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.

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.

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.

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 Air Pollution Exposure and Pneumonia-related Mortality in a Large Pooled European Cohort.

Rationale: Ambient air pollution exposure has been linked to mortality from chronic cardiorespiratory diseases, while evidence on respiratory infections remains more limited. Objectives: We examined the association between long-term exposure to air pollution and pneumonia-related mortality in adults in a pool of eight European cohorts. Methods: Within the multicenter project ELAPSE (Effects of Low-Level Air Pollution: A Study in Europe), we pooled data from eight cohorts among six European countries. Annual mean residential concentrations in 2010 for fine particulate matter, nitrogen dioxide (NO2), black carbon (BC), and ozone 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 pneumonia, influenza, and acute lower respiratory infections (ALRI) mortality. Measurements and Main Results: Of 325,367 participants, 712 died from pneumonia and influenza combined, 682 from pneumonia, and 695 from ALRI during a mean follow-up of 19.5 years. NO2 and BC were associated with 10-12% increases in pneumonia and influenza combined mortality, but 95% confidence intervals included unity (hazard ratios, 1.12 [0.99-1.26] per 10 µg/m3 for NO2; 1.10 [0.97-1.24] per 0.5 10-5m-1 for BC). Associations with pneumonia and ALRI mortality were almost identical. We detected effect modification suggesting stronger associations with NO2 or BC in overweight, employed, or currently smoking participants compared with normal weight, unemployed, or nonsmoking participants. Conclusions: Long-term exposure to combustion-related air pollutants NO2 and BC may be associated with mortality from lower respiratory infections, but larger studies are needed to estimate these associations more precisely.

Perspectives on environment and health research in Denmark.

AIMS: We provide an overview of nationwide environmental data available for Denmark and its linkage potentials to individual-level records with the aim of promoting research on the potential impact of the local surrounding environment on human health. BACKGROUND: Researchers in Denmark have unique opportunities for conducting large population-based studies treating the entire Danish population as one big, open and dynamic cohort based on nationally complete population and health registries. So far, most research in this area has utilised individual- and family-level information to study the clustering of disease in families, comorbidities, risk of, and prognosis after, disease onset, and social gradients in disease risk. Linking environmental data in time and space to individuals enables novel possibilities for studying the health effects of the social, built and physical environment. METHODS: We describe the possible linkage between individuals and their local surrounding environment to establish the exposome - that is, the total environmental exposure of an individual over their life course. CONCLUSIONS: The currently available nationwide longitudinal environmental data in Denmark constitutes a valuable and globally rare asset that can help explore the impact of the exposome on human health.

Long-term exposure to ambient air pollution and bladder cancer incidence in a pooled European cohort: the ELAPSE project.

BACKGROUND: The evidence linking ambient air pollution to bladder cancer is limited and mixed. METHODS: We assessed the associations of bladder cancer incidence with residential exposure to fine particles (PM2.5), nitrogen dioxide (NO2), black carbon (BC), warm season ozone (O3) and eight PM2.5 elemental components (copper, iron, potassium, nickel, sulfur, silicon, vanadium, and zinc) in a pooled cohort (N = 302,493). Exposures were primarily assessed based on 2010 measurements and back-extrapolated to the baseline years. We applied Cox proportional hazard models adjusting for individual- and area-level potential confounders. RESULTS: During an average of 18.2 years follow-up, 967 bladder cancer cases occurred. We observed a positive though statistically non-significant association between PM2.5 and bladder cancer incidence. Hazard Ratios (HR) were 1.09 (95% confidence interval (CI): 0.93-1.27) per 5 µg/m3 for 2010 exposure and 1.06 (95% CI: 0.99-1.14) for baseline exposure. Effect estimates for NO2, BC and O3 were close to unity. A positive association was observed with PM2.5 zinc (HR 1.08; 95% CI: 1.00-1.16 per 10 ng/m3). CONCLUSIONS: We found suggestive evidence of an association between long-term PM2.5 mass exposure and bladder cancer, strengthening the evidence from the few previous studies. The association with zinc in PM2.5 suggests the importance of industrial emissions.

Air pollution and fecundability: Results from a Danish preconception cohort study.

