Prevalence of dementia among people experiencing homelessness in Ontario, Canada: a population-based comparative analysis
Bibliographic record
Abstract
BACKGROUND: Cognitive decline in people experiencing homelessness is an increasingly recognised issue. We compared the prevalence of dementia among people experiencing homelessness to housed individuals in the general population and those living in low-income neighbourhoods. METHODS: We conducted a population-based, cross-sectional, comparative analysis using linked health-care administrative data from Ontario, Canada. We included individuals aged 45 years or older on Jan 1, 2019, who visited hospital-based ambulatory care (eg, emergency department), were hospitalised, or visited a community health centre in 2019; and identified people experiencing homelessness if they had one or more health-care records with an indication of homelessness or unstable housing. Prevalence of dementia was ascertained as of Dec 31, 2019, using a validated case definition for Alzheimer's disease and related dementia that was modified to include diagnoses made at a community health centre. Poisson models were used to generate estimates of prevalence. Estimates were compared with Ontarians that accessed any of the same health-care services over the same time, overall (general population group), and among those who were in the lowest quintile of area-based neighbourhood income (low-income group). FINDINGS: 12 863 people experiencing homelessness, 475 544 people in the low-income comparator group, and 2 273 068 people in the general population comparator group were included in the study. Dementia prevalence was 68·7 per 1000 population among people experiencing homelessness, 62·6 per 1000 population in the low-income group, and 51·0 per 1000 population in the general population group. Descriptively, prevalence ratios between people experiencing homelessness and the comparator groups were highest within the ages of 55-64 years and 65-74 years in both sexes, ranging from 2·98 to 5·00. After adjusting for age, sex, geographical location of residence (urban vs rural), and health conditions associated with dementia, the prevalence ratio of dementia among people experiencing homelessness was 1·71 (95% CI 1·60-1·82) compared with the low-income group and 1·90 (1·79-2·03) compared with the general population group. INTERPRETATION: People experiencing homelessness experience a high burden of dementia compared with housed populations in Ontario. Findings suggest that people experiencing homelessness might experience dementia at younger ages and could benefit from the development of proactive screening and housing interventions. FUNDING: The Public Health Agency of Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".