Long-term exposure to air pollution and incidence of gastric and the upper aerodigestive tract cancers in a pooled European cohort: The ELAPSE project.

Air pollution has been shown to significantly impact human health including cancer. Gastric and upper aerodigestive tract (UADT) cancers are common and increased risk has been associated with smoking and occupational exposures. However, the association with air pollution remains unclear. We pooled European subcohorts (N = 287,576 participants for gastric and N = 297,406 for UADT analyses) and investigated the association between residential exposure to fine particles (PM2.5), nitrogen dioxide (NO2), black carbon (BC) and ozone in the warm season (O3w) with gastric and UADT cancer. We applied Cox proportional hazards models adjusting for potential confounders at the individual and area-level. During 5,305,133 and 5,434,843 person-years, 872 gastric and 1139 UADT incident cancer cases were observed, respectively. For gastric cancer, we found no association with PM2.5, NO2 and BC while for UADT the hazard ratios (95% confidence interval) were 1.15 (95% CI: 1.00-1.33) per 5 µg/m3 increase in PM2.5, 1.19 (1.08-1.30) per 10 µg/m3 increase in NO2, 1.14 (1.04-1.26) per 0.5 × 10-5 m-1 increase in BC and 0.81 (0.72-0.92) per 10 µg/m3 increase in O3w. We found no association between long-term ambient air pollution exposure and incidence of gastric cancer, while for long-term exposure to PM2.5, NO2 and BC increased incidence of UADT cancer was observed.

Socioeconomic Inequalities in the External Exposome in European Cohorts: The EXPANSE Project.

Socioeconomic inequalities in the exposome have been found to be complex and highly context-specific, but studies have not been conducted in large population-wide cohorts from multiple countries. This study aims to examine the external exposome, encompassing individual and environmental factors influencing health over the life course, and to perform dimension reduction to derive interpretable characterization of the external exposome for multicountry epidemiological studies. Analyzing data from over 25 million individuals across seven European countries including 12 administrative and traditional cohorts, we utilized domain-specific principal component analysis (PCA) to define the external exposome, focusing on air pollution, the built environment, and air temperature. We conducted linear regression to estimate the association between individual- and area-level socioeconomic position and each domain of the external exposome. Consistent exposure patterns were observed within countries, indicating the representativeness of traditional cohorts for air pollution and the built environment. However, cohorts with limited geographical coverage and Southern European countries displayed lower temperature variability, especially in the cold season, compared to Northern European countries and cohorts including a wide range of urban and rural areas. The individual- and area-level socioeconomic determinants (i.e., education, income, and unemployment rate) of the urban exposome exhibited significant variability across the European region, with area-level indicators showing stronger associations than individual variables. While the PCA approach facilitated common interpretations of the external exposome for air pollution and the built environment, it was less effective for air temperature. The diverse socioeconomic determinants suggest regional variations in environmental health inequities, emphasizing the need for targeted interventions across European countries.

Socio-environmental modifiers of heat-related mortality in eight Swiss cities: A case time series analysis.

In the light of growing urbanization and projected temperature increases due to climate change, heat-related mortality in urban areas is a pressing public health concern. Heat exposure and vulnerability to heat may vary within cities depending on structural features and socioeconomic factors. This study examined the effect modification of the temperature-mortality association of three socio-environmental factors in eight Swiss cities and population subgroups (<75 and ≥ 75 years, males, females): urban heat islands (UHI) based on within-city temperature contrasts, residential greenness measured as normalized difference vegetation index (NDVI) and neighborhood socioeconomic position (SEP). We used individual death records from the Swiss National Cohort occurring during the warm season (May to September) in the years 2003-2016. We performed a case time series analysis using conditional quasi-Poisson and distributed lag non-linear models with a lag of 0-3 days. As exposure variables, we used daily maximum temperatures (Tmax) and a binary indicator for warm nights (Tmin ≥20 °C). In total, 53,593 deaths occurred during the study period. Overall across the eight cities, the mortality risk increased by 31% (1.31 relative risk (95% confidence interval: 1.20-1.42)) between 22.5 °C (the minimum mortality temperature) and 35 °C (the 99th percentile) for warm-season Tmax. Stratified analysis suggested that the heat-related risk at 35 °C is 26% (95%CI: -4%, 67%) higher in UHI compared to non-UHI areas. Indications of smaller risk differences were observed between the low vs. high greenness strata (Relative risk difference = 13% (95%CI: -11%; 44%)). Living in low SEP neighborhoods was associated with an increased heat related risk in the non-elderly population (<75 years). Our results indicate that UHI are associated with increased heat-related mortality risk within Swiss cities, and that features beyond greenness are responsible for such spatial risk differences.

