Frequency and source of prescription eyewear insurance coverage in Ontario: a repeated population-based cross-sectional study using survey data
Bibliographic record
Abstract
Background: Insurance coverage may reduce cost barriers to obtain vision correction. Our aim was to determine the frequency and source of prescription eyewear insurance to understand how Canadians finance optical correction. Methods: We conducted a repeated population-based cross-sectional study using 2003, 2005 and 2013–2014 Canadian Community Health Survey data from respondents aged 12 years or older from Ontario, Canada. In this group, the cost of prescription eyewear is not covered by the government unless one is registered with a social assistance program or belongs to a specific population. We determined the frequency and source of insurance coverage for prescription eyewear in proportions. We used survey weights provided by Statistics Canada in all analyses to account for sample selection, a complex survey, and adjustments for seasonal effect, poststratification, nonresponse and calibration. We compared unadjusted proportions and adjusted prevalence ratios (PRs) of having insurance. Results: Insurance covered all or part of the costs of prescription eyewear for 62% of Ontarians in all 3 survey years. Of those insured, 84.1%–86.0% had employer-sponsored coverage, 9.0%–10.3% had government-sponsored coverage, and 5.7%–6.8% had private plans. Employer-sponsored coverage remained constant for those in households with postsecondary graduation but decreased significantly for those in households with less than secondary school graduation, from 67.0% (95% confidence interval [CI] 63.2%–70.8%) (n = 175 000) in 2005 to 54.6% (95% CI 50.1%–59.2%) (n = 123 500) in 2013–2014. Government-sponsored coverage increased significantly for those in households with less than secondary school graduation, from 29.2% (95% CI 25.5%–32.9%) (n = 76 400) in 2005 to 41.7% (95% CI 37.2%–46.1%) (n = 93 900) in 2013–2014. In 2013–2014, Ontarians in households with less than secondary school graduation were less likely than those with secondary school graduation to report employer-sponsored coverage (adjusted PR 0.79, 95% CI 0.75–0.84) but were more likely to have government-sponsored coverage (adjusted PR 1.27, 95% CI 1.06–1.53). Interpretation: Sixty-two percent of Ontarians had prescription eyewear insurance in 2003, 2005 and 2013–2014; the largest source of insurance was employers, primarily covering those with higher education levels, whereas government-sponsored insurance increased significantly among those with lower education levels. Further research is needed to elucidate barriers to obtaining prescription eyewear and the degree to which affordability impairs access to vision correction.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".