The prevalence and economic burden of 1<sup>st</sup>‐generation NNRTI resistance
Bibliographic record
Abstract
The prevalence of NNRTI resistance in the community and costs associated with HIV disease and treatment failure are examined. NNRTI‐based HAART therapy using EFV is recommended as a 1st‐line treatment choice in international guidelines, and it is the most common component of initial therapy. Resistance to NNRTIs is common in the community (treated or untreated) and may impact economic burden. Published articles and conference abstracts detailing the epidemiology of NNRTI resistance, the economic burden of HIV disease progression, or the costs associated with treatment failure were located using PubMed/MEDLINE and Embase. NNRTI resistance data sources included randomized or observational trials, or cohort studies of European, US, or Canadian patients, published between years 2005–2011 in English. Inclusion criteria for cost data were studies reporting total direct medical costs of HIV disease in the US. Prevalence of NNRTI resistance early in infection (generally pre‐antibody response) was reported in 6 European studies and in 3 US studies. Among chronically‐infected treatment‐naïve patients, 13 studies reported prevalence of NNRTI resistance in Europe, 2 in US, and 2 in Canada. All studies collected data from 2009 and earlier, when 2nd‐generation NNRTIs were not available. The reported prevalence was generally higher in US/Canada than it was in Europe (range=7–10% vs. 2–10%). The prevalence of resistance among patients was similar in early vs. later infection for both regions (range=2.0–6.7% [median=3.7%] vs. 1.7–10.2% [3.0%] in Europe and 7.0–13.0% [8.8%] vs. 3.8–11.5% [7.6%] in US/Canada, respectively). Using UDS, the prevalence of NNRTI resistance in treatment‐naïve patients was reported at 24% (intra‐quasi‐species prevalences 0.34–98.8%). The most recent time‐based trends suggest that NNRTI‐resistance prevalence may be stable or reducing. Annual HIV medical costs increased 1) as CD4 cells decreased, driven in part by hospitalization at lower CD4 cell counts; 2) for treatment changes (cost of 3rd line 1.5‐fold higher than 1st or 2nd line), and 3) for each virologic failure. The economic burden of regimen failure and disease progression underlines the importance of ensuring optimal initial therapy choices and regimen succession. The possible erosion of efficacy or of therapy choices through resistance transmission or selection, even when present in an individual as a 1% minority, may become a barrier to the use of 1st generation NNRTIs.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".