Prevalence of self-reported visual impairment among people in Canada with and without diabetes: findings from population-based surveys from 1994 to 2014
Bibliographic record
Abstract
BACKGROUND: Diabetes, a leading cause of visual impairment, is on the rise in Canada. We assessed trends in the prevalence of visual impairment among people in Canada with and without diabetes to inform the development of strategies and policies for the management of visual impairment. METHODS: We analyzed self-reported data from respondents aged 45 years and older in 7 cycles of nationwide surveys (National Population Health Survey and Canadian Community Health Survey) from 1994/95 to 2013/14. The age- and sex-standardized prevalence of visual impairment was calculated. We assessed comparisons by levels of education and income, using sex-standardized prevalence owing to sparse data. RESULTS: Among people in Canada with diabetes, the age- and sex-standardized prevalence of visual impairment was 7.37% (95% confidence interval [CI] 5.31%-9.43%) in 1994/95 and 1996/97 combined, decreasing to 3.03% (95% CI 2.48%-3.57%) in 2013/14, giving a standardized prevalence ratio of 0.41 (95% CI 0.30-0.56) comparing 2013/14 with 1994/95 and 1996/97 combined. Among people in Canada without diabetes, visual impairment prevalence decreased from 3.72% (95% CI 3.31%-4.14%) in 1994/95 and 1996/97 combined to 1.69% (95% CI 1.52%-1.87%) in 2013/14, with a standardized prevalence ratio of 0.45 (95% CI 0.40-0.52). Decreased sex-standardized prevalence of visual impairment was observed among people with high and low education levels and incomes among those with and without diabetes. INTERPRETATION: Visual impairment prevalence was roughly 2 times higher among those with versus without diabetes in all survey years; from 1994 to 2014, visual impairment prevalence decreased among those with and without diabetes irrespective of education and income levels. These results suggest effective collective efforts by clinicians, researchers, the public and government.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".