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.

Equity in the recovery of elective and oncological surgery volumes after the COVID-19 lockdown: a multicentre cohort study in Italy.

BACKGROUND: The COVID-19 pandemic has had, and still has, a profound impact on national health systems, altering trajectories of care and exacerbating existing inequalities in health. Postponement of surgeries and cancellation of elective surgical procedures have been reported worldwide. In Italy, the lock-down measures following the COVID-19 pandemic caused cancellations of surgical procedures and important backlogs; little is known about potential social inequalities in the recovery process that occurred during the post-lockdown period. This study aims at evaluating whether all population social strata benefited equally from the surgical volumes' recovery in four large Italian regions. METHODS: This multicentre cohort study covers a population of approximately 11 million people. To assess if social inequalities exist in the recovery of eight indicators of elective and oncological surgery, we estimated Risk Ratios (RR) through Poisson models, comparing the incidence proportions of events recorded during COVID-19 (2020-21) with those in pre-pandemic years (2018-19) for each pandemic period and educational level. RESULTS: Compared to 2018-19, volumes of elective surgery showed a U-shape with the most significant drops during the second wave or the vaccination phase. The recovery was socially unequal. At the end of 2021, incidence proportions among highly educated people generally exceeded the expected ones; RRs were 1.31 (95%CI 1.21-1.42), 1.24 (95%CI 1.17-1.23), 1.17 (95%CI 1.08-1.26) for knee and hip replacement and prostatic surgery, respectively. Among low educated patients, RR remained always < 1. Oncological surgery indicators showed a similar social gradient. Whereas volumes were preserved among the highly educated, the low educated were still lagging behind at the end of 2021. CONCLUSIONS: Surgical procedures generally returned to pre-pandemic levels but the low educated experienced the slowest recovery. An equity-oriented appraisal of trends in healthcare provision should be included in pandemic preparedness plans, to ensure that social inequalities are promptly recognised and tackled.

Sector of Employment and Mortality: A Cohort Based on Different Administrative Archives.

Administrative data can be precious in connecting information from different sectors. For the first time, we used data from the National Social Insurance Agency (INPS) to investigate the association between the occupational sectors and both non-accidental and accidental mortality. We retrieved information on occupational sectors from 1974 to 2011 for private sector workers included in the 2011 census cohort of Rome. We classified the occupational sectors into 25 categories and analyzed occupational exposure as ever/never have been employed in a sector or as the lifetime prevalent sector. We followed the subjects from the census reference day (9 October 2011) to 31 December 2019. We calculated age-standardized mortality rates for each occupational sector, separately in men and women. We used Cox regression to investigate the association between the occupational sectors and mortality, producing hazard ratios (HRs) and 95% confidence intervals (95%CI). We analyzed 910,559 30+-year-olds (53% males) followed for 7 million person-years. During the follow-up, 59,200 and 2560 died for non-accidental and accidental causes, respectively. Several occupational sectors showed high mortality risks in men in age-adjusted models: food and tobacco production with HR = 1.16 (95%CI: 1.09-8.22), metal processing (HR = 1.66, 95%CI: 1.21-11.8), footwear and wood (HR = 1.19, 95%CI: 1.11-1.28), construction (HR = 1.15, 95%CI: 1.12-1.18), hotels, camping, bars, and restaurants (HR = 1.16, 95%CI: 1.11-1.21) and cleaning (HR = 1.42, 95%CI: 1.33-1.52). In women, the sectors that showed higher mortality than the others were hotels, camping, bars, and restaurants (HR = 1.17, 95%CI: 1.10-1.25) and cleaning services (HR = 1.23, 95%CI: 1.17-1.30). Metal processing and construction sectors showed elevated accidental mortality risks in men. Social Insurance Agency data have the potential to characterize high-risk sectors and identify susceptible groups in the population.

[BIGEPI project: environmental and health data]. / Progetto BIGEPI: dati ambientali e dati sanitari.

