Enhanced screening for tuberculosis infection among immigrants in southern New Brunswick: A cross-sectional pilot study
Bibliographic record
Abstract
Background: In 2021, approximately 77% of active tuberculosis (TB) disease (TBD) cases in Canada were among foreign-born individuals. Less than 3% of TBD cases in Canada are detected through pre-arrival Canadian immigration medical examinations (i.e., chest X-rays), and the remaining 97% are likely due to reactivation of undiagnosed latent TB infection (TBI) post-arrival. In New Brunswick, the proportion of TBD cases among foreign-born individuals gradually increased from about 33% (1/3 individuals) in 2013 to 100% (14/14 individuals) in 2023. The objective of this study was to estimate the prevalence of TBI among immigrants in southern New Brunswick, identify potential predictors for positive TBI screening and assess participant experiences with the pilot TBI screening procedure. Methods: A cross-sectional study was conducted from November 2021 to November 2023 among immigrants ≥19 years old who had no history of TBD and were born in a country with a TB incidence rate of ≥40/100,000 population or were referred by healthcare professionals. Participants were recruited through various channels and underwent TBI screening using the interferon-gamma release assay, followed by a survey on their screening experience. Results: Of the 264 participants, 49 (18.6%) screened positive for TBI. Factors associated with higher odds of screening TBI-positive included birthplace in a "highly to severely endemic" (≥300/100,000 population) TB-incidence country (OR=3.24; 95% CI: 1.07-9.81) and increased age (OR=1.05; 95% CI: 1.01-1.08). Participants rated the pilot TBI screening procedure positively (mean scores ranged from 4.03-4.55 on a five-point Likert scale). Conclusion: Results suggest that immigrants born in countries with TB incidences of ≥300/100,000 population should be considered for screening and treatment of TBI. The pilot TBI screening procedure yielded positive feedback. Further research with a larger sample is recommended.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".