Impact of HIV self-testing for oral pre-exposure prophylaxis scale-up on drug resistance and HIV outcomes in western Kenya: a modelling study
Bibliographic record
Abstract
BACKGROUND: Community-based oral pre-exposure prophylaxis (PrEP) provision has the potential to expand PrEP coverage. HIV self-testing can facilitate PrEP community-based delivery but might have lower sensitivity than facility-based HIV testing, potentially leading to inappropriate PrEP use among people with HIV and subsequent development of drug resistance. We aimed to evaluate the impact of HIV self-testing use for PrEP scale-up. METHODS: We parameterised an agent-based network model, EMOD-HIV, to simulate generic tenofovir disoproxil fumarate and emtricitabine PrEP scale-up in western Kenya using four testing scenarios: provider-administered nucleic acid testing, provider-administered rapid diagnostic tests detecting antibodies, blood-based HIV self-testing, or oral fluid HIV self-testing. Scenarios were compared with a no PrEP counterfactual. Individuals aged 18-49 years with one or more heterosexual partners who screened HIV-negative were eligible for PrEP. We assessed the cost and health impact of rapid PrEP scale-up with high coverage over 20 years, and the budget impact over 5 years, using various HIV testing modalities. FINDINGS: PrEP coverage of 29% was projected to avert approximately 54% of HIV infections and 17% of HIV-related deaths among adults aged 18-49 years over 20 years; health impacts were similar across HIV testing modalities used to deliver PrEP. The percentage of HIV infections with PrEP-associated nucleoside reverse transcriptase inhibitor (NRTI) drug resistance was 0·6% (95% uncertainty intervals 0·4-0·9) in the blood HIV self-testing scenario and 0·8% (0·6-1·0) in the oral HIV self-testing scenario, compared with 0·3% (0·2-0·3) in the antibody rapid diagnostic testing scenario and 0·2% (0·1-0·2) in the nucleic acid testing scenario. Accounting for background NRTI resistance, we found similarly low proportions of drug resistance across scenarios. The budget impact of implementing PrEP using HIV self-testing and provider-administered rapid diagnostic tests were similar, while nucleic acid testing was approximately 50% more costly. INTERPRETATION: Scaling up PrEP using HIV self-testing has similar health impacts, costs, and low risk of drug resistance as provider-administered rapid diagnostic tests. Policy makers should consider leveraging HIV self-testing to expand PrEP access among those at HIV risk. FUNDING: The Bill and Melinda Gates Foundation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".