Proportion of Violent Injuries Unreported to Law Enforcement

Temporal and spatial shifts in gun violence, before and after a historic police killing in Minneapolis

OBJECTIVE

To determine the impact of the police murder of George Floyd in Minneapolis, MN on firearm violence, and examine the spatial and social heterogeneity of the effect.

METHODS

We analyzed a uniquely constructed panel dataset of Minneapolis Zip Code Tabulation Areas from 2016-2020 (n = 5742), consisting of Minnesota Hospital Association, Minneapolis Police Department, Minneapolis Public Schools, Census Bureau, and Minnesota Department of Natural Resources data. Interrupted time-series and random effects panel models were used to model the spatiotemporal effects of police killing event on the rate of firearm assault injuries.

RESULTS

Findings reveal a rising and falling temporal pattern post-killing and a spatial pattern in which disadvantaged, historically Black communities near earlier sites of protest against police violence experienced the brunt of the post-killing increase in firearm assault injury. These effects remain after adjusting for changes in police activity and pandemic-related restrictions, indicating that rising violence was not a simple byproduct of changes in police behavior or COVID-19 response.

CONCLUSIONS

The results suggest that the increases in firearm violence as a result of police violence are disproportionately borne by underserved communities.

Linking emergency care and police department data to strengthen timely information on violence-related paediatric injuries

BACKGROUND

Combined ED and police department (PD) data have improved violence surveillance in the UK, enabling significantly improved prevention. We sought to determine if the addition of emergency medical service (EMS) data to ED data would contribute meaningful information on violence-related paediatric injuries beyond PD record data in a US city.

METHODS

Cross-sectional data on self-reported violence-related injuries of youth treated in the ED between January 2015 and September 2016 were combined with incidents classified by EMS as intentional interpersonal violence and incidents in which the PD responded to a youth injury from a simple or aggravated assault, robbery or sexual offence. Nearest neighbour hierarchical spatial clustering detected areas in which 10 or more incidents occurred during this period (hotspots), with the radii of the area being 1000, 1500, 2000 and 3000 ft. Overlap of PD incidents within ED&EMS hotspots (and vice versa) was calculated and Spearman's r tested statistical associations between the data sets, or ED&EMS contribution to PD violence information.

RESULTS

There were 935 unique ED&EMS records (ED=381; EMS=554). Of these, 877 (94%) were not in PD records. In large hotspots >2000 ft, ED&EMS records identified one additional incident for every three in the PD database. ED and EMS provided significant numbers of incidents not reported to PD. Significant correlations of ED&EMS incidents in PD hotspots imply that the ED&EMS incidents are as pervasive across the city as that reported by PD. In addition, ED and EMS provided unique violence information, as ED&EMS hotspots never included a majority (>50%) of PD records. Most (676/877; 77%) incidents unique to ED&EMS records were within 1000 ft of a school or park.

CONCLUSIONS

Many violence locations in ED and EMS data were not present in PD records. A combined PD, ED and EMS database resulted in new knowledge of the geospatial distribution of violence-related paediatric injuries and can be used for data-informed and targeted prevention of violence in which children are injured-especially in and around schools and parks.

Assault-related injuries reported to police and treated by healthcare providers in the United States

Research suggests that assault-related injuries known by the police significantly differ from those known by healthcare providers, but the magnitude and nature of these differences are poorly understood. To address this gap, our study examined the empirical differences between assault-related injuries reported to police and treated by healthcare providers. In June of 2021, we analyzed the National Crime Victimization Survey (1993-2019) to estimate the prevalence of police reporting and healthcare use among 5093 nonfatal victimizations that caused injury and were either reported to the police or treated by healthcare in the United States. Quasi-Poisson models identified the factors associated with whether people who sustained the injuries used healthcare (v. only reported to police) and reported to police (v. only used healthcare). Among victimizations that caused only minor injuries, 43% involved only a police report, 11% involved only healthcare, and 46% involved both services. Among victimizations that caused serious injuries, 14% involved only a police report, 13% involved only healthcare, and 73% involved both services. Whether people with violent injuries used healthcare (v. only reported to police) and reported to police (v. only used healthcare) was significantly associated with 13 different person- and incident-level factors. The number and nature of assault-related injuries reported to law enforcement significantly differ from those treated by healthcare providers. Therefore, public health efforts to link police and healthcare data are warranted and recommended.

Implementation and initial analysis of Cardiff Model data collection procedures in a level I trauma adult emergency department

OBJECTIVES

Our understanding of community violence is limited by incomplete information, which can potentially be resolved by collecting violence-related injury information through healthcare systems in tandem with prior data streams. This study assessed the feasibility of implementing Cardiff Model data collection procedures in the emergency department (ED) setting to improve multisystem data sharing capabilities and create more representative datasets.

