1018. Health Technology Assessment of New Long-Acting, Directly-Observed HIV Treatments in Canada: Impact of Real-World Adherence to Daily Oral Therapy on Treatment, Transmission and Cost-Effectiveness
Bibliographic record
Abstract
Abstract Background Current antiretroviral therapy (ART) has dramatically improved outcomes for people living with HIV (PLWHIV), however adherence to daily oral dosing remains a challenge for some. New, long-acting (LA) ARTs which are directly administered by physicians eliminate the need to adhere to daily oral dosing and may improve clinical outcomes. The study objective was to evaluate costs and QALYs associated with improved adherence achieved via a novel, directly-observed therapy (DOT) of a monthly LA injectable ART, compared to standard of care (SoC), daily oral therapy. Methods A published Markov cohort state-transition model was adapted to model the impact of treatment adherence and subsequent disease transmission. Without the need to adhere to daily dosing, the efficacy of the injectable was modelled independent of adherence whereas virologic suppression in the SoC arm was adjusted to reflect published data on adherence to daily dosing (8.12% below optimal levels observed in clinical trials). Results This evidence-based approach of accounting for adherence revealed an increase in lifetime costs for oral SOC of approximately $850, and QALY loss of 0.109 when compared to results without accounting for adherence. Disease transmission results yielded 3 cases averted of HIV per 1,000 patients with LA’s impact on adherence. Conclusion In the absence of comparative adherence estimates between a LA, injectable DOT and daily oral therapy in the real world, an evidence-based approach provides a method to address the uncertainty around the true impact on costs and QALYs of a novel mode of administration. Disclosures Erin Arthurs, MSc, GlaxoSmithKline (Employee) Ben Parker, MSc, HEOR Ltd. (Employee) Ian Jacob, MSc, HEOR Ltd (Employee) Debbie Becker, MSc, GSK (Consultant) Amy Lee, MSc, PhD, GSK (Consultant) Olivia Hayward, PhD, HEOR ltd. (Employee) Vasiliki Chounta, MSc, GlaxoSmithKline (Shareholder)ViiV Healthcare (Employee) Sarah-Jane Anderson, PhD, GlaxoSmithKline (Employee, Shareholder) Nicolas Van de Velde, PhD, GlaxoSmithKline (Shareholder)ViiV Healthcare (Employee)
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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 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".