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 exposure to several constituents and sources of PM2.5 is associated with incidence of upper aerodigestive tract cancers but not gastric cancer: Results from the large pooled European cohort of the ELAPSE project

It is unclear whether cancers of the upper aerodigestive tract (UADT) and gastric cancer are related to air pollution, due to few studies with inconsistent results. The effects of particulate matter (PM) may vary across locations due to different source contributions and related PM compositions, and it is not clear which PM constituents/sources are most relevant from a consideration of overall mass concentration alone. We therefore investigated the association of UADT and gastric cancers with PM2.5 elemental constituents and sources components indicative of different sources within a large multicentre population based epidemiological study. Cohorts with at least 10 cases per cohort led to ten and eight cohorts from five countries contributing to UADT- and gastric cancer analysis, respectively. Outcome ascertainment was based on cancer registry data or data of comparable quality. We assigned home address exposure to eight elemental constituents (Cu, Fe, K, Ni, S, Si, V and Zn) estimated from Europe-wide exposure models, and five source components identified by absolute principal component analysis (APCA). Cox regression models were run with age as time scale, stratified for sex and cohort and adjusted for relevant individual and neighbourhood level confounders. We observed 1139 UADT and 872 gastric cancer cases during a mean follow-up of 18.3 and 18.5 years, respectively. UADT cancer incidence was associated with all constituents except K in single element analyses. After adjustment for NO2, only Ni and V remained associated with UADT. Residual oil combustion and traffic source components were associated with UADT cancer persisting in the multiple source model. No associations were found for any of the elements or source components and gastric cancer incidence. Our results indicate an association of several PM constituents indicative of different sources with UADT but not gastric cancer incidence with the most robust evidence for traffic and residual oil combustion.

Long-term exposure to ambient air pollution and risk of leukemia and lymphoma in a pooled European cohort

Leukemia and lymphoma are the two most common forms of hematologic malignancy, and their etiology is largely unknown. Pathophysiological mechanisms suggest a possible association with air pollution, but little empirical evidence is available. We aimed to investigate the association between long-term residential exposure to outdoor air pollution and risk of leukemia and lymphoma. We pooled data from four cohorts from three European countries as part of the "Effects of Low-level Air Pollution: a Study in Europe" (ELAPSE) collaboration. We used Europe-wide land use regression models to assess annual mean concentrations of fine particulate matter (PM2.5), nitrogen dioxide (NO2), black carbon (BC) and ozone (O3) at residences. We also estimated concentrations of PM2.5 elemental components: copper (Cu), iron (Fe), zinc (Zn); sulfur (S); nickel (Ni), vanadium (V), silicon (Si) and potassium (K). We applied Cox proportional hazards models to investigate the associations. Among the study population of 247,436 individuals, 760 leukemia and 1122 lymphoma cases were diagnosed during 4,656,140 person-years of follow-up. The results showed a leukemia hazard ratio (HR) of 1.13 (95% confidence intervals [CI]: 1.01-1.26) per 10 μg/m3 NO2, which was robust in two-pollutant models and consistent across the four cohorts and according to smoking status. Sex-specific analyses suggested that this association was confined to the male population. Further, the results showed increased lymphoma HRs for PM2.5 (HR = 1.16; 95% CI: 1.02-1.34) and potassium content of PM2.5, which were consistent in two-pollutant models and according to sex. Our results suggest that air pollution at the residence may be associated with adult leukemia and lymphoma.

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 Low-Level PM2.5 and Mortality: Investigation of Heterogeneity by Harmonizing Analyses in Large Cohort Studies in Canada, United States, and Europe

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.

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.

Breast Cancer Incidence in Relation to Long-Term Low-Level Exposure to Air Pollution in the ELAPSE Pooled Cohort

BACKGROUND

Established risk factors for breast cancer include genetic disposition, reproductive factors, hormone therapy, and lifestyle-related factors such as alcohol consumption, physical inactivity, smoking, and obesity. More recently a role of environmental exposures, including air pollution, has also been suggested. The aim of this study, was to investigate the relationship between long-term air pollution exposure and breast cancer incidence.

METHODS

We conducted a pooled analysis among six European cohorts (n = 199,719) on the association between long-term residential levels of ambient nitrogen dioxide (NO2), fine particles (PM2.5), black carbon (BC), and ozone in the warm season (O3) and breast cancer incidence in women. The selected cohorts represented the lower range of air pollutant concentrations in Europe. We applied Cox proportional hazards models adjusting for potential confounders at the individual and area-level.

RESULTS

During 3,592,885 person-years of follow-up, we observed a total of 9,659 incident breast cancer cases. The results of the fully adjusted linear analyses showed a HR (95% confidence interval) of 1.03 (1.00-1.06) per 10 μg/m³ NO2, 1.06 (1.01-1.11) per 5 μg/m³ PM2.5, 1.03 (0.99-1.06) per 0.5 10-5 m-1 BC, and 0.98 (0.94-1.01) per 10 μg/m³ O3. The effect estimates were most pronounced in the group of middle-aged women (50-54 years) and among never smokers.

CONCLUSIONS

The results were in support of an association between especially PM2.5 and breast cancer.

IMPACT

The findings of this study suggest a role of exposure to NO2, PM2.5, and BC in development of breast cancer.

Long-term air pollution exposure and Parkinson's disease mortality in a large pooled European cohort: An ELAPSE study

BACKGROUND

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.

OBJECTIVE

We examined the association between long-term exposure to ambient air pollution and PD mortality in seven European cohorts.

METHODS

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 (NO₂), black carbon (BC), and ozone (O₃), 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.

RESULTS

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), NO₂ (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 O₃ (0.74; 0.58-0.94 per 10 µg/m³). Associations of PM_2.5, NO₂, 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 NO₂ (1.24; 0.95-1.62) or BC (1.28; 0.96-1.71), whereas associations with NO₂ or BC attenuated to null. O₃ 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.

CONCLUSION

Long-term exposure to PM_2.5, at levels well below current EU air pollution limit values, may contribute to PD 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.

Long-term exposure to air pollution and mortality from dementia, psychiatric disorders, and suicide in a large pooled European cohort: ELAPSE study

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 (NO₂), black carbon (BC), ozone (O₃), 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 NO₂ (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 (NO₂: 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 O₃ and 8 PM_2.5 components with any of the three mortality outcomes. Long-term exposure to NO₂, PM_2.5, and BC may lead to premature mortality from psychiatric disorders and suicide.

Exposure to surrounding greenness and natural-cause and cause-specific mortality in the ELAPSE pooled cohort

BACKGROUND

The majority of studies have shown higher greenness exposure associated with reduced mortality risks, but few controlled for spatially correlated air pollution and traffic noise exposures. We aim to address this research gap in the ELAPSE pooled cohort.

