Literature DB >> 26844439

[Factors associated with the timely treatment of women with breast cancer supported by a non-governmental organization in Bogotá, Colombia].

Guillermo Sánchez1, Carlos Gustavo Niño1, Carolina Estupiñán2.   

Abstract

INTRODUCTION: The prognosis for a woman with breast cancer is related to the time that elapses before diagnosis and integral treatment. Colombian women face barriers that determine effective access to the health system.
OBJECTIVE: To establish the determinants of timely treatment for breast cancer in a group of women supported by a non-governmental organization in Bogotá.
MATERIALS AND METHODS: An observational analytical study was carried out on 136 women with breast cancer supported by the non-governmental organization. The cut-off point for timely treatment was defined as 90 days, calculated as the time between the appearance of symptoms and the initiation of treatment. Predictors of timely treatment were explored by means of multivariate analysis.
RESULTS: Although 96% of the women had health insurance only 26.4% received timely treatment, and 36 of them reported being denied medical services. Of these women, 23% took legal action to gain access to their healthcare rights. Significant associations were established by multivariate analysis for timely treatment among women belonging to socioeconomic strata IV and V (OR=3.39), as well as those with higher education (OR=2.72).
CONCLUSIONS: According to the international literature, the prognosis for women with breast cancer improves when they are able to access opportune treatment. In this group of women socioeconomic factors appeared to determine effective access to treatment, revealing the existence of inequalities that may be socially determined.

Entities:  

Mesh:

Year:  2015        PMID: 26844439     DOI: 10.7705/biomedica.v35i4.2378

Source DB:  PubMed          Journal:  Biomedica        ISSN: 0120-4157            Impact factor:   0.935


  2 in total

1.  Barriers to timely surgery for breast cancer in Rwanda.

Authors:  Lauren E Schleimer; Jean-Marie Vianney Dusengimana; John Butonzi; Catherine Kigonya; Abirami Natarajan; Aline Umwizerwa; Daniel S O'Neil; Ainhoa Costas-Chavarri; Jean-Paul Majyambere; Lawrence N Shulman; Nancy L Keating; Cyprien Shyirambere; Tharcisse Mpunga; Lydia E Pace
Journal:  Surgery       Date:  2019-08-27       Impact factor: 3.982

2.  Predicting depression among rural and urban disabled elderly in China using a random forest classifier.

Authors:  Yu Xin; Xiaohui Ren
Journal:  BMC Psychiatry       Date:  2022-02-15       Impact factor: 3.630

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.