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Rosellini, Anthony

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Rosellini

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Anthony

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Rosellini, Anthony
Author's Publications: search.filters.isAuthorOfPublication.be72128c-82d1-4c65-bdb3-0c654081ccd2

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    Mental Disorders, Comorbidity, and Pre-enlistment Suicidal Behavior Among New Soldiers in the U.S. Army: Results from the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS)
    (Wiley-Blackwell, 2015) Nock, Matthew; Ursano, Robert J.; Heeringa, Steven G.; Stein, Murray B.; Jain, Sonia; Raman, Rema; Sun, Xiaoying; Chiu, Wai; Colpe, Lisa J.; Fullerton, Carol S.; Gilman, Stephen Edward; Hwang, Irving; Naifeh, James A.; Rosellini, Anthony; Sampson, Nancy; Schoenbaum, Michael; Zaslavsky, Alan; Kessler, Ronald
    We examined the associations between mental disorders and suicidal behavior (ideation, plans, and attempts) among new soldiers using data from the New Soldier Study (NSS) component of the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS; n=38,507). Most new soldiers with a pre-enlistment history of suicide attempt reported a prior mental disorder (59.0%). Each disorder examined was associated with increased odds of suicidal behavior (ORs=2.6–8.6). Only PTSD and disorders characterized by irritability and impulsive/aggressive behavior (i.e., bipolar disorder, conduct disorder, oppositional defiant disorder, and attention-deficit/hyperactivity disorder) predicted unplanned attempts among ideators. Mental disorders are important predictors of pre-enlistment suicidal behavior among new soldiers and should figure prominently in suicide screening and prevention efforts.
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    Prevalence and correlates of suicidal behavior among new soldiers in the US Army: results from the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS)
    (Wiley-Blackwell, 2014) Ursano, Robert J.; Heeringa, Steven G.; Stein, Murray B.; Jain, Sonia; Raman, Rema; Sun, Xiaoying; Chiu, Wai; Colpe, Lisa J.; Fullerton, Carol S.; Gilman, Stephen Edward; Hwang, Irving; Naifeh, James A.; Nock, Matthew; Rosellini, Anthony; Sampson, Nancy; Schoenbaum, Michael; Zaslavsky, Alan; Kessler, Ronald
    Background The prevalence of suicide among U.S. Army soldiers has risen dramatically in recent years. Prior studies suggest that most soldiers with suicidal behaviors (i.e., ideation, plans, and attempts) had first onsets prior to enlistment. However, those data are based on retrospective self-reports of soldiers later in their Army careers. Unbiased examination of this issue requires investigation of suicidality among new soldiers. Method The New Soldier Study (NSS) of the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS) used fully structured self-administered measures to estimate preenlistment histories of suicide ideation, plans, and attempts among new soldiers reporting for Basic Combat Training in 2011–2012. Survival models examined sociodemographic correlates of each suicidal outcome. Results Lifetime prevalence estimates of preenlistment suicide ideation, plans, and attempts were 14.1, 2.3, and 1.9%, respectively. Most reported onsets of suicide plans and attempts (73.3–81.5%) occurred within the first year after onset of ideation. Odds of these lifetime suicidal behaviors among new soldiers were positively, but weakly associated with being female, unmarried, religion other than Protestant or Catholic, and a race/ethnicity other than non-Hispanic White, non-Hispanic Black, or Hispanic. Conclusions Lifetime prevalence estimates of suicidal behaviors among new soldiers are consistent with retrospective reports of preenlistment prevalence obtained from soldiers later in their Army careers. Given that prior suicidal behaviors are among the strongest predictors of later suicides, consideration should be given to developing methods of obtaining valid reports of preenlistment suicidality from new soldiers to facilitate targeting of preventive interventions.
