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Length of stay for inpatient incompetent to stand trial patients: importance of clinical and demographic variables

Published online by Cambridge University Press:  09 June 2020

Charles Broderick Open the ORCID record for Charles Broderick [Opens in a new window] ,
Allen Azizian  and
Katherine Warburton Open the ORCID record for Katherine Warburton [Opens in a new window]
Charles Broderick*
Affiliation:
California Department of State Hospitals, Sacramento, California, USA
Allen Azizian
Affiliation:
California Department of State Hospitals, Sacramento, California, USA Department of Criminology, California State University, Fresno, California, USA
Katherine Warburton
Affiliation:
California Department of State Hospitals, Sacramento, California, USA Department of Psychiatry, University of California Davis, Davis, California, USA
*
*Address correspondence to: Charles Broderick, PhD, California Department of State Hospitals, Clinical Operations Division, 1600 9th Street, Room 400, Sacramento, CA 95814, USA. (Email: [email protected])
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Abstract

Objective

We investigated clinical and demographic variables to better understand their relationship to hospital length of stay for patients involuntarily committed to California state psychiatric hospitals under the state’s incompetent to stand trial (IST) statutes. Additionally, we determined the most important variables in the model that influenced patient length of stay.

Methods

We retrospectively studied all patients admitted as IST to California state psychiatric hospitals during the period January 1, 2010 through June 30, 2018 (N = 20 041). Primary diagnosis, total number of violent acts while hospitalized, age at admission, treating hospital, level of functioning at admission, ethnicity, sex, and having had a previous state hospital admission were evaluated using a parametric survival model.

Results

The analysis showed that the most important variables related to length of stay were (1) diagnosis, (2) number of violent acts while hospitalized, and (3) age of admission. Specifically, longer length of stay was associated with (1) having a diagnosis of schizophrenia or neurocognitive disorder, (2) one or more violent acts, and (3) older age at admission. The other variables studied were also statistically significant, but not as influential in the model.

Conclusions

We found significant relations between length of stay and the variables studied, with the most important variables being (1) diagnosis, (2) number of physically violent acts, and (3) age at admission. These findings emphasize the need for treatments to target cognitive issues in the seriously mentally ill as well as treatment of violence and early identification of violence risk factors.

Keywords

Competency to stand trialinpatientlength of stayvariable importanceviolencecognition.
Type
Original Research
Information
CNS Spectrums , Volume 25 , Issue 5 , October 2020 , pp. 734 - 742
Copyright
© Cambridge University Press 2020

