Multidrug-resistant tuberculosis imported into low-incidence countries—a GeoSentinel analysis, 2008–2020
Bibliographic record
Abstract
BACKGROUND: Early detection of imported multidrug-resistant tuberculosis (MDR-TB) is crucial, but knowledge gaps remain about migration- and travel-associated MDR-TB epidemiology. The aim was to describe epidemiologic characteristics among international travellers and migrants with MDR-TB. METHODS: Clinician-determined and microbiologically confirmed MDR-TB diagnoses deemed to be related to travel or migration were extracted from GeoSentinel, a global surveillance network of travel and tropical medicine clinics, from January 2008 through December 2020. MDR-TB was defined as resistance to both isoniazid and rifampicin. Additional resistance to either a fluoroquinolone or a second-line injectable drug was categorized as pre-extensively drug-resistant (pre-XDR) TB, and as extensively drug-resistant (XDR) TB when resistance was detected for both. Sub-analyses were performed based on degree of resistance and country of origin. RESULTS: Of 201 patients, 136 had MDR-TB (67.7%), 25 had XDR-TB (12.4%), 23 had pre-XDR TB (11.4%) and 17 had unspecified MDR- or XDR-TB (8.5%); 196 (97.5%) were immigrants, of which 92 (45.8%) originated from the former Soviet Union. The median interval from arrival to presentation was 154 days (interquartile range [IQR]: 10-751 days); 34.3% of patients presented within 1 month after immigration, 30.9% between 1 and 12 months and 34.9% after ≥1 year. Pre-XDR- and XDR-TB patients from the former Soviet Union other than Georgia presented earlier than those with MDR-TB (26 days [IQR: 8-522] vs. 369 days [IQR: 84-827]), while patients from Georgia presented very early, irrespective of the level of resistance (8 days [IQR: 2-18] vs. 2 days [IQR: 1-17]). CONCLUSIONS: MDR-TB is uncommon in traditional travellers. Purposeful medical migration may partly explain differences in time to presentation among different groups. Public health resources are needed to better understand factors contributing to cross-border MDR-TB spread and to develop strategies to optimize care of TB-infected patients in their home countries before migration.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".