Recent National Trends in Time to Breast Cancer Surgery Before and After the COVID-19 Pandemic Onset and Predictors of Failure to Receive Timely Surgery.

Sullivan S, Mott S, Nataliansyah MM, Mobley EM, Lizarraga IM

Journal ArticleAbstract only — full text under license

AI summary — for doctors, generated from the abstract only

Time to breast cancer surgery has lengthened since the pandemic, with fewer patients meeting the 60-day quality target, especially uninsured and minority patients.

What was studied

National Cancer Database analysis of 520,967 women with stage I-III breast cancer treated with upfront surgery in 2014-2023, using linear and logistic regression for time to surgery and non-compliance with 60 days.

Key findings

Mean time to surgery rose from 37 to 50 days, and the share within 60 days fell from 88% to 74%.
2021-2023 had 1.90 times the odds of missing the 60-day target compared with 2014-2019.
Strongest predictors of delay were uninsured status, diagnosis elsewhere, immediate reconstruction, Black race and Hispanic ethnicity.

Clinical relevance

Delays are tied to worse survival, and the new CoC quality measure exposes systematic gaps in access and coordination.

Who should care

Radiation OncologyMedical Oncology

Limitations

Registry data are retrospective and cannot capture reasons for delay or workup details, so associations are not causal.

Audit local time-to-surgery, and improve care coordination for uninsured patients, those diagnosed elsewhere and those needing reconstruction.

This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.

Abstract

BACKGROUND

Time to surgery (TTS) for breast cancer has been increasing for decades, with delays associated with worse survival. A 2022 Commission on Cancer (CoC) quality measure requires upfront surgery within 60 days after diagnosis (BCSdx). The long-term effects of the COVID-19 pandemic on TTS and compliance with BCSdx are unknown.

METHODS

Using the National Cancer Database, the study identified 520,967 women with a diagnosis of clinical stages I to III invasive breast cancer from 2014 to 2023 who underwent upfront surgery. Time to surgery was defined as days from diagnosis to definitive surgery. Diagnosis year was modeled continuously and categorically (pre-pandemic 2014-2019, pandemic onset 2020, post-pandemic onset 2021-2023). Linear and logistic regression were used to identify factors associated with TTS and odds of not having surgery in ≤ 60 days.

RESULTS

Between 2014 and 2023, the mean TTS increased from 37 to 50 days (+ 13 days). Compared with 2014-2019, the 2021-2023 period was associated with + 7.51 mean days (95% confidence interval [CI], 7.35-7.67 days) and 1.90 times (95% CI, 1.87-1.94 times) increased odds of not meeting BCSdx in the multivariable analysis. Longer TTS also was associated with immediate reconstruction (β = + 8.27 days), uninsured status (β = + 7.99 days), and diagnosis at another facility (β = + 6.55 days). The proportion of patients with a TTS of ≤ 60 days declined from 88% to 74%. The strongest predictors of noncompliance were uninsured status (odds ratio [OR], 2.10), diagnosis at another facility (OR, 1.81), immediate reconstruction (OR, 1.81), black race (OR, 1.72), and Hispanic ethnicity (OR, 1.70).

CONCLUSIONS

The increase in TTS for breast cancer accelerated after the onset of the COVID-19 pandemic, with a concomitant decrease in BCSdx compliance. Strategies to improve care coordination, particularly for at-risk populations, are needed to address this quality measure.

DOI

10.1245/s10434-026-20719-9

PMID

42897381

Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.

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