High prevalence of syndemic health problems in patients seeking post-exposure prophylaxis for sexual exposures to HIV
Bibliographic record
Abstract
INTRODUCTION: The standard clinical approach to non-occupational HIV post-exposure prophylaxis (nPEP) focuses on biomedical aspects of the intervention, but may overlook co-occurring or 'syndemic' psychosocial problems that reinforce future vulnerability to HIV. We therefore sought to determine the prevalence of syndemic health problems in a cohort of Ontario nPEP patients, and explored the relationship between syndemic burden and HIV risk. METHODS: Between 07/2013-08/2016, we distributed a self-administered questionnaire to patients presenting to three clinics in Toronto and Ottawa seeking nPEP for sexual HIV exposures. We used validated screening tools to estimate the prevalence of depression (CES-D score ≥16), harmful alcohol use (AUDIT ≥8), problematic drug use (DUDIT ≥6 men/≥2 women), and sexual compulsivity (SCS ≥24) among men who have sex with men (MSM) respondents. In exploratory analyses, we examined the relationships between syndemic conditions using univariable logistic regression models, and the relationship between syndemic count (total number of syndemic conditions per participant) and HIV risk, as estimated by the HIRI-MSM score, using linear regression models. RESULTS: The 186 MSM included in the analysis had median age 31 (IQR = 26-36), including 87.6% having a college/undergraduate degree or higher. Overall, 53.8% screened positive for depression, 34.4% for harmful alcohol use, 30.1% for problematic drug use, and 16.1% for sexual compulsivity. Most participants (74.2%) had at least one syndemic condition and 46.8% had more than one. Exploratory analyses suggested positive associations between depression and harmful alcohol use (OR = 2.11, 95%CI = 1.13, 3.94) and between harmful alcohol use and problematic drug use (OR = 1.22, 95%CI = 0.65, 2.29). Syndemic count was associated with increased HIRI-MSM risk scores in univariable (2.2, 95%CI = 1.0, 3.3 per syndemic condition) and multivariable (2.1, 95%CI = 0.6, 3.6) linear regression models. CONCLUSIONS: The prevalence of syndemic conditions in MSM seeking nPEP for sexual exposure is alarmingly high, and is associated with underlying HIV risk. Routine screening for these conditions may identify opportunities for intervention and could alleviate future vulnerability to HIV.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".