Canada’s Enhanced Dementia Surveillance Initiative: Improving national dementia measures to inform public health actions
Bibliographic record
Abstract
Abstract Background The Enhanced Dementia Surveillance Initiative (EDSI), led by the Public Health Agency of Canada (PHAC), supports the implementation of Canada’s first national dementia strategy. To improve the national monitoring of dementia and its health impacts, the EDSI projects focused on priority data gaps: dementia by cause, progression stages and impacts; socio‐demographic characteristics, risk and protective factors; and caregivers. Method PHAC collaborated on 15 projects with multiple stakeholders (universities/research institutions, health organizations, and federal/provincial government departments). Each employed novel methods and data sources in the Canadian public health surveillance context. Existing and linked health administrative data, electronic medical records (EMR), various primary data collections, and longitudinal cohorts were leveraged to apply new approaches addressing the listed data gaps. Result Projects enabled various surveillance advancements in dementia health measures; examples are presented under four categories. 1. By leveraging existing databases with new linkages, algorithm validation and data visualization techniques, projects were able to better identify and characterize (clinical and sociodemographic characteristics) people living with dementia and their caregivers. 2. By enhancing linked health administrative data for national surveillance, new methods to report on dementia in long term care and dementia comorbidities were developed, and health care costs across dementia progression stages were calculated. 3. By developing new conceptual and data models, it was possible to enhance the understanding of the dementia national data landscape. The impact of selected factors on dementia risk in the population was also estimated, and dementia patients’ trajectories across care settings were described. 4. By applying a health equity lens, progress was made towards an improved monitoring of dementia in diverse populations, such as Indigenous communities and individuals experiencing homelessness. Conclusion Through a knowledge synthesis of the results and findings from the EDSI projects, PHAC aims to identify and integrate best practices for national dementia surveillance. The expected long‐term outcome of this initiative is to contribute to more robust evidence on dementia in Canada to help inform related public health actions.
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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 | 0.047 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".