Frequency of False Positive Rapid HIV Serologic Tests in African Men and Women Receiving PrEP for HIV Prevention: Implications for Programmatic Roll-Out of Biomedical Interventions
Bibliographic record
Abstract
BACKGROUND: Rapid HIV assays are the mainstay of HIV testing globally. Delivery of effective biomedical HIV prevention strategies such as antiretroviral pre-exposure prophylaxis (PrEP) requires periodic HIV testing. Because rapid tests have high (>95%) but imperfect specificity, they are expected to generate some false positive results. METHODS: We assessed the frequency of true and false positive rapid results in the Partners PrEP Study, a randomized, placebo-controlled trial of PrEP. HIV testing was performed monthly using 2 rapid tests done in parallel with HIV enzyme immunoassay (EIA) confirmation following all positive rapid tests. RESULTS: A total of 99,009 monthly HIV tests were performed; 98,743 (99.7%) were dual-rapid HIV negative. Of the 266 visits with ≥1 positive rapid result, 99 (37.2%) had confirmatory positive EIA results (true positives), 155 (58.3%) had negative EIA results (false positives), and 12 (4.5%) had discordant EIA results. In the active PrEP arms, over two-thirds of visits with positive rapid test results were false positive results (69.2%, 110 of 159), although false positive results occurred at <1% (110/65,945) of total visits. CONCLUSIONS: When HIV prevalence or incidence is low due to effective HIV prevention interventions, rapid HIV tests result in a high number of false relative to true positive results, although the absolute number of false results will be low. Program roll-out for effective interventions should plan for quality assurance of HIV testing, mechanisms for confirmatory HIV testing, and counseling strategies for persons with positive rapid test results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".