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The epidemiology of trauma and post-traumatic stress disorder in a representative cohort of young people in England and Wales

Summary Background Despite the emphasis placed on childhood trauma in psychiatry, comparatively little is known about the epidemiology of trauma and trauma-related psychopathology in young people. We therefore aimed to evaluate the prevalence, clinical features, and risk factors associated with trauma exposure and post-traumatic stress disorder (PTSD) in young people. Methods We carried out a comprehensive epidemiological study based on participants from the Environmental Risk Longitudinal Twin Study, a population-representative birth-cohort of 2232 children born in England and Wales in 1994–95. At the follow-up home visit at age 18 years, participants were assessed with structured interviews for trauma exposure, PTSD, other psychopathology, risk events, functional impairment, and service use. Risk factors for PTSD were measured prospectively over four previous assessments between age 5 and 12 years. The key outcomes were the prevalence, clinical features, and risk factors associated with trauma exposure and PTSD. We also derived and tested the internal validity of a PTSD risk calculator. Findings We found that 642 (31·1%) of 2064 participants reported trauma exposure and 160 (7·8%) of 2063 experienced PTSD by age 18 years. Trauma-exposed participants had high rates of psychopathology (187 [29·2%] of 641 for major depressive episode, 146 [22·9%] of 638 for conduct disorder, and 102 [15·9%] of 641 for alcohol dependence), risk events (160 [25·0%] of 641 for self-harm, 53 [8·3%] of 640 for suicide attempt, and 42 [6·6%] of 640 for violent offence), and functional impairment. Participants with lifetime PTSD had even higher rates of psychopathology (87 [54·7%] of 159 for major depressive episode, 43 [27·0%] of 159 for conduct disorder, and 41 [25·6%] of 160 for alcohol dependence), risk events (78 [48·8%] of 160 for self-harm, 32 [20·1%] of 159 for suicide attempt, and 19 [11·9%] of 159 for violent offence), and functional impairment. However, only 33 (20·6%) of 160 participants with PTSD received help from mental health professionals. The PTSD risk calculator had an internally validated area under the receiver operating characteristic curve of 0·74, indicating adequate discrimination of trauma-exposed participants with and without PTSD, and internally validated calibration-in-the-large of −0·10 and calibration slope of 0·90, indicating adequate calibration. Interpretation Trauma exposure and PTSD are associated with complex psychiatric presentations, high risk, and significant impairment in young people. Improved screening, reduced barriers to care provision, and comprehensive clinical assessment are needed to ensure that trauma-exposed young people and those with PTSD receive appropriate treatment. Funding The Medical Research Council, the National Institute of Child Health and Development, the Jacobs Foundation, the Nuffield Foundation, the National Society for Prevention of Cruelty to Children, the Economic and Social Research Council, the National Institute for Health Research, MQ, and Canadian Institutes for Advanced Research.


Supplementary Methods
Page 1 Supplementary Results Page 3 Supplementary Tables   Table S1. Assessment of trauma-exposure, PTSD diagnosis, clinical features, and childhood risk factors Page 4 Table S2. Characteristics of the sample Page 7  Table S8. PTSD risk calculator sensitivity analysis to compare different methods of internal validation and penalized models Page 13 Table S9. PTSD risk calculator sensitivity analysis to assess for bias in using twin data Page 14 Table S10. PTSD risk score in trauma-exposed participants Page 15 Supplementary References Page 16 Overall prediction performance captures aspects of discrimination and calibration. Overall prediction performance was assessed with the Brier score. The Brier score indicates the mean squared difference between the observed and predicted probabilities of PTSD; good prediction is demonstrated by low values. As the Brier score varies depending on the prevalence of the outcome, Brier scores scaled by their maximum possible scores were calculated, where Brierscaled = 1 -Brier / Briermax. The scaled Brier score corresponds to the proportion of the mean squared difference between observed and predicted values associated with a non-informative model that is accounted for by the current model, and ranges from 0% (non-informative prediction) to 100% (perfect prediction). Of note, it is not possible to calculate scaled Brier scores using the bootstrap internal validation method described above and, thus, scaled Brier scores were only calculated for models internally validated with cross-validation.

PTSD risk prediction sensitivity analysis to assess for bias in using twin data
We sought to examine whether the presence of non-independent observations within our twin sample may have biased our measures of prediction performance. Specifically, higher levels of similarity in the risk profiles of twins within each pair (compared to unrelated individuals) may have overestimated prediction performance if, during internal validation, the model was 'trained' on one twin and 'tested' on their co-twin. To address these concerns, we performed a sensitivity analysis using ten sub-samples consisting of only one twin per twin pair (randomly selected in twin pairs where both were trauma-exposed), and compared the average model prediction performance obtained from bootstrap internal validation in these sub-samples to the performance in the full trauma-exposed sample.

