Economic evaluations of eye care services for Indigenous populations in high-income countries: a scoping review
Bibliographic record
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 ).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.036 | 0.161 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".