Multi-episode survival analysis: An application modelling readmission rates of heroin dependents at an inpatient detoxification unit

• Published in: Addictive behaviors Vol. 32; no. 10; pp. 2391 - 2397
• Main Authors: Trujols, Joan, Guàrdia, Joan, Peró, Maribel, Freixa, Montserrat, Siñol, Núria, Tejero, Antonio, Pérez de los Cobos, José
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.10.2007; Elsevier Science Ltd
• Subjects: Adult; Detoxification; Drug addiction; Female; Heroin; Heroin Dependence - therapy; Humans; Likelihood Functions; Male; Multi-episode survival analysis; Opioid dependence; Patient admissions; Patient Readmission; Psychiatry; Recurrence; Recurrent event data; Relapse; Retrospective Studies; Studies; Substance Abuse Treatment Centers; Survival Analysis; Treatment career perspective; Treatment Failure; Opioid dependence; Treatment career perspective; Relapse; Multi-episode survival analysis; Detoxification; Recurrent event data
• ISSN: 0306-4603; 1873-6327
• DOI: 10.1016/j.addbeh.2007.02.008

Abstract The purpose of this study is to describe the characteristics of a statistical technique appropriate for analysing multi-episode data (multi-episode survival analysis), and to show its application in modelling the flow of readmissions at an inpatient detoxification unit. Data are from 784 opioid-dependent patients admitted at an inpatient detoxification unit, who totalled 1255 admission episodes. Information stored prospectively at the unit database was reviewed for the following variables at the time of each patient discharge: episode serial number, sex, route of heroin administration, reason for discharge, time of discharge, and transition time (re-entry into the inpatient detoxification unit). Cox's semi-parametric regression model seems the most appropriate for describing the series of episodes. Amongst the parametric models, most noteworthy was the superior fit of the Gompertz–Makeham model, suggesting that the transition rate decreases monotonically with time. The influence of the variables assessed differed based on the serial number of the episode. The results suggest that multi-episode survival analysis is a statistical method that can fully address the long-term perspective on treatment utilization.