Cost‐Utility Analysis to Control <i>Campylobacter</i> on Chicken Meat—Dealing with Data Limitations
Bibliographic record
Abstract
The current article describes the economic evaluation of interventions to control Campylobacter on chicken meat by means of a cost-utility analysis. Apart from the methodology used, the main focus of this article is on data gaps and assumptions made, and their impact on results and conclusions. The direct intervention costs, the relative risk, the disease burden (expressed in disability-adjusted life years (DALYs)), and the costs of illness for the various interventions are necessary inputs for the cost-utility analysis. The cost-utility ratio (CUR) -- the measure for efficiency -- is expressed in net costs per avoided DALY. Most data gaps were of a biological order, but for some interventions, information on costs was also scarce. As a consequence, assumptions had to be made, which had some impact on the estimated CUR. A higher (lower) incidence of Campylobacter infections associated with chicken meat, higher (lower) effectiveness, and lower (higher) intervention costs, respectively, would result in absolute better (worse) CUR estimates. By taking the perspective of all consumers eating Dutch chicken meat, rather than only the Dutch society, absolute better CUR estimates could be obtained. Indirect costs or a shift toward non-Dutch chicken meat would both result in higher CUR estimates. Despite the assumptions made, three interventions showed for most of the applied sensitivity analyses relatively favorable CUR estimates: limiting fecal leakage during processing, carcass decontamination by dipping in a chemical solution, and the phage therapy. However, all three do have some clauses.
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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 | 0.039 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".