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1.
Psychol Trauma ; 10(2): 253-262, 2018 Mar.
Article in English | MEDLINE | ID: mdl-27428553

ABSTRACT

OBJECTIVE: This study examined the prevalence and the psychosocial predictors of probable PTSD among Chinese adolescents in Kunming (approximately 444 miles from the epicenter), China, who were indirectly exposed to the Sichuan Earthquake in 2008. METHOD: Using a longitudinal study design, primary and secondary school students (N = 3577) in Kunming completed questionnaires at baseline (June 2008) and 6 months afterward (December 2008) in classroom settings. Participants' exposure to earthquake-related imagery and content, perceptions and emotional reactions related to the earthquake, and posttraumatic stress symptoms were measured. Univariate and forward stepwise multivariable logistic regression models were fit to identify significant predictors of probable PTSD at the 6-month follow-up. RESULTS: Prevalences of probable PTSD (with a Children's Revised Impact of Event Scale score ≥30) among the participants at baseline and 6-month follow-up were 16.9% and 11.1% respectively. In the multivariable analysis, those who were frequently exposed to distressful imagery had experienced at least two types of negative life events, perceived that teachers were distressed due to the earthquake, believed that the earthquake resulted from damages to the ecosystem, and felt apprehensive and emotionally disturbed due to the earthquake reported a higher risk of probable PTSD at 6-month follow-up (all ps < .05). CONCLUSION: Exposure to distressful media images, emotional responses, and disaster-related perceptions at baseline were found to be predictive of probable PTSD several months after indirect exposure to the event. Parents, teachers, and the mass media should be aware of the negative impacts of disaster-related media exposure on adolescents' psychological health. (PsycINFO Database Record


Subject(s)
Earthquakes , Mass Media , Models, Psychological , Stress Disorders, Post-Traumatic/epidemiology , Survivors/psychology , Adolescent , China , Emotions , Environmental Exposure , Female , Follow-Up Studies , Humans , Longitudinal Studies , Male , Prevalence , Stress Disorders, Post-Traumatic/psychology , Stress, Psychological/epidemiology , Stress, Psychological/etiology , Visual Perception
2.
PLoS One ; 9(2): e89860, 2014.
Article in English | MEDLINE | ID: mdl-24587080

ABSTRACT

BACKGROUND: Necrotizing enterocolitis (NEC) is a major source of neonatal morbidity and mortality. Since there is no specific diagnostic test or risk of progression model available for NEC, the diagnosis and outcome prediction of NEC is made on clinical grounds. The objective in this study was to develop and validate new NEC scoring systems for automated staging and prognostic forecasting. STUDY DESIGN: A six-center consortium of university based pediatric teaching hospitals prospectively collected data on infants under suspicion of having NEC over a 7-year period. A database comprised of 520 infants was utilized to develop the NEC diagnostic and prognostic models by dividing the entire dataset into training and testing cohorts of demographically matched subjects. Developed on the training cohort and validated on the blind testing cohort, our multivariate analyses led to NEC scoring metrics integrating clinical data. RESULTS: Machine learning using clinical and laboratory results at the time of clinical presentation led to two nec models: (1) an automated diagnostic classification scheme; (2) a dynamic prognostic method for risk-stratifying patients into low, intermediate and high NEC scores to determine the risk for disease progression. We submit that dynamic risk stratification of infants with NEC will assist clinicians in determining the need for additional diagnostic testing and guide potential therapies in a dynamic manner. ALGORITHM AVAILABILITY: http://translationalmedicine.stanford.edu/cgi-bin/NEC/index.pl and smartphone application upon request.


Subject(s)
Algorithms , Enterocolitis, Necrotizing/diagnosis , Enterocolitis, Necrotizing/pathology , Female , Humans , Infant, Newborn , Male
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