Applying Health Technology Assessment Methodologies to Support Health Policy for Lyme Disease: Assessing Health and Economic Burden, and Cost-effectiveness of Potential Interventions
Bibliographic record
Abstract
Lyme disease (LD) is an increasingly common vector-borne disease reported in temperate climate zones in North America. In Canada, the number of confirmed LD cases reported has increased from 144 in 2009 to 2,851 in 2021. An increasing number of LD cases along with the controversies on clinical management within the medical community and patient advocacy groups prompted the government to commit to addressing the challenges of recognition, timely diagnosis, and treatment of LD, mandated by the Federal Framework on Lyme Disease Act. However, evidence gaps related to LD health outcomes, the economic burden to the healthcare system, and cost-effectiveness of LD interventions remained. In the last decade, high-quality evidence has been more commonly produced by health technology assessment (HTA) methodologies for infectious diseases to support public health planning and the evaluation of innovative technologies. Therefore, the goal of this thesis was to use specific HTA methodologies to generate evidence to inform health policy for LD in Ontario. The first study conducted was a systematic review of long-term sequelae, prognostic factors, health-related quality of life, and health state utility values associated with LD in North America and Europe. The second study estimated the economic burden and health outcomes attributable to laboratory-confirmed LD in Ontario using a population-based retrospective cohort study, health administrative data and laboratory test data. The third study involved the development of a microsimulation model to simulate the natural disease history of LD and estimate the number of LD cases, sequelae, and projected population burden, expressed as quality-adjusted life years (QALYs). The fourth study used the microsimulation model to conduct a cost-utility analysis of potential risk-based LD vaccination programs in Ontario assuming a vaccine candidate profile. This thesis summarized long-term outcomes associated with LD, which can be used to shape or support clinical management guidelines, and future cohort studies. While investing in universal vaccination programs are typically good value, a tailored approach may be optimal for LD. This body of work demonstrated how HTA methodologies can be used to synthesize clinical, epidemiological, and economic evidence to support current and future health policy decision-making for emerging infectious diseases.
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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 | 0.097 | 0.247 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| 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".