Browsing by Author "Petukhova, Maria"

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    How well can post-traumatic stress disorder be predicted from pre-trauma risk factors? an exploratory study in the WHO World Mental Health Surveys
    (2014) Kessler, Ronald C; Rose, Sherri; Koenen, Karestan C; Karam, Elie G; Stang, Paul E; Stein, Dan J; Heeringa, Steven G; Hill, Eric D; Liberzon, Israel; McLaughlin, Katie A; McLean, Samuel A; Pennell, Beth E; Petukhova, Maria; Rosellini, Anthony J; Ruscio, Ayelet M; Shahly, Victoria; Shalev, Arieh Y; Silove, Derrick; Zaslavsky, Alan M; Angermeyer, Matthias C; Bromet, Evelyn J; de Almeida, José Miguel Caldas; de Girolamo, Giovanni; de Jonge, Peter; Demyttenaere, Koen; Florescu, Silvia E; Gureje, Oye; Haro, Josep Maria; Hinkov, Hristo; Kawakami, Norito; Kovess-Masfety, Viviane; Lee, Sing; Medina-Mora, Maria Elena; Murphy, Samuel D; Navarro-Mateu, Fernando; Piazza, Marina; Posada-Villa, Jose; Scott, Kate; Torres, Yolanda; Viana, Maria Carmen
    Post-traumatic stress disorder (PTSD) should be one of the most preventable mental disorders, since many people exposed to traumatic experiences (TEs) could be targeted in first response settings in the immediate aftermath of exposure for preventive intervention. However, these interventions are costly and the proportion of TE-exposed people who develop PTSD is small. To be cost-effective, risk prediction rules are needed to target high-risk people in the immediate aftermath of a TE. Although a number of studies have been carried out to examine prospective predictors of PTSD among people recently exposed to TEs, most were either small or focused on a narrow sample, making it unclear how well PTSD can be predicted in the total population of people exposed to TEs. The current report investigates this issue in a large sample based on the World Health Organization (WHO)'s World Mental Health Surveys. Retrospective reports were obtained on the predictors of PTSD associated with 47,466 TE exposures in representative community surveys carried out in 24 countries. Machine learning methods (random forests, penalized regression, super learner) were used to develop a model predicting PTSD from information about TE type, socio-demographics, and prior histories of cumulative TE exposure and DSM-IV disorders. DSM-IV PTSD prevalence was 4.0% across the 47,466 TE exposures. 95.6% of these PTSD cases were associated with the 10.0% of exposures (i.e., 4,747) classified by machine learning algorithm as having highest predicted PTSD risk. The 47,466 exposures were divided into 20 ventiles (20 groups of equal size) ranked by predicted PTSD risk. PTSD occurred after 56.3% of the TEs in the highest-risk ventile, 20.0% of the TEs in the second highest ventile, and 0.0-1.3% of the TEs in the 18 remaining ventiles. These patterns of differential risk were quite stable across demographic-geographic sub-samples. These results demonstrate that a sensitive risk algorithm can be created using data collected in the immediate aftermath of TE exposure to target people at highest risk of PTSD. However, validation of the algorithm is needed in prospective samples, and additional work is warranted to refine the algorithm both in terms of determining a minimum required predictor set and developing a practical administration and scoring protocol that can be used in routine clinical practice.
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    Post-traumatic stress disorder associated with life-threatening motor vehicle collisions in the WHO World Mental Health Surveys
    (BioMed Central, 2016-07-22) Stein, Dan J; Karam, Elie G; Shahly, Victoria; Hill, Eric D; King, Andrew; Petukhova, Maria; Atwoli, Lukoye; Bromet, Evelyn J; Florescu, Silvia; Haro, Josep M; Hinkov, Hristo; Karam, Aimee; Medina-Mora, María E; Navarro-Mateu, Fernando; Piazza, Marina; Shalev, Arieh; Torres, Yolanda; Zaslavsky, Alan M; Kessler, Ronald C
    Background: Motor vehicle collisions (MVCs) are a substantial contributor to the global burden of disease and lead to subsequent post-traumatic stress disorder (PTSD). However, the relevant literature originates in only a few countries, and much remains unknown about MVC-related PTSD prevalence and predictors. Methods: Data come from the World Mental Health Survey Initiative, a coordinated series of community epidemiological surveys of mental disorders throughout the world. The subset of 13 surveys (5 in high income countries, 8 in middle or low income countries) with respondents reporting PTSD after lifethreatening MVCs are considered here. Six classes of predictors were assessed: socio-demographics, characteristics of the MVC, childhood family adversities, MVCs, other traumatic experiences, and respondent history of prior mental disorders. Logistic regression was used to examine predictors of PTSD. Mental disorders were assessed with the fully-structured Composite International Diagnostic Interview using DSM-IV criteria. Results: Prevalence of PTSD associated with MVCs perceived to be life-threatening was 2.5 % overall and did not vary significantly across countries. PTSD was significantly associated with low respondent education, someone dying in the MVC, the respondent or someone else being seriously injured, childhood family adversities, prior MVCs (but not other traumatic experiences), and number of prior anxiety disorders. The final model was significantly predictive of PTSD, with 32 % of all PTSD occurring among the 5 % of respondents classified by the model as having highest PTSD risk. Conclusion: Although PTSD is a relatively rare outcome of life-threatening MVCs, a substantial minority of PTSD cases occur among the relatively small proportion of people with highest predicted risk. This raises the question whether MVC-related PTSD could be reduced with preventive interventions targeted to high-risk survivors using models based on predictors assessed in the immediate aftermath of the MVCs.
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    Trauma and posttraumatic stress disorder in South Africa: analysis from the South African Stress and Health Study
    (BioMed Central Ltd, 2013) Atwoli, Lukoye; Stein, Dan; Williams, David; Mclaughlin, Katie; Petukhova, Maria; Kessler, Ronald; Koenen, Karestan
    BACKGROUND: South Africa's unique history, characterised by apartheid, a form of constitutional racial segregation and exploitation, and a long period of political violence and state-sponsored oppression ending only in 1994, suggests a high level of trauma exposure in the general population. The aim of this study was to document the epidemiology of trauma and posttraumatic stress disorder (PTSD) in the South African general population. METHODS: The South African Stress and Health Study is a nationally representative survey of South African adults using the WHO's Composite International Diagnostic Interview (CIDI) to assess exposure to trauma and presence of DSM-IV mental disorders. RESULTS: The most common traumatic events were the unexpected death of a loved one and witnessing trauma occurring to others. Lifetime and 12-month prevalence rates of PTSD were 2.3% and 0.7% respectively, while the conditional prevalence of PTSD after trauma exposure was 3.5%. PTSD conditional risk after trauma exposure and probability of chronicity after PTSD onset were both highest for witnessing trauma. Socio-demographic factors such as sex, age and education were largely unrelated to PTSD risk. CONCLUSIONS: The occurrence of trauma and PTSD in South Africa is not distributed according to the socio-demographic factors or trauma types observed in other countries. The dominant role of witnessing in contributing to PTSD may reflect the public settings of trauma exposure in South Africa and highlight the importance of political and social context in shaping the epidemiology of PTSD.