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Geospatial Analysis of Inflammatory Breast Cancer and Associated Community Characteristics in the United States

Inflammatory breast cancer (IBC) is a rare and aggressive form of breast cancer, almost always diagnosed at late stage where mortality outcomes and morbidity burdens are known to be worse. Missed by mammography screening, IBC progresses rapidly and reaches late stage by the time of diagnosis. With a...

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Detalles Bibliográficos
Autores principales: Scott, Lia, Mobley, Lee R., Il’yasova, Dora
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5409605/
https://www.ncbi.nlm.nih.gov/pubmed/28398259
http://dx.doi.org/10.3390/ijerph14040404
Descripción
Sumario:Inflammatory breast cancer (IBC) is a rare and aggressive form of breast cancer, almost always diagnosed at late stage where mortality outcomes and morbidity burdens are known to be worse. Missed by mammography screening, IBC progresses rapidly and reaches late stage by the time of diagnosis. With an unknown etiology and poor prognosis, it is crucial to evaluate the distribution of the disease in the population as well as identify area social and economic contextual risk factors that may be contributing to the observed patterns of IBC incidence. In this study, we identified spatial clustering of county-based IBC rates among US females and examined the underlying community characteristics associated with the clusters. IBC accounted for ~1.25% of all primary breast cancers diagnoses in 2004–2012 and was defined by the Collaborative Stage (CS) Extension code 710 and 730. Global and local spatial clusters of IBC rates were identified and mapped. The Mann-Whitney U test was used to compare median differences in key contextual variables between areas with high and low spatial clusters of IBC rates. High clusters are counties and their neighbors that all exhibit above average rates, clustered together in a fashion that would be extremely unlikely to be observed by chance, and conversely for low clusters. There was statistically significant evidence of spatial clustering into high and low rate clusters. The average rate in the high rate clusters (n = 46) was approximately 12 times the average rate in low rate clusters (n = 126), and 2.2 times the national average across all counties. Significant differences were found in the medians of the underlying race, poverty, and urbanicity variables when comparing the low cluster counties with the high cluster counties (p < 0.05). Cluster analysis confirms that IBC rates differ geographically and may be influenced by social and economic environmental factors. Particular attention may need to be paid to race, urbanicity and poverty when considering risk factors for IBC and when developing interventions and alternative prevention strategies.