Trends in the Cost and Utilization of Publicly Reimbursed Cancer Medications Dispensed as Take-Home Treatments from 2017–2021
Bibliographic record
Abstract
Background: The cost and uptake of cancer medications dispensed as take-home treatments are not well understood. Therefore, in this study, we describe trends and the impact of SARS-CoV-2 on the utilization and cost of take-home cancer medications dispensed through the public payer system in Ontario, Canada. Methods: We conducted a repeated cross-sectional time-series analysis examining monthly and fiscal-year trends in the utilization and cost of take-home cancer medications reimbursed by the public payer between 1 April 2017 and 31 March 2021, in Ontario, Canada. Our primary outcome was per-beneficiary spending. Total public payer spending and the number of unique beneficiaries who were dispensed take-home cancer medications were reported as secondary outcomes. All outcomes were reported overall and stratified by drug class. We used autoregressive integrated moving average (ARIMA) models to assess the impact of the SARS-CoV-2 pandemic on the aforementioned trends. Results: Annual per-beneficiary spending on take-home cancer medications increased by 32.8% (from CAD 4422 in 2017/18 to CAD 6579 in 2020/21) over the study period. The rise in per-beneficiary spending was driven by the cost of medications within the small-molecule targeted therapy and immunotherapy drug classes, which accounted for three-quarters of total public payer spending on take-home cancer medications in 2020/21 despite being dispensed to less than 8% of beneficiaries. Upon the declaration of emergency for SARS-CoV-2, a short-term decline in per-beneficiary spending (CAD −179 per month; p-value < 0.01) was observed between March and June 2020. This temporary decline was driven by an increase in the number of beneficiaries (5582 per month; p-value < 0.01) receiving low-cost take-home cancer medications within the cytotoxic chemotherapy and hormonal therapy drug class without a corresponding rise in public payer spending. Conclusion: Future research should investigate barriers to the widespread uptake of take-home cancer medications during periods of public emergencies, particularly for high-cost drugs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".