Prevalence of neglected tropical diseases among migrants living in Europe: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Migration to Europe has intensified due to recent political conflicts, economic crises, and climate change, introducing an increased risk of neglected tropical diseases (NTDs) within this population. While NTDs typically impact tropical regions, their presence among migrants in Europe presents a growing challenge, compounded by limited research in this area. This study provides the first meta-analysis on the prevalence of NTDs in migrants across European nations. METHODS: A systematic review and meta-analysis was conducted focusing on studies that included NTD prevalence among migrant populations in Europe, with data sourced until July 2024. Cross-sectional and longitudinal studies were eligible, with bias assessed using the Newcastle-Ottawa Scale. Prevalence rates for various NTDs were calculated using a random-effects model, and meta-regressions were performed to assess potential moderators like sample size, age, and gender. RESULTS: A total of 148 studies comprising 228,798 migrants were analyzed. The most prevalent NTDs were strongyloidiasis (11.53 %) and schistosomiasis (10.8 %), with American trypanosomiasis also present. Dengue and lymphatic filariasis showed significant rates, though high heterogeneity was noted. Data quality was frequently low, with most studies at a high risk of bias. CONCLUSIONS: This study underscores the need for robust screening and diagnostic protocols in Europe for NTDs, particularly as clinician familiarity with these diseases is limited. Test-and-treat strategies appear promising, yet more comprehensive efforts are necessary. Establishing a European NTD registry could improve monitoring and management. Future studies should prioritize higher-quality data and address the barriers migrants face in accessing health services.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".