Decision Analytic Models to Assess the Cost-Effectiveness of Prevention, Detection, and Treatment in Multiple Long-Term Conditions: A Scoping Review

Objectives: To comprehensively examine decision-analytic models used to assess the cost-effectiveness of prevention and treatment of MLTCs and appraise their quality. Methods: We searched MEDLINE and EMBASE for studies published up to July 15, 2024. Studies were included if they used cost-effectiveness models to evaluate interventions targeting 2 or more long-term conditions. A second reviewer screened 10% of titles and abstracts to ensure consistency. Results: Out of 6900 titles and abstracts screened, 51 studies were selected for full-text review. After exclusions and citation tracking, 43 studies were included. Most models (n = 22, 50%) addressed only 2 LTCs. Markov state transition models were the most common (n = 30, 70%), followed by individual-level microsimulations (n = 5, 12%) and discrete event simulations or decision trees (n = 4, 9% each). Nearly all studies (n = 42, 99%) reported outcomes in quality-adjusted life-years. Conclusions: Few models adequately captured the complexity of MLTCs, such as interactions between conditions, patient heterogeneity, and lifetime trajectories. There is a clear need for more advanced economic models that reflect the multifaceted nature of MLTCs and support efficient, equitable intervention prioritization.