OBJECTIVES: the BIGEPI project, co-funded by INAIL, has used big data to identify the health risks associated with short and long-term exposure to air pollution, extreme temperatures and occupational exposures. DESIGN: the project consists of 5 specific work packages (WP) aimed at assessing: 1. the acute effects of environmental exposures over the national territory; 2. the acute effects of environmental exposures in contaminated areas, such as Sites of National Interest (SIN) and industrial sites; 3. the chronic effects of environmental exposures in 6 Italian longitudinal metropolitan studies; 4. the acute and chronic effects of environmental exposures in 7 epidemiological surveys on population samples; 5. the chronic effects of occupational exposures in the longitudinal metropolitan studies of Rome and Turin. SETTING AND PARTICIPANTS: BIGEPI analyzed environmental and health data at different levels of detail: the whole Italian population (WP1); populations living in areas contaminated by pollutants of industrial origin (WP2); the entire longitudinal cohorts of the metropolitan areas of Bologna, Brindisi, Rome, Syracuse, Taranto and Turin (WP3 and WP5); population samples participating in the epidemiological surveys of Ancona, Palermo, Pavia, Pisa, Sassari, Turin and Verona (WP4). MAIN OUTCOME MEASURES: environmental exposure: PM10, PM2,5, NO2 and O3 concentrations and air temperature at 1 Km2 resolution at national level. Occupational exposures: employment history of subjects working in at least one of 25 sectors with similar occupational exposures to chemicals/carcinogens; self-reported exposure to dust/fumes/gas in the workplace. Health data: cause-specific mortality/hospitalisation; symptoms/diagnosis of respiratory/allergic diseases; respiratory function and bronchial inflammation. RESULTS: BIGEPI analyzed data at the level of the entire Italian population, data on 2.8 million adults (>=30 yrs) in longitudinal metropolitan studies and on about 14,500 individuals (>=18 yrs) in epidemiological surveys on population samples. The population investigated in the longitudinal metropolitan studies had an average age of approximately 55 years and that of the epidemiological surveys was about 48 years; in both cases, 53% of the population was female. As regards environmental exposure, in the period 2013-2015, at national level average values for PM10, PM2.5, NO2 and summer O3 were: 21.1±13.6, 15.1±10.9, 14.7±9.1 and 80.3±17.3 µg/m3, for the temperature the average value was 13.9±7.2 °C. Data were analyzed for a total of 1,769,660 deaths from non-accidental causes as well as 74,392 incident cases of acute coronary event and 45,513 of stroke. Epidemiological investigations showed a high prevalence of symptoms/diagnoses of rhinitis (range: 14.2-40.5%), COPD (range: 4.7-19.3%) and asthma (range: 3.2-13.2%). The availability of these large datasets has made it possible to implement advanced statistical models for estimating the health effects of short- and long-term exposures to pollutants. The details are reported in the BIGEPI papers already published in other international journals and in those published in this volume of E&P. CONCLUSIONS: BIGEPI has confirmed the great potential of using big data in studies of the health effects of environmental and occupational factors, stimulating new directions of scientific research and confirming the need for preventive action on air quality and climate change for the health of the general population and the workers.

[Employment sector and respiratory mortality in Rome and Turin longitudinal metropolitan studies]. / Settore occupazionale e mortalità respiratoria negli studi longitudinali metropolitani di Roma e Torino.