METHODS

Mean Normalized Difference Vegetation Index (NDVI) in a 300-m grid cell and 1-km radius were assigned to participants' baseline home addresses as a measure of surrounding greenness exposure. We used Cox proportional hazards models to estimate the association of NDVI exposure with natural-cause and cause-specific mortality, adjusting for a number of potential confounders including socioeconomic status and lifestyle factors at individual and area-levels. We further assessed the associations between greenness exposure and mortality after adjusting for fine particulate matter (PM2.5), nitrogen dioxide (NO2) and road traffic noise.

RESULTS

The pooled study population comprised 327,388 individuals who experienced 47,179 natural-cause deaths during 6,374,370 person-years of follow-up. The mean NDVI in the pooled cohort was 0.33 (SD 0.1) and 0.34 (SD 0.1) in the 300-m grid and 1-km buffer. In the main fully adjusted model, 0.1 unit increment of NDVI inside 300-m grid was associated with 5% lower risk of natural-cause mortality (Hazard Ratio (HR) 0.95 (95% CI: 0.94, 0.96)). The associations attenuated after adjustment for air pollution [HR (95% CI): 0.97 (0.96, 0.98) adjusted for PM2.5; 0.98 (0.96, 0.99) adjusted for NO2]. Additional adjustment for traffic noise hardly affected the associations. Consistent results were observed for NDVI within 1-km buffer. After adjustment for air pollution, NDVI was inversely associated with diabetes, respiratory and lung cancer mortality, yet with wider 95% confidence intervals. No association with cardiovascular mortality was found.

CONCLUSIONS

We found a significant inverse association between surrounding greenness and natural-cause mortality, which remained after adjusting for spatially correlated air pollution and traffic noise.

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 (PM_2.5) in a pooled European cohort of 323,782 participants. Cox proportional hazard models were applied to estimate mortality hazard ratios (HRs) for source-specific PM_2.5 identified through a source apportionment analysis. Exposure to 2010 annual average concentrations of source-specific PM_2.5 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 PM_2.5 had the strongest association (HR: 1.13; 95% CI: 1.05 and 1.22), which was substantially higher than that for generic PM_2.5 mass, suggesting that past estimates using the generic PM_2.5 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 fine particle elemental components and mortality in Europe: Results from six European administrative cohorts within the ELAPSE project

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 (NO₂) 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/m³ Cu; 1.069 (1.031, 1.108) per 100 ng/m³ Fe; 1.039 (1.018, 1.062) per 50 ng/m³ K; 1.024 (1.006, 1.043) per 1 ng/m³ Ni; 1.036 (1.016, 1.057) per 200 ng/m³ S; 1.152 (1.048, 1.266) per 100 ng/m³ Si; 1.020 (1.006, 1.034) per 2 ng/m³ V]. Only K and Si were robust to PM_2.5 or NO₂ adjustment [pooled HRs (95% CI) per 50 ng/m³ in K: 1.025 (1.008, 1.044), 1.020 (0.999, 1.042) and per 100 ng/m³ in Si: 1.121 (1.039, 1.209), 1.068 (1.022, 1.117) adjusted for PM_2.5 and NO₂ correspondingly]. Our findings indicate an association of natural mortality with most components, which was reduced after adjustment for PM_2.5 and especially NO₂.

Associations between the fast-food environment and diabetes prevalence in the Netherlands: a cross-sectional study

BACKGROUND

Diabetes is a major health concern and is influenced by lifestyle, which can be affected by the neighbourhood environment. Specifically, a fast-food environment can influence eating behaviours and thus diabetes prevalence. Therefore, our aim was to assess the relationship between fast-food environment and diabetes prevalence for urban and rural environments in the Netherlands, using multiple indicators and buffer sizes.

METHODS

In this cross-sectional study, data on a nationwide sample of adults older than 19 years in the Netherlands were taken from the 2012 Dutch national health survey (from Public Health Monitor), in which participants were surveyed on topics related to health and lifestyle behaviour. Fast-food outlet exposures were determined within street-network buffers of 100 m, 400 m, 1000 m, and 1500 m around residential addresses. For each of these buffers, three indicators were calculated: presence (yes or no) of fast-food outlets, fast-food outlet density, and ratio. Logistic regression analyses were carried out to assess associations of these indicators with diabetes, adjusting for potential confounders and stratifying into urban and rural areas.

FINDINGS

387 195 adults were surveyed, 284 793 of whom were included in the study. 22 951 (8%) reported having diabetes. Fast-food outlet exposures were positively associated with diabetes prevalence. We did not observe large differences between urban and rural areas. The effect estimates were small for all indicators. For example, in the 400 m buffer in the urban environment, the odds ratio (OR) for having diabetes among people with a fast-food outlet present compared with those without, was 1·006 (95% CI 1·003-1·009) using the presence indicator. The presence indicator showed higher effect estimates and the most consistent results across buffer sizes (ranging from OR 1·005 [95% CI 1·000-1·010] with the 1000 m buffer to 1·016 [1·005-1·028] with the 1500 m buffer in urban areas and from 1·002 [0·998-1·005] with the 1500 m buffer to 1·009 [1·006-1·018] with the 100 m buffer in rural areas) compared with the density and ratio indicators.

INTERPRETATION

The results confirm the evidence that the fast-food outlet environment is a diabetes risk factor. All data included were at the individual level and the variability was ensured by the spatial distribution and number of participants. In this study, we only accounted for residential exposure because we were unable to account for exposure outside the residential environment. The findings of this study encourage local governments to consider the potential adverse effects of fast-food exposures and aim at minimising unhealthy food access.

FUNDING

Global Geo Health Data Centre, Utrecht University, Netherlands.

Long term exposure to low level air pollution and mortality in eight European cohorts within the ELAPSE project: pooled analysis

OBJECTIVE

To investigate the associations between air pollution and mortality, focusing on associations below current European Union, United States, and World Health Organization standards and guidelines.

DESIGN

Pooled analysis of eight cohorts.

SETTING

Multicentre project Effects of Low-Level Air Pollution: A Study in Europe (ELAPSE) in six European countries.

PARTICIPANTS

325 367 adults from the general population recruited mostly in the 1990s or 2000s with detailed lifestyle data. Stratified Cox proportional hazard models were used to analyse the associations between air pollution and mortality. Western Europe-wide land use regression models were used to characterise residential air pollution concentrations of ambient fine particulate matter (PM_2.5), nitrogen dioxide, ozone, and black carbon.

MAIN OUTCOME MEASURES

Deaths due to natural causes and cause specific mortality.

