OA2074. Accidents in Germany 2024 – first results of a survey by the National Public Health Institute
Bibliographic record
Abstract
Abstract Background In 2023, over 33,000 people died in accidents in Germany (ICD10: V01-X59), most of them in accidents at home and during leisure activities (30,000). Germany-wide monitoring and reporting is only available for individual areas, with a particular lack of data on accidents at home and during leisure activities. In 2024, after a 14-year interval, a nationwide survey was conducted to close this gap. Methods In the accident module of the Robert Koch Institute’s ‘Health in Germany 2024’ study, 27,020 people aged 18 and over were asked about accident-related injuries in the previous 12 months. The data set was weighted according to age, gender, education and regional characteristics and analysed descriptively. Results 9.0% of women and 10.3% of men in Germany report at least one accident-related injury within 12 months that required medical treatment. Over a third of accidents happened at home, a quarter during leisure time. Women report fewer accidents at work than men, but more accidents at home. The most significant age differences are found in leisure-time accidents, with a significantly higher proportion of accidents happening during leisure time among younger people than among older people. The pattern is reversed for accidents at home. There are educational differences in the locations of accidents: accidents at work were reported more frequently by men with a low level of education, while leisure accidents were reported more frequently by men with a high level of education. One-fifth of accident victims were treated in hospital. (Data as of 31 March 2025). Conclusions The initial analysis provides important key data for informing health policymakers and prevention experts in Germany. The accident module contains numerous additional questions about the circumstances of the accident, the resulting injuries and the care provided. In future, this will enable concrete recommendations for prevention to be made, because an accident is no coincidence. Key messages • 9.0% of women and 10.3% of men in Germany received medical treatment for an accident within a year. • After 14 years, this survey provides urgently needed detailed information on accidents for health policymakers and prevention experts in Germany. Topic Accident, prevalence, adults.
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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 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".