OBJECTIVES: to assess the association between the occupational sector and respiratory mortality in the metropolitan longitudinal studies of Rome and Turin. DESIGN: retrospective cohort study. SETTING AND PARTICIPANTS: the 2011 census cohorts of residents of Rome and Turin aged 30 years and older who had worked for at least one year in the private sector between 1970s and 2011 was analysed. The individuals included in the study were followed from 9 October 2011 to 31 December 2018. Occupational history was obtained from archives of private sector contributions at the National Social Insurance Agency (INPS) and then was linked to data from the longitudinal studies. MAIN OUTCOME MEASURES: the study outcome was non-malignant respiratory mortality. The exposure of interest was whether or not individuals had worked in one of the 25 occupational sectors considered (agriculture and fishing, steel industry, paper and printing, pharmaceuticals, manufacturing, textile, energy and water, food and tobacco industry, non-metal mining, glass & cement industry, metal processing, electrical construction, footwear and wood industry, construction, trade, hotel and restaurants, transportation, insurance, healthcare, services, laundries, waste management, hairdressing, cleaning services, and gas stations). The association between the occupational sector and respiratory mortality, adjusted for potential confounders (age, marital status, place of birth, educational level), was estimated using Cox models. All analyses were stratified by sex and city. RESULTS: a total of 910,559 people were analysed in Rome and 391,541 in Turin. During the eight years of follow-up, 4,133 people in Rome and 2,772 people in Turin died from respiratory causes. The sectors associated with high respiratory mortality in both cities among men were footwear and wood industry (adjusted HR for age: 1.37 (95%CI 1.07-1.76) and 1.48 (95%CI 1.08-2.03) in Rome and Turin, respectively), construction (HR: 1.31 (95%CI 1.20-1.44) in Rome and 1.51 (95%CI 1.31-1.74) in Turin), hotel and restaurant sector (HR: 1.25 (95%CI 1.07-1.46) in Rome and 1.68 (95%CI 1.20-2.33) in Turin), and cleaning services (HR: 1.57 (95%CI 1.19-2.06) in Rome and 1.97 (95%CI 1.51-2.58) in Turin). Some sectors had high respiratory mortality only in one of the two cities: in Rome, the food& tobacco industry, and gas stations, while in Turin, the metal processing industry. Among female workers, the cleaning services sector was associated with higher respiratory mortality in both Rome and Turin (HR: 1.52, 95%CI 1.27-1.82, e 1.58, 95%CI 1.17-2.12, respectively). CONCLUSIONS: the data confirm the previously known associations between occupational sectors and respiratory mortality for exposures characteristic of specific sectors, such as construction, hotel and restaurant sector, and cleaning services. The differences reported between the two cities reflect the different composition of the workforce and the size of the two study populations. Administrative social insurance data can provide helpful information for epidemiological studies of occupational exposure.

Long-term exposure to low ambient air pollution concentrations and mortality among 28 million people: results from seven large European cohorts within the ELAPSE project.

BACKGROUND: Long-term exposure to ambient air pollution has been associated with premature mortality, but associations at concentrations lower than current annual limit values are uncertain. We analysed associations between low-level air pollution and mortality within the multicentre study Effects of Low-Level Air Pollution: A Study in Europe (ELAPSE). METHODS: In this multicentre longitudinal study, we analysed seven population-based cohorts of adults (age ≥30 years) within ELAPSE, from Belgium, Denmark, England, the Netherlands, Norway, Rome (Italy), and Switzerland (enrolled in 2000-11; follow-up until 2011-17). Mortality registries were used to extract the underlying cause of death for deceased individuals. Annual average concentrations of fine particulate matter (PM2·5), nitrogen dioxide (NO2), black carbon, and tropospheric warm-season ozone (O3) from Europe-wide land use regression models at 100 m spatial resolution were assigned to baseline residential addresses. We applied cohort-specific Cox proportional hazard models with adjustment for area-level and individual-level covariates to evaluate associations with non-accidental mortality, as the main outcome, and with cardiovascular, non-malignant respiratory, and lung cancer mortality. Subset analyses of participants living at low pollutant concentrations (as per predefined values) and natural splines were used to investigate the concentration-response function. Cohort-specific effect estimates were pooled in a random-effects meta-analysis. FINDINGS: We analysed 28 153 138 participants contributing 257 859 621 person-years of observation, during which 3 593 741 deaths from non-accidental causes occurred. We found significant positive associations between non-accidental mortality and PM2·5, NO2, and black carbon, with a hazard ratio (HR) of 1·053 (95% CI 1·021-1·085) per 5 µg/m3 increment in PM2·5, 1·044 (1·019-1·069) per 10 µg/m3 NO2, and 1·039 (1·018-1·059) per 0·5 × 10-5/m black carbon. Associations with PM2·5, NO2, and black carbon were slightly weaker for cardiovascular mortality, similar for non-malignant respiratory mortality, and stronger for lung cancer mortality. Warm-season O3 was negatively associated with both non-accidental and cause-specific mortality. Associations were stronger at low concentrations: HRs for non-accidental mortality at concentrations lower than the WHO 2005 air quality guideline values for PM2·5 (10 µg/m3) and NO2 (40 µg/m3) were 1·078 (1·046-1·111) per 5 µg/m3 PM2·5 and 1·049 (1·024-1·075) per 10 µg/m3 NO2. Similarly, the association between black carbon and non-accidental mortality was highest at low concentrations, with a HR of 1·061 (1·032-1·092) for exposure lower than 1·5× 10-5/m, and 1·081 (0·966-1·210) for exposure lower than 1·0× 10-5/m. INTERPRETATION: Long-term exposure to concentrations of PM2·5 and NO2 lower than current annual limit values was associated with non-accidental, cardiovascular, non-malignant respiratory, and lung cancer mortality in seven large European cohorts. Continuing research on the effects of low concentrations of air pollutants is expected to further inform the process of setting air quality standards in Europe and other global regions. FUNDING: Health Effects Institute.

