title: How well can post-traumatic stress disorder be predicted from pre-trauma risk factors? An exploratory study in the WHO World Mental Health Surveys.
abstract: 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.
authors.affiliation: Department of Health Care Policy, Harvard Medical School, 180 Longwood Ave., Boston, MA, 02115, USA.
authors.forename: Ronald C
authors.initials: RC
authors.lastname: Kessler
authors.forename: Sherri
authors.initials: S
authors.lastname: Rose
authors.forename: Karestan C
authors.initials: KC
authors.lastname: Koenen
authors.forename: Elie G
authors.initials: EG
authors.lastname: Karam
authors.forename: Paul E
authors.initials: PE
authors.lastname: Stang
authors.forename: Dan J
authors.initials: DJ
authors.lastname: Stein
authors.forename: Steven G
authors.initials: SG
authors.lastname: Heeringa
authors.forename: Eric D
authors.initials: ED
authors.lastname: Hill
authors.forename: Israel
authors.initials: I
authors.lastname: Liberzon
authors.forename: Katie A
authors.initials: KA
authors.lastname: McLaughlin
authors.forename: Samuel A
authors.initials: SA
authors.lastname: McLean
authors.forename: Beth E
authors.initials: BE
authors.lastname: Pennell
authors.forename: Maria
authors.initials: M
authors.lastname: Petukhova
authors.forename: Anthony J
authors.initials: AJ
authors.lastname: Rosellini
authors.forename: Ayelet M
authors.initials: AM
authors.lastname: Ruscio
authors.forename: Victoria
authors.initials: V
authors.lastname: Shahly
authors.forename: Arieh Y
authors.initials: AY
authors.lastname: Shalev
authors.forename: Derrick
authors.initials: D
authors.lastname: Silove
authors.forename: Alan M
authors.initials: AM
authors.lastname: Zaslavsky
authors.forename: Matthias C
authors.initials: MC
authors.lastname: Angermeyer
authors.forename: Evelyn J
authors.initials: EJ
authors.lastname: Bromet
authors.forename: José Miguel Caldas
authors.initials: JM
authors.lastname: de Almeida
authors.forename: Giovanni
authors.initials: G
authors.lastname: de Girolamo
authors.forename: Peter
authors.initials: P
authors.lastname: de Jonge
authors.forename: Koen
authors.initials: K
authors.lastname: Demyttenaere
authors.forename: Silvia E
authors.initials: SE
authors.lastname: Florescu
authors.forename: Oye
authors.initials: O
authors.lastname: Gureje
authors.forename: Josep Maria
authors.initials: JM
authors.lastname: Haro
authors.forename: Hristo
authors.initials: H
authors.lastname: Hinkov
authors.forename: Norito
authors.initials: N
authors.lastname: Kawakami
authors.forename: Viviane
authors.initials: V
authors.lastname: Kovess-Masfety
authors.forename: Sing
authors.initials: S
authors.lastname: Lee
authors.forename: Maria Elena
authors.initials: ME
authors.lastname: Medina-Mora
authors.forename: Samuel D
authors.initials: SD
authors.lastname: Murphy
authors.forename: Fernando
authors.initials: F
authors.lastname: Navarro-Mateu
authors.forename: Marina
authors.initials: M
authors.lastname: Piazza
authors.forename: Jose
authors.initials: J
authors.lastname: Posada-Villa
authors.forename: Kate
authors.initials: K
authors.lastname: Scott
authors.forename: Yolanda
authors.initials: Y
authors.lastname: Torres
authors.forename: Maria
authors.initials: M
authors.lastname: Carmen Viana
keywords: Post-traumatic stress disorder; machine learning; penalized regression; predictive modeling; random forests; ridge regression