Trajectories of traumatic stress symptoms during conflict: A latent class growth analysis

Greene, Talya; Gelkopf, Marc; Grinapol, Shulamit; Werbeloff, Nomi; Carlson, Eve B.; Lapid, Liron

doi:10.1016/j.jad.2017.05.036

Cited by 31 publications

(30 citation statements)

References 63 publications

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“…Despite the limitations of a single definition of resilience, the literature as a whole continues to examine and quantify resilience using a single definition, most commonly the presence/absence of PTSD symptoms (e.g. Bryant et al, 2015; Greene et al, 2017; Polousny et al, 2017). While useful, given that trauma is a transdiagnostic factor, this approach does not capture other, important patterns of responding.…”

Section: Discussionmentioning

confidence: 99%

“…Examination of resilience from multiple perspectives, as compared to absence of PTSD symptoms (e.g. Bonanno, Galea, Bucciarelli, & Vlahov, 2006; Greene et al, 2017; Polousny et al, 2017) or high self-reported resilience coping (e.g. Green, Beckham, Youssef, & Elbogen, 2014; Pietrzak et al, 2010) in isolation, will be important for practitioners and the military for purposes of prevention and intervention programming, as well as assessment of redeployment readiness.…”

Section: Discussionmentioning

confidence: 99%

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Exploring resilience models in a sample of combat-exposed military service members and veterans: a comparison and commentary

Sheerin

Stratton

Amstadter

et al. 2018

European Journal of Psychotraumatology

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Background: The term resilience is applied in numerous ways in the mental health field, leading to different perspectives of what constitutes a resilient response and disparate findings regarding its prevalence following trauma. Objective: illustrate the impact of various definitions on our understanding and prevalence of resilience, we compared various resilience definitions (absence of PTSD, absence of current mental health diagnosis, absence of generalized psychological distress, and an alternative trauma load–resilience discrepancy model of the difference between actual and predicted distress given lifetime trauma exposure) within a combat-exposed military personnel and veteran sample. Method: In this combat-trauma exposed sample (N = 849), of which approximately half were treatment seeking, rates of resilience were determined across all models, the kappa statistic was used to determine the concordance and strength of association across models, and t-tests examined the models in relation to a self-reported resilience measure. Results: Prevalence rates were 43.7%, 30.7%, 87.4%, and 50.1% in each of the four models. Concordance analyses identified 25.7% (n = 218) considered resilient by all four models (kappa = .40, p < .001). Correlations between models and self-reported resilience were strong, but did not fully overlap. Conclusions:The discussion highlights theoretical considerations regarding the impact of various definitions and methodologies on resilience classifications, links current findings to a systems-based perspective, and ends with suggestions for future research approaches on resilience.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Exploring resilience models in a sample of combat-exposed military service members and veterans: a comparison and commentary

Sheerin

Stratton

Amstadter

et al. 2018

European Journal of Psychotraumatology

View full text Add to dashboard Cite

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“…The present paper utilized data collected within a large prospective ESM study conducted during and after the Israel–Gaza conflict that took place in July and August 2014 (Gelkopf et al., ; Greene, ; Greene et al., ; Lapid Pickman, Greene, & Gelkopf, ). During the 50‐day conflict, rockets and mortar shells were fired at Israeli communities from Gaza, and the Israel Defence Forces conducted ground and air attacks on Gaza.…”

Section: Methodsmentioning

confidence: 99%

Dynamic Network Analysis of Negative Emotions and DSM‐5 Posttraumatic Stress Disorder Symptom Clusters During Conflict

