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• W2765193733 abstract "Vol. 125, No. 10 ResearchOpen AccessLong-Term Exposure to Ambient Air Pollution and Incidence of Postmenopausal Breast Cancer in 15 European Cohorts within the ESCAPE Projectis companion ofAir Pollution and Breast Cancer in Postmenopausal Women: Evidence across Cohorts Zorana J. Andersen, Massimo Stafoggia, Gudrun Weinmayr, Marie Pedersen, Claudia Galassi, Jeanette T. Jørgensen, Anna Oudin, Bertil Forsberg, David Olsson, Bente Oftedal, Gunn Marit Aasvang, Geir Aamodt, Andrei Pyko, Göran Pershagen, Michal Korek, Ulf De Faire, Nancy L. Pedersen, Claes-Göran Östenson, Laura Fratiglioni, Kirsten T. Eriksen, Anne Tjønneland, Petra H. Peeters, Bas Bueno-de-Mesquita, Michelle Plusquin, Timothy J. Key, Andrea Jaensch, Gabriele Nagel, Alois Lang, Meng Wang, Ming-Yi Tsai, Agnes Fournier, Marie-Christine Boutron-Ruault, Laura Baglietto, Sara Grioni, Alessandro Marcon, Vittorio Krogh, Fulvio Ricceri, Carlotta Sacerdote, Enrica Migliore, Ibon Tamayo-Uria, Pilar Amiano, Miren Dorronsoro, Roel Vermeulen, Ranjeet Sokhi, Menno Keuken, Kees de Hoogh, Rob Beelen, Paolo Vineis, Giulia Cesaroni, Bert Brunekreef, Gerard Hoek, and Ole Raaschou-Nielsen Zorana J. Andersen Address correspondence to Z.J. Andersen, Centre for Epidemiology and Screening, Department of Public Health, University of Copenhagen, Øster Farimagsgade 5, 1014 København K, Denmark; Telephone: 45 20740462; Email: E-mail Address: [email protected] Centre for Epidemiology and Screening, Department of Public Health, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author , Massimo Stafoggia Department of Epidemiology, Lazio Regional Health Service, Local Health Unit Azienda Sanitaria Locale Roma 1 (ASL RM1), Rome, Italy Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden Search for more papers by this author , Gudrun Weinmayr Institute of Epidemiology and Medical Biometry, Ulm University, Ulm, Germany Search for more papers by this author , Marie Pedersen Centre for Epidemiology and Screening, Department of Public Health, University of Copenhagen, Copenhagen, Denmark The Danish Cancer Society Research Center, Copenhagen, Denmark Search for more papers by this author , Claudia Galassi Unit of Cancer Epidemiology, Città della Salute e della Scienza University-Hospital and Center for Cancer Prevention (CPO), Turin, Italy Search for more papers by this author , Jeanette T. Jørgensen Centre for Epidemiology and Screening, Department of Public Health, University of Copenhagen, Copenhagen, Denmark Search for more papers by this author , Anna Oudin Occupational and Environmental Medicine, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden Search for more papers by this author , Bertil Forsberg Occupational and Environmental Medicine, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden Search for more papers by this author , David Olsson Occupational and Environmental Medicine, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden Search for more papers by this author , Bente Oftedal Norwegian Institute of Public Health, Oslo, Norway Search for more papers by this author , Gunn Marit Aasvang Norwegian Institute of Public Health, Oslo, Norway Search for more papers by this author , Geir Aamodt Norwegian Institute of Public Health, Oslo, Norway Search for more papers by this author , Andrei Pyko Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden Search for more papers by this author , Göran Pershagen Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden Search for more papers by this author , Michal Korek Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden Search for more papers by this author , Ulf De Faire Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden Search for more papers by this author , Nancy L. Pedersen Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden Search for more papers by this author , Claes-Göran Östenson Department of Molecular Medicine and Surgery, Karolinska Institute, Stockholm, Sweden Search for more papers by this author , Laura Fratiglioni Aging Research Center, Department of Neurobiology Care Science and