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Cost-effectiveness of Universal School- and Community-Based Vision Testing Strategies to Detect Amblyopia in Children in Ontario, Canada

2023· article· en· W4313482834 on OpenAlexaffabout
Afua O. Asare, Daphne Maurer, Agnes Wong, Natasha Saunders, Wendy J. Ungar

Bibliographic record

VenueJAMA Network Open · 2023
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Visual Impairment Studies
Canadian institutionsInstitute for Clinical Evaluative SciencesSickKids FoundationHospital for Sick ChildrenMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsOptometryPsychologyMedicine

Abstract

fetched live from OpenAlex

Importance: Screening for amblyopia in primary care visits is recommended for young children, yet screening rates are poor. Although the prevalence of amblyopia is low (3%-5%) among young children, universal screening in schools and mandatory optometric examinations may improve vision care, but the cost-effectiveness of these vision testing strategies compared with the standard in primary care is unknown. Objective: To evaluate the relative cost-effectiveness of universal school screening and mandated optometric examinations compared with standard care vision screening in primary care visits in Toronto, Canada, with the aim of detecting and facilitating treatment of amblyopia and amblyopia risk factors from the Ontario government's perspective. Design, Setting, and Participants: An economic evaluation was conducted from July 2019 to May 2021 using a Markov model to compare 15-year costs and quality-adjusted life-years (QALYs) between school screening and optometric examination compared with primary care screening in Toronto, Canada. Parameters were derived from published literature, the Ontario Schedule of Benefits and Fees, and the Kindergarten Vision Testing Program. A hypothetical cohort of 25 000 children aged 3 to 5 years was simulated. It was assumed that children in the cohort had irreversible vision impairment if not diagnosed by an optometrist. In addition, incremental costs and outcomes of 0 were adjusted to favor the reference strategy. Vision testing programs were designed to detect amblyopia and amblyopia risk factors. Main Outcomes and Measures: For each strategy, the mean costs per child included the costs of screening, optometric examinations, and treatment. The mean health benefits (QALYs) gained were informed by the presence of vision impairment and the benefits of treatment. Incremental cost-effectiveness ratios were calculated for each alternative strategy relative to the standard primary care screening strategy as the additional cost required to achieve an additional QALY at a willingness-to-pay threshold of $50 000 Canadian dollars (CAD) ($37 690) per QALY gained. Results: School screening relative to primary care screening yielded cost savings of CAD $84.09 (95% CI, CAD $82.22-$85.95) (US $63.38 [95% CI, US $61.97-$64.78]) per child and an incremental gain of 0.0004 (95% CI, -0.0047 to 0.0055) QALYs per child. Optometric examinations relative to primary care screening yielded cost savings of CAD $74.47 (95% CI, CAD $72.90-$76.03) (US $56.13 [95% CI, $54.95-$57.30]) per child and an incremental gain of 0.0508 (95% CI, 0.0455-0.0561) QALYs per child. At a willingness-to-pay threshold of CAD $50 000 (US $37 690) per QALY gained, school screening and optometric examinations were cost-effective relative to primary care screening in only 20% and 29% of iterations, respectively. Conclusions and Relevance: In this study, because amblyopia prevalence is low among young children and most children in the hypothetical cohort had healthy vision, universal school screening and optometric examinations were not cost-effective relative to primary care screening for detecting amblyopia in young children in Toronto, Canada. The mean added health benefits of school screening and optometric examinations compared with primary care screening did not warrant the resources used.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.455

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.069
GPT teacher head0.363
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2023
Admission routes2
Has abstractyes

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