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

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

Sex differences in factors associated with heart failure and diastolic left ventricular dysfunction: a cross-sectional population-based study.

BACKGROUND: Although sex differences in cardiovascular diseases are recognised, including differences in incidence, clinical presentation, response to treatments, and outcomes, most of the practice guidelines are not sex-specific. Heart failure (HF) is a major public health challenge, with high health care expenditures, high prevalence, and poor clinical outcomes. The objective was to analyse the sex-specific association of socio-demographics, life-style factors and health characteristics with the prevalence of HF and diastolic left ventricular dysfunction (DLVD) in a cross-sectional population-based study. METHODS: A random sample of 2001 65-84 year-olds underwent physical examination, laboratory measurements, including N-terminal pro-B-type natriuretic peptide (NT-proBNP), electrocardiography, and echocardiography. We selected the subjects with no missing values in covariates and echocardiographic parameters and performed a complete case analysis. Sex-specific multivariable logistic regression models were used to identify the factors associated with the prevalence of the diseases, multinomial logistic regression was used to investigate the factors associated to asymptomatic and symptomatic LVD, and spline curves to display the relationship between the conditions and both age and NT-proBNP. RESULTS: In 857 men included, there were 66 cases of HF and 408 cases of DLVD (77% not reporting symptoms). In 819 women, there were 51 cases of HF and 382 of DLVD (79% not reporting symptoms). In men, the factors associated with prevalence of HF were age, ischemic heart disease (IHD), and suffering from three or more comorbid conditions. In women, the factors associated with HF were age, lifestyles (smoking and alcohol), BMI, hypertension, and atrial fibrillation. Age and diabetes were associated to asymptomatic DLVD in both genders. NT-proBNP levels were more strongly associated with HF in men than in women. CONCLUSIONS: There were sex differences in the factors associated with HF. The results suggest that prevention policies should consider the sex-specific impact on cardiac function of modifiable cardiovascular risk factors.

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.

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

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

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

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

Long-term low-level ambient air pollution exposure and risk of lung cancer - A pooled analysis of 7 European cohorts.

