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 We identified 49 (1·8%) eligible articles of 2653 assessed and included 29 (59·2%) in the various data syntheses. 15 394 participants (mean sample size n=530 [SD 824]; mean age of 39·3 years [SD 9·65]; 1167 [7·6%] of 15 279 female) were included. The prevalence of vision impairment among road users ranged from 1·2% to 26·4% (26 studies), colour vision defects from 0·5% to 17·1% (15 studies), and visual field defects from 2·0% to 37·3% (ten studies). A substantial proportion (range 10·6–85·4%) received licences without undergoing mandatory vision testing. The meta-analysis revealed a 46% greater risk of having a road traffic crash among those with central acuity visual impairment (risk ratio [RR] 1·46 [95% CI 1·20–1·78]; p=0·0002, 13 studies) and a greater risk among those with defects in colour vision (RR 1·36 [1·01–1·82]; p=0·041, seven studies) or the visual field (RR 1·36 [1·25–1·48]; p<0·0001, seven studies). The I 2 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.
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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 | 0.015 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.036 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".