Trends in Medicare versus Medicaid spending on CFTR modulator therapy – an economic evaluation
Bibliographic record
Abstract
The introduction of cystic fibrosis transmembrane conductance regulator (CFTR) modulators has changed the landscape of therapy for persons with cystic fibrosis. However, the steep cost of targeted therapy poses significant financial burden for individuals and health systems. We aimed to determine the trends in Medicare and Medicaid spending on CFTR modulators between the years 2015 and 2022 through retrospective analysis of the Medicare and Medicaid claims data. The outcome measures included total dosage units prescribed, number of claims, spending per claim, and total spending on CFTR modulators for Medicare and Medicaid between 2015 to 2022. Average annual percentage changes (AAPC) were calculated for all outcome measures. Our results show that from 2015 to 2022, Medicaid consistently had higher total dosage units prescribed, number of claims, spending per claim, and overall spending on CFTR modulators compared to Medicare. Total spending for both Medicaid [AAPC 38.9, 95 % confidence interval [CI] 27.2-51.6, p < 0.01] and Medicare [AAPC 39.2, 95 % CI 30.2-55.1, p < 0.01] increased significantly during this period. Increases in CFTR spending was accelerated beginning in 2019, when the triple combination CFTR modulator therapy, elexacaftor/tezacaftor/ivacaftor, was introduced to the US market. This increase has been accompanied by a reduction in spending and use of other CFTR agents. This study evaluated the increases in spending for CFTR modulators over the years and the main drivers behind them, which may help inform future negotiations between healthcare systems and pharmaceutical companies, as well as policy makers and stakeholders.
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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".