The Incidence and Prevalence of Dementia in Alberta, Canada between 2014 and 2022
Bibliographic record
Abstract
BACKGROUND: The number of people affected by dementia in Canada is anticipated to increase significantly in coming years. The province of Alberta is expected to have the largest relative increase in the number of people affected by dementia among all provinces in Canada. We determined the prevalence and incidence of diagnosed all-cause dementia in Alberta, Canada between 2014 and 2022 using population-based health data. METHODS: Population-based administrative health data were used to identify all individuals ages 65 years and older who were diagnosed with dementia between 2014 and 2022 in Alberta, Canada. A validated case ascertainment algorithm was used to identify cases of dementia. The prevalence of all-cause dementia was reported for all individuals on April 1, of each year. Annual numbers of incident cases of dementia were reported on April 1 for each year using individuals newly diagnosed with dementia in the preceding year. RESULTS: The overall prevalence of dementia in Alberta increased from 30,050 in 2014 to 43,802 in 2022 (45% relative increase). The number of incident cases of dementia increased from 6,884 individuals in 2014 to 8,741 in 2022 (27% relative increase). The incidence rate of dementia declined during this time period from 1.48/100 person years in 2014 to 1.37/100 person years in 2022 (p <0.001). CONCLUSION: The number of prevalent and incident cases of dementia increased significantly in Alberta between 2014 and 2022 although the incidence rate of dementia is decreasing during this time. This information has important implications for the planning supports and services for this population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".