Health Economic Evaluations in Immunotherapy and Biologic Treatments for Food Allergy: A Systematic Review
Bibliographic record
Abstract
The use of immunotherapy and biologics has garnered increased interest as a potential treatment option for managing food allergies. Food allergy imposes significant economic burdens through treatment costs, healthcare utilisation, and reductions in health-related quality of life (HRQoL). We conducted a comprehensive systematic review to identify studies evaluating cost-effectiveness in immunotherapy and biologics in food allergy management. Findings indicate that non-commercial oral immunotherapy is the dominant economic strategy compared to no treatment, offering lower costs and improved HRQoL. In comparison, commercial products frequently exceeded cost-effectiveness thresholds compared to no treatment. Biologics such as omalizumab were less cost-effective compared to no treatment. Variability in health state utility calculations, cost inputs and models were noted among the eight included studies. The most often reported levers for cost-effectiveness on sensitivity analysis were the health state utility impact for food allergy and the HRQoL benefits associated with treatment. Overall, this review summarises the economic evaluations to date for immunotherapy and biologics in food allergy management. Future research should refine utility measurements, consider the direct and indirect costs of food allergy and integrate patient-centred perspectives and long-term treatment outcomes to better inform policy decisions and resource allocation in the evolving landscape of food allergy therapies.
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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 | 0.013 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".