Burden of injury during the complex political emergency in northern Uganda.
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».