Use of an HIV‐risk screening tool to identify optimal candidates for PrEP scale‐up among men who have sex with men in Toronto, Canada: disconnect between objective and subjective HIV risk
Bibliographic record
Abstract
INTRODUCTION: Identifying appropriate pre-exposure prophylaxis (PrEP) candidates is a challenge in planning for the safe and effective roll-out of this strategy. We explored the use of a validated HIV risk screening tool, HIV Incidence Risk Index for Men who have Sex with Men (HIRI-MSM), to identify "optimal" candidates among MSM testing at a busy sexual health clinic's community testing sites in Toronto, Canada. METHODS: Between November 2014 and April 2015, we surveyed MSM undergoing anonymous HIV testing at community testing sites in Toronto, Canada, to quantify "optimal" candidates for scaling up PrEP roll-out, defined as being at high objective HIV risk (scoring ≥10 on the HIRI-MSM), perceiving oneself at moderate-to-high HIV risk and being willing to use PrEP. Cascades were constructed to identify barriers to broader PrEP uptake. The association between HIRI-MSM score and both willingness to use PrEP and perceived HIV risk were explored in separate multivariable logistic regression analyses. RESULTS: Of 420 respondents, 64.4% were objectively at high risk, 52.5% were willing to use PrEP and 27.2% perceived themselves at moderate-to-high HIV risk. Only 16.4% were "optimal" candidates. Higher HIRI-MSM scores were positively associated with both willingness to use PrEP (aOR=1.7 per 10 score increase, 95%CI=1.3-2.2) and moderate-to-high perceived HIV risk (aOR=1.7 per 10 score increase, 95%CI=1.2-2.3). The proportion of men who were "optimal" candidates increased to 42.9% when the objective HIV risk cut-off was changed to top quartile of HIRI-MSM scores (≥26). In our full cascade, a very low proportion (5.3%) of MSM surveyed could potentially benefit from PrEP under current conditions. The greatest barrier in the cascade was low perception of HIV risk among high-risk men, but considerable numbers were also lost in downstream cascade steps. Of men at high objective HIV risk, 68.3% did not perceive themselves to be at moderate-to-high HIV risk, 23.6% were unaware of PrEP, 40.1% were not willing to use PrEP, 47.6% lacked a family physician with whom they felt comfortable discussing sexual health, and 31.6% had no means to cover the cost of PrEP. CONCLUSIONS: A higher HIRI-MSM cut-off may be helpful for identifying candidates for PrEP scale-up. Improving engagement in the PrEP cascade will require interventions to simultaneously address multiple barriers.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".