Global cost of correcting vision impairment from uncorrected refractive error
Bibliographic record
Abstract
OBJECTIVE: To estimate the global cost of establishing and operating the educational and refractive care facilities required to provide care to all individuals who currently have vision impairment resulting from uncorrected refractive error (URE). METHODS: The global cost of correcting URE was estimated using data on the population, the prevalence of URE and the number of existing refractive care practitioners in individual countries, the cost of establishing and operating educational programmes for practitioners and the cost of establishing and operating refractive care facilities. The assumptions made ensured that costs were not underestimated and an upper limit to the costs was derived using the most expensive extreme for each assumption. FINDINGS: There were an estimated 158 million cases of distance vision impairment and 544 million cases of near vision impairment caused by URE worldwide in 2007. Approximately 47 000 additional full-time functional clinical refractionists and 18 000 ophthalmic dispensers would be required to provide refractive care services for these individuals. The global cost of educating the additional personnel and of establishing, maintaining and operating the refractive care facilities needed was estimated to be around 20 000 million United States dollars (US$) and the upper-limit cost was US$ 28 000 million. The estimated loss in global gross domestic product due to distance vision impairment caused by URE was US$ 202 000 million annually. CONCLUSION: The cost of establishing and operating the educational and refractive care facilities required to deal with vision impairment resulting from URE was a small proportion of the global loss in productivity associated with that vision impairment.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".