RESULTS

Of 325 367 adults followed-up for an average of 19.5 years, 47 131 deaths were observed. Higher exposure to PM_2.5, nitrogen dioxide, and black carbon was associated with significantly increased risk of almost all outcomes. An increase of 5 µg/m³ in PM_2.5 was associated with 13% (95% confidence interval 10.6% to 15.5%) increase in natural deaths; the corresponding figure for a 10 µg/m³ increase in nitrogen dioxide was 8.6% (7% to 10.2%). Associations with PM_2.5, nitrogen dioxide, and black carbon remained significant at low concentrations. For participants with exposures below the US standard of 12 µg/m³ an increase of 5 µg/m³ in PM_2.5 was associated with 29.6% (14% to 47.4%) increase in natural deaths.

CONCLUSIONS

Our study contributes to the evidence that outdoor air pollution is associated with mortality even at low pollution levels below the current European and North American standards and WHO guideline values. These findings are therefore an important contribution to the debate about revision of air quality limits, guidelines, and standards, and future assessments by the Global Burden of Disease.

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 for PM2.5 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.

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 (PM_2.5 ), black carbon (BC), warm-season ozone (O₃ ), and eight elemental components of PM_2.5 (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 ), PM_2.5 (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 PM_2.5 . Most components of PM_2.5 were associated with the risk of liver cancer, with the strongest associations for sulfur and vanadium, which were robust to adjustment for PM_2.5 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 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 (PM_2.5), 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 PM_2.5, 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^-5m^-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 NO₂. NO₂ 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.

Long-Term Exposure to Fine Particle Elemental Components and Natural and Cause-Specific Mortality-a Pooled Analysis of Eight European Cohorts within the ELAPSE Project

BACKGROUND

Inconsistent associations between long-term exposure to particles with an aerodynamic diameter ≤ 2.5   μ m [fine particulate matter (PM 2.5 )] components and mortality have been reported, partly related to challenges in exposure assessment.

OBJECTIVES

We investigated the associations between long-term exposure to PM 2.5 elemental components and mortality in a large pooled European cohort; to compare health effects of PM 2.5 components estimated with two exposure modeling approaches, namely, supervised linear regression (SLR) and random forest (RF) algorithms.

METHODS

We pooled data from eight European cohorts with 323,782 participants, average age 49 y at baseline (1985-2005). Residential exposure to 2010 annual average concentration of eight PM 2.5 components [copper (Cu), iron (Fe), potassium (K), nickel (Ni), sulfur (S), silicon (Si), vanadium (V), and zinc (Zn)] was estimated with Europe-wide SLR and RF models at a 100 × 100   m scale. We applied Cox proportional hazards models to investigate the associations between components and natural and cause-specific mortality. In addition, two-pollutant analyses were conducted by adjusting each component for PM 2.5 mass and nitrogen dioxide (NO 2 ) separately.

RESULTS

We observed 46,640 natural-cause deaths with 6,317,235 person-years and an average follow-up of 19.5 y. All SLR-modeled components were statistically significantly associated with natural-cause mortality in single-pollutant models with hazard ratios (HRs) from 1.05 to 1.27. Similar HRs were observed for RF-modeled Cu, Fe, K, S, V, and Zn with wider confidence intervals (CIs). HRs for SLR-modeled Ni, S, Si, V, and Zn remained above unity and (almost) significant after adjustment for both PM 2.5 and NO 2 . HRs only remained (almost) significant for RF-modeled K and V in two-pollutant models. The HRs for V were 1.03 (95% CI: 1.02, 1.05) and 1.06 (95% CI: 1.02, 1.10) for SLR- and RF-modeled exposures, respectively, per 2   ng / m 3 , adjusting for PM 2.5 mass. Associations with cause-specific mortality were less consistent in two-pollutant models.

CONCLUSION

Long-term exposure to V in PM 2.5 was most consistently associated with increased mortality. Associations for the other components were weaker for exposure modeled with RF than SLR in two-pollutant models. https://doi.org/10.1289/EHP8368.

Modeling multi-level survival data in multi-center epidemiological cohort studies: Applications from the ELAPSE project

BACKGROUND

We evaluated methods for the analysis of multi-level survival data using a pooled dataset of 14 cohorts participating in the ELAPSE project investigating associations between residential exposure to low levels of air pollution (PM2.5 and NO2) and health (natural-cause mortality and cerebrovascular, coronary and lung cancer incidence).

METHODS

We applied five approaches in a multivariable Cox model to account for the first level of clustering corresponding to cohort specification: (1) not accounting for the cohort or using (2) indicator variables, (3) strata, (4) a frailty term in frailty Cox models, (5) a random intercept under a mixed Cox, for cohort identification. We accounted for the second level of clustering due to common characteristics in the residential area by (1) a random intercept per small area or (2) applying variance correction. We assessed the stratified, frailty and mixed Cox approach through simulations under different scenarios for heterogeneity in the underlying hazards and the air pollution effects.

RESULTS

Effect estimates were stable under approaches used to adjust for cohort but substantially differed when no adjustment was applied. Further adjustment for the small area grouping increased the effect estimates' standard errors. Simulations confirmed identical results between the stratified and frailty models. In ELAPSE we selected a stratified multivariable Cox model to account for between-cohort heterogeneity without adjustment for small area level, due to the small number of subjects and events in the latter.

CONCLUSIONS

Our study supports the need to account for between-cohort heterogeneity in multi-center collaborations using pooled individual level data.

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 (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.

METHODS

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).

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 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 (NO₂). 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.

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 PM_2.5 components may prove helpful in future lung cancer prevention strategies.

Comparison of associations between mortality and air pollution exposure estimated with a hybrid, a land-use regression and a dispersion model

INTRODUCTION

To characterize air pollution exposure at a fine spatial scale, different exposure assessment methods have been applied. Comparison of associations with health from different exposure methods are scarce. The aim of this study was to evaluate associations of air pollution based on hybrid, land-use regression (LUR) and dispersion models with natural cause and cause-specific mortality.

METHODS

We followed a Dutch national cohort of approximately 10.5 million adults aged 29+ years from 2008 until 2012. We used Cox proportional hazard models with age as underlying time scale and adjusted for several potential individual and area-level socio-economic status confounders to evaluate associations of annual average residential NO2, PM2.5 and BC exposure estimates based on two stochastic models (Dutch LUR, European-wide hybrid) and deterministic Dutch dispersion models.

RESULTS

Spatial variability of PM2.5 and BC exposure was smaller for LUR compared to hybrid and dispersion models. NO2 exposure variability was similar for the three methods. Pearson correlations between hybrid, LUR and dispersion modeled NO2 and BC ranged from 0.72 to 0.83; correlations for PM2.5 were slightly lower (0.61-0.72). In general, all three models showed stronger associations of air pollutants with respiratory disease and lung cancer mortality than with natural cause and cardiovascular disease mortality. The strength of the associations differed between the three exposure models. Associations of air pollutants estimated by LUR were generally weaker compared to associations of air pollutants estimated by hybrid and dispersion models. For natural cause mortality, we found a hazard ratio (HR) of 1.030 (95% confidence interval (CI): 1.019, 1.041) per 10 µg/m3 for hybrid modeled NO2, a HR of 1.003 (95% CI: 0.993, 1.013) per 10 µg/m3 for LUR modeled NO2 and a HR of 1.015 (95% CI: 1.005, 1.024) per 10 µg/m3 for dispersion modeled NO2.

