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    Modeling the growth of long-stay populations in public mental hospitals

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    Authors
    Fisher, William H.
    Phillips, Barbara F.
    UMass Chan Affiliations
    Department of Psychiatry
    Document Type
    Journal Article
    Publication Date
    1990-01-01
    Keywords
    Algorithms
    Chronic Disease
    Deinstitutionalization
    Follow-Up Studies
    Forecasting
    Hospitals, Psychiatric
    Hospitals, State
    Humans
    Length of Stay
    Life Tables
    Massachusetts
    *Models, Statistical
    Population Dynamics
    Time Factors
    United States
    Health Services Research
    Mental and Social Health
    Psychiatric and Mental Health
    Psychiatry
    Psychiatry and Psychology
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    Link to Full Text
    http://dx.doi.org/10.1016/0277-9536(90)90314-I
    Abstract
    Long-stay, chronic patients have been a problematic subpopulation in public mental hospitals for over a century. Despite three decades of deinstitutionalization and a major shift toward shorter episodes of hospitalization, there continues to exist a group of patients who experience lengthy hospital stays. As the number of such patients increases in a facility, its ability to provide acute care may be compromised, and the size of this subpopulation must therefore be anticipated. This paper examines the length-of-stay patterns of a sample of public mental hospital admissions through the use of life table analysis, and develops a dynamic modeling algorithm using sample survival function values. Life table analysis revealed a declining hazard function, indicating a diminishing probability of discharge with increased hospital stay. The dynamic model showed that, after 2 years of operation of a hypothetical facility, current length-of-stay patterns would generate an inpatient population 40% of which had been hospitalized for over 6 months. Goodness-of-fit tests comparing the algorithm's forecast with actual hospital utilization data showed its predictions to be reliable. The authors discuss the use of this methodology to anticipate the effects of programmatic or other types of changes in mental hospitals, and also suggest other types of settings where such modeling techniques might profitably be applied.
    Source
    Soc Sci Med. 1990;30(12):1341-7.
    Permanent Link to this Item
    http://hdl.handle.net/20.500.14038/45155
    PubMed ID
    2367879
    Related Resources
    Link to Article in PubMed
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    UMass Chan Faculty and Researcher Publications

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