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Mammographic density in the environs of multiple industrial sources.
Jiménez, Tamara; Pollán, Marina; Domínguez-Castillo, Alejandro; Lucas, Pilar; Sierra, María Ángeles; Castelló, Adela; Fernández de Larrea-Baz, Nerea; Lora-Pablos, David; Salas-Trejo, Dolores; Llobet, Rafael; Martínez, Inmaculada; Pino, Marina Nieves; Martínez-Cortés, Mercedes; Pérez-Gómez, Beatriz; Lope, Virgina; García-Pérez, Javier.
Afiliación
  • Jiménez T; Department of Preventive Medicine, Public Health and Microbiology, Universidad Autónoma de Madrid (UAM), Madrid, Spain.
  • Pollán M; Cancer and Environmental Epidemiology Unit, Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Carlos III Institute of Health (Instituto de Salud Carlos III), Madrid, Spain; Consortium for Biomedical Research in Epidemiology & Public Health (CIBER en Epidemiología
  • Domínguez-Castillo A; Cancer and Environmental Epidemiology Unit, Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Carlos III Institute of Health (Instituto de Salud Carlos III), Madrid, Spain. Electronic address: a.dominguez@isciii.es.
  • Lucas P; Cancer and Environmental Epidemiology Unit, Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Carlos III Institute of Health (Instituto de Salud Carlos III), Madrid, Spain. Electronic address: pmlucas@isciii.es.
  • Sierra MÁ; Cancer and Environmental Epidemiology Unit, Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Carlos III Institute of Health (Instituto de Salud Carlos III), Madrid, Spain; Consortium for Biomedical Research in Epidemiology & Public Health (CIBER en Epidemiología
  • Castelló A; Cancer and Environmental Epidemiology Unit, Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Carlos III Institute of Health (Instituto de Salud Carlos III), Madrid, Spain; Consortium for Biomedical Research in Epidemiology & Public Health (CIBER en Epidemiología
  • Fernández de Larrea-Baz N; Cancer and Environmental Epidemiology Unit, Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Carlos III Institute of Health (Instituto de Salud Carlos III), Madrid, Spain; Consortium for Biomedical Research in Epidemiology & Public Health (CIBER en Epidemiología
  • Lora-Pablos D; Scientific Support Unit, Instituto de Investigación Sanitaria Hospital Universitario 12 de Octubre (imas12), Madrid, Spain; Spanish Clinical Research Network (SCReN), Madrid, Spain; Faculty of Statistical Studies, Universidad Complutense de Madrid (UCM), Madrid, Spain. Electronic address: david@h12o
  • Salas-Trejo D; Consortium for Biomedical Research in Epidemiology & Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP), Spain; Valencian Breast Cancer Screening Program, General Directorate of Public Health, Valencia, Spain; Center for Public Health Research CSISP, FISABIO, Valencia, Spain. Elec
  • Llobet R; Institute of Computer Technology, Universitat Politècnica de València, Valencia, Spain. Electronic address: rllobet@dsic.upv.es.
  • Martínez I; Valencian Breast Cancer Screening Program, General Directorate of Public Health, Valencia, Spain; Center for Public Health Research CSISP, FISABIO, Valencia, Spain. Electronic address: martinez_inm@gva.es.
  • Pino MN; Servicio de Prevención y Promoción de la Salud, Madrid Salud, Ayuntamiento de Madrid, Madrid, Spain. Electronic address: pinoemn@madrid.es.
  • Martínez-Cortés M; Servicio de Prevención y Promoción de la Salud, Madrid Salud, Ayuntamiento de Madrid, Madrid, Spain. Electronic address: martinezcme@madrid.es.
  • Pérez-Gómez B; Cancer and Environmental Epidemiology Unit, Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Carlos III Institute of Health (Instituto de Salud Carlos III), Madrid, Spain; Consortium for Biomedical Research in Epidemiology & Public Health (CIBER en Epidemiología
  • Lope V; Cancer and Environmental Epidemiology Unit, Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Carlos III Institute of Health (Instituto de Salud Carlos III), Madrid, Spain; Consortium for Biomedical Research in Epidemiology & Public Health (CIBER en Epidemiología
  • García-Pérez J; Cancer and Environmental Epidemiology Unit, Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Carlos III Institute of Health (Instituto de Salud Carlos III), Madrid, Spain; Consortium for Biomedical Research in Epidemiology & Public Health (CIBER en Epidemiología
Sci Total Environ ; 876: 162768, 2023 Jun 10.
Article en En | MEDLINE | ID: mdl-36907418
BACKGROUND: Mammographic density (MD), defined as the percentage of dense fibroglandular tissue in the breast, is a modifiable marker of the risk of developing breast cancer. Our objective was to evaluate the effect of residential proximity to an increasing number of industrial sources in MD. METHODS: A cross-sectional study was conducted on 1225 premenopausal women participating in the DDM-Madrid study. We calculated distances between women's houses and industries. The association between MD and proximity to an increasing number of industrial facilities and industrial clusters was explored using multiple linear regression models. RESULTS: We found a positive linear trend between MD and proximity to an increasing number of industrial sources for all industries, at distances of 1.5 km (p-trend = 0.055) and 2 km (p-trend = 0.083). Moreover, 62 specific industrial clusters were analyzed, highlighting the significant associations found between MD and proximity to the following 6 industrial clusters: cluster 10 and women living at ≤1.5 km (ß = 10.78, 95 % confidence interval (95%CI) = 1.59; 19.97) and at ≤2 km (ß = 7.96, 95%CI = 0.21; 15.70); cluster 18 and women residing at ≤3 km (ß = 8.48, 95%CI = 0.01; 16.96); cluster 19 and women living at ≤3 km (ß = 15.72, 95%CI = 1.96; 29.49); cluster 20 and women living at ≤3 km (ß = 16.95, 95%CI = 2.90; 31.00); cluster 48 and women residing at ≤3 km (ß = 15.86, 95%CI = 3.95; 27.77); and cluster 52 and women living at ≤2.5 km (ß = 11.09, 95%CI = 0.12; 22.05). These clusters include the following industrial activities: surface treatment of metals/plastic, surface treatment using organic solvents, production/processing of metals, recycling of animal waste, hazardous waste, urban waste-water treatment plants, inorganic chemical industry, cement and lime, galvanization, and food/beverage sector. CONCLUSIONS: Our results suggest that women living in the proximity to an increasing number of industrial sources and those near certain types of industrial clusters have higher MD.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Residuos Peligrosos / Densidad de la Mama Tipo de estudio: Etiology_studies / Observational_studies / Prevalence_studies / Prognostic_studies / Risk_factors_studies Límite: Animals Idioma: En Revista: Sci Total Environ Año: 2023 Tipo del documento: Article País de afiliación: España Pais de publicación: Países Bajos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Residuos Peligrosos / Densidad de la Mama Tipo de estudio: Etiology_studies / Observational_studies / Prevalence_studies / Prognostic_studies / Risk_factors_studies Límite: Animals Idioma: En Revista: Sci Total Environ Año: 2023 Tipo del documento: Article País de afiliación: España Pais de publicación: Países Bajos