IMPORTANCE Understanding suicide ideation (SI) during combat deployment can inform prevention and treatment during and after deployment. OBJECTIVE To examine associations of sociodemographic characteristics, lifetime and past-year stressors, and mental disorders with 30-day SI among a representative sample of US Army soldiers deployed in Afghanistan. DESIGN, SETTING, AND PARTICIPANTS In this survey study, soldiers deployed to Afghanistan completed self-administered questionnaires in July 2012. The sample was weighted to represent all 87 032 soldiers serving in Afghanistan. Prevalence of lifetime, past-year, and 30-day SI and mental disorders was determined. Logistic regression analyses examined risk factors associated with SI. Data analyses for this study were conducted between August 2018 and August 2019. MAIN OUTCOMES AND MEASURES Suicide ideation, lifetime and 12-month stressors, and mental disorders were assessed with questionnaires. Administrative records identified sociodemographic characteristics and suicide attempts. RESULTS A total of 3957 soldiers (3473 [weighted 87.5%] male; 2135 [weighted 52.6%] aged Յ29 years) completed self-administered questionnaires during their deployment in Afghanistan. Lifetime, past-year, and 30-day SI prevalence estimates were 11.7%, 3.0%, and 1.9%, respectively. Among soldiers with SI, 44.2% had major depressive disorder (MDD) and 19.3% had posttraumatic stress disorder in the past 30-day period. A series of analyses of the 23 grouped variables potentially associated with SI resulted in a final model of sex; race/ethnicity; lifetime noncombat trauma; past 12-month relationship problems, legal problems, and death or illness of a friend or family member; and MDD. In this final multivariable model, white race/ethnicity (odds ratio [OR], 3.1 [95% CI, 1.8-5.1]), lifetime noncombat trauma (OR, 2.1 [95% CI, 1.1-4.0]), and MDD (past 30 days: OR, 31.8 [95% CI, 15.0-67.7]; before past 30 days: OR, 4.9 [95% CI, 2.5-9.6]) were associated with SI. Among the 85 soldiers with past 30-day SI, from survey administration through 12 months after returning from deployment, 6% (5 participants) had a documented suicide attempt vs 0.14% (6 participants) of the 3872 soldiers without SI. CONCLUSIONS AND RELEVANCE This study suggests that major depressive disorder and noncombat trauma are important factors in identifying SI risk during combat deployment.

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Objectives. To examine opioid-related outcomes by using hospitalization and mortality data as an indicator of the current opioid crisis in West Virginia. Methods. We used data from the West Virginia University Medicine health care system to examine the trend in opioid overdoses and percentage of patients with a repeat overdose from 2008 to 2016. We obtained the opioid overdose death rate for the state from Centers for Disease Control and Prevention WONDER (Wide-ranging ONline Data for Epidemiologic Research) mortality data for 2008 to 2016. Results. The hospitalization rate for opioid overdoses increased (13%) on average each year in a similar fashion to the opioid overdose death rate for the state (12%) between 2008 and 2016. During the same time, the percentage of patients with a repeat opioid overdose increased annually by 13% on average. Conclusions. There continues to be a surge of opioid overdoses in West Virginia. These findings suggest a need to amplify comprehensive prevention and treatment efforts throughout the state. Public health initiatives to reduce the morbidity and mortality associated with overdoses should consider how the changes in potency may be influencing these outcomes.

Objective Evaluate factors associated with treatment delays and their effect on survival in laryngeal squamous cell carcinoma. Study Design Retrospective cohort study. Setting National Cancer Database. Methods Patients receiving primary radiation or surgery for laryngeal squamous cell carcinoma were included from 2004 to 2017. The primary outcomes were the diagnosis-to-treatment interval (DTI) and 5-year survival. Variables of prolonged DTI (>30 days) were assessed via logistic regression models. Survival was then assessed through Cox proportional hazards models. Candidate variables for both outcomes included age, sex, race, ethnicity, distance to treatment facility, insurance coverage, treatment facility type, TNM T stage, nodal status, and DTI (in models estimating survival). Results An overall 136,203 patients with laryngeal cancer were identified, from which 51,747 remained after exclusions were applied: 18,499 received primary surgery and 33,248 received primary radiation. Being a member of a racial or ethnic minority, advanced age, female sex, ≥30 miles from treatment facility, lack of insurance, treatment at an academic cancer center, and primary radiation were associated with a prolonged DTI. However, in spite of a faster DTI, treatment at a community cancer center was independently associated with higher mortality (hazard ratio, 1.2; P < .0001). Conclusions Despite being associated with prolonged DTI, receiving treatment at a high-volume academic facility was associated with significantly improved survival. Our results indicate that improved referral pathways or outreach may help improve survival in laryngeal cancer, especially in high-risk populations.

This issue contains a thoughtful report by Gradus et al. on a machine learning (ML) analysis of administrative variables to predict suicide attempts over two decades throughout Denmark. This is one of numerous recent studies that document strong concentration of risk of suicide-related behaviors (SRBs) among patients with high scores on ML models. The clear exposition of Gradus et al. provides an opportunity to review major challenges in developing, interpreting, and using such models: defining appropriate controls and time horizons, selecting comprehensive predictors, dealing with imbalanced outcomes, choosing classifiers, tuning hyperparameters, evaluating predictor variable importance, and evaluating operating characteristics. We close by calling for ML SRB research to move beyond merely demonstrating significant prediction, as this is by now well established, and to focus instead on using such models to target specific preventive interventions and to develop individualized treatment rules that can be used to help guide clinical decisions that address the growing problems of suicide attempts, suicide deaths, and other injuries and deaths in the same spectrum.