158 A comparative analysis of global child restraint use in nine cities
Bibliographic record
Abstract
Background Road traffic injuries are the leading cause of death for children over five, and these deaths occur predominantly in low- and middle-income countries (LMICs). Despite the proven effectiveness of child restraints in reducing mortality by 60%, utilization rates are critically low in these regions. Unfortunately, child restraint use is extremely low in LMICs, with rates below 10% in some Latin American countries. Moreover, the WHO has no child restraint use data in major regions including Africa, Eastern Mediterranean, and South-East Asia. This study, funded by the Bloomberg Philanthropies Initiative for Global Road Safety, evaluates child restraint usage across nine global cities. Methods Observational data were collected in eight rounds from February 2015 to March 2019, in Accra, Addis Ababa, Bandung, Bangkok, Bogota, Fortaleza, Ho Chi Minh City, Mumbai, and Sao Paulo. Observations included the child’s age, use of child restraint, seat position, vehicle type, day, and road type. Results From 43,619 observations, the prevalence of child restraint use was 16.23% for under-fives and 3.24% for ages five to eleven. Sao Paulo had the highest under-five usage at 53.54%, and Bandung had the lowest, at 2.23%. Children over five showed the highest restraint usage in Bangkok and Sao Paulo, both above 16%, while the remaining cities fell below 10%. Sedans and SUVs had higher restraint usage rates, with lower rates noted on Fridays and on local roads. A significant number of children were improperly seated in the front seat. Conclusion The low and variable rates of child restraint use in these nine cities highlight a critical public health issue. Potential barriers to child restraint use include availability, affordability, and lack of necessary infrastructure, such as seat belts in older vehicles. The ongoing analysis aims to identify factors influencing restraint use and track changes over time. The findings underscore the urgency for improved vehicle standards and stronger policies to enhance child passenger safety globally.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".