Delays in the initiation of breast cancer treatment in Brazil: an analysis by age and region
DOI:
https://doi.org/10.29289/259453942025V35S1088Palavras-chave:
breast neoplasms, treatment delayResumo
Introduction: Breast cancer is the most prevalent malignancy among women in Brazil. Objective: This study aimed to
identify the regions and age groups most affected by delays in the initiation of breast cancer treatment and to assess the
potential clinical consequences of such delays on patient outcomes. Methods: Data were collected from the Department
of Informatics of the Unified Health System (DATASUS) platform, which provided information on the date of diagnostic
examination, date of first treatment, regions, and age groups of patients. According to guidelines from recent cancer committees, the optimal time to initiate oncological treatment is within 60 days of the diagnostic examination. Consequently,
delay in treatment initiation was defined as any interval exceeding 60 days from the diagnostic exam. Results: In 2024,
a total of 53,401 breast cancer patients initiated treatment, with 31.5% (n=16,830) experiencing delays in treatment commencement. The Central-West region exhibited the highest delay rate, with 39.9% (n=1,190) of cases, followed by the North
region with 34.0% (n=735). Among the age groups, patients aged 65–69 years experienced the highest treatment delay,
with a 35.1% (n=2,038) rate. Delays in initiating breast cancer treatment can result in a range of clinical consequences,
which may vary according to the tumor’s grade and histological type. However, studies consistently demonstrate that
delayed treatment is associated with an increased risk of metastasis, local disease progression, and decreased survival
rates. Conclusion: These findings underscore the critical importance of initiating treatment as early as possible for the
progression of the disease, highlighting that regional and social disparities significantly influence the timing of treatment initiation. Further in-depth studies are necessary to better understand the underlying causes of these disparities
reflected in the statistics.
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Copyright (c) 2026 Gabriel Maciel Almeida, Valbert Oliveira Costa Filho, Eduarda Severo Alvarenga, Carlos Alberto Barbosa Neto, Fabrícia Cardoso Marques, Gabriel Maciel Almeida, João Luiz Lima Pinheiro, Anelise Poluboiarinov Cappellaro

Este trabalho está licenciado sob uma licença Creative Commons Attribution 4.0 International License.




