Predicting suicides after outpatient mental health visits in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS).

The 2013 US Veterans Administration/Department of Defense Clinical Practice Guidelines (VA/DoD CPG) require comprehensive suicide risk assessments for VA/DoD patients with mental disorders but provide minimal guidance on how to carry out these assessments. Given that clinician-based assessments are not known to be strong predictors of suicide, we investigated whether a precision medicine model using administrative data after outpatient mental health specialty visits could be developed to predict suicides among outpatients. We focused on male nondeployed Regular US Army soldiers because they account for the vast majority of such suicides. Four machine learning classifiers (naive Bayes, random forests, support vector regression and elastic net penalized regression) were explored. Of the Army suicides in 2004-2009, 41.5% occurred among 12.0% of soldiers seen as outpatient by mental health specialists, with risk especially high within 26 weeks of visits. An elastic net classifier with 10-14 predictors optimized sensitivity (45.6% of suicide deaths occurring after the 15% of visits with highest predicted risk). Good model stability was found for a model using 2004-2007 data to predict 2008-2009 suicides, although stability decreased in a model using 2008-2009 data to predict 2010-2012 suicides. The 5% of visits with highest risk included only 0.1% of soldiers (1047.1 suicides/100 000 person-years in the 5 weeks after the visit). This is a high enough concentration of risk to have implications for targeting preventive interventions. An even better model might be developed in the future by including the enriched information on clinician-evaluated suicide risk mandated by the VA/DoD CPG to be recorded

Cigarette smoking, cytochrome P4501A1 polymorphisms, and breast cancer among African-American and white women

INTRODUCTION: Previous epidemiologic studies suggest that women with variant cytochrome P4501A1 (CYP1A1) genotypes who smoke cigarettes are at increased risk for breast cancer. METHODS: We evaluated the association of breast cancer with CYP1A1 polymorphisms and cigarette smoking in a population-based, case–control study of invasive breast cancer in North Carolina. The study population consisted of 688 cases (271 African Americans and 417 whites) and 702 controls (285 African Americans and 417 whites). Four polymorphisms in CYP1A1 were genotyped using PCR/restriction fragment length polymorphism analysis: M1 (also known as CYP1A1*2A), M2 (CYP1A1*2C), M3 (CYP1A1*3), and M4 (CYP1A1*4) RESULTS: No associations were observed for CYP1A1 variant alleles and breast cancer, ignoring smoking. Among women who smoked for longer than 20 years, a modest positive association was found among women with one or more M1 alleles (odds ratio [OR] = 2.1, 95% confidence interval [CI] = 1.2–3.5) but not among women with non-M1 alleles (OR = 1.2, 95% CI = 0.9–1.6). Odds ratios for smoking longer than 20 years were higher among African-American women with one or more M3 alleles (OR = 2.5, 95% CI = 0.9–7.1) compared with women with non-M3 alleles (OR = 1.3, 95% CI = 0.8–2.2). ORs for smoking in white women did not differ appreciably based upon M2 or M4 genotypes. CONCLUSIONS: Cigarette smoking increases breast cancer risk in women with CYP1A1 M1 variant genotypes and in African-American women with CYP1A1 M3 variant genotypes, but the modifying effects of the CYP1A1 genotype are quite weak

Polychlorinated biphenyls, cytochrome P450 1A1 (CYP1A1) polymorphisms, and breast cancer risk among African American women and white women in North Carolina: a population-based case-control study

