Cardiometabolic Diseases Prevention Policy Models: A Systematic Review to Inform Conceptual Model Development

Abstract

Abstract Background: Cardiometabolic diseases (CMDs) such as cardiovascular disease (CVD) and type 2 diabetes (T2DM) are the leading cause of disability and mortality, as well as contributing to rising healthcare costs worldwide. In order to enhance disease prevention programs, the use of a decision model is beneficial to obtain long-term evidence of interventions, particularly in terms of effectiveness, cost-effectiveness, and further policy directions. This study aimed to systematically review the existing published literature on CMD policy models. In particular, we intend to provide (i) a comprehensive overview of CMD policy models, and (ii) conduct a critical appraisal of CMD policy models and their application for primordial prevention programs. Methods: The search strategy was developed and run on 6th December 2022 in MEDLINE (Ovid), EMBASE (Ovid), CINAHL, Google Scholar, and Open Grey restricting the publication year from 1st January 2000 to December 2022, applying Medical Subject Heading (MesH) for “cardiovascular”, “diabetes”, “decision model” and “policy model”. The retrieved full-text article was critically appraised by three independent reviewers using Phillips et al., checklist and we followed PRISMA guidelines for reporting the review process. Results: Forty-one (n=41) articles were identified that met our inclusion criteria and were eligible for critical appraisal. We presented assessments for three distinct categories: structure, data, and consistency. Most policy models (81%) fulfilled the criteria for the ‘model structure’. Modeling input and objectives were mostly consistent with the stated perspective and initial justifications. Less than 60% of studies that clearly reported data and parameters used in the model as well as validation tests reported. There was also a limited amount of information on consistency. Overall, the discussed papers utilize various methodologies and modelling approaches, including parameters incorporation, modelling simulation, analysis, and expected outcomes. The suitability of a policy model depends on the specific research question and data availability. Conclusion: There are heterogeneous results in terms of model structure, simulation level, type of data used, as well as its overall modelling quality. Based on our systematic review, we provided a list of recommendations to improve CMD policy model conceptualization and development.