A call to improve understanding of Undetectable equals Untransmittable (U = U) in Brazil: a web‐based survey
Bibliographic record
Abstract
INTRODUCTION: Currently, the slogan "Undetectable = Untransmittable" (U = U), launched to disseminate scientific evidence on how people living with HIV (PLHIV) on antiretroviral treatment with suppressed viral load cannot transmit HIV to their sexual partners, is still challenged by individuals with differential acceptance across populations. In this study, we documented the perceived accuracy of U = U in Brazil in three different groups: PLHIV, HIV-negative/unknown cisgender gay/bisexual men who have sex with men (GBM) and HIV-negative/unknown other populations (POP). METHODS: Adult (age ≥ 18y) Brazilians were recruited during October 2019 to complete a web-based survey advertised on Grindr, Facebook and WhatsApp. Perceived accuracy of U = U was assessed with the question: "With regards to HIV-positive individuals transmitting HIV through sexual contact, how accurate do you believe the slogan U = U is?" Response options ranged from 1 (Completely inaccurate) to 4 (Completely accurate) plus a fifth option (I don't know what "undetectable" means). Participants' characteristics were described according to perceived accuracy of U = U. Logistic regression models assessed the factors associated with perceived accuracy of U = U (completely accurate vs. partially accurate/inaccurate or completely inaccurate) by group. RESULTS: Of 2311 individuals accessing the questionnaire, 1690 (73.1%) met inclusion/exclusion criteria and completed it. Of these, 347 (20.5%) were PLHIV, 785 (46.4%) GBM and 558 (33.0%) POP. More PLHIV perceived U = U as completely accurate (79.0%), compared to 44.2% GBM and 17.2% POP (p < 0.001). Among PLHIV, Black identity was associated with decreased odds of perceiving U = U as completely accurate while having a steady partner was associated with increased odds. Among GBM, being gay, having middle/higher income, being a resident of state capital metropolitan areas and ever testing for HIV were associated with increased odds. Lastly, among POP, ever testing for HIV increased the odds of perceiving U = U as completely accurate. CONCLUSIONS: There was a significant difference in perceived accuracy of U = U across population groups. Accurate understanding of the slogan needs to be promoted in more vulnerable populations such as PLHIV of Black identity and GBM of lower income to maximize individual and societal prevention benefits. Moreover, broader understating of U = U among the general population can help decrease societal stigma towards PLHIV.
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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 | 0.010 | 0.027 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".