Major depressive disorder seven years after the conflict in northern Uganda: burden, risk factors and impact on outcomes (The Wayo-Nero Study)
Bibliographic record
Abstract
BACKGROUND: Major depressive disorder (MDD) is a major public health burden in conflict areas. However, it is not known for how long and by how much the observed high rates of MDD seen in conflict settings persist into the post-conflict period. METHODS: A cross sectional survey was employed seven years after the conflict in northern Uganda had ended in the three districts of Amuru, Gulu and Nwoya. RESULTS: The prevalence of major depressive disorder (MDD) was 24.7% (95% CI: 22.9%-26.4%). The distribution by gender was females 29.2% (95% CI: 14.6%-19.5%) and males 17.0% (95% CI: 26.9%-31.5%). The risk factors for MDD fell under the broad domains of socio-demographic factors (female gender, increasing age, being widowed and being separated/divorced); distal psychosocial vulnerability factors ( being HIV positive, low social support, increasing war trauma events previously experienced, war trauma stress scores previously experienced, past psychiatric history, family history of mental illness, negative coping style, increasing childhood trauma scores, life-time attempted suicide, PTSD, generalized anxiety disorder and alcohol dependency disorder) and the psychosocial stressors (food insufficiency, increasing negative life event scores, increasing stress scores). 'Not receiving anti-retroviral therapy' for those who were HIV positive was the only negative clinical and behavioral outcome associated with MDD. CONCLUSIONS: These findings indicate that post-conflict northern Uganda still has high rates for MDD. The risk factors are quite many (including psychiatric, psychological and social factors) hence the need for effective multi-sectoral programs to address the high rates of MDD in the region. These programs should be long term in order to address the long term effects of war. Longitudinal studies are recommended to continuously assess the trends of MDD in the region and remedial action taken.
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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".