- Poster presentation
- Open Access
Using primary care data to evaluate the 10-year cost-effectiveness of cardiovascular disease risk algorithms in patients with serious mental illness: a patient level simulation
© Zomer et al. 2015
- Published: 16 November 2015
- Serious Mental Illness
- Prescribe Statins
- Time Dependent Transition
- Risk Algorithm
- Primary Care Database
Patients with serious mental illness (SMI) are at increased risk of cardiovascular disease (CVD), but there is limited evidence on the cost-effectiveness of SMI specific CVD risk management strategies.
To develop a 10 year patient level simulation of the cost-effectiveness of an SMI-specific CVD risk algorithm compared to standard CVD risk algorithm for primary CVD prevention.
Patient data was extracted from The Health Improvement Network (THIN), a primary care database, to populate the patient level simulation. Patients had SMI, were aged 30 to 74 years and free of CVD. A CVD risk algorithm was applied and patients scoring above 10% prescribed statins. We tested four CVD risk algorithms against no algorithm; lipid and body mass index (BMI) versions of SMI specific and general population algorithms. Time dependent transition probabilities for fatal and non-fatal CVD events were calculated using survival analysis. Utility scores to calculate quality adjusted life years (QALYs) were obtained from the literature.
All four risk algorithms plus statins for those at high risk resulted in more QALYs for less cost than no risk algorithm. The general population lipid and the SMI BMI algorithm had the highest probability of being cost-effective, resulting in an additional 12 QALYs and a cost saving of £37,310 and £36,431 respectively, per 1000 patients over 10 years compared to no algorithm.
Patient level simulations using primary care data provide a mechanism for assessing the cost-effectiveness of CVD risk management strategies for high risk populations where there is limited evidence.
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.