BACKGROUND: Animal and epidemiologic studies indicate that air pollution may adversely affect fertility. Epidemiologic studies have been restricted largely to couples undergoing fertility treatment or have retrospectively ascertained time-to-pregnancy among pregnant women. OBJECTIVES: We examined the association between residential ambient air pollution and fecundability, the per-cycle probability of conception, in a large preconception cohort of Danish pregnancy planners. METHODS: During 2007-2018, we used the Internet to recruit and follow women who were trying to conceive without the use of fertility treatment. Participants completed an online baseline questionnaire eliciting socio-demographic characteristics, lifestyle factors, and medical and reproductive histories and follow-up questionnaires every 8 weeks to ascertain pregnancy status. We determined concentrations of ambient nitrogen oxides (NOx ), nitrogen dioxide (NO2 ), carbon monoxide (CO), ozone (O3 ), particulate matter <2.5 µm (PM2.5 ) and <10 µm (PM10 ), and sulphur dioxide (SO2 ) at each participant's residential address. We calculated average exposure during the year before baseline, during each menstrual cycle over follow-up and during the entire pregnancy attempt time. We used proportional probabilities regression models to estimate fecundability ratios (FRs) and 95% confidence intervals (CIs), adjusting for potential confounders and co-pollutants. The analysis was restricted to the 10,183 participants who were trying to conceive for <12 cycles at study entry whose addresses could be geocoded. RESULTS: During 12 months of follow-up, 73% of participants conceived. Higher concentrations of PM2.5 and PM10 were associated with small reductions in fecundability. For example, the FRs for a one interquartile range (IQR) increase in PM2.5 (IQR = 3.2 µg/m3 ) and PM10 (IQR = 5.3 µg/m3 ) during each menstrual cycle were 0.93 (95% CI: 0.87, 0.99) and 0.91 (95% CI: 0.84, 0.99), respectively. Other air pollutants were not appreciably associated with fecundability. CONCLUSIONS: In this preconception cohort study of Danish women, residential exposures to PM2.5 and PM10 were associated with reduced fecundability.

Mixed-Effects Modeling Framework for Amsterdam and Copenhagen for Outdoor NO2 Concentrations Using Measurements Sampled with Google Street View Cars.

High-resolution air quality (AQ) maps based on street-by-street measurements have become possible through large-scale mobile measurement campaigns. Such campaigns have produced data-only maps and have been used to produce empirical models [i.e., land use regression (LUR) models]. Assuming that all road segments are measured, we developed a mixed model framework that predicts concentrations by an LUR model, while allowing road segments to deviate from the LUR prediction based on between-segment variation as a random effect. We used Google Street View cars, equipped with high-quality AQ instruments, and measured the concentration of NO2 on every street in Amsterdam (n = 46.664) and Copenhagen (n = 28.499) on average seven times over the course of 9 and 16 months, respectively. We compared the data-only mapping, LUR, and mixed model estimates with measurements from passive samplers (n = 82) and predictions from dispersion models in the same time window as mobile monitoring. In Amsterdam, mixed model estimates correlated rs (Spearman correlation) = 0.85 with external measurements, whereas the data-only approach and LUR model estimates correlated rs = 0.74 and 0.75, respectively. Mixed model estimates also correlated higher rs = 0.65 with the deterministic model predictions compared to the data-only (rs = 0.50) and LUR model (rs = 0.61). In Copenhagen, mixed model estimates correlated rs = 0.51 with external model predictions compared to rs = 0.45 and rs = 0.50 for data-only and LUR model, respectively. Correlation increased for 97 locations (rs = 0.65) with more detailed traffic information. This means that the mixed model approach is able to combine the strength of data-only mapping (to show hyperlocal variation) and LUR models by shrinking uncertain concentrations toward the model output.

Long-Term Exposure to Source-Specific Fine Particles and Mortality─A Pooled Analysis of 14 European Cohorts within the ELAPSE Project.

We assessed mortality risks associated with source-specific fine particles (PM2.5) in a pooled European cohort of 323,782 participants. Cox proportional hazard models were applied to estimate mortality hazard ratios (HRs) for source-specific PM2.5 identified through a source apportionment analysis. Exposure to 2010 annual average concentrations of source-specific PM2.5 components was assessed at baseline residential addresses. The source apportionment resulted in the identification of five sources: traffic, residual oil combustion, soil, biomass and agriculture, and industry. In single-source analysis, all identified sources were significantly positively associated with increased natural mortality risks. In multisource analysis, associations with all sources attenuated but remained statistically significant with traffic, oil, and biomass and agriculture. The highest association per interquartile increase was observed for the traffic component (HR: 1.06; 95% CI: 1.04 and 1.08 per 2.86 µg/m3 increase) across five identified sources. On a 1 µg/m3 basis, the residual oil-related PM2.5 had the strongest association (HR: 1.13; 95% CI: 1.05 and 1.22), which was substantially higher than that for generic PM2.5 mass, suggesting that past estimates using the generic PM2.5 exposure response function have underestimated the potential clean air health benefits of reducing fossil-fuel combustion. Source-specific associations with cause-specific mortality were in general consistent with findings of natural mortality.

