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Cost-effectiveness analysis of artificial intelligence-based diabetic retinopathy screening in rural China based on the Markov model

Author

Listed:
  • Huilin Li
  • Guanyan Li
  • Na Li
  • Changyan Liu
  • Ziyou Yuan
  • Qingyue Gao
  • Shaofeng Hao
  • Shengfu Fan
  • Jianzhou Yang

Abstract

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 (

Suggested Citation

  • Huilin Li & Guanyan Li & Na Li & Changyan Liu & Ziyou Yuan & Qingyue Gao & Shaofeng Hao & Shengfu Fan & Jianzhou Yang, 2023. "Cost-effectiveness analysis of artificial intelligence-based diabetic retinopathy screening in rural China based on the Markov model," PLOS ONE, Public Library of Science, vol. 18(11), pages 1-29, November.
    • Handle: RePEc:plo:pone00:0291390
    DOI: 10.1371/journal.pone.0291390
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    References listed on IDEAS

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    1. Lei Liu & Xiaomei Wu & Limin Liu & Jin Geng & Zhe Yuan & Zhongyan Shan & Lei Chen, 2012. "Prevalence of Diabetic Retinopathy in Mainland China: A Meta-Analysis," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-8, September.
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