Prevalence and sequence of chronic conditions in older people with dementia: a multi-province, population-based cohort study
Bibliographic record
Abstract
INTRODUCTION: Comorbid chronic conditions contribute to increased health service use and poor outcomes for people with dementia, but there is little information about the prevalence of these conditions in this population. METHODS: We used linked administrative data from British Columbia (BC), Ontario (ON), Quebec (QC) and Prince Edward Island (PE) to identify a cohort of 287 453 individuals aged 65 years and older with prevalent dementia in April 2015, and followed this population until March 2020. We determined the prevalence of comorbid chronic conditions and ascertainment dates using Canadian Chronic Disease Surveillance System definitions, and used descriptive statistics to compare patterns across provinces. RESULTS: Sociodemographic characteristics were similar across provinces (mean age: 83.0 [PE]-84.3 [BC] years; female sex: 61.8% [BC]-66.2% [QC]; and long-term care facility residence: 39.5% [QC]-41.6% [BC]). People with dementia commonly experienced five or more comorbid conditions (38.8% [PE]-53.5% [ON]); the most prevalent were hypertension (76.4% [PE]-81.4% [ON]), mental illness and alcohol- or druginduced disorders (44.4% [QC]-91.2% [BC]) and osteoarthritis (43.8% [PE]-60.4% [ON]). Hypertension, diabetes and stroke were frequently apparent before dementia ascertainment, whereas heart failure and traumatic brain injury were apparent almost as frequently after dementia ascertainment as before. CONCLUSION: Patterns of comorbid chronic conditions were similar across provinces, with most present prior to dementia ascertainment. Health service planning strategies should be developed and shared across provinces to address the complex health care needs of people with dementia.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".