Society, Karolinska Institute, Stockholm, Sweden Search for more papers by this author , Kirsten T. Eriksen The Danish Cancer Society Research Center, Copenhagen, Denmark Search for more papers by this author , Anne Tjønneland The Danish Cancer Society Research Center, Copenhagen, Denmark Search for more papers by this author , Petra H. Peeters Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, Netherlands MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College, London, UK Search for more papers by this author , Bas Bueno-de-Mesquita MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College, London, UK Department for Determinants of Chronic Diseases (DCD), National Institute for Public Health and the Environment (RIVM), Bilthoven, Netherlands Department of Social and Preventive Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia Search for more papers by this author , Michelle Plusquin MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College, London, UK Search for more papers by this author , Timothy J. Key Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK Search for more papers by this author , Andrea Jaensch Institute of Epidemiology and Medical Biometry, Ulm University, Ulm, Germany Search for more papers by this author , Gabriele Nagel Institute of Epidemiology and Medical Biometry, Ulm University, Ulm, Germany Agency for Preventive and Social Medicine, Bregenz, Austria Search for more papers by this author , Alois Lang Vorarlberg Cancer Registry, Agency for Preventive and Social Medicine (aks), Bregenz, Austria Search for more papers by this author , Meng Wang Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA Search for more papers by this author , Ming-Yi Tsai Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA Swiss Tropical and Public Health Institute, Basel, Switzerland University of Basel, Basel, Switzerland Search for more papers by this author , Agnes Fournier Centre de recherche en Épidémiologie et Santé des Populations (CESP) “Health across Generations”, Institut national de la santé et de la recherche médicale (Inserm), Université Paris-Saclay, Villejuif, France Institut Gustave Roussy, Villejuif, France Search for more papers by this author , Marie-Christine Boutron-Ruault Centre de recherche en Épidémiologie et Santé des Populations (CESP) “Health across Generations”, Institut national de la santé et de la recherche médicale (Inserm), Université Paris-Saclay, Villejuif, France Institut Gustave Roussy, Villejuif, France Search for more papers by this author , Laura Baglietto Centre de recherche en Épidémiologie et Santé des Populations (CESP) “Health across Generations”, Institut national de la santé et de la recherche médicale (Inserm), Université Paris-Saclay, Villejuif, France Institut Gustave Roussy, Villejuif, France Search for more papers by this author , Sara Grioni Epidemiology and Prevention Unit, Department of Preventive and Predictive Medicine, Fondazione Istituto di ricovero e cura a carattere scientifico (IRCCS) Istituto Nazionale dei Tumori, Milan, Italy Search for more papers by this author , Alessandro Marcon Unit of Epidemiology and Medical Statistics, Department of Diagnostics and Public Health, University of Verona, Verona, Italy Search for more papers by this author , Vittorio Krogh Epidemiology and Prevention Unit, Department of Preventive and Predictive Medicine, Fondazione Istituto di ricovero e cura a carattere scientifico (IRCCS) Istituto Nazionale dei Tumori, Milan, Italy Search for more papers by this author , Fulvio Ricceri Unit of Cancer Epidemiology, Città della Salute e della Scienza University-Hospital and Center for Cancer Prevention (CPO), Turin, Italy Unit of Epidemiology, Regional Health Service Azienda Sanitaria Locale Torino 3 (ASL TO3), Grugliasco, Italy Search for more papers by this author , Carlotta Sacerdote Unit of Cancer Epidemiology, Città della Salute e della Scienza University-Hospital and Center for Cancer Prevention (CPO), Turin, Italy Search for more papers by this author , Enrica Migliore Unit of Cancer Epidemiology, Città della Salute e della Scienza University-Hospital and Center for Cancer