Does Housing Improve Health Care Utilization and Costs? A Longitudinal Analysis of Health Administrative Data Linked to a Cohort of Individuals With a History of Homelessness
Bibliographic record
Abstract
BACKGROUND: Individuals who are homeless have complex health care needs, which contribute to the frequent use of health services. In this study, we investigated the relationship between housing and health care utilization among adults with a history of homelessness in Ontario. METHODS: Survey data from a 4-year prospective cohort study were linked with administrative health records in Ontario. Annual rates of health encounters and mean costs were compared across housing categories (homeless, inconsistently housed, housed), which were based on the percentage of time an individual was housed. Generalized estimating equations were applied to estimate the average annual effect of housing status on health care utilization and costs. RESULTS: Over the study period, the proportion of individuals who were housed increased from 37% to 69%. The unadjusted rates of ambulatory care visits, prescription medications, and laboratory tests were highest during person-years spent housed or inconsistently housed and the rate of emergency department visits was lowest during person-years spent housed. Following adjustment, the rate of prescription claims remained higher during person-years spent housed or inconsistently housed compared with the homeless. Rate ratios for other health care encounters were not significant (P>0.05). An interaction between time and housing status was observed for total health care costs; as the percentage of days housed increased, the average costs increased in year 1 and decreased in years 2-4. CONCLUSIONS: These findings highlight the effects of housing on health care encounters and costs over a 4-year study period. The rate of prescription medications was higher during person-years spent housed or inconsistently housed compared with the homeless. The cost analysis suggests that housing may reduce health care costs over time; however, future work is needed to confirm the reason for the reduction in total costs observed in later years.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".