Dataset Information

Mental Health Over Time in a Military Sample: The Impact of Alcohol Use Disorder on Trajectories of Psychopathology After Deployment.

ABSTRACT: To identify trajectories of depression and posttraumatic stress (PTS) symptom groups after deployment and determine the effect of alcohol use disorder on these trajectories, depression symptoms were modeled using the 9-item Patient Health Questionnaire in 727 Ohio National Guard members, and PTS symptoms were modeled using the PTSD Checklist in 472 Ohio National Guard members. There were 55.8% who were resistant to depression symptoms across the 4 years of study, and 41.5% who were resistant to PTS symptoms. There were 18.7% and 42.2% of participants who showed resilience (experiencing slightly elevated symptoms followed by a decline, according to Bonanno et al., 2002) to depression and PTS symptoms, respectively. Mild and chronic dysfunction constituted the smallest trajectory groups across disorders. Marital status, deployment to an area of conflict, and number of lifetime stressors were associated with membership into different latent groups for depression (unstandardized ? estimates range = 0.69 to 1.37). Deployment to an area of conflict, number of lifetime traumatic events and education predicted membership into different latent groups for PTS (significant unstandardized ? estimate range = 0.83 to 3.17). AUD was associated with an increase in both symptom outcomes (significant unstandardized ? estimate range = 0.20 to 9.45). These results suggested that alcohol use disorder may have contributed substantially to trajectories of psychopathology in this population.

SUBMITTER: Sampson L

PROVIDER: S-EPMC4681498 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Mental Health Over Time in a Military Sample: The Impact of Alcohol Use Disorder on Trajectories of Psychopathology After Deployment.

Sampson Laura L Cohen Gregory H GH Calabrese Joseph R JR Fink David S DS Tamburrino Marijo M Liberzon Israel I Chan Philip P Galea Sandro S

Journal of traumatic stress 20151201 6

To identify trajectories of depression and posttraumatic stress (PTS) symptom groups after deployment and determine the effect of alcohol use disorder on these trajectories, depression symptoms were modeled using the 9-item Patient Health Questionnaire in 727 Ohio National Guard members, and PTS symptoms were modeled using the PTSD Checklist in 472 Ohio National Guard members. There were 55.8% who were resistant to depression symptoms across the 4 years of study, and 41.5% who were resistant to ...[more]

PMID: 26625353

Similar Datasets

Project description:Background and aimsAlcohol consumption changes markedly over the life course, with important implications for health and social development. Assessment of these patterns often relies on cross-sectional data, which cannot fully capture how individuals' drinking changes as they age. This study used data from 18 waves of a general population panel survey to measure drinking trajectories over the life course in Australia.Design and settingLongitudinal survey data from the Household, Income and Labour Dynamics in Australia (HILDA) survey between 2001 and 2018.ParticipantsA total of 20 593 individuals ages 15 or above in two samples assessing quantity-frequency (n = 20 569, 52.0% female) and risky single occasion drinking (RSOD), respectively, (n = 17 340, 52.5% female), interviewed as part of HILDA.MeasurementsUsual quantity of alcohol consumed per drinking occasion; frequency of drinking occasions per week; average daily consumption, calculated by combining reported usual quantity and frequency; and average reported frequency of RSOD per week.FindingsMultilevel, mixed effects models run with fractional polynomial terms found similar male and female alcohol consumption trajectories for quantity-frequency and RSOD measures. Usual quantity of alcohol consumed per drinking occasion (5.4 drinks for men, 3.8 for women) and RSOD frequency (0.56 occasions/week for men, 0.38 for women) peaked in young adulthood, whereas frequency of drinking occasions (2.5 occasions/week for men, 1.7 for women) peaked in middle age. Middle-age drinkers had the highest average daily consumption of alcohol (1.4 drinks/day for 54-year-old men, 0.6 drinks for 57-year-old women) and engaged in RSOD slightly less than young adults.ConclusionsAlcohol consumption in Australia appears to vary substantially over the life course, with usual quantity per drinking occasion and frequency of risky single occasion drinking peaking during early adulthood and average daily consumption and frequency of consumption peaking in middle age.

