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Predictors of Study Drop-Out and Service Disengagement in Patients at Clinical High Risk for Psychosis
Journal
Social psychiatry and psychiatric epidemiology
Volume
55
Number
5
Pages / Article-Number
539-548
Keywords
At-risk mental state; Attrition; Clinical service; Early intervention; Service use
Mesh terms
Adolescent; Adult; Demography; Female; Humans; Male; Patient Compliance, statistics & numerical data; Predictive Value of Tests; Prospective Studies; Psychometrics; Psychotic Disorders, therapy; Risk Factors; Socioeconomic Factors; Switzerland, epidemiology; Young Adult
Abstract
Study drop-out during follow-up and service disengagement frequently occur in patients at clinical high risk for psychosis (CHR-P). However, little is known about their predictors. Therefore, we aimed to analyze the rate and reasons for drop-out and service disengagement in CHR-P patients and investigate their sociodemographic and clinical predictors.; Data from 200 patients of the prospective Früherkennung von Psychosen (FePsy) study were analyzed with competing risks survival models, considering drop-out and transition to psychosis as competing events. To investigate whether symptoms changed immediately before drop-out, t tests were applied.; Thirty-six percent of patients dropped out within 5 years. Almost all drop-outs also disengaged from our service. Hence, study drop-out was used as a proxy for service disengagement. Patients with more severe baseline disorganized symptoms and a late inclusion into the study were significantly more likely to disengage. Immediately before disengagement, there was significant improvement in negative symptoms only.; A considerable proportion of CHR-P patients disengaged from our clinical study and service. Patients who were included during a later study period with more assessments disengaged more often, which might have been due to more frequent invitations to follow-up assessments and thereby increasing participation burden. Hence, our study provides a cautionary note on high-frequency follow-up assessments. Larger-scale studies evaluating predictors on multiple domains would help to further elucidate drop-out and disengagement.
