Cardiometabolic Disease Policy Model: Supporting Long-Term Health Economic and Inequality Outcomes
Author(s)
Septiara Putri, BSc, MPH, Giorgio Ciminata, MSc, PhD, Jim Lewsey, BSc, PhD, Claudia Geue, PhD.
Health Economics and Health Technology Assessment (HEHTA), University of Glasgow, Glasgow, United Kingdom.
Health Economics and Health Technology Assessment (HEHTA), University of Glasgow, Glasgow, United Kingdom.
OBJECTIVES: To develop a cardiometabolic disease policy model capable of projecting long-term health, economic, and inequality outcomes in a UK setting.
METHODS: A cohort of 184,845 individuals from the Clinical Practice Research Datalink (CPRD) Aurum was analysed, with linkage to Hospital Episode Statistics (HES), mortality records, and a deprivation index. A multi-state parametric survival model with a semi-Markov structure was applied to estimate transition-specific hazards, state occupancy probabilities, and transition probabilities, capturing time-dependent risks and enabling extrapolation. Model diagnostics and performance evaluations were conducted to assess model validity.
RESULTS: The model comprises seven health states: disease-free, type 2 diabetes mellitus (T2DM), myocardial infarction (MI), post-MI, stroke, post-stroke, and death, linked by 13 clinically relevant transitions. We compared models from non-parametric to parametric approaches. For standard parametric models, it produced more abrupt transitions with earlier exits from the disease-free state, offering computational simplicity. In contrast, flexible parametric models yielded smoother transitions and delayed onset of T2DM, MI, stroke, and death, better reflecting real-world disease patterns.The model operates as a "hybrid" of cohort and microsimulation approaches. Like a cohort model, it estimates state occupancy over time and aggregates outcomes such as life expectancy and disease prevalence. Reflecting microsimulation features, it supports individual-level risk stratification using covariates, allowing transition probabilities to vary across individuals. Hazards are converted into time-dependent transition probabilities, enabling discrete-time simulation of disease trajectories.
CONCLUSIONS: By leveraging UK real world complex and linked data and by applying robust multi-state parametric survival modelling, the model provides: a) a transparent, flexible platform for simulating cardiometabolic disease progression, and b) evaluation of long-term health, health economics, and impact on inequalities of preventative interventions.
METHODS: A cohort of 184,845 individuals from the Clinical Practice Research Datalink (CPRD) Aurum was analysed, with linkage to Hospital Episode Statistics (HES), mortality records, and a deprivation index. A multi-state parametric survival model with a semi-Markov structure was applied to estimate transition-specific hazards, state occupancy probabilities, and transition probabilities, capturing time-dependent risks and enabling extrapolation. Model diagnostics and performance evaluations were conducted to assess model validity.
RESULTS: The model comprises seven health states: disease-free, type 2 diabetes mellitus (T2DM), myocardial infarction (MI), post-MI, stroke, post-stroke, and death, linked by 13 clinically relevant transitions. We compared models from non-parametric to parametric approaches. For standard parametric models, it produced more abrupt transitions with earlier exits from the disease-free state, offering computational simplicity. In contrast, flexible parametric models yielded smoother transitions and delayed onset of T2DM, MI, stroke, and death, better reflecting real-world disease patterns.The model operates as a "hybrid" of cohort and microsimulation approaches. Like a cohort model, it estimates state occupancy over time and aggregates outcomes such as life expectancy and disease prevalence. Reflecting microsimulation features, it supports individual-level risk stratification using covariates, allowing transition probabilities to vary across individuals. Hazards are converted into time-dependent transition probabilities, enabling discrete-time simulation of disease trajectories.
CONCLUSIONS: By leveraging UK real world complex and linked data and by applying robust multi-state parametric survival modelling, the model provides: a) a transparent, flexible platform for simulating cardiometabolic disease progression, and b) evaluation of long-term health, health economics, and impact on inequalities of preventative interventions.
Conference/Value in Health Info
2025-11, ISPOR Europe 2025, Glasgow, Scotland
Value in Health, Volume 28, Issue S2
Code
MSR54
Topic
Methodological & Statistical Research, Real World Data & Information Systems, Study Approaches
Disease
Cardiovascular Disorders (including MI, Stroke, Circulatory), Diabetes/Endocrine/Metabolic Disorders (including obesity)