Interventions for High-Burden Infectious Diseases in Children and Adolescents: A Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Approximately 2.2 million deaths were reported among school-age children and young people in 2019, and infectious diseases remain the leading causes of morbidity and mortality, especially in low and middle-income countries. We aim to synthesize evidence on interventions for high-burden infectious diseases among children and adolescents aged 5 to 19 years. METHODS: We conducted a comprehensive literature search until December 31, 2020. Two review authors independently screened studies for relevance, extracted data, and assessed risk of bias. RESULTS: We included a total of 31 studies, including 81 596 participants. Sixteen studies focused on diarrhea; 6 on tuberculosis; 2 on human immunodeficiency virus; 2 on measles; 1 study each on acute respiratory infections, malaria, and urinary tract infections; and 2 studies targeted multiple diseases. We did not find any study on other high burden infectious diseases among this age group. We could not perform meta-analysis for most outcomes because of variances in interventions and outcomes. Findings suggests that for diarrhea, water treatment, water filtration, and zinc supplementation have some protective effect. For tuberculosis, peer counseling, contingency contract, and training of health care workers led to improvements in tuberculosis detection and treatment completion. Continuation of cotrimoxazole therapy reduced the risk of tuberculosis and hospitalizations among human immunodeficiency virus-infected children and reduced measles complications and pneumonia cases among measles-infected children. Zinc supplementation led to a faster recovery in urinary tract infections with a positive effect in reducing symptoms. CONCLUSIONS: There is scarcity of data on the effectiveness of interventions for high-burden infectious diseases among school-aged children and adolescents.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.057 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".