HIV self-testing with digital supports as the new paradigm: A systematic review of global evidence (2010–2021)
Bibliographic record
Abstract
BACKGROUND: HIV self-testing (HIVST) is recommended by the WHO as an innovative strategy to reach UNAIDS targets to end HIV by 2030. HIVST with digital supports is defined as the use of digital interventions (e.g., website-based, social media, mobile HIVST applications (apps), text messaging (SMS), digital vending machines (digital VMs)) to improve the efficiency and impact of HIVST. HIVST deployment and integration in health services is an emerging priority. We conducted a systematic review aiming to close the gap in evidence that summarizes the impact of digitally supported HIVST and to inform policy recommendations. METHODS: We searched PubMed and Embase for articles and abstracts on HIVST with digital supports published during the period February 1st, 2010 to June 15th, 2021, following Cochrane guidelines and PRISMA methodology. We assessed feasibility, acceptability, preference, and impact outcomes across all populations and study designs. Metrics reported were willingness to use HIVST, preferences for HIVST delivery, proportion of first-time testers, HIVST uptake, HIVST kit return rate, and linkage to care. Heterogeneity of the interventions and reported metrics precluded us from conducting a meta-analysis. FINDINGS: 46 studies were narratively synthesized, of which 72% were observational and 28% were RCTs. Half of all studies (54%, 25/46) assessed web-based innovations (e.g., study websites, videos, chatbots), followed by social media (26%, 12/46), HIVST-specific apps (7%, 3/46), SMS (9%, 4/46), and digital VMs (4%, 2/46). Web-based innovations were found to be acceptable (77-97%), preferred over in-person and hybrid options by more first-time testers (47-48%), highly feasible (93-95%), and were overall effective in supporting linkage to care (53-100%). Social media and app-based innovations also had high acceptability (87-95%) and linkage to care proportions (80-100%). SMS innovations increased kit return rates (54-94%) and HIVST uptake among hard-to-reach groups. Finally, digital VMs were highly acceptable (54-93%), and HIVST uptake was six times greater when using digital VMs compared to distribution by community workers. INTERPRETATION: HIVST with digital supports was deemed feasible, acceptable, preferable, and was shown to increase uptake, engage first-time testers and hard-to-reach populations, and successfully link participants to treatment. Findings pave the way for greater use of HIVST interventions with digital supports globally.
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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 | 0.027 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".