Browsing by Author "Koenen, Karestan C"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Open Access
    Associations between lifetime potentially traumatic events and chronic physical conditions in the South African Stress and Health Survey: a cross-sectional study
    (BioMed Central, 2016-07-07) Atwoli, Lukoye; Platt, Jonathan M; Basu, Archana; Williams, David R; Stein, Dan J; Koenen, Karestan C
    Background: This study examined the association between the type, and cumulative number of lifetime potentially traumatic events (PTEs), and chronic physical conditions, in a South African sample. PTE exposures have been associated with an increased risk for a wide range of chronic physical conditions, but it is unclear whether psychiatric disorders mediate this association. Given the established differences in trauma occurrence, and the epidemiology of posttraumatic stress disorder (PTSD) in South Africa relative to other countries, examining associations between PTEs and chronic physical conditions, particularly while accounting for psychiatric comorbidity is important. Methods: Data were drawn from the South African Stress and Health Study, a cross-sectional population-representative study of psychological and physical health of South African adults. Twenty-seven PTEs, based on the World Health Organization Composite International Diagnostic Interview Version 3.0, DSM-IV PTSD module were grouped into seven PTE types (war events, physical violence, sexual violence, accidents, unexpected death of a loved one, network events, and witnessing PTEs). Five clusters of physical conditions (cardiovascular, arthritis, respiratory, chronic pain, and other health conditions) were examined. Logistic regressions assessed the odds of reporting a physical condition in relation to type and cumulative number of PTEs. Cochran-Armitage test for trend was used to examine dose-response effect of cumulative PTEs on physical conditions. Results: After adjusting for sociodemographic variables and psychiatric disorders, respondents with any PTE had increased odds of all assessed physical conditions, ranging between 1.48 (95 % CI: 1.06–2.07) for arthritis and 2.07 (95 % CI: 1.57–2.73) for respiratory conditions, compared to those without PTE exposure. Sexual violence, physical violence, unexpected death of a loved one, and network PTEs significantly increased the odds of all or nearly all the physical conditions assessed. There was a dose-response relationship between number of PTEs and increased odds of all physical conditions. Conclusions: Results from this study, the first in an African general population, are consistent with other population-based studies; PTEs confer a broad-spectrum risk for chronic physical conditions, independent of psychiatric disorders. These risks increase with each cumulative PTE exposure. Clinically, comprehensive evaluations for risk of mental and physical health morbidities should be considered among PTE survivors.
  • Thumbnail Image
    Item
    Restricted
    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.