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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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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The comparisons of findings to prior meta-analyses indirectly corroborate that sexual assault may be associated with greater prevalence and symptom severity of PTSD relative to other forms of trauma.
“these comparisons of our findings to prior meta-analyses indirectly corroborate extant literature suggesting sexual assault may be associated with greater prevalence and symptom severity of PTSD relative to other forms of trauma”
From PTSD in the Year Following Sexual Assault: A Meta-Analysis of Prospective Studies (2023) · cites this paper as Kessler et al., 2014
The “Distribution and associations of TEs with PTSD” section reports higher conditional PTSD risk after rape and other sexual assault than after many other traumatic experiences. The paper also describes prior meta-analyses as linking physical or sexual assault to high PTSD risk, but the passages do not establish greater PTSD symptom severity after sexual assault. (LLM, conf=0.88)
Experimental — accuracy not yet measuredSexual assault is associated with higher rates of PTSD than other traumas.
“Sexual assault is associated with higher rates of PTSD than other traumas (Brewin et al., 2000; Dworkin, 2020; Kessler et al., 2014; Kilpatrick et al., 2003)”
From PTSD in the Year Following Sexual Assault: A Meta-Analysis of Prospective Studies (2023) · cites this paper as Kessler et al., 2014
Sexual assault appears to carry relatively greater risk for PTSD than other potentially traumatic events.
“Compared to other potentially traumatic events (e.g., physical assault, accidents, natural disasters), for which only a minority of survivors experience significant levels of PTSD (Galatzer-Levy et al., 2018; Lowe et al., 2020), sexual assault appears to carry relatively greater risk for PTSD (Brewin et al., 2000; Dworkin, 2020; Kessler et al., 2014; Kilpatrick et al., 2003), and may be associated”
From PTSD in the Year Following Sexual Assault: A Meta-Analysis of Prospective Studies (2023) · cites this paper as Kessler et al., 2014
No prior work has quantitatively summarized the natural course of PTSD recovery specifically for adolescent and adult sexual assault survivors.
“Second, despite evidence that sexual assault is more strongly associated with PTSD than other forms of trauma (Brewin et al., 2000; Dworkin, 2020; Kessler et al., 2014; Kilpatrick et al., 2003), no prior work has quantitatively summarized the natural course of PTSD recovery specifically for adolescent and adult sexual assault survivors.”
From PTSD in the Year Following Sexual Assault: A Meta-Analysis of Prospective Studies (2023) · cites this paper as Kessler et al., 2014
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The roles of monuments for the dead during the aftermath of the Great East Japan Earthquake
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The role of suboptimal mitochondrial function in vulnerability to post‐traumatic stress disorder
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Self-citationGenetics of stress-related disorders
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Systematic review of machine learning in PTSD studies for automated diagnosis evaluation
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Diagnosing post-traumatic stress disorder using electronic medical record data
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Trauma exposure and post-traumatic stress disorder in a cohort of pregnant Peruvian women
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Self-citationIncidence Trajectories of Psychiatric Disorders After Assault, Injury, and Bereavement
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Impact of post-traumatic stress disorder on oral health
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Stabilizing group treatment for childhood-abuse related PTSD: a randomized controlled trial
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Jann, P., Netzer, J., & Hecker, T. (2024) · European Journal of Psychotraumatology · doi:10.1080/20008066.2024.2371762 · Cited by 23 · see citing works
Computational health economics for identification of unprofitable health care enrollees
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Self-citationA Multimodal Approach for Predicting Changes in PTSD Symptom Severity
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Self-citationA preliminary evaluation of the unified protocol among trauma-exposed adults with and without PTSD
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Pre-trauma predictors of severe psychiatric comorbidity 5 years following traumatic experiences
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Self-citationUsing Machine Learning Algorithms to Enhance the Management of Suicide Ideation
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Big Data and Machine Learning Meet the Health Sciences
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- Authors
- Kessler, R. C., Rose, S., Koenen, K. C., Karam, E. G., Stang, P. E., Stein, D. J., Heeringa, S. G., Hill, E. D., Liberzon, I., McLaughlin, K. A., McLean, S. A., Pennell, B. E., Petukhova, M., Rosellini, A. J., Ruscio, A. M., Shahly, V., Shalev, A. Y., Silove, D., Zaslavsky, A. M., Angermeyer, M. C., Bromet, E. J., de Almeida, J. M. C., de Girolamo, G., de Jonge, P., Demyttenaere, K., Florescu, S. E., Gureje, O., Haro, J. M., Hinkov, H., Kawakami, N., Kovess-Masfety, V., Lee, S., Medina-Mora, M. E., Murphy, S. D., Navarro-Mateu, F., Piazza, M., Posada-Villa, J., Scott, K., Torres, Y., & Carmen Viana, M.