BACKGROUND/AIM: Ambient air pollution has been associated with lung cancer, but the shape of the exposure-response function - especially at low exposure levels - is not well described. The aim of this study was to address the relationship between long-term low-level air pollution exposure and lung cancer incidence. METHODS: The "Effects of Low-level Air Pollution: a Study in Europe" (ELAPSE) collaboration pools seven cohorts from across Europe. We developed hybrid models combining air pollution monitoring, land use data, satellite observations, and dispersion model estimates for nitrogen dioxide (NO2), fine particulate matter (PM2.5), black carbon (BC), and ozone (O3) to assign exposure to cohort participants' residential addresses in 100 m by 100 m grids. We applied stratified Cox proportional hazards models, adjusting for potential confounders (age, sex, calendar year, marital status, smoking, body mass index, employment status, and neighborhood-level socio-economic status). We fitted linear models, linear models in subsets, Shape-Constrained Health Impact Functions (SCHIF), and natural cubic spline models to assess the shape of the association between air pollution and lung cancer at concentrations below existing standards and guidelines. RESULTS: The analyses included 307,550 cohort participants. During a mean follow-up of 18.1 years, 3956 incident lung cancer cases occurred. Median (Q1, Q3) annual (2010) exposure levels of NO2, PM2.5, BC and O3 (warm season) were 24.2 µg/m3 (19.5, 29.7), 15.4 µg/m3 (12.8, 17.3), 1.6 10-5m-1 (1.3, 1.8), and 86.6 µg/m3 (78.5, 92.9), respectively. We observed a higher risk for lung cancer with higher exposure to PM2.5 (HR: 1.13, 95% CI: 1.05, 1.23 per 5 µg/m3). This association was robust to adjustment for other pollutants. The SCHIF, spline and subset analyses suggested a linear or supra-linear association with no evidence of a threshold. In subset analyses, risk estimates were clearly elevated for the subset of subjects with exposure below the EU limit value of 25 µg/m3. We did not observe associations between NO2, BC or O3 and lung cancer incidence. CONCLUSIONS: Long-term ambient PM2.5 exposure is associated with lung cancer incidence even at concentrations below current EU limit values and possibly WHO Air Quality Guidelines.

Mortality inequalities by occupational status and type of job in men and women: results from the Rome Longitudinal Study.

OBJECTIVES: Socioeconomic inequalities have a strong impact on population health all over the world. Occupational status is a powerful determinant of health in rich societies. We aimed at investigating the association between occupation and mortality in a large metropolitan study. DESIGN: Cohort study. SETTING: Rome, capital of Italy. PARTICIPANTS: We used the Rome Longitudinal Study, the administrative cohort of residents in Rome at the 2001 general census, followed until 2015. We selected residents aged 15-65 years at baseline. For each subject, we had information on sex, age and occupation (occupational status and type of job) according to the Italian General Census recognition. MAIN OUTCOME MEASURES: We investigated all-cause, cancer, cardiovascular and accidental mortality, major causes of death in the working-age population. We used Cox proportional hazards models to investigate the association between occupation and all-cause and cause-specific mortality in men and women. RESULTS: We selected 1 466 726 subjects (52.1% women). 42 715 men and 29 915 women died during the follow-up. In men, 47.8% of deaths were due to cancer, 26.7% to cardiovascular causes and 6.4% to accidents, whereas in women 57.8% of deaths were due to cancer, 19.3% to cardiovascular causes and 3.5% to accidents. We found an association between occupational variables and mortality, more evident in men than in women. Compared with employed, unemployed had a higher risk of mortality for all causes with an HR=1.99 (95% CI 1.92 to 2.06) in men and an HR=1.49 (95% CI 1.39 to 1.60) in women. Compared with high-qualified non-manual workers, non-specialised manual workers had a higher mortality risk (HR=1.68, 95% CI 1.59 to 1.77 and HR=1.30, 95% CI 1.20 to 1.40, for men and women, respectively). CONCLUSIONS: This study shows the importance of occupational variables as social health determinants and provides evidence for policy-makers on the necessity of integrated and preventive policies aimed at improving the safety of the living and the working environment.

Global estimates of mortality associated with long-term exposure to outdoor fine particulate matter.

