Treatment Dropout in Child and Adolescent Substance Use Disorders: A Systematic Literature Review of Associated Factors

Substance use disorders (SUDs) are a major public health concern during adolescence and can cause substantial individual and societal burden. Despite the availability of specialized treatment, only a small proportion of affected adolescents access addiction services or initiate treatment (Thomasius et al., 2022), and among those who do, premature treatment termination remains a major challenge, with only around 59% of adolescents and young adults completing psychosocial substance use treatment (Wells et al., 2024), thereby limiting treatment effectiveness. Previous research has identified patient-, treatment-, and organizational-level predictors of dropout, however the evidence is largely based on adult populations and North American treatment systems, with limited applicability to child and adolescent psychiatry and psychotherapy (CAPP) services in Europe. Moreover, existing studies have predominantly focused on pretreatment patient characteristics, while potentially modifiable treatment-process and organizational factors have received considerably less attention. Across studies treatment dropout is operationalized inconsistently, and patient-initiated withdrawal is rarely distinguished from clinician-initiated treatment termination (“pushout”). This systematic review addresses these gaps by synthesizing predictors of treatment dropout using a conceptual framework that distinguishes patient-, treatment-, and organizational-level factors, as well as fixed versus dynamic patient characteristics and pretreatment versus treatment-process factors. Studies will be grouped by age for the subgroup analysis (sample mean < 18 years vs. ≥ 18 years) to reflect the distinct legal and organizational frameworks governing patient treatment. A systematic search of five bibliographic databases, assessed against the PRESS 2015 checklist (McGowan et al., 2016), was conducted between June and August 2026 (final search 1 August 2026). Titles and abstracts will be screened with AI-aided prioritization (ASReview), including independent dual screening of a calibration sample and verification by a second reviewer. Full texts will be screened independently by two reviewers. Data will be extracted by one reviewer and verified by a second, and results will be synthesized narratively along the predictor framework. With this review we aim to identify clinically relevant and potentially modifiable predictors of treatment dropout to inform strategies for improving treatment retention in child and adolescent addiction services, with a particular focus on European healthcare systems.