Ambient air pollution and hypertensive disorders of pregnancy in Rome.

BACKGROUND: Ambient air pollution has been associated with hypertensive disorders of pregnancy (HDP), but few studies rely on assessment of fine-scale variation in air quality, specific subtypes and multi-pollutant exposures. AIM: To study the impact of long-term exposure to individual and mixture of air pollutants on all and specific subtypes of HDP. METHODS: We obtained data from 130,470 liveborn singleton pregnacies in Rome during 2014-2019. Spatiotemporal land-use random-forest models at 1 km spatial resolution assigned to the maternal residential addresses were used to estimate the exposure to particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), and ozone (O3). RESULTS: For PM2.5, PM10 and NO2, there was suggestive evidence of increased risk of preeclampsia (PE, n = 442), but no evidence of increased risk for all subtypes of HDP (n = 2297) and gestational hypertension (GH, n = 1901). For instance, an interquartile range of 7.0 µg/m3 increase in PM2.5 exposure during the first trimester of pregnancy was associated with an odds ratio (OR) of 1.06 (95% confidence interval: 0.81, 1.39) and 1.04 (0.92, 1.17) after adjustment for NO2 and the corresponding results for a 15.7 µg/m3 increase in NO2 after adjustment for PM2.5 were 1.11 (0.92, 1.34) for PE and 0.83 (0.76, 0.90) for HDP. Increased risks for HDP and GH were suggested for O3 in single-pollutant models and for PM after adjustment for NO2, but all other associations were stable or attenuated in two-pollutant models. CONCLUSIONS: The results of our study suggest that PM2.5, PM10 and NO2 increases the risk of PE and that these effects are robust to adjustment for O3 while the increased risks for GH and HDP suggested for O3 attenuated after adjustment for PM or NO2. Additional studies are needed to evaluate the effects of source-specific component of PM on subtypes as well as all types of HDP which would help to target preventive actions.

Spatiotemporal modelling of airborne birch and grass pollen concentration across Switzerland: A comparison of statistical, machine learning and ensemble methods.

BACKGROUND: Statistical and machine learning models are commonly used to estimate spatial and temporal variability in exposure to environmental stressors, supporting epidemiological studies. We aimed to compare the performances, strengths and limitations of six different algorithms in the retrospective spatiotemporal modeling of daily birch and grass pollen concentrations at a spatial resolution of 1 km across Switzerland. METHODS: Daily birch and grass pollen concentrations were available from 14 measurement sites in Switzerland for 2000-2019. To develop the spatiotemporal models, we considered spatiotemporal, spatial and temporal predictors including meteorological factors, land-use, elevation, species distribution and Normalized Difference Vegetation Index (NDVI). We used six statistical and machine learning algorithms: LASSO, Ridge, Elastic net, Random forest, XGBoost and ANNs. We optimized model structures through feature selection and grid search techniques to obtain the best predictive performance. We used train-test split and cross-validation to avoid overfitting and overoptimistic performance indicators. We then combined these six models through multiple linear regression to develop an ensemble hybrid model. RESULTS: The 5th-95th percentiles of birch and grass pollen concentrations were 0-151 and 0-105 grains/m3, respectively. The hybrid ensemble model achieved the best RMSE on the test dataset for both birch and grass pollen with 94.4 and 19.7 grains/m3, respectively. Nonlinear models (Random forest, XGBoost and ANNs) achieved lower test RMSE's than linear models (LASSO, Ridge, Elastic net) for both pollen types, with RMSE's ranging from 105.9 to 140.5 grains/m3 for birch and from 20.0 to 25.4 grains/m3 for grass pollen. The Random forest algorithm yielded the best spatial and temporal performance among the six evaluated modelling methods. The ensemble hybrid model outperformed the six linear and nonlinear algorithms. Country-wide pollen concentration, land use, weather, and NDVI were important predictors. CONCLUSION: Nonlinear algorithms outperformed linear models and accurately explained complex, nonlinear relationships between environmental factors and measured concentrations.