Prevention (CPO), Turin, Italy Search for more papers by this author , Ibon Tamayo-Uria ISGlobal Institute de Salut Global Barcelona, Barcelona, Spain Consortium for Biomedical Research in Epidemiology and Public Health (CIBER en Epidemiología y Salud Pública–CIBERESP), Madrid, Spain Universitat Pompeu Fabra, Barcelona, Spain Search for more papers by this author , Pilar Amiano Consortium for Biomedical Research in Epidemiology and Public Health (CIBER en Epidemiología y Salud Pública–CIBERESP), Madrid, Spain Public Health Department of Gipuzkoa, BioDonostia Research Institute, San Sebastian, Spain Search for more papers by this author , Miren Dorronsoro Consortium for Biomedical Research in Epidemiology and Public Health (CIBER en Epidemiología y Salud Pública–CIBERESP), Madrid, Spain Public Health Department of Gipuzkoa, BioDonostia Research Institute, San Sebastian, Spain Search for more papers by this author , Roel Vermeulen Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, Netherlands MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College, London, UK Institute for Risk Assessment Sciences, Utrecht University, Utrecht, Netherlands Search for more papers by this author , Ranjeet Sokhi Centre for Atmospheric and Instrumentation Research, University of Hertfordshire, Hatfield, UK Search for more papers by this author , Menno Keuken Netherlands Organization for Applied Scientific Research, Utrecht, Netherlands Search for more papers by this author , Kees de Hoogh Swiss Tropical and Public Health Institute, Basel, Switzerland University of Basel, Basel, Switzerland Search for more papers by this author , Rob Beelen Institute for Risk Assessment Sciences, Utrecht University, Utrecht, Netherlands National Institute for Public Health and the Environment (RIVM), Bilthoven, Netherlands Search for more papers by this author , Paolo Vineis MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College, London, UK Molecular and Epidemiology Unit, Human Genetics Foundation (HuGeF), Torino, Italy Search for more papers by this author , Giulia Cesaroni Department of Epidemiology, Lazio Regional Health Service, Local Health Unit Azienda Sanitaria Locale Roma 1 (ASL RM1), Rome, Italy Search for more papers by this author , Bert Brunekreef Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, Netherlands Consortium for Biomedical Research in Epidemiology and Public Health (CIBER en Epidemiología y Salud Pública–CIBERESP), Madrid, Spain Search for more papers by this author , Gerard Hoek Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, Netherlands Institute for Risk Assessment Sciences, Utrecht University, Utrecht, Netherlands Search for more papers by this author , and Ole Raaschou-Nielsen The Danish Cancer Society Research Center, Copenhagen, Denmark Department of Environmental Science, Aarhus University, Roskilde, Denmark Search for more papers by this author Published:13 October 2017CID: 107005https://doi.org/10.1289/EHP1742Cited by:9AboutSectionsPDF Supplemental Materials ToolsDownload CitationsTrack CitationsCopy LTI LinkHTMLAbstractPDF ShareShare onFacebookTwitterLinked InRedditEmail AbstractBackground: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 2.5–10μm 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/EHP1742IntroductionEstablished risk factors for breast cancer, including genetic mutations, age, family history, alcohol consumption, smoking, reproductive history, and postmenopausal hormone therapy (HT) use, explain only approximately one-third of new cases (Brody et al. 2007b). Observations of higher incidence of breast cancer in urban than in rural areas (Binachon et al. 2014; Reynolds et al. 2004), as well as an increase in breast cancer incidence along with increasing traffic emissions over the last 30 y (Chen and Bina 2012; Wei et al. 2012) suggested the relevance of air pollution for breast cancer etiology. Air pollution is a risk factor for lung cancer (Hamra et al. 2015; Raaschou-Nielsen et al. 2013), and it was recently classified as carcinogenic to humans (Loomis et al. 2014). Experimental data provide some evidence supporting a link between a number of