Cost-Effectiveness of Emerging Treatments for Atopic Dermatitis: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Numerous therapies have recently emerged for treatment of patients with atopic dermatitis (AD), a common skin disease, and understanding their cost-effectiveness is of high importance for policy makers. This systematic literature review (SLR) aimed to provide an overview of full economic evaluations that assessed cost-effectiveness of emerging AD treatments. METHODS: The SLR was conducted in Medline, Embase, UK National Health Service Economic Evaluation Database and EconLit. Reports published by the National Institute for Health and Care Excellence, the Institute for Clinical and Economic Review and the Canadian Agency for Drugs and Technologies in Health were manually searched. Economic evaluations published from 2017 to September 2022 that compared emerging AD treatments with any comparator were included. Quality assessment was conducted by using the Consensus on Health Economic Criteria list. RESULTS: A total of 1333 references were screened after removing duplicates. Among those references, 15 that conducted a total of 24 comparisons were included. Most studies were from the USA, UK or Canada. Seven different emerging treatments were compared, mostly with usual care. In 15 comparisons (63%), the emerging treatment was cost-effective, and 11 out of 14 dupilumab comparisons (79%) reported that dupilumab was cost-effective. Upadacitinib was the only emerging therapy that was never classified as cost-effective. On average, 13 out of 19 quality criteria (68%) per reference were rated as fulfilled while manuscripts and health technology reports received generally higher quality assessment scores than published abstracts. DISCUSSION: This study revealed some discrepancies in the cost-effectiveness of emerging therapies for AD. A variety of designs and guidelines made comparison difficult. Therefore, we recommend that future economic evaluations use more similar modelling approaches to improve comparability of results. OTHERS: The protocol was published in PROSPERO (ID: CRD42022343993).
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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 | 0.015 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.013 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".