IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i22p15127-d974746.html
   My bibliography  Save this article

Predictors of Length of Hospitalization and Impact on Early Readmission for Mental Disorders

Author

Listed:
  • Lia Gentil

    (Department of Psychiatry, McGill University, 1033, Pine Avenue West, Montreal, QC H3A 1A1, Canada
    Douglas Hospital Research Centre, Douglas Mental Health University Institute, 6875 LaSalle Blvd, Montreal, QC H4H 1R3, Canada)

  • Guy Grenier

    (Douglas Hospital Research Centre, Douglas Mental Health University Institute, 6875 LaSalle Blvd, Montreal, QC H4H 1R3, Canada)

  • Helen-Maria Vasiliadis

    (Département Des Sciences de la Santé Communautaire, Université de Sherbrooke, Longueuil, QC J4K 0A8, Canada
    Centre de Recherche Charles-Le Moyne-Saguenay-Lac-Saint-Jean sur les Innovations en Santé (CR-CSIS), Campus de Longueuil-Université de Sherbrooke, 150 Place Charles-Lemoyne, Longueuil, QC J4K 0A8, Canada)

  • Marie-Josée Fleury

    (Department of Psychiatry, McGill University, 1033, Pine Avenue West, Montreal, QC H3A 1A1, Canada
    Douglas Hospital Research Centre, Douglas Mental Health University Institute, 6875 LaSalle Blvd, Montreal, QC H4H 1R3, Canada)

Abstract

Length of hospitalization, if inappropriate to patient needs, may be associated with early readmission, reflecting sub-optimal hospital treatment, and translating difficulties to access outpatient care after discharge. This study identified predictors of brief-stay (1–6 days), mid-stay (7–30 days) or long-stay (≥31 days) hospitalization, and evaluated how lengths of hospital stay impacted on early readmission (within 30 days) among 3729 patients with mental disorders (MD) or substance-related disorders (SRD). This five-year cohort study used medical administrative databases and multinomial logistic regression. Compared to patients with brief-stay or mid-stay hospitalization, more long-stay patients were 65+ years old, had serious MD, and had a usual psychiatrist rather than a general practitioner (GP). Predictors of early readmission were brief-stay hospitalization, residence in more materially deprived areas, more diagnoses of MD/SRD or chronic physical illnesses, and having a usual psychiatrist with or without a GP. Patients with long-stay hospitalization (≥31 days) and early readmission had more complex conditions, especially more co-occurring chronic physical illnesses, and more serious MD, while they tended to have a usual psychiatrist with or without a GP. For patients with more complex conditions, programs such as assertive community treatment, intensive case management or home treatment would be advisable, particularly for those living in materially deprived areas.

Suggested Citation

  • Lia Gentil & Guy Grenier & Helen-Maria Vasiliadis & Marie-Josée Fleury, 2022. "Predictors of Length of Hospitalization and Impact on Early Readmission for Mental Disorders," IJERPH, MDPI, vol. 19(22), pages 1-19, November.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:22:p:15127-:d:974746
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/22/15127/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/19/22/15127/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hyunyoung Baek & Minsu Cho & Seok Kim & Hee Hwang & Minseok Song & Sooyoung Yoo, 2018. "Analysis of length of hospital stay using electronic health records: A statistical and data mining approach," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-16, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Marie-Josée Fleury & Zhirong Cao & Guy Grenier, 2024. "Emergency Department Use among Patients with Mental Health Problems: Profiles, Correlates, and Outcomes," IJERPH, MDPI, vol. 21(7), pages 1-18, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kuluski, Kerry & Cadel, Lauren & Marcinow, Michelle & Sandercock, Jane & Guilcher, Sara JT, 2022. "Expanding our understanding of factors impacting delayed hospital discharge: Insights from patients, caregivers, providers and organizational leaders in Ontario, Canada," Health Policy, Elsevier, vol. 126(4), pages 310-317.
    2. Addisu Jember Zeleke & Serena Moscato & Rossella Miglio & Lorenzo Chiari, 2022. "Length of Stay Analysis of COVID-19 Hospitalizations Using a Count Regression Model and Quantile Regression: A Study in Bologna, Italy," IJERPH, MDPI, vol. 19(4), pages 1-18, February.
    3. Emmanuel Helm & Anna M. Lin & David Baumgartner & Alvin C. Lin & Josef Küng, 2020. "Towards the Use of Standardized Terms in Clinical Case Studies for Process Mining in Healthcare," IJERPH, MDPI, vol. 17(4), pages 1-12, February.
    4. Anthony Gramaje & Fadi Thabtah & Neda Abdelhamid & Sayan Kumar Ray, 2021. "Patient Discharge Classification Using Machine Learning Techniques," Annals of Data Science, Springer, vol. 8(4), pages 755-767, December.
    5. Davide Golinelli & Francesco Sanmarchi & Fabrizio Toscano & Andrea Bucci & Nicola Nante, 2024. "Analyzing the 20-year declining trend of hospital length-of-stay in European countries with different healthcare systems and reimbursement models," International Journal of Health Economics and Management, Springer, vol. 24(3), pages 375-392, September.
    6. Braulio A Marfil-Garza & Pablo F Belaunzarán-Zamudio & Alfonso Gulias-Herrero & Antonio Camiro Zuñiga & Yanink Caro-Vega & David Kershenobich-Stalnikowitz & José Sifuentes-Osornio, 2018. "Risk factors associated with prolonged hospital length-of-stay: 18-year retrospective study of hospitalizations in a tertiary healthcare center in Mexico," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-14, November.
    7. Reyes-Santias, Francisco & Reboredo, Juan C. & de Assis, Edilson Machado & Rivera-Castro, Miguel A., 2021. "Does length of hospital stay reflect power-law behavior? A q-Weibull density approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 568(C).
    8. Rita Matos & Diogo Ferreira & Maria Isabel Pedro, 2021. "Economic Analysis of Portuguese Public Hospitals Through the Construction of Quality, Efficiency, Access, and Financial Related Composite Indicators," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 157(1), pages 361-392, August.
    9. Minsu Cho & Minseok Song & Junhyun Park & Seok-Ran Yeom & Il-Jae Wang & Byung-Kwan Choi, 2020. "Process Mining-Supported Emergency Room Process Performance Indicators," IJERPH, MDPI, vol. 17(17), pages 1-20, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:22:p:15127-:d:974746. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.