Source attribution of campylobacteriosis in Australia circa 2018
Bibliographic record
Abstract
Abstract Background: Campylobacter spp. infections are the leading cause of foodborne gastroenteritis in high-income countries, including Australia. Campylobacter colonises a variety of mammalian and avian hosts that are reservoirs for human campylobacteriosis. Though most Australian outbreak investigations implicate chicken meat, the proportions of sporadic cases attributable to different animal reservoirs are unknown. Methods: Campylobacter isolates were obtained from notified human cases, and raw meat and offal from the major livestock in Australia: chickens, pigs, and ruminants (cattle and sheep) between 2017 and 2019. Isolates were speciated, with sequence types determined using multi-locus sequence genotyping. We used Bayesian source attribution models to estimate the proportion of human cases attributable to each livestock source by comparing the frequency of sequence types in cases and each animal source. We employed a model comparison approach with ten base models and explored adjusting these for age, gender, jurisdiction, rurality, and season. Four of the ten base models included an ‘unsampled’ source to estimate the proportion of cases attributable to wild, feral, or domestic animal reservoirs not sampled in our study. Results: We included 612 food and 710 human case isolates. The best fitting models attributed >80% of Campylobacter cases to chickens, with a greater proportion of Campylobacter coli (>84%) than Campylobacter jejuni (>77%). The best fitting model that included an unsampled source attributed 14% (95% CrI: 0.3-32%) to the unsampled source and only 2% to ruminants (95% CrI: 0.3-12%) and 2% to pigs (95% CrI: 0.2-11%.) The best fitting model that did not include an unsampled source attributed 12% to ruminants (95%CrI: 1.3-33%) and 6% to pigs (95%CrI: 1.1-19%.) Model fit was not improved by inclusion of case covariates. Conclusions: Chickens were the leading source of Campylobacter infections in our data and should remain the focus of interventions to reduce the burden in Australia.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.009 | 0.001 |
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".