Malaria after international travel: a GeoSentinel analysis, 2003–2016
Bibliographic record
Abstract
BACKGROUND: More than 30,000 malaria cases are reported annually among international travellers. Despite improvements in malaria control, malaria continues to threaten travellers due to inaccurate perception of risk and sub-optimal pre-travel preparation. METHODS: Records with a confirmed malaria diagnosis after travel from January 2003 to July 2016 were obtained from GeoSentinel, a global surveillance network of travel and tropical medicine providers that monitors travel-related morbidity. Records were excluded if exposure country was missing or unascertainable or if there was a concomitant acute diagnosis unrelated to malaria. Records were analyzed to describe the demographic and clinical characteristics of international travellers with malaria. RESULTS: There were 5689 travellers included; 325 were children <18 years. More than half (53%) were visiting friends and relatives (VFRs). Most (83%) were exposed in sub-Saharan Africa. The median trip duration was 32 days (interquartile range 20-75); 53% did not have a pre-travel visit. More than half (62%) were hospitalized; children were hospitalized more frequently than adults (73 and 62%, respectively). Ninety-two per cent had a single Plasmodium species diagnosis, most frequently Plasmodium falciparum (4011; 76%). Travellers with P. falciparum were most frequently VFRs (60%). More than 40% of travellers with a trip duration ≤7 days had Plasmodium vivax. There were 444 (8%) travellers with severe malaria; 31 children had severe malaria. Twelve travellers died. CONCLUSION: Malaria remains a serious threat to international travellers. Efforts must focus on preventive strategies aimed on children and VFRs, and chemoprophylaxis access and preventive measure adherence should be emphasized.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".