Cost-effectiveness analysis of biologic sequential treatments for moderate-to-severe psoriasis: A Malaysian healthcare system perspective
Bibliographic record
Abstract
OBJECTIVE: In Malaysia, there is now a dearth of recommendations pertaining to the priority of biologic treatments for the effective management of psoriasis, given the multitude of available therapeutic alternatives. Present analysis reports results of a cost-effectiveness model that determines the most optimal arrangement of biologic treatments, with a particular focus of adding biosimilars to the existing treatment pathway for psoriasis in Malaysia. METHODS: A Markov model was developed to compare the cost effectiveness of various biologic sequential treatments in a hypothetical cohort of moderate to severe psoriasis patient in Malaysia over a lifetime horizon. The model simulated the progression of patients through three lines of active biologic therapy, before transitioning to best supportive care. Costs and effects were discounted annually at a rate of 3%. RESULTS: First line secukinumab has produced lowest incremental cost effectiveness ratios (ICERs) when compared to first line systemic [ICERs value; US$152,474 (first set analysis) and US$110,572 (second set analysis)] and first line phototherapy [ICERs value; US$147,057 (first set analysis) and US$107,616 (second set analysis)]. However, these values were slightly higher than the Malaysian based threshold of three times gross domestic product per capita, US$104,337. A 40% reduction in the unit costs of reference biologics renders most of the evaluated treatment sequences cost-effective. CONCLUSION: Adding biosimilar to the current treatment sequence could achieve cost savings ranging from 4.3% to 10.8% without significant loss of effectiveness. Given the significant impact of comorbidities and the resulting decline in quality of life among individuals with psoriasis, it may be justifiable to establish a threshold of up to US$184,000 per quality-adjusted life year (QALY) for the provision of therapies in the context of Malaysia.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".