- Affiliations
- Harvard University (US)
- Columbia University (US)
- University of Balamand (LB)
- Janssen Research & Development; Titusville NJ USA
- University of Cape Town (ZA)
- University of Michigan (US)
- University of Washington (US)
- University of North Carolina at Chapel Hill (US)
- University of Pennsylvania (US)
- Hadassah Medical Center (IL)
- Liverpool Hospital (AU)
- Center for Public Mental Health; Gösingam Wagram Austria
- State University of New York (US)
- Universidade Nova de Lisboa; Lisbon Portugal
- IRCCS Centro San Giovanni di Dio Fatebenefratelli; Brescia Italy
- University of Groningen; Groningen The Netherlands
- Universitair Ziekenhuis Leuven (BE)
- National School of Public Health, Management and Professional Development (RO)
- University of Ibadan (NG)
- Parc Sanitari Sant Joan de Déu (ES)
- National Center for Public Health Protection; Sofia Bulgaria
- The University of Tokyo (JP)
- Université Paris Descartes and EHESP School for Public Health; Paris France
- Chinese University of Hong Kong (HK)
- Instituto Nacional de Psiquiatria Ramon de La Fuente Muñiz; Tlalpan Mexico City Mexico
- University of Ulster; Londonderry Northern Ireland UK
- Servicio Murciano de Salud and CIBER de Epidemiologia y Salud Publica (CIBERESP); El Palmar Spain
- Universidad Peruana Cayetano Heredia (PE)
- Universidad Colegio Mayor de Cundinamarca; Bogota Colombia
- University of Otago (NZ)
- University Center of Excellence on Mental Health Research; Medellín Colombia
- Federal University of Espirito Santo (UFES); Vitoria Brazil
- ORCID
- 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.
- Petukhova, Maria
- Rosellini, Anthony J.
- Shalev, Arieh Y.
- Silove, Derrick
- Zaslavsky, Alan M.
- Bromet, Evelyn J.
- Demyttenaere, Koen
- Florescu, Silvia E.
- Gureje, Oye
- Haro, Josep Maria
- Hinkov, Hristo
- Kawakami, Norito
- Piazza, Marina
- Scott, Kate
- Torres, Yolanda
- Venue
- World Psychiatry
- Publisher
- Wiley
- Published
- 2014-10
- Volume
- 13
- Issue
- 3
- Pages
- 265-274
- ISSN
- 1723-8617 (ISSN-L)
- PMC
- PMC4219068
- OpenAlex
- W2077086070
- Cited by
- 255
- References
- 33
- Citations by year
- 2026: 9 · 2025: 15 · 2024: 22 · 2023: 13 · 2022: 29 · 2021: 23 · 2020: 31 · 2019: 33 · 2018: 22 · 2017: 27 · 2016: 21 · 2015: 10
- FWCI
- 22.88
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- World Psychiatry (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
- Deep Blue (University of Michigan) (submittedVersion)
- Open University of Cape Town (University of Cape Town) (submittedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion)
- UNC Libraries
- Funders
- John D. and Catherine T. MacArthur Foundation (R01 DA016558, R13-MH066849)
- Bristol-Myers Squibb
- Eli Lilly and Company
- Pfizer
- GlaxoSmithKline
- National Institutes of Health (5r13mh066849-04, 5r01mh069864-04, 5r01mh070884-05, 5r01mh093612-03)
- U.S. Public Health Service (R01-DA016558, R13‐MH066849, R01- MH069864)
- Pfizer Foundation (R01‐MH069864, R01-DA016558, R13-MH066849, R03-TW006481)
- Pan American Health Organization
- National Institute of Mental Health (R13-MH-066849, MH093612, R01‐MH070884, MH070884, R01-MH093612, K01MH092526, R01 MH093612-01, R01‐MH069864, R01MH101227)
- National Institute on Drug Abuse (R01DA016558)
- Fogarty International Center (FIRCA R03TW006481, R03TW006481)
- Topics
- Social Sciences › Psychology › Clinical Psychology › Posttraumatic Stress Disorder Research
- Social Sciences › Psychology › Clinical Psychology › Migration, Health and Trauma
- Social Sciences › Psychology › Clinical Psychology › Child Abuse and Trauma
- Keywords
- post-traumatic stress disorder, risk prediction, traumatic events, cumulative trauma, PTSD prevalence, high-risk individuals, machine learning prediction models, random forest, super learner, penalized regression, World Mental Health Surveys
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