Single- and two-pollutant Concentration-Response Functions for PM2.5 and NO2 for Quantifying Mortality Burden in Health Impact Assessments.

OBJECTIVE: Health Impact Assessments (HIAs) for air pollutant mixtures are challenging because risk estimates are primarily derived from single-pollutant models. Combining risk estimates from multiple pollutants requires new approaches, as a simple addition of single pollutant risk estimates from correlated air pollutants may result in double counting. We investigated approaches applying concentration-response functions (CRFs) from single- and two-pollutant models in HIAs, focusing on long-term exposure to particulate matter with a diameter less than 2.5 micrometers (PM2.5) and nitrogen dioxide (NO2) and their associations with all-cause mortality. METHODS: A systematic literature search of MEDLINE and EMBASE identified cohort studies employing single- and two-pollutant models of long-term exposure to PM2.5 and NO2 with all-cause mortality. Pooled CRFs were calculated through random-effects meta-analyses of risk estimates from single- and two-pollutant models. Coefficient differences were calculated by comparing single- and two-pollutant model estimates. Four approaches to estimating population-attributable fractions (PAFs) were compared: PM2.5 or NO2 single-pollutant models to represent the mixture, the sum of single-pollutant models, the sum of two-pollutant models and the sum of single-pollutant models from a larger body of evidence adjusted by coefficient difference. RESULTS: Seventeen papers reported both single and two-pollutant estimates. Pooled hazard ratios (HRs) for mortality from single- and two-pollutant models were 1.053 (95% confidence interval: 1.034-1.071) and 1.035 (1.014-1.057), respectively, for a 5 µg/m3 increase in PM2.5. HRs for a 10 µg/m3 increase in NO2 were 1.032 (1.014-1.049) and 1.024 (1.000-1.049) for single- and two-pollutant models, respectively. The average coefficient difference between single- and two-pollutant models was 0.017 for PM2.5 and 0.007 for NO2. Combined PAFs for the PM2.5-NO2 mixture using joint HRs from single- and two-pollutant model CRFs were 0.09 and 0.06, respectively. CONCLUSION: Utilizing CRFs from two-pollutant models or applying the coefficient difference to a more extensive evidence base seems to mitigate the potential overestimation of mixture health impacts from adding single-pollutant CRFs.

Residential exposure to air pollution and incidence of leukaemia in the industrial area of Viadana, Northern Italy.

BACKGROUND: Exposure to air pollution has been proposed as one of the potential risk factors for leukaemia. Work-related formaldehyde exposure is suspected to cause leukaemia. METHODS: We conducted a nested register-based case-control study on leukaemia incidence in the Viadana district, an industrial area for particleboard production in Northern Italy. We recruited 115 cases and 496 controls, frequency-matched by age, between 1999 and 2014. We assigned estimated exposures to particulate matter (PM10, PM2.5), nitrogen dioxide (NO2), and formaldehyde at residential addresses, averaged over the susceptibility window 3rd to 10th year prior to the index date. We considered potential confounding by sex, age, nationality, socio-economic status, occupational exposures to benzene and formaldehyde, and prior cancer diagnoses. RESULTS: There was no association of exposures to PM10, PM2.5, and NO2 with leukaemia incidence. However, an indication of increased risk emerged for formaldehyde, despite wide statistical uncertainty (OR 1.46, 95%CI 0.65-3.25 per IQR-difference of 1.2 µg/m3). Estimated associations for formaldehyde were higher for acute (OR 2.07, 95%CI 0.70-6.12) and myeloid subtypes (OR 1.79, 95%CI 0.64-5.01), and in the 4-km buffer around the industrial facilities (OR 2.78, 95%CI 0.48-16.13), although they remained uncertain. CONCLUSIONS: This was the first study investigating the link between ambient formaldehyde exposure and leukaemia incidence in the general population. The evidence presented suggests an association, although it remains inconclusive, and a potential significance of emissions related to industrial activities in the district. Further research is warranted in larger populations incorporating data on other potential risk factors.

