Mammographic density in the environs of multiple industrial sources.

Female Animals Breast Density Cross-Sectional Studies Hazardous Waste Industry Metals Risk Factors
Breast cancer Breast density Industrial cluster Industrial pollution Industry

Journal

The Science of the total environment
ISSN: 1879-1026
Titre abrégé: Sci Total Environ
Pays: Netherlands
ID NLM: 0330500

Informations de publication

Date de publication:
10 Jun 2023
Historique:
received: 20 12 2022
revised: 02 03 2023
accepted: 06 03 2023
medline: 1 5 2023
pubmed: 13 3 2023
entrez: 12 3 2023
Statut: ppublish

Résumé

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. 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. 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 (β = 10.78, 95 % confidence interval (95%CI) = 1.59; 19.97) and at ≤2 km (β = 7.96, 95%CI = 0.21; 15.70); cluster 18 and women residing at ≤3 km (β = 8.48, 95%CI = 0.01; 16.96); cluster 19 and women living at ≤3 km (β = 15.72, 95%CI = 1.96; 29.49); cluster 20 and women living at ≤3 km (β = 16.95, 95%CI = 2.90; 31.00); cluster 48 and women residing at ≤3 km (β = 15.86, 95%CI = 3.95; 27.77); and cluster 52 and women living at ≤2.5 km (β = 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. 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.

Sections du résumé

BACKGROUND 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 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 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 (β = 10.78, 95 % confidence interval (95%CI) = 1.59; 19.97) and at ≤2 km (β = 7.96, 95%CI = 0.21; 15.70); cluster 18 and women residing at ≤3 km (β = 8.48, 95%CI = 0.01; 16.96); cluster 19 and women living at ≤3 km (β = 15.72, 95%CI = 1.96; 29.49); cluster 20 and women living at ≤3 km (β = 16.95, 95%CI = 2.90; 31.00); cluster 48 and women residing at ≤3 km (β = 15.86, 95%CI = 3.95; 27.77); and cluster 52 and women living at ≤2.5 km (β = 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 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.

Identifiants

DOI: 10.1016/j.scitotenv.2023.162768 PMID: 36907418
pubmed: 36907418
pii: S0048-9697(23)01384-0
doi: 10.1016/j.scitotenv.2023.162768
pii:
doi:

Substances chimiques

Hazardous Waste 0
Metals 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

162768

Informations de copyright

Copyright © 2023 Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Tamara Jiménez (T)

Department of Preventive Medicine, Public Health and Microbiology, Universidad Autónoma de Madrid (UAM), Madrid, Spain.

Marina 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 y Salud Pública - CIBERESP), Spain. Electronic address: mpollan@isciii.es.

Alejandro 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.

Pilar 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.

María Ángeles 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 y Salud Pública - CIBERESP), Spain. Electronic address: masierra@isciii.es.

Adela 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 y Salud Pública - CIBERESP), Spain. Electronic address: acastello@isciii.es.

Nerea 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 y Salud Pública - CIBERESP), Spain. Electronic address: nfernandez@isciii.es.

David 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.es.

Dolores 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. Electronic address: salas_dol@gva.es.

Rafael Llobet (R)

Institute of Computer Technology, Universitat Politècnica de València, Valencia, Spain. Electronic address: rllobet@dsic.upv.es.

Inmaculada 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.

Marina Nieves Pino (MN)

Servicio de Prevención y Promoción de la Salud, Madrid Salud, Ayuntamiento de Madrid, Madrid, Spain. Electronic address: pinoemn@madrid.es.

Mercedes Martínez-Cortés (M)

Servicio de Prevención y Promoción de la Salud, Madrid Salud, Ayuntamiento de Madrid, Madrid, Spain. Electronic address: martinezcme@madrid.es.

Beatriz 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 y Salud Pública - CIBERESP), Spain. Electronic address: bperez@isciii.es.

Virgina 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 y Salud Pública - CIBERESP), Spain. Electronic address: vicarvajal@isciii.es.

Javier 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 y Salud Pública - CIBERESP), Spain. Electronic address: jgarcia@isciii.es.

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Classifications MeSH

questionsmedicales.fr › Eucaryotes › Animaux › Mammographic density in the environs of...
questionsmedicales.fr › Phénomènes physiologiques › Constitution physique › Densité mammaire › Mammographic density in the environs of...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Caractéristiques des études épidémiologiques › Études épidémiologiques › Études transversales › Mammographic density in the environs of...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Pollution de l'environnement › Déchets › Déchets dangereux › Mammographic density in the environs of...
questionsmedicales.fr › Technologie, industrie et agriculture › Industrie › Mammographic density in the environs of...
questionsmedicales.fr › Produits chimiques inorganiques › Métaux › Mammographic density in the environs of...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Statistiques comme sujet › Probabilité › Risque › Facteurs de risque › Mammographic density in the environs of...