There is currently no definitive method for identifying individuals with psychosis in secondary care on a population-level using administrative healthcare data from England.

To develop various algorithms to identify individuals with psychosis in the Mental Health Services Data Set (MHSDS), guided by national estimates of the prevalence of psychosis.

Using a combination of data elements in the MHSDS for financial years 2017–2018 and 2018–2019 (mental health cluster (a way to describe and classify a group of individuals with similar characteristics), Health of the Nation Outcome Scale (HoNOS) scores, reason for referral, primary diagnosis, first-episode psychosis flag, early intervention in psychosis team flag), we developed 12 unique algorithms to detect individuals with psychosis seen in secondary care. The resulting numbers were then compared with national estimates of the prevalence of psychosis to ascertain whether they were reasonable or not.

The 12 algorithms produced 99 204–138 516 and 107 545–134 954 cases of psychosis for financial years 2017–2018 and 2018–2019, respectively, in line with national prevalence estimates. The numbers of cases of psychosis identified by the different algorithms differed according to the type and number (3–6) of data elements used. Most algorithms identified the same core of patients.

The MHSDS can be used to identify individuals with psychosis in secondary care in England. Users can employ several algorithms to do so, depending on the objective of their analysis and their preference regarding the data elements employed. These algorithms could be used for surveillance, research and/or policy purposes.

People with mental disorders have worse physical health compared with the general population, which could be attributable to receiving poorer quality healthcare.

To examine the relationship between severe and common mental disorders and risk of emergency hospital admissions for ambulatory care sensitive conditions (ACSCs), and factors associated with increased risk.

Baseline data for England (N = 445 814) were taken from UK Biobank, which recruited participants aged 37–73 years during 2006–2010, and linked to hospital admission records up to 31 December 2019. Participants were grouped into those with a history of either schizophrenia, bipolar disorder, depression or anxiety, or no mental disorder. Survival analysis was used to assess the risk of hospital admission for ACSCs among those with mental disorders compared with those without, adjusting for factors in different domains (sociodemographic, socioeconomic, health and biomarkers, health-related behaviours, social isolation and psychological).

People with schizophrenia had the highest (unadjusted) risk of hospital admission for ACSCs compared with those with no mental disorder (hazard ratio 4.40, 95% CI 4.04–4.80). People with bipolar disorder (hazard ratio 2.48, 95% CI 2.28–2.69) and depression or anxiety (hazard ratio 1.76, 95% CI 1.73–1.80) also had higher risk. Associations were more conservative when including all admissions, as opposed to first admissions only. The observed associations persisted after adjusting for a range of factors.

People with severe mental disorders have the highest risk of preventable hospital admissions. Ensuring people with mental disorders receive adequate ambulatory care is essential to reduce the large health inequalities they experience.

People with serious mental illness (SMI) experience higher mortality partially attributable to higher long-term condition (LTC) prevalence. However, little is known about multiple LTCs (MLTCs) clustering in this population.

People from South London with SMI and two or more existing LTCs aged 18+ at diagnosis were included using linked primary and mental healthcare records, 2012–2020. Latent class analysis (LCA) determined MLTC classes and multinominal logistic regression examined associations between demographic/clinical characteristics and latent class membership.

The sample included 1924 patients (mean (s.d.) age 48.2 (17.3) years). Five latent classes were identified: ‘substance related’ (24.9%), ‘atopic’ (24.2%), ‘pure affective’ (30.4%), ‘cardiovascular’ (14.1%), and ‘complex multimorbidity’ (6.4%). Patients had on average 7–9 LTCs in each cluster. Males were at increased odds of MLTCs in all four clusters, compared to the ‘pure affective’. Compared to the largest cluster (‘pure affective’), the ‘substance related’ and the ‘atopic’ clusters were younger [odds ratios (OR) per year increase 0.99 (95% CI 0.98–1.00) and 0.96 (0.95–0.97) respectively], and the ‘cardiovascular’ and ‘complex multimorbidity’ clusters were older (ORs 1.09 (1.07–1.10) and 1.16 (1.14–1.18) respectively). The ‘substance related’ cluster was more likely to be White, the ‘cardiovascular’ cluster more likely to be Black (compared to White; OR 1.75, 95% CI 1.10–2.79), and both more likely to have schizophrenia, compared to other clusters.

The current study identified five latent class MLTC clusters among patients with SMI. An integrated care model for treating MLTCs in this population is recommended to improve multimorbidity care.

