Mood instability as a transdiagnostic predictor of cannabis use in ADHD and depression: a natural language processing analysis of electronic health records from 13,025 adolescents
A. Seker1, E. Bullock1, S. Chandler1, R. Patel2, D. Quattrone3, C. Colling4, E. Sonuga-Barke1, J. Downs1
1Institute of Psychiatry, Psychology & Neuroscience, Child and Adolescent Psychiatry, London, United Kingdom, 2University of Cambridge, Psychiatry, Cambridge, United Kingdom, 3Institute of Psychiatry, Psychology & Neuroscience, Social, Genetic & Developmental Psychiatry Centre, London, United Kingdom, 4South London & Maudsley NHS Foundation Trust, CAMHS, London, United Kingdom
Background: Cannabis use is elevated in youth with depression and attention-deficit/hyperactivity disorder (ADHD), but drivers of this increase remain underexplored. The self-medication hypothesis suggests cannabis is used by patients for mood regulation, a common difficulty in ADHD and depression.
Aims & Objectives: This study aimed to examine associations between mood instability and cannabis use in a large, representative clinical cohort of adolescents diagnosed with ADHD and/or depression.
Method: Natural language processing (NLP) approaches were utilised to identify references to mood instability and cannabis use in the electronic health records of adolescents (aged 11–18 years) with primary diagnoses of ADHD (n = 7,985) or depression (n = 5,738). Logistic regression was used to examine mood instability as the main exposure for cannabis use in models stratified by ADHD and depression.
Results: Mood instability was associated with a 25% higher probability of cannabis use in adolescents with ADHD compared to those with depression. Following adjustment for available sociodemographic and clinical covariates, mood instability was associated with increased cannabis use in both ADHD (aOR: 1.61 [95% CI: 1.41–1.84]) and depression (aOR: 1.38 [95% CI: 1.21–1.57]) groups.
Discussion & Conclusions: This was the first study to explore the differential impact of mood instability on adolescent cannabis use across distinct diagnostic profiles. NLP analysis proved an efficient tool for examining large populations of adolescents accessing psychiatric services and provided preliminary evidence of a link between mood instability and cannabis use in ADHD and depression. Longitudinal studies using direct measures or tailored NLP techniques can further establish the directionality of these associations.
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