carcinogens present in ambient air pollution and breast cancer (Brody et al. 2007a), most consistently for polycyclic aromatic hydrocarbons (PAHs), which can cause oxidative stress and mammary tumors in laboratory animals (Mordukhovich et al. 2010). Furthermore, benzene, present in traffic exhaust, has been linked to mammary tumors in mice (Huff et al. 1989), and particulate matter (PM) showed DNA-damaging activity and estrogenicity in human breast cancer cells (Chen et al. 2013).Epidemiological evidence is inconsistent and sparse, consisting of five case–control (Bonner et al. 2005; Crouse et al. 2010; Hystad et al. 2015; Lewis-Michl et al. 1996; Nie et al. 2007) and four cohort (Andersen et al. 2016; Hart et al. 2016; Reding et al. 2015; Raaschou-Nielsen et al. 2011a) studies. An early case–control study found no association between either pre- or postmenopausal breast cancer risk and living close to busy roads in Long Island, New York (Lewis-Michl et al. 1996). The Western New York Exposures and Breast Cancer (WEB) Study reported relevance of early (at birth) but not of later life (at menarche, first birth, 10- and 20-y before breast cancer) exposure to total suspended particles (TSP), assumed to be a proxy for PAHs, to postmenopausal breast cancer (Bonner et al. 2005). A study adding traffic emissions data to the WEB study found a statistically significantly increased risk of postmenopausal breast cancer risk with exposures at first birth, but none with other exposure windows (Nie et al. 2007). A Canadian case–control study found a statistically significant increased risk of postmenopausal breast cancer with increasing levels of nitrogen dioxide (NO2) at the residence 10 y before diagnosis (Crouse et al. 2010). Another Canadian case–control study study found an increased risk of premenopausal and none with postmenopausal breast cancer related to NO2 levels over the 20-y period before diagnosis (Hystad et al. 2015). In contrast, cohort studies found no association between breast cancer (primarily postmenopausal) and nitrogen oxides (NOx) levels over 35 y (Raaschou-Nielsen et al. 2011a) or NO2 levels over a few years before diagnosis (Andersen et al. 2016; Reding et al. 2015) except for a statistically significant positive association of NO2 with the risk of estrogen receptor (ER)+/ progesterone receptor (PR)+ breast cancer subtype (Reding et al. 2015). Finally, three recent cohort studies, all in primarily postmenopausal women, found no association between exposure to PM with diameter <2.5μm (PM2.5) or <10μm (PM10) at the time window close to diagnosis and breast cancer (Andersen et al. 2016; Hart et al. 2016; Reding et al. 2015). With air pollution established as carcinogenic to humans, suggestive experimental evidence on the biological plausibility, and inconclusive epidemiological evidence, it is important to further examine associations between air pollution and breast cancer.We aimed to examine the association between long-term exposure to ambient air pollution and incidence of postmenopausal breast cancer in 15 European cohorts within the framework of the European Study of Cohorts for Air Pollution Effects (ESCAPE; http://www.escapeproject.eu/) (Beelen et al. 2014; Raaschou-Nielsen et al. 2013).MethodsStudy PopulationWe approached 22 cohorts that have contributed to earlier ESCAPE analyses on the association of ambient air pollution with lung cancer (Raaschou-Nielsen et al. 2013) and mortality (Beelen et al. 2014). We included 15 cohorts from nine European countries (Table 1, Figure 1) that had information on postmenopausal breast cancer incidence and that had the resources (statistical analyst available) for participation. We included five Swedish cohorts: European Prospective Investigation into Cancer and Nutrition (EPIC)-Umeå, Swedish National Study on Aging and Care in Kungsholmen (SNAC-K), Stockholm Screening Across the Lifespan Twin study and TwinGene (SALT/TwinGene), Stockholm 60 y old/IMPROVE study (60YO/IMPROVE), and Stockholm Diabetes Prevention Program (SDPP); one Norwegian cohort: Oslo Health Study (HUBRO); one Danish cohort: Diet, Cancer and Health (DCH) study; two