Incidence and Clinical Profile of Norovirus Disease in Guatemala, 2008–2013
Bibliographic record
Abstract
Background: Acute gastroenteritis (AGE) is a leading infectious cause of morbidity worldwide, particularly among children in developing countries. With the decline of rotavirus disease rates following introduction of rotavirus vaccines, the relative importance of norovirus will likely increase. Our objectives in this study were to determine the incidence and clinical profile of norovirus disease in Guatemala. Methods: We analyzed data from a population-based surveillance study conducted in Guatemala from 2008 through 2013. Demographic information, clinical data, and stool samples were collected from patients who presented with AGE (≥3 liquid stools within 24 hours that initiated 7 days before presentation). Estimated incidence of hospitalized, outpatient, and total community norovirus disease was calculated using surveillance data and household surveys of healthcare use. Results: We included 999 AGE hospitalizations and 3189 AGE outpatient visits at facilities, of which 164 (16%) and 370 (12%), respectively, were positive for norovirus. Severity of norovirus was milder than of rotavirus. Community incidence of norovirus ranged from 2068 to 4954 per 100000 person-years (py) in children aged<5 years. Children aged <5 years also had higher incidence of norovirus-associated hospitalization (51-105 per 100000 py) compared with patients aged ≥5 years (0-1.6 per 100000 py and 49-80 per 100000 py, respectively). Conclusions: This study highlights the burden of norovirus disease in Guatemala, especially among young children. These data can help prioritize development of control strategies, including the potential use of vaccines, and provide a baseline to evaluate the impact of such interventions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".