Effectiveness and cost-effectiveness of a cardiovascular risk prediction algorithm for people with severe mental illness (PRIMROSE). Issue 9 (5th September 2017)

• Record Type: Journal Article
• Authors: Zomer, Ella; Osborn, David; Nazareth, Irwin; Blackburn, Ruth; Burton, Alexandra; Hardoon, Sarah; Holt, Richard Ian Gregory; King, Michael; Marston, Louise; Morris, Stephen; Omar, Rumana; Petersen, Irene; Walters, Kate; Hunter, Rachael Maree
• Is Part Of: BMJ open. Volume 7:Issue 9(2017)
• Publication Date: 2017-09-05
• Subjects: health economics -- mental health -- coronary heart disease; Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjopen-2017-018181
• Languages: English
• ISSNs: 2044-6055

Abstract

Abstract : Objectives: To determine the cost-effectiveness of two bespoke severe mental illness (SMI)-specific risk algorithms compared with standard risk algorithms for primary cardiovascular disease (CVD) prevention in those with SMI. Setting: Primary care setting in the UK. The analysis was from the National Health Service perspective. Participants: 1000 individuals with SMI from The Health Improvement Network Database, aged 30–74 years and without existing CVD, populated the model. Interventions: Four cardiovascular risk algorithms were assessed: (1) general population lipid, (2) general population body mass index (BMI), (3) SMI-specific lipid and (4) SMI-specific BMI, compared against no algorithm. At baseline, each cardiovascular risk algorithm was applied and those considered high risk (> 10%) were assumed to be prescribed statin therapy while others received usual care. Primary and secondary outcome measures: Quality-adjusted life years (QALYs) and costs were accrued for each algorithm including no algorithm, and cost-effectiveness was calculated using the net monetary benefit (NMB) approach. Deterministic and probabilistic sensitivity analyses were performed to test assumptions made and uncertainty around parameter estimates. Results: The SMI-specific BMI algorithm had the highest NMB resulting in 15 additional QALYs and a cost saving of approximately £53 000 per 1000 patients with SMI over 10 years, followed by the general population lipid algorithm (13 additional QALYs and a cost saving of £46 000). Conclusions: The general population lipid and SMI-specific BMI algorithms performed equally well. The ease and acceptability of use of an SMI-specific BMI algorithm (blood tests not required) makes it an attractive algorithm to implement in clinical settings.