Road traffic injuries and alcohol use in the emergency department in Tanzania: a case-crossover study
Bibliographic record
Abstract
Abstract Introduction: Alcohol is the leading risk factor for road traffic injury (RTI). Africa has the second-highest rate of alcohol dependence and the highest road traffic fatality rate. We describe the proportion of Tanzanian emergency department (ED) patients presenting with RTIs who are blood alcohol content (BAC) positive and determine the dose-response relationship between drinking and injury risk.Methods: Analysis of data from EDs in Tanzania from 2013 to 2014 was performed. Adults presenting to an ED within 6 hours of injury had BAC testing and were asked whether and how much alcohol was consumed prior to the injury. Data also included self-reported alcohol use during control periods 1 day and 1 week prior to the injury. Case-crossover analysis of injury risk used logistic regression to determine matched-pair odds ratios (ORs) and 95% confidence intervals (CIs).Results: Of 513 injury patients, 375 (73%) suffered RTIs. Overall, 29% of RTI patients were BAC-positive. Approximately 40% of those who reported using alcohol prior to RTI reported drinking more than 5 standard drinks. With any alcohol, drivers of both motorcycles and cars/trucks had increased odds of RTI (4.90 OR (CI 2.5-9.5) and 5.70 OR (CI 2.8-11.6) respectively). While the odds of RTI in car/truck drivers demonstrated a dose-dependent response, that in motorcyclists was highest after 3-4 drinks (5.60 OR, CI 2.22-14.10).Discussion: The RTI burden in Tanzania is high. Any alcohol can increase RTI risk. These findings should guide drunk-driving legislation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".