Pretreatment and acquired HIV drug resistance in Belize—results of nationally representative surveys, 2021–22
Bibliographic record
Abstract
BACKGROUND: The rising prevalence of pretreatment drug resistance (PDR) to non-nucleoside reverse-transcriptase inhibitors threatens the effectiveness of ART. In response, the WHO recommends dolutegravir-based ART regimens due to their high genetic barrier to resistance and better treatment outcomes. This is expected to contribute to achieving the Joint United Nations Programme on HIV/AIDS (UNAIDS) target of 95% viral suppression in people on ART. OBJECTIVES: To estimate the prevalence of PDR among adults initiating ART and assess viral suppression and acquired HIV drug resistance (ADR) among individuals receiving ART in Belize. PATIENTS AND METHODS: Nationally representative cross-sectional PDR and ADR surveys were conducted between 2021 and 2022. Sixty-seven adults were included in the PDR survey, and 43 children and adolescents and 331 adults were included in the ADR survey. Demographic and clinic data and blood specimens were collected. HIV drug resistance (HIVDR) was predicted using the Stanford HIVdb tool. RESULTS: The prevalence of PDR to efavirenz or nevirapine in adults was 49.3% (95% CI 42.2%-56.4%) and was significantly higher in those with previous antiretroviral exposure (OR: 7.16; 95% CI 2.71-18.95; P = 0.002). Among children and adolescents receiving ART, 50.0% had viral suppression, with better rates for those receiving dolutegravir-based ART (OR: 5.31; 95% CI 3.02-9.34; P < 0.001). In adults, 79.6% achieved viral suppression. No resistance to integrase inhibitors was observed in those on dolutegravir-based ART. CONCLUSIONS: Prioritizing dolutegravir-based ART is critical for achieving HIV epidemic control in Belize. Efforts should focus on retention in care and adherence support to prevent HIVDR.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".