Predictors of Medication Non-Adherence Among Hepatitis B Patients in South Sudan: A Health-Facility-Based Cross-Sectional Study
Bibliographic record
Abstract
Background: Despite the effectiveness of hepatitis B virus (HBV) antiviral treatment therapy in reducing the risk of liver-related complications, such as cirrhosis and hepatocellular carcinoma among chronically infected patients, medication non-adherence continues to hamper the successful management of the infection. The extent of HBV treatment adherence, associated facilitators, and barriers in South Sudan is not established. This study aimed to explore the predictors of medication non-adherence among HBV patients attending a public health facility in, South Sudan. Methods: We conducted a facility-based cross-sectional study of 392 convenience-selected patients using a pretested interviewer-administered questionnaire premised on the information-motivation-behavioral skills (IMB) adherence model between December 2023 and March 2024. The relationship between medication non-adherence and antecedent variables was ascertained by logistic regression analysis. Results: The sample was predominantly male (64.3%), and the mean age was 31.06 (30.19-31.93) years, with 28.1% reporting no formal education. The patients demonstrated inadequate HBV information (4.33±1.93), low motivation (8.20±2.69), and inadequate behavioral skills toward medication adherence (8.45±2.99), as measured on their respective rating scales. Further, more than two-thirds of the patients (70.2%) were HBV medication non-adherent. Younger age (AOR = 4.74, 95% CI = 2.13-10.56), being currently unmarried (AOR = 3.25, 95% CI = 1.76-6.01), unemployment (AOR = 4.19, 95% CI = 1.84-9.56), and increased behavioral skills (AOR = 1.12, 95% CI = 1.84-9.56) significantly influenced medication non-adherence. Lower education (AOR = 0.21, 95% CI = 0.10-0.46) and information adequacy (AOR = 0.63, 95% CI = 0.53-0.75) were associated with lower odds of non-adherence. Conclusion: The study highlights key factors influencing the concerning rate of medication non-adherence among HBV patients in South Sudan. While these identified factors may explain the lingering burden of HBV-related complications, targeted interventions addressing demographic, socioeconomic barriers, and HBV-specific education are essential to enhance adherence and improve health outcomes.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".