Critical Analysis of Markov Modeling for the Economic Evaluation of Obesity Interventions: A Systematic Review.

Background: Obesity poses significant health and economic burdens globally, with interventions requiring robust cost-effectiveness evaluations. Markov models are widely utilized in economic evaluation of obesity interventions, their structure, assumptions, and related uncertainties have not yet been thoroughly evaluated. Objective: This study aimed to systematically review the Markov models used for the economic evaluation of anti-obesity interventions, describe their structural characteristics, identify key uncertainties, and provide insights for future research. Methods: The review was conducted across three databases (PubMed, Embase, the Cochrane Library) and health technology assessment agency websites to identify published Markov model-based full economic evaluations in adults with obesity from their inception to 2 June 2024. Model structure, model uncertainty, and validation were extracted from the included studies. Philips checklist for the methodology quality of modeling studies was performed. Results: The review included 21 primary publications with 21 unique Markov models. Two modeling approaches regarding the progression of obesity and its impact were identified: direct BMI to cost and utility; and BMI-linked complications, with diabetes and cardiovascular diseases most frequently modeled. Validation practices were inconsistently reported (43% of models), and structural uncertainty (eg, BMI trajectory assumptions) was rarely addressed. Quality assessment revealed moderate rigor (a mean compliance rate of 78% across all criteria), with gaps in transparency and generalizability, particularly for non-Western populations. Probabilistic sensitivity analysis was universal, yet scenario analyses highlighted outcome sensitivity to complication inclusion and time horizons. Conclusion: While Markov models are commonly utilized in obesity intervention evaluations, methodological heterogeneity and insufficient validation limit comparability and reliability. Future models should prioritize standardized validation (eg, ISPOR guidelines), broader complication spectrum, and diverse population data. Enhancing transparency in structural assumptions and uncertainty analysis is critical for robust policy recommendations.