COVID-19 pandemic impact on the potential exacerbation of screening mammography disparities: A population-based study in Ontario, Canada
Bibliographic record
Abstract
Strategies to ramp up breast cancer screening after COVID-19 require data on the influence of the pandemic on groups of women with historically low screening uptake. Using data from Ontario, Canada, our objectives were to 1) quantify the overall pandemic impact on weekly bilateral screening mammography rates (per 100,000) of average-risk women aged 50-74 and 2) examine if COVID-19 has shifted any mammography inequalities according to age, immigration status, rurality, and access to material resources. Using a segmented negative binomial regression model, we estimated the mean change in rate at the start of the pandemic (the week of March 15, 2020) and changes in weekly trend of rates during the pandemic period (March 15-December 26, 2020) compared to the pre-pandemic period (January 3, 2016-March 14, 2020) for all women and for each subgroup. A 3-way interaction term (COVID-19*week*subgroup variable) was added to the model to detect any pandemic impact on screening disparities. Of the 3,481,283 mammograms, 8.6 % (n = 300,064) occurred during the pandemic period. Overall, the mean weekly rate dropped by 93.4 % (95 % CI 91.7 % - 94.8 %) at the beginning of COVID-19, followed by a weekly increase of 8.4 % (95 % CI 7.4 % - 9.4 %) until December 26, 2020. The pandemic did not shift any disparities (all interactions p > 0.05) and that women who were under 60 or over 70, immigrants, or with a limited access to material resources had persistently low screening rate in both periods. Interventions should proactively target these underserved populations with the goals of reducing advanced-stage breast cancer presentations and mortality.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".