The Impact of Lack of Government-Insured Routine Eye Examinations on the Incidence of Self-Reported Glaucoma, Cataracts, and Vision Loss
Bibliographic record
Abstract
PURPOSE: We determined the impact of lack of government insured routine eye examinations on the incidence of self-reported glaucoma, cataracts and vision loss. METHODS: We analyzed data from the Canadian longitudinal National Population Health Survey (1994-2011). White respondents aged 65+ in 1994/1995 were included (n = 2618). Three cohorts were established at baseline: those free of glaucoma, cataracts, and vision loss (i.e., unable to see close or distance when wearing glasses or contact lenses). Incident cases were identified through self-reporting of these conditions during the follow-up period. RESULTS: The incidence (per 1000 person-years) of glaucoma was lower in uninsured provinces (8.1; 95% confidence interval [CI], 5.5-10.7) than in insured provinces (12.8; 95% CI, 10.5-15.1). The incidence of cataracts was also lower in the uninsured (67.2; 95% CI, 55.7-78.6) versus insured provinces (75.7; 95% CI, 69.2-82.2). The incidence of vision loss was higher in the uninsured (26.6; 95% CI, 20.2-33.0) versus insured provinces (22.5; 95% CI, 20.0-25.5). Adjusting for confounders, seniors in insured provinces had a 59% increased risk of glaucoma (incidence rate ratio [IRR], 1.59; 95% CI, 1.07-2.37), a 13% greater risk of cataracts (IRR, 1.13; 95% CI, 0.93-1.37), and a 12% reduced risk of vision loss (IRR, 0.88; 95% CI, 0.67-1.16). CONCLUSIONS: Lack of government-funded routine eye examinations is associated with a reduced incidence of self-reported glaucoma and cataracts, likely due to reduced detection. Lack of insurance also is associated with a higher incidence of self-reported vision loss, likely due to poorer access to eye care and late treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.010 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".