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Understanding social and clinical associations with unemployment for people with schizophrenia and bipolar disorders: large-scale health records study

Author

Listed:
  • Chilman, Natasha
  • Laporte, Dionne
  • Dorrington, Sarah
  • Hatch, Stephani L.
  • Morgan, Craig
  • Okoroji, Celestin
  • Stewart, Robert
  • Das-Munshi, Jayati

Abstract

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.

Suggested Citation

  • Chilman, Natasha & Laporte, Dionne & Dorrington, Sarah & Hatch, Stephani L. & Morgan, Craig & Okoroji, Celestin & Stewart, Robert & Das-Munshi, Jayati, 2024. "Understanding social and clinical associations with unemployment for people with schizophrenia and bipolar disorders: large-scale health records study," LSE Research Online Documents on Economics 122156, London School of Economics and Political Science, LSE Library.
  • Handle: RePEc:ehl:lserod:122156
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    References listed on IDEAS

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    1. Huajie Jin & Iris Mosweu, 2017. "The Societal Cost of Schizophrenia: A Systematic Review," PharmacoEconomics, Springer, vol. 35(1), pages 25-42, January.
    2. Hector Tsang & Bacon Ng & Ip Yee Chiu & Stephen Mann, 2000. "Predictors of Post-Hospital Employment Status for Psychiatric Patients in Hong Kong: From Perceptions of Rehabilitation Professionals To Empirical Evidence," International Journal of Social Psychiatry, , vol. 46(4), pages 306-313, December.
    3. Nordt, Carlos & Müller, Brigitte & Rössler, Wulf & Lauber, Christoph, 2007. "Predictors and course of vocational status, income, and quality of life in people with severe mental illness: A naturalistic study," Social Science & Medicine, Elsevier, vol. 65(7), pages 1420-1429, October.
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    More about this item

    Keywords

    employment; occupation; schizophrenia; bipolar disorder; ethnicity; natural language processing; Specific occupation application development was supported by the Health Foundation working together with the Academy of Medical Sciences; in a clinician scientist fellowship held by JD-M; NC is funded by the Economic and Social Research Council for a PhD studentship under the London Interdisciplinary Social Science Doctoral Training Partnership (grand number ES/P000703/1); JD-M; SLH and CM are part supported by the ESRC Centre for Society and Mental Health at King’s College London (ESRC Reference: ES/S012567/1); Specific occupation application development was supported by the Health Foundation working together with the Academy of Medical Sciences; in a clinician scientist fellowship held by JD-M; This work was supported by the Clinical Record Interactive Search system which is funded by the National Institute for Health Research (NIHR) Maudsley Biomedical Research Centre at South London and Maudsley National Health Service (NHS) Foundation Trust and King’s College London and a joint infrastructure grant from Guy’s and St Thomas’ Charity and the Maudsley Charity; JDM and SLH are partly supported by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London; JDM and RS are supported by the National Institute for Health Research (NIHR) Applied Research Collaboration South London (NIHR ARC South London) at King’s College Hospital NHS Foundation Trust; RS is additionally supported by UKRI – Medical Research Council through the DATAMIND HDR UK Mental Health Data Hub (MRC reference: MR/W014386); RS is additionally supported [...] through the UK Prevention Research Partnership (Violence; Health and Society; MR-VO49879/1); an initiative funded by UK Research and Innovation Councils; the Department of Health and Social Care (England) and the UK devolved administrations; and leading health research charities;
    All these keywords.

    JEL classification:

    • R14 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Land Use Patterns
    • J01 - Labor and Demographic Economics - - General - - - Labor Economics: General

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