Nicotine Dependence and Pre-Enlistment Suicidal Behavior Among U.S. Army Soldiers

Suicidal Behaviors Among Active-Duty US Service Members: Data from the 2018 Health-Related Behaviors Survey

Background

This study aimed to 1) determine the prevalence of past-year suicidal ideation (SI) and attempts (SA) among active-duty SMs; 2) determine whether differences exist by age, sex, and race; and 3) assess whether prevalence estimates vary by risk profiles of mental health conditions and substance use.

Methods

Data were from the 2018 Health-Related Behavior Survey (HRBS), a cross-sectional survey of active-duty SMs (n = 17,166). We used the logistic model to identify the factors of SI and SA and latent class analysis (LCA) to identify the risk profiles.

Results

Among active duty SMs, 8.26% had SI and 1.25% had SA in the past year. Gender and age have been shown to influence how race might contribute to suicidal behaviors. Mental health conditions were associated with higher odds of SI and SA, as were younger ages; LGB identity; being separated, divorced, or widowed; use of e-cigarettes, dual use of e-cigarettes and cigarettes, or drugs; and history of deployment of less than 12 months. Frequencies of cigarette and e-cigarette use were also associated with SI and SA, indicating the odds were increasing by 0.3% for every additional cigarette or e-cigarette used. Five risk profiles were identified: class 1 (illegal drug use), class 2 (mental health needs with tobacco and alcohol use), class 3 (mental health conditions only), class 4 ("low risk" SMs with low levels of illegal drug use, mental health visits, tobacco use, and alcohol use), and class 5 (alcohol use). Compared to class 4 ("low risk"), all other risk profiles were associated with increased odds of suicidal behaviors.

Conclusion

Despite the resources and increased access provided for mental health support, the prevalence of SI among active-duty SMs is greater than in the general population of the same age, likely due to additional military exposures and stressors.

Identifying long-term and imminent suicide predictors in a general population and a clinical sample with machine learning

BACKGROUND

Machine learning (ML) is increasingly used to predict suicide deaths but their value for suicide prevention has not been established. Our first objective was to identify risk and protective factors in a general population. Our second objective was to identify factors indicating imminent suicide risk.

METHODS

We used survival and ML models to identify lifetime predictors using the Cohort of Norway (n=173,275) and hospital diagnoses in a Saskatoon clinical sample (n=12,614). The mean follow-up times were 17 years and 3 years for the Cohort of Norway and Saskatoon respectively. People in the clinical sample had a longitudinal record of hospital visits grouped in six-month intervals. We developed models in a training set and these models predicted survival probabilities in held-out test data.

RESULTS

In the general population, we found that a higher proportion of low-income residents in a county, mood symptoms, and daily smoking increased the risk of dying from suicide in both genders. In the clinical sample, the only predictors identified were male gender and older age.

CONCLUSION

Suicide prevention probably requires individual actions with governmental incentives. The prediction of imminent suicide remains highly challenging, but machine learning can identify early prevention targets.