The economic burden of road traffic injuries: evidence from a provincial general hospital in Vietnam
Bibliographic record
Abstract
OBJECTIVE: To examine the costs of road traffic injuries (RTIs) in Vietnam and factors associated with increased costs. METHOD: RTI data were collected in a prospective cohort study on the impact of injuries in Vietnam. Participants were persons admitted to the Thai Binh General Hospital because of RTI. All costs incurred by participants and their family members during hospitalisation were collected, including direct medical costs, direct non-medical costs and indirect costs. Generalised linear models were employed to examine predictors of increased costs including demographic and injury context characteristics. RESULTS: Each RTI hospitalisation costs the patient and family on average US$363 or 6 months of average salary. Income, injury severity, principal region of injury and length of hospital stay were statistically significant predictors of increased costs; age, gender, occupation and road user group were not. After controlling for injury characteristics and income, participants with principal injuries to the lower extremities had a cost 1.28 (95% CI 1.07 to 1.54) times higher than those with principal injuries to the face. Analyses of motorcycle-related RTIs with principal injury to the head also showed increased costs among those without a helmet (1.41 times higher, 95% CI 1.17 to 1.71). CONCLUSIONS: RTIs can cause a substantial economic burden to the patient and family. During hospitalisation on average, an RTI would cost approximately 6 months of salary. In addition to interventions to decrease the risk of RTIs, those reducing the severity, such as wearing a motorcycle helmet, should be enforced to minimise the economic and health consequences of injury.
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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 | 0.000 | 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 | 0.000 |
| 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".