Project description:OBJECTIVE:The primary objective was to explore differences in mental health problems (MHP) between serving Canadian Armed Forces (CAF) components (Regular Force (RegF); Reserve Force (ResF)) with an Afghanistan deployment and to assess the contribution of both component and deployment experiences to MHP using covariate-adjusted prevalence difference estimates. Additionally, mental health services use (MHSU) was descriptively assessed among those with a mental disorder. DESIGN:Data came from the 2013 CAF Mental Health Survey, a cross-sectional survey of serving personnel (n=72?629). Analyses were limited to those with an Afghanistan deployment (population n=35?311; sampled n=4854). Logistic regression compared MHP between RegF and ResF members. Covariate-adjusted prevalence differences were computed. PRIMARY OUTCOME MEASURE:The primary outcomes were MHP, past-year mental disorders, identified using the WHO's Composite International Diagnostic Interview, and past-year suicide ideation. RESULTS:ResF personnel were less likely to be identified with a past-year anxiety disorder (adjusted OR (AOR)=0.72 (95% CI 0.58 to 0.90)), specifically both generalised anxiety disorder and panic disorder, but more likely to be identified with a past-year alcohol abuse disorder (AOR=1.63 (95% CI 1.04 to 2.58)). The magnitude of the covariate-adjusted disorder prevalence differences for component was highest for the any anxiety disorder outcome, 2.8% (95% CI 1.0 to 4.6); lower for ResF. All but one deployment-related experience variable had some association with MHP. The 'ever felt responsible for the death of a Canadian or ally personnel' experience had the strongest association with MHP; its estimated covariate-adjusted disorder prevalence difference was highest for the any (of the six measured) mental disorder outcome (11.2% (95% CI 6.6 to 15.9)). Additionally, ResF reported less past-year MHSU and more past-year civilian MHSU. CONCLUSIONS:Past-year MHP differences were identified between components. Our findings suggest that although deployment-related experiences were highly associated with MHP, these only partially accounted for MHP differences between components. Additional research is needed to further investigate MHSU differences between components.

Project description:ImportanceMilitary deployment involves significant risk for life-threatening experiences that can lead to posttraumatic stress disorder (PTSD). Accurate predeployment prediction of PTSD risk may facilitate the development of targeted intervention strategies to enhance resilience.ObjectiveTo develop and validate a machine learning (ML) model to predict postdeployment PTSD.Design, setting, and participantsThis diagnostic/prognostic study included 4771 soldiers from 3 US Army brigade combat teams who completed assessments between January 9, 2012, and May 1, 2014. Predeployment assessments occurred 1 to 2 months before deployment to Afghanistan, and follow-up assessments occurred approximately 3 and 9 months post deployment. Machine learning models to predict postdeployment PTSD were developed in the first 2 recruited cohorts using as many as 801 predeployment predictors from comprehensive self-report assessments. In the development phase, cross-validated performance metrics and predictor parsimony were considered to select an optimal model. Next, the selected model's performance was evaluated with area under the receiver operating characteristics curve and expected calibration error in a temporally and geographically distinct cohort. Data analyses were performed from August 1 to November 30, 2022.Main outcomes and measuresPosttraumatic stress disorder diagnosis was assessed by clinically calibrated self-report measures. Participants were weighted in all analyses to address potential biases related to cohort selection and follow-up nonresponse.ResultsThis study included 4771 participants (mean [SD] age, 26.9 [6.2] years), 4440 (94.7%) of whom were men. In terms of race and ethnicity, 144 participants (2.8%) identified as American Indian or Alaska Native, 242 (4.8%) as Asian, 556 (13.3%) as Black or African American, 885 (18.3%) as Hispanic, 106 (2.1%) as Native Hawaiian or other Pacific Islander, 3474 (72.2%) as White, and 430 (8.9%) as other or unknown race or ethnicity; participants could identify as of more than 1 race or ethnicity. A total of 746 participants (15.4%) met PTSD criteria post deployment. In the development phase, models had comparable performance (log loss range, 0.372-0.375; area under the curve range, 0.75-0.76). A gradient-boosting machine with 58 core predictors was selected over an elastic net with 196 predictors and a stacked ensemble of ML models with 801 predictors. In the independent test cohort, the gradient-boosting machine had an area under the curve of 0.74 (95% CI, 0.71-0.77) and low expected calibration error of 0.032 (95% CI, 0.020-0.046). Approximately one-third of participants with the highest risk accounted for 62.4% (95% CI, 56.5%-67.9%) of the PTSD cases. Core predictors cut across 17 distinct domains: stressful experiences, social network, substance use, childhood or adolescence, unit experiences, health, injuries, irritability or anger, personality, emotional problems, resilience, treatment, anxiety, attention or concentration, family history, mood, and religion.Conclusions and relevanceIn this diagnostic/prognostic study of US Army soldiers, an ML model was developed to predict postdeployment PTSD risk with self-reported information collected before deployment. The optimal model showed good performance in a temporally and geographically distinct validation sample. These results indicate that predeployment stratification of PTSD risk is feasible and may facilitate the development of targeted prevention and early intervention strategies.

Dataset Information

Mental Health Over Time in a Military Sample: The Impact of Alcohol Use Disorder on Trajectories of Psychopathology After Deployment.

Publications

Mental Health Over Time in a Military Sample: The Impact of Alcohol Use Disorder on Trajectories of Psychopathology After Deployment.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets