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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".