Long term exposure to air pollution and kidney parenchyma cancer - Effects of low-level air pollution: a Study in Europe (ELAPSE).

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 (PM2.5), nitrogen dioxide (NO2), black carbon (BC), warm season ozone (O3) and eight elemental components of PM2.5 (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 NO2, PM2.5, BC and O3 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 NO2, 1.04 (95% CI: 0.88, 1.21) per 5 µg/m³ PM2.5, 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 O3. We did not find associations between any of the elemental components of PM2.5 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.

Enhanced Ambient Sensing Environment-A New Method for Calibrating Low-Cost Gas Sensors.

Accurate calibration of low-cost gas sensors is, at present, a time consuming and difficult process. Laboratory calibration and field calibration methods are currently used, but laboratory calibration is generally discounted due to poor transferability, and field methods requiring several weeks are standard. The Enhanced Ambient Sensing Environment (EASE) method described in this article, is a hybrid of the two, combining the advantages of a laboratory calibration with the increased accuracy of a field calibration. It involves calibrating sensors inside a duct, drawing in ambient air with similar properties to the site where the sensors will operate, but with the added feature of being able to artificially increases or decrease pollutant levels, thus condensing the calibration period required. Calibration of both metal-oxide (MOx) and electrochemical (EC) gas sensors for the measurement of NO2 and O3 (0-120 ppb) were conducted in EASE, laboratory and field environments, and validated in field environments. The EC sensors performed marginally better than MOx sensors for NO2 measurement and sensor performance was similar for O3 measurement, but the EC sensor nodes had less node inter-node variability and were more robust. For both gasses and sensor types the EASE calibration outperformed the laboratory calibration, and performed similarly to or better than the field calibration, whilst requiring a fraction of the time.

Long-term exposure to air pollution and liver cancer incidence in six European cohorts.

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.

Long-term air pollution and road traffic noise exposure and COPD: the Danish Nurse Cohort.

BACKGROUND: While air pollution has been linked to the development of chronic obstructive pulmonary disease (COPD), evidence on the role of environmental noise is just emerging. We examined the associations of long-term exposure to air pollution and road traffic noise with COPD incidence. METHODS: We defined COPD incidence for 24 538 female nurses from the Danish Nurse Cohort (age >44 years) as the first hospital contact between baseline (1993 or 1999) and 2015. We estimated residential annual mean concentrations of particulate matter with an aerodynamic diameter <2.5 µm (PM2.5) since 1990 and nitrogen dioxide (NO2) since 1970 using the Danish Eulerian Hemispheric Model/Urban Background Model/Air Geographic Information System modelling system, and road traffic noise (Lden) since 1970 using the Nord2000 model. Time-varying Cox regression models were applied to assess the associations of air pollution and road traffic noise with COPD incidence. RESULTS: 977 nurses developed COPD during a mean of 18.6 years' follow-up. We observed associations with COPD for all three exposures with HRs and 95% CIs of 1.19 (1.01-1.41) per 6.26 µg·m-3 for PM2.5, 1.13 (1.05-1.20) per 8.19 µg·m-3 for NO2 and 1.15 (1.06-1.25) per 10 dB for Lden. Associations with NO2 and Lden attenuated slightly after mutual adjustment, but were robust to adjustment for PM2.5. Associations with PM2.5 were attenuated to null after adjustment for either NO2 or Lden. No potential interaction effect was observed between air pollutants and noise. CONCLUSION: Long-term exposure to air pollution, especially traffic-related NO2, and to road traffic noise were independently associated with COPD.

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

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

Outdoor light at night and breast cancer incidence in the Danish Nurse Cohort.

