Budgetary impact of the utilization of buprenorphine/naloxone sublingual film and tablet for Medicaid in the United States
Bibliographic record
Abstract
OBJECTIVES: The buprenorphine/naloxone combination for the treatment of opioid dependence is available in a film or tablet formulation. Recent retrospective studies demonstrated that treatment with the sublingual film formulation is associated with improved treatment retention and lower healthcare costs. In March 2013, generic buprenorphine/naloxone tablets were approved in the US. A budget impact model was built to compare healthcare expenditures for different market shares of sublingual film and tablet. METHODS: A Markov model was developed to track a cohort of opioid dependent patients treated with sublingual film or tablet through the following treatment phases: initiation, maintenance, discontinuation, off-treatment and reinitiation. Transition probabilities and costs for each phase were estimated from the MarketScan Medicaid database for the period between 1 March 2010 and 30 June 2012. The total expenditure for the plan and expenditure per plan member per month were predicted over 5 years. Two market share scenarios were considered: 1) sublingual film is progressively replaced by generic tablet (current situation) and 2) the sublingual film holds a market share of 100%. RESULTS: Predicted total costs over 5 years were $6400 million when the sublingual film holds a market share of 100% (as per Scenario 2) which is lower than when sublingual film is progressively replaced by generic tablet (current situation as per Scenario 1) by $64 million. These savings were mostly driven by inpatient care ($56 million saved over 5 years), followed by emergency room care ($27 million) and pharmaceutical costs ($24 million). Costs of outpatient care attenuated the difference as they were predicted to be higher by $44 million in Scenario 2. The reduction in total cost per member per month reached $0.027 in the fifth year. Results were most sensitive to price rebates and to the probability of non-psychiatric hospitalization. CONCLUSIONS: While using the sublingual film formulation for more patients treated with buprenorphine/naloxone is predicted to increase outpatient care costs, it would generate savings in emergency care and hospitalizations. In the treatment of opioid dependence, total direct medical costs for Medicaid would be lower for sublingual film treated patients, at current drug prices.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".