Fatal road traffic injuries in Ibadan, using the mortuary as a data source
Bibliographic record
Abstract
BACKGROUND: Road Traffic Injury (RTI) in Africa represents 14% of global RTI deaths. Lack of timely, reliable data undermines road safety interventions. Available fatality data are aggregated, limited in detail or scarce in surveys. This is the first fatal RTI surveillance study in Nigeria. OBJECTIVE: To pilot a systematic mortuary-based data collection in Ibadan, determine the nature and circumstances of fatal RTI and assess data quality against existing data sources. METHODS: Using a draft data collection system developed jointly by WHO and Monash University, the detailed information was prospectively collected on RTI University College Hospital mortuary admissions in Ibadan September 2010 to February 2011. Demographics, road user type, counterpart vehicle, intent, manner and medical cause of death were recorded. RESULTS: Mortuary admissions included 80 fatal RTI cases: 81.3% males. By road user category, 28 (35.0%) were pedestrians; 28 (35.0%) motorised 2-wheeler users; 18.8% car occupants; and 11.3% bus occupants. In 70% of cases, medical cause of death was head injury, including 25 of 28 motorised 2-wheeler users (89.3%). Estimates from this study indicate apparent increased mortuary capture of fatal RTI compared with police data. CONCLUSIONS: This study demonstrates the feasibility of collecting detailed, timely RTI fatality data through mortuary-based surveillance in Ibadan. While not all RTI deaths are reported to any authority in Ibadan, this large case series complements existing data sources and suggests that pedestrians and motorised 2-wheeler users die most often in road traffic crashes. Frequent head injuries among motorised 2-wheeler users strongly support the need for helmet wearing interventions.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".