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
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».