COVID-19 impacts on the breast cancer care pathway among systemically marginalized communities in Ontario
Bibliographic record
Abstract
PURPOSE: Healthcare system pauses occurred worldwide due to COVID-19, and may have worsened pre-existing disparities in breast cancer care. In this population-based, retrospective cohort study, we investigated indicators of breast cancer care (i.e., adherence to screening guidelines, early vs. late-stage diagnosis, and mastectomy vs. breast-conserving surgery) before and after COVID-19 lockdowns in Ontario, with an emphasis on immigrant women. METHODS: We had three binary outcomes and corresponding cohorts, and each outcome/cohort was ascertained relative to two time periods: April 1, 2018-March 31, 2020 ("pre-pandemic") and April 1, 2020-March 31, 2022 ("pandemic"): i) up to date on screening, ii) early vs late stage of breast cancer diagnosis, and iii) mastectomy vs breast-conserving surgery at any time after diagnosis for women who were diagnosed at stages I-III during each two-year time period. We conducted descriptive analyses, and used logistic regression, both unadjusted and adjusted, to determine odds ratios for our dichotomous outcomes. RESULTS: Breast cancer screening rates dropped from 59.4% to 51.0%, and the number of women diagnosed dropped from 18,821 to 14,269, in the pre-pandemic vs pandemic period. In multivariable analyses, screening significantly dropped (AOR = 0.69 [95% CI (0.69-0.69)]), there was no significant difference for diagnostic stage (AOR = 0.99 [95% CI (0.92-1.05)]), and the use of mastectomy vs breast-conserving surgery was higher in the pandemic period (AOR = 1.14 [95% CI (1.08-1.20)]). Women from the Caribbean had lower odds of early-stage diagnosis in the pre-pandemic period despite a screening advantage. CONCLUSION: Future work should further explore the reasons for these findings and potential system-level solutions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".