A story of innovation: Building a national surveillance system for dementia in Canada
Bibliographic record
Abstract
Abstract Background Accurately capturing cases of dementia at the population‐level is complex, given the nature of the condition. Over the past decade, Canada has focused on this challenge, evolving and improving dementia surveillance in collaboration with multiple stakeholders. Method Originally based on self‐reported data, dementia data in Canada were limited to the prevalence of the condition and were significantly underestimating its burden. Canada sought to develop an innovative and systematic approach to enhance dementia surveillance by, initially, undertaking the National Population Health Study of Neurological Conditions and, then, exploring the feasibility of using linked health administrative data. The Canadian Chronic Disease Surveillance System (CCDSS), a network of provincial/territorial surveillance systems supported by the Public Health Agency of Canada, since became the core platform to support national reporting on dementia. Result A validated case definition, incorporating data from hospitalizations, physician claims and drug prescriptions, was developed and applied to the CCDSS linked databases. As a result, since 2016, national and provincial/territorial estimates of dementia incidence, prevalence, and mortality over time (dating back to 2002) are released annually. With the recent adoption of Canada’s National Dementia Strategy in 2019, other innovative methods are being explored to enhance surveillance, including: analysing the trajectory of the condition, with the development of comorbidities and impacts on health outcomes; creating new data linkages to enrich the sociodemographic information on individuals living with dementia; assessing the feasibility to monitor early onset dementia. Conclusion Utilizing the unique capacity of the CCDSS, a new national surveillance system for dementia in Canada has been implemented. The evolution and innovation of dementia surveillance over the course of the past decade can provide valuable lessons for the surveillance of dementia and other complex conditions in other countries.
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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 | 0.056 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".