Burden and Cost of <i>Campylobacter</i> Risk Factors in Australia
Bibliographic record
Abstract
Campylobacter is a globally important pathogen with well-studied risk factors, but the burden of risk factors has not been quantified. We quantified the cost of illness attributable to specific domestic risk factors for C. jejuni and C. coli in Australia. We used data from a 2018–2019 case–control study to estimate odds ratios and attributable fractions for risk factors. We used data on national incidence, hospitalization, and premature mortality to quantify burden. We then applied costs related to healthcare utilization, pain and suffering, premature mortality, and lost productivity to each risk factor. In Australia, C. jejuni caused 83.0% of campylobacteriosis infections and chicken consumption resulted in the highest attributable fraction (30.0%), costing approximately US$110 million annually. The excess burden of campylobacteriosis associated with the use of proton-pump inhibitors (PPIs) was US$45 million, with almost half these costs due to disease in adults over 65 years of age. Contact with young dogs (US$30 million) and chicken feces (US$10 million) also contributed to costs and burden. Campylobacteriosis is a significant cost to Australia, particularly because of lost productivity. Effective cross-sectoral interventions to improve chicken meat safety and reduce inappropriate use of PPIs might have substantial economic and human benefits.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".