Aging with intellectual and developmental disabilities and dementia in Manitoba
Bibliographic record
Abstract
Purpose Information on the risk of dementia in aging persons with intellectual and/or developmental disability (IDD) in Manitoba, Canada is lacking. The purpose of this paper is to estimate dementia prevalence in adults with IDD. Design/methodology/approach Anonymized population-level health and non-health administrative data (1979-2012) contained in the Population Health Research Data Repository of the Manitoba Centre for Health Policy (MCHP) were linked to identify adults with IDD, and estimate the prevalence of dementia based on the presence of ICD codes. Prevalence of dementia was estimated for persons aged 18-55 years and 55+ years, and was reported by sex, type of residence, region of residence, neighbourhood income quintiles, and IDD diagnostic category. Findings Of the 8,655 adults with IDD identified, 8.1 per cent had an indication of dementia in their medical records; an estimate three times greater than that found for those without IDD (2.6 per cent). More than 17 per cent of Manitobans with IDD aged 55+ years had an indication of dementia, which was nearly twice the rate reported previously. Of those with IDD and dementia, 34.7 per cent lived in long-term care facilities. Originality/value Health and social support services are typically available to individuals with dementia aged 65+ years; thus, younger adults with IDD and dementia may not be eligible for those supports. To promote equity in health and access to care, dementia screening and increased supports for aging individuals with IDD are recommended.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".