CONCLUSION

Air pollution was positively associated with natural cause and cause-specific mortality, but the strength of the associations differed between the three exposure models. Our study documents that the selected exposure model may contribute to heterogeneity in effect estimates of associations between air pollution and health.

Long-term exposure to low-level air pollution and incidence of chronic obstructive pulmonary disease: The ELAPSE project

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 (NO₂), 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/m³ for PM_2.5, 1.11 (1.06, 1.16) per 10 µg/m³ for NO₂, and 1.11 (1.06, 1.15) per 0.5 10^-5m^-1 for BC. Associations persisted in subset participants with PM_2.5 or NO₂ levels below current EU and US limit values and WHO guidelines, with no evidence for a threshold. HRs for NO₂ and BC remained unchanged in two-pollutant models with PM_2.5, whereas the HR for PM_2.5 was attenuated to unity with NO₂ 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 NO₂ and BC may be the most relevant.

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 (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.

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.

A Global Analysis of Associations between Fine Particle Air Pollution and Cardiovascular Risk Factors: Feasibility Study on Data Linkage

Background

This paper presents a feasibility study of data linkage between global air pollution data and clinical medical data to assess the associations of PM2.5 with cardiovascular risk factors.

Methods

Cardiovascular risk factor data were obtained from the SUrvey of Risk Factors (SURF) for coronary heart disease (CHD) patients from 10 countries in Europe, Asia, and the Middle-East. Annual average PM2.5 concentrations were estimated using recent global WHO PM2.5 maps combining satellite and surface monitoring data for the location of the 71 participating centers. Associations of PM2.5 with risk factors were assessed by mixed-effect generalized estimation equation models adjusted by sex, age, exercise, body mass index, and smoking. In the final model there was further adjustment for country.

Results

Linkage between cardiovascular risk factor data and PM2.5 via the postal address of participating hospitals was shown to be feasible, however with several limitations noted.Eight thousand three hundred and ninety two patients (30% women) were included. Globally, an increase of 10 μg/m3 in PM2.5 was significantly associated with decreased BP and increased glucose. After controlling for country, an increase of 10 μg/m3 in PM2.5 was associated with decreased BP and increased LDL (SBP: -0.45 mmHg [95% CI: -0.85, -0.06]; DBP: -0.47 mmHg [-0.73, -0.20]; LDL: 0.04 mmol/L [0.01, 0.08]). The association with glucose attenuated (0.08 mmol/L [-0.23, 0.16]).

Conclusion

It is feasible to link PM2.5 and cardiovascular risk factors but it is still challenging to interpret these observed associations due to unavailability of potential confounders. After country adjustment, PM2.5 was associated with small increases in LDL and small decreases in BP.

Highlights

- There are limited studies on the association between air pollution and cardiovascular risk factors for patients with established coronary heart disease in low- and middle-income countries;- Data linkage is an efficient and cost-effective method to maximize the use of existing data to investigate more health related research questions;- It is feasible to determine global associations of air pollution and cardiovascular risk factors by data linkage but it is still challenging in terms of interpretation.

Exposure to Air Pollution during Pregnancy and Childhood, and White Matter Microstructure in Preadolescents

BACKGROUND

Air pollution has been related to brain structural alterations, but a relationship with white matter microstructure is unclear.

OBJECTIVES

We assessed whether pregnancy and childhood exposures to air pollution are related to white matter microstructure in preadolescents.

METHODS

We used data of 2,954 children from the Generation R Study, a population-based birth cohort from Rotterdam, Netherlands (2002-2006). Concentrations of 17 air pollutants including nitrogen oxides (NOX), particulate matter (PM), and components of PM were estimated at participants' homes during pregnancy and childhood using land-use regression models. Diffusion tensor images were obtained at child's 9-12 years of age, and fractional anisotropy (FA) and mean diffusivity (MD) were computed. We performed linear regressions adjusting for socioeconomic and lifestyle characteristics. Single-pollutant analyses were followed by multipollutant analyses using the Deletion/Substitution/Addition (DSA) algorithm.

RESULTS

In the single-pollutant analyses, higher concentrations of several air pollutants during pregnancy or childhood were associated with significantly lower FA or higher MD (p<0.05). In multipollutant models of pregnancy exposures selected by DSA, higher concentration of fine particles was associated with significantly lower FA [-0.71 (95% CI: -1.26, -0.16) per 5 μg/m3 fine particles] and higher concentration of elemental silicon with significantly higher MD [0.06 (95% CI: 0.01, 0.11) per 100 ng/m3 silicon]. Multipollutant models of childhood exposures selected by DSA indicated significant associations of NOX with FA [-0.14 (95% CI: -0.23, -0.04) per 20-μg/m3 NOX increase], and of elemental zinc and the oxidative potential of PM with MD [0.03 (95% CI: 0.01, 0.04) per 10-ng/m3 zinc increase and 0.07 (95% CI: 0.00, 0.44) per 1-nmol DTT/min/m3 oxidative potential increase]. Mutually adjusted models of significant exposures during pregnancy and childhood indicated significant associations of silicon during pregnancy, and zinc during childhood, with MD.

DISCUSSION

Exposure in pregnancy and childhood to air pollutants from tailpipe and non-tailpipe emissions were associated with lower FA and higher MD in white matter of preadolescents. https://doi.org/10.1289/EHP4709.

High resolution annual average air pollution concentration maps for the Netherlands

Long-term exposure to air pollution is considered a major public health concern and has been related to overall mortality and various diseases such as respiratory and cardiovascular disease. Due to the spatial variability of air pollution concentrations, assessment of individual exposure to air pollution requires spatial datasets at high resolution. Combining detailed air pollution maps with personal mobility and activity patterns allows for an improved exposure assessment. We present high-resolution datasets for the Netherlands providing average ambient air pollution concentration values for the year 2009 for NO₂, NO_x, PM_2.5, PM_{2.5absorbance} and PM_10. The raster datasets on 5×5 m grid cover the entire Netherlands and were calculated using the land use regression models originating from the European Study of Cohorts for Air Pollution Effects (ESCAPE) project. Additional datasets with nationwide and regional measurements were used to evaluate the generated concentration maps. The presented datasets allow for spatial aggregations on different scales, nationwide individual exposure assessment, and the integration of activity patterns in the exposure estimation of individuals.

Spatial PM2.5, NO2, O3 and BC models for Western Europe - Evaluation of spatiotemporal stability

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, NO₂, BC, O₃) across Europe.

