The Prevalence of Tuberculosis Infection Among Foreign-Born Canadians: A Modelling Study
Bibliographic record
Abstract
Abstract Background The prevalence of tuberculosis infection (TBI) is critical to designing tuberculosis (TB) prevention strategies, yet it is unknown in Canada. We estimated TBI prevalence among foreign-born Canadians. Methods Using a previously developed Gaussian process regression model, annual risk of infection trends abroad were constructed and used to estimate TBI prevalence by age and year of migration to Canada for persons from each of 168 countries. These stratified TBI prevalence estimates were combined with Canadian census data to estimate overall TBI prevalence among foreign-born residents during census years 2001, 2006, 2011, and 2016. We also estimated TBI prevalence according to age, WHO-estimated TB incidence in country of origin, and province/territory of residence. Finally, we estimated the prevalence of TBI acquired within the two preceding years. Results Estimated TBI prevalence among foreign-born Canadians overall was 25% (95% uncertainty interval [UI]: 20-35%), 24% (20-33%), 23% (19-30%), and 22% (19-28%) for census years 2001, 2006, 2011, and 2016, respectively. TBI prevalence increased with age at migration and TB incidence in the country of origin. In 2016, estimated TBI prevalence was lowest in Quebec (19%, 95% UI: 16-25) and highest in Alberta and British Columbia, at 24% each. Among all foreign-born Canadian residents with TBI in 2016, we estimated that only 1 in 440 were infected within the two preceding years. Interpretation Approximately 1 in 4 foreign-born Canadians has TBI; estimated prevalence has remained quite stable over the last two decades. However, a very small minority of people with TBI were infected within the last two years—the highest risk period for progression to TB disease. These data may inform future TBI screening policies.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".