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Association between socioeconomic factors at diagnosis and survival in breast cancer: A population‐based study

BACKGROUND: The associations between socioeconomic statuses and survival outcomes of breast cancer remain unclear. No model has included both histological and socioeconomic factors to predict the survival of breast cancer. This study was designed to develop nomograms to predict breast cancer–specifi...

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Autores principales: Ji, Peng, Gong, Yue, Jiang, Chang‐Chuan, Hu, Xin, Di, Gen‐Hong, Shao, Zhi‐Ming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7050085/
https://www.ncbi.nlm.nih.gov/pubmed/31960597
http://dx.doi.org/10.1002/cam4.2842
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author Ji, Peng
Gong, Yue
Jiang, Chang‐Chuan
Hu, Xin
Di, Gen‐Hong
Shao, Zhi‐Ming
author_facet Ji, Peng
Gong, Yue
Jiang, Chang‐Chuan
Hu, Xin
Di, Gen‐Hong
Shao, Zhi‐Ming
author_sort Ji, Peng
collection PubMed
description BACKGROUND: The associations between socioeconomic statuses and survival outcomes of breast cancer remain unclear. No model has included both histological and socioeconomic factors to predict the survival of breast cancer. This study was designed to develop nomograms to predict breast cancer–specific survival (BCSS) and overall survival (OS) with consideration of socioeconomic factors for breast cancer patients. MATERIALS AND METHODS: We included a total of 207 749 female patients, diagnosed with malignant breast cancer between 2007 and 2012 from the Surveillance, Epidemiology, and End Results database. BCSS and OS were evaluated with Gray's test and log‐rank tests, respectively. Marital statuses, insurance statuses, residence, median household income, poverty rate, unemployment rate, and education level were included as socioeconomic factors in univariate and multivariate Cox regression analyses. Clinicopathological factors and socioeconomic factors were integrated to construct nomograms. Calibration plots and concordance indexes (C‐indexes) were used to evaluate the accuracy and discrimination of the models. RESULTS: Four and three socioeconomic factors were involved in constructing the nomograms for 3‐, 5‐, and 7‐year BCSS and OS, respectively. The C‐indexes of the final nomograms were higher than those of the TNM staging system for predicting BCSS (0.835 vs 0.782; P < .001) and OS (0.773 vs 0.676; P < .001). The performance of the nomograms for predicting OS was significantly lower when excluding socioeconomic factors (P < .001). CONCLUSION: These findings may highlight the importance of developing health‐related policies and the necessity of targeted social support‐based interventions for high‐risk patients.
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spelling pubmed-70500852020-03-05 Association between socioeconomic factors at diagnosis and survival in breast cancer: A population‐based study Ji, Peng Gong, Yue Jiang, Chang‐Chuan Hu, Xin Di, Gen‐Hong Shao, Zhi‐Ming Cancer Med Cancer Prevention BACKGROUND: The associations between socioeconomic statuses and survival outcomes of breast cancer remain unclear. No model has included both histological and socioeconomic factors to predict the survival of breast cancer. This study was designed to develop nomograms to predict breast cancer–specific survival (BCSS) and overall survival (OS) with consideration of socioeconomic factors for breast cancer patients. MATERIALS AND METHODS: We included a total of 207 749 female patients, diagnosed with malignant breast cancer between 2007 and 2012 from the Surveillance, Epidemiology, and End Results database. BCSS and OS were evaluated with Gray's test and log‐rank tests, respectively. Marital statuses, insurance statuses, residence, median household income, poverty rate, unemployment rate, and education level were included as socioeconomic factors in univariate and multivariate Cox regression analyses. Clinicopathological factors and socioeconomic factors were integrated to construct nomograms. Calibration plots and concordance indexes (C‐indexes) were used to evaluate the accuracy and discrimination of the models. RESULTS: Four and three socioeconomic factors were involved in constructing the nomograms for 3‐, 5‐, and 7‐year BCSS and OS, respectively. The C‐indexes of the final nomograms were higher than those of the TNM staging system for predicting BCSS (0.835 vs 0.782; P < .001) and OS (0.773 vs 0.676; P < .001). The performance of the nomograms for predicting OS was significantly lower when excluding socioeconomic factors (P < .001). CONCLUSION: These findings may highlight the importance of developing health‐related policies and the necessity of targeted social support‐based interventions for high‐risk patients. John Wiley and Sons Inc. 2020-01-20 /pmc/articles/PMC7050085/ /pubmed/31960597 http://dx.doi.org/10.1002/cam4.2842 Text en © 2020 The Authors. Cancer Medicine published by John Wiley & Sons Ltd. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Cancer Prevention
Ji, Peng
Gong, Yue
Jiang, Chang‐Chuan
Hu, Xin
Di, Gen‐Hong
Shao, Zhi‐Ming
Association between socioeconomic factors at diagnosis and survival in breast cancer: A population‐based study
title Association between socioeconomic factors at diagnosis and survival in breast cancer: A population‐based study
title_full Association between socioeconomic factors at diagnosis and survival in breast cancer: A population‐based study
title_fullStr Association between socioeconomic factors at diagnosis and survival in breast cancer: A population‐based study
title_full_unstemmed Association between socioeconomic factors at diagnosis and survival in breast cancer: A population‐based study
title_short Association between socioeconomic factors at diagnosis and survival in breast cancer: A population‐based study
title_sort association between socioeconomic factors at diagnosis and survival in breast cancer: a population‐based study
topic Cancer Prevention
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7050085/
https://www.ncbi.nlm.nih.gov/pubmed/31960597
http://dx.doi.org/10.1002/cam4.2842
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