METHODS

We developed West-European land use regression models (LUR) for 2010 estimating annual mean PM_2.5, NO₂, BC and O₃ concentrations (including cold and warm season estimates for O₃). The models were based on AirBase routine monitoring data (PM_2.5, NO₂ and O₃) and ESCAPE monitoring data (BC), and incorporated satellite observations, dispersion model estimates, land use and traffic data. Kriging was performed on the residual spatial variation from the LUR models and added to the exposure estimates. One model was developed using all sites (100%). Robustness of the models was evaluated by performing a five-fold hold-out validation and for PM_2.5 and NO₂ additionally with independent comparison at ESCAPE measurements. To evaluate the stability of each model's spatial structure over time, separate models were developed for different years (NO₂ and O₃: 2000 and 2005; PM_2.5: 2013).

RESULTS

The PM_2.5, BC, NO₂, O₃ annual, O₃ warm season and O₃ cold season models explained respectively 72%, 54%, 59%, 65%, 69% and 83% of spatial variation in the measured concentrations. Kriging proved an efficient technique to explain a part of residual spatial variation for the pollutants with a strong regional component explaining respectively 10%, 24% and 16% of the R² in the PM_2.5, O₃ warm and O₃ 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 NO₂ (49% vs 57%). Predictions from the 2010 model correlated highly with models developed in other years at the overall European scale.

CONCLUSIONS

We developed robust PM_2.5, NO₂, O₃ and BC hybrid LUR models. At the West-European scale models were robust in time, becoming less robust at smaller spatial scales. Models were applied to 100 × 100 m surfaces across Western Europe to allow for exposure assignment for 35 million participants from 18 European cohorts participating in the ELAPSE study.

Modeled and perceived RF-EMF, noise and air pollution and symptoms in a population cohort. Is perception key in predicting symptoms?

BACKGROUND

Psychosocial research has shown that perceived exposure can influence symptom reporting, regardless of actual exposure. The impact of this phenomenon on the interpretation of results from epidemiological research on environmental determinants of symptoms is unclear.

OBJECTIVE

Our aim was to compare associations between modeled exposures, the perceived level of these exposures and reported symptoms (non-specific symptoms, sleep disturbances, and respiratory symptoms) for three different environmental exposures (radiofrequency electromagnetic fields (RF-EMF), noise, and air pollution). These environmental exposures vary in the degree to which they can be sensorially observed.

METHODS

Participant characteristics, perceived exposures, and self-reported health were assessed with a baseline (n = 14,829, 2011/2012) and follow-up (n = 7905, 2015) questionnaire in the Dutch population-based Occupational and Environmental Health Cohort (AMIGO). Environmental exposures were estimated at the home address using spatial models. Cross-sectional and longitudinal regression models were used to examine the associations between modeled and perceived exposures, and reported symptoms.

RESULTS

The extent to which exposure sources could be observed by participants likely influenced correlations between modeled and perceived exposure as correlations were moderate for air pollution (r_Sp = 0.34) and noise (r_Sp = 0.40), but less so for RF-EMF (r_Sp = 0.11). Perceived exposures were consistently associated with increased symptom scores (respiratory, sleep, non-specific). Modeled exposures, except RF-EMF, were associated with increased symptom scores, but these associations disappeared or strongly diminished when accounted for perceived exposure in the analyses.

DISCUSSION

Perceived exposure has an important role in symptom reporting. When environmental determinants of symptoms are studied without acknowledging the potential role of both modeled and perceived exposures, there is a risk of bias in health risk assessment. However, the etiological role of exposure perceptions in relation to symptom reporting requires further research.

Relations between the residential fast-food environment and the individual risk of cardiovascular diseases in The Netherlands: A nationwide follow-up study

Background

The food environment has been hypothesized to influence cardiovascular diseases such as hypertension and coronary heart disease. This study determines the relation between fast-food outlet density (FFD) and the individual risk for cardiovascular disease, among a nationwide Dutch sample.

Methods

After linkage of three national registers, a cohort of 2,472,004 adults (≥35 years), free from cardiovascular disease at January 1st 2009 and living at the same address for ≥15 years was constructed. Participants were followed for one year to determine incidence of cardiovascular disease, including coronary heart disease, stroke and heart failure. Street network-based buffers of 500 m, 1000 m and 3000 m around residential addresses were calculated, while FFD was determined using a retail outlet database. Logistic regression analyses were conducted. Models were stratified by degree of urbanization and adjusted for age, sex, ethnicity, marital status, comorbidity, neighbourhood-level income and population density.

Results

In urban areas, fully adjusted models indicated that the incidence of cardiovascular disease and coronary heart disease was significantly higher within 500 m buffers with one or more fast-food outlets as compared with areas with no fast-food outlets. An elevated FFD within 1000 m was associated with an significantly increased incidence of cardiovascular disease and coronary heart disease. Evidence was less pronounced for 3000 m buffers, or for stroke and heart-failure incidence.

Conclusions

Elevated FFD in the urban residential environment (≤1000 m) was related to an increased incidence of cardiovascular heart disease and coronary heart disease. To better understand how FFD is associated with cardiovascular disease, future studies should account for a wider range of lifestyle and environmental confounders than was achieved in this study.

Green space definition affects associations of green space with overweight and physical activity

INTRODUCTION

In epidemiological studies, exposure to green space is inconsistently associated with being overweight and physical activity, possibly because studies differ widely in their definition of green space exposure, inclusion of important confounders, study population and data analysis.

OBJECTIVES

We evaluated whether the association of green space with being overweight and physical activity depended upon definition of greenspace.

METHODS

We conducted a cross-sectional study using data from a Dutch national health survey of 387,195 adults. Distance to the nearest park entrance and surrounding green space, based on the Normalized Difference Vegetation Index (NDVI) or a detailed Dutch land-use database (TOP10NL), was calculated for each residential address. We used logistic regression analyses to study the association of green space exposure with being overweight and being moderately or vigorously physically active outdoors at least 150min/week (self-reported). To study the shape of the association, we specified natural splines and quintiles.

RESULTS

The distance to the nearest park entrance was not associated with being overweight or outdoor physical activity. Associations of surrounding green space with being overweight or outdoor physical activity were highly non-linear. For NDVI surrounding greenness, we observed significantly decreased odds of being overweight [300m buffer, odds ratio (OR) = 0.88; 95% CI: 0.86, 0.91] and increased odds for outdoor physical activity [300m buffer, OR = 1.14; 95% CI: 1.10, 1.17] in the highest quintile compared to the lowest quintile. For TOP10NL surrounding green space, associations were mostly non-significant. Associations were generally stronger for subjects living in less urban areas and for the smaller buffers.

CONCLUSION

Associations of green space with being overweight and outdoor physical activity differed considerably between different green space definitions. Associations were strongest for NDVI surrounding greenness.