DESIGN

Information collection fields were incorporated into the ED electronic health record (EHR), which gathered additional information from patients reporting assaultive injuries. ED nurses were surveyed to evaluate implementation and feasibility of information collection. Logistic regression was performed to determine associations between missing location information and patient demographic data.

SETTING

60-bed academic level I trauma adult ED in a large Midwestern city.

PARTICIPANTS

2648 patients screened positive for assault injuries between 2017 and 2020. 198 patients were omitted due to age outside the range served by this ED. Unselected inclusion of 150 ED nurses was surveyed.

MAIN OUTCOME MEASURES

Main outcomes include nursing staff survey responses and ORs for providing complete injury information across various patient demographics.

RESULTS

Most ED nurses believed that information collection aligned with the hospital's mission (92%), wanted information collection to continue (88%), did not believe that information collection impacted their workflow (88%), and reported taking under 1 min to screen and document violence information (77%). 825 patients (31.2%) provided sufficient information for geospatial mapping. Likelihood of providing complete location information was significantly associated with patient gender, race, arrival means, accompaniment, trauma type and year.

CONCLUSIONS

It is feasible to implement information collection procedures about location-based, assault-related injuries through the EHR in the adult ED setting. Nurses reported being receptive to collecting information. Analyses suggest patient-level and time variables impact information collection completeness. The geospatial information collected can greatly improve preexisting law enforcement and emergency medical systems datasets.

Business and property types experiencing excess violent crime: a micro-spatial analysis

Background:

Beyond alcohol retail establishments, most business and property types receive limited attention in studies of violent crime. We sought to provide a comprehensive examination of which properties experience the most violent crime in a city and how that violence is distributed throughout a city.

Methods:

For a large urban city, we merged violent incident data from police reports with municipal tax assessor data from 2012-2017 and tabulated patterns of violent crime for 15 commercial and public property types. To describe outlier establishments, we calculated the proportion of individual parcels within each property-type that experienced more than 5 times the average number of crimes for that property-type and also mapped the 25 parcels with the highest number of violent incidents to explore what proportion of violent crime in these block groups were contributed by the outlier establishments.

Results:

While the hotel/lodging property-type experienced the highest number of violent crimes per parcel (2.72), each property-type had outlier establishments experiencing more than 5 times the average number of violent crimes per business. Twelve of 15 property-types (80%) had establishments with more than 10 times the mean number of violent incidents. The 25 parcels with the most violent crime comprised a wide variety of establishments, ranging from a shopping center, grocery store, gas station, motel, public park, vacant lot, public street, office building, transit station, hospital, pharmacy, school, community center, and movie theatre, and were distributed across the city. Eight of the 25 parcels with the highest amount of violent crime, accounted for 50% or more of the violent crime within a 400-meter buffer.

Conclusions:

All property-types had outlier establishments experiencing elevated counts of violent crimes. Furthermore, the 25 most violent properties in the city demonstrated remarkable diversity in property-type. Further studies assessing the risk of violent crime among additional property-types may aid in violence prevention.

The limitations of hospital and law enforcement databases in characterizing the epidemiology of firearm injury

BACKGROUND

Current data on the epidemiology of firearm injury in the United States are incomplete. Common sources include hospital, law enforcement, consumer, and public health databases, but each database has limitations that exclude injury subgroups. By integrating hospital (inpatient and outpatient) and law enforcement databases, we hypothesized that a more accurate depiction of the totality of firearm injury in our region could be achieved.

METHODS

We constructed a collaborative firearm injury database consisting of all patients admitted as inpatients to the regional level 1 trauma hospital (inpatient registry), patients treated and released from the emergency department (ED), and subjects encountering local law enforcement as a result of firearm injury in Jefferson County, Kentucky. Injuries recorded from January 1, 2016, to December 31, 2020, were analyzed. Outcomes, demographics, and injury detection rates from individual databases were compared with those of the combined collaborative database and compared using χ2 testing across databases.

RESULTS

The inpatient registry (n = 1,441) and ED database (n = 1,109) were combined, resulting in 2,550 incidents in the hospital database. The law enforcement database consisted of 2,665 patient incidents, with 2,008 incidents in common with the hospital database and 657 unique incidents. The merged collaborative database consisted of 3,207 incidents. In comparison with the collaborative database, the inpatient, total hospital (inpatient and ED), and law enforcement databases failed to include 55%, 20%, and 17% of all injuries, respectively. The hospital captured nearly 94% of survivors but less than 40% of nonsurvivors. Law enforcement captured 93% of nonsurvivors but missed 20% of survivors. Mortality (11-26%) and injury incidence were markedly different across the databases.

