Incidence of hydatid disease in children: A systematic review and meta-analysis
Bibliographic record
Abstract
Hydatid disease (echinococcosis) is a zoonotic infection caused by Echinococcus species, characterized by cyst formation in various organs, most commonly the liver and lungs. While often asymptomatic in the early stages, progressive cyst growth can lead to severe complications, including organ dysfunction, secondary infections, or rupture. In this review, we aimed to assess the incidence of hydatid disease among pediatric populations across different regions. We searched PubMed, Scopus, Cochrane, and Web of Science for studies reporting the incidence of hydatid disease in children up to February 20, 2025. A random-effects meta-analysis was performed using STATA version 28. Subgroup analyses were conducted based on country, geographic region, and cyst location. Study quality was assessed using the Newcastle–Ottawa Scale. We pooled data from nine studies, yielding an overall incidence of 3.37 per 1,000 children (95% confidence interval [CI]: 0.52–8.48), with substantial heterogeneity ( I 2 = 99.97%). The highest incidence was reported in China (17.86 per 1,000), followed by Turkey (1.48 per 1,000) and Bulgaria (1.47 per 1,000). Lower incidence rates were reported in Iran (0.62 per 1,000) and Romania (0.12 per 1,000). Studies conducted in rural areas showed a higher incidence (14.84 per 1,000) compared to those including patients from diverse geographic regions (1.91 per 1,000). Based on the available evidence, we conclude that the incidence of hydatid cysts in children varies across countries, with the highest rates observed in China and in rural areas. Echinococcosis poses a significant threat to both public health and livestock; therefore, effective monitoring and control strategies are crucial to reduce its 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 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.036 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".