Exposure to ambient fine particulate matter (PM2.5) is a major global health concern. Quantitative estimates of attributable mortality are based on disease-specific hazard ratio models that incorporate risk information from multiple PM2.5 sources (outdoor and indoor air pollution from use of solid fuels and secondhand and active smoking), requiring assumptions about equivalent exposure and toxicity. We relax these contentious assumptions by constructing a PM2.5-mortality hazard ratio function based only on cohort studies of outdoor air pollution that covers the global exposure range. We modeled the shape of the association between PM2.5 and nonaccidental mortality using data from 41 cohorts from 16 countries-the Global Exposure Mortality Model (GEMM). We then constructed GEMMs for five specific causes of death examined by the global burden of disease (GBD). The GEMM predicts 8.9 million [95% confidence interval (CI): 7.5-10.3] deaths in 2015, a figure 30% larger than that predicted by the sum of deaths among the five specific causes (6.9; 95% CI: 4.9-8.5) and 120% larger than the risk function used in the GBD (4.0; 95% CI: 3.3-4.8). Differences between the GEMM and GBD risk functions are larger for a 20% reduction in concentrations, with the GEMM predicting 220% higher excess deaths. These results suggest that PM2.5 exposure may be related to additional causes of death than the five considered by the GBD and that incorporation of risk information from other, nonoutdoor, particle sources leads to underestimation of disease burden, especially at higher concentrations.

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

INTRODUCTION: Previous analysis from the large European multicentre ESCAPE study showed an association of ambient particulate matter <2.5 µm (PM2.5) air pollution exposure at residence with the incidence of gastric cancer. It is unclear which components of PM are most relevant for gastric and also upper aerodigestive tract (UADT) cancer and some of them may not be strongly correlated with PM mass. We evaluated the association between long-term exposure to elemental components of PM2.5 and PM10 and gastric and UADT cancer incidence in European adults. METHODS: Baseline addresses of individuals were geocoded and exposure was assessed by land-use regression models for copper (Cu), iron (Fe) and zinc (Zn) representing non-tailpipe traffic emissions; sulphur (S) indicating long-range transport; nickel (Ni) and vanadium (V) for mixed oil-burning and industry; silicon (Si) for crustal material and potassium (K) for biomass burning. Cox regression models with adjustment for potential confounders were used for cohort-specific analyses. Combined estimates were determined with random effects meta-analyses. RESULTS: Ten cohorts in six countries contributed data on 227,044 individuals with an average follow-up of 14.9 years with 633 incident cases of gastric cancer and 763 of UADT cancer. The combined hazard ratio (HR) for an increase of 200 ng/m3 of PM2.5_S was 1.92 (95%-confidence interval (95%-CI) 1.13;3.27) for gastric cancer, with no indication of heterogeneity between cohorts (I2 = 0%), and 1.63 (95%-CI 0.88;3.01) for PM2.5_Zn (I2 = 70%). For the other elements in PM2.5 and all elements in PM10 including PM10_S, non-significant HRs between 0.78 and 1.21 with mostly wide CIs were seen. No association was found between any of the elements and UADT cancer. The HR for PM2.5_S and gastric cancer was robust to adjustment for additional factors, including diet, and restriction to study participants with stable addresses over follow-up resulted in slightly higher effect estimates with a decrease in precision. In a two-pollutant model, the effect estimate for total PM2.5 decreased whereas that for PM2.5_S was robust. CONCLUSION: This large multicentre cohort study shows a robust association between gastric cancer and long-term exposure to PM2.5_S but not PM10_S, suggesting that S in PM2.5 or correlated air pollutants may contribute to the risk of gastric cancer.

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

Air pollution has been classified as carcinogenic to humans. However, to date little is known about the relevance for cancers of the stomach and upper aerodigestive tract (UADT). We investigated the association of long-term exposure to ambient air pollution with incidence of gastric and UADT cancer in 11 European cohorts. Air pollution exposure was assigned by land-use regression models for particulate matter (PM) below 10 µm (PM10 ), below 2.5 µm (PM2.5 ), between 2.5 and 10 µm (PMcoarse ), PM2.5 absorbance and nitrogen oxides (NO2 and NOX ) as well as approximated by traffic indicators. Cox regression models with adjustment for potential confounders were used for cohort-specific analyses. Combined estimates were determined with random effects meta-analyses. During average follow-up of 14.1 years of 305,551 individuals, 744 incident cases of gastric cancer and 933 of UADT cancer occurred. The hazard ratio for an increase of 5 µg/m3 of PM2.5 was 1.38 (95% CI 0.99; 1.92) for gastric and 1.05 (95% CI 0.62; 1.77) for UADT cancers. No associations were found for any of the other exposures considered. Adjustment for additional confounders and restriction to study participants with stable addresses did not influence markedly the effect estimate for PM2.5 and gastric cancer. Higher estimated risks of gastric cancer associated with PM2.5 was found in men (HR 1.98 [1.30; 3.01]) as compared to women (HR 0.85 [0.5; 1.45]). This large multicentre cohort study shows an association between long-term exposure to PM2.5 and gastric cancer, but not UADT cancers, suggesting that air pollution may contribute to gastric cancer risk.

