Understanding the cost-utility of implementing HIV self-testing with digital-based supports
Bibliographic record
Abstract
Introduction: HIV self-testing (HIVST) is an innovative strategy that has been shown to increase uptake of HIV testing compared to conventional facility-based testing. HIVST implementation with digital-based supports may help facilitate testing accessibility and linkage to care after a reactive self-test. Economic evidence around community-based implementation of HIVST is growing; however, economic evidence around digital-based HIVST approaches remains limited. Methods: We used previously published cost and efficacy data from HIVST interventions, with the specific intervention model differing between scenarios. Digital-based interventions included text messaging campaigns and online websites that promoted uptake and linkage to HIVST care. Community-based interventions included door-to-door distribution, peer-incentivized distribution, and mobile testing units. Using data obtained from the literature, we parameterized a combined Markov and decision analytic model to evaluate the cost-utility of digital-based HIVST implementation across Malawi, South Africa, and Brazil compared to both community-based HIVST and facility-based testing. Results: We found that HIVST was cost-effective compared to facility-based testing in all settings investigated. Our scenarios predicted that digital-based HIVST was associated with an incremental cost in the range of $769-$17,839/DALY (disability-adjusted life year) averted compared to facility-based testing across Malawi, South Africa, and Brazil. Digital-based HIVST cost savings had an incremental cost of $7,300/DALY averted compared to community-based HIVST. The main drivers of cost-utility included HIV test and treatment costs, HIV test-positivity, rates of linkage to care, and antiretroviral therapy (ART) initiation rates. Digital-based supports were associated with an increased cost compared to facility-based testing, but they also had increased utility, which led to favorable cost-utility estimates. Discussion: HIVST with digital supports has the potential to be a highly cost-effective approach, with the potential to make HIV testing more available and accessible, thereby increasing overall uptake and coverage of HIV testing. Digital supports can also support linkage to care, which we have identified as a major driver of cost-utility. Strategies to improve cost-utility include reducing testing costs, targeting key populations with increased rates of HIV test-positivity, and ensuring strong support for linkage to care.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".