Is It Time to Re-think the Post-immigration Tuberculosis Surveillance System in Canada?
Bibliographic record
Abstract
Background. The post-arrival tuberculosis (TB) surveillance system in Canada is inefficient. Ideally, it identifies new permanent residents at increased risk of post-arrival TB. However, the current system has low coverage, poor post-arrival compliance, and low rates of active TB diagnosis in the immediate post-arrival period. The focus is not on latent TB infection (LTBI) diagnosis and/or treatment. The objective of this study was to simulate the effect of improving the current two-year post-immigration surveillance system on TB incidence. Methods. The 2014 Canadian permanent resident cohort was used as the reference population for this evaluation. A discrete event simulation model was developed in Simio, simulating pre-entry surveillance flagging, post-immigration follow-up, LTBI diagnosis and treatment, and incident TB over a two-year time horizon. Post-immigration TB incidence data from Ontario and California were used to populate the model. The model was validated against two-year TB incidence in Ontario. Increased LTBI treatment uptake and surveillance compliance and their impact on two-year TB incidence were modeled. Results. The current surveillance system flags 2.4% of new permanent residents for surveillance, with 59.8% complying post-arrival, and 15-68% completing LTBI treatment. TB incidence in the first two years under the current system was predicted to be 27.3 per 100,000 person years (PY). Removing the surveillance program entirely would result in 27.7 cases per 100,000 PY, corresponding to only a 1.5% increase. A perfect program with full surveillance compliance and LTBI treatment completion would result in 26.2 cases per 100,000 PY, corresponding to a 4.4% decrease. Conclusion. The current post-landing surveillance program does little to prevent active TB diagnoses in Canada. Canada could consider shifting to pre-immigration LTBI screening and treatment, reconsider criteria for surveillance to target LTBI post-landing, or help support TB control efforts in high TB incidence countries. Disclosures. All authors: No reported disclosures.
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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 | 0.007 | 0.017 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 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".