Vision impairment and traffic safety outcomes in low-income and middle-income countries: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Road traffic injuries are a major public health concern and their prevention requires concerted efforts. We aimed to systematically analyse the current evidence to establish whether any aspects of vision, and particularly interventions to improve vision function, are associated with traffic safety outcomes in low-income and middle-income countries (LMICs). METHODS: We did a systematic review and meta-analysis to assess the association between poor vision and traffic safety outcomes. We searched MEDLINE, Embase, PsycINFO, CINAHL, Web of Science, Cochrane Database of Systematic Reviews, and the Cochrane Central Register of Controlled Trials in the Cochrane Library from database inception to April 2, 2020. We included any interventional or observational studies assessing whether vision is associated with traffic safety outcomes, studies describing prevalence of poor vision among drivers, and adherence to licensure regulations. We excluded studies done in high-income countries. We did a meta-analysis to explore the associations between vision function and traffic safety outcomes and a narrative synthesis to describe the prevalence of vision disorders and adherence to licensure requirements. We used random-effects models with residual maximum likelihood method. The systematic review protocol was registered on PROSPERO, CRD-42020180505. FINDINGS: value for overall statistical heterogeneity was 63·4%. INTERPRETATION: This systematic review shows a positive association between vision impairment and traffic crashes in LMICs. Our findings provide support for mandatory vision function assessment before issuing a driving licence. FUNDING: None.
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 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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.018 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".