Estimated number of seriously injured road users admitted to hospital in France between 2010 and 2017, based on medico-administrative data
DOI: 10.1186/s12889-021-10437-0
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Summary
This study addresses the significant underestimation of serious road traffic injuries in France, where official statistics rely primarily on police data that often fail to capture the full scope of hospital admissions or injury severity. While fatality data from police records are considered reliable, estimates for serious injuries are biased, particularly for vulnerable road users like cyclists and motorcyclists involved in single-vehicle accidents. The research aimed to provide accurate estimates of seriously injured road users admitted to hospitals between 2010 and 2017 using national medico-administrative data, aligning with European Commission definitions to facilitate international comparison. The researchers utilized the French national hospital discharge registry (PMSI-MCO), which contains standardized discharge summaries coded with ICD-10 diagnoses. Two primary data challenges were addressed: the frequent absence of external cause codes (indicating road accidents) and the lack of injury severity metrics in ICD-10. To correct for missing external causes, the authors employed a logistic regression model to estimate the probability of a cause being recorded based on patient and hospital variables, then applied inverse probability weighting to the dataset. To determine severity, they used the AAAM10 conversion instrument to map ICD-10 codes to the Abbreviated Injury Scale (AIS), identifying serious injuries as those with a Maximum AIS score of 3 or higher (MAIS3+). The study validated its methodology by comparing PMSI estimates against the Rhône Road Trauma Registry, a local exhaustive database. The results indicate that approximately 100,000 road accident victims were admitted to French hospitals annually during the study period. The number of serious injuries (MAIS3+) ranged from roughly 17,000 in 2012–2013 to nearly 20,000 in 2017, representing about 17.5% of all admissions. The incidence rate averaged 161.6 per 100,000 inhabitants. Demographic analysis revealed that males were admitted twice as often as females, with the disparity widening to nearly threefold for serious injuries among those aged 20–39. Validation against the Rhône Registry showed that while national PMSI estimates slightly overcounted local cases due to regional hospital catchment areas, restricting the analysis to local residents yielded strong agreement with registry data. Additionally, the study estimated that deaths occurring more than 30 days post-accident constituted a negligible fraction (1–2%) of total fatalities. The significance of this work lies in providing a robust, reproducible method for estimating serious road injuries using widely available hospital discharge data. By bridging the gap between ICD coding and AIS severity scales, the study offers estimates that are comparable across European nations. These findings highlight that police data significantly underreport hospital admissions and underscore the utility of medico-administrative databases for assessing the true burden of road trauma, enabling better cost estimation and policy planning for road safety.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-20 |
| archive | success | canonical_url | — | — | 1 | 2026-06-26 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-20 |
| chunk | success | chunk | — | — | 1 | 2026-06-20 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-20 |
| promote | success | — | — | — | 1 | 2026-06-20 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-20 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
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- Empirical Findings: crash risk outcomes