Factors associated with higher healthcare costs in a cohort of homeless adults with a mental illness and a general cohort of adults with a history of homelessness
Bibliographic record
Abstract
BACKGROUND: Healthcare costs are disproportionately incurred by a relatively small group of people often described as high-cost users. Understanding the factors associated with high-cost use of health services among people experiencing homelessness could help guide service planning. METHODS: Survey data from a general cohort of adults with a history of homelessness and a cohort of homeless adults with mental illness were linked with administrative healthcare records in Ontario, Canada. Total costs were calculated using a validated costing algorithm and categorized based on population cut points for the top 5%, top 6-10%, top 11-50% and bottom 50% of users in Ontario. Multinomial logistic regression was used to identify the predisposing, enabling, and need factors associated with higher healthcare costs (with bottom 50% as the reference). RESULTS: Sixteen percent of the general homeless cohort and 30% percent of the cohort with a mental illness were in the top 5% of healthcare users in Ontario. Most healthcare costs for the top 5% of users were attributed to emergency department and inpatient service costs, while the costs from other strata were mostly for physician services, hospital outpatient clinics, and medications. The odds of being within the top 5% of users were higher for people who reported female gender, a regular medical doctor, past year acute service use, poor perceived general health and two or more diagnosed chronic conditions, and were lower for Black participants and other racialized groups. Older age was not consistently associated with higher cost use; the odds of being in the top 5% were highest for 35-to-49-year year age group in the cohort with a mental illness and similar for the 35-49 and ≥ 50-year age groups in the general homeless cohort. CONCLUSIONS: This study combines survey and administrative data from two cohorts of homeless adults to describe the distribution of healthcare costs and identify factors associated with higher cost use. These findings can inform the development of targeted interventions to improve healthcare delivery and support for people experiencing homelessness.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".