Cutaneous and mucocutaneous leishmaniasis in travellers and migrants: a 20-year GeoSentinel Surveillance Network analysis
Bibliographic record
Abstract
BACKGROUND: Cutaneous leishmaniasis (CL) may be emerging among international travellers and migrants. Limited data exist on mucocutaneous leishmaniasis (MCL) in travellers. We describe the epidemiology of travel-associated CL and MCL among international travellers and immigrants over a 20-year period through descriptive analysis of GeoSentinel data. METHODS: Demographic and travel-related data on returned international travellers diagnosed with CL or MCL at a GeoSentinel Surveillance Network site between 1 September 1997 and 31 August 2017 were analysed. RESULTS: A total of 955 returned travellers or migrants were diagnosed with travel-acquired CL (n = 916) or MCL during the study period, of whom 10% (n = 97) were migrants. For the 858 non-migrant travellers, common source countries were Bolivia (n = 156, 18.2%) and Costa Rica (n = 97, 11.3%), while for migrants, they were Syria (n = 34, 35%) and Afghanistan (n = 22, 22.7%). A total of 99 travellers (10%) acquired their disease on trips of ≤ 2 weeks. Of 274 cases for which species identification was available, Leishmania Viannia braziliensis was the most well-represented strain (n = 117, 42.7%), followed by L. major (n = 40, 14.6%) and L. V. panamensis (n = 38, 13.9%). Forty cases of MCL occurred, most commonly in tourists (n = 29, 72.5%) and from Bolivia (n = 18, 45%). A total of 10% of MCL cases were acquired in the Old World. CONCLUSIONS: Among GeoSentinel reporting sites, CL is predominantly a disease of tourists travelling mostly to countries in Central and South America such as Bolivia where risk of acquiring L. V. braziliensis and subsequent MCL is high. The finding that some travellers acquired leishmaniasis on trips of short duration challenges the common notion that CL is a disease of prolonged travel. Migrants from areas of conflict and political instability, such as Afghanistan and Syria, were well represented, suggesting that as mass migration of refugees continues, CL will be increasingly encountered in intake countries.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".