Long-term exposure to ambient air pollution and risk of lung cancer - A comparative analysis of incidence and mortality in four administrative cohorts in the ELAPSE study.

BACKGROUND: Studies have linked air pollution to lung cancer incidence and mortality, but few have compared these associations, which may differ due to cancer survival variations. We aimed to evaluate the association between long-term air pollution exposure and lung cancer incidence and compare findings with previous lung cancer mortality analyses within the same cohorts. METHODS: We analyzed four population-based administrative cohorts in Denmark (2000-2015), England (2011-2017), Norway (2001-2016) and Rome (2001-2015). We assessed residential exposure to annual average fine particulate matter (PM2.5), nitrogen dioxide (NO2), black carbon (BC), and warm-season ozone (O3) using Europe-wide land use regression models. We used Cox proportional hazard models to evaluate cohort-specific hazard ratios (HRs) and 95% confidence intervals (CIs) for lung cancer incidence identified using hospital admission records (English and Roman cohorts) or cancer registries (Danish and Norwegian cohorts). We evaluated the associations at low exposure levels using subset analyses and natural cubic splines. Cohort-specific HRs were pooled using random-effects meta-analyses, separately for incidence and mortality. RESULTS: Over 93,733,929 person-years of follow-up, 111,949 incident lung cancer cases occurred. Incident lung cancer was positively associated with PM2.5, NO2 and BC, and negatively associated with O3. The negative O3 association became positive after adjustment for NO2. Associations were almost identical or slightly stronger for lung cancer incidence than mortality in the same cohorts, with respective meta-analytic HRs (95% CIs) of 1.14 (1.06, 1.22) and 1.12 (1.02, 1.22) per 5 µg/m3 increase in PM2.5, and 1.10 (1.04, 1.16) and 1.09 (1.02, 1.16) per 10 µg/m3 increase in NO2. Positive associations persisted for both incidence and mortality at low pollution levels with similar magnitude. CONCLUSIONS: We found similarly elevated risks of lung cancer incidence and mortality in association with residential exposure to PM2.5, NO2 and BC in meta-analyses of four European administrative cohorts, which persisted at low pollution levels.

Long-term exposure to air pollution and chronic kidney disease-associated mortality-Results from the pooled cohort of the European multicentre ELAPSE-study.

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 (NO2), black carbon (BC), ozone (O3), particulate matter ≤2.5 µm (PM2.5) and several elemental constituents of PM2.5. 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 PM2.5 (hazard ratio (HR) with 95% confidence interval (CI) of 1.31 (1.03-1.66) per 5 µg/m3, BC (1.26 (1.03-1.53) per 0.5 × 10- 5/m), NO2 (1.13 (0.93-1.38) per 10 µg/m3) and inverse for O3 (0.71 (0.54-0.93) per 10 µg/m3). 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.

Does residential address-based exposure assessment for outdoor air pollution lead to bias in epidemiological studies?

