A SYSTEMATIC REVIEW OF CARDIOVASCULAR DISEASE POLICY MODELS FOR PREDICTING HEALTH AND COST OUTCOMES
Author(s)
Taihang Shao, MSc1, Kailu Wang, PhD2, Eliza LY Wong, PhD2.
1Student, The Chinese University of Hong Kong, Shatin, NT, Hong Kong, 2The Chinese University of Hong Kong, Shatin, NT, Hong Kong.
1Student, The Chinese University of Hong Kong, Shatin, NT, Hong Kong, 2The Chinese University of Hong Kong, Shatin, NT, Hong Kong.
OBJECTIVES: Cardiovascular disease (CVD) policy models are increasingly used to project long-term health and economic outcomes and guide resource allocation. However, existing reviews have focused on specific populations, regions, interventions, or economic outcomes. This systematic review aimed to comprehensively summarize published CVD policy models, identify research priorities, and appraise methodological quality.
METHODS: PubMed, Embase, Web of Science, and Scopus were searched for English-language studies published from January 2006 to June 2025. Eligible studies reported mathematical or simulation-based models predicting long-term CVD-related health outcomes, healthcare utilization, costs, or cost-effectiveness at the population or health-system level. Study characteristics were narratively synthesized, and model quality was assessed using a modified Phillips checklist.
RESULTS: Of 25,691 records identified, 166 studies met the inclusion criteria. Most were conducted in high-income settings (n = 121) and published after 2016 (n = 119). Primary prevention was the dominant focus, particularly pharmacological and dietary interventions. Markov models were most common in population-level analyses, whereas microsimulation predominated in individual-level models. Coronary heart disease and stroke were the most frequently modelled conditions. Direct medical costs were almost universally included, but direct non-medical, indirect, and programme costs were less frequently reported. Healthcare utilization outcomes appeared in only eight studies. Equity-related analyses were reported in 68 studies, mainly through subgroup analyses. Sensitivity analysis was common, but calibration and validation were reported in fewer than half of studies. The mean quality score was 70.45%, with recurring limitations in data quality assessment, structural uncertainty, and justification of modelling assumptions.
CONCLUSIONS: This review highlights geographic disparities in the economic aspects and methodological heterogeneity in CVD policy models. Future models should improve transparency, validation, uncertainty analysis, equity assessment, and the use of locally relevant data to strengthen policy relevance and credibility.
METHODS: PubMed, Embase, Web of Science, and Scopus were searched for English-language studies published from January 2006 to June 2025. Eligible studies reported mathematical or simulation-based models predicting long-term CVD-related health outcomes, healthcare utilization, costs, or cost-effectiveness at the population or health-system level. Study characteristics were narratively synthesized, and model quality was assessed using a modified Phillips checklist.
RESULTS: Of 25,691 records identified, 166 studies met the inclusion criteria. Most were conducted in high-income settings (n = 121) and published after 2016 (n = 119). Primary prevention was the dominant focus, particularly pharmacological and dietary interventions. Markov models were most common in population-level analyses, whereas microsimulation predominated in individual-level models. Coronary heart disease and stroke were the most frequently modelled conditions. Direct medical costs were almost universally included, but direct non-medical, indirect, and programme costs were less frequently reported. Healthcare utilization outcomes appeared in only eight studies. Equity-related analyses were reported in 68 studies, mainly through subgroup analyses. Sensitivity analysis was common, but calibration and validation were reported in fewer than half of studies. The mean quality score was 70.45%, with recurring limitations in data quality assessment, structural uncertainty, and justification of modelling assumptions.
CONCLUSIONS: This review highlights geographic disparities in the economic aspects and methodological heterogeneity in CVD policy models. Future models should improve transparency, validation, uncertainty analysis, equity assessment, and the use of locally relevant data to strengthen policy relevance and credibility.
Conference/Value in Health Info
2026-09, ISPOR Asia Pacific 2026, Bangkok, Thailand
Value in Health, Volume 55, Issue S1
Code
MSR11
Topic
Methodological & Statistical Research
Disease
No Additional Disease & Conditions/Specialized Treatment Areas, SDC: Cardiovascular Disorders (including MI, Stroke, Circulatory)