1275. Will an App-Optimized HIV Self-testing Strategy Work for South Africans? Results From a Large Cohort Study
Bibliographic record
Abstract
Abstract Background HIV self-testing (HIVST) offers a potential for expanded test access; challenges remain in operationalizing rapid personalized linkages and referrals to care. We investigated if an app-optimized personalized HIVST strategy improved referrals, detected new infections and expedited linkages to care and treatment. Methods In an ongoing cohort study (n = 2,000) based in South Africa, from November 2016 to January 2018, to participants presenting to self-test at community township based clinics, we offered a choice of the following strategies: (a) unsupervised HIVST; (b) supervised HIVST. We also observed participants opting for conventional HIV testing (ConvHT) in geographically separated clinics. We observed outcomes (i.e., linkage initiation, referrals, disease detection) and compared it between the two (HIVST vs. ConvHT) for the same duration. Results Of 2,000 participants, 1,000 participants were on HIVST, 599 (59.9%) chose unsupervised HIVST, 401 (40.1%) on supervised HIVST; compared with 1,000 participants on ConvHT. Participants in HIVST vs. ConvHT were comparable young (mean age 27.7 [SD = 9.0] vs. 29.5 [SD = 8.4]); female (64.0% vs. 74.7%); poor monthly income <3,000 RAND ($253 USD) (79.9% vs. 76.4%). With HIV ST (vs. ConvHT), many more referrals (17.4% [15.1–19.9] vs. 2.6% [1.7–3.8]; RR 6.69 [95% CI: 4.47–10.01]), and many new infections (86 (8.6% (6.9–10.5)) vs. 57 (5.7% (4.3–7.3)); Odds Ratio 1.55 [95% CI 1.1–2.2]) were noted. Break up: 45 infections in supervised HIVST 45 (52.3%); 41 infections in unsupervised HIVST (47.6%)]. Preference for HIVST was at 91.6%. With an app-optimized HIVST strategy, linkages to care were operationalized within a day in all participants (99.7% (HIVST) vs. 99.2% (ConvHT); RR 1.005 [95% CI: 0.99–1.01]); 99.8% supervised HIVST, 99.7% unsupervised HIVST. Conclusion Our app-optimized HIVST strategy successfully increased test referrals, detected new infections, and operationalized linkages within a day. This innovative, patient preferred strategy holds promise for a global scale up in digitally literate populations worldwide. Disclosures All authors: No reported disclosures.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".