P17.09 Cost-effectiveness of hiv self-testing promotion through grindr™, a smartphone social networking application
Bibliographic record
Abstract
Introduction Currently, the cost per new HIV diagnosis in the United States is estimated at $17,700. HIV self-testing promotion through smartphone social networking applications (apps) might present an affordable way to help improve case finding. We evaluated the cost-effectiveness of an HIV self-testing program that linked geo-targeted mobile advertisements to an online self-test request system. Methods The HIV self-testing program was offered in Los Angeles from April 17 to May 29, 2014, and from October 13 to November 11, 2014. During those periods, we placed advertisements for free HIV self-tests on Grindr™, a smartphone geosocial networking app popular with men who have sex with men (MSM). Users were linked to http://freehivselftests.weebly.com/to submit self-test requests. African American and Latino MSM ≥18 years old were asked if they used the self-test and what the result of the self-test was. Cost-effectiveness was measured by the cost per person tested and the cost per new case of HIV identified. Results Through the two offerings of the program, an estimated 455 users received and used an HIV self-test. Among 112 (63%) survey respondents of 178 invited, study-eligible participants who self-identified as not being previously diagnosed with HIV, 4 (4%) reported testing HIV positive; all 4 (100%) sought medical care. The total direct costs of the program incurred from two waves of advertising (US$2,670), self-test purchases (US$13,130 at US$26 per test), and personnel time (US$1,800) was US$17,600. The cost per person tested was US$39, and the cost per new case of HIV identified was US$4,400. Conclusion Free HIV self-testing promotion through Grindr™ is an effective and affordable means of identifying previously undiagnosed cases of HIV among African American and Latino MSM. Future work should compare advertising on different smartphone social networking apps and evaluate methods to confirm self-reported HIV test results and linkage-to-care activities. Disclosure of interest statement The authors have no conflicts of interest to disclose.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.002 |
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".