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    Lifetime Prevalence of Dsm-Iv Mental Disorders Among New Soldiers in the U.S. Army: Results From the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS)
    (Wiley-Blackwell, 2014) Rosellini, Anthony; Heeringa, Steven G.; Stein, Murray B.; Ursano, Robert J.; Chiu, Wai; Colpe, Lisa J.; Fullerton, Carol S.; Gilman, Stephen Edward; Hwang, Irving; Naifeh, James A.; Nock, Matthew; Petukhova, Maria; Sampson, Nancy; Schoenbaum, Michael; Zaslavsky, Alan; Kessler, Ronald
    Background The prevalence of 30-day mental disorders with retrospectively-reported early onsets is significantly higher in the U.S. Army than among socio-demographically matched civilians. This difference could reflect high prevalence of pre-enlistment disorders and/or high persistence of these disorders in the context of the stresses associated with military service. These alternatives can to some extent be distinguished by estimating lifetime disorder prevalence among new Army recruits. Methods The New Soldier Study (NSS) in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS) used fully-structured measures to estimate lifetime prevalence of 10 DSM-IV disorders in new soldiers reporting for Basic Combat Training in 2011-2012 (n=38,507). Prevalence was compared to estimates from a matched civilian sample. Multivariate regression models examined socio-demographic correlates of disorder prevalence and persistence among new soldiers. Results Lifetime prevalence of having at least one internalizing, externalizing, or either type of disorder did not differ significantly between new soldiers and civilians, although three specific disorders (generalized anxiety, posttraumatic stress, and conduct disorders) and multi-morbidity were significantly more common among new soldiers than civilians. Although several socio-demographic characteristics were significantly associated with disorder prevalence and persistence, these associations were uniformly weak. Conclusions New soldiers differ somewhat, but not consistently, from civilians in lifetime pre-enlistment mental disorders. This suggests that prior findings of higher prevalence of current disorders with pre-enlistment onsets among soldiers than civilians are likely due primarily to a more persistent course of early-onset disorders in the context of the special stresses experienced by Army personnel.
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    Occupational differences in US Army suicide rates
    (Cambridge University Press (CUP), 2015) Kessler, Ronald; Stein, M. B.; Bliese, P. D.; Bromet, E. J.; Chiu, Wai; Cox, K. L.; Colpe, L. J.; Fullerton, C. S.; Gilman, Stephen Edward; Gruber, Michaela; Heeringa, S. G.; Lewandowski-Romps, L.; Millikan-Bell, A.; Naifeh, J. A.; Nock, Matthew; Petukhova, Maria; Rosellini, Anthony; Sampson, Nancy; Schoenbaum, M.; Zaslavsky, Alan; Ursano, R. J.
    Background Civilian suicide rates vary by occupation in ways related to occupational stress exposure. Comparable military research finds suicide rates elevated in combat arms occupations. However, no research has evaluated variation in this pattern by deployment history, the indicator of occupation stress widely considered responsible for the recent rise in the military suicide rate. Method The joint associations of Army occupation and deployment history in predicting suicides were analysed in an administrative dataset for the 729 337 male enlisted Regular Army soldiers in the US Army between 2004 and 2009. Results There were 496 suicides over the study period (22.4/100 000 person-years). Only two occupational categories, both in combat arms, had significantly elevated suicide rates: infantrymen (37.2/100 000 person-years) and combat engineers (38.2/100 000 person-years). However, the suicide rates in these two categories were significantly lower when currently deployed (30.6/100 000 person-years) than never deployed or previously deployed (41.2–39.1/100 000 person-years), whereas the suicide rate of other soldiers was significantly higher when currently deployed and previously deployed (20.2–22.4/100 000 person-years) than never deployed (14.5/100 000 person-years), resulting in the adjusted suicide rate of infantrymen and combat engineers being most elevated when never deployed [odds ratio (OR) 2.9, 95% confidence interval (CI) 2.1–4.1], less so when previously deployed (OR 1.6, 95% CI 1.1–2.1), and not at all when currently deployed (OR 1.2, 95% CI 0.8–1.8). Adjustment for a differential ‘healthy warrior effect’ cannot explain this variation in the relative suicide rates of never-deployed infantrymen and combat engineers by deployment status. Conclusions Efforts are needed to elucidate the causal mechanisms underlying this interaction to guide preventive interventions for soldiers at high suicide risk.
