Markov model parameters.

This study assessed the cost-effectiveness of different diabetic retinopathy (DR) screening strategies in rural regions in China by using a Markov model to make health economic evaluations. In this study, we determined the structure of a Markov model according to the research objectives, which required parameters collected through field investigation and literature retrieval. After perfecting the model with parameters and assumptions, we developed a Markov decision analytic model according to the natural history of DR in TreeAge Pro 2011. For this model, we performed Markov cohort and cost-effectiveness analyses to simulate the probabilistic distributions of different developments in DR and the cumulative cost-effectiveness of artificial intelligence (AI)-based screening and ophthalmologist screening for DR in the rural population with diabetes mellitus (DM) in China. Additionally, a model-based health economic evaluation was performed by using quality-adjusted life years (QALYs) and incremental cost-effectiveness ratios. Last, one-way and probabilistic sensitivity analyses were performed to assess the stability of the results. From the perspective of the health system, compared with no screening, AI-based screening cost more (the incremental cost was 37,257.76 RMB (approximately 5,211.31 US dollars)), but the effect was better (the incremental utility was 0.33). Compared with AI-based screening, the cost of ophthalmologist screening was higher (the incremental cost was 14,886.76 RMB (approximately 2,070.19 US dollars)), and the effect was worse (the incremental utility was -0.31). Compared with no screening, the incremental cost-effectiveness ratio (ICER) of AI-based DR screening was 112,146.99 RMB (15,595.47 US dollars)/QALY, which was less than the threshold for the ICER (本研究采用马尔可夫模型（Markov model）开展卫生经济学评价，评估中国农村地区不同糖尿病视网膜病变（diabetic retinopathy, DR）筛查方案的成本效益。本研究依据研究目标确定马尔可夫模型的结构，所需参数通过实地调研与文献检索获取。在通过参数与假设完善模型框架后，本研究基于糖尿病视网膜病变的自然病程，借助TreeAge Pro 2011搭建马尔可夫决策分析模型。针对该模型，本研究开展马尔可夫队列分析与成本效益分析，模拟中国农村糖尿病（diabetes mellitus, DM）人群中糖尿病视网膜病变的不同进展概率分布，以及基于人工智能（artificial intelligence, AI）筛查与眼科医师筛查的累计成本效益。此外，本研究采用质量调整生命年（quality-adjusted life years, QALYs）与增量成本效益比开展基于模型的卫生经济学评价。最后，通过单因素敏感性分析与概率敏感性分析评估研究结果的稳定性。从卫生系统视角出发，与无筛查方案相比，基于人工智能的筛查成本更高（增量成本为37257.76元人民币，约合5211.31美元），但健康获益更优（增量效用为0.33）。与基于人工智能的筛查方案相比，眼科医师筛查的成本更高（增量成本为14886.76元人民币，约合2070.19美元），但健康获益更差（增量效用为-0.31）。与无筛查方案相比，基于人工智能的糖尿病视网膜病变筛查的增量成本效益比（incremental cost-effectiveness ratio, ICER）为112146.99元人民币（15595.47美元）每质量调整生命年，低于增量成本效益比阈值（