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

被引:3
|
作者
Jochum, Floriane [1 ,2 ]
Hamy, Anne-Sophie [3 ]
Gaillard, Thomas [4 ]
Lecointre, Lise [5 ,6 ]
Gougis, Paul [1 ]
Dumas, Elise [1 ]
Grandal, Beatriz [1 ]
Feron, Jean-Guillaume [4 ]
Laas, Enora [4 ]
Fourchotte, Virginie [4 ]
Girard, Noemie [4 ]
Pauly, Lea [4 ]
Osdoit, Marie [4 ]
Gauroy, Elodie [4 ]
Darrigues, Lauren [4 ]
Reyal, Fabien [4 ]
Akladios, Cherif [2 ]
Lecuru, Fabrice [4 ]
机构
[1] INSERM, Translat Res Dept, Residual Tumor & Response Treatment Lab RT2Lab, U932 Immun & Canc, F-75005 Paris, France
[2] Strasbourg Univ Hosp, Dept Gynecol, F-67000 Strasbourg, France
[3] Univ Paris Cite, Inst Curie, Dept Med Oncol, F-75005 Paris, France
[4] Univ Paris Cite, Inst Curie, Dept Surg, F-75005 Paris, France
[5] Univ Strasbourg, ICube UMR 7357, Lab Sci Ingn Informat & Imagerie, F-67000 Strasbourg, France
[6] Univ Strasbourg, Inst Minimally Invas Hybrid Image Guided Surg, Inst Hosp Univ IHU, F-67000 Strasbourg, France
关键词
ovarian cancer; area of residence; hierarchical cluster algorithm; sociodemographic factor; NEIGHBORHOOD SOCIOECONOMIC-STATUS; POPULATION-BASED ANALYSIS; COMORBIDITY; STAGE; WOMEN; INEQUALITIES; ASSOCIATION; SEGREGATION; DISPARITIES; MORTALITY;
D O I
10.3390/cancers14235987
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
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.
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页数:14
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