Is There an Association Between Ambient Air Pollution and Bladder Cancer Incidence? Analysis of 15 European Cohorts.

BACKGROUND: Ambient air pollution contains low concentrations of carcinogens implicated in the etiology of urinary bladder cancer (BC). Little is known about whether exposure to air pollution influences BC in the general population. OBJECTIVE: To evaluate the association between long-term exposure to ambient air pollution and BC incidence. DESIGN, SETTING, AND PARTICIPANTS: We obtained data from 15 population-based cohorts enrolled between 1985 and 2005 in eight European countries (N=303431; mean follow-up 14.1 yr). We estimated exposure to nitrogen oxides (NO2 and NOx), particulate matter (PM) with diameter <10µm (PM10), <2.5µm (PM2.5), between 2.5 and 10µm (PM2.5-10), PM2.5absorbance (soot), elemental constituents of PM, organic carbon, and traffic density at baseline home addresses using standardized land-use regression models from the European Study of Cohorts for Air Pollution Effects project. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: We used Cox proportional-hazards models with adjustment for potential confounders for cohort-specific analyses and meta-analyses to estimate summary hazard ratios (HRs) for BC incidence. RESULTS AND LIMITATIONS: During follow-up, 943 incident BC cases were diagnosed. In the meta-analysis, none of the exposures were associated with BC risk. The summary HRs associated with a 10-µg/m3 increase in NO2 and 5-µg/m3 increase in PM2.5 were 0.98 (95% confidence interval [CI] 0.89-1.08) and 0.86 (95% CI 0.63-1.18), respectively. Limitations include the lack of information about lifetime exposure. CONCLUSIONS: There was no evidence of an association between exposure to outdoor air pollution levels at place of residence and risk of BC. PATIENT SUMMARY: We assessed the link between outdoor air pollution at place of residence and bladder cancer using the largest study population to date and extensive assessment of exposure and comprehensive data on personal risk factors such as smoking. We found no association between the levels of outdoor air pollution at place of residence and bladder cancer risk.

Long-term exposure to ambient air pollution and incidence of brain tumor: the European Study of Cohorts for Air Pollution Effects (ESCAPE).

Background: Epidemiological evidence on the association between ambient air pollution and brain tumor risk is sparse and inconsistent. Methods: In 12 cohorts from 6 European countries, individual estimates of annual mean air pollution levels at the baseline residence were estimated by standardized land-use regression models developed within the ESCAPE and TRANSPHORM projects: particulate matter (PM) ≤2.5, ≤10, and 2.5-10 µm in diameter (PM2.5, PM10, and PMcoarse), PM2.5 absorbance, nitrogen oxides (NO2 and NOx) and elemental composition of PM. We estimated cohort-specific associations of air pollutant concentrations and traffic intensity with total, malignant, and nonmalignant brain tumor, in separate Cox regression models, adjusting for risk factors, and pooled cohort-specific estimates using random-effects meta-analyses. Results: Of 282194 subjects from 12 cohorts, 466 developed malignant brain tumors during 12 years of follow-up. Six of the cohorts also had data on nonmalignant brain tumor, where among 106786 subjects, 366 developed brain tumor: 176 nonmalignant and 190 malignant. We found a positive, statistically nonsignificant association between malignant brain tumor and PM2.5 absorbance (hazard ratio and 95% CI: 1.67; 0.89-3.14 per 10-5/m3), and weak positive or null associations with the other pollutants. Hazard ratio for PM2.5 absorbance (1.01; 0.38-2.71 per 10-5/m3) and all other pollutants were lower for nonmalignant than for malignant brain tumors. Conclusion: We found suggestive evidence of an association between long-term exposure to PM2.5 absorbance indicating traffic-related air pollution and malignant brain tumors, and no association with overall or nonmalignant brain tumors.

