Assessing the Value and Impacts of Vision Care Services for Children in Ontario
Bibliographic record
Abstract
BACKGROUND: Refractive errors and amblyopia cause the most vision impairment in children. Early vision testing is crucial to prevent permanent impairment. The uptake of optometry exams amongst children in Ontario is unknown. Limited evidence exists on the accessibility and value of vision care services. This evidence is needed for equitable and cost-effective services to prevent needless vision loss in Ontario. OBJECTIVES: To 1) determine the uptake of comprehensive eye exams and test its associations with socioeconomic status amongst young children in Ontario, 2) synthesize and appraise the literature on economic evaluations of vision screening to detect vision impairment in children, and 3) determine the cost-effectiveness of (a) mandatory comprehensive eye exams by optometrists once between 2 to 5 years of age (CEE); and (b) universal school screening by contracted screeners at age 5 years (USS) compared to (c) usual care (i.e., vision screening as part of well-child visits annually from age 3 to 5 years [WCC(RS)] in Toronto, Ontario. METHODS: A population-based cohort study, systematic review of the literature, and a cost-utility analysis using a probabilistic health-state microsimulation model. RESULTS: Sixty-five percent of children in Ontario had at least one comprehensive eye exam by their 7th birthday, with children in neighborhoods of least material deprivation having a higher odds [adjusted odds ratio (aOR) 1.43; 95%CI 1.36, 1.51] compared to children in most deprived neighborhoods. The review yielded 13 economic evaluation studies, six of high quality with incremental cost-effectiveness ratios (ICER) between C$1,056 to C$151,274/case detected or prevented and between C$9,429 to C$30,254,703/QALY gained. From the societal perspective of the cost-utility analysis, USS was less costly and less effective than WCC(RS), and the ICER was C$500/QALY gained comparing CEE to WCC(RS). There was significant parameter uncertainty in the probabilistic analysis. At a willingness-to pay of C$50,000/QALY gained, USS was cost-effective in 19% of iterations relative to WCC(RS), CEE was cost-effective in 26% of iterations relative to WCC(RS). CONCLUSION: Low socioeconomic status is associated with poor uptake of comprehensive eye exams in Ontario however, USS and CEE are not cost-effective alternatives to usual care screening [WCC(RS)] at a willingness-to-pay of C$50,000/QALY gained.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".