Browsing by Subject "Psychological trauma"

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    Development and Validation of a Model to Predict Posttraumatic Stress Disorder and Major Depression After a Motor Vehicle Collision
    (American Medical Association, 2021) Ziobrowski, Hannah N.; Kennedy, Chris J.; Ustun, Berk; House, Stacey L.; Beaudoin, Francesca L.; An, Xinming; Zeng, Donglin; Bollen, Kenneth A.; Petukhova, Maria; Sampson, Nancy A.; Puac-Polanco, Victor; Lee, Sue; Koenen, Karestan C.; Ressler, Kerry J.; McLean, Samuel A.; Kessler, Ronald C.; AURORA Consortium; Stevens, Jennifer S.; Neylan, Thomas C.; Clifford, Gari D.; Jovanovic, Tanja; Linnstaedt, Sarah D.; Germine, Laura T.; Rauch, Scott L.; Haran, John P.; Storrow, Alan B.; Lewandowski, Christopher; Musey, Paul I., Jr.; Hendry, Phyllis L.; Sheikh, Sophia; Jones, Christopher W.; Punches, Brittany E.; Lyons, Michael S.; Murty, Vishnu P.; McGrath, Meghan E.; Pascual, Jose L.; Seamon, Mark J.; Datner, Elizabeth M.; Chang, Anna M.; Pearson, Claire; Peak, David A.; Jambaulikar, Guruprasad; Merchant, Roland C.; Domeier, Robert M.; Rathlev, Niels K.; O'Neil, Brian J.; Sergot, Paulina; Sanchez, Leon D.; Bruce, Steven E.; Pietrzak, Robert H.; Joormann, Jutta; Barch, Deanna M.; Pizzagalli, Diego A.; Sheridan, John F.; Harte, Steven E.; Elliott, James M.; van Rooij, Sanne J.H.; Emergency Medicine, School of Medicine
    Importance: A substantial proportion of the 40 million people in the US who present to emergency departments (EDs) each year after traumatic events develop posttraumatic stress disorder (PTSD) or major depressive episode (MDE). Accurately identifying patients at high risk in the ED would facilitate the targeting of preventive interventions. Objectives: To develop and validate a prediction tool based on ED reports after a motor vehicle collision to predict PTSD or MDE 3 months later. Design, setting, and participants: The Advancing Understanding of Recovery After Trauma (AURORA) study is a longitudinal study that examined adverse posttraumatic neuropsychiatric sequalae among patients who presented to 28 US urban EDs in the immediate aftermath of a traumatic experience. Enrollment began on September 25, 2017. The 1003 patients considered in this diagnostic/prognostic report completed 3-month assessments by January 31, 2020. Each patient received a baseline ED assessment along with follow-up self-report surveys 2 weeks, 8 weeks, and 3 months later. An ensemble machine learning method was used to predict 3-month PTSD or MDE from baseline information. Data analysis was performed from November 1, 2020, to May 31, 2021. Main outcomes and measures: The PTSD Checklist for DSM-5 was used to assess PTSD and the Patient Reported Outcomes Measurement Information System Depression Short-Form 8b to assess MDE. Results: A total of 1003 patients (median [interquartile range] age, 34.5 [24-43] years; 715 [weighted 67.9%] female; 100 [weighted 10.7%] Hispanic, 537 [weighted 52.7%] non-Hispanic Black, 324 [weighted 32.2%] non-Hispanic White, and 42 [weighted 4.4%] of non-Hispanic other race or ethnicity were included in this study. A total of 274 patients (weighted 26.6%) met criteria for 3-month PTSD or MDE. An ensemble machine learning model restricted to 30 predictors estimated in a training sample (patients from the Northeast or Midwest) had good prediction accuracy (mean [SE] area under the curve [AUC], 0.815 [0.031]) and calibration (mean [SE] integrated calibration index, 0.040 [0.002]; mean [SE] expected calibration error, 0.039 [0.002]) in an independent test sample (patients from the South). Patients in the top 30% of predicted risk accounted for 65% of all 3-month PTSD or MDE, with a mean (SE) positive predictive value of 58.2% (6.4%) among these patients at high risk. The model had good consistency across regions of the country in terms of both AUC (mean [SE], 0.789 [0.025] using the Northeast as the test sample and 0.809 [0.023] using the Midwest as the test sample) and calibration (mean [SE] integrated calibration index, 0.048 [0.003] using the Northeast as the test sample and 0.024 [0.001] using the Midwest as the test sample; mean [SE] expected calibration error, 0.034 [0.003] using the Northeast as the test sample and 0.025 [0.001] using the Midwest as the test sample). The most important predictors in terms of Shapley Additive Explanations values were symptoms of anxiety sensitivity and depressive disposition, psychological distress in the 30 days before motor vehicle collision, and peritraumatic psychosomatic symptoms. Conclusions and relevance: The results of this study suggest that a short set of questions feasible to administer in an ED can predict 3-month PTSD or MDE with good AUC, calibration, and geographic consistency. Patients at high risk can be identified in the ED for targeting if cost-effective preventive interventions are developed.
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    “I just keep quiet about it and act as if everything is alright” – The cascade from trauma to disengagement among adolescents living with HIV in western Kenya
    (Wiley, 2021-04) Enane, Leslie A.; Apondi, Edith; Omollo, Mark; Toromo, Judith J.; Bakari, Salim; Aluoch, Josephine; Morris, Clemette; Kantor, Rami; Braitstein, Paula; Fortenberry, J. Dennis; Nyandiko, Winstone M.; Wools-Kaloustian, Kara; Elul, Batya; Vreeman, Rachel C.; Pediatrics, School of Medicine
    Introduction: There are approximately 1.7 million adolescents living with HIV (ALHIV, ages 10 to 19) globally, including 110,000 in Kenya. While ALHIV experience poor retention in care, limited data exist on factors underlying disengagement. We investigated the burden of trauma among disengaged ALHIV in western Kenya, and its potential role in HIV care disengagement. Methods: We performed in-depth qualitative interviews with ALHIV who had disengaged from care at two sites, their caregivers and healthcare workers (HCW) at 10 sites, from 2018 to 2020. Disengagement was defined as not attending clinic ≥60 days past a missed scheduled visit. ALHIV and their caregivers were traced through phone calls and home visits. Interviews ascertained barriers and facilitators to adolescent retention in HIV care. Dedicated questions elicited narratives surrounding traumatic experiences, and the ways in which these did or did not impact retention in care. Through thematic analysis, a conceptual model emerged for a cascade from adolescent experience of trauma to disengagement from HIV care. Results: Interviews were conducted with 42 disengaged ALHIV, 34 caregivers and 28 HCW. ALHIV experienced a high burden of trauma from a range of stressors, including experiences at HIV disclosure or diagnosis, the loss of parents, enacted stigma and physical or sexual violence. A confluence of factors - trauma, stigma and isolation, and lack of social support - led to hopelessness and depression. These factors compounded each other, and resulted in complex mental health burdens, poor antiretroviral adherence and care disengagement. HCW approaches aligned with the factors in this model, suggesting that these areas represent targets for intervention and provision of trauma-informed care. Conclusions: Trauma is a major factor underlying disengagement from HIV care among Kenyan adolescents. We describe a cascade of factors representing areas for intervention to support mental health and retention in HIV care. These include not only the provision of mental healthcare, but also preventing or addressing violence, trauma and stigma, and reinforcing social and familial support surrounding vulnerable adolescents. In this conceptualization, supporting retention in HIV care requires a trauma-informed approach, both in the individualized care of ALHIV and in the development of strategies and policies to support adolescent health outcomes.