BACKGROUND: Epidemiological studies of long-term exposure to outdoor air pollution have consistently documented associations with morbidity and mortality. Air pollution exposure in these epidemiological studies is generally assessed at the residential address, because individual time-activity patterns are seldom known in large epidemiological studies. Ignoring time-activity patterns may result in bias in epidemiological studies. The aims of this paper are to assess the agreement between exposure assessed at the residential address and exposures estimated with time-activity integrated and the potential bias in epidemiological studies when exposure is estimated at the residential address. MAIN BODY: We reviewed exposure studies that have compared residential and time-activity integrated exposures, with a focus on the correlation. We further discuss epidemiological studies that have compared health effect estimates between the residential and time-activity integrated exposure and studies that have indirectly estimated the potential bias in health effect estimates in epidemiological studies related to ignoring time-activity patterns. A large number of studies compared residential and time-activity integrated exposure, especially in Europe and North America, mostly focusing on differences in level. Eleven of these studies reported correlations, showing that the correlation between residential address-based and time-activity integrated long-term air pollution exposure was generally high to very high (R > 0.8). For individual subjects large differences were found between residential and time-activity integrated exposures. Consistent with the high correlation, five of six identified epidemiological studies found nearly identical health effects using residential and time-activity integrated exposure. Six additional studies in Europe and North America showed only small to moderate potential bias (9 to 30% potential underestimation) in estimated exposure response functions using residence-based exposures. Differences of average exposure level were generally small and in both directions. Exposure contrasts were smaller for time-activity integrated exposures in nearly all studies. The difference in exposure was not equally distributed across the population including between different socio-economic groups. CONCLUSIONS: Overall, the bias in epidemiological studies related to assessing long-term exposure at the residential address only is likely small in populations comparable to those evaluated in the comparison studies. Further improvements in exposure assessment especially for large populations remain useful.

Does growing up in a physical activity-friendly neighborhood increase the likelihood of remaining active during adolescence and early adulthood?

BACKGROUND: The SOPHYA-cohort-study investigated whether the objectively characterized and perceived residential neighborhood of Swiss youth predict accelerometer-measured physical activity and activity in specific domains (participation in a sports club and cycling) five years later. METHODS: At baseline in 2014, 1230 children and adolescents aged 6 to 16 years participated and wore accelerometers for 7 days. Of these children, 447 participated again in the follow-up study in 2019 and provided longitudinal accelerometer measurements. Sociodemographic factors and perceptions of the local neighbourhood were assessed by questionnaire. Specific objective environmental data (e.g. built environment or social environment) was modelled to the children's address at baseline. Multivariate linear and logistic regression models were applied to identify short- and long-term characteristics that are associated with accelerometer-based physical activity, cycling and participation in organised sport. RESULTS: If the neighborhood-score as perceived by the parents in 2014 was in the middle or lowest tertile, children were significantly less active cross-sectionally in 2014 (-41.1 (-78.0;-4.2) and -52.4 (-88.6;-16.2) counts per minute, cpm), and five years later (-52.4 (-88.6;-16.2) and 48.1 (-86.6;-9.7) cpm). In addition, they were also less likely to accumulate active minutes above the median at both measuring points compared to peers of the same age and sex. Using objective environmental data modeled around the children's residential address, similar associations were found: In the tertile with the lowest proportion of green space children achieved less cpm in 2014, while a high main street density and a low socioeconomic environment, respectively, hindered physical activity tracking above the median longitudinally. Also for cycling and participation in a sport club, the associations with the perceived and objective environment were more pronounced in the longitudinal analyses. CONCLUSION: The results suggest that growing up in a physical activity friendly neighborhood increases the likelihood of remaining active during adolescence and early adulthood. Interventions should be implemented to ensure that children growing up in an unfavorable neighborhood do not fall behind at an early stage.

Commentary: A road map for future data-driven urban planning and environmental health research.

Recent advances in data science and urban environmental health research utilise large-scale databases (100s-1000s of cities) to explore the complex interplay of urban characteristics such as city form and size, climate, mobility, exposure, and environmental health impacts. Cities are still hotspots of air pollution and noise, suffer urban heat island effects and lack of green space, which leads to disease and mortality burdens preventable with better knowledge. Better understanding through harmonising and analysing data in large numbers of cities is essential to identifying the most effective means of disease prevention and understanding context dependencies important for policy.

Long-term air pollution exposure and malignant intracranial tumours of the central nervous system: a pooled analysis of six European cohorts.