Neighbourhood social and physical environment and general practitioner assessed morbidity

The aim of our study was to investigate the association between health enhancing and threatening, and social and physical aspects of the neighbourhood environment and general practitioner (GP) assessed morbidity of the people living there, in order to find out whether the effects of environmental characteristics add up or modify each other. We combined GP electronic health records with environmental data on neighbourhoods in the Netherlands. Cross-classified logistic multilevel models show the importance of taking into account several environmental characteristics and confounders, as social capital effects on the prevalence of morbidity disappear when other area characteristics are taken into account. Stratification by area socio-economic status, shows that the association between environmental characteristics and the prevalence of morbidity is stronger for people living in low SES areas. In low SES areas, green space seems to alleviate effects of air pollution on the prevalence of high blood pressure and diabetes, while the effects of green space and social capital reinforce each other.

Long-term exposure to particulate matter, NO2 and the oxidative potential of particulates and diabetes prevalence in a large national health survey

BACKGROUND

The evidence from observational epidemiological studies of a link between long-term air pollution exposure and diabetes prevalence and incidence is currently mixed. Some studies found the strongest associations of diabetes with fine particles, other studies with nitrogen dioxide and some studies found no associations.

OBJECTIVES

Our aim was to investigate associations between long-term exposure to multiple air pollutants and diabetes prevalence in a large national survey in the Netherlands.

METHODS

We performed a cross-sectional analysis using the 2012 Dutch national health survey to investigate the associations between the 2009 annual average concentrations of multiple air pollutants (PM₁₀, PM_2.5, PM_10-2.5, PM_2.5 absorbance, OP^DTT, OP^ESR and NO₂) and diabetes prevalence, among 289,703 adults. Air pollution exposure was assessed by land use regression models. Diabetes was defined based on a combined measure of self-reported physician diagnosis and medication prescription from an external database. Using logistic regression, we adjusted for potential confounders, including neighborhood- and individual socio-economic status and lifestyle-related risk factors such as smoking habits, alcohol consumption, physical activity and BMI.

RESULTS

After adjustment for potential confounders, all pollutants (except PM_2.5) were associated with diabetes prevalence. In two-pollutant models, NO₂ and OP^DTT remained associated with increased diabetes prevalence. For NO₂ and OP^DTT, single-pollutant ORs per interquartile range were 1.07 (95% CI: 1.05, 1.09) and 1.08 (95% CI: 1.05, 1.10), respectively. Stratified analysis showed no consistent effect modification by any of the included known diabetes risk factors.

CONCLUSIONS

Long-term residential air pollution exposure was associated with diabetes prevalence in a large health survey in the Netherlands, strengthening the evidence of air pollution being an important diabetes risk factor. Most consistent associations were observed for NO₂ and oxidative potential of PM_2.5 measured by the DTT assay. The finding of an association with the oxidative potential of fine particles but not with PM_2.5, suggests that particle composition may be important for a potential effect on diabetes.

The impact of ambient air pollution on the human blood metabolome

BACKGROUND

Biological perturbations caused by air pollution might be reflected in the compounds present in blood originating from air pollutants and endogenous metabolites influenced by air pollution (defined here as part of the blood metabolome). We aimed to assess the perturbation of the blood metabolome in response to short term exposure to air pollution.

METHODS

We exposed 31 healthy volunteers to ambient air pollution for 5h. We measured exposure to particulate matter, particle number concentrations, absorbance, elemental/organic carbon, trace metals, secondary inorganic components, endotoxin content, gaseous pollutants, and particulate matter oxidative potential. We collected blood from the participants 2h before and 2 and 18h after exposure. We employed untargeted metabolite profiling to monitor 3873 metabolic features in 493 blood samples from these volunteers. We assessed lung function using spirometry and six acute phase proteins in peripheral blood. We assessed the association of the metabolic features with the measured air pollutants and with health markers that we previously observed to be associated with air pollution in this study.

RESULTS

We observed 89 robust associations between air pollutants and metabolic features two hours after exposure and 118 robust associations 18h after exposure. Some of the metabolic features that were associated with air pollutants were also associated with acute health effects, especially changes in forced expiratory volume in 1s. We successfully identified tyrosine, guanosine, and hypoxanthine among the associated features. Bioinformatics approach Mummichog predicted enriched pathway activity in eight pathways, among which tyrosine metabolism.

CONCLUSIONS

This study demonstrates for the first time the application of untargeted metabolite profiling to assess the impact of air pollution on the blood metabolome.

Associations between lifestyle and air pollution exposure: Potential for confounding in large administrative data cohorts

BACKGROUND

Cohorts based on administrative data have size advantages over individual cohorts in investigating air pollution risks, but often lack in-depth information on individual risk factors related to lifestyle. If there is a correlation between lifestyle and air pollution, omitted lifestyle variables may result in biased air pollution risk estimates. Correlations between lifestyle and air pollution can be induced by socio-economic status affecting both lifestyle and air pollution exposure.

OBJECTIVES

Our overall aim was to assess potential confounding by missing lifestyle factors on air pollution mortality risk estimates. The first aim was to assess associations between long-term exposure to several air pollutants and lifestyle factors. The second aim was to assess whether these associations were sensitive to adjustment for individual and area-level socioeconomic status (SES), and whether they differed between subgroups of the population. Using the obtained air pollution-lifestyle associations and indirect adjustment methods, our third aim was to investigate the potential bias due to missing lifestyle information on air pollution mortality risk estimates in administrative cohorts.

METHODS

We used a recent Dutch national health survey of 387,195 adults to investigate the associations of PM₁₀, PM_2.5, PM_2.5-10, PM_2.5 absorbance, OP^DTT, OP^ESR and NO₂ annual average concentrations at the residential address from land use regression models with individual smoking habits, alcohol consumption, physical activity and body mass index. We assessed the associations with and without adjustment for neighborhood and individual SES characteristics typically available in administrative data cohorts. We illustrated the effect of including lifestyle information on the air pollution mortality risk estimates in administrative cohort studies using a published indirect adjustment method.

RESULTS

Current smoking and alcohol consumption were generally positively associated with air pollution. Physical activity and overweight were negatively associated with air pollution. The effect estimates were small (mostly <5% of the air pollutant standard deviations). Direction and magnitude of the associations depended on the pollutant, use of continuous vs. categorical scale of the lifestyle variable, and level of adjustment for individual and area-level SES. Associations further differed between subgroups (age, sex) in the population. Despite the small associations between air pollution and smoking intensity, indirect adjustment resulted in considerable changes of air pollution risk estimates for cardiovascular and especially lung cancer mortality.

CONCLUSIONS

Individual lifestyle-related risk factors were weakly associated with long-term exposure to air pollution in the Netherlands. Indirect adjustment for missing lifestyle factors in administrative data cohort studies may substantially affect air pollution mortality risk estimates.

