Determinants of nonsuppression of HIV viral load among children receiving antiretroviral therapy in the Simiyu region: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Despite substantial antiretroviral therapy (ART) coverage among individuals with human immunodeficiency virus (HIV) infection in Tanzania, viral load suppression (VLS) among HIV-positive children receiving ART remains intolerably low. This study was conducted to determine factors affecting the nonsuppression of VL in children with HIV receiving ART in the Simiyu region; thus, an effective, sustainable intervention to address VL nonsuppression can be developed in the future. METHODS: We conducted a cross-sectional study including children with HIV aged 2-14 years who were currently presenting to care and treatment clinics in the Simiyu region. We collected data from the children/caregivers and care and treatment center databases. We used Stata™ to perform data analysis. We used statistics, including means, standard deviations, medians, interquartile ranges (IQRs), frequencies, and percentages, to describe the data. We performed forward stepwise logistic regression, where the significance level for removal was 0.10 and that for entry was 0.05. The median age of the patients at ART initiation was 2.0 years (IQR, 1.0-5.0 years), and the mean age at HIV VL (HVL) nonsuppression was 8.8 ± 2.99 years. Of the 253 patients, 56% were female, and the mean ART duration was 64 ± 33.07 months. In multivariable analysis, independent predictors of HVL nonsuppression were older age at ART initiation (adjusted odds ratio [AOR] = 1.21; 95% confidence interval [CI] 1.012-1.443) and poor medication adherence (AOR, 0.06; 95% CI 0.004-0.867). CONCLUSIONS: This study showed that older age at ART initiation and poor medication adherence play significant roles in HVL nonsuppression. HIV/AIDS programs should have intensive interventions targeting early identification, ART initiation, and adherence intensification.
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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".