Publication:
Mammographic density in the environs of multiple industrial sources

dc.contributor.authorJiménez, Tamara
dc.contributor.authorPollan-Santamaria, Marina
dc.contributor.authorDomínguez-Castillo, Alejandro
dc.contributor.authorde Lucas, Maria Pilar
dc.contributor.authorSierra, Maria Angeles
dc.contributor.authorCastelló Pastor, Adela
dc.contributor.authorFernandez de Larrea-Baz, Nerea
dc.contributor.authorLora-Pablos, David
dc.contributor.authorSalas-Trejo, Dolores
dc.contributor.authorLlobet, Rafael
dc.contributor.authorMartínez, Inmaculada
dc.contributor.authorPino, Marina Nieves
dc.contributor.authorMartínez-Cortés, Mercedes
dc.contributor.authorPerez-Gomez, Beatriz
dc.contributor.authorLope Carvajal, Virginia
dc.contributor.authorGarcía-Pérez, Javier
dc.contributor.funderInstituto de Salud Carlos III
dc.date.accessioned2023-12-13T09:23:25Z
dc.date.available2023-12-13T09:23:25Z
dc.date.issued2023-06-10
dc.description.abstractBackground: 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 (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: 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.es_ES
dc.description.peerreviewedes_ES
dc.description.sponsorshipThis work was supported by the Carlos III Institute of Health (AESI PI15CIII/0029, AESI PI15CIII/00013, and EPY-505/19-PFIS). The article presents independent research. The views expressed are those of the authors and not necessarily those of the Carlos III Institute of Health.es_ES
dc.format.page162768es_ES
dc.format.volume876es_ES
dc.identifier.citationSci Total Environ. 2023 Jun 10:876:162768.es_ES
dc.identifier.doi10.1016/j.scitotenv.2023.162768
dc.identifier.e-issn1879-1026es_ES
dc.identifier.journalThe Science of the total environmentes_ES
dc.identifier.urihttp://hdl.handle.net/20.500.12105/16789
dc.language.isoenges_ES
dc.publisherElsevier
dc.relation.projectFISinfo:eu-repo/grantAgreement/ES/PI15CIII/0029es_ES
dc.relation.projectFISinfo:eu-repo/grantAgreement/ES/PI15CIII/00013es_ES
dc.relation.projectFISinfo:eu-repo/grantAgreement/ES/EPY-505/19-PFISes_ES
dc.relation.publisherversionhttps://doi.org/10.1016/j.scitotenv.2023.162768es_ES
dc.repisalud.centroISCIII::Centro Nacional de Epidemiologíaes_ES
dc.repisalud.institucionISCIIIes_ES
dc.rights.accessRightsopen accesses_ES
dc.rights.licenseAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectBreast canceres_ES
dc.subjectBreast densityes_ES
dc.subjectIndustrial clusteres_ES
dc.subjectIndustrial pollutiones_ES
dc.subjectIndustryes_ES
dc.subject.meshBreast Densityes_ES
dc.subject.meshHazardous Wastees_ES
dc.subject.meshFemalees_ES
dc.subject.meshAnimalses_ES
dc.subject.meshCross-Sectional Studieses_ES
dc.subject.meshIndustryes_ES
dc.subject.meshMetalses_ES
dc.subject.meshRisk Factorses_ES
dc.titleMammographic density in the environs of multiple industrial sourceses_ES
dc.typeresearch articlees_ES
dc.type.hasVersionAMes_ES
dspace.entity.typePublication
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