Prevalence of dementia among people experiencing homelessness in Ontario, Canada: a population-based comparative analysis
Bibliographic record
Abstract
BackgroundCognitive decline in people experiencing homelessness (PEH) is an increasingly recognized issue. We compared the prevalence of dementia among PEH to housed individuals in the general population and those living in low-income neighborhoods. ApproachA population-based cross-sectional comparative analysis using linked healthcare administrative data from Ontario, Canada. We included individuals ≥45 years on January 1, 2019 who visited hospital-based ambulatory care (eg, emergency department), were hospitalized, or visited a Community Health Centre in 2019; and identified PEH if they had one or more healthcare records with an indication of homelessness or unstable housing. Prevalence of dementia was ascertained using a validated case definition for Alzheimer’s disease and related dementia. Poisson models were used to generate estimates of prevalence. Findings12,863 PEH, 475,544 low-income comparators, and 2,273,068 general population comparators were assembled. Among these groups, dementia prevalence was 68.7, 62.6, and 51.0 per 1,000, respectively. Descriptively, prevalence ratios between PEH and the comparator groups were highest within the ages of 55-64 and 65-74 in both sexes, ranging from 2.98 to 5.00. After adjusting for age, sex, geographical location of residence, and health conditions associated with dementia, prevalence of dementia among PEH was found to be 1.71 (95% CI: 1.60–1.82) and 1.90 (1.79–2.03) times greater than the low-income and general population comparators, respectively. InterpretationPEH experience high burden of dementia compared to housed populations in Ontario. Findings suggest that PEH may experience dementia at younger ages and may benefit from the development of proactive screening and housing interventions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".