Changes in female cancer diagnostic billing rates over the COVID-19 period in the Ontario Health Insurance Plan
Bibliographic record
Abstract
Background: The initial response to coronavirus disease 2019 (COVID-19) in Ontario included suspension of cancer screening programs and deferral of diagnostic procedures and many treatments. Although the short-term impact of these measures on female cancers is well documented, few studies have assessed the mid- to long-term impacts. Objectives: To compare annual billing prevalence and incidence rates of female cancers during the COVID-19 period (2020-2022) to pre-COVID-19 levels (2015-2019). Design: Retrospective analysis of aggregated claims data for female cancer diagnostic codes from the Ontario Health Insurance Plan (OHIP). Methods: Linear regression analysis was used to fit pre-COVID-19 (2015-2019) data for each OHIP billing code and extrapolate counterfactual values for the years of 2020-2022. Excess billing rates were calculated as the difference between projected and actual rates for each year. Results: In 2020, OHIP billing prevalence rates for cervical, breast, uterine, and ovarian cancers decreased relative to projected values for that year by -50.7/100k, -13.9/100k, -3.5/100k, and -3.8/100k, respectively. The reverse was observed in 2021 with rate increases of 47.8/100k, 59.1/100k, 2.5/100k, and 3.7/100k, respectively. In 2022, the excesses were further amplified, especially for cervical and breast cancers (111.2/100k and 78.67/100k, respectively). The net excess patient billing rate for 2020-2022 was largely positive for all female cancer types (108.3/100k, 123.7/100k, 5.2/100k, and 1.8/100k, respectively). Analysis of billing incidence rates showed similar trends. Conclusion: The expected female cancer billing rate decreases in 2020 were followed by large increases in 2021 and 2022, resulting in a cumulative excess during the COVID-19 period. Further research is required to assess the nature of these changes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".