BACKGROUND: Risk factors for malignant tumours of the central nervous system (CNS) are largely unknown. METHODS: We pooled six European cohorts (N = 302,493) and assessed the association between residential exposure to nitrogen dioxide (NO2), fine particles (PM2.5), black carbon (BC), ozone (O3) and eight elemental components of PM2.5 (copper, iron, potassium, nickel, sulfur, silicon, vanadium, and zinc) and malignant intracranial CNS tumours defined according to the International Classification of Diseases ICD-9/ICD-10 codes 192.1/C70.0, 191.0-191.9/C71.0-C71.9, 192.0/C72.2-C72.5. We applied Cox proportional hazards models adjusting for potential confounders at the individual and area-level. RESULTS: During 5,497,514 person-years of follow-up (average 18.2 years), we observed 623 malignant CNS tumours. The results of the fully adjusted linear analyses showed a hazard ratio (95% confidence interval) of 1.07 (0.95, 1.21) per 10 µg/m³ NO2, 1.17 (0.96, 1.41) per 5 µg/m³ PM2.5, 1.10 (0.97, 1.25) per 0.5 10-5m-1 BC, and 0.99 (0.84, 1.17) per 10 µg/m³ O3. CONCLUSIONS: We observed indications of an association between exposure to NO2, PM2.5, and BC and tumours of the CNS. The PM elements were not consistently associated with CNS tumour incidence.

Long-term exposure to ambient air pollution and asthma symptom score in the CONSTANCES cohort.

BACKGROUND: The asthma symptom score allows to consider asthma as a continuum and to investigate its risk factors. One previous study has investigated the association between asthma score and air pollution and only for nitrogen dioxide (NO2). We aimed to study the associations between particulate matter with an aerodynamic diameter lower than 2.5 µm (PM2.5), black carbon (BC) and NO2 and the asthma symptom score in adults from CONSTANCES, a French population-based cohort. METHODS: Asthma symptom score (range: 0-5) was based on the number of five self-reported symptoms of asthma in the last 12 months. Annual individual exposure to PM2.5, BC and NO2 was estimated at participants' residential address using hybrid land-use regression models. Cross-sectional associations of each pollutant with asthma symptom score were estimated using negative binomial regressions adjusted for age, sex, smoking status and socioeconomic position. Associations with each symptom were estimated using logistic regression. The effect of BC independent of total PM2.5 was investigated with a residual model. RESULTS: Analyses were conducted on 135 165 participants (mean age: 47.2 years, 53.3% women, 19.0% smokers, 13.5% ever asthma). The ratio of mean score was 1.12 (95% CI 1.10 to 1.14), 1.14 (95% CI 1.12 to 1.16) and 1.12 (95% CI 1.10 to 1.14) per one IQR increase of PM2.5 (4.86 µg/m3), BC (0.88 10-5 m-1) and NO2 (17.3 µg/m3). Positive and significant associations were also found for each asthma symptom separately. BC effect persisted independently of total PM2.5. CONCLUSION: Exposure to each pollutant was associated with increased asthma symptom score in adults. This study highlights that BC could be one of the most harmful particulate matter components.

Integrating large-scale stationary and local mobile measurements to estimate hyperlocal long-term air pollution using transfer learning methods.

Mobile air quality measurements are collected typically for several seconds per road segment and in specific timeslots (e.g., working hours). These short-term and on-road characteristics of mobile measurements become the ubiquitous shortcomings of applying land use regression (LUR) models to estimate long-term concentrations at residential addresses. This issue was previously found to be mitigated by transferring LUR models to the long-term residential domain using routine long-term measurements in the studied region as the transfer target (local scale). However, long-term measurements are generally sparse in individual cities. For this scenario, we propose an alternative by taking long-term measurements collected over a larger geographical area (global scale) as the transfer target and local mobile measurements as the source (Global2Local model). We empirically tested national, airshed countries (i.e., national plus neighboring countries) and Europe as the global scale in developing Global2Local models to map nitrogen dioxide (NO2) concentrations in Amsterdam. The airshed countries scale provided the lowest absolute errors, and the Europe-wide scale had the highest R2. Compared to a "global" LUR model (trained exclusively with European-wide long-term measurements), and a local mobile LUR model (using mobile data from Amsterdam only), the Global2Local model significantly reduced the absolute error of the local mobile LUR model (root-mean-square error, 6.9 vs 12.6 µg/m3) and improved the percentage explained variances compared to the global model (R2, 0.43 vs 0.28, assessed by independent long-term NO2 measurements in Amsterdam, n = 90). The Global2Local method improves the generalizability of mobile measurements in mapping long-term residential concentrations with a fine spatial resolution, which is preferred in environmental epidemiological studies.

