The Burden of Tuberculosis Disease in Ontario: A Health Technology Assessment Approach to Inform Policy and Resource Allocation
Bibliographic record
Abstract
Despite being both preventable and curable, tuberculosis (TB) remains a persistent public health concern in Ontario, with nearly 1,000 new cases reported in 2024. The burden of disease falls disproportionately on newcomers to Canada, with 79% of reported cases occurring among individuals born outside the country. While clinical management is well defined, there remains limited understanding of the full economic impact of TB across the health system, for individuals, and at the societal level. This dissertation aimed to address this gap using a multi-method approach to estimate the costs associated with TB disease in Ontario.The first study was a scoping review of model-based economic evaluations in low-incidence settings. It found that most studies focused narrowly on medical interventions, with limited attention to non-medical interventions despite their established importance in TB prevention and control. The second study used linked population-based administrative data and a matched cohort design to estimate TB-attributable health care costs across defined phases of care, generating generalizable cost estimates specific to the Ontario context. In the third study, a cross-sectional survey captured out-of-pocket costs and productivity-related outcomes among people receiving TB treatment at two centres in Toronto, highlighting the financial burden experienced even within a universal health care system. The final study developed a model-based cost-of-illness analysis to estimate the lifetime costs of TB from the health system, patient, and societal perspectives. By capturing costs from multiple perspectives, this work highlights opportunities to tailor interventions that reduce the financial burden for different payers. Together, these studies provide a comprehensive assessment of the economic burden of TB in Ontario. They demonstrate the value of integrating administrative data, patient-reported outcomes, and decision-analytic modeling to inform evidence-based decision-making. This work contributes to the broader use of health technology assessment to address the economic dimensions of communicable disease control in low-incidence, high-resource settings.
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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 | 0.029 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.012 | 0.016 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".