P.065 Alzheimer’s disease (AD) and dementias in Canada: First national surveillance data from the Canadian Chronic Disease Surveillance System (CCDSS)
Bibliographic record
Abstract
Background: With a growing and aging population, the number of individuals with AD and dementias and their associated costs are expected to increase in Canada. Up to now, no national mechanism was in place to monitor the epidemiological burden of AD and dementias. This presentation will showcase the first CCDSS data available on these conditions. Methods: Through the CCDSS, a Federal/Provincial/Territorial partnership, health administrative databases are linked to collect data on chronic conditions. Using selected ICD-9(CM)/ICD-10 codes for AD and dementias, the validated case definition implemented to identify relevant cases aged 65+ is: 1+ hospitalizations; or 3+ physician claims within 2 years, with a 30-day-gap between each claim; or 1+ anti-dementia drug prescriptions. Prevalence and incidence rates will be presented by 5-year age group, sex, province/territory, and fiscal year. Results: Overall, incidence and prevalence rates were higher in women. The prevalence rate approximately doubled between 5-year age groups and sex differences tended to widen with age. While aged-standardised data show increasing prevalence rates over time, incidence rates fluctuated but suggest a decline since 2009/10. Conclusions: CCDSS data can be used to monitor the burden of AD and dementias in Canada. This information is important for the assessment of prevention actions and the planning of health care resources.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".