Economic impact of elexacaftor/tezacaftor/ivacaftor on healthcare expenditure in Canada
Bibliographic record
Abstract
The introduction of elexacaftor/tezacaftor/ivacaftor (ETI) has led to improved outcomes and survival in patients living with cystic fibrosis (PwCF) although imposes a substantial economic burden. Despite the reduced healthcare utilization that follows ETI initiation, the economic impact on healthcare spending is not well understood. To try and better understand this, the estimated economic impact on healthcare spending of ETI was calculated in Canada. A treatment naïve cohort of PwCF receiving their first ETI prescription during the 2021-2022 fiscal year from 7 provinces had their healthcare utilization and costs collected one year prior and one year following the initiation of ETI for each patient. Data available included physician visits, emergency department presentations, hospitalizations, drug utilization and laboratory and other diagnostic charges. In the year prior to the first ETI prescription, there was an estimated direct health care cost of $17.6 million CDN. The spending decreased significantly in the year post ETI by $6.9 million with the majority attributed to a 75% reduction in hospitalization-associated costs. When the list price of ETI is accounted for, up to an additional $203 million was spent in the first year after ETI. Irrespective of improvements in life quality brought about by ETI, a price of approximately $10,000/year would be required for it to be cost neutral.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".