Prevalence of Strongyloides and Schistosomiasis among migrants: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: The public health impact of Strongyloides stercoralis and Schistosoma spp, two neglected diseases endemic in tropical regions, has increased in North America, Europe and Australia due to increasing global migration. Both infections may result in significant morbidity and mortality that could be prevented through early screening and treatment. The burden of these diseases among migrants from endemic countries is poorly understood and has not been previously summarized. The aim of this study was to conduct a systematic review and meta-analysis of the prevalence of strongyloidiasis and schistosomiasis by different diagnostic methods among migrants born in endemic countries to inform screening guidelines for this population. Methods & Materials: Three electronic databases were searched from inception until March 1st, 2017 for studies reporting on the prevalence of Strongyloides and Schistosoma larvae/eggs by stool or urine microscopy or antibodies by serology. Pooled stool and serologic prevalence estimates for each parasite were calculated using a random-effects model. Heterogeneity was explored by stratifying the data by age, region of origin, migrant class, decade of study and type of serologic antigen used. Results: A total of 1981 articles were identified, full text review was conducted on 270 and 81 articles were included. Pooled Strongyloides seroprevalence was 12.7% (95% CI 8.6–17.5, I2 97%) and stool prevalence was 1.87% (1.21–2.68; 99%). Migrants from Sub-Saharan Africa, East Asia & Pacific and Latin America & Caribbean demonstrated the highest seroprevalence [11.5% (6.9-17.1) to 17.9% (2.6-42.8)]. Pooled Schistosoma seroprevalence was 16.72% (11.45–22.76; 97%) and stool prevalence was 0.69% (0.10–1.82; 99%). Sub-Saharan African (22.7% [14.8-31.7]) and Latin-American migrants (20% [9.0–34.0]) had highest seroprevalence and was 0% to 5% from other regions. Study results were limited by heterogeneity for all estimates and small sample size for certain sub-groups. Conclusion: Seroprevalence of Strongyloides was high among migrants from all endemic regions except Eastern Europe supporting broad based screening of migrants for this infection. Schistosoma seroprevalence was highest among migrants from Sub-Saharan Africa and Latin America supporting more a targeted screening approach.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.035 |
| Bibliometrics | 0.008 | 0.009 |
| 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) | 0.004 | 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".