Effects of large-scale oceanic phenomena on non-cholera vibriosis incidence in the United States: implications for climate change
Bibliographic record
Abstract
Non-cholera Vibrio (NCV) species are important causes of disease. These pathogens are thermophilic and climate change could increase the risk of NCV infection. The El Niño Southern Oscillation (ENSO) is a 'natural experiment' that may presage ocean warming effects on disease incidence. In order to evaluate possible climatic contributions to observed increases in NCV infection, we obtained NCV case counts for the United States from publicly available surveillance data. Trends and impacts of large-scale oceanic phenomena, including ENSO, were evaluated using negative binomial and distributed non-linear lag models (DNLM). Associations between latitude and changing risk were evaluated with meta-regression. Trend models demonstrated expected seasonality (P < 0.001) and a 7% (6.1%-8.1%) annual increase in incidence from 1999 to 2014. DNLM demonstrated increased vibriosis risk following ENSO conditions over the subsequent 12 months (relative risk 1.940, 95% confidence interval (CI) 1.298-2.901). The 'relative-relative risk' (RRR) of annual disease incidence increased with latitude (RRR per 10° increase 1.066, 95% CI 1.027-1.107). We conclude that NCV risk in the United States is impacted by ocean warming, which is likely to intensify with climate change, increasing NCV risk in vulnerable populations.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.007 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".