Case Study
Examining Patterns in Breast Cancer Patient Characteristics and Treatment
Throughout the COVID-19 Pandemic
Introduction
Patterns in breast cancer patient presentation and time to treatment throughout the totality of the COVID-19 pandemic have not been comprehensively investigated. The objective of this study is to aid in filling this gap.
Methods
28,048 breast cancer patients with a date of diagnosis occurring from January of 2019 to June of 2023 were extracted from the Q-Centrix Clinical Data Warehouse, a proprietary database of de-identified clinical data produced through expert-driven human abstraction. The subset sourced from the Clinical Data Warehouse includes information from 49 hospitals, health systems, and cancer centers nationwide.
- The time from January of 2019 to June of 2023 was analyzed by 6-month time periods.
- Chi-Square tests were utilized to compare differences in patient characteristics and treatment variables across the 9 time-periods. A p-value cutoff of <0.05 was considered significant.
- Post-hoc pairwise chi-square tests were run with a Bonferroni correction to control for multiple tests.
Findings
- 38.3% of all patients diagnosed from late 2019 to early 2021 received their first treatment within 30 days of diagnosis
- 24.6% of patients diagnosed from late 2021 to 2023 received treatment within 30 days of diagnosis
- 60.8% of patients diagnosed from 2019 to early 2021 received their first treatment 31+ days post diagnosis
- 70.7% of all patients diagnosed from late 2021 to 2023 received their first treatment 31+ days post diagnosis (p-value <0.001)
Conclusion
This study depicts a shift in time-to-treat in periods occurring from mid to late pandemic (late 2021 out to 2023) as compared to the pre- and early pandemic periods (2019 to the beginning of 2021). Patients received their first treatment an average of 1 week later in the latter portion of the pandemic than they did in the pre and early periods of the pandemic. Additional studies are needed to understand the drivers of these dynamics.
This abstract was originally presented at the 2025 ISPOR Conference in Montreal.
