Tuberculosis elimination in the Canadian First Nations population: assessment by a state-transfer, compartmental epidemic model
Bibliographic record
Abstract
BACKGROUND: Tuberculosis (TB) remains an important public health problem in Canadian Aboriginal (First Nations and Inuit) communities. The objectives of this study were to predict future disease burden and set feasible targets for the elimination of TB in the First Nations population, using retrospective data and an epidemic model. METHODS: Reported TB incidence data (1974-2002), previously published TB meningitis data from the pre-chemotherapy era, and previous estimates of disease risk following infection were used to estimate a trend in the annual risk of infection from 1929 to 2002, and the age-specific prevalence of infection in 2002. A state-transfer, compartmental model was then developed to predict future disease burden. Two scenarios were simulated, with different disease risk parameters. RESULTS: The estimated prevalence of infection in 2002 was 20.9% in scenario 1 and 25.5% in scenario 2. Predicted incidence rates in 2015 were 16.8 per 100,000 and 11.7 per 100,000 for the two scenarios, respectively. The incidence of disease was not lower than 1 per 100,000 for either scenario in 2034, the arbitrarily chosen last year of the model. CONCLUSIONS: The goal of eliminating TB among Aboriginal peoples in Canada is a feasible one, but will only be achieved with continued investment in programs designed to control and prevent transmission. Reactivation disease cases may occur for a number of years to come, making rapid elimination a difficult goal.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| 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".