Disparities in healthcare costs of people experiencing homelessness in Toronto, Canada in the post COVID-19 pandemic era: a matched cohort study
Bibliographic record
Abstract
BACKGROUND: Evidence is limited about healthcare cost disparities associated with homelessness, particularly in recent years after major policy and resource changes affecting people experiencing homelessness occurred after the onset of the COVID-19 pandemic. We estimated 1-year healthcare expenditures, overall and by type of service, among a representative sample of people experiencing homelessness in Toronto, Canada, in 2021 and 2022, and compared these to costs among matched housed and low-income housed individuals. METHODS: Data from individuals experiencing homelessness participating in the Ku-gaa-gii pimitizi-win cohort study were linked with Ontario health administrative databases. Participants (n = 640) were matched 1:5 by age, sex-assigned-at-birth and index month to presumed housed individuals (n = 3,200) and to low-income presumed housed individuals (n = 3,200). Groups were followed over 1 year to ascertain healthcare expenditures, overall and by healthcare type. Generalized linear models were used to assess unadjusted and adjusted mean cost ratios between groups. RESULTS: Average 1-year costs were $12,209 (95% CI $9,762-$14,656) among participants experiencing homelessness compared to $1,769 ($1,453-$2,085) and $1,912 ($1,510-$2,314) among housed and low-income housed individuals. Participants experiencing homelessness had nearly seven times (6.90 [95% confidence interval [CI] 5.98-7.97]) the unadjusted mean ratio (MR) of costs as compared to housed persons. After adjustment for number of comorbidities and history of healthcare for mental health and substance use disorders, participants experiencing homelessness had nearly six times (adjusted MR 5.79 [95% CI 4.13-8.12]) the expected healthcare costs of housed individuals. The two housed groups had similar costs. CONCLUSIONS: Homelessness is associated with substantial excess healthcare costs. Programs to quickly resolve and prevent cases of homelessness are likely to better meet the health and healthcare needs of this population while being a more efficient use of public resources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".