PTSD risk prediction using cumulative risk score
A risk calculator with multiple predictors may not be straight forward to use in clinical practice. Therefore, we also derived a PTSD risk calculator with a single cumulative risk score predictor that could be very easily applied in clinical settings. We generated this risk score by combining statistically significant risk factors for lifetime PTSD in trauma-exposed participants (from Table 2) into a cumulative risk score (range 0-2; using criteria: childhood victimization=1, and direct interpersonal index trauma=1). We used this variable to develop a logistic regression risk prediction model. We tested the validity of this model using 1,000 bootstrap resamples to obtain overfitting (optimism) bias-corrected estimates of prediction performance. These estimates may be overoptimistic and this method is not true internal validation because the variables used to generate the score were pre-selected based on results from this dataset. Model prediction performance was measured in terms of discrimination, calibration, and overall prediction performance (as above).

PTSD risk prediction internal validation
The results of the bootstrap internal validation of the standard (non-penalized) logistic regression model are described in the main text, and are outlined in Supplementary Table S8. We found similar prediction performance results for internal validation of the standard (non-penalized) logistic regression model using tenfold cross-validation (Supplementary Table S8). Therefore, we have presented only the results from the preferred bootstrap internal validation method in the main text. Additionally, we found similar prediction performance results for internal validation of the penalized logistic regression models (Supplementary Table  S8), indicating that these methods do not substantially improve accuracy of our model. Therefore, we have presented the results from the standard (non-penalized) logistic regression model in the main text.

PTSD risk prediction sensitivity analysis to assess for bias in using twin data
We performed a sensitivity analysis to test whether using non-independent (twin) data biased our internal validation results. We compared bootstrap internal validation results from ten sub-samples consisting of only one twin per twin pair (randomly selected in twin pairs where both were trauma-exposed) with results from the full trauma-exposed sample, and found that the average prediction performance in the sub-samples was similar to the results in the full trauma-exposed sample. These findings indicate that using twins has not biased our results. A full description of these analyses is provided in Supplementary Table S9.

PTSD risk prediction using cumulative risk score
We tested the validity of a PTSD risk calculator based on the PTSD cumulative risk score in trauma-exposed participants (see Supplementary Methods). First, higher risk scores were more common in participants with PTSD than in trauma-exposed participants without PTSD (Supplementary Table S10, Panel A), and bootstrap validated AUC was 0·73, indicating adequate discrimination of trauma-exposed participants with and without PTSD. Second, validated calibration-in-the-large was 0·00 and calibration slope was 0·99, indicating good calibration. The observed prevalences of PTSD for each risk score agreed well with bias-corrected predictions, further indicating good calibration (Supplementary Table S10, Panels B and C). Finally, this prediction model had a validated Brier score of 0·16, indicating adequate overall risk prediction performance. Index trauma type and category were classified based on qualitative data from the PTSD interview 10 and Juvenile Victimization Questionnaire 2 nd Revision (JVQ-R2) 11 adapted as a clinical interview. 12 First, we coded index traumas in a set of trauma types.

