<scp>HIV</scp> molecular network in Mexico City (2021–2022): Understanding transmission dynamics through the role of newly diagnosed cases
Bibliographic record
Abstract
OBJECTIVE: We aimed to infer and describe Mexico City's HIV genetic transmission network from 2021 through 2022 by characterizing its members based on time since HIV acquisition, as well as sociodemographic, clinical, and behavioural characteristics. Additionally, we assessed clustering potential according to time since HIV acquisition. METHODS: Individuals with a recent HIV diagnosis at the largest HIV clinic in Mexico City were invited to participate, completing self-administered questionnaires on sociodemographic, clinical, and behavioural characteristics. Blood samples were collected for analysis of the HIV pol gene using next-generation sequencing. The stage of infection at diagnosis was determined using an algorithm that includes antibody avidity tests. Genetic transmission network analysis used the Seguro HIV-TRACE tool. RESULTS: Of 6703 participants, 561 (8.4%) were identified as people newly living with HIV (PNLH). Transmission network analysis identified 896 clusters; 30.2% had at least one PNLH. Among all individuals, 43.5% formed clusters, with 11.8% being PNLH. PNLH added to a cluster showed higher odds for higher education, engaging in commercial sex, use of dating apps, annual HIV screening, and engaging in high-risk sexual practices (p < 0.05). Clusters with PNLH exhibited greater growth rates than those without PNLH (p < 0.05). CONCLUSIONS: The presence of PNLH in clusters was associated with a higher growth rate. Tailored prevention strategies are crucial, including using dating apps for risk communication, promoting PrEP use, and safe sexual practices in sex venues, and enhancing harm reduction related to drug use. PNLH could be key candidates for interventions aimed at breaking transmission chains, including contact tracing.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".