Predicting non-familial major physical violent crime perpetration in the US Army from administrative data.

BACKGROUND: Although interventions exist to reduce violent crime, optimal implementation requires accurate targeting. We report the results of an attempt to develop an actuarial model using machine learning methods to predict future violent crimes among US Army soldiers. METHOD: A consolidated administrative database for all 975 057 soldiers in the US Army in 2004-2009 was created in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS). Of these soldiers, 5771 committed a first founded major physical violent crime (murder-manslaughter, kidnapping, aggravated arson, aggravated assault, robbery) over that time period. Temporally prior administrative records measuring socio-demographic, Army career, criminal justice, medical/pharmacy, and contextual variables were used to build an actuarial model for these crimes separately among men and women using machine learning methods (cross-validated stepwise regression, random forests, penalized regressions). The model was then validated in an independent 2011-2013 sample. RESULTS: Key predictors were indicators of disadvantaged social/socioeconomic status, early career stage, prior crime, and mental disorder treatment. Area under the receiver-operating characteristic curve was 0.80-0.82 in 2004-2009 and 0.77 in the 2011-2013 validation sample. Of all administratively recorded crimes, 36.2-33.1% (male-female) were committed by the 5% of soldiers having the highest predicted risk in 2004-2009 and an even higher proportion (50.5%) in the 2011-2013 validation sample. CONCLUSIONS: Although these results suggest that the models could be used to target soldiers at high risk of violent crime perpetration for preventive interventions, final implementation decisions would require further validation and weighing of predicted effectiveness against intervention costs and competing risks.

Planning for emergency ambulance service systems.

The application of a computer model to evaluate ambulance deployment configurations in an urban ambulance service is described. A planned expansion of the Boston Emergency Ambulance Service was accomplished using computer projections. The use of analytic models in the planning and implementation of an Emergency Medical Services (EMS) system allows for greater understanding of the interactions between various performance measures, facilitating the effective and cost-efficient allocation of resources.

The importance of secondary trauma exposure for post-disaster mental disorder.

BACKGROUND: Interventions to treat mental disorders after natural disasters are important both for humanitarian reasons and also for successful post-disaster physical reconstruction that depends on the psychological functioning of the affected population. A major difficulty in developing such interventions, however, is that large between-disaster variation exists in the prevalence of post-disaster mental disorders, making it difficult to estimate need for services in designing interventions without carrying out a post-disaster mental health needs assessment survey. One of the daunting methodological challenges in implementing such surveys is that secondary stressors unique to the disaster often need to be discovered to understand the magnitude, type, and population segments most affected by post-disaster mental disorders. METHODS: This problem is examined in the current commentary by analyzing data from the WHO World Mental Health (WMH) Surveys. We analyze the extent to which people exposed to natural disasters throughout the world also experienced secondary stressors and the extent to which the mental disorders associated with disasters were more proximally due to these secondary stressors than to the disasters themselves. RESULTS. Lifetime exposure to natural disasters was found to be high across countries (4.4-7.5%). 10.7-11.4% of those exposed to natural disasters reported the occurrence of other related stressors (e.g. death of a loved one and destruction of property). A monotonic relationship was found between the number of additional stressors and the subsequent onset of mental disorders CONCLUSIONS. These results document the importance of secondary stressors in accounting for the effects of natural disasters on mental disorders. Implications for intervention planning are discussed.