Local Transmission Plays No Important Role in the Occurrence of Multidrug-Resistant Tuberculosis in Immigrants to Canada: An In-depth Epidemiologic Analysis
Bibliographic record
Abstract
BACKGROUND: Multidrug-resistant (MDR) tuberculosis has increased among migrants in Canada. The cause(s) of this increase is unknown. METHODS: We performed a retrospective cohort study in a Canadian province with substantially increased immigration between 1982-2001 and 2002-2019. The proportion of MDR tuberculosis among migrants arriving from high MDR (HMDR) tuberculosis burden countries during these 2 periods was used to estimate the proportion of cases due to immigration versus change in proportion in the country of birth. Epidemiologic, spatiotemporal, and drug resistance pattern data were used to confirm local transmission. RESULTS: Fifty-two of 3514 (1.48%) foreign-born culture-positive tuberculosis patients had MDR tuberculosis: 8 (0.6%) in 1982-2001 and 44 (2.0%) in 2002-2019. Between time periods, the proportion of MDR tuberculosis among migrants with tuberculosis from HMDR tuberculosis countries increased from 1.11% to 3.62%, P = .003; 31.6% attributable to recent immigration and 68.4% to a higher proportion of MDR tuberculosis in cases arrived from HMDR tuberculosis countries. No cases of MDR tuberculosis were attributable to local transmission. CONCLUSIONS: In stark contrast to HMDR tuberculosis countries, local transmission plays no important role in the occurrence of MDR tuberculosis in Canada. Improved tuberculosis programming in HMDR tuberculosis countries is urgently needed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".