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    Understanding the elevated suicide risk of female soldiers during deployments
    (Cambridge University Press (CUP), 2014) Street, A. E.; Gilman, Stephen Edward; Rosellini, Anthony; Stein, M. B.; Bromet, E. J.; Cox, K. L.; Colpe, L. J.; Fullerton, C. S.; Gruber, M; Heeringa, S. G.; Lewandowski-Romps, L.; Little, R. J. A.; Naifeh, J. A.; Nock, Matthew; Sampson, Nancy; Schoenbaum, M.; Ursano, R. J.; Zaslavsky, Alan; Kessler, Ronald
    Background The Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS) has found that the proportional elevation in the US Army enlisted soldier suicide rate during deployment (compared with the never-deployed or previously deployed) is significantly higher among women than men, raising the possibility of gender differences in the adverse psychological effects of deployment. Method Person-month survival models based on a consolidated administrative database for active duty enlisted Regular Army soldiers in 2004–2009 (n = 975 057) were used to characterize the gender × deployment interaction predicting suicide. Four explanatory hypotheses were explored involving the proportion of females in each soldier’s occupation, the proportion of same-gender soldiers in each soldier’s unit, whether the soldier reported sexual assault victimization in the previous 12 months, and the soldier’s pre-deployment history of treated mental/behavioral disorders. Results The suicide rate of currently deployed women (14.0/100 000 person-years) was 3.1–3.5 times the rates of other (i.e. never-deployed/previously deployed) women. The suicide rate of currently deployed men (22.6/100 000 person-years) was 0.9–1.2 times the rates of other men. The adjusted (for time trends, sociodemographics, and Army career variables) female:male odds ratio comparing the suicide rates of currently deployed v. other women v. men was 2.8 (95% confidence interval 1.1–6.8), became 2.4 after excluding soldiers with Direct Combat Arms occupations, and remained elevated (in the range 1.9–2.8) after adjusting for the hypothesized explanatory variables. Conclusions These results are valuable in excluding otherwise plausible hypotheses for the elevated suicide rate of deployed women and point to the importance of expanding future research on the psychological challenges of deployment for women.
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    Predicting Suicides After Psychiatric Hospitalization in US Army Soldiers
    (American Medical Association (AMA), 2015) Kessler, Ronald; Warner, Christopher H.; Ivany, Christopher; Petukhova, Maria; Rose, Sherri; Bromet, Evelyn J.; Brown, Millard; Cai, Tianxi; Colpe, Lisa J.; Cox, Kenneth L.; Fullerton, Carol S.; Gilman, Stephen Edward; Gruber, M; Heeringa, Steven G.; Lewandowski-Romps, Lisa; Li, Junlong; Millikan-Bell, Amy M.; Naifeh, James A.; Nock, Matthew K.; Rosellini, Anthony; Sampson, Nancy; Schoenbaum, Michael; Stein, Murray B.; Wessely, Simon; Zaslavsky, Alan; Ursano, Robert J.
