Development and Evaluation of a Digital HIV Risk Assessment Tool Incorporated Within an App-Based Self-Testing Program
Bibliographic record
Abstract
BACKGROUND: Low-risk perception is an important barrier to the utilization of HIV services. In this context, offering an online platform for people to assess their risk of HIV and inform their decision to test can be impactful in increasing testing uptake. Using secondary data from the HIVSmart! quasirandomized trial, we aimed to identify predictors of HIV, develop a risk staging model for South African township populations, and validate it in combination with the HIVSmart! digital self-testing program. SETTING: Townships in Cape Town, South Africa. METHODS: Using Bayesian predictive projection, we identified predictors of HIV and constructed a risk assessment model that we validated in external data. RESULTS: Our analyses included 3095 participants from the HIVSmart! trial. We identified a model of 5 predictors (being unmarried, HIV testing history, having had sex with a partner living with HIV, dwelling situation, and education) that performed best during external validation (area under the receiver operating characteristic curve, 89% credible intervals: 0.71, 0.68 to 0.72). The sensitivity of our HIV risk staging model was 91.0% (89.1% to 92.7%) and the specificity was 13.2% (8.5% to 19.8%) but increased when combined with a digital HIV self-testing program, the specificity was 91.6% (95.9% to 96.4%) and sensitivity remained similar at 90.9% (89.1% to 92.6%). CONCLUSIONS: This is the first validated digital HIV risk assessment tool developed for South African township populations and the first study to evaluate the added value of a risk assessment tool with an app-based HIV self-testing program. Study findings are relevant for application of digital programs to improve utilization of HIV testing services.
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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 | 0.020 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".