Are violence, harmful alcohol/substance use and poor mental health associated with increased genital inflammation?: A longitudinal cohort study with HIV-negative female sex workers in Nairobi, Kenya
Bibliographic record
Abstract
Violence, alcohol use, substance use and poor mental health have been linked with increased HIV acquisition risk, and genital inflammation enhances HIV susceptibility. We examined whether past 6 month experience of these exposures was associated with increased genital inflammation, thereby providing a biological link between these exposures and HIV acquisition risk. The Maisha Fiti study was a longitudinal mixed-methods study of female sex workers in Nairobi, Kenya. Behavioural-biological surveys were conducted at baseline (June-December 2019) and endline (June 2020-March 2021). Analyses were restricted to HIV-negative women (n = 746). Women with raised levels of at least 5 of 9 genital inflammatory cytokines were defined as having genital inflammation. Multivariable logistic regression models were used to estimate (i) baseline associations between genital inflammation and violence, harmful alcohol/substance use, and poor mental health, and (ii) longitudinal associations between these exposures at different survey rounds, and genital inflammation at follow-up. Inflammation data was available for 711 of 746 (95.3%) women at baseline; 351 (50.1%) had genital inflammation, as did 247 (46.7%) at follow-up. At baseline, 67.8% of women had experienced physical and/or sexual violence in the past 6 months, 33.9% had harmful alcohol use, 26.4% had harmful substance use, 25.5% had moderate/severe depression/anxiety, and 13.9% had post-traumatic stress disorder. In adjusted analyses, there was no evidence that these exposures were associated cross-sectionally or longitudinally with genital inflammation. We report no associations between past 6 month experience of violence, harmful alcohol/substance use, or poor mental health, and immune parameters previously associated with HIV risk. This suggests that the well-described epidemiological associations between these exposures and HIV acquisition do not appear to be mediated by genital immune changes, or that any such changes are relatively short-lived. High prevalences of these exposures suggest an urgent need for sex-worker specific violence, alcohol/substance use and mental health interventions.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".