Trends in Prevalence of Bacterial Infections in Febrile Infants During the COVID-19 Pandemic
Bibliographic record
Abstract
OBJECTIVES: Our objective was to describe the prevalence of urinary tract infection (UTI) and invasive bacterial infection (IBI) in febrile infants during the coronavirus disease 2019 pandemic. METHODS: We conducted a multicenter cross-sectional study that included 97 hospitals in the United States and Canada. We included full-term, well-appearing infants 8 to 60 days old with a temperature of ≥38°C and an emergency department visit or hospitalization at a participating site between November 1, 2020 and March 31, 2022. We used logistic regression to determine trends in the odds of an infant having UTI and IBI by study month and to determine the association of COVID-19 prevalence with the odds of an infant having UTI and IBI. RESULTS: We included 9112 infants; 603 (6.6%) had UTI, 163 (1.8%) had bacteremia without meningitis, and 43 (0.5%) had bacterial meningitis. UTI prevalence decreased from 11.2% in November 2020 to 3.0% in January 2022. IBI prevalence was highest in February 2021 (6.1%) and decreased to 0.4% in January 2022. There was a significant downward monthly trend for odds of UTI (odds ratio [OR] 0.93; 95% confidence interval [CI]: 0.91-0.94) and IBI (OR 0.90; 95% CI: 0.87-0.93). For every 5% increase in COVID-19 prevalence in the month of presentation, the odds of an infant having UTI (OR 0.97; 95% CI: 0.96-0.98) or bacteremia without meningitis decreased (OR 0.94; 95% CI: 0.88-0.99). CONCLUSIONS: The prevalence of UTI and IBI in eligible febrile infants decreased to previously published, prepandemic levels by early 2022. Higher monthly COVID-19 prevalence was associated with lower odds of UTI and bacteremia.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".