The burden of tuberculosis among foreign-born Canadians: estimates with dynamic models
Bibliographic record
Abstract
Abstract Background Despite only comprising about a quarter of the total population of Canada, foreign-born individuals bear about three-quarters of the burden of active tuberculosis (TB) cases. New immigrants arriving in Canada are screened for active TB, but generally not for latent TB infection (LTBI); thus the burden of LTBI among foreign-born Canadians is not well understood. Methods To investigate the impact of immigration on the burden of TB among foreign-born Canadians, we develop an SEIR-compartment model that distinguishes between actively infected, latently infected, and uninfected individuals. Unknown parameters are calibrated to reports on the incidence and prevalence of active TB in Canada. We validate our model by comparing model computed quantities to other estimates of tuberculosis burden among foreign-born Canadians, including an estimate of the prevalence of LTBI among immigrants entering Canada. Results If the profile and number of immigrants arriving into Canada in the next decade is similar to the past decade, our model predicts that among the foreign-born population, Canada will not meet the End TB 2035 goal of reducing incidence by 90% compared to 2015. In fact, Canada would still fail to meet the incidence goal if no new immigrants are allowed to enter, primarily due to the activation of foreign-born Canadians with LTBI. Author summary Our model examines how immigration affects the burden of tuberculosis among foreign-born Canadians. We fit a compartment model to calibration data, then find a feasible parameter set based on validation data. We forecast the incidence of TB in 2035 and demonstrate that regardless of which WHO geographic region immigrants originate from, Canada will fail to meet the WHO End TB’s 2035 incidence goal (90% less than 2015) among the foreign-born population.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".