Patient perspectives on de‐simplifying their single‐tablet co‐formulated antiretroviral therapy for societal cost savings
Bibliographic record
Abstract
OBJECTIVES: The incremental costs of expanding antiretroviral (ARV) drug treatment to all HIV-infected patients are substantial, so cost-saving initiatives are important. Our objectives were to determine the acceptability and financial impact of de-simplifying (i.e. switching) more expensive single-tablet formulations (STFs) to less expensive generic-based multi-tablet components. We determined physician and patient perceptions and acceptance of STF de-simplification within the context of a publicly funded ARV budget. METHODS: Programme costs were calculated for patients on ARVs followed at the Southern Alberta Clinic, Canada during 2016 (Cdn$). We focused on patients receiving Triumeq® and determined the savings if patients de-simplified to eligible generic co-formulations. We surveyed all prescribing physicians and a convenience sample of patients taking Triumeq® to see if, for budgetary purposes, they felt that de-simplification would be acceptable. RESULTS: Of 1780 patients receiving ARVs, 62% (n = 1038) were on STF; 58% (n = 607) of patients on STF were on Triumeq®. The total annual cost of ARVs was $26 222 760. The cost for Triumeq® was $8 292 600. If every patient on Triumeq® switched to generic abacavir/lamivudine and Tivicay® (dolutegravir), total costs would decrease by $4 325 040. All physicians (n = 13) felt that de-simplifying could be safely achieved. Forty-eight per cent of 221 patients surveyed were agreeable to de-simplifying for altruistic reasons, 27% said no, and 25% said maybe. CONCLUSIONS: De-simplifying Triumeq® generates large cost savings. Additional savings could be achieved by de-simplifying other STFs. Both physicians and patients agreed that selective de-simplification was acceptable; however, it may not be acceptable to every patient. Monitoring the medical and cost impacts of de-simplification strategies seems warranted.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".