Chronic disease prevalence and preventive care among Ontario social housing residents compared with the general population: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Older adults living in social housing report poor health and access to healthcare services. This study aimed to estimate the prevalence of chronic diseases, influenza vaccination and cancer screenings among social housing residents versus non-residents in Ontario, Canada. METHODS: We conducted a population-based cohort study for all health-insured Ontarians alive and aged 40 or older as of 1 January 2020. Social housing residents were identified using postal codes. Validated health administrative data case definitions were used to identify individuals with diabetes, hypertension, chronic obstructive pulmonary disease, asthma, congestive heart failure and cardiovascular disease. Influenza vaccination and mammography, Pap and colorectal cancer screenings were identified among screen-eligible residents using health administrative data. RESULTS: The prevalence of all chronic diseases was higher among social housing residents across all age groups: 40-59, 60-79 and 80+ years. Influenza vaccination rates in 2018-2019 were lower among social housing residents aged 60-79 and 80+ years. Mammography rates for women aged 50-69 years in 2018-2019 were 10-11% lower among social housing residents across all age groups compared with non-residents. Pap screening rates for women aged 40-69 in 2018-2019 were 6-8% lower among social housing residents. The percentage of colorectal screening in both women and men aged 52-74 was lower (9-10% in men and 6-7% in women) in social housing compared with the general population in 2019-2020. CONCLUSION: There is a higher prevalence of chronic diseases and lower cancer screening rates among the growing population of older adults in social housing in Ontario, Canada.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| 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".