Contribution of influenza viruses to medically attended acute respiratory illnesses in children in high‐income countries: a meta‐analysis
Bibliographic record
Abstract
AIM: The burden of disease in children attributable to influenza viruses is difficult to quantify given the similarity of symptoms caused by infection due to influenza and other viruses. This uncertainty impacts clinical decision-making and estimates of burden. We aimed to systematically review the literature to determine the proportion of healthy children presenting for health care with an acute respiratory illness (ARI) who have laboratory-confirmed seasonal influenza (PROSPERO ID#CRD42014013896). METHOD: We searched Ovid MEDLINE, EMBASE, Scopus, and references of included articles. We included studies that used polymerase chain reaction methods to test for influenza in healthy children aged ≤5 years who presented for health care in high-income countries with an influenza-like or ARI. A standardized form was used to collect data on positivity and other relevant study elements. RESULTS: Seventeen studies covering 12 different influenza seasons were included. The proportion of influenza positivity ranged from 11% to 56%. Subgroup analyses were performed by influenza season, continent, healthcare setting, age group, and vaccination status. Higher influenza positivity was reported among children aged 3-5 years compared with children aged ≤2 years, and for unvaccinated children. CONCLUSION: The minority of healthy patients aged ≤5 years with medically attended influenza-like or acute respiratory symptoms have laboratory-confirmed influenza virus infection, although this varied by influenza season. Prevention efforts should be targeted accordingly. STATEMENT: Most influenza-like illnesses are not laboratory-confirmed and have similar clinical presentations. Consequently, the true contribution of influenza to acute respiratory infections in children remains uncertain. Our systematic review estimates that this proportion ranges from 11% to 56%. This finding can help both clinicians and public health professionals target prevention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".