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Timeliness of Treatment Initiation in Newly Diagnosed Patients With Breast Cancer
Jinghui Dong1, Kimberly S Esham2, Lauren Boehm1
1Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA.
Background:
Evidence-based timeliness benchmarks have been established to assess quality of breast cancer care, as delays in treatment are associated with poor clinical outcomes. However, few studies have evaluated how current breast cancer care meets these benchmarks and what factors may delay the timely initiation of treatment.
Patients And Methods:
Demographic and disease characteristics of 377 newly diagnosed patients with breast cancer who initiated treatment at Tufts Medical Center (2009-2015) were extracted from electronic medical records. Time from diagnosis to initial surgery and time from diagnosis to initiation of hormone therapy were estimated with Kaplan-Meier curves. Multivariable regression analysis was used to identify factors associated with treatment delays. Thematic analysis was performed to categorize reasons for delay.
Results:
Of 319 patients who had surgery recommended as the first treatment, 248 (78%) met the 45-day benchmark (median, 28 days; 25th-75th %, 19-43). After adjusting for potential confounders, multivariable regression analysis revealed that negative hormone receptor status (odds ratio, 3.48; 95% confidence interval, 1.44-8.43) and mastectomy (odds ratio, 4.07; 95% confidence interval, 2.10-8.06) were significantly associated with delays in surgery. Delays were mostly owing to clinical complexity or logistical/financial reasons. Of 241 patients eligible for hormone therapy initiation, 232 (96%) met the 1-year benchmark (median, 147 days; 25th-75th %, 79-217).
Conclusion:
Most patients met timeliness guidelines for surgery and initiation of hormone therapy, although risk factors for delay were identified. Knowledge of reasons for breast cancer treatment delay, including clinical complexity and logistical/financial issues, may allow targeting interventions for patients at greatest risk of care delays.
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