Epidemiology of alcohol use and alcohol use disorders among people living with HIV on antiretroviral therapy in Northwest Tanzania: implications for ART adherence and case management
Bibliographic record
Abstract
Alcohol use disorders (AUD) among people living with HIV (PLHIV) are associated with poor health outcomes. This cross-sectional study examined current alcohol use and AUD among 300 PLHIV on ART at four HIV care centres in Northwest Tanzania. Participants' data were collected using questionnaires. Alcohol use was assessed using Alcohol Use Disorders Identification Test (AUDIT). Logistic regression was used to examine associations between each outcome (current drinking and AUD) and sociodemographic and clinical factors. Association between alcohol use and ART adherence was also studied. The median age of participants was 43 years (IQR 19-71) and 41.3% were male. Twenty-two (7.3%) participants failed to take ART at least once in the last seven days. The prevalence of current drinking was 29.3% (95% CI 24.2-34.8%) and that of AUD was 11.3% (8.2%-15.5%). Males had higher odds of alcohol use (OR 3.03, 95% CI 1.79-5.14) and AUD (3.89, 1.76-8.60). Alcohol use was associated with ART non-adherence (OR = 2.78, 1.10-7.04). There was a trend towards an association between AUD and non-adherence (OR = 2.91, 0.92-9.21). Alcohol use and AUD were common among PLHIV and showed evidence of associations with ART non-adherence. Screening patients for alcohol use and AUD in HIV clinics may increase ART adherence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".