The Revolving Door Phenomenon Revisited: Time to Readmission in 17’415 Patients with 37’697 Hospitalisations at a German Psychiatric Hospital

Ulrich Frick,Hannah Frick,Berthold Langguth,Michael Landgrebe,Göran Hajak,Bettina Hübner-Liebermann,James D. Clelland

doi:10.1371/journal.pone.0075612

Abstract

ObjectiveDespite the recurring nature of the disease process in many psychiatric patients, individual careers and time to readmission rarely have been analysed by statistical models that incorporate sequence and velocity of recurrent hospitalisations. This study aims at comparing four statistical models specifically designed for recurrent event history analysis and evaluating the potential impact of predictor variables from different sources (patient, treatment process, social environment).MethodThe so called Andersen-Gil counting process model, two variants of the conditional models of Prentice, Williams, and Peterson (gap time model, conditional probability model), and the so called frailty model were applied to a dataset of 17’415 patients observed during a 12 years period starting from 1996 and leading to 37’697 psychiatric hospitalisations. Potential prognostic factors stem from a standardized patient documentation form.ResultsEstimated regression coefficients over different models were highly similar, but the frailty model best represented the sequentiality of individual treatment careers and differing velocities of disease progression. It also avoided otherwise likely misinterpretations of the impact of gender, partnership, historical time and length of stay. A widespread notion of psychiatric diseases as inevitably chronic and worsening could be rejected. Time in community was found to increase over historical time for all patients. Most important protective factors beyond diagnosis were employment, partnership, and sheltered living situation. Risky conditions were urban living and a concurrent substance use disorder.ConclusionPrognostic factors for course of diseases should be determined only by statistical models capable of adequately incorporating the recurrent nature of psychiatric illnesses.

Highlights

Serious mental illness is often believed to follow a natural course with chronic, recurrent episodes of the underlying disease
Estimated regression coefficients over different models were highly similar, but the frailty model best represented the sequentiality of individual treatment careers and differing velocities of disease progression
After the introduction of modern drug treatment in the second half of the 20th century a formerly permanent seclusion of psychiatric patients within closed hospitals has been replaced by recurrent hospitalisations

Summary

Results

Effect sizes were quite large and estimated at odds ratios between 0.38 and 0.61 Another characteristic of the disease process, ‘‘first psychiatric hospitalisation before age of 21’’, did not reach statistical significance as a predictor for time in community under any of the models and was not included in table 1. Sex = female Current diagnosis = F1 Current diagnosis = F3 Interaktion F3 with Course of Illness (# hosp.) Current diagnosis = F4 Current diagnosis = F5 Place of residence = urban Spouse/partner Compulsory hospitalisation Higher education Age at discharge Employment after discharge GAF score at discharge No private housing after discharge Referral to hospital’s outpatient clinic Referral to general practitioner Length of stay (before last discharge) Historical year (discharge) Indenture number of hospitalisation. 1.015 (0.964–1.068) 1.448 (1.351–1.552) 0.865 (0.806–0.929) n.a. 0.554 (0.508–0.604) 0.537 (0.375–0.771) 1.103 (1.046–1.163) 1.000 (1.000–1.000) 0.997 (0.916–1.043) 0.896 (0.827–0.971) 0.996 (0.994–0.998) 0.830 (0.765–0.901) 0.989 (0.988–0.991) 0.810 (0.682–0.961) 1.072 (0.980–1.173) 0.989 (0.983–1.043) 1.002 (1.001–1.002) 0.994 (0.986–1.003) 1.080 (1.078–1.083)

Introduction

Discussion

Strengths and Limitations