SUPPLEMENTARY TABLES
Where index traumas consisted of several trauma types, for example both sexual and physical assault, these were all coded; therefore, trauma types were not mutually exclusive. Coded trauma types were adapted from the Composite International Diagnostic Interview (CIDI). 13 Next, we coded index traumas in a set of trauma categories. This variable described mutually exclusive subgroups of trauma-exposed individuals, required for analyses which compared subgroups of trauma-exposed participants. Coded trauma categories were developed based on previous research findings that interpersonal index traumas are associated with higher rates of PTSD than non-interpersonal index traumas, and that direct index traumas are associated with higher rates of PTSD than witnessed index traumas. [14][15][16][17] In order to compare these groups, we developed categories to describe whether the index trauma was interpersonal (actions of another person intentionally causing or threatening death, injury, or sexual violation), or not interpersonal (accident or illness); and within these groups further specified whether the index trauma was directly experienced by the participant, or witnessed only. Other trauma categories were network trauma (a traumatic event affecting someone in the participant's network that they learned details of, but did not did not directly experience or witness), other trauma (any other trauma that did not fall into these categories), and declined to answer (where participants declined to disclose details of their experience, often due to distress; classed as missing in analyses of predictors of PTSD (n=6)). Trauma types and categories are listed in Supplementary Table S3. Trauma dossiers from each trauma-exposed participant were independently coded by two psychiatrists blind to any other information about participants, to indicate types (inter-rater reliability of kappa=0·83) and categories (inter-rater reliability of kappa=0·89) of index traumas experienced.   Mothers were asked to report on their own mental health history and the mental health history of their biological mother, biological father, biological sisters, biological brothers, as well as the twins' biological father. Mothers were asked to report if anyone on the aforementioned list experienced difficulties with substance-use problems, alcohol problems, depression, psychosis, or suicide attempts.  The table presents summary statistics for characteristics of the E-Risk Study members who participated in the age-18 assessments (n=2,066). Where data was missing, we have used pairwise deletion. A full description of these characteristics is provided in Supplementary Table S1, Panel C. In addition to this information, the prevalence of mental health conditions, risk events, functional impairment, and service use in E-Risk participants at age 18 is provided in Table 1. IQ = intelligence quotient. Panel A lists the prevalence of lifetime trauma exposure, by index trauma type and category, in the overall sample. Panel B lists the prevalence of lifetime PTSD in participants exposed to each index trauma type and category. Panel C lists the PTSD burden of each index trauma type and category, that is the percentage of all lifetime PTSD cases associated with each index trauma type and category. Where data was missing, we have used pairwise deletion. A full description of these trauma types and categories is provided in Supplementary Table S1, Panel A. a Type of index trauma, not mutually exclusive -each trauma-exposed participant's index traumatic experience may consist of several trauma types, for example both sexual and physical assault. b Category of index trauma, mutually exclusive -each trauma-exposed participant's index trauma is classified into one of these categories. The table presents unadjusted odds ratios (95% confidence intervals) for the associations between lifetime trauma exposure and each clinical feature in the overall sample (Panel A), between lifetime PTSD and each clinical feature in the overall sample (Panel B), and between lifetime PTSD and each clinical feature in trauma-exposed participants (Panel C), all calculated using logistic regression models with robust standard errors accounting for clustering within families. Where data was missing, we have used pairwise deletion. A full description of these clinical features is provided in Supplementary Table S1, Panel B. Bold text signifies statistical significance with p<0·05. PTSD = post-traumatic stress disorder; ADHD = attention-deficit/hyperactivity disorder; NEET = not in education, employment, or training. The table lists the prevalence of each clinical feature in the overall sample (Panel A), trauma-unexposed participants (Panel B), trauma-exposed participants (Panel C), participants without 12-month PTSD in the overall sample (Panel D), participants without 12-month PTSD in the trauma-exposed sub-sample (Panel E), and participants with 12-month PTSD (Panel F)· Where data was missing, we have used pairwise deletion. A full description of these clinical features is provided in Supplementary Table S1, Panel B. PTSD = post-traumatic stress disorder; ADHD = attention-deficit/hyperactivity disorder; NEET = not in education, employment, or training.  The table presents unadjusted odds ratios (95% confidence intervals) for the associations between lifetime trauma exposure and each clinical feature in the overall sample (Panel A), between 12-month PTSD and each clinical feature in the overall sample (Panel B), and between 12-month PTSD and each clinical feature in trauma-exposed participants (Panel C), all calculated using logistic regression models with robust standard errors accounting for clustering within families. Where data was missing, we have used pairwise deletion. A full description of these clinical features is provided in Supplementary Table S1, Panel B. Bold text signifies statistical significance with p<0·05. PTSD = post-traumatic stress disorder; ADHD = attention-deficit/hyperactivity disorder; NEET = not in education, employment, or training. The table lists odds ratios (95% confidence intervals) for the associations between [1] lifetime trauma exposure and each clinical feature in the overall sample, [2] lifetime PTSD and each clinical feature in the overall sample, and [3] lifetime PTSD and each clinical feature in trauma-exposed participants, adjusted for mental health conditions as indicated in the first column (major depressive episode, psychotic symptoms, or count of mental health conditions), calculated using logistic regression models with robust standard errors accounting for clustering within families. Where data was missing, we have used pairwise deletion. A full description of these clinical features is provided in Supplementary Table S1, Panel B. Bold text signifies statistical significance with p<0·05. PTSD = post-traumatic stress disorder; NEET = not in education, employment, or training. The PTSD risk calculator was first derived using the standard multivariate logistic regression model predicting lifetime PTSD in trauma-exposed participants ( Table 2). The table shows the internally validated risk prediction performance of this model, using bootstrap resampling and ten-fold cross-validation. The PTSD risk calculator was then derived using penalized multivariate logistic regression models predicting lifetime PTSD in trauma-exposed participants (penalty terms were an average of the optimal penalties found across the ten folds). The table shows the internally validated risk prediction performance of these models, using ten-fold cross-validation. The table also shows odds ratios for predictors based on these models. The PTSD risk calculator was derived using the multivariate logistic regression model predicting lifetime PTSD in trauma-exposed participants ( Table 2). Panel A shows the bootstrap internally validated risk prediction performance of this model which was developed in all trauma-exposed participants. We were concerned that using nonindependent (twin) data may bias our internal validation results, and undertook a sensitivity analysis to test this, shown in Panel B. In this sensitivity analysis, we tested ten subsamples consisting of only one twin per twin pair (randomly selected in twin pairs where both were trauma-exposed). The average prediction performance was similar to the results of the full trauma-exposed sample (Panel A), indicating that using twins has not biased our results. We generated this PTSD cumulative risk score by combining statistically significant risk factors for lifetime PTSD in trauma-exposed participants (from Table 2) into a cumulative risk score (range 0-2; using criteria: childhood victimization=1, and direct interpersonal index trauma=1). Panel A lists the percentage of trauma-exposed participants without PTSD and those with lifetime PTSD who had each risk score. Panel B lists the observed prevalence of lifetime PTSD in trauma-exposed participants with each risk score, and the odds ratios (95% confidence intervals) for associations between each risk score versus 0 and PTSD, calculated using logistic regression with robust standard errors. We tested the validity of this model using 1,000 bootstrap resamples to obtain overfitting (optimism) bias-corrected estimates of prediction performance. Panel C lists the bias-corrected predicted probability of lifetime PTSD in trauma-exposed participants with each risk score, based on this validation.