BACKGROUND: Knowledge of the role of melatonin, xenograft experiments, and epidemiological studies suggests that exposure to light at night (LAN) may disturb circadian rhythms, possibly increasing the risk of developing breast cancer. OBJECTIVES: We examined the association between residential outdoor LAN and the incidence of breast cancer: overall and subtypes classified by estrogen (ER) and progesterone (PR) receptor status. METHODS: We used data on 16,941 nurses from the Danish Nurse Cohort who were followed-up from the cohort baseline in 1993 or 1999 through 2012 in the Danish Cancer Registry for breast cancer incidence and the Danish Breast Cancer Cooperative Group for breast cancer ER and PR status. LAN exposure data were obtained from the U.S. Defense Meteorological Satellite Program (DMSP) available for 1996, 1999, 2000, 2003, 2004, 2006, and 2010 in nW/cm2/sr unit, and assigned to the study participants' residence addresses during the follow-up. Time-varying Cox regression models were used to calculate the hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between LAN and breast cancer, adjusting for individual characteristics, road traffic noise, and air pollution. RESULTS: Of 16,941 nurses, 745 developed breast cancer in total during 320,289 person-years of follow-up. We found no association between exposure to LAN and overall breast cancer. In the fully adjusted models, HRs for the highest (65.8-446.4 nW/cm2/sr) and medium (22.0-65.7 nW/cm2/sr) LAN tertiles were 0.97 (95% CI: 0.77, 1.23) and 1.09 (95% CI: 0.90, 1.31), respectively, compared to the lowest tertile of LAN exposure (0-21.9 nW/cm2/sr). We found a suggestive association between LAN and ER-breast cancer. CONCLUSION: This large cohort study of Danish female nurses suggests weak evidence of the association between LAN and breast cancer incidence.

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

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

Long-term exposure to road traffic noise and stroke incidence: a Danish Nurse Cohort study.

BACKGROUND: Road traffic noise has been linked to increased risk of ischemic heart disease, yet evidence on stroke shows mixed results. We examine the association between long-term exposure to road traffic noise and incidence of stroke, overall and by subtype (ischemic or hemorrhagic), after adjustment for air pollution. METHODS: Twenty-five thousand six hundred and sixty female nurses from the Danish Nurse Cohort recruited in 1993 or 1999 were followed for stroke-related first-ever hospital contact until December 31st, 2014. Full residential address histories since 1970 were obtained and annual means of road traffic noise (Lden [dB]) and air pollutants (particulate matter with diameter < 2.5 µm and < 10 µm [PM2.5 and PM10], nitrogen dioxide [NO2], nitrogen oxides [NOx]) were determined using validated models. Time-varying Cox regression models were used to estimate hazard ratios (HR) (95% confidence intervals [CI]) for the associations of one-, three-, and 23-year running means of Lden preceding stroke (all, ischemic or hemorrhagic), adjusting for stroke risk factors and air pollutants. The World Health Organization and the Danish government's maximum exposure recommendations of 53 and 58 dB, respectively, were explored as potential Lden thresholds. RESULTS: Of 25,660 nurses, 1237 developed their first stroke (1089 ischemic, 148 hemorrhagic) during 16 years mean follow-up. For associations between a 1-year mean of Lden and overall stroke incidence, the estimated HR (95% CI) in the fully adjusted model was 1.06 (0.98-1.14) per 10 dB, which attenuated to 1.01 (0.93-1.09) and 1.00 (0.91-1.09) in models further adjusted for PM2.5 or NO2, respectively. Associations for other exposure periods or separately for ischemic or hemorrhagic stroke were similar. There was no evidence of a threshold association between Lden and stroke. CONCLUSIONS: Long-term exposure to road traffic noise was suggestively positively associated with the risk of overall stroke, although not after adjusting for air pollution.

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

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

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

We developed Europe-wide models of long-term exposure to eight elements (copper, iron, potassium, nickel, sulfur, silicon, vanadium, and zinc) in particulate matter with diameter <2.5 µm (PM2.5) using standardized measurements for one-year periods between October 2008 and April 2011 in 19 study areas across Europe, with supervised linear regression (SLR) and random forest (RF) algorithms. Potential predictor variables were obtained from satellites, chemical transport models, land-use, traffic, and industrial point source databases to represent different sources. Overall model performance across Europe was moderate to good for all elements with hold-out-validation R-squared ranging from 0.41 to 0.90. RF consistently outperformed SLR. Models explained within-area variation much less than the overall variation, with similar performance for RF and SLR. Maps proved a useful additional model evaluation tool. Models differed substantially between elements regarding major predictor variables, broadly reflecting known sources. Agreement between the two algorithm predictions was generally high at the overall European level and varied substantially at the national level. Applying the two models in epidemiological studies could lead to different associations with health. If both between- and within-area exposure variability are exploited, RF may be preferred. If only within-area variability is used, both methods should be interpreted equally.