ObjectivesViolence is a major public health problem in the USA. In 2016, more than 1.6 million assault-related injuries were treated in US emergency departments (EDs). Unfortunately, information about the magnitude and patterns of violent incidents is often incomplete and underreported to law enforcement (LE). In an effort to identify more complete information on violence for the development of prevention programme, a cross-sectoral Cardiff Violence Prevention Programme (Cardiff Model) partnership was established at a large, urban ED with a level I trauma designation and local metropolitan LE agency in the Atlanta, Georgia metropolitan area. The Cardiff Model is a promising violence prevention approach that promotes combining injury data from hospitals and LE. The objective was to describe the Cardiff Model implementation and collaboration between hospital and LE partners.MethodsThe Cardiff Model was replicated in the USA. A process evaluation was conducted by reviewing project materials, nurse surveys and interviews and ED–LE records.ResultsCardiff Model replication centred around four activities: (1) collaboration between the hospital and LE to form a community safety partnership locally called the US Injury Prevention Partnership; (2) building hospital capacity for data collection; (3) data aggregation and analysis and (4) developing and implementing violence prevention interventions based on the data.ConclusionsThe Cardiff Model can be implemented in the USA for sustainable violent injury data surveillance and sharing. Key components include building a strong ED–LE partnership, communicating with each other and hospital staff, engaging in capacity building and sustainability planning.

Identifying geographic areas and time periods of increased violence is of considerable importance in prevention planning. This study compared the performance of multiple data sources to prospectively forecast areas of increased interpersonal violence. We used 2011-2014 data from a large metropolitan county on interpersonal violence (homicide, assault, rape and robbery) and forecasted violence at the level of census block-groups and over a one-month moving time window. Inputs to a Random Forest model included historical crime records from the police department, demographic data from the US Census Bureau, and administrative data on licensed businesses. Among 279 block groups, a model utilizing all data sources was found to prospectively improve the identification of the top 5% most violent block-group months (positive predictive value = 52.1%; negative predictive value = 97.5%; sensitivity = 43.4%; specificity = 98.2%). Predictive modelling with simple inputs can help communities more efficiently focus violence prevention resources geographically.

No abstract

Role of the Funder/Sponsor: The funding organizations had no role in design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Health systems capture injuries using International Statistical Classification of Diseases and Related Health Problems, 10th Revision, Clinical Modification (ICD-10-CM) diagnostic codes and share data with public health to inform injury surveillance. This study analyses provider-assigned ICD-10-CM injury codes among self-reported injuries to determine the effectiveness of ICD-10-CM coding in capturing injury and assault.MethodsSelf-reported injury screen records from an urban, level 1 trauma centre collected between 20 November 2015 and 30 September 2019 were compared with corresponding provider-assigned ICD-10-CM codes discerning the frequency in which intentions are indicated among patients reporting (1) any injury and (2) assault.ResultsOf 380 922 patients screened, 32 788 (8.61%) reported any injury and 6763 (1.78%) reported assault. ICD-10-CM codes had a sensitivity of 67.40% (95% CI 66.89% to 67.91%) for any injury and specificity of 89.79% (95% CI 89.69% to 89.89%]). For assault, ICD-10-CM codes had sensitivity of 2.25% (95% CI 1.91% to 2.63%) and specificity of 99.97% (95% CI 99.97% 99.98%).DiscussionThis study found provider-assigned ICD-10-CM had limited sensitivity to identify injury and low sensitivity for assault. This study more fully characterises ICD-10-CM coding system effectiveness in identifying assaults.