O2-S6.05 Sexual violence against men who have sex with men and transgenders (MSM-T) in southern India
Notice bibliographique
Résumé
Introduction Sexual violence (SV) has been associated globally with vulnerability to HIV and other sexually transmitted infections (STIs) in marginalised populations. There is little information on SV among men who have sex with men and transgendered individuals (MSM-T) in South Asia. Societal sanctions resulting in stigma and discrimination may place MSM-T at especially high risk for both SV and HIV/STIs. We investigated factors associated with SV among MSM-T, and the relationship between health care utilisation patterns and reported SV. Methods Data were obtained from cross-sectional surveys in four districts in Karnataka state, south India. Bivariate and multivariable logistic regression models were constructed to examine factors related to SV. Multivariable negative binomial regression models examined the association between physician visits and SV. Normalised weights were used to account for a complex sampling design. Results The total sample size was 543. The prevalence of SV in the past year was 18%, with a range by district of 12%–31%. HIV prevalence among those reporting SV was 20%, compared to 12% among those not reporting SV. In a multivariable model, among those reporting sex work involvement, having anal sex with five or more casual sex partners in the past week was associated with SV (AOR: 4.1; 95% CI: 1.2% to 14.3%, p=0.03). Of those not reporting sex work, feminised sexual identities (AOR: 6.10; 95% CI: 1.3% to 28.8%, p=0.03) and younger age (AOR: 0.9; 95% CI: 0.9% to 1.0%, p=0.002) were associated with SV. There was no significant association between numbers of partners and SV among those not reporting sex work. Increased physician visits among those reporting SV was reported only for those involved in sex work (AOR: 1.7; 95% CI: 1.1% to 2.7%, p=0.01). Conclusions There was a trend towards higher HIV prevalence overall among MSM-T reporting sexual violence, as well higher HIV prevalence among important sub-groups experiencing SV. These findings highlight the importance of integrating programs to address sexual violence into STI/HIV prevention programs among MSM-T, and of understanding that there is considerable contextual heterogeneity in vulnerability to violence in this population. The demonstrated higher rates of physician utilisation by male sex workers who report sexual violence suggests an important point of contact for a population which may otherwise be quite hidden.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».