Identifying the environmental drivers of Campylobacter infection risk in southern Ontario, Canada using a One Health approachs
Bibliographic record
Abstract
BACKGROUND: Campylobacter bacteria infect both humans and animals. Sources of human exposure include contaminated food and water, contact with animals and/or their faeces, and contact with infected individuals. The objectives of this study were to: (a) identify environmental conditions associated with the occurrence of Campylobacter in humans in four regions of Ontario, and (b) identify pooled measures of effect across all four regions and potential sources of heterogeneity. METHODS: To address objective 1, human Campylobacter cases from four health regions of Ontario, Canada were analysed using negative binomial regression and case cross-over analysis to identify relationships between environmental factors (temperature, precipitation and hydrology of the local watershed) and the risk of human infection. To address objective 2, meta-analytic models were used to explore pooled measures of effect and when appropriate, meta-regression models were used to explore potential sources of heterogeneity. RESULTS: Human incidence exhibited strong seasonality with cases peaking in the late spring and summer. There was a decreasing yearly effect in three of the four health regions. A significant pooled effect was found for mean temperature after a 1-week lag (OR = 1.03, 95% CI 1.02, 1.04). No significant pooled effects were found for precipitation or water flow. However, increased precipitation was associated with lower odds of campylobacteriosis in Wellington and York regions at 2- and 3-week lags, respectively, from the case cross-over analysis. CONCLUSION: These results demonstrate that a climatic factor (specifically, mean temperature in the week prior) was associated with human case occurrence after a biologically plausible time period, but hydrologic factors are not.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".