Comparison of adherence measurement tools used in a pre-exposure prophylaxis demonstration study among female sex workers in Benin
Bibliographic record
Abstract
BACKGROUND: Measuring adherence to PrEP (pre-exposure prophylaxis) remains challenging. Biological adherence measurements are reported to be more accurate than self-reports and pill counts but can be expensive and not suitable on a daily basis in resource-limited countries. Using data from a demonstration project on PrEP among female sex workers in Benin, we aimed to measure adherence to PrEP and compare self-report and pill count adherence to tenofovir (TFV) disoproxil fumarate (TDF) concentration in plasma to determine if these 2 measures are reliable and correlate well with biological adherence measurements. METHODS: Plasma TFV concentrations were analyzed in samples collected at day 14 follow-up visit and months 6, 12, 18, and 24 (or at last visit when follow-up was shorter). Self-reported adherence was captured at day 14 follow-up visit and then quarterly by asking participants to report the number of missed pills within the last week. For pill count, medications were refilled monthly and participants were asked to bring in their medication bottles at each follow-up visit. Using generalized estimating equations adherence measured by self-report and pill count was compared to plasma drug concentrations. RESULTS: Of 255 participants, 47.1% completed follow-up. Weighted optimal adherence combining data from all visits was 26.8% for TFV concentration, 56.0% by self-report and 18.9% by pill count. Adherence measured by both TFV concentrations and self-report decreased over time (P = .009 and P = .019, respectively), while the decreasing trend in adherence by pill count was not significant (P = .087). The decrease in adherence was greater using TFV concentrations than the other 2 adherence measures. CONCLUSION: With high levels of misreporting of adherence using self-report and pill count, the objective biomedical assessment of adherence via laboratory testing is optimal and more accurately reflects PrEP uptake and persistence. Alternative inexpensive and accurate approaches to monitor PrEP adherence should be investigated.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".