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