HIV Pretreatment Drug Resistance Trends in Mexico City, 2017–2020
Bibliographic record
Abstract
In response to increasing pretreatment drug resistance (PDR), Mexico changed its national antiretroviral treatment (ART) policy, recommending and procuring second-generation integrase strand-transfer inhibitor (INSTI)-based regimens as preferred first-line options since 2019. We present a four-year observational study describing PDR trends across 2017–2020 at the largest HIV diagnosis and primary care center in Mexico City. A total of 6688 baseline protease-reverse transcriptase and 6709 integrase sequences were included. PDR to any drug class was 14.4% (95% CI, 13.6–15.3%). A significant increasing trend for efavirenz/nevirapine PDR was observed (10.3 to 13.6%, p = 0.02). No increase in PDR to second-generation INSTI was observed, remaining under 0.3% across the study period. PDR was strongly associated with prior exposure to ART (aOR: 2.9, 95% CI: 1.9–4.6, p < 0.0001). MSM had higher odds of PDR to efavirenz/nevirapine (aOR: 2.0, 95% CI: 1.0–3.7, p = 0.04), reflecting ongoing transmission of mutations such as K103NS and E138A. ART restarters showed higher representation of cisgender women and injectable drug users, higher age, and lower education level. PDR to dolutegravir/bictegravir remained low in Mexico City, although further surveillance is warranted given the short time of ART optimization. Our study identifies demographic characteristics of groups with higher risk of PDR and lost to follow-up, which may be useful to design differentiated interventions locally.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".