Greene

Gelkopf

Fried

et al. 2019

Journal of Traumatic Stress

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Investigating dynamic associations between specific negative emotions and PTSD symptom clusters may provide novel insights into the ways in which PTSD symptoms interact with, emerge from, or are reinforced by negative emotions. The present study estimated the associations among negative emotions and the four DSM‐5 PTSD symptom clusters (intrusions, avoidance, negative alterations in cognitions and mood [NACM], and arousal) in a sample of Israeli civilians (n = 96) during the Israel–Gaza War of July–August 2014. Data were collected using experience sampling methodology, with participants queried via smartphone about PTSD symptoms and negative emotions twice a day for 30 days. We used a multilevel vector auto‐regression model to estimate temporal and contemporaneous temporal networks. Contrary to our hypothesis, in the temporal network, PTSD symptom clusters were more predictive of negative emotions than vice versa, with arousal emerging as the strongest predictor that negative emotions would be reported at the next measurement point; fear and sadness were also strong predictors of PTSD symptom clusters. In the contemporaneous network, negative emotions exhibited the strongest associations with the NACM and arousal PTSD symptom clusters. The negative emotions of sadness, stress, fear, and loneliness had the strongest associations to the PTSD symptom clusters. Our findings suggest that arousal has strong associations to both PTSD symptoms and negative emotions during ongoing trauma and highlights the potentially relevant role of arousal for future investigations in primary or early interventions.

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“…Analyses performed on the same data set as was used in the current study (Gelkopf et al., Greene et al., ; Lapid Pickman, Greene, & Gelkopf, ) have also suggested that the dynamics of P‐PTSS in civilian populations are affected by the level of objective threat (e.g., sounding of sirens at the time of incoming rocket fire). The aforementioned studies, as well as others, have also suggested gender (Freedman et al., ); a history of severe mental health problems, including poor emotional self‐regulation (Koenen, ), elevated perceived threat (Conway, Starr, Espejo, & Brennan, ), and weakened ability for contextual integration of information (Holmes & Steel, ); and trauma history (Goslin, Stover, Berkowitz, & Marans, ; Harvey & Bryant, ) as risk factors for acute stress reactions after a traumatic event.…”

mentioning

confidence: 71%

The Dynamic Relations Among Peritraumatic Posttraumatic Stress Symptoms: An Experience Sampling Study During Wartime

Gelkopf

Pickman

Carlson

et al. 2019

Journal of Traumatic Stress

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The associations among peritraumatic posttraumatic stress symptoms (P‐PTSS) in the immediate aftermath of trauma exposure, including those in the posttraumatic stress disorder clusters of intrusions, avoidance, negative cognitions and mood (NCM), and arousal, might indicate mechanisms through which enduring PTSD develops. During a period of war, exposed participants (N = 181) were sent twice‐daily questionnaires for 30 days via smartphone. We repeatedly assessed the predictive associations between the P‐PTSS clusters over time. We performed a multilevel pathway analysis built of multiple triple sequence responses (6,221 cases) on each of the four P‐PTSS clusters at a mean time lag of 12 hr (Model A) and 24 hr (Model B) for 181 participants, 85 of whom had been diagnosed with a serious mental illness. Arousal predicted intrusion in Models A and B, bA = 0.08, 95% CI [0.03, 0.12], p < .001 and bB = 0.03, 95% CI [0.00, 0.07], p = .051, respectively; and NCM in Models A and B, bA = 0.09, 95% CI [0.05, 0.12], p < .001 and bB = 0.06, 95% CI [0.03, 0.09], p < .001, respectively. Intrusion predicted arousal in Model B, bB = 0.05, 95% CI [0.01, 0.08], p = .010. NCM predicted arousal, bA = 0.10, 95% CI [0.05, 0.14], p < .001, and avoidance bA = 0.05, 95% CI [0.00, 0.11], p = .052, in Model A. Avoidance did not predict any other cluster. Arousal seemed to be acting as a hub, strengthening feedback loops to and from NCM and intrusion.

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Trajectories of traumatic stress symptoms during conflict: A latent class growth analysis

Cited by 31 publications

References 63 publications

Exploring resilience models in a sample of combat-exposed military service members and veterans: a comparison and commentary

Exploring resilience models in a sample of combat-exposed military service members and veterans: a comparison and commentary

Dynamic Network Analysis of Negative Emotions and DSM‐5 Posttraumatic Stress Disorder Symptom Clusters During Conflict

The Dynamic Relations Among Peritraumatic Posttraumatic Stress Symptoms: An Experience Sampling Study During Wartime

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