Economic evaluation of a digital health intervention for preventing dementia in Canadians with mild cognitive impairment
Bibliographic record
Abstract
Dementia poses an on-going Canadian challenge due to an aging population, with cases projected to rise significantly by 2050. This study evaluates the cost-effectiveness of a conceptual digital health intervention designed to prevent dementia in Canadians with mild cognitive impairment (MCI). The analysis is exploratory and conceptual, comparing different scenarios for the possible effectiveness of the digital intervention for dementia prevention. Using data from the Global Burden of Disease (GBD) 2021 study, a long-term economic evaluation was conducted from a healthcare payer perspective, comparing intervention costs to usual care between 2030 and 2050. Health outcome was assessed using disability-adjusted life years (DALYs) averted. The analysis revealed favorable incremental cost-effectiveness ratios (ICERs) well below conventional willingness-to-pay thresholds across all scenarios. Sensitivity analyses confirmed the robustness of these findings, underscoring the intervention's potential to cost-effectively reduce dementia burden. The findings are based on modeled assumptions in the absence of empirical efficacy data and should therefore be interpreted with caution until validated in real-world settings. Yet, these results provide valuable insights for Canadian policymakers on scalable, proactive dementia prevention strategies.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".