Cost‐Utility Analysis to Control <i>Campylobacter</i> on Chicken Meat—Dealing with Data Limitations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it