DISCUSSION

The utilization of trauma registry or law enforcement databases alone do not accurately reflect the epidemiology of firearm injury and may misrepresent areas in need of greater injury prevention efforts.

LEVEL OF EVIDENCE

Epidemiological, level IV.

Identifying nonfatal firearm assault incidents through linking police data and clinical records: Cohort study in Indianapolis, Indiana, 2007-2016

Nonfatal firearm assault incidents are more prevalent than gun homicides, however, little is understood about nonfatal firearm assault incidents due to a lack of accurate data in the United States. This is a descriptive study of all nonfatal firearm assault incidents identified through police and clinical records from 2007 to 2016 in Indianapolis, Indiana. Records were linked at the incident level to demonstrate the overlap and non-overlap of nonfatal firearm assault incidents in police and clinical records and describe differences in demographic characteristics of the victims. Incidents were matched within a 24-h time window of the recorded date of the police incident. Data were analyzed in fall 2020. There were 3797 nonfatal firearm assault incidents identified in police reports and 3131 clinical encounters with an ICD 9/10 diagnosis-based nonfatal firearm-related injury. 62% (n = 2366) of nonfatal firearm assault incidents matched within 24 h to a clinical encounter, 81% (n = 1905) had a firearm related ICD code: 40% (n = 947) were coded as a firearm-related assault, 32% (n = 754) were coded as a firearm-related accident; and 8.6% (n = 198) were coded as undetermined, self-inflicted or law enforcement firearm-related. The other 20% (n = 461) did not have an ICD firearm related diagnosis code. Results indicate most nonfatal firearm assault incidents overlap between police and clinical records systems, however, discrepancies between the systems exist. These findings also demonstrate an undercounting of nonfatal firearm assault incidents when relying on clinical data systems alone and more efforts are needed to link administrative police and clinical data in the study of nonfatal firearm assaults.

Exploring injury intentionality and mechanism via ICD-10-CM injury codes and self-reported injury in a large, urban emergency department

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.

METHODS

Self-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.

RESULTS

Of 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%).

DISCUSSION

This 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.

Advancing injury and violence prevention through data science

INTRODUCTION

The volume of new data that is created each year relevant to injury and violence prevention continues to grow. Furthermore, the variety and complexity of the types of useful data has also progressed beyond traditional, structured data. In order to more effectively advance injury research and prevention efforts, the adoption of data science tools, methods, and techniques, such as natural language processing and machine learning, by the field of injury and violence prevention is imperative.

METHOD

The Centers for Disease Control and Prevention's (CDC) National Center for Injury Prevention and Control has conducted numerous data science pilot projects and recently developed a Data Science Strategy. This strategy includes goals on expanding the availability of more timely data systems, improving rapid identification of health threats and responses, increasing access to accurate health information and preventing misinformation, improving data linkages, expanding data visualization efforts, and increasing efficiency of analytic and scientific processes for injury and violence, among others.

RESULTS

To achieve these goals, CDC is expanding its data science capacity in the areas of internal workforce, partnerships, and information technology infrastructure. Practical Application: These efforts will expand the use of data science approaches to improve how CDC and the field address ongoing injury and violence priorities and challenges.

Location of Violent Crime Relative to Trauma Resources in Detroit: Implications for Community Interventions

Introduction

Detroit, Michigan, is among the leading United States cities for per-capita homicide and violent crime. Hospital- and community-based intervention programs could decrease the rate of violent-crime related injury but require a detailed understanding of the locations of violence in the community to be most effective.

Methods

We performed a retrospective geospatial analysis of all violent crimes reported within the city of Detroit from 2009–2015 comparing locations of crimes to locations of major hospitals. We calculated distances between violent crimes and trauma centers, and applied summary spatial statistics.

Results

Approximately 1.1 million crimes occurred in Detroit during the study period, including approximately 200,000 violent crimes. The distance between the majority of violent crimes and hospitals was less than five kilometers (3.1 miles). Among violent crimes, the closest hospital was an outlying Level II trauma center 60% of the time.

Conclusion

Violent crimes in Detroit occur throughout the city, often closest to a Level II trauma center. Understanding geospatial components of violence relative to trauma center resources is important for effective implementation of hospital- and community-based interventions and targeted allocation of resources.

Building capacity for injury prevention: a process evaluation of a replication of the Cardiff Violence Prevention Programme in the Southeastern USA

OBJECTIVES

Violence 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.

METHODS

The Cardiff Model was replicated in the USA. A process evaluation was conducted by reviewing project materials, nurse surveys and interviews and ED-LE records.

RESULTS

Cardiff 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.

CONCLUSIONS

The 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.