Predicting the impact of clustered risk and testing behaviour patterns on the population-level effectiveness of pre-exposure prophylaxis against HIV among gay, bisexual and other men who have sex with men in Greater Vancouver, Canada
Why this work is in the frame
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Bibliographic record
Abstract
Pre-exposure prophylaxis (PrEP) has the potential to greatly reduce transmission of HIV. However, significant questions remain around how behavioural factors may influence its impact within target populations. We used a 2014 sexual behaviour survey to modify and recalibrate a mathematical model of HIV infection dynamics within the population of gay, bisexual and other men who have sex with men (GBMSM) in the Greater Vancouver area of British Columbia, Canada. We performed a clustering analysis on the survey data to divide the population into categories associated with their reported risk of HIV exposure as well as their reported testing habits and attitudes towards PrEP. We found a positive association between reported risk and testing behaviour and level of awareness/interest in PrEP. Using the cluster groups to structure the population, we then estimated the impact of PrEP on HIV transmission in our study population. We found that the association between behaviour and interest in PrEP substantially boosted the population-level effectiveness of PrEP. Within our model, if PrEP adoption was unrelated to risk and testing, an additional 206 (95% credible interval 5-261), new infections representing 15% of total infections are predicted to occur among GBMSM over ten years, compared to where PrEP is adopted by individuals according to their level of interest. Our results underscore the importance of incorporating behavioural data into models when predicting the impact of future public health interventions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it