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

Should cities hosting mass gatherings invest in public health surveillance and planning? Reflections from a decade of mass gatherings in Sydney, Australia

<p>Abstract</p> <p>Background</p> <p>Mass gatherings have been defined by the World Health Organisation as "events attended by a sufficient number of people to strain the planning and response resources of a community, state or nation". This paper explores the public health response to mass gatherings in Sydney, the factors that influenced the extent of deployment of resources and the utility of planning for mass gatherings as a preparedness exercise for other health emergencies.</p> <p>Discussion</p> <p>Not all mass gatherings of people require enhanced surveillance and additional response. The main drivers of extensive public health planning for mass gatherings reflect geographical spread, number of international visitors, event duration and political and religious considerations. In these instances, the implementation of a formal risk assessment prior to the event with ongoing daily review is important in identifying public health hazards.</p> <p>Developing and utilising event-specific surveillance to provide early-warning systems that address the specific risks identified through the risk assessment process are essential. The extent to which additional resources are required will vary and depend on the current level of surveillance infrastructure.</p> <p>Planning the public health response is the third step in preparing for mass gatherings. If the existing public health workforce has been regularly trained in emergency response procedures then far less effort and resources will be needed to prepare for each mass gathering event. The use of formal emergency management structures and co-location of surveillance and planning operational teams during events facilitates timely communication and action.</p> <p>Summary</p> <p>One-off mass gathering events can provide a catalyst for innovation and engagement and result in opportunities for ongoing public health planning, training and surveillance enhancements that outlasted each event.</p

National suicide rates a century after Durkheim: Do we know enough to estimate error?

Durkheim's nineteenth-century analysis of national suicide rates dismissed prior concerns about mortality data fidelity. Over the intervening century, however, evidence documenting various types of error in suicide data has only mounted, and surprising levels of such error continue to be routinely uncovered. Yet the annual suicide rate remains the most widely used population-level suicide metric today. After reviewing the unique sources of bias incurred during stages of suicide data collection and concatenation, we propose a model designed to uniformly estimate error in future studies. A standardized method of error estimation uniformly applied to mortality data could produce data capable of promoting high quality analyses of cross-national research questions. © 2010 The American Association of Suicidology.link_to_subscribed_fulltex