4.10-P3Epidemiology of tuberculosis among refugees in British Columbia, Canada: retrospective cohort study
Bibliographic record
Abstract
Background: In order to develop more effective tuberculosis (TB) prevention programmes among refugee populations in Canada, better understanding is needed about who is most at risk of developing active TB disease after migration. Our objective was to describe the epidemiology of TB in a refugee population in British Columbia (BC), Canada over a 29-year period, and to identify factors predictive of TB development. Methods: This retrospective cohort included all individuals who became permanent residents of Canada within a refugee immigration class between 1985 and 2012, and were residents of BC at any time between 1985 and 2013. Multiple administrative databases and disease registries were linked to the provincial TB registry. We identified predictors of TB development after immigration to BC using Cox proportional hazards regression. Results: The cohort included 86,803 people from 184 different countries/territories. Active TB developed in 362 (0.4%) people, with a median time to diagnosis of 4.2 years after immigration. Significant predictors of TB included: high TB-incidence (>100 TB cases/100,000 population) in birth country (adjusted hazard ratio(HR)=7.8; 95%CI: 5.7, 10.7), being a government-sponsored vs asylum refugee (HR = 1.8; 1.3,2.4), aged over 50 years at time of immigration (HR = 2.7; 1.6,4.5), and having TB-related co-morbidities (HR = 2.5; 1.8,3.5) or contact with an active TB case (HR = 6.9; 4.6,10.3). Conclusions: Refugees in BC represented a diverse population. Most active TB was diagnosed within the first 5 years after immigration as refugees, with the highest TB incidence rates observed in adults migrating from high TB-incidence birth countries. Main messages: Our results support current Canadian TB Standards recommendations. Targeting of preventive TB screening and treatment programmes for refugee populations in BC should focus on adults migrating from high TB-incidence countries.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 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 | 0.001 |
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".