Objective: To analyze the temporal trend and characteristics of notifications of violence among the transgender population from 2015 to 2022 in Brazilian municipalities.
Methods: This was a repeated panel epidemiological study, based on violence incidents reported among the transgender population aged 20 to 59 years, available in the Notifiable Health Conditions Information System. An annual temporal trend analysis was performed by means of generalized linear regression, using the Prais-Winsten method and spatial distribution of notifying municipalities in Brazil.
Results: Notifications of violence in the transgender population decreased during the period (1.7%; β = -0.07; p = 0.010), but there was an increase in the number of notifying municipalities (45.8%), self-inflicted violence (28.9%; β = 2.21; p < 0.001) and sexual violence (β = 0.79; p < 0.001). The majority of perpetrators were male and in an affective relationship, especially with transgender women (43.4%; p < 0.001).
Conclusion: Notification of violence does not yet fully reflect the reality of this population, but it represents the first step towards visibility and addressing the issue.
Main Results: Notifications of violence against transgender people accounted for 1.7% of the total. Self-inflicted and sexual violence increased from 2015 to 2022. Almost half of Brazilian municipalities have already been reporting cases of violence against this population.
Implications For Services: Continuous and high-quality notification will contribute to monitoring and understand violence in this population group, enabling the adaptation of services to meet their specific needs.
Perspectives: The development of research on the transgender population will allow for a better understand and guidance of specific health actions for this group. Information on violence against this population is crucial for informing public policies.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1590/S2237-96222024v33e2024296.especial.en | DOI Listing |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11654394 | PMC |
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!