Approximately 60 000 people in England have coexisting type 2 diabetes mellitus (T2DM) and severe mental illness (SMI). They are more likely to have poorer health outcomes and require more complex care pathways compared with those with T2DM alone. Despite increasing prevalence, little is known about the healthcare resource use and costs for people with both conditions.

To assess the impact of SMI on healthcare resource use and service costs for adults with T2DM, and explore the predictors of healthcare costs and lifetime costs for people with both conditions.

This was a matched-cohort study using data from the Clinical Practice Research Datalink linked to Hospital Episode Statistics for 1620 people with comorbid SMI and T2DM and 4763 people with T2DM alone. Generalised linear models and the Bang and Tsiatis method were used to explore cost predictors and mean lifetime costs respectively.

There were higher average annual costs for people with T2DM and SMI (£1930 higher) than people with T2DM alone, driven primarily by mental health and non-mental health-related hospital admissions. Key predictors of higher total costs were older age, comorbid hypertension, use of antidepressants, use of first-generation antipsychotics, and increased duration of living with both conditions. Expected lifetime costs were approximately £35 000 per person with both SMI and T2DM. Extrapolating nationally, this would generate total annual costs to the National Health Service of around £250 m per year.

Our estimates of resource use and costs for people with both T2DM and SMI will aid policymakers and commissioners in service planning and resource allocation.

To systematically review and synthesise qualitative evidence about determinants of self-management in adults with SMI. The goal is to use findings from this review to inform the design of effective self-management strategies for people with SMI and LTCs.

People living with serious mental illness (SMI) have a reduced life expectancy by around 15–20 years, mainly due to the high prevalence of long-term physical conditions such as diabetes and heart disease. People with SMI face many challenges when trying to manage their physical health. Little is known about the determinants of self-management – managing the emotional and practical issues – of long-term conditions (LTCs) for people with SMI.

Six databases, including CINAHL and MEDLINE, were searched to identify qualitative studies that explored people's perceptions about determinants of self-management in adults with SMI (with or without comorbid LTCs). Self-management was defined according to the American Association of Diabetes Educator's self-care behaviours (AADE7). Determinants were defined according to the Capabilities, Opportunity, Motivations and Behaviours (COM-B) framework. Eligible studies were purposively sampled for synthesis according to the richness of the data (assessed using Ames et al (2017)'s data richness scale), and thematically synthesised.

Twenty-six articles were included in the synthesis. Seven studies focused on self-management of LTCs, with the remaining articles exploring self-management of SMI. Six analytic themes and 28 sub-themes were identified from the synthesis. The themes included: the additional burden of SMI; living with comorbidities; beliefs and attitudes about self-management; support from others for self-management; social and environmental factors; routine, structure and planning. Capabilities for self-management were linked to people's perceptions about the support they received for their SMI and LTC from healthcare professionals, family and friends. Opportunities for self-management were more commonly expressed in the context of social and environmental factors. Motivation for self-management was influenced by beliefs and attitudes, whilst being closely related to the burden of SMI.

The themes identified from the synthesis suggest that capabilities, opportunities and motivations for self-management can be negatively influenced by the experience of SMI, whilst social and professional support, improved access to resources, and increased involvement in care, could promote self-management. Support programmes for people with SMI and LTCs need to account for these experiences and adapt to meet the unique needs of this population.

• • Understand the different payment models currently being used and proposed in mental health services in England

• • Understand the role of clustering in measuring mental health activity and providing a basis for funding

• • Understand how a robust model of clustering can benefit the provision of mental health services

None.

1. Understand the difference between retrospective and prospective reimbursement systems and the incentives generated by each financing system.

2. Understand the three key building blocks of PbR: currency, reference costs and tariffs.

3. Consider the evidence on PbR in acute physical care and the implications for mental health services.

The collection of results of a specific outcome measure, the Health of the Nation Outcome Scales (HoNOS), is mandatory for mental healthcare providers in the National Health Service in England. Not all providers collect HoNOS data and coverage varies widely. This paper explores, by means of interviews with clinicians and policy makers and econometric analysis of HoNOS data, the barriers and incentives to the uptake of HoNOS and outcomes more generally, and the key characteristics associated with providers who do undertake HoNOS.

The main barriers to the collection of outcomes involve a lack of adequate feedback mechanisms, a lack of perceived clinical relevance and poor information technology infrastructure. Econometric results show HoNOS collection is associated with providers who produce high-quality data.

Initiatives should focus on putting systems in place to encourage feedback mechanisms for clinicians.