Do motorcycle helmets reduce road traffic injuries, hospitalizations and mortalities in low and lower-middle income countries in Africa? A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Studies in Africa have examined the association between helmet use and injury prevention, however, there has been no systematic review to synthesize the literature within an African context nor has there been any meta-analysis examining the effect of helmet use on injury prevention. METHODS: The review was performed in accordance with the Joanna Briggs Institute for Systematic Reviews. Articles were searched using several databases (e.g. CINAHL, OVID Medline) and select gray literature (e.g. TRID) sources. Articles were included if they were quantitative studies published in English between 2000 and 2019 and examined the association between motorcycle helmet use with head injuries, hospitalizations, and deaths in low- and lower-middle income countries in Africa with comprehensive motorcycle helmet laws. A meta-analysis was performed using pooled effect sizes assessing the impact of helmet use on reducing head injuries. RESULTS: After screening 491 articles, eight studies met the inclusion criteria. Helmet use ranged from 0 to 43%. The mean age of being involved in a crash was 30 years with males being two times more likely to be involved in motorcycle crashes than females. Drivers (riders) were more likely to be involved in a crash, followed by passengers and then pedestrians. Helmet use reduced injury severity and provided an 88% reduction in serious head injuries (OR 0.118, 95% CI: 0.014-0.968, p = 0.049). CONCLUSIONS: In our study, helmet usage significantly reduced the likelihood of fatal head injuries. African countries with no helmet laws should consider adopting helmet use policies to reduce severe head related injuries from motorcycle crashes.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".