    IMPORTANCE: The US Army experienced a sharp increase in soldier suicides beginning in 2004. Administrative data reveal that among those at highest risk are soldiers in the 12 months after inpatient treatment of a psychiatric disorder. OBJECTIVE: To develop an actuarial risk algorithm predicting suicide in the 12 months after US Army soldier inpatient treatment of a psychiatric disorder to target expanded posthospitalization care. DESIGN, SETTING, AND PARTICIPANTS: There were 53,769 hospitalizations of active duty soldiers from January 1, 2004, through December 31, 2009, with International Classification of Diseases, Ninth Revision, Clinical Modification psychiatric admission diagnoses. Administrative data available before hospital discharge abstracted from a wide range of data systems (sociodemographic, US Army career, criminal justice, and medical or pharmacy) were used to predict suicides in the subsequent 12 months using machine learning methods (regression trees and penalized regressions) designed to evaluate cross-validated linear, nonlinear, and interactive predictive associations. MAIN OUTCOMES AND MEASURES: Suicides of soldiers hospitalized with psychiatric disorders in the 12 months after hospital discharge. RESULTS: Sixty-eight soldiers died by suicide within 12 months of hospital discharge (12.0% of all US Army suicides), equivalent to 263.9 suicides per 100,000 person-years compared with 18.5 suicides per 100,000 person-years in the total US Army. The strongest predictors included sociodemographics (male sex [odds ratio (OR), 7.9; 95% CI, 1.9-32.6] and late age of enlistment [OR, 1.9; 95% CI, 1.0-3.5]), criminal offenses (verbal violence [OR, 2.2; 95% CI, 1.2-4.0] and weapons possession [OR, 5.6; 95% CI, 1.7-18.3]), prior suicidality [OR, 2.9; 95% CI, 1.7-4.9], aspects of prior psychiatric inpatient and outpatient treatment (eg, number of antidepressant prescriptions filled in the past 12 months [OR, 1.3; 95% CI, 1.1-1.7]), and disorders diagnosed during the focal hospitalizations (eg, nonaffective psychosis [OR, 2.9; 95% CI, 1.2-7.0]). A total of 52.9% of posthospitalization suicides occurred after the 5% of hospitalizations with highest predicted suicide risk (3824.1 suicides per 100,000 person-years). These highest-risk hospitalizations also accounted for significantly elevated proportions of several other adverse posthospitalization outcomes (unintentional injury deaths, suicide attempts, and subsequent hospitalizations). CONCLUSIONS AND RELEVANCE: The high concentration of risk of suicide and other adverse outcomes might justify targeting expanded posthospitalization interventions to soldiers classified as having highest posthospitalization suicide risk, although final determination requires careful consideration of intervention costs, comparative effectiveness, and possible adverse effects.
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    Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports
    (2015) Kessler, Ronald; van Loo, Hanna M.; Wardenaar, Klaas J.; Bossarte, Robert M.; Brenner, Lisa A.; Cai, Tianxi; Ebert, David Daniel; Hwang, Irving; Li, Junlong; de Jonge, Peter; Nierenberg, Andrew; Petukhova, Maria; Rosellini, Anthony; Sampson, Nancy; Schoevers, Robert A.; Wilcox, Marsha A.; Zaslavsky, Alan
    Heterogeneity of major depressive disorder (MDD) illness course complicates clinical decision-making. While efforts to use symptom profiles or biomarkers to develop clinically useful prognostic subtypes have had limited success, a recent report showed that machine learning (ML) models developed from self-reports about incident episode characteristics and comorbidities among respondents with lifetime MDD in the World Health Organization World Mental Health (WMH) Surveys predicted MDD persistence, chronicity, and severity with good accuracy. We report results of model validation in an independent prospective national household sample of 1,056 respondents with lifetime MDD at baseline. The WMH ML models were applied to these baseline data to generate predicted outcome scores that were compared to observed scores assessed 10–12 years after baseline. ML model prediction accuracy was also compared to that of conventional logistic regression models. Area under the receiver operating characteristic curve (AUC) based on ML (.63 for high chronicity and .71–.76 for the other prospective outcomes) was consistently higher than for the logistic models (.62–.70) despite the latter models including more predictors. 34.6–38.1% of respondents with subsequent high persistence-chronicity and 40.8–55.8% with the severity indicators were in the top 20% of the baseline ML predicted risk distribution, while only 0.9% of respondents with subsequent hospitalizations and 1.5% with suicide attempts were in the lowest 20% of the ML predicted risk distribution. These results confirm that clinically useful MDD risk stratification models can be generated from baseline patient self-reports and that ML methods improve on conventional methods in developing such models.