouth, which may prevent contact transmission of COVID-19 among the general population in public areas.

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 (PM 2.5 , NO 2 , BC, O 3 ) across Europe., Methods: We developed West-European land use regression models (LUR) for 2010 estimating annual mean PM 2.5 , NO 2 , BC and O 3 concentrations (including cold and warm season estimates for O 3 ). The models were based on AirBase routine monitoring data (PM 2.5 , NO 2 and O 3 ) 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 PM 2.5 and NO 2 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 (NO 2 and O 3 : 2000 and 2005; PM 2.5 : 2013)., Results: The PM 2.5 , BC, NO 2 , O 3 annual, O 3 warm season and O 3 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 R 2 in the PM 2.5 , O 3 warm and O 3 cold models. Explained variance at fully independent sites vs the internal hold-out validation was slightly lower for PM 2.5 (65% vs 66%) and lower for NO 2 (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 PM 2.5 , NO 2 , O 3 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., (Copyright © 2018 Elsevier Ltd. All rights reserved.)