“The Cango Lyec Project - Healing the Elephant”: HIV related vulnerabilities of post-conflict affected populations aged 13–49 years living in three Mid-Northern Uganda districts
Bibliographic record
Abstract
BACKGROUND: The protracted war between the Government of Uganda and the Lord's Resistance Army in Northern Uganda (1996-2006) resulted in widespread atrocities, destruction of health infrastructure and services, weakening the social and economic fabric of the affected populations, internal displacement and death. Despite grave concerns that increased spread of HIV/AIDS may be devastating to post conflict Northern Uganda, empirical epidemiological data describing the legacy of the war on HIV infection are scarce. METHODS: The 'Cango Lyec' Project is an open cohort study involving conflict-affected populations living in three districts of Gulu, Nwoya and Amuru in mid-northern Uganda. Between November 2011 and July 2012, 8 study communities randomly selected out of 32, were mapped and house-to-house census conducted to enumerate the entire community population. Consenting participants aged 13-49 years were enrolled and interviewer-administered data were collected on trauma, depression and socio-demographic-behavioural characteristics, in the local Luo language. Venous blood was taken for HIV and syphilis serology. Multivariable logistic regression was used to determine factors associated with HIV prevalence at baseline. RESULTS: A total of 2954 participants were eligible, of whom 2449 were enrolled. Among 2388 participants with known HIV status, HIV prevalence was 12.2% (95%CI: 10.8-13.8), higher in females (14.6%) than males (8.5%, p < 0.001), higher in Gulu (15.2%) than Nwoya (11.6%, p < 0.001) and Amuru (7.5%, p = 0.006) districts. In this post-conflict period, HIV infection was significantly associated with war trauma experiences (Adj. OR = 2.50; 95%CI: 1.31-4.79), the psychiatric problems of PTSD (Adj. OR = 1.44; 95%CI: 1.06-1.96), Major Depressive Disorder (Adj. OR = 1.89; 95%CI: 1.28-2.80) and suicidal ideation (Adj. OR = 1.87; 95%CI: 1.34-2.61). Other HIV related vulnerabilities included older age, being married, separated, divorced or widowed, residing in an urban district, ulcerative sexually transmitted infections, and staying in a female headed household. There was no evidence in this study to suggest that people with a history of abduction were more likely to be HIV positive. CONCLUSIONS: HIV prevalence in this post conflict-affected population is high and is significantly associated with age, trauma, depression, history of ulcerative STIs, and residing in more urban districts. Evidence-based HIV/STI prevention programs and culturally safe, gender and trauma-informed are urgently needed.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 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".