Cargando…

Impact of the Area of Residence of Ovarian Cancer Patients on Overall Survival

SIMPLE SUMMARY: The disparities in ovarian cancer care and outcomes have been linked to socioeconomic indicators. Our study mainly demonstrated that women living in economically and socially deprived areas had a significantly higher risk of death after adjustment for individual factors. Our results...

Descripción completa

Detalles Bibliográficos
Autores principales: Jochum, Floriane, Hamy, Anne-Sophie, Gaillard, Thomas, Lecointre, Lise, Gougis, Paul, Dumas, Élise, Grandal, Beatriz, Feron, Jean-Guillaume, Laas, Enora, Fourchotte, Virginie, Girard, Noemie, Pauly, Lea, Osdoit, Marie, Gauroy, Elodie, Darrigues, Lauren, Reyal, Fabien, Akladios, Cherif, Lecuru, Fabrice
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9736843/
https://www.ncbi.nlm.nih.gov/pubmed/36497469
http://dx.doi.org/10.3390/cancers14235987
Descripción
Sumario:SIMPLE SUMMARY: The disparities in ovarian cancer care and outcomes have been linked to socioeconomic indicators. Our study mainly demonstrated that women living in economically and socially deprived areas had a significantly higher risk of death after adjustment for individual factors. Our results reflect the complexity of the environment in which the patients live and the impact on overall survival of the combination of negative social, economic and educational factors within that environment. ABSTRACT: Survival disparities persist in ovarian cancer and may be linked to the environments in which patients live. The main objective of this study was to analyze the global impact of the area of residence of ovarian cancer patients on overall survival. The data were obtained from the Surveillance, Epidemiology and End Results (SEER) database. We included all the patients with epithelial ovarian cancers diagnosed between 2010 and 2016. The areas of residence were analyzed by the hierarchical clustering of the principal components to group similar counties. A multivariable Cox proportional hazards model was then fitted to evaluate the independent effect of each predictor on overall survival. We included a total of 16,806 patients. The clustering algorithm assigned the 607 counties to four clusters, with cluster 1 being the most disadvantaged and cluster 4 having the highest socioeconomic status and best access to care. The area of residence cluster remained a statistically significant independent predictor of overall survival in the multivariable analysis. The patients living in cluster 1 had a risk of death more than 25% higher than that of the patients living in cluster 4. This study highlights the importance of considering the sociodemographic factors within the patient’s area of residence when developing a care plan and follow-up.