A Population-Based Approach to Determine the Prevalence of Transmitted Drug-Resistant HIV Among Recent Versus Established HIV Infections
Bibliographic record
Abstract
OBJECTIVES: Published results on primary or transmitted HIV drug resistance may be biased because they have been largely derived from specific cohort studies or higher risk individuals who present symptomatically. Here, we present results from a representative population-based study of newly diagnosed cases of HIV in Canada and compare the prevalence of transmitted drug resistance between recent and established infections. METHODS: Available archived sera taken for the purpose of diagnostic HIV testing from all treatment-naive HIV-positive individuals who were newly diagnosed between 2000 and 2001 were tested for recency of infection, HIV-1 subtype, and mutations conferring reduced susceptibility to reverse transcriptase inhibitors and protease inhibitors (PIs). Recent infections were identified using the Organon Teknika Vironostika HIV-1-LS assay. After full-length sequencing of the pol gene, drug resistance mutations were identified using the 2004 International AIDS Society-USA mutations panel. Differences in drug resistance profiles between recent and prevalent infections were examined using the chi test and the Fisher exact test. The variables examined included gender, age at diagnosis, year of diagnosis, exposure category, ethnicity, and HIV-1 subtype. RESULTS: Among the study population, 8.1% had genotypic evidence of transmitted drug resistance: 4.1% against nucleoside reverse transcriptase inhibitors, 1.4% against nonnucleoside reverse transcriptase inhibitors, 1.5% against PIs, and 1% against > or =2 classes of drugs. A higher proportion of recent infections had genotypic evidence of transmitted drug resistance when compared with established infections (12.2% vs. 6.1%, respectively; P = 0.005). Transmitted drug resistance was identified mainly among recently infected Caucasian men who have sex with men but it was not limited to this group. Compared with the year 2000, a higher proportion of recently infected individuals with resistance-conferring mutations were diagnosed during the year 2001 (66.7% vs. 46.6%). CONCLUSIONS: In Canada, transmitted drug resistance is occurring within all 3 drug classes and across different population groups. The results suggest that the prevalence rates may be higher among recent versus established infections. Given the public health implications of transmitting drug-resistant HIV, it is important to continue population-based drug resistance surveillance to guide optimum prevention and treatment of HIV infection.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".