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References

National Association of State Mental Health Program Directors. Forensic patients in state psychiatric hospitals: 1999-2016 (August 2017). https://nasmhpd.org/sites/default/files/TACPaper.10.Forensic-Patients-in-State-Hospitals_508C_v2.pdf. Accessed January 17, 2020.Google Scholar
Bellisle, M. After paying $83 million in fines, Washington settles jail mental-health lawsuit. Seattle Times. December 12, 2018.Google Scholar
Pirelli, G, Gottdiener, WH, Zapf, PA. A meta-analytic review of competency to stand trial research. Psychol Public Policy Law. 2011;17(1):1–53.CrossRefGoogle Scholar
Gay, JG, Vitacco, MJ, Ragatz, L. Mental health symptoms predict competency to stand trial and competency restoration success. Legal Criminol Psychol. 2017;22(2):288–301.CrossRefGoogle Scholar
Morris, DR, DeYoung, NJ. Long-term competence restoration. J Am Acad Psychiatry Law Online. 2014;42(1):81–90.Google ScholarPubMed
Morris, DR, Parker, GF. Effects of advanced age and dementia on restoration of competence to stand trial. Int J Law Psychiatry. 2009;32(3):156–160.CrossRefGoogle ScholarPubMed
Grossi, LM, Green, D, Schneider, M, et al. Personality, psychiatric, and cognitive predictors of length of time for competency to stand trial restoration. Int J Forens Mental Health. 2018;17(2):167–180.CrossRefGoogle Scholar
Colwell, LH, Gianesini, J. Demographic, criminogenic, and psychiatric factors that predict competency restoration. J Am Acad Psychiatry Law. 2011;39(3):297–306.Google ScholarPubMed
Toofanian Ross, P, Padula, CB, Nitch, SR, et al. Cognition and competency restoration: using the RBANS to predict length of stay for patients deemed incompetent to stand trial. Clin Neuropsychol. 2015;29(1):150–165.CrossRefGoogle Scholar
Nicholson, RA, Barnard, GW, Robbins, L, et al. Predicting treatment outcome for incompetent defendants. Bull Am Acad Psychiatry Law. 1994;22(3):367–377.Google ScholarPubMed
Mossman, D. Predicting restorability of incompetent criminal defendants. J Am Acad Psychiatry Law. 2007;35:34–43.Google ScholarPubMed
Morris, DR, Parker, GF. Jackson's Indiana: state hospital competence restoration in Indiana. J Am Acad Psychiatry Law. 2008;36(4):522–534.Google ScholarPubMed
Renner, M, Newark, C, Bartos, BJ, et al. Length of stay for 25,791 California patients found incompetent to stand trial. J Forens Legal Med. 2017;51:22–26.Google Scholar
Department of State Hospitals. Violence Report DSH 2010-2017. https://web.archive.org/web/20190729220318/https://www.dsh.ca.gov/Publications/Reports_and_Data/docs/Violence_Report_DSH_2010-2017.pdf. Accessed January 17, 2020.Google Scholar
American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR®). Washington, DC: American Psychiatric Association Publishing; 2000.Google Scholar
American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5®). Washington, DC: American Psychiatric Association Publishing; 2013.Google Scholar
Broderick, C, Azizian, A, Kornbluh, R, et al. Prevalence of physical violence in a forensic psychiatric hospital system during 2011-2013: patient assaults, staff assaults, and repeatedly violent patients. CNS Spectr. 2015;20(3):319–330.CrossRefGoogle Scholar
AAS. Global Assessment of Functioning (GAF) properties and frontier of current knowledge; 2010.CrossRefGoogle Scholar
R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2018.Google Scholar
Harrell, FE. Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis. Berlin, Germany: Springer International Publishing; 2015.Google Scholar
Carroll, KJ. On the use and utility of the Weibull model in the analysis of survival data. Control Clin Trials. 2003;24(6):682–701.CrossRefGoogle ScholarPubMed
Hilbe, JM. Logistic Regression Models. Boca Raton, FL: CRC Press; 2009.CrossRefGoogle Scholar
Kinon, BJ. The group of treatment resistant schizophrenias. Heterogeneity in treatment resistant schizophrenia (TRS). Front Psychiatry. 2018;9(9):757.CrossRefGoogle Scholar
Bak, N, Ebdrup, BH, Oranje, B, et al. Two subgroups of antipsychotic-naive, first-episode schizophrenia patients identified with a Gaussian mixture model on cognition and electrophysiology. Transl Psychiatry. 2017;7(4):e1087.CrossRefGoogle ScholarPubMed
Chang, WC, Ho, RWH, Tang, JYM, et al. Early-stage negative symptom trajectories and relationships with 13-year outcomes in first-episode nonaffective psychosis. Schizophr Bull. 2019;45(3):610–619.CrossRefGoogle ScholarPubMed
Elvevag, B, Goldberg, TE. Cognitive impairment in schizophrenia is the core of the disorder. Crit Rev Neurobiol. 2000;14(1):1–21.Google ScholarPubMed
Green, MF. Cognitive impairment and functional outcome in schizophrenia and bipolar disorder. J Clin Psychiatry. 2006;67:673–678.CrossRefGoogle ScholarPubMed
MacCabe, JH. Population-based cohort studies on premorbid cognitive function in schizophrenia. Epidemiol Rev. 2008;30(1):77–83.CrossRefGoogle Scholar
Schwalbe, E, Medalia, A. Cognitive dysfunction and competency restoration: using cognitive remediation to help restore the unrestorable. J Am Acad Psychiatry Law. 2007;35(4):518–525.Google ScholarPubMed
Palmer, BW, Loughran, CI, Meeks, TW. Cognitive impairment among older adults with late-life schizophrenia or bipolar disorder. CONTINUUM Lifelong Learn Neurol. 2010;16(2):135–152.Google ScholarPubMed
Reichenberg, A, Weiser, M, Rabinowitz, J, et al. A population-based cohort study of premorbid intellectual, language, and behavioral functioning in patients with schizophrenia, schizoaffective disorder, and nonpsychotic bipolar disorder. Am J Psychiatry. 2002;159(12):2027–2035.CrossRefGoogle ScholarPubMed
Lewandowski, KE, Whitton, AE, Pizzagalli, DA, et al. Reward learning, neurocognition, social cognition, and symptomatology in psychosis. Front Psychiatry. 2016;7:100.Google ScholarPubMed
Fagiolini, A, Goracci, A. The effects of undertreated chronic medical illnesses in patients with severe mental disorders. J Clin Psychiatry. 2009;70:322–329.CrossRefGoogle ScholarPubMed
Scurich, N. Personal communication requesting clarification of table dated November 14, 2017. ed.Google Scholar