The impact of particle filtration on indoor air quality in a classroom near a highway

A pilot study was performed to investigate whether the application of a new mechanical ventilation system with a fine F8 (MERV14) filter could improve indoor air quality in a high school near the Amsterdam ring road. PM10, PM2.5, and black carbon (BC) concentrations were measured continuously inside an occupied intervention classroom and outside the school during three sampling periods in the winter of 2013/2014. Initially, 3 weeks of baseline measurements were performed, with the existing ventilation system and normal ventilation habits. Next, an intervention study was performed. A new ventilation system was installed in the classroom, and measurements were performed during 8 school weeks, in alternating 2-week periods with and without the filter in the ventilation system under otherwise identical ventilation conditions. Indoor/outdoor ratios measured during the weeks with filter were compared with those measured without filter to evaluate the ability of the F8 filter to improve indoor air quality. During teaching hours, the filter reduced BC exposure by, on average, 36%. For PM10 and PM2.5, a reduction of 34% and 30% was found, respectively. This implies that application of a fine filter can reduce the exposure of schoolchildren to traffic exhaust at hot spot locations by about one-third.

Air pollution exposure affects circulating white blood cell counts in healthy subjects: the role of particle composition, oxidative potential and gaseous pollutants - the RAPTES project

Studies have linked air pollution exposure to cardiovascular health effects, but it is not clear which components drive these effects. We examined the associations between air pollution exposure and circulating white blood cell (WBC) counts in humans. To investigate independent contributions of particulate matter (PM) characteristics, we exposed 31 healthy volunteers at five locations with high contrast and reduced correlations amongst pollutant components: two traffic sites, an underground train station, a farm and an urban background site. Each volunteer visited at least three sites and was exposed for 5 h with intermittent exercise. Exposure measurements on-site included PM mass and number concentration, oxidative potential (OP), elemental- and organic carbon, metals, O3 and NO2. Total and differential WBC counts were performed on blood collected before and 2 and 18 h post-exposure (PE). Changes in total WBC counts (2 and 18 h PE), number of neutrophils (2 h PE) and monocytes (18 h PE) were positively associated with PM characteristics that were high at the underground site. These time-dependent changes reflect an inflammatory response, but the characteristic driving this effect could not be isolated. Negative associations were observed for NO2 with lymphocytes and eosinophils. These associations were robust and did not change after adjustment for a large suite of PM characteristics, suggesting an independent effect of NO2. We conclude that short-term air pollution exposure at real-world locations can induce changes in WBC counts in healthy subjects. Future studies should indicate if air pollution exposure-induced changes in blood cell counts results in adverse cardiovascular effects in susceptible individuals.

Oxidative potential of particulate matter collected at sites with different source characteristics

BACKGROUND

The oxidative potential (OP) of particulate matter (PM) has been proposed as a more health relevant metric than PM mass. Different assays exist for measuring OP and little is known about how the different assays compare.

AIM

To assess the OP of PM collected at different site types and to evaluate differences between locations, size fractions and correlation with PM mass and PM composition for different measurement methods for OP.

METHODS

PM2.5 and PM10 was sampled at 5 sites: an underground station, a farm, 2 traffic sites and an urban background site. Three a-cellular assays; dithiothreitol (OP(DTT)), electron spin resonance (OP(ESR)) and ascorbate depletion (OP(AA)) were used to characterize the OP of PM.

RESULTS

The highest OP was observed at the underground, where OP of PM10 was 30 (OP(DTT)) to >600 (OP(ESR)) times higher compared to the urban background when expressed as OP/m(3) and 2-40 times when expressed as OP/μg. For the outdoor sites, samples from the farm showed significantly lower OP(ESR) and OP(AA), whereas samples from the continuous traffic site showed the highest OP for all assays. Contrasts in OP between sites were generally larger than for PM mass and were lower for OP(DTT) compared to OP(ESR) and OP(AA). Furthermore, OP(DTT)/μg was significantly higher in PM2.5 compared to PM10, whereas the reverse was the case for OP(ESR). OP(ESR) and OP(AA) were highly correlated with traffic-related PM components (i.e. EC, Fe, Cu, PAHs), whereas OP(DTT) showed the highest correlation with PM mass and OC.

CONCLUSIONS

Contrasts in OP between sites, differences in size fractions and correlation with PM composition depended on the specific OP assay used, with OP(ESR) and OP(AA) showing the most similar results. This suggests that either OP(ESR) or OP(AA) and OP(DTT) can complement each other in providing information regarding the oxidative properties of PM, which can subsequently be used to study its health effects.

Composition of PM affects acute vascular inflammatory and coagulative markers - the RAPTES project

BACKGROUND

Exposure to ambient particulate matter (PM) has been associated with adverse cardiovascular effects in epidemiological studies. Current knowledge of independent effects of individual PM characteristics remains limited.

METHODS

Using a semi-experimental design we investigated which PM characteristics were consistently associated with blood biomarkers believed to be predictive of the risk of cardiovascular events. We exposed healthy adult volunteers at 5 different locations chosen to provide PM exposure contrasts with reduced correlations among PM characteristics. Each of the 31 volunteers was exposed for 5 h, exercising intermittently, 3-7 times at different sites from March to October 2009. Extensive on-site exposure characterization included measurements of PM mass and number concentration, elemental- (EC) and organic carbon (OC), trace metals, sulfate, nitrate, and PM oxidative potential (OP). Before and 2 h and 18 h after exposure we measured acute vascular blood biomarkers - C-reactive protein, fibrinogen, platelet counts, von Willebrand Factor, and tissue plasminogen activator/plasminogen activator inhibitor-1 complex. We used two-pollutant models to assess which PM characteristics were most consistently associated with the measured biomarkers.

RESULTS AND CONCLUSION

We found OC, nitrate and sulfate to be most consistently associated with different biomarkers of acute cardiovascular risk. Associations with PM mass concentrations and OP were less consistent, whereas other measured components of the air pollution mixture, including PNC, EC, trace metals and NO2, were not associated with the biomarkers after adjusting for other pollutants.

Components of ambient air pollution affect thrombin generation in healthy humans: the RAPTES project

OBJECTIVES

Increases in ambient particulate matter (PM) have been associated with an elevated risk of stroke, myocardial ischaemia and coronary heart disease, with activation of blood coagulation likely playing an important role. PM-mediated activation of two major activation pathways of coagulation provides a potential mechanism for the observed association between PM and cardiovascular disease. However, it remains unclear which specific characteristics and components of air pollution are responsible.

