Firearm violence is an issue of public health concern leading to more than 30,000 deaths and 80,000 nonfatal injuries in the United States annually. To date, firearm-related studies among Veterans have focused primarily on suicide and attempted suicide. Herein, we examine firearm violence among VHA enrollees for all manners/ intents, including assault, unintentional, self-inflicted, undetermined and other firearm-related injury encounters in both the inpatient and outpatient settings. 

Many methods to detect outbreaks currently exist, although most are ineffective in the face of real data, resulting in high false positivity. More complicated methods have better precision, but can be difficult to interpret and justify. Praedico™ is a next generation biosurveillance application built on top of a Hadoop High Performance Cluster that incorporates multiple syndromic surveillance methods of alerting, and a machine-learning (ML) model using a decision tree classifier  evaluating over 100 different signals simultaneously, within a user friendly interface.

The National Strategy for Biosurveillance promotes a national effort to improve early detection and enable ongoing situational awareness of all-hazards threats. Implicit in the Strategy’s implementation plan is the need to upgrade capabilities and integrate multiple disparate data sources, including more complete electronic health record (EHR) data into future biosurveillance capabilities. Thus, new biosurveillance applications are clearly needed.