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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".