The impact of public coverage of newer hepatitis C medications on utilization, adherence, and costs in British Columbia
Bibliographic record
Abstract
BACKGROUND: Sofosbuvir and ledipasvir-sofosbuvir are both newer direct-acting antiviral agents for the treatment of hepatitis C. The high list prices for both drugs have led to concern about the budget impact for public drug coverage programs. Therefore, we studied the impact of public prescription drug coverage for both drugs on utilization, adherence, and public and private expenditure in British Columbia, Canada. METHODS: We used provincial administrative claims data from January 2014 to June 2017 for all individuals historically tested for either hepatitis C and/or human immunodeficiency virus. Using interrupted time series analysis, we examined the impact of public insurance coverage on treatment uptake, adherence (proportion of days covered), and public and private expenditures. RESULTS: Over our study period, 4,462 treatment initiations were eligible for analysis (1,131 sofosbuvir and 3,331 ledipasvir-sofosbuvir, which include 19 patients initiated on both treatments). We found the start of public coverage for sofosbuvir and ledipasvir-sofosbuvir increased treatment uptake by 154%. Adherence rates were consistently high and did not change with public coverage. Finally, public expenditure increased after the policy change, and crowded out some private expenditure. CONCLUSION: Public coverage for high-cost drugs for hepatitis C dramatically increased use of these drugs, but did not reduce adherence. From a health policy perspective, public payers should be prepared for increased treatment uptake following the availability of public coverage. However, they should not be concerned that populations without private insurance coverage will be less adherent and not finish their treatment course.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".