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

Apr 1, 2026, 00:00
10.1016/j.jval.2025.10.010
https://www.valueinhealthjournal.com/article/S1098-3015(25)05661-X/fulltext
Title : Decision Analytic Models to Assess the Cost-Effectiveness of Prevention, Detection, and Treatment in Multiple Long-Term Conditions: A Scoping Review
Citation : https://www.valueinhealthjournal.com/action/showCitFormats?pii=S1098-3015(25)05661-X&doi=10.1016/j.jval.2025.10.010
First page : 605
Section Title : COMPARATIVE-EFFECTIVENESS RESEARCH/HTA
Open access? : No
Section Order : 605

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.

Categories :
Tags :
  • chronic conditions
  • cost-effectiveness
  • multiple long-term conditions
Regions :
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