Effects of Large-Scale Oceanic Phenomena on Non-Cholera Vibriosis Incidence in the United States: Implications for Climate Change
Bibliographic record
Abstract
Abstract Purpose Epidemics of diarrhea caused by toxigenic strains of Vibrio cholerae are of global public health concern, but non-cholera Vibrio (NCV) species are also 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. Method We obtained vibriosis case counts in the United States by digitizing annual reports from the U.S. Cholera and Other Vibrio Illness Surveillance system. Trends and environmental impacts (of ENSO and the North Atlantic Oscillation) were evaluated using negative binomial and distributed nonlinear lag models. Associations between latitude and changing risk were evaluated with meta-regression. Results Trend models demonstrated significant seasonality (P < 0.001) and a 7% annual increase in disease risk from 1999 to 2014 (annual IRR 1.071, 95% CI 1.061-1.081). Distributed lag models demonstrated increased vibriosis risk following ENSO conditions over the subsequent 12 months (integrated RR 1.940, 95% CI 1.298-2.901). The rate of change in vibriosis risk increased with state latitude (RR per 10° increase 1.066, 95% CI 1.027-1.107). Conclusion Vibriosis risk in the United States appears to be impacted by irregular large-scale ocean warming and exhibits a north-south gradient in rate of change as would be expected if changing disease incidence is attributable to ocean warming. Vulnerable populations, which include high-income countries with well-developed public health systems, may experience increased risk of this disease as a result of climate change.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.011 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".