INTRODUCTION: Epidemiologic studies have not shown a strong relationship between blood levels of polychlorinated biphenyls (PCBs) and breast cancer risk. However, two recent studies showed a stronger association among postmenopausal white women with the inducible M2 polymorphism in the cytochrome P450 1A1 (CYP1A1) gene. METHODS: In a population-based case-control study, we evaluated breast cancer risk in relation to PCBs and the CYP1A1 polymorphisms M1 (also known as CYP1A1*2A), M2 (CYP1A1*2C), M3 (CYP1A1*3), and M4 (CYP1A1*4). The study population consisted of 612 patients (242 African American, 370 white) and 599 controls (242 African American, 357 white). RESULTS: There was no evidence of strong joint effects between CYP1A1 M1-containing genotypes and total PCBs in African American or white women. Statistically significant multiplicative interactions were observed between CYP1A1 M2-containing genotypes and elevated plasma total PCBs among white women (P value for likelihood ratio test = 0.02). Multiplicative interactions were also observed between CYP1A1 M3-containing genotypes and elevated total PCBs among African American women (P value for likelihood ratio test = 0.10). CONCLUSIONS: Our results confirm previous reports that CYP1A1 M2-containing genotypes modify the association between PCB exposure and risk of breast cancer. We present additional evidence suggesting that CYP1A1 M3-containing genotypes modify the effects of PCB exposure among African American women. Additional studies are warranted, and meta-analyses combining results across studies will be needed to generate more precise estimates of the joint effects of PCBs and CYP1A1 genotypes

Sociodemographic and career history predictors of suicide mortality in the United States Army 2004–2009

The US Army suicide rate has increased sharply in recent years. Identifying significant predictors of Army suicides in Army and Department of Defense (DoD) administrative records might help focus prevention efforts and guide intervention content. Previous studies of administrative data, although documenting significant predictors, were based on limited samples and models. A career history perspective is used here to develop more textured models. The analysis was carried out as part of the Historical Administrative Data Study (HADS) of the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS). De-identified data were combined across numerous Army and DoD administrative data systems for all Regular Army soldiers on active duty in 2004–2009. Multivariate associations of sociodemographics and Army career variables with suicide were examined in subgroups defined by time in service, rank and deployment history. Several novel results were found that could have intervention implications. The most notable of these were significantly elevated suicide rates (69.6–80.0 suicides per 100 000 person-years compared with 18.5 suicides per 100 000 person-years in the total Army) among enlisted soldiers deployed either during their first year of service or with less than expected (based on time in service) junior enlisted rank; a substantially greater rise in suicide among women than men during deployment; and a protective effect of marriage against suicide only during deployment. A career history approach produces several actionable insights missed in less textured analyses of administrative data predictors. Expansion of analyses to a richer set of predictors might help refine understanding of intervention implications.Psycholog

Occupational differences in US Army suicide rates

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.Psycholog

Predicting Suicides After Psychiatric Hospitalization in US Army Soldiers

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

Comprehensive analysis of the ATM, CHEK2 and ERBB2 genes in relation to breast tumour characteristics and survival: a population-based case-control and follow-up study

BACKGROUND: Mutations in the ataxia-telangiectasia mutated (ATM) and checkpoint kinase 2 (CHEK2) genes and amplification of the v-erb-b2 avian erythroblastic leukemia viral oncogene homolog 2 (ERBB2) gene have been suggested to have an important role in breast cancer aetiology. However, whether common variation in these genes has a role in the development of breast cancer or breast cancer survival in humans is still not clear. METHODS: We performed a comprehensive haplotype analysis of the ATM, CHEK2 and ERBB2 genes in a Swedish population-based study, which included 1,579 breast cancer cases and 1,516 controls. We followed the cases for 8.5 years, on average, and retrieved information on the date and cause of death during that period from the nationwide Swedish causes of death registry. We selected seven haplotype-tagging SNPs (tagSNPs) in the ATM gene, six tagSNPs in the CHEK2 gene and seven tagSNPs in the ERBB2 gene that predicted both haplotypic and single locus variations in the respective genes with R(2 )values ≥ 0.8. These tagSNPs were genotyped in the complete set of cases and controls. We computed expected haplotype dosages of the tagSNP haplotypes and included the dosages as explanatory variables in Cox proportional hazards or logistic regression models. RESULTS: We found no association between any genetic variation in the ATM, CHEK2 or ERBB2 genes and breast cancer survival or the risk of developing tumours with certain characteristics. CONCLUSION: Our results indicate that common variants in the ATM, CHEK2 or ERBB2 genes are not involved in modifying breast cancer survival or the risk of tumour-characteristic-defined breast cancer