Impact of Oral Targeted Therapy on the Economic Burden of Chronic Lymphocytic Leukemia in Canada
Bibliographic record
Abstract
Background: Continuous oral targeted therapy (OTT) for chronic lymphocytic leukemia (CLL) represents an effective therapy but also a major economic burden on the healthcare system. This study aimed to estimate future direct costs, along with the prevalence, of CLL in the era of continuous OTT in Canada. Methods: The economic burden of OTT was modelled and compared to chemoimmunotherapy (CIT), for CLL treatment. The burden was assessed/projected from 2011 to 2025. For the OTT scenario, CIT was considered the standard of care before 2015, while OTT was considered standard of care for patients with either unmutated immunoglobulin heavy-chain variable (IGHV) or del(17p)/TP53 mutations starting in 2015 and, from 2020 onwards, for all first-line treatments except for patients with mutated IGHV. A Markov model was developed including four health states: watchful-waiting, first-line treatment, relapse and death. Costs of therapy, follow-up/monitoring and adverse events were included. Key clinical parameters were extracted from pivotal clinical trials. Results: As incidence rates and rate of survival are increasing, the prevalence of CLL in Canada is projected to increase 1.8-fold, from 8301 patients in 2011 to 14,654 by 2025. Correspondingly, the total annual costs of CLL management are predicted to increase 15.7-fold, from $60.8 million to $957.5 million during that same period. Conclusions: Although OTT enhances survival for patients with CLL, it is nonetheless associated with an important economic burden due to the projected vast increase in costs from 2011 to 2025. Changes in clinical strategies, such as implementation of a fixed OTT treatment duration, could help alleviate financial burden.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".