Dutch cohorts: EPIC-Monitoring Project on Risk Factors and Chronic Diseases in Netherlands (EPIC-MORGEN) and EPIC-Prospect; one United Kingdom cohort: EPIC-Oxford; one Austrian cohort: Vorarlberg Health Monitoring and Prevention Programme (VHM&PP); one French cohort: EPIC-E3N; two Italian cohorts: EPIC-Varese and EPIC-Turin; and one Spanish cohort: EPIC-San Sebastian (Figure 1). The majority of cohorts recruited participants from large cities and the surrounding suburban or rural communities, and a few covered large regions of the country, such as EPIC-MORGEN in Netherlands, EPIC-Oxford in the United Kingdom, and the VHM&PP cohort in Austria. For DCH, EPIC-Oxford, EPIC-E3N and VHM&PP, exposure to air pollution was assessed for part of the original cohort only. Data from the four Swedish cohorts from Stockholm (SNAC-K, SALT/TwinGene, 60YO/IMPROVE, and SDPP) as well as from the two Dutch cohorts (EPIC-MORGEN and EPIC-Prospect) were pooled and analyzed as single cohorts, which were named Cardiovascular Effects of Air pollution and Noise in Stockholm (CEANS) and EPIC Netherlands (EPIC-NL), respectively. All of the cohorts that contributed data to the present analysis received ethical approval, and all participants provided informed consent.Figure 1. Map of the study sites in the breast cancer analyses.Table 1 Description of the 74,750 postmenopausal women (n=3,612) from 15 European cohorts included in the study.Table 1 lists cohort and country in the first column. The corresponding enrollment years; original number; final number; percentage of original number; mean plus or minus SD, years; number of cases; person-years at risk; mean follow-up time, years; and IR values are listed in the following columns.Cohort, countryEnrollmentOriginal naFinal n% Original nMean±SD age, yn CasesPerson-years at riskMean follow-up time, yearsIREPIC-Umeå, Sweden1992–964,2383,76288.854.4±6.017550,72013.53.45HUBRO, Norway2000–014,0771,93147.457.2±5.76816,6068.64.10CEANS, Swedenb1992–20026,9305,99786.559.8±12.922657,2159.53.95DCH, Denmark1993–9715,91015,83599.557.7±4.21,054237,65515.04.44EPIC-NL, Netherlandsc1993–9714,21912,83790.358.6±5.9542147,78811.53.67EPIC-Oxford, UK1993–200110,7427,29967.359.7±8.331995,43013.23.34VHM&PP, Austria1985–200514,55213,38792.065.1±7.5628218,96016.42.87EPIC-E3N, France1993–9611,2075,31947.557.2±5.626768,24812.83.91EPIC-Varese, Italy1993–974,9324,72795.856.6±6.520151,85111.03.88EPIC-Turin, Italy1993–982,3761,95082.155.2±5.17625,02812.83.04EPIC-San Sebastian, Spain1992–951,8061,77698.355.3±5.75721,85212.32.61Note: CEANS, Cardiovascular Effects of Air Pollution and Noise in Stockholm; DCH, Danish Diet, Health and Cancer cohort; EPIC, European Prospective Investigation into Cancer and Nutrition; EPIC-E3N, French cohort of the Etude Epidemiologique de Femmes de la Mutuelle Générale de l'Education Nationale; HUBRO, Oslo Health Study; IR, incidence rate per 1,000 person-years; SD, standard deviation; VHM&PP, Vorarlberg Health Monitoring and Prevention Programme.aNumber of postmenopausal women in the original cohort.bPooled data from the 4 cohorts from Stockholm Sweden: SNAC-K, SALT/TwinGene, 60YO/IMPROVE, and SDPP.cPooled data from 2 Dutch cohorts: EPIC MORGEN and EPIC Prospect.Breast Cancer DefinitionCohorts have followed participants for cancer incidence via linkage to national or regional cancer registries or via self-administered questionnaires (in EPIC-E3N). Analyses were restricted to women who were postmenopausal or who were older than 55 y at the cohort baseline (in cohorts without information on menopausal status) and who did not have cancer before the study baseline (excluding nonmelanoma skin cancers) to study incidence of breast cancer. We chose not to exclude women with nonmelanoma skin cancers before baseline because these cancers are very commonly diagnosed and, unlike other malignant cancers, are easily treated if detected early, have very low case fatality (<5%), and very low risk of metastasis. The reason for excluding cancer before baseline (i.e., including only first cancer) is that receiving a cancer diagnosis likely changes the risk of a subsequent cancer for (at least) two reasons: carcinogenic cancer treatment and change in lifestyle habits because of the cancer diagnosis; neither of these applies to nonmelanoma skin cancer. Moreover, many cancer registries do not even register nonmelanoma skin cancer. We chose to focus on postmenopausal women only based on the existing evidence available at the time the present study was planned in 2014, suggesting the relevance of air pollution for postmenopausal breast cancer only, with no associations reported for premenopausal breast cancer (Bonner et al. 2005; Crouse et al. 2010; Lewis-Michl et al. 1996; Nie et al. 2007). Our outcome was incident, malignant, primary breast cancer, defined according to International Classification of Diseases and Related Health Problems, 10th Revision (ICD-10; WHO 1990) code C50, International Classification of Diseases and Related Health Problems, 9th Revision (ICD-9; WHO 1977) code 174, and International Classification of Diseases and Related Health Problems, 7th Revision (ICD-7; WHO 1955) code 170.Exposure AssessmentWe estimated individual levels of air pollution at the baseline residence for each cohort participant using standardized area-specific land-use regression (LUR) models developed within the ESCAPE study (Beelen et al. 2013; Eeftens et al. 2012b). In brief, the LUR models were based on measurements of NO2 and NOx in all 15 study areas and on measurements of PM2.5, PM10, and PM2.5 absorbance in 11 study areas (owing to budgetary reasons) for 1-y period and were conducted between October 2008 and May 2011 (Cyrys et al. 2012; Eeftens et al. 2012a). The concentration of PMcoarse was calculated as the difference between PM10 and PM2.5. Subsequently, LUR models were developed for each pollutant in each study area to predict air pollution levels at the residences of the cohort participants using traffic and land-use predictors obtained from Geographic Information Systems (GIS). Data from the nearest routine monitoring stations were used to back-extrapolate the LUR estimates to the baseline year in 14 of the 15 study areas. Air pollution measurements were performed in 2008–2011, but the exposure window relevant for development of breast cancer extends further back in time. We therefore extrapolated air-pollution concentrations predicted by the LUR models around 2010 back to the time of enrollment in the 1990s for the majority of cohorts, using the absolute difference and the ratio between the two periods, based on data from routine background monitoring network site(s) in each study area. Details on this procedure can be found here: http://www.escapeproject.eu/manuals/. We also used traffic intensity on the nearest road (vehicles per day) as an indicator of exposure to traffic-related air pollution. Furthermore, we used estimated concentrations of eight elements in PM2.5 and PM10 (copper, iron, zinc, sulfur, nickel, vanadium, silicon, and potassium) as indicators of exposure (de Hoogh et al. 2013). PM filters were then sent to Cooper Environmental Services (Portland, OR) to analyze elemental compositions using X-ray fluorescence (XRF) (de Hoogh et al. 2013). We selected eight of the 48 measured elements for epidemiological evaluation based on evidence of their health effects (toxicity), their representivity of major anthropogenic sources, a high percentage of detected samples (>75%), and precise measurements. We selected copper, iron, and zinc as indicators mainly of nontailpipe traffic emissions such as brake and tire wear, sulfur mainly of long-range transport, nickel and vanadium of mixed oil-burning and industry, silicon of crustal material, and potassium of biomass burning (de Hoogh et al. 2013; Eeftens et al. 2014; Viana et al. 2008; Wang et al. 2014). Each element can have multiple sources. We collected information about potential predictor variables relating to nearby traffic intensity, population/household density, and land use from GIS and used regression modeling to evaluate this information to explain spatial variation of annual average concentrations. We have previously reported the LUR model results for all study areas (Raaschou-Nielsen et al. 