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

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Title: Long-Term Exposure to Ambient Air Pollution and Incidence of Postmenopausal Breast Cancer in 15 European Cohorts within the ESCAPE Project.
Authors: Andersen, Zorana J.1 zorana.andersen@sund.ku.dk, Stafoggia, Massimo2,3, Weinmayr, Gudrun4, Pedersen, Marie1,5, Galassi, Claudia6, Jørgensen, Jeanette T.1, Oudin, Anna7, Forsberg, Bertil7, Olsson, David7, Oftedal, Bente8, Aasvang, Gunn Marit8, Aamodt, Geir8, Pyko, Andrei3, Pershagen, Göran3, Korek, Michal3, De Faire, Ulf3, Pedersen, Nancy L.9, Östenson, Claes-Göran10, Fratiglioni, Laura11, Eriksen, Kirsten T.5
Source: Environmental Health Perspectives. Oct2017, Vol. 125 Issue 10, p1-14. 14p. 10 Charts, 1 Map.
Subject Terms: *Air pollution, *Automobiles, *Epidemiological research, *Nitrogen oxides, *Environmental exposure, *Particulate matter, Breast tumors, Chi-squared test, Confidence intervals, Longitudinal method, Medical cooperation, Meta-analysis, Probability theory, Regression analysis, Research, Research funding, Evidence-based medicine, Professional practice, Statistical significance, Proportional hazards models, Postmenopause, Data analysis software, Descriptive statistics, Odds ratio
Geographic Terms: Europe
Abstract: 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) ≤2:5 μm, ≤10 μm, and 2:5-10 μm 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. [ABSTRACT FROM AUTHOR]
Copyright of Environmental Health Perspectives is the property of National Institute of Environmental Health Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Long-Term Exposure to Ambient Air Pollution and Incidence of Postmenopausal Breast Cancer in 15 European Cohorts within the ESCAPE Project.
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  Data: 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) ≤2:5 μm, ≤10 μm, and 2:5-10 μm 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Environmental Health Perspectives is the property of National Institute of Environmental Health Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1289/EHP1742
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