Purpose: People with severe mental illness (SMI) experience high levels of unemployment. We aimed to better understand the associations between clinical, social, and demographic inequality indicators and unemployment.
Methods: Data were extracted from de-identified health records of people with SMI in contact with secondary mental health services in south London, UK. A Natural Language Processing text-mining application was applied to extract information on unemployment in the health records. Multivariable logistic regression was used to assess associations with unemployment, in people with SMI.
Results: Records from 19,768 service users were used for analysis, 84.9% (n = 16,778) had experienced unemployment. In fully adjusted models, Black Caribbean and Black African service users were more likely to experience unemployment compared with White British service users (Black Caribbean: aOR 1.62, 95% CI 1.45-1.80; Black African: 1.32, 1.15-1.51). Although men were more likely to have experienced unemployment relative to women in unadjusted models (OR 1.36, 95% CI 1.26-1.47), differences were no longer apparent in the fully adjusted models (aOR 1.05, 95% CI 0.97-1.15). The presence of a non-affective (compared to affective) diagnosis (1.24, 1.13-1.35), comorbid substance use (2.02, 1.76-2.33), previous inpatient admissions (4.18, 3.71-4.70), longer inpatient stays (78 + days: 7.78, 6.34-9.54), and compulsory admissions (3.45, 3.04-3.92) were associated with unemployment, in fully adjusted models.
Conclusion: People with SMI experience high levels of unemployment, and we found that unemployment was associated with several clinical and social factors. Interventions to address low employment may need to also address these broader inequalities.
Keywords: Bipolar disorder; Employment; Ethnicity; Natural language processing; Occupation; Schizophrenia.
© 2024. The Author(s).