Prediction Models for Individual-Level Healthcare Costs Associated with Cardiovascular Events in the UK
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Updated: Aug 9, 2025
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Published on: August 9, 2024
Junwen Zhou1, Runguo Wu2, Claire Williams1
1Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Old Road Campus, Headington, OX3 7LF, Oxford, UK.
Insights
This study developed prediction models for individual healthcare costs related to cardiovascular events in the UK. These models can inform health technology assessments and cost-reduction policies.
Area of Science:
- Health Economics
- Cardiovascular Disease Epidemiology
- Healthcare Cost Analysis
Background:
- Cardiovascular events significantly impact individual healthcare costs.
- Accurate prediction of these costs is crucial for healthcare resource allocation and policy development.
- Existing models may not fully capture the individual-level economic burden of cardiovascular disease and related conditions.
Purpose of the Study:
- To develop and validate individual-level prediction models for UK healthcare costs.
- To quantify the impact of cardiovascular events (myocardial infarction, stroke) and other conditions on primary and hospital care expenditures.
- To identify key individual characteristics associated with higher healthcare costs.
Main Methods:
- Utilized UK Biobank data (2006-2010) with linked primary and hospital care records for individuals aged 40-70.
- Employed regression models, including generalized linear models (GLMs) and logistic regression, to predict annual costs.
- Modeled costs separately for individuals with and without previous cardiovascular disease (CVD).
Main Results:
- Developed prediction models for individual healthcare costs associated with cardiovascular events and other diseases.
- Identified significant excess costs following myocardial infarction (£190 primary care, £4340 hospital in year of event) and stroke (£360 primary care, £5590 hospital in year of event).
- Found that individuals with previous CVD incurred over twice the annual costs, and factors like older age, deprivation, and comorbidities increased costs.
Conclusions:
- Individual-level healthcare cost prediction models for cardiovascular events are now available.
- These models can inform health technology assessments and policies aimed at reducing disease risks and healthcare expenditures.
- An accompanying Excel calculator facilitates the practical application of these cost prediction models.
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.
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