METHODS

In order to investigate those characteristics and components, we semiexperimentally exposed healthy adult volunteers at five different locations with increased contrasts and reduced correlations among PM characteristics. Volunteers were exposed for 5 h, exercising intermittently, 3-7 times at different sites from March to October 2009. On site, we measured PM mass and number concentration, its oxidative potential (OP), content of elemental/organic carbon, trace metals, sulphate, nitrate and gaseous pollutants (ozone, nitrogen oxides). Before and 2 and 18 h after exposure we sampled blood from the participants and measured thrombin generation using the calibrated automated thrombogram.

RESULTS

We found that thrombin generation increases in the intrinsic (FXII-mediated) blood coagulation pathway in relation to ambient air pollution exposure. The associations with NO2, nitrate and sulphate were consistent and robust, insensitive to adjustment for other pollutants. The associations with tissue factor-mediated thrombogenicity were not very consistent.

CONCLUSIONS

Ex vivo thrombin generation was associated with exposure to NO2, nitrate and sulphate, but not PM mass, PM OP or other measured air pollutants.

Respiratory health effects of airborne particulate matter: the role of particle size, composition, and oxidative potential-the RAPTES project

BACKGROUND

Specific characteristics of particulate matter (PM) responsible for associations with respiratory health observed in epidemiological studies are not well established. High correlations among, and differential measurement errors of, individual components contribute to this uncertainty.

OBJECTIVES

We investigated which characteristics of PM have the most consistent associations with acute changes in respiratory function in healthy volunteers.

METHODS

We used a semiexperimental design to accurately assess exposure. We increased exposure contrast and reduced correlations among PM characteristics by exposing volunteers at five different locations: an underground train station, two traffic sites, a farm, and an urban background site. Each of the 31 participants was exposed for 5 hr while exercising intermittently, three to seven times at different locations during March-October 2009. We measured PM10, PM2.5, particle number concentrations (PNC), absorbance, elemental/organic carbon, trace metals, secondary inorganic components, endotoxin content, gaseous pollutants, and PM oxidative potential. Lung function [FEV1 (forced expiratory volume in 1 sec), FVC (forced vital capacity), FEF25-75 (forced expiratory flow at 25-75% of vital capacity), and PEF (peak expiratory flow)] and fractional exhaled nitric oxide (FENO) were measured before and at three time points after exposure. Data were analyzed with mixed linear regression.

RESULTS

An interquartile increase in PNC (33,000 particles/cm3) was associated with an 11% [95% confidence interval (CI): 5, 17%] and 12% (95% CI: 6, 17%) FENO increase over baseline immediately and at 2 hr postexposure, respectively. A 7% (95% CI: 0.5, 14%) increase persisted until the following morning. These associations were robust and insensitive to adjustment for other pollutants. Similarly consistent associations were seen between FVC and FEV1 with PNC, NO2 (nitrogen dioxide), and NOx (nitrogen oxides).

CONCLUSIONS

Changes in PNC, NO2, and NOx were associated with evidence of acute airway inflammation (i.e., FENO) and impaired lung function. PM mass concentration and PM10 oxidative potential were not predictive of the observed acute responses.

In vitro toxicity of particulate matter (PM) collected at different sites in the Netherlands is associated with PM composition, size fraction and oxidative potential--the RAPTES project

Background

Ambient particulate matter (PM) exposure is associated with respiratory and cardiovascular morbidity and mortality. To what extent such effects are different for PM obtained from different sources or locations is still unclear. This study investigated the in vitro toxicity of ambient PM collected at different sites in the Netherlands in relation to PM composition and oxidative potential.

Method

PM was sampled at eight sites: three traffic sites, an underground train station, as well as a harbor, farm, steelworks, and urban background location. Coarse (2.5-10 μm), fine (< 2.5 μm) and quasi ultrafine PM (qUF; < 0.18 μm) were sampled at each site. Murine macrophages (RAW 264.7 cells) were exposed to increasing concentrations of PM from these sites (6.25-12.5-25-50-100 μg/ml; corresponding to 3.68-58.8 μg/cm²). Following overnight incubation, MTT-reduction activity (a measure of metabolic activity) and the release of pro-inflammatory markers (Tumor Necrosis Factor-alpha, TNF-α; Interleukin-6, IL-6; Macrophage Inflammatory Protein-2, MIP-2) were measured. The oxidative potential and the endotoxin content of each PM sample were determined in a DTT- and LAL-assay respectively. Multiple linear regression was used to assess the relationship between the cellular responses and PM characteristics: concentration, site, size fraction, oxidative potential and endotoxin content.

Results

Most PM samples induced a concentration-dependent decrease in MTT-reduction activity and an increase in pro-inflammatory markers with the exception of the urban background and stop & go traffic samples. Fine and qUF samples of traffic locations, characterized by a high concentration of elemental and organic carbon, induced the highest pro-inflammatory activity. The pro-inflammatory response to coarse samples was associated with the endotoxin level, which was found to increase dramatically during a three-day sample concentration procedure in the laboratory. The underground samples, characterized by a high content of transition metals, showed the largest decrease in MTT-reduction activity. PM size fraction was not related to MTT-reduction activity, whereas there was a statistically significant difference in pro-inflammatory activity between Fine and qUF PM. Furthermore, there was a statistically significant negative association between PM oxidative potential and MTT-reduction activity.

Conclusion

The response of RAW264.7 cells to ambient PM was markedly different using samples collected at various sites in the Netherlands that differed in their local PM emission sources. Our results are in support of other investigations showing that the chemical composition as well as oxidative potential are determinants of PM induced toxicity in vitro.

Respiratory health effects of ultrafine and fine particle exposure in cyclists

OBJECTIVES

Monitoring studies have shown that commuters are exposed to high air pollution concentrations, but there is limited evidence of associated health effects. We carried out a study to investigate the acute respiratory health effects of air pollution related to commuting by bicycle.

METHODS

Twelve healthy adults cycled a low- and a high-traffic intensity route during morning rush hour in Utrecht, The Netherlands. Exposure to traffic-related air pollution was characterised by measurements of PM(10), soot and particle number. Before, directly after and 6 h after cycling we measured lung function (FEV(1), FVC, PEF), exhaled NO (FE(NO)) and respiratory symptoms. The association between post- minus pre-exposure difference in health effects and exposure during cycling was evaluated with linear regression models.

RESULTS

The average particle number concentration was 59% higher, while the average soot concentration was 39% higher on the high-traffic route than on the low-traffic route. There was no difference for PM(10). Contrary to our hypothesis, associations between air pollution during cycling and lung function changes immediately after cycling were mostly positive. Six hours after cycling, associations between air pollution exposure and health were mostly negative for lung function changes and positive for changes in exhaled NO, although non-significant.

CONCLUSIONS

We found substantial differences in ultrafine particle number and soot exposure between two urban cycling routes. Exposure to ultrafine particles and soot during cycling was weakly associated with increased exhaled NO, indicative of airway inflammation, and decrements in lung function 6 h after exposure. A limitation of the study was the relatively small sample size.