Prevalence of strongyloidiasis and schistosomiasis among migrants: a systematic review and meta-analysis
Bibliographic record
Abstract
Background Global migration from regions where strongyloidiasis and schistosomiasis are endemic to non-endemic countries has increased the potential individual and public health effect of these parasitic diseases. We aimed to estimate the prevalence of these infections among migrants to establish which groups are at highest risk and who could benefit from screening. Methods We did a systematic review and meta-analysis of strongyloidiasis and schistosomiasis prevalence among migrants born in endemic countries. Original studies that included data for the prevalence of Strongyloides or Schistosoma antibodies in serum or the prevalence of larvae or eggs in stool or urine samples among migrants originating from countries endemic for these parasites and arriving or living in host countries with low endemicity—specifically the USA, Canada, Australia, New Zealand, Israel, and 23 western European countries—were eligible for inclusion. Pooled estimates of the prevalence of strongyloidiasis and schistosomiasis by stool or urine microscopy for larvae or eggs or serum antibodies were calculated with a random-effects model. Heterogeneity was explored by stratification by age, region of origin, migrant class, period of study, and type of serological antigen used. Findings 88 studies were included. Pooled strongyloidiasis seroprevalence was 12·2% (95% CI 9·0–15·9%; I 2 96%) and stool-based prevalence was 1·8% (1·2–2·6%; 98%). Migrants from east Asia and the Pacific (17·3% [95% CI 4·1–37·0]), sub-Saharan Africa (14·6% [7·1–24·2]), and Latin America and the Caribbean (11·4% [7·8–15·7]) had the highest seroprevalence. Pooled schistosomiasis seroprevalence was 18·4% (95% CI 13·1–24·5; I 2 97%) and stool-based prevalence was 0·9% (0·2–1·9; 99%). Sub-Saharan African migrants had the highest seroprevalence (24·1·% [95% CI 16·4–32·7]). Interpretation Strongyloidiasis affects migrants from all global regions, whereas schistosomiasis is focused in specific regions and most common among sub-Saharan African migrants. Serological prevalence estimates were several times higher than stool estimates for both parasites. These data can be used to inform screening decisions for migrants and support the use of serological screening, which is more sensitive and easier than stool testing. Funding None.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.034 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".