Risk of suicide-related behaviors is elevated among military personnel transitioning to civilian life. An earlier report showed that high-risk U.S. Army soldiers could be identified shortly before this transition with a machine learning model that included predictors from administrative systems, self-report surveys, and geospatial data. Based on this result, a Veterans Affairs and Army initiative was launched to evaluate a suicide-prevention intervention for high-risk transitioning soldiers. To make targeting practical, though, a streamlined model and risk calculator were needed that used only a short series of self-report survey questions.

We revised the original model in a sample of n = 8335 observations from the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS) who participated in one of three Army STARRS 2011–2014 baseline surveys while in service and in one or more subsequent panel surveys (LS1: 2016–2018, LS2: 2018–2019) after leaving service. We trained ensemble machine learning models with constrained numbers of item-level survey predictors in a 70% training sample. The outcome was self-reported post-transition suicide attempts (SA). The models were validated in the 30% test sample.

Twelve-month post-transition SA prevalence was 1.0% (s.e. = 0.1). The best constrained model, with only 17 predictors, had a test sample ROC-AUC of 0.85 (s.e. = 0.03). The 10–30% of respondents with the highest predicted risk included 44.9–92.5% of 12-month SAs.

An accurate SA risk calculator based on a short self-report survey can target transitioning soldiers shortly before leaving service for intervention to prevent post-transition SA.

The transition from military service to civilian life is a high-risk period for suicide attempts (SAs). Although stressful life events (SLEs) faced by transitioning soldiers are thought to be implicated, systematic prospective evidence is lacking.

Participants in the Army Study to Assess Risk and Resilience in Servicemembers (STARRS) completed baseline self-report surveys while on active duty in 2011–2014. Two self-report follow-up Longitudinal Surveys (LS1: 2016–2018; LS2: 2018–2019) were subsequently administered to probability subsamples of these baseline respondents. As detailed in a previous report, a SA risk index based on survey, administrative, and geospatial data collected before separation/deactivation identified 15% of the LS respondents who had separated/deactivated as being high-risk for self-reported post-separation/deactivation SAs. The current report presents an investigation of the extent to which self-reported SLEs occurring in the 12 months before each LS survey might have mediated/modified the association between this SA risk index and post-separation/deactivation SAs.

The 15% of respondents identified as high-risk had a significantly elevated prevalence of some post-separation/deactivation SLEs. In addition, the associations of some SLEs with SAs were significantly stronger among predicted high-risk than lower-risk respondents. Demographic rate decomposition showed that 59.5% (s.e. = 10.2) of the overall association between the predicted high-risk index and subsequent SAs was linked to these SLEs.

It might be possible to prevent a substantial proportion of post-separation/deactivation SAs by providing high-risk soldiers with targeted preventive interventions for exposure/vulnerability to commonly occurring SLEs.

Personal protective equipment (PPE) is a critical need during the coronavirus disease 2019 (COVID-19) pandemic. Alternative sources of surgical masks, including 3-dimensionally (3D) printed approaches that may be reused, are urgently needed to prevent PPE shortages. Few data exist identifying decontamination strategies to inactivate viral pathogens and retain 3D-printing material integrity.

To test viral disinfection methods on 3D-printing materials.

The viricidal activity of common disinfectants (10% bleach, quaternary ammonium sanitizer, 3% hydrogen peroxide, or 70% isopropanol and exposure to heat (50°C, and 70°C) were tested on four 3D-printed materials used in the healthcare setting, including a surgical mask design developed by the Veterans’ Health Administration. Inactivation was assessed for several clinically relevant RNA and DNA pathogenic viruses, including severe acute respiratory coronavirus virus 2 (SARS-CoV-2) and human immunodeficiency virus 1 (HIV-1).

SARS-CoV-2 and all viruses tested were completely inactivated by a single application of bleach, ammonium quaternary compounds, or hydrogen peroxide. Similarly, exposure to dry heat (70°C) for 30 minutes completely inactivated all viruses tested. In contrast, 70% isopropanol reduced viral titers significantly less well following a single application. Inactivation did not interfere with material integrity of the 3D-printed materials.

Several standard decontamination approaches effectively disinfected 3D-printed materials. These approaches were effective in the inactivation SARS-CoV-2, its surrogates, and other clinically relevant viral pathogens. The decontamination of 3D-printed surgical mask materials may be useful during crisis situations in which surgical mask supplies are limited.