Impacts of weather and air pollution on Legionnaires' disease in Switzerland: A national case-crossover study.

BACKGROUND: The number of reported cases of Legionnaires' disease (LD) has risen markedly in Switzerland (6.5/100,000 inhabitants in 2021) and abroad over the last decade. Legionella, the causative agent of LD, are ubiquitous in the environment. Therefore, environmental changes can affect the incidence of LD, for example by increasing bacterial concentrations in the environment or by facilitating transmission. OBJECTIVES: The aim of this study is to understand the environmental determinants, in particular weather conditions, for the regional and seasonal distribution of LD in Switzerland. METHODS: We conducted a series of analyses based on the Swiss LD notification data from 2017 to 2021. First, we used a descriptive and hotspot analysis to map LD cases and identify regional clusters. Second, we applied an ecological model to identify environmental determinants on case frequency at the district level. Third, we applied a case-crossover design using distributed lag non-linear models to identify short-term associations between seven weather variables and LD occurrence. Lastly, we performed a sensitivity analysis for the case-crossover design including NO2 levels available for the year 2019. RESULTS: Canton Ticino in southern Switzerland was identified as a hotspot in the cluster analysis, with a standardised notification rate of 14.3 cases/100,000 inhabitants (CI: 12.6, 16.0). The strongest association with LD frequency in the ecological model was found for large-scale factors such as weather and air pollution. The case-crossover study confirmed the strong association of elevated daily mean temperature (OR 2.83; CI: 1.70, 4.70) and mean daily vapour pressure (OR: 1.52, CI: 1.15, 2.01) 6-14 days before LD occurrence. DISCUSSION: Our analyses showed an influence of weather with a specific temporal pattern before the onset of LD, which may provide insights into the effect mechanism. The relationship between air pollution and LD and the interplay with weather should be further investigated.

Mortality attributable to ambient fine particulate matter and nitrogen dioxide in Switzerland in 2019: Use of two-pollutant effect estimates.

INTRODUCTION: Air pollution health risk assessments have traditionally used single-pollutant effect estimates for one proxy ambient air pollutant such as PM2.5. Two-pollutant effect estimates, i.e. adjusted for another correlated pollutant, theoretically enable the aggregation of pollutant-specific health effects minimizing double-counting. Our study aimed at estimating the adult mortality in Switzerland in 2019 attributable to PM2.5 from a single-pollutant effect estimate and to the sum of PM2.5 and NO2 from two-pollutant estimates; comparing the results with those from alternative global, European and Swiss effect estimates. METHODS: For the single-pollutant approach, we used a PM2.5 summary estimate of European cohorts from the project ELAPSE, recommended by the European Respiratory Society and International Society for Environmental Epidemiology (ERS-ISEE). To derive the two-pollutant effect estimates, we applied ELAPSE-based conversion factors to ERS-ISEE PM2.5 and NO2 single-pollutant effect estimates. Additionally, we used World Health Organization 2021 Air Quality Guidelines as counterfactual scenario, exposure model data from 2019 and Swiss lifetables. RESULTS: The single-pollutant effect estimate for PM2.5 (1.118 [1.060; 1.179] per 10 µg/m3) resulted in 2240 deaths (21,593 years of life lost). Using our derived two-pollutant effect estimates (1.023 [1.012; 1.035] per 10 µg/m3 PM2.5 adjusted for NO2 and 1.040 [1.023; 1.058] per 10 µg/m3 NO2 adjusted for PM2.5), we found 1977 deaths (19,071 years of life lost) attributable to PM2.5 and NO2 together (23% from PM2.5). Deaths using alternative effect estimates ranged from 1042 to 5059. DISCUSSION: Estimated premature mortality attributable to PM2.5 alone was higher than to both PM2.5 and NO2 combined. Furthermore, the proportion of deaths from PM2.5 was lower than from NO2 in the two-pollutant approach. These seemingly paradoxical results, also found in some alternative estimates, are due to statistical imprecisions of underlying correction methods. Therefore, using two-pollutant effect estimates can lead to interpretation challenges in terms of causality.

