P3.431 Impact of Social Marketing to Promote Awareness of “Early” HIV Testing in Addition to Pooled Nucleic Acid Amplification Testing in Men Who Have Sex with Men in Vancouver, British Columbia
Bibliographic record
Abstract
Background The contribution of acute HIV infection (AHI) to transmission is widely recognised. Increasing AHI diagnosis capacity can enhance HIV prevention through subsequent behaviour change or intervention. We examined the impact of targeted pooled NAAT and social marketing to increase AHI diagnosis among men who have sex with men (MSM) in Vancouver, British Columbia. Methods We implemented pooled NAAT following negative 3rd generation EIA testing for males ≥ 19 years in six clinics accessed by MSM, accompanied by two social marketing campaigns developed in partnership with a community-based gay men’s health organisation (campaigns emphasised availability of “early” testing for AHI and promoted early testing following potential exposure or new relationship). We compared test volume and diagnosis rates for pre- (April 2006-March 2009) and post-pooling (April 2009-March 2012) periods, and calculated diagnostic rate and yield, RNA copy number, time to results and cost per diagnosis. Results Post-pooling, the AHI diagnosis rate increased from 1.0 to 1.84 per 1,000 tests; quarterly AHI diagnosis rates increased significantly. Of 217 new HIV diagnoses post-pooling, 54 (24.9%) were AHI (25 detected by pooled NAAT only) for an increased diagnostic yield of 11.5%. AHI detected by pooled NAAT had higher median RNA copies (7.77 × 105 copies/mL) and similar time to result (median 7 days) compared to AHI detected through 3rd generation EIA (4.96 × 105 copies/mL; median 6 days). The incremental cost per AHI diagnosis through pooled NAAT was $9,124 CDN. Conclusions Few studies have assessed the contribution of social marketing in enhancing capacity for AHI diagnosis. Our study suggests that targeted implementation of pooled NAAT at clinics accessed by MSM accompanied by local social marketing campaigns is effective at increasing AHI diagnoses, and is likely cost-saving.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".