Cost and quality of life of overlooked eye care needs of children
Bibliographic record
Abstract
BACKGROUND: The objective of this research was to conduct a systematic review and cost analysis to summarize, from the Ministry of Health perspective, the costs families might incur because of their child's prescription for refractive errors and amblyopia correction. METHODS: Databases including MEDLINE, Embase, BIOSIS, CINAHL, HEED, ISI Web of Science, and the Cochrane Library as well as the gray literature were searched. Systematic review was conducted using EPPI-Reviewer 4. Percentage difference in cost of glasses and patches per patient per various diagnoses were computed. The cost of glasses and patches was projected over a 5-year time horizon. Cost-utility analysis was performed. RESULTS: In total, 302 records were retrieved from multiple databases and an additional 48 records were identified through gray literature search. From these, a total of 14 studies (10,388 subjects) were eligible for quantitative analysis. The cost of glasses increased significantly for congenital cataract patients to US$1,820, esotropia patients to US$840, myopes to US$411, amblyopes (mixed) to US$916, anisometropes to US$521, and patients with strabismus to US$728 over a 5-year period making them unaffordable for low-income families. Incremental cost of glasses of congenital cataract patients with delayed treatment was computed to be US$1,690 per health utility gained. Incremental cost of glasses for high refractive error was US$93 per health utility gained in non-compliant children. For amblyopia patients, incremental cost of glasses per quality-adjusted life years gained was US$3,638. CONCLUSION: Cost of corrective lenses is associated with significant financial burden and thus other means of mitigating costs should be considered. Eyesight problems in children are perceived as low-priority health needs. Thus, educational interventions on substantial visual deficits of not wearing glasses should be offered to families and governmental health agencies.
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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 | 0.010 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".