Burden of injury during the complex political emergency in northern Uganda.
Bibliographic record
Abstract
BACKGROUND: War injury is a public health problem that warrants global attention. This study aims to determine the burden of injury during a complex emergency in sub-Saharan Africa. METHODS: To determine the magnitude, causes, distribution, risk factors and cumulative burden of injury in a population experiencing armed conflict in northern Uganda since 1986 and to evaluate the living conditions and access to care for injury victims, we took a multistage, stratified, random sampling from the Gulu district to determine the rates of injury from 1994 to 1999. The Gulu district is endemic for malaria, tuberculosis, HIV and malnutrition and has a high maternal death rate. It is 1 of 3 districts in northern Uganda affected by war since 1986. The study participants included 8595 people from 1475 households. Of these, 73.0% lived in temporary housing, 46.0% were internally displaced and 81.0% were under 35 years of age. Trained interviewers administered a 3-part household survey in the local language. Quantitative data on injury, household environment, health care and demography were analyzed. Qualitative data from part 3 of the survey will be reported elsewhere. A similar rural district (Mukono) not affected by war was used for comparison. We studied injury risk factors, mortality and disability rates, accumulated deaths, access to care and living conditions. RESULTS: Of the study population, 14% were injured annually: gunshot injuries were the leading cause of death. The annual death rate from war injury was 7.8/1000 (95% confidence interval [CI] 7.0-8.5) and the disability rate was 11.3/1000 (95% CI 10.4-12.2). The annual excess injury mortality was 6.85/1000. Only 4.5% of the injured were combatants. Fifty percent of the injured received first aid, but only 13.0% of those who died reached hospital. The injury mortality in Gulu was 8.35-fold greater than that for Mukono. CONCLUSIONS: The crisis in Gulu can be considered a complex political emergency. Protracted conflicts should not be ignored because of a low rate of injury death since the cumulative total is high. Political emergencies should be monitored, and when the mortality exceeds 3.5%, international intervention is indicated. The international and national failings of this protracted conflict should be critically analyzed so that such political emergencies can be prevented or terminated.
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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".