Preventing tuberculosis in the foreign-born population of Canada: a mathematical modelling study
Bibliographic record
Abstract
BACKGROUND: Foreign-born persons in Canada contribute 67% of all tuberculosis (TB) cases annually, but represent only 21% of the total population. Molecular epidemiological studies suggest that most foreign-born TB cases result from the reactivation of latent tuberculous infection (LTBI) acquired before immigration. OBJECTIVE: To estimate the effect on incidence of a prevention strategy that would screen selected immigrants at arrival for LTBI and offer preventive treatment to those who test positive. DESIGN: A deterministic model was developed to quantify the incidence of active TB in immigrants to Canada and validated with national immigration and TB case data. RESULTS: Model simulations suggested that it would be optimal to screen and treat LTBI in new immigrants from countries of birth with an estimated TB incidence rate in excess of 50 per 100 000 person-years. If this strategy had been implemented in 1986, the national TB incidence rate would have fallen by 18.5%, from 5.4 to 4.4 cases per 100 000 population by 2002. CONCLUSION: This study suggests that screening and treating LTBI in foreign-born persons from high TB incidence countries is the most effective strategy in terms of total persons screened and treated and percentage reduction in national incidence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".