Kathryn Wiens1, Laura C Rosella2, Paul Kurdyak3, Simon Chen4, Tim Aubry5, Vicky Stergiopoulos3, Stephen W Hwang6. 1. Dalla Lana School of Public Health, University of Toronto, 155 College St., Toronto, Ontario, M5T 1P8, Canada. kathryn.wiens@mail.utoronto.ca. 2. Dalla Lana School of Public Health, University of Toronto, 155 College St., Toronto, Ontario, M5T 1P8, Canada. 3. Centre for Addiction and Mental Health, 33 Russel St, Toronto, Ontario, M5S 3M1, Canada. 4. ICES, 2075 Bayview Avenue, Toronto, Ontario, M4N 3M5, Canada. 5. School of Psychology & Centre for Research on Educational and Community Services, University of Ottawa, 136 Jean-Jacques-Lussier Private, Ottawa, Ontario, K1N 9A8, Canada. 6. MAP Centre for Urban Health Solutions, St. Michael's Hospital, 30 Bond St, Toronto, Ontario, M5B 1X1, Canada.
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.
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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