Piecing the puzzle together: foreign-born tuberculosis in an immigrant-receiving country
Bibliographic record
Abstract
In major immigrant-receiving countries, annual foreign-born tuberculosis (TB) case counts and rates are relatively constant. Why this is so, and who might be a high-yield target for screening for latent TB infection, remain open questions. Foreign-born TB in Canada during 1986-2002 was retrospectively examined using national TB and immigration data as well as census data. Case counts and rates were analysed in relation to demographics, immigration period and time since arrival. Pre-1986 immigrants (n=3,860,853) and 1986-2002 immigrants (n=3,463,283) contributed 8,662 and 9,613 TB cases, respectively. Immigrants arriving ≤ 5 yrs ago and those arriving >10 yrs ago contributed almost equally to the annual foreign-born TB case count despite a 3.5-fold difference in in-country person-yrs. Remarkably stable and relatively low TB incidence was observed among immigrants >10 yrs post-arrival. Conversely, TB incidence within 5 yrs of arrival was dynamic, demonstrating a strong inverse association with time since arrival and higher sensitivity to changes in immigration level than shifts toward higher incidence source countries. Relative constancy in foreign-born TB incidence is explained by a complex convergence of several factors. Immigrants born in high-incidence countries who arrived ≤ 2 yrs ago and were aged 15-34 yrs upon arrival constitute high-yield targets for preventive therapy.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| 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".