Estimated Impact of World Health Organization Latent Tuberculosis Screening Guidelines in a Region With a Low Tuberculosis Incidence: Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Latent tuberculosis infection (LTBI) screening and treatment is a key component of the World Health Organization (WHO) EndTB Strategy, but the impact of LTBI screening and treatment at a population level is unclear. We aimed to estimate the impact of LTBI screening and treatment in a population of migrants to British Columbia (BC), Canada. METHODS: This retrospective cohort included all individuals (N = 1 080 908) who immigrated to Canada as permanent residents between 1985 and 2012 and were residents in BC at any time up to 2013. Multiple administrative databases were linked to identify people with risk factors who met the WHO strong recommendations for screening: people with tuberculosis (TB) contact, with human immunodeficiency virus, on dialysis, with tumor necrosis factor-alpha inhibitors, who had an organ/haematological transplant, or with silicosis. Additional TB risk factors included immunosuppressive medications, cancer, diabetes, and migration from a country with a high TB burden. We defined active TB as preventable if diagnosed ≥6 months after a risk factor diagnosis. We estimated the number of preventable TB cases, given optimal LTBI screening and treatment, based on these risk factors. RESULTS: There were 16 085 people (1.5%) identified with WHO strong risk factors. Of the 2814 people with active TB, 118 (4.2%) were considered preventable through screening with WHO risk factors. Less than half (49.4%) were considered preventable with expanded screening to include people migrating from countries with high TB burdens, people who had been prescribed immunosuppressive medications, or people with diabetes or cancer. CONCLUSIONS: The application of WHO LTBI strong recommendations for screening would have minimally impacted the TB incidence in this population. Further high-risk groups must be identified to develop an effective LTBI screening and treatment strategy for low-incidence regions.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".