Universal Credit trajectories among individuals who access secondary mental health services: a sequence analysis of linked data.

ObjectivesUniversal Credit (UC) was introduced in 2013 as part of welfare benefit reforms in the UK. It replaced six previous benefits, including jobseeker’s allowance, income support, and housing benefit. We aimed to examine UC trajectories, and transitions between UC conditionality regimes, among secondary mental health service users between 2016-2020. MethodsWe used a linked dataset established by combining electronic secondary mental health records from South London and Department for Work and Pensions (DWP) administrative benefits data. 4,876 individuals who received UC for the first time in 2016 were included as our entry cohort, and followed up for approximately 4.5 years. We used sequence analysis to analyse transitions between UC regime states, followed by cluster analysis to identify typical UC trajectories. We used multinominal logistic regression to test associations between sociodemographic and diagnostic characteristics with UC trajectory membership; presenting these results as average marginal effects. ResultsOur analysis identified six distinct UC trajectory clusters. These clusters indicated: short-term UC searching for work (18.7%), medium-term UC searching for work (19.1%), long-term UC searching for work (21.4%), no work requirements (11.9%), UC working cluster (6.1%), and no work searching and caring responsibilities cluster (22.8%). Distinct characteristic profiles were associated with each of these clusters. Women were overrepresented in the ‘no work searching and caring responsibilities’ cluster and a cluster indicating more low-paid or part-time work. Older people were more likely to be in the ‘long-term searching for work’ and ‘no work requirements’ clusters. Those with diagnosis of severe mental illness were over-represented in the no work requirements group. ConclusionsUsing a novel analysis method, we identified six distinct UC trajectories among users of secondary mental health services and identified characteristics of patients represented within these groups. Findings can be used to inform and tailor support to groups vulnerable to conditionality or longer-term UC receipt.

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Publication Details

Journal
International Journal for Population Data Science
Published
2025-08-28
DOI
https://doi.org/10.23889/ijpds.v10i4.3172
Primary Topic
Chronic Disease Management Strategies
Type
article
Field-Weighted Citation Impact
0.00
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article

Universal Credit trajectories among individuals who access secondary mental health services: a sequence analysis of linked data.

Matthew Hotopf, Sharon A. M. Stevelink, Ira Madan, Thomas Lorentzen et al.
International Journal for Population Data Science
Chronic Disease Management Strategies
article

Universal Credit trajectories among individuals who access secondary mental health services: a sequence analysis of linked data.

Matthew Hotopf, Sharon A. M. Stevelink, Ira Madan, Thomas Lorentzen, Ioannis Bakolis, Sarah Ledden, Ray Leal, Nicola Fear
article en

Abstract

ObjectivesUniversal Credit (UC) was introduced in 2013 as part of welfare benefit reforms in the UK. It replaced six previous benefits, including jobseeker’s allowance, income support, and housing benefit. We aimed to examine UC trajectories, and transitions between UC conditionality regimes, among secondary mental health service users between 2016-2020. MethodsWe used a linked dataset established by combining electronic secondary mental health records from South London and Department for Work and Pensions (DWP) administrative benefits data. 4,876 individuals who received UC for the first time in 2016 were included as our entry cohort, and followed up for approximately 4.5 years. We used sequence analysis to analyse transitions between UC regime states, followed by cluster analysis to identify typical UC trajectories. We used multinominal logistic regression to test associations between sociodemographic and diagnostic characteristics with UC trajectory membership; presenting these results as average marginal effects. ResultsOur analysis identified six distinct UC trajectory clusters. These clusters indicated: short-term UC searching for work (18.7%), medium-term UC searching for work (19.1%), long-term UC searching for work (21.4%), no work requirements (11.9%), UC working cluster (6.1%), and no work searching and caring responsibilities cluster (22.8%). Distinct characteristic profiles were associated with each of these clusters. Women were overrepresented in the ‘no work searching and caring responsibilities’ cluster and a cluster indicating more low-paid or part-time work. Older people were more likely to be in the ‘long-term searching for work’ and ‘no work requirements’ clusters. Those with diagnosis of severe mental illness were over-represented in the no work requirements group. ConclusionsUsing a novel analysis method, we identified six distinct UC trajectories among users of secondary mental health services and identified characteristics of patients represented within these groups. Findings can be used to inform and tailor support to groups vulnerable to conditionality or longer-term UC receipt.

International Journal for Population Data ScienceVol. 10(4)
St Thomas' Hospital (GB), King's College London (GB), Guy's and St Thomas' NHS Foundation Trust (GB), South London and Maudsley NHS Foundation Trust (GB), University of Bergen (NO)
Openalex Percentile: Top 69%
Chronic Disease Management Strategies
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