Epidemiology of Adenovirus Infection in Hospitalized Children in the United States From 1997 to 2019
Bibliographic record
Abstract
OBJECTIVE: The study aimed to explore the prevalence, clinical features, resource utilization, temporal trends and outcomes associated with adenoviral infections in hospitalized children. METHODS: A retrospective analysis using the Healthcare Cost and Utilization Project's Kids' Inpatient Database from 1997 to 2019 was performed. Children 29 days to 17 years of age with adenoviral infection were selected. Chi-square, Kruskal-Wallis tests, linear trend analysis and multivariable analysis were used for data analysis. RESULTS: A total of 40,135 children under 18 years of age with adenoviral infection were discharged in the United States with an overall prevalence of 18.9 per 10,000 discharges and 6.9 children per 100,000 population. By linear trend analysis, the hospitalization rate has significantly increased with the highest prevalence in 2019. Adenoviral infection was more prevalent in Black children, in winter months, in the Midwest region, in children with government insurance and in the lowest income quartile. The majority (85%) of adenovirus-related hospitalizations occurred under 6 years of age. Mechanical ventilation, extracorporeal membrane oxygenation support, acute kidney injury and liver failure were documented in 11.9%, 0.4%, 2.7% and 0.4%, respectively. The overall case fatality rate was 1.4%, which decreased from 1997 to 2019 ( P < 0.05). By regression analysis, an increased mortality rate was associated with the need for mechanical ventilation, the presence of complex chronic conditions, immune deficiency, central nervous system infection and pneumonia/bronchiolitis. CONCLUSIONS: Most human adenovirus infections occur in children under 6 years of age and cause mild illness. Human adenovirus can lead to serious illness in children with complex chronic conditions and immune deficiency conditions.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".