Cango Lyec (Healing the Elephant): Chronic Hepatitis B Virus among post-conflict affected populations living in mid-Northern Uganda
Bibliographic record
Abstract
BACKGROUND: The legacy of war in Northern Uganda continues to impact people's health and wellbeing in the Acholi region. Despite increasing attention to Hepatitis B Virus (HBV) in Uganda and globally, concerns remain that unique drivers of infection, and barriers to screening, and treatment, persist among those affected by conflict. METHODS: Cango Lyec (Healing the Elephant) cohort survey involved conflict-affected adults aged 13-49 in three mid-Northern Uganda districts (Gulu, Amuru and Nwoya). Baseline (2011-2012) samples were tested for HBV surface antigen (HBsAg), HBV e-antigen (HBeAg), antibodies to HBV surface antigen (HBsAb), antibodies to HBV e-antigen (HBeAb), and antibodies to HBV core antigen (HBcAb). All HBsAg positive samples were tested for IgM antibodies to HBV B core antigen (HBc-IgM) and where available, >6-month follow-up samples were tested for HBeAg and HBV DNA. Data were analyzed using STATA 15 software. Logistic regression accounted for variance due to complex two-stage sampling that included stratification, unequal selection probabilities and community clustering. Odds ratios measured effect potential risk factors associated with chronic HBV infection. RESULTS: Among 2,421 participants, 45.7% were still susceptible to HBV infection. HBsAg seropositivity was 11.9% (10.9-13.0), chronic HBV was 11.6% (10.4-12.8), acquired immunity resulting from vaccination was 10.9%, and prior natural infection was 31.5%. Older age (OR:0.570; 95%CI:0.368-0.883) and higher education (OR:0.598; 95%CI:0.412-0.868) were associated with reduced odds of chronic HBV infection. Being male (OR:1.639; 95%CI:1.007-2.669) and having been abducted (OR:1.461; 95%CI:1.055-2.023) were associated with increased odds of infection. Among women, having 1 or 2 pregnancies (compared to none or >2) was associated with increased odds of infection (OR:1.764; 95%CI:1.009-3.084). CONCLUSION: Chronic HBV is endemic in Gulu, Amuru and Nwoya districts. Recommended strategies to reduce post-conflict prevalence include establishment of Northern Uganda Liver Wellness Centres, integration of screening and treatment into antenatal care, and roll out of birth-dose vaccination.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".