Predisposing Characteristics, Enabling Resources and Need as Predictors of Utilization and Clinical Outcomes for Veterans Receiving Mental Health Services
Abstract
BACKGROUND: Though demand for mental health services (MHS) among US veterans is increasing, MHS utilization per veteran is decreasing. With health and social service needs competing for limited resources, it is important to understand the association between patient factors, MHS utilization, and clinical outcomes. OBJECTIVES: We use a framework based on Andersen's behavioral model of health service utilization to examine predisposing characteristics, enabling resources, and clinical need as predictors of MHS utilization and clinical outcomes. METHODS: This was a prospective observational study of veterans receiving inpatient or outpatient MHS through Veterans Administration programs. Clinician ratings (Global Assessment of Functioning [GAF]) and self-report assessments (Behavior and Symptom Identification Scale-24) were completed for 421 veterans at enrollment and 3 months later. Linear and logistic regression analyses were conducted to examine: (1) predisposing characteristics, enabling resources, and need as predictors of MHS inpatient, residential, and outpatient utilization and (2) the association between individual characteristics, utilization, and clinical outcomes. RESULTS: Being older, female, having greater clinical need, lack of enabling resources (employment, stable housing, and social support), and easy access to treatment significantly predicted greater MHS utilization at 3-month follow-up. Less clinical need and no inpatient psychiatric hospitalization predicted better GAF and Behavior and Symptom Identification Scale-24 scores. White race and residential treatment also predicted better GAF scores. Neither enabling resources, nor number of outpatient mental health visits predicted clinical outcomes. CONCLUSIONS: This application of Andersen's behavioral model of health service utilization confirmed associations between some predisposing characteristics, need, and enabling resources on MHS utilization but only predisposing characteristics, need, and utilization were associated with clinical outcomes.
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The number of psychiatric hospitalizations has been used in other outcome studies of mental health conditions among veterans.
“the number of psychiatric hospitalizations has been used in other outcome studies55,56 of mental health conditions among veterans.”
From Treatment Engagement and Outcomes of Mindfulness-Based Cognitive Therapy for Veterans with Psychiatric Disorders (2019) · cites this paper as [55]
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- Authors
- Fasoli, D. R., Glickman, M. E., & Eisen, S. V.
- Affiliations
- Edith Nourse Rogers Memorial Veterans Hospital (US)
- Boston University (US)
- Corresponding author
- Fasoli, DiJon R.
- Glickman, Mark E.
- Eisen, Susan V.
- Venue
- Medical Care
- Published
- 2010-04
- Volume
- 48
- Issue
- 4
- Pages
- 288-295
- ISSN
- 0025-7079 (ISSN-L)
- OpenAlex
- W1969584992
- Cited by
- 69
- References
- 46
- Citations by year
- 2025: 2 · 2023: 2 · 2021: 3 · 2020: 4 · 2019: 5 · 2018: 9 · 2017: 7 · 2016: 5 · 2015: 9 · 2014: 10 · 2013: 6 · 2012: 3
- FWCI
- 5.66
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Medical Care (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Psychology › Social Psychology › Mental Health Treatment and Access
- Social Sciences › Psychology › Clinical Psychology › Psychiatric care and mental health services
- Social Sciences › Psychology › Clinical Psychology › Migration, Health and Trauma
- Keywords
- veterans, mental health service utilization, Andersen Behavioral Model, enabling resources, clinical needs, Global Assessment of Functioning, psychiatric hospitalization, residential treatment, outpatient mental health visits, social support, housing stability, employment, race, clinical outcomes
- MeSH terms
- Adult, Boston, Female, Forecasting, Health Services Needs and Demand, Humans, Male, Mental Health Services, Middle Aged, Prospective Studies, Veterans, Linear Models, Logistic Models, Outcome Assessment, Health Care, Observation, Health Care Surveys
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