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Economic evaluation of combined population-based screening for multiple blindness-causing eye diseases in China: a cost-effectiveness analysis
Open accessAbstract
Background More than 90% of vision impairment is avoidable. However, in China, a routine screening programme is currently unavailable in primary health care. With the dearth of economic evidence on screening programmes for multiple blindness-causing eye diseases, delivery options, and screening frequencies, we aimed to evaluate the costs and benefits of a population-based screening programme for multiple eye diseases in China. Methods We developed a decision-analytic Markov model for a cohort of individuals aged 50 years and older with a total of 30 1-year cycles. We calculated the cost-effectiveness and cost-utility of screening programmes for multiple major blindness-causing eye diseases in China, including age-related macular degeneration, glaucoma, diabetic retinopathy, cataracts, and pathological myopia, from a societal perspective (including direct and indirect costs). We analysed rural and urban settings separately by different screening delivery options (non-telemedicine [ie, face-to-face] screening, artificial intelligence [AI] telemedicine screening, and non-AI telemedicine screening) and frequencies. We calculated incremental cost-utility ratios (ICURs) using quality-adjusted life-years and incremental cost-effectiveness ratios (ICERs) in terms of the cost per blindness year avoided. One-way deterministic and simulated probabilistic sensitivity analyses were used to assess the robustness of the main outcomes. Findings Compared with no screening, non-telemedicine co…
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Previous studies only focused on theoretical scenarios where sensitivity and specificity can change independently.
“However, these studies only focused on theoretical scenarios where sensitivity and specificity can change independently, overlooking that in practical screening tools, these two parameters have an inverse correlation.”
From Economic evaluation for medical artificial intelligence: accuracy vs. cost-effectiveness in a diabetic retinopathy screening case (2024) · cites this paper as [14]
Previous studies evaluating cost-effectiveness of AI models assigned different values to sensitivity and specificity.
“To evaluate cost-effectiveness change, previous studies assigned different values to the sensitivity and specificity of AI models14–16.”
From Economic evaluation for medical artificial intelligence: accuracy vs. cost-effectiveness in a diabetic retinopathy screening case (2024) · cites this paper as [14]
In economically disadvantaged rural areas of China, the cost of one additional case of blindness is equivalent to ~13.6 additional referrals.
“In economically disadvantaged rural areas of China, the cost of one additional case of blindness is equivalent to ~13.6 additional referrals14.”
From Economic evaluation for medical artificial intelligence: accuracy vs. cost-effectiveness in a diabetic retinopathy screening case (2024) · cites this paper as [14]
Most previous health economic studies in China leaned towards adopting AI models with a high specificity (over 97%).
“Most of these studies leaned towards adopting AI models with a high specificity (over 97%).”
From Economic evaluation for medical artificial intelligence: accuracy vs. cost-effectiveness in a diabetic retinopathy screening case (2024) · cites this paper as [14]
Compliance with referral suggestion for a full ocular examination among diabetic patients was 33% in one rural region of the Lifeline Express Program and 50.7% in urban regions.
“Compliance with referral suggestion for a full ocular examination among diabetic patients was reported as 33% in one rural region of the Lifeline Express Program and as 50.7% in urban regions14,43,44.”
From Economic evaluation for medical artificial intelligence: accuracy vs. cost-effectiveness in a diabetic retinopathy screening case (2024) · cites this paper as [14]
Previous health economic studies in China have only compared a specific AI-based screening strategy with conventional manual screening.
“Previous health economic studies in China have only compared a specific AI-based screening strategy with conventional manual screening, to indicate the cost-effectiveness of using AI for screening DR14,15,37–39.”
From Economic evaluation for medical artificial intelligence: accuracy vs. cost-effectiveness in a diabetic retinopathy screening case (2024) · cites this paper as [14]
The transition rate from non-referable DR to referable DR in China was estimated as 7%.
“To capture the general progression rate of DR in China, we estimated the transition rates from non-referable DR to referable DR as 7%.”
From Economic evaluation for medical artificial intelligence: accuracy vs. cost-effectiveness in a diabetic retinopathy screening case (2024) · cites this paper as [14]
The utility values for each DR status (non-referable DR, referable DR, and treated referable DR) and blindness were derived from previous studies in China.
“The utility values for each DR status (non-referable DR, referable DR, and treated referable DR) and blindness were derived from previous studies in China that closely matched the situation in the current study14,15,37,39,51,52, and discounted at an annual rate of 3.5% according to the recommendations from the National Institute for Health and Clinical Excellence53.”
From Economic evaluation for medical artificial intelligence: accuracy vs. cost-effectiveness in a diabetic retinopathy screening case (2024) · cites this paper as [14]
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Metadata
- Authors
- Liu, H., Li, R., Zhang, Y., Zhang, K., Yusufu, M., Liu, Y., Mou, D., Chen, X., Tian, J., Li, H., Fan, S., Tang, J., & Wang, N.
- Affiliations
- Beijing Institute of Technology (CN)
- Beijing Tongren Hospital (CN)
- The University of Melbourne (AU)
- Han Dan City Eye Hospital - The Third Hospital of Handan (CN)
- Renmin University of China (CN)
- ORCID
- Venue
- The Lancet Global Health
- Publisher
- Elsevier BV
- Published
- 2023-03
- Volume
- 11
- Issue
- 3
- Pages
- e456-e465
- ISSN
- 2214-109X (ISSN-L)
- OpenAlex
- W4317790704
- Cited by
- 111
- References
- 28
- Citations by year
- 2026: 32 · 2025: 42 · 2024: 29 · 2023: 8
- FWCI
- 22.60
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- The Lancet Global Health (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Greater South Information System
- Funders
- National Natural Science Foundation of China (82171051)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Health Systems, Economic Evaluations, Quality of Life
- Health Sciences › Medicine › Ophthalmology › Ophthalmology and Visual Impairment Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Keywords
- blindness prevention, cost-effectiveness, age-related macular degeneration, glaucoma, diabetic retinopathy, cataract, pathologic myopia, telemedicine screening, artificial intelligence screening, urban-rural differences, visual impairment, China, Markov model, quality-adjusted life years, screening frequency, primary care
- MeSH terms
- Cost-Effectiveness Analysis, Aged, Artificial Intelligence, China, Cost-Benefit Analysis, Glaucoma, Humans, Middle Aged, Ophthalmology, Telemedicine, Quality-Adjusted Life Years
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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