Economic evaluations of eye care services for Indigenous populations in high-income countries: a scoping review
Bibliographic record
Abstract
Abstract Background Indigenous people in high-income countries have worse eye health outcomes when compared to non-Indigenous people, contributing to ongoing socioeconomic disadvantage. Although services have been designed to address these disparities, it is unclear if they have undergone comprehensive economic evaluation. Our scoping review aimed to identify the number, type, quality, and main findings of such evaluations. Methods MEDLINE, Embase, Web of Science, Cochrane Library Database, the National Health Service Economic Evaluation Database, EconLit, and relevant grey literature were systematically searched as per our pre-registered protocol. All economic evaluations of real or model services designed to meet the eye care needs of Indigenous populations in high-income countries were included. Two reviewers independently screened studies, extracted data, and assessed quality using the Quality of Health Economic Studies instrument. Results We identified 20 studies evaluating services for Indigenous populations in Australia (n = 9), Canada (n = 7), and the United States of America (n = 4). Common services included diabetic retinopathy (DR) screening through fundus photographs acquired in local primary health care clinics (n = 7) or by mobile teams (n = 6), and general eye care through teleophthalmology (n = 2), outreach ophthalmology (n = 2) or an Indigenous health care clinic optometrist (n = 1). These services were economically favourable in 85% of comparisons with conventional alternatives, mainly through reduced costs of travel, in-person consults, and vision loss. Only four studies assessed the benefits of increased patient uptake. Only five included patient evaluations, but none integrated these into their quantitative analysis. Methodological issues included no stated economic perspective (n = 10), no sensitivity analysis (n = 12), no discounting (n = 9), inappropriate measurement of costs (n = 13) or outcomes (n = 5), and unjustified assumptions (n = 15). Conclusion Several Indigenous eye care services are cost-effective, particularly remote DR screening. Other services are promising but require evaluation, with attention to avoid common methodological pitfalls. Well-designed evaluations can guide the allocation of scarce resources to services with demonstrated effectiveness and sustainability. Trial registration Our scoping review protocol was pre-registered (Open Science Framework DOI: https://doi.org/10.17605/OSF.IO/YQKWN ).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.149 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".