Prediction Models for Individual-Level Healthcare Costs Associated with Cardiovascular Events in the UK
Junwen Zhou, Runguo Wu, Claire Williams, Jonathan Emberson, Christina Reith, Anthony Keech, John Robson, K. G. Wilkinson, Jane Armitage, Alastair Gray, John Simes, Colin Baigent, Borislava Mihaylova
Abstract
OBJECTIVES: The aim of this study was to develop prediction models for the individual-level impacts of cardiovascular events on UK healthcare costs. METHODS: In the UK Biobank, people 40-70 years old, recruited in 2006-2010, were followed in linked primary (N = 192,983 individuals) and hospital care (N = 501,807 individuals) datasets. Regression models of annual primary and annual hospital care costs (2020 UK£) associated with individual characteristics and experiences of myocardial infarction (MI), stroke, coronary revascularization, incident diabetes mellitus and cancer, and vascular and nonvascular death are reported. RESULTS: For both people without and with previous cardiovascular disease (CVD), primary care costs were modelled using one-part generalised linear models (GLMs) with identity link and Poisson distribution, and hospital costs with two-part models (part 1: logistic regression models the probability of incurring costs; part 2: GLM with identity link and Poisson distribution models the costs conditional on incurring any). In people without previous CVD, mean annual primary and hospital care costs were £360 and £514, respectively. The excess primary care costs were £190 and £360 following MI and stroke, respectively, whereas excess hospital costs decreased from £4340 and £5590, respectively, in the year of these events, to £190 and £410 two years later. People with previous CVD had more than twice higher annual costs, and incurred higher excess costs for cardiovascular events. Other characteristics associated with higher costs included older age, female sex, south Asian ethnicity, higher socioeconomic deprivation, smoking, lower level of physical activities, unhealthy body mass index, and comorbidities. CONCLUSIONS: These individual-level healthcare cost prediction models could inform assessments of the value of health technologies and policies to reduce cardiovascular and other disease risks and healthcare costs. An accompanying Excel calculator is available to facilitate the use of the models.