Global epidemiology of dengue in paediatric and adolescent populations: a systematic review and meta-analysis
Bibliographic record
Abstract
Background Dengue infection is a major public health threat, especially to children.Objectives To comprehensively summarise the global epidemiological evidence on dengue in paediatric and adolescent populations by reporting incidence proportions, seroprevalence by region, country, and age group, and presenting pooled odds ratio (OR) estimates between age, sex, and infection risk.Methods MEDLINE and Embase were searched until April 2024. This meta-analysis included studies reporting or allowing calculation of incidence proportion, seroprevalence, and ORs; pooled estimates were derived using random-effects models.Results One-hundred fifty-eight articles were included. Incidence proportions were similar across the Americas, Africa, and Asia; country-specific incidence proportion was highest for Yemen (65%) and Brazil (53%). Seroprevalence was highest in Oceania and the Americas; country-specific seroprevalence was highest in Nicaragua (91%), and Honduras (88%). ORs of dengue infection did not differ between males and females [1.04 (95%CI: 0.95–1.14), p = 0.36]. However, dengue infection increased by age group with OR of 1.84 (95%CI: 1.51–2.24), p < 0.001 for children <10-years and 2.53 (95%CI: 2.03–3.16), p < 0.001 for those aged ≥10-years. The World Health Organisation’s 2018 and 2024 position statements recommend CYD-TDV vaccination in countries where the seroprevalence reaches ≥80% and TAK-003 where it is ≥60% by nine years of age. However, our data indicate that no country meets the 80% threshold, and very few reach 60% seroprevalence by age 10.Conclusion Dengue infection affects youth globally across all endemic regions, with similar incidence distribution among continents. These findings illuminate the global and country-specific dengue epidemiology, emphasising the need for enhanced mitigation measures to reduce viral spread and impact.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.032 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".