Typologies of violence against women in Brazil: A latent class analysis of how violence and HIV intersect.
Kiyomi TsuyukiJamila K StockmanDaniela KnauthChristina J CatabayFeng HeNoor A Al-AlusiFlavia Bulegon PileccoSonia JainRegina Maria BarbosaPublished in: Global public health (2020)
We address the limited understanding around the overlap between violence and HIV in Brazil. Data was from two clinic-based samples of HIV-positive (n = 1534) and HIV-negative women (n = 1589) in São Paulo and Porto Alegre. We conducted latent class analysis and identified violence typologies by type of violence, life course timing, frequency, and perpetrator, stratified by city and HIV-status. Overall, HIV-positive women experienced more lifetime physical and sexual violence than HIV-negative women. Twelve unique violence latent classes were identified. In São Paulo, HIV-positive women were likely to have endured physical violence several times (Conditional Probability [CP]: 0.80) by an intimate partner (CP: 0.85), and sexual violence several times (CP: 0.46) by an intimate partner (CP: 0.62). In Porto Alegre, HIV-positive women endured physical violence several times (CP: 0.80) by an intimate partner (CP: 0.70) during childhood/adolescence (CP: 0.48), and sexual violence several times (CP: 0.54) by an intimate partner (CP: 0.60). Findings inform interventions to educate around gender equity, violence, and the health effects of violence including HIV, integrate HIV and violence services, and improve the provision of bio-medical HIV prevention among HIV-negative women who experience violence.
Keyphrases
- hiv positive
- mental health
- antiretroviral therapy
- men who have sex with men
- hiv testing
- south africa
- hiv infected
- human immunodeficiency virus
- polycystic ovary syndrome
- hiv aids
- healthcare
- intimate partner violence
- public health
- primary care
- hepatitis c virus
- insulin resistance
- palliative care
- cervical cancer screening
- electronic health record
- risk assessment
- breast cancer risk
- big data
- climate change
- depressive symptoms
- deep learning
- human health