Lin Qiucen, Ung Carolina Oi Lam, Lai Yunfeng, Hu Hao, Jakovljevic Mihajlo
State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao, People's Republic of China.
Centre for Pharmaceutical Regulatory Sciences, University of Macau, Macao, People's Republic of China.
Risk Manag Healthc Policy. 2025 Jun 28;18:2169-2187. doi: 10.2147/RMHP.S528064. eCollection 2025.
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.
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.
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.
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.
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.
肥胖在全球范围内带来了重大的健康和经济负担，干预措施需要进行有力的成本效益评估。马尔可夫模型在肥胖干预措施的经济评估中被广泛应用，但其结构、假设及相关不确定性尚未得到充分评估。
本研究旨在系统回顾用于抗肥胖干预措施经济评估的马尔可夫模型，描述其结构特征，识别关键不确定性，并为未来研究提供见解。
通过三个数据库（PubMed、Embase、Cochrane图书馆）和卫生技术评估机构网站进行检索，以识别从模型建立至2024年6月2日发表的基于马尔可夫模型的针对肥胖成年人的全面经济评估。从纳入研究中提取模型结构、模型不确定性和验证情况。采用飞利浦建模研究方法质量清单进行评估。
该综述纳入了21篇主要文献及21个独特的马尔可夫模型。确定了两种关于肥胖进展及其影响的建模方法：直接将体重指数（BMI）与成本和效用关联；以及与BMI相关的并发症，其中糖尿病和心血管疾病是最常建模的并发症。验证方法的报告不一致（43%的模型），且结构不确定性（如BMI轨迹假设）很少被提及。质量评估显示严谨性中等（所有标准的平均符合率为78%），在透明度和可推广性方面存在差距，尤其是针对非西方人群。概率敏感性分析普遍存在，但情景分析突出了结果对并发症纳入和时间范围的敏感性。
虽然马尔可夫模型在肥胖干预评估中普遍使用，但方法的异质性和验证不足限制了可比性和可靠性。未来的模型应优先考虑标准化验证（如国际药物经济学和结果研究协会（ISPOR）指南）、更广泛的并发症谱和多样化的人群数据。提高结构假设和不确定性分析的透明度对于得出可靠的政策建议至关重要。