Association of long-term exposure to ambient air pollution with retinal neurodegeneration: the prospective Alienor study.

Chronic exposure to air pollution may have adverse effects on neurodegenerative diseases. Glaucoma, the second leading cause of blindness worldwide, is a neurodegenerative disease of the optic nerve, characterized by progressive thinning of the retinal nerve fiber layer (RNFL). We investigated the relationship of air pollution exposure with longitudinal changes of RNFL thickness in the Alienor study, a population-based cohort of residents of Bordeaux, France, aged 75 years or more. Peripapillary RNFL thickness was measured using optical coherence tomography imaging every 2 years from 2009 to 2020. Measurements were acquired and reviewed by specially trained technicians to control quality. Air pollution exposure (particulate matter ≤2.5 µm (PM2.5), black carbon (BC), nitrogen dioxide (NO2)) was estimated at the participants' geocoded residential address using land-use regression models. For each pollutant, the 10-year average of past exposure at first RNFL thickness measurement was estimated. Associations of air pollution exposure with RNFL thickness longitudinal changes were assessed using linear mixed models adjusted for potential confounders, allowing for intra-eye and intra-individual correlation (repeated measurements). The study included 683 participants with at least one RNFL thickness measurement (62% female, mean age 82 years). The average RNFL was 90 µm (SD:14.4) at baseline. Exposure to higher levels of PM2.5 and BC in the previous 10 years was significantly associated with a faster RNFL thinning during the 11-year follow-up (-0.28 µm/year (95% confidence interval (CI) [-0.44;-0.13]) and -0.26 µm/year (95% CI [-0.40;-0.12]) per interquartile range increment; p < 0.001 for both). The size of the effect was similar to one year of age in the fitted model (-0.36 µm/year). No statistically significant associations were found with NO2 in the main models. This study evidenced a strong association of chronic exposure to fine particulate matter with retinal neurodegeneration, at air pollution levels below the current recommended thresholds in Europe.

Multiple myeloma risk in relation to long-term air pollution exposure - A pooled analysis of four European cohorts.

BACKGROUND: Air pollution is a growing concern worldwide, with significant impacts on human health. Multiple myeloma is a type of blood cancer with increasing incidence. Studies have linked air pollution exposure to various types of cancer, including leukemia and lymphoma, however, the relationship with multiple myeloma incidence has not been extensively investigated. METHODS: We pooled four European cohorts (N = 234,803) and assessed the association between residential exposure to nitrogen dioxide (NO2), fine particles (PM2.5), black carbon (BC), and ozone (O3) and multiple myeloma. We applied Cox proportional hazards models adjusting for potential confounders at the individual and area-level. RESULTS: During 4,415,817 person-years of follow-up (average 18.8 years), we observed 404 cases of multiple myeloma. The results of the fully adjusted linear analyses showed hazard ratios (95% confidence interval) of 0.99 (0.84, 1.16) per 10 µg/m³ NO2, 1.04 (0.82, 1.33) per 5 µg/m³ PM2.5, 0.99 (0.84, 1.18) per 0.5 10-5 m-1 BCE, and 1.11 (0.87, 1.41) per 10 µg/m³ O3. CONCLUSIONS: We did not observe an association between long-term ambient air pollution exposure and incidence of multiple myeloma.

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.