Infections with long latency in international refugees, immigrants, and migrants seen at GeoSentinel sites, 2016–2018
Bibliographic record
Abstract
BACKGROUND: The continued increase in global migration compels clinicians to be aware of specific health problems faced by refugees, immigrants, and migrants (RIM). This analysis aimed to characterize RIM evaluated at GeoSentinel sites, their migration history, and infectious diseases detected through screening and diagnostic workups. METHODS: A case report form was used to collect data on demographics, migration route, infectious diseases screened, test results, and primary infectious disease diagnosis for RIM patients seen at GeoSentinel sites. Descriptive statistics were performed. RESULTS: Between October 2016 and November 2018, 5,319 RIM patients were evaluated at GeoSentinel sites in 19 countries. Africa was the region of birth for 2,436 patients (46 %), followed by the Americas (1,644, 31 %), and Asia (1,098, 21 %). Tuberculosis (TB) was the most common infection screened and reported as positive (853/2,273, 38 % positive by any method). TB, strongyloidiasis, and hepatitis B surface antigen positivity were observed across all migration administrative categories and regions of birth. Chagas disease was reported only among RIM patients from the Americas (393/394, 100 %) and schistosomiasis predominantly in those from Africa (480/510, 94 %). TB infection (694/5,319, 13 %) and Chagas disease (524/5,319, 10 %) were the leading primary infectious disease diagnoses. CONCLUSIONS: Several infections of long latency (e.g. TB, hepatitis B, and strongyloidiasis) with potential for long-term sequelae were seen among RIM patients across all migration administrative categories and regions of origin. Obtaining detailed epidemiologic information from RIM patients is critical to optimize detection of diseases of individual and public health importance, particularly those with long latency periods.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".