Patient-Reported Experiences of Breast Cancer Screening, Diagnosis, and Treatment Delay, and Telemedicine Adoption during COVID-19
Bibliographic record
Abstract
Purpose: To evaluate and quantify potential sociodemographic disparities in breast cancer screening, diagnosis, and treatment due to the COVID-19 pandemic, and the use of telemedicine. Methods: We fielded a 52-item web-based questionnaire from 14 May 2020 to 1 July 2020 in partnership with several U.S.-based breast cancer advocacy groups. Individuals aged 18 or older were eligible for this study if they: (1) received routine breast cancer screening; OR (2) were undergoing diagnostic evaluation for breast cancer; OR (3) had ever been diagnosed with breast cancer. We used descriptive statistics to understand the extent of cancer care delay and telemedicine adoption and used multivariable logistic regression models to estimate the association of sociodemographic factors with odds of COVID-19-related delays in care and telemedicine use. Results: Of 554 eligible survey participants, 493 provided complete data on demographic and socioeconomic factors and were included in the analysis. Approximately half (n = 248, 50.3%) had a personal history of breast cancer. Overall, 188 (38.1%) participants had experienced any COVID-19-related delay in care including screening, diagnosis, or treatment, and 339 (68.8) reported having at least one virtual appointment during the study period. Compared to other insurance types, participants with Medicaid insurance were 2.58 times more likely to report a COVID-19-related delay in care (OR 2.58, 95% Cl: 1.05, 6.32; p = 0.039). Compared to participants with a household income of less than USD 50,000, those with a household income of USD 150,000 or more were 2.38 (OR 2.38, 95% Cl: 1.09, 5.17; p = 0.029) times more likely to adopt virtual appointments. Self-insured participants were 70% less likely to use virtual appointment compared to those in other insurance categories (OR 0.28, 95% Cl: 0.11, 0.73; p = 0.009). Conclusions: The COVID-19 pandemic has had a significant impact on breast cancer screening, diagnosis, and treatment, and accelerated the delivery of virtual care. Lower-income groups and patients with certain insurance categories such as Medicaid or self-insured could be more likely to experience care delay or less likely to use telemedicine. Careful attention must be paid to vulnerable groups to insure equity in breast cancer-related service utilization and telemedicine access during and beyond the COVID-19 pandemic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".