Long-Term Exposure to Ambient Air Pollution and Incidence of Postmenopausal Breast Cancer in 15 European Cohorts within the ESCAPE Project.

BACKGROUND: Epidemiological evidence on the association between ambient air pollution and breast cancer risk is inconsistent. OBJECTIVE: We examined the association between long-term exposure to ambient air pollution and incidence of postmenopausal breast cancer in European women. METHODS: In 15 cohorts from nine European countries, individual estimates of air pollution levels at the residence were estimated by standardized land-use regression models developed within the European Study of Cohorts for Air Pollution Effects (ESCAPE) and Transport related Air Pollution and Health impacts ­ Integrated Methodologies for Assessing Particulate Matter (TRANSPHORM) projects: particulate matter (PM) ≤2.5µm, ≤10µm, and 2.5­10µm in diameter (PM2.5, PM10, and PMcoarse, respectively); PM2.5 absorbance; nitrogen oxides (NO2 and NOx); traffic intensity; and elemental composition of PM. We estimated cohort-specific associations between breast cancer and air pollutants using Cox regression models, adjusting for major lifestyle risk factors, and pooled cohort-specific estimates using random-effects meta-analyses. RESULTS: Of 74,750 postmenopausal women included in the study, 3,612 developed breast cancer during 991,353 person-years of follow-up. We found positive and statistically insignificant associations between breast cancer and PM2.5 {hazard ratio (HR)=1.08 [95% confidence interval (CI): 0.77, 1.51] per 5 µg/m3}, PM10 [1.07 (95% CI: 0.89, 1.30) per 10 µg/m3], PMcoarse[1.20 (95% CI: 0.96, 1.49 per 5 µg/m3], and NO2 [1.02 (95% CI: 0.98, 1.07 per 10 µg/m3], and a statistically significant association with NOx [1.04 (95% CI: 1.00, 1.08) per 20 µg/m3, p=0.04]. CONCLUSIONS: We found suggestive evidence of an association between ambient air pollution and incidence of postmenopausal breast cancer in European women. https://doi.org/10.1289/EHP1742.

Road Traffic Pollution and Childhood Leukemia: A Nationwide Case-control Study in Italy.

BACKGROUND: The association of childhood leukemia with traffic pollution was considered in a number of studies from 1989 onwards, with results not entirely consistent and little information regarding subtypes. AIM OF THE STUDY: We used the data of the Italian SETIL case-control on childhood leukemia to explore the risk by leukemia subtypes associated to exposure to vehicular traffic. METHODS: We included in the analyses 648 cases of childhood leukemia (565 Acute lymphoblastic-ALL and 80 Acute non lymphoblastic-AnLL) and 980 controls. Information on traffic exposure was collected from questionnaire interviews and from the geocoding of house addresses, for all periods of life of the children. RESULTS: We observed an increase in risk for AnLL, and at a lower extent for ALL, with indicators of exposure to traffic pollutants. In particular, the risk was associated to the report of closeness of the house to traffic lights and to the passage of trucks (OR: 1.76; 95% CI 1.03-3.01 for ALL and 6.35; 95% CI 2.59-15.6 for AnLL). The association was shown also in the analyses limited to AML and in the stratified analyses and in respect to the house in different period of life. CONCLUSIONS: Results from the SETIL study provide some support to the association of traffic related exposure and risk for AnLL, but at a lesser extent for ALL. Our conclusion highlights the need for leukemia type specific analyses in future studies. Results support the need of controlling exposure from traffic pollution, even if knowledge is not complete.