Evidence on Digital HIV Self-Testing From Accuracy to Impact: Updated Systematic Review (Preprint)
Bibliographic record
Abstract
BACKGROUND HIV self-testing has gained momentum following the approval of self-testing methods and novel technological advancements. Digital HIV self-testing involves completing an oral or blood-based HIV self-test with support from a digital innovation. OBJECTIVE We conducted a systematic review on the existing data analyzing digital HIV self-testing accuracy while updating research on digital HIV self-test acceptability, preference, feasibility, and impact. METHODS We searched Embase and PubMed for records on HIV self-testing with digital support. Included studies significantly incorporated a form of digital innovation throughout the HIV self-test process and reported quantitative data. For accuracy measures, the search spanned January 1, 2013, to October 15, 2024; for patient-centered and impact outcomes, we updated existing literature (June 16, 2021, to October 15, 2024) reported in a previous systematic review. Studies’ quality was assessed using the QUADAS 2 Tool, Newcastle-Ottawa Scale, and Cochrane Risk of Bias Tool 2. RESULTS Fifty-five studies (samples ranging 120-21,035, median 1267 participants) were summarized from 19 middle- to high-income countries. Seven studies reported on the accuracy of HIV self-testing with innovations from >5000 participants. Diagnostic performance metrics, including point estimates of specificity, sensitivity, positive predictive value, and negative predictive value were measured (n=3), and ranged from: 96.8% to 99.9%, 92.9% to 100.0%, 76.5% to 99.2%, and 99.2% to 100.0%, respectively. The percentage of invalid test results for oral and blood-based self-tests ranged from 0.2% to 12.7% (n=4). Fifty-one studies reported data on metrics beyond accuracy, including acceptability, preference, feasibility, and impact outcomes from >30,000 participants. Majority (38/51, 74.5%) were observational studies, while 25.5% (13/51) reported data from randomized controlled trials. Acceptability and preference outcomes varied from 64.5% to 99.0% (14/51) and 4.6% to 99.3% (8/51), respectively. Feasibility outcomes included test uptake (30.9% to 98.2%; 28/51), response rate (26.0% to 94.8%; 7/51), and visits to web-based providers (43.0% to 70.7%; n=4). Impact outcomes assessed new infections (0.0% to 25.8%; 31/51), first-time testers (2.0% to 53.0%; 26/51), result return proportions (22.1% to 100.0%; 24/51), linkage to care as both connections to confirmatory testing and counseling (53.0% to 100.0%; 16/51), and referrals for treatment initiation (44.4% to 98.1%; 8/51). The quality of studies varied, though they generally demonstrated low risk of bias. CONCLUSIONS Digital innovations improved the accuracy of HIV self-test results, and were well-accepted and preferred by participants. Operationally, they were found to be feasible and reported impacting the HIV self-testing process. These findings are in favor of the use of digital HIV self-test innovations as a promising support tool and suggest that digital HIV self-tests’ service delivery models hold promise in not only facilitating HIV testing but also impacting operational outcomes that are crucial to reaching Joint United Nations Program on HIV/AIDS targets in middle- to high-income countries. CLINICALTRIAL PROSPERO CRD42020205025; https://www.crd.york.ac.uk/prospero/CRD42020205025
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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 | 0.024 | 0.163 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".