Disparities in Injury Mortality Between Uganda and the United States: Comparative Analysis of a Neglected Disease
Bibliographic record
Abstract
BACKGROUND: The burden of global injury-related deaths predominantly affects developing countries, which have little infrastructure to evaluate these disparities. We describe injury-related mortality patterns in Kampala, Uganda and compare them with data from the United States and San Francisco (SF), California. METHODS: We created a database in Kampala of deaths recorded by the City Mortuary, the Mulago Hospital Mortuary, and the Uganda Ministry of Health from July to December 2007. We analyzed the rate and odds ratios and compared them to data from the U.S. Centers for Disease Control and Prevention and the California Department of Public Health. RESULTS: In Kampala, 25% of all deaths were due to injuries (812/3303) versus 6% in SF and 7% in the United States. The odds of dying of injury in Kampala were 5.0 times higher than in SF and 4.2 times higher than in the United States. Age-standardized death rates indicate a 93% greater risk of dying from injury in Kampala than in SF. The mean age was lower in Kampala than in SF (29 vs. 44 years). The adult injury death rate (rate ratio, or RR) was higher in Kampala than in SF (2.3) or the United States (1.5). Head/neck injury was reported in 65% of injury deaths in Kampala compared to 34% in SF [odds ratio (OR) 3.7] and 28% in the US (OR 4.8). CONCLUSIONS: Urban injury-related mortality is significantly higher in Uganda than in the United States. Injury preferentially affects adults in the prime of their economically productive years. These findings serve as a call for stronger injury prevention and control policies in Uganda.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".