scholarly journals A Longitudinal Examination of Interpersonal Violence Exposure, Concern for Loved Ones During a Disaster, and Web-Based Intervention Effects on Posttraumatic Stress Disorder Among Adolescent Victims of the Spring 2011 Tornadoes

2018 ◽  
pp. 088626051879123 ◽  
Author(s):  
Amanda K. Gilmore ◽  
Matthew Price ◽  
Kaitlin E. Bountress ◽  
Kelly L. Zuromski ◽  
Ken Ruggiero ◽  
...  
2016 ◽  
Vol 184 (11) ◽  
pp. 796-805 ◽  
Author(s):  
Jennifer J. Vasterling ◽  
Mihaela Aslan ◽  
Susan P. Proctor ◽  
John Ko ◽  
Brian P. Marx ◽  
...  

2019 ◽  
Vol 283 ◽  
pp. 34-44
Author(s):  
Virginie C. Perizzolo ◽  
Cristina Berchio ◽  
Dominik A. Moser ◽  
Cristina Puro Gomez ◽  
Marylène Vital ◽  
...  

2020 ◽  
Author(s):  
Joviana Avanci ◽  
Fernanda Serpeloni ◽  
Thiago Pires de Oliveira ◽  
Simone Gonçalves de Assis

Abstract Background: The frequency of trauma and violence exposure in urban areas and their effects on mental health in adolescents in developing countries are poorly investigated. Most information about traumatized young people comes from war scenarios or disasters. This study aimed to determine the prevalence of PTSD in trauma-exposed students in a low-resource city of the state of Rio de Janeiro, Brazil. The effects of sociodemographic, individual, family factors in the development of PTSD were also investigated.Methods: Through multi-stage cluster sampling, 862 adolescents (Mage = 15 years old, 65% female) from public and private schools in the city of São Gonçalo were selected for the study. Self-rating structured questionnaires were applied to assess sociodemographic profile, exposure to physical and psychological violence (family, school, community), sexual abuse, social support, social functional impairment, resilience, and posttraumatic stress disorder. The data were grouped in blocks regarding sociodemographic, individual, family, and community variables. For statistical analysis, chi-square, Fisher's exact test, and logistic regression were performed. Results: The PTSD prevalence was 7.8% among adolescents. Boys were significantly exposed to more events of community violence while girls to family violence. The adjusted odds ratio (OR) for PTSD were statistically significant for age (OR, 1.45, [95% CI, 1.043–2.007]), social functional impairment (OR, 4.82, [95% CI, 1.77–13.10]), severe physical violence of the mother (OR, 2.79, [95% CI, 0.79–9.93]), psychological violence by significant people (OR, 3.96, [95% CI, 1.89–8.31]) and a high number of episodes of community violence (OR, 3.52, [95% CI, 1.47–8.40).Conclusions: There was a high prevalence of PTSD within this population associated with violence exposure. Not only physical but also psychological violence contributed to PTSD. The results also raise awareness for the differences in life trajectories between boys and girls regarding violence. These differences need to be better understood in order to develop effective preventative interventions. Treating and preventing mental health disorders presents a challenge for countries, especially those with a lower degree of social and economic development and who have high community violence.


2020 ◽  
pp. 088626052097819
Author(s):  
Matthew C. Morris ◽  
Francisco Sanchez-Sáez ◽  
Brooklynn Bailey ◽  
Natalie Hellman ◽  
Amber Williams ◽  
...  

A substantial minority of women who experience interpersonal violence will develop posttraumatic stress disorder (PTSD). One critical challenge for preventing PTSD is predicting whose acute posttraumatic stress symptoms will worsen to a clinically significant degree. This 6-month longitudinal study adopted multilevel modeling and exploratory machine learning (ML) methods to predict PTSD onset in 58 young women, ages 18 to 30, who experienced an incident of physical and/or sexual assault in the three months prior to baseline assessment. Women completed baseline assessments of theory-driven cognitive and neurobiological predictors and interview-based measures of PTSD diagnostic status and symptom severity at 1-, 3-, and 6-month follow-ups. Higher levels of self-blame, generalized anxiety disorder severity, childhood trauma exposure, and impairment across multiple domains were associated with a pattern of high and stable posttraumatic stress symptom severity over time. Predictive performance for PTSD onset was similarly strong for a gradient boosting machine learning model including all predictors and a logistic regression model including only baseline posttraumatic stress symptom severity. The present findings provide directions for future work on PTSD prediction among interpersonal violence survivors that could enhance early risk detection and potentially inform targeted prevention programs.


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