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MDPI, Medicina, 11(56), p. 613, 2020

DOI: 10.3390/medicina56110613

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Pre-Discharge Predictors of 1-Year Rehospitalization in Adolescents and Young Adults with Severe Mental Disorders: A Retrospective Cohort Study

This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Data provided by SHERPA/RoMEO

Abstract

Background and objectives: Readmissions of youths hospitalized for a severe mental disorder are common events and bear a remarkable human, social, and economic burden. The current study aimed at evaluating predictors of 1-year rehospitalization in a sample of adolescents and young adults with severe mental disorders. Materials and Methods: Data for ≤25-year-old inpatients with a severe mental disorder and consecutively admitted between 1 January 2016 and 30 June 2019 were collected. Subjects were retrospectively assessed over a follow-up period of one year after the index discharge to track readmissions—i.e., the primary outcome variable. Standard descriptive statistics were used. The association between variables and 1-year rehospitalization was estimated using the univariate Cox proportional hazards regression model. We then carried out a multivariable Cox regression model, also estimating the covariate-adjusted survivor function. Hazard ratios (HRs) with related 95% confidence intervals (95% CIs) were provided. Results: The final sample included 125 individuals. The multivariable Cox regression model estimated that co-occurring substance use disorders (HR = 2.14; 95% CI: 1.08 to 4.26; p = 0.029) and being admitted for a suicide attempt (HR = 2.49; 95% CI: 1.13 to 5.49; p = 0.024) were both significant predictors of 1-year rehospitalization. Conclusions: Our study showed that comorbid substance use disorders and being admitted for a suicide attempt were predictors of early readmission in youths with severe mental disorders. Although their generalizability is limited, our findings could contribute to improve the quality of young patients’ mental health care by identifying vulnerable subjects who may benefit from tailored interventions to prevent rehospitalizations.