Prevention and Management of High-Burden Noncommunicable Diseases in School-Age Children: A Systematic Review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Noncommunicable diseases (NCDs) are chronic conditions requiring health care, education, social and community services, addressing prevention, treatment, and management. This review aimed to summarize and synthesize the available evidence on interventions from systematic reviews of high-burden NCDs and risk factors among school-aged children. METHODS: The following databases were used for this research: Medline, Embase, The Cochrane Library, and the Campbell library. The search dates were from 2000 to 2021. We included systematic reviews that synthesized studies to evaluate intervention effectiveness in children aged 5 to 19 years globally. Two reviewers independently extracted data and assessed methodological quality of included reviews using the AMSTAR 2 tool. RESULTS: Fifty studies were included. Asthma had the highest number of eligible reviews (n = 19). Of the reviews reporting the delivery platform, 27% (n = 16) reported outpatient settings, 13% (n = 8) home and community-based respectively, and 8% (n = 5) school-based platforms. Included reviews primarily (69%) reported high-income country data. This may limit the results' generalizability for school-aged children and adolescents in low- and middle- income countries. CONCLUSIONS: School-aged children and adolescents affected by NCDs require access to quality care, treatment, and support to effectively manage their diseases into adulthood. Strengthening research and the capacity of countries, especially low- and middle- income countries, for early screening, risk education and management of disease are crucial for NCD prevention and control.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".