Estimating the costs and quality of life impact of vision loss in the population aged 50-80 years in Malta: evidence from The Malta Eye Study
Bibliographic record
Abstract
Background: Visual impairment and related ocular conditions impose substantial direct, indirect, and intangible costs, encompassing healthcare expenses, productivity losses, and reduced quality of life. Despite the global relevance of visual impairment, no comprehensive cost analysis has yet been conducted in an older adult Maltese population aged 50-80 years. Methods: Prevalence estimates from the population-based Malta Eye Study were used to calculate indirect costs via the gross national income per capita method with disability weight assumptions. Direct medical costs for key ocular conditions, including refractive error, cataract, age-related macular degeneration, diabetic retinopathy, and glaucoma, were estimated using prevalence, hospital, and private data performance indices and relevant cost data, enabling estimation of service coverage and unmet care needs. Intangible costs were derived from quality-of-life measures using the National Eye Institute Visual Function Questionnaire-39 to calculate disability weights and years lived with disability (YLD). Results: The productivity losses from blindness and moderate-severe visual impairment among individuals aged 50-80 were estimated at €16.0 million per annum (95% CI €6.0-€43.0 million). The estimated annual direct medical costs from the main ocular causes were estimated to sum up to €53.4 million (95% CI €44.6-€67.0 million), with unmet needs amounting to €20.8 million (95% CI €15.5-€28.5 million). Cataract (56.9%) and refractive error (24.5%) accounted for the highest shares of such costs. Vision-related quality of life correlated with the severity and laterality of visual impairment. Mild unilateral visual impairment carried the highest YLD rate 2264.4 YLDs per 100,000 while uncorrected refractive error carried the highest YLD rate among the visually impairing causes (2452.7 YLDs per 100,000). Discussion: Visual impairment imposes a considerable economic and quality-of-life burden on an older adult population in Malta, driven largely by cataracts, refractive error, and productivity losses. These results emphasize the need for preventive and treatment strategies and underscore the importance of future cost-benefit and cost-effectiveness analyses to help guide eye health policy in Malta.
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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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".