2016). LUR models for copper, iron, and zinc in both fractions (PM10 and PM2.5) had average cross-validation–explained variance (r2) between 52% and 84% with a large variability between areas (Eeftens et al. 2012a). Traffic variables contributed to most of these models, reflecting nontailpipe emissions. Models for the other elements performed moderately with average cross-validation r2 generally between ∼50% and ∼60%. For PM2.5, the average cross-validation r2 was 32% and ranged from 2% to 67%, consistent with the relatively low spatial variation of sulfur concentrations within the cohort areas.Statistical AnalysesWe used Cox proportional hazards models for the cohort-specific analyses with age as the underlying timescale and censoring at the time of any other cancer diagnosis (except nonmelanoma skin cancer), death, emigration, or end of follow-up, whichever came first. We analyzed air pollution exposure as a continuous variable. Potential confounders were available from questionnaires at baseline. We specified three confounder models a priori: Model 1, adjusted for age (time scale) and calendar time (years of enrollment); Model 2, additionally adjusted for smoking status (never, former, or current), smoking intensity (grams/day), smoking duration (years), alcohol consumption (grams/day; linear term), physical activity in leisure time (yes/no), body mass index (BMI; kilograms per meter squared; linear term), educational level (low, medium, or high), employment (yes/no), parity (yes/no), number of children (linear term), breastfeeding (yes/no), age at first childbirth (years; linear term), postmenopausal hormone therapy (HT) use (never/previous/current, never/ever), HT use duration (years; linear term), oral contraceptive use (never/ever); and Model 3, adjusted for Model 2 and additionally adjusted for area-level socioeconomic status variables (mean income of the neighborhood or municipality, in the majority of cohorts), using random effects of the spatial area units in each cohort to check for spatial clustering of residuals of the models. 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• W2765193733 title "Long-Term Exposure to Ambient Air Pollution and Incidence of Postmenopausal Breast Cancer in 15 European Cohorts within the ESCAPE Project" @default.
• W2765193733 cites W1573746641 @default.
• W2765193733 cites W1635514373 @default.
• W2765193733 cites W1893149856 @default.
• W2765193733 cites W1964886571 @default.
• W2765193733 cites W1976755741 @default.
• W2765193733 cites W1980161869 @default.
• W2765193733 cites W1980353330 @default.
• W2765193733 cites W1991350060 @default.
• W2765193733 cites W1998561407 @default.
• W2765193733 cites W2004655066 @default.
• W2765193733 cites W2018454781 @default.
• W2765193733 cites W2023201844 @default.
• W2765193733 cites W2023255438 @default.
• W2765193733 cites W2039430775 @default.
• W2765193733 cites W2041566535 @default.
• W2765193733 cites W2043839611 @default.
• W2765193733 cites W2047270591 @default.
• W2765193733 cites W2048204036 @default.
• W2765193733 cites W2050801271 @default.
• W2765193733 cites W2053925165 @default.
• W2765193733 cites W2061273963 @default.
• W2765193733 cites W2063304727 @default.
• W2765193733 cites W2064480245 @default.
• W2765193733 cites W2065753326 @default.
• W2765193733 cites W2066544083 @default.
• W2765193733 cites W2073935849 @default.
• W2765193733 cites W2074644961 @default.
• W2765193733 cites W2076549499 @default.
• W2765193733 cites W2076634454 @default.
• W2765193733 cites W2080936852 @default.
• W2765193733 cites W2087621972 @default.
• W2765193733 cites W2088992650 @default.
• W2765193733 cites W2105002037 @default.
• W2765193733 cites W2105150627 @default.
• W2765193733 cites W2107328434 @default.
• W2765193733 cites W2107552147 @default.
• W2765193733 cites W2109695403 @default.
• W2765193733 cites W2111970963 @default.
• W2765193733 cites W2113755967 @default.
• W2765193733 cites W2120081385 @default.
• W2765193733 cites W2126930838 @default.
• W2765193733 cites W2133032064 @default.
• W2765193733 cites W2138085930 @default.

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