Modeling socio-demographic and clinical factors influencing psychiatric inpatient service use: a comparison of models fo
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(2020) 20:232
RESEARCH ARTICLE
Open Access
Modeling socio-demographic and clinical factors influencing psychiatric inpatient service use: a comparison of models for zero-Inflated and overdispersed count data Sharmin Sharker1 , Lloyd Balbuena2 , Gene Marcoux2 and Cindy Xin Feng1,3*
Abstract Background: Psychiatric disorders may occur as a single episode or be persistent and relapsing, sometimes leading to suicidal behaviours. The exact causes of psychiatric disorders are hard to determine but easy access to health care services can help to reduce their severity. The aim of this study was to investigate the factors associated with repeated hospitalizations among the patients with psychiatric illness, which may help the policy makers to target the high-risk groups in a more focused manner. Methods: A large linked administrative database consisting of 200,537 patients with psychiatric diagnosis in the years of 2008-2012 was used in this analysis. Various counts regression models including zero-inflated and hurdle models were considered for analyzing the hospitalization rate among patients with psychiatric disorders within three months follow-up since their index visit dates. The covariates for this study consisted of socio-demographic and clinical characteristics of the patients. Results: The results show that the odds of hospitalization are significantly higher among registered Indians, male patients and younger patients. Hospitalization rate depends on the patients’ disease types. Having previously visited a general physician served a protective role for psychiatric hospitalization during the study period. Patients who had seen an outpatient psychiatrist were more likely to have a higher number of psychiatric hospitalizations. This may indicate that psychiatrists tend to see patients with more severe illnesses, who require hospital-based care for managing their illness. Conclusions: Providing easier access to registered Indian people and youth may reduce the need for hospital-based care. Patients with mental health conditions may benefit from greater and more timely access to primary care. Keywords: Repeated hospitalizations, Zero-inflated models, Hurdle models, Model comparison, Model diagnosis
*Correspondence: [email protected] School of Public Health, University of Saskatchewan, 104 Clinic Place, Saskatoon, Canada 3 Department of Community Health and Epidemiology, Faculty of Medicine, Dalhousie University, 5790 University Avenue, B3H 1V7 Halifax, Canada Full list of author information is available at the end of the article 1
© The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licenc
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