Changes in perceived social support and PTSD symptomatology among Danish army military personnel

Purpose

Previous research has identified social support to be associated with risk of posttraumatic stress disorder (PTSD) symptoms among military personnel. While the lack of social support influences PTSD symptomatology, it is unknown how changes in perceived social support affect the PTSD symptom level in the aftermath of deployment. Furthermore, the influence of specific sources of social support from pre- to post-deployment on level of PTSD symptoms is unknown. We aim to examine how changes in perceived social support (overall and from specific sources) from pre- to 2.5 year post-deployment are associated with the level of post-deployment PTSD symptoms.

Methods

Danish army military personnel deployed to Afghanistan in 2009 and 2013 completed questionnaires at pre-deployment and at 2.5 year post-deployment measuring perceived social support and PTSD symptomatology and sample characteristics of the two cohorts. Data were analyzed using univariate and multivariate nominal logistic regression.

Results

Negative changes in perceived social support from pre- to post-deployment were associated with both moderate (OR 1.99, CI 1.51–2.57) and high levels (OR 2.71, CI 1.94–3.78) of PTSD symptoms 2.5 year post-deployment (adjusted analysis). Broadly, the same direction was found for specific sources of social support and level of PTSD symptoms. In the adjusted analyses, pre-deployment perceived social support and military rank moderated the associations.

Conclusions

Deterioration in perceived social support (overall and specific sources) from pre- to 2.5 year post-deployment increases the risk of an elevated level of PTSD symptoms 2.5 year post-deployment.

     

Exploring the benefit of synaesthetic colours: Testing for “pop-out” in individuals with grapheme–colour synaesthesia

In grapheme–colour synaesthesia, letters, numbers, and words elicit involuntary colour experiences. Recently, there has been much emphasis on individual differences and possible subcategories of synaesthetes with different underlying mechanisms. In particular, there are claims that for some, synaesthesia occurs prior to attention and awareness of the inducing stimulus. We first characterized our sample using two versions of the “Synaesthetic Congruency Task” to distinguish “projector” and “associator” synaesthetes who may differ in the extent to which their synaesthesia depends on attention and awareness. We then used a novel modification of the “Embedded Figures Task” that included a set-size manipulation to look for evidence of preattentive “pop-out” from synaesthetic colours, at both a group and an individual level. We replicate an advantage for synaesthetes over nonsynaesthetic controls on the Embedded Figures Task in accuracy, but find no support for pop-out of synaesthetic colours. We conclude that grapheme–colour synaesthetes are fundamentally similar in their visual processing to the general population, with the source of their unusual conscious colour experiences occurring late in the cognitive hierarchy.     

The co-occurrence of PTSD and dissociation: differentiating severe PTSD from dissociative-PTSD

Purpose

A dissociative-posttraumatic stress disorder (PTSD) subtype has been included in the DSM-5. However, it is not yet clear whether certain socio-demographic characteristics or psychological/clinical constructs such as comorbid psychopathology differentiate between severe PTSD and dissociative-PTSD. The current study investigated the existence of a dissociative-PTSD subtype and explored whether a number of trauma and clinical covariates could differentiate between severe PTSD alone and dissociative-PTSD.

Methods

The current study utilized a sample of 432 treatment seeking Canadian military veterans. Participants were assessed with the Clinician Administered PTSD Scale (CAPS) and self-report measures of traumatic life events, depression, and anxiety. CAPS severity scores were created reflecting the sum of the frequency and intensity items from each of the 17 PTSD and 3 dissociation items. The CAPS severity scores were used as indicators in a latent profile analysis (LPA) to investigate the existence of a dissociative-PTSD subtype. Subsequently, several covariates were added to the model to explore differences between severe PTSD alone and dissociative-PTSD.

Results

The LPA identified five classes: one of which constituted a severe PTSD group (30.5 %), and one of which constituted a dissociative-PTSD group (13.7 %). None of the included, demographic, trauma, or clinical covariates were significantly predictive of membership in the dissociative-PTSD group compared to the severe PTSD group.

Conclusions

In conclusion, a significant proportion of individuals report high levels of dissociation alongside their PTSD, which constitutes a dissociative-PTSD subtype. Further investigation is needed to identify which factors may increase or decrease the likelihood of membership in a dissociative-PTSD subtype group compared to a severe PTSD only group.     

What we talk about when we talk about trauma: Content overlap and heterogeneity in the assessment of trauma exposure

The accurate definition and assessment of trauma exposure is the foundation for replicable studies of mental health problems following trauma exposure. However, scales developed to assess trauma exposure might vary widely in terms of item content; overlap; and specifications of trauma intensity, frequency, duration, and timing. We compared eight frequently used self-report measures of trauma exposure to address content overlap and measurement heterogeneity. Combined, these measures assess 44 disparate exposures. Mean overlap across scales was moderate (M = 0.41, range: 0.25–0.48 across scales). Pairwise overlap between scales ranged from .19 to .59. We found 18 exposures (40.9%) that were included in one scale and three exposures (6.8%) that were included in all eight scales. Four of the included scales assess trauma frequency, five assess intensity or perceived danger, two assess duration, and four assess timing. The implications of measurement heterogeneity for clinical research as well as for comparability and replication of trauma-related research are discussed.     

Prediction models of suicide and non-fatal suicide attempt after discharge from a psychiatric inpatient stay: A machine learning approach on nationwide Danish registers

Introduction

To develop machine learning models capable of predicting suicide and non-fatal suicide attempt as separate outcomes in the first 30 days after discharge from a psychiatric inpatient stay.

Methods

Prospective cohort study using nationwide Danish registry data. We included individuals who were 18 years or older, and all discharges from psychiatric hospitalizations in Denmark from 1995 to 2018. We trained predictive models using 10-fold cross validation on 80% of the data and did testing on the remaining 20%.

Results

The best model for predicting non-fatal suicide attempt was an ensemble of predictions from gradient boosting (XGBoost) and categorical boosting (catBoost). The ROC-AUC for predicting suicide attempt was 0.85 (95% CI: 0.84–0.85). At a risk threshold of 4.36%, positive predictive value (PPV) was 11.0% and sensitivity was 47.2%. The best model for predicting suicide was an ensemble of predictions from random forest, XGBoost and catBoost. For suicide, the ROC-AUC was 0.71 (95% CI: 0.70–0.73). At a risk threshold of 0.15%, PPV was 0.34% and sensitivity was 56.0%. The most contributing predictors differed when predicting suicide and suicide attempt, indicating that separate models are needed. The ensemble model was fair across sex and age, and more so than the penalized logistic regression model.

Conclusions

We achieved good performance for predicting suicide attempts and demonstrated a clinical application of ensemble models. Our results indicate a difference in predictive performance for models predicting suicide and suicide attempt, respectively. Thus, we recommend that suicide and suicide attempt are treated as two separate endpoints, in particular for clinical application. We demonstrated that the ensemble model is fairer across sex and age compared with a penalized logistic regression, and therefore we recommend the use of well-tested ensembles despite a more complex explainability.