Prevalence and Risk Factors for Near and Far Visual Difficulty in Burkina Faso
Bibliographic record
Abstract
PURPOSE: To determine the prevalence and risk factors for near and far visual difficulty in Burkina Faso. METHODS: Population-based data were used from the World Health Survey done in Burkina Faso in 2002-2003;2003 (n=4,822 adults). Near and far visual difficulty were assessed by questions about difficulty seeing and recognizing an object at arm's length and about difficulty seeing and recognizing a person across the road. Prevalence estimates were adjusted for the multi-stage, stratified, random cluster sampling design. Logistic regression was used to identify independent risk factors. RESULTS: The overall prevalence of any near and far visual difficulty was 10% (standard error [SE] = 0.7%) and 13% (SE=0.9%) respectively. Prevalence estimates were strongly associated with age with 48% (SE=4.2%) and 66% (SE=3.9%) of those >or= 65 years old having near or far visual difficulty (P < 0.001). Only 5% (SE=0.6%) of people wore glasses. We identified two potentially modifiable variables associated with near visual difficulty: a cooking stove in the same room as sleeping area (Odds Ratio [OR]=1.45, 95% Confidence Interval [CI] 1.01, 2.02) and high fruit consumption (OR=0.65, 95% CI 0.50, 0.86). CONCLUSION: The prevalence of visual difficulty was high in Burkina Faso. Efforts to confirm these findings with cooking stove location and fruit consumption should be undertaken in this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".