How well can post-traumatic stress disorder be predicted from pre-trauma risk factors? an exploratory study in the WHO World Mental Health Surveys

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dc.contributor.authorKessler, Ronald C
dc.contributor.authorRose, Sherri
dc.contributor.authorKoenen, Karestan C
dc.contributor.authorKaram, Elie G
dc.contributor.authorStang, Paul E
dc.contributor.authorStein, Dan J
dc.contributor.authorHeeringa, Steven G
dc.contributor.authorHill, Eric D
dc.contributor.authorLiberzon, Israel
dc.contributor.authorMcLaughlin, Katie A
dc.contributor.authorMcLean, Samuel A
dc.contributor.authorPennell, Beth E
dc.contributor.authorPetukhova, Maria
dc.contributor.authorRosellini, Anthony J
dc.contributor.authorRuscio, Ayelet M
dc.contributor.authorShahly, Victoria
dc.contributor.authorShalev, Arieh Y
dc.contributor.authorSilove, Derrick
dc.contributor.authorZaslavsky, Alan M
dc.contributor.authorAngermeyer, Matthias C
dc.contributor.authorBromet, Evelyn J
dc.contributor.authorde Almeida, José Miguel Caldas
dc.contributor.authorde Girolamo, Giovanni
dc.contributor.authorde Jonge, Peter
dc.contributor.authorDemyttenaere, Koen
dc.contributor.authorFlorescu, Silvia E
dc.contributor.authorGureje, Oye
dc.contributor.authorHaro, Josep Maria
dc.contributor.authorHinkov, Hristo
dc.contributor.authorKawakami, Norito
dc.contributor.authorKovess-Masfety, Viviane
dc.contributor.authorLee, Sing
dc.contributor.authorMedina-Mora, Maria Elena
dc.contributor.authorMurphy, Samuel D
dc.contributor.authorNavarro-Mateu, Fernando
dc.contributor.authorPiazza, Marina
dc.contributor.authorPosada-Villa, Jose
dc.contributor.authorScott, Kate
dc.contributor.authorTorres, Yolanda
dc.contributor.authorViana, Maria Carmen
dc.date.accessioned2018-06-07T08:25:07Z
dc.date.available2018-06-07T08:25:07Z
dc.date.issued2014
dc.date.updated2016-01-13T14:50:48Z
dc.description.abstractPost-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.
dc.identifierhttp://dx.doi.org/10.1002/wps.20150
dc.identifier.apacitationKessler, R. C., Rose, S., Koenen, K. C., Karam, E. G., Stang, P. E., Stein, D. J., ... Viana, M. C. (2014). How well can post-traumatic stress disorder be predicted from pre-trauma risk factors? an exploratory study in the WHO World Mental Health Surveys. <i>World Psychiatry</i>, http://hdl.handle.net/11427/28242en_ZA
dc.identifier.chicagocitationKessler, Ronald C, Sherri Rose, Karestan C Koenen, Elie G Karam, Paul E Stang, Dan J Stein, Steven G Heeringa, et al "How well can post-traumatic stress disorder be predicted from pre-trauma risk factors? an exploratory study in the WHO World Mental Health Surveys." <i>World Psychiatry</i> (2014) http://hdl.handle.net/11427/28242en_ZA
dc.identifier.citationKessler, R. C., Rose, S., Koenen, K. C., Karam, E. G., Stang, P. E., Stein, D. J., . . . Carmen Viana, M. (2014). How well can post-traumatic stress disorder be predicted from pre-trauma risk factors? an exploratory study in the WHO world mental health surveys. World Psychiatry, 13(3), 265-274. doi:10.1002/wps.20150
dc.identifier.ris TY - AU - Kessler, Ronald C AU - Rose, Sherri AU - Koenen, Karestan C AU - Karam, Elie G AU - Stang, Paul E AU - Stein, Dan J AU - Heeringa, Steven G AU - Hill, Eric D AU - Liberzon, Israel AU - McLaughlin, Katie A AU - McLean, Samuel A AU - Pennell, Beth E AU - Petukhova, Maria AU - Rosellini, Anthony J AU - Ruscio, Ayelet M AU - Shahly, Victoria AU - Shalev, Arieh Y AU - Silove, Derrick AU - Zaslavsky, Alan M AU - Angermeyer, Matthias C AU - Bromet, Evelyn J AU - de Almeida, José Miguel Caldas AU - de Girolamo, Giovanni AU - de Jonge, Peter AU - Demyttenaere, Koen AU - Florescu, Silvia E AU - Gureje, Oye AU - Haro, Josep Maria AU - Hinkov, Hristo AU - Kawakami, Norito AU - Kovess-Masfety, Viviane AU - Lee, Sing AU - Medina-Mora, Maria Elena AU - Murphy, Samuel D AU - Navarro-Mateu, Fernando AU - Piazza, Marina AU - Posada-Villa, Jose AU - Scott, Kate AU - Torres, Yolanda AU - Viana, Maria Carmen AB - 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. DA - 2014 DB - OpenUCT DP - University of Cape Town J1 - World Psychiatry LK - https://open.uct.ac.za PB - University of Cape Town PY - 2014 T1 - How well can post-traumatic stress disorder be predicted from pre-trauma risk factors? an exploratory study in the WHO World Mental Health Surveys TI - How well can post-traumatic stress disorder be predicted from pre-trauma risk factors? an exploratory study in the WHO World Mental Health Surveys UR - http://hdl.handle.net/11427/28242 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/28242
dc.identifier.vancouvercitationKessler RC, Rose S, Koenen KC, Karam EG, Stang PE, Stein DJ, et al. How well can post-traumatic stress disorder be predicted from pre-trauma risk factors? an exploratory study in the WHO World Mental Health Surveys. World Psychiatry. 2014; http://hdl.handle.net/11427/28242.en_ZA
dc.language.isoeng
dc.publisher.departmentDepartment of Psychiatry and Mental Healthen_ZA
dc.publisher.facultyFaculty of Health Sciencesen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.sourceWorld Psychiatry
dc.source.urihttps://onlinelibrary.wiley.com/journal/20515545
dc.subject.otherPost-traumatic stress disorder
dc.subject.otherpredictive modeling
dc.subject.othermachine learning
dc.subject.otherpenalized regression
dc.subject.otherrandom forests
dc.subject.otherridge regression
dc.titleHow well can post-traumatic stress disorder be predicted from pre-trauma risk factors? an exploratory study in the WHO World Mental Health Surveys
dc.typeJournal Article
uct.type.filetypeText
uct.type.filetypeImage
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