Policy implications of regional variations in eye disease detection and treatment on Prince Edward Island: a repeated cross-sectional analysis, 2010–2012
Bibliographic record
Abstract
BACKGROUND: In Canada, government insurance covers eye care services provided by ophthalmologists and other physicians. However, government coverage for services provided by optometrists, non-medical school trained primary eye care providers, varies regionally. Little is known about the impact of a funding model in which ophthalmologist services are government-insured but services provided by optometrists are not, on eye care utilization and eye disease detection and treatment. We aimed to address this question by examining geographic variations in eye care service utilization on Prince Edward Island (PEI). METHODS: PEI physician-billing data from 2010 to 2012 was analyzed across five distinct geographic regions (Charlottetown, Summerside, Prince, Queens & Kings and Stratford). The residential location of patients and practice locations of eye care providers were identified using the first three digits of their respective postal code. Age-standardized rates were computed for comparisons across different regions. RESULTS: There were six ophthalmologists practicing on PEI, five with offices in Charlottetown. Twenty optometrists practiced on the island with offices across the province. Stratford is closest and Prince farthest from Charlottetown. Age-standardized utilization rates of ophthalmologists per 100 populations were 10.44 in Charlottetown and 10.90 in Stratford, which was significantly higher than in other regions (7.74-8.92; p < 0.05). The disparities were most pronounced amongst the elderly. The prevalence of glaucoma visits was higher in Charlottetown (6.10%) and Stratford (6.38%) and lower in other regions. A similar pattern was observed for the prevalence of cataract visits. While the prevalence of diabetes visits was higher in Prince and Summerside, the utilization of ophthalmologists by people with diabetes was almost twice as high in Charlottetown (6.49%) than in Prince (3.88%). CONCLUSIONS: The observed discrepancies in vision care utilization across geographic regions were likely attributed to barriers in accessing government-insured, geographically concentrated ophthalmologists, as opposed to a reflection of the true differences in eye disease occurrence. The lower prevalence of glaucoma visits in regions farther away from ophthalmologist offices may result in delayed detection and blindness in this population. Encouraging ophthalmologists to work in other areas of the province and/or to publicly fund services provided by optometrists may mitigate the observed disparities. TRIAL REGISTRATION: Not applicable.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".