Spectrum of Illness in International Migrants Seen at GeoSentinel Clinics in 1997–2009, Part 2: Migrants Resettled Internationally and Evaluated for Specific Health Concerns
Bibliographic record
Abstract
BACKGROUND: Increasing international migration may challenge healthcare providers unfamiliar with acute and long latency infections and diseases common in this population. This study defines health conditions encountered in a large heterogenous group of migrants. METHODS: Migrants seen at GeoSentinel clinics for any reason, other than those seen at clinics only providing comprehensive protocol-based health screening soon after arrival, were included. Proportionate morbidity for syndromes and diagnoses by country or region of origin were determined and compared. RESULTS: A total of 7629 migrants from 153 countries were seen at 41 GeoSentinel clinics in 19 countries. Most (59%) were adults aged 19-39 years; 11% were children. Most (58%) were seen >1 year after arrival; 27% were seen after >5 years. The most common diagnoses were latent tuberculosis (22%), viral hepatitis (17%), active tuberculosis (10%), human immunodeficiency virus (HIV)/AIDS (7%), malaria (7%), schistosomiasis (6%), and strongyloidiasis (5%); 5% were reported healthy. Twenty percent were hospitalized (24% for active tuberculosis and 21% for febrile illness [83% due to malaria]), and 13 died. Tuberculosis diagnoses and HIV/AIDS were reported from all regions, strongyloidiasis from most regions, and chronic hepatitis B virus (HBV) particularly in Asian immigrants. Regional diagnoses included schistosomiasis (Africa) and Chagas disease (Americas). CONCLUSIONS: Eliciting a migration history is important at every encounter; migrant patients may have acute illness or chronic conditions related to exposure in their country of origin. Early detection and treatment, particularly for diagnoses related to tuberculosis, HBV, Strongyloides, and schistosomiasis, may improve outcomes. Policy makers should consider expansion of refugee screening programs to include all migrants.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".