Utilizing Emergency Department Data to Understand Violent Injuries and Reporting Status Público
Baas, Gretchen (Spring 2021)
Abstract
Background: Personal demographic characteristics, violent injury type, and medical history’s association with reporting violent injuries to law enforcement remain under-research areas within both the public health and criminal justice spheres. Although violence impacts millions of people each year, there is a dearth of research bridging data from law enforcement and healthcare facilities to provide accurate data on these violent injuries and how they are, or are not, reported to law enforcement. This study aims to examine the associations and predictors of reporting status by personal demographic characteristics, violent injury type, and a chart review.
Methods: This is a secondary data analysis of the Cardiff Model dataset from Grady Memorial Hospital in Atlanta, Georgia. The matched dataset from the original pilot study from May 2015-November 2017 was utilized for secondary data analysis. An additional chart review was conducted and merged to the main dataset before analyses were conducted. In the pilot study, participants were screened in the emergency room at Grady Memorial Hospital by the Information Sharing to Tackle Violence (ISTV) screen. approximately 152,000 patients were screened of those approximately 3000 presented with intentional injuries, and 300 were mappable violent injuries regardless of reporting. Emergency department data was matched with police department data at three location sensitivities: 100m, 500m, and 1000m. Preliminary analysis was conducted through descriptive statistics, chi-square, and simple logistic regressions at each location sensitivity. Primary analyses were conducted through three multivariable logistic regressions at 100m, 500m, and 1000m respectively.
Results: Chi-square results conclude significant associations between means of arrival, mechanism of injury, acuity, gender, chief complaint, and financial class at various location sensitivities. Multivariable logistic regressions revealed significant predictors between means of arrival (walk-in) (100m, p=0.044; 500m, p=0.028), location of injury (street) (500m, p=0.031), and gender (500m, p=0.015; 1000m, p=0.010) at various location sensitivities.
Conclusions: There are associations between personal demographic characteristics, violent injury type, and chart review variables and reporting status at all three location sensitivities. Additionally, we can conclude that means of arrival, location of injury, and gender are significant predictors of reporting status. However, conclusions also exemplify the need to further research these concepts within different populations and geographic locations to understand these trends.
Table of Contents
CHAPTER ONE
Introduction
Comorbidities of Violent Injuries
Risk Factors for Violence Victimization and Perpetration
Cardiff Model
Research Questions
Purpose of the Study
Significance of the Study
Limitations
Definition of Terms
CHAPTER TWO
Literature Review
Overview
Comorbidities of Violent Injuries
Psychological
Violent Injury Recidivism
Substance Use
Risk Factors for Violence Victimization and Perpetration
Need for Data Linkage
Crime Reporting
Hot-Spots
Cardiff Model
Summary
CHAPTER THREE
Methods
Statement of the Problem
Human Subjects Approval
Participant Recruitment Methods
Procedures
Study Protocol
Pilot Model Protocol
Information Sharing to Tackle Violence (ISTV) Hospital Screen Questionnaire
Measures
Personal Demographic Characteristics
Information Sharing to Tackle Violence (ISTV) Hospital Screen Measures
Reporting Status
Medical Chart Review Measures
Length of Stay
Means of Arrival
Acuity
Injury Date and Time
Chief Complaint
Emergency Department Disposition
Treatment of Data
Preliminary Analyses
Specific Analysis by Research Questions
CHAPTER FOUR
Results
Full Sample Personal Demographic Characteristics Descriptive Statistics
Non-Reporting Group Personal Demographic Characteristics Descriptive Statistics
Reporting Group Personal Demographics Descriptive Statistics
Full Sample Violent Injury Type Descriptive Statistics
Non-Reporting Group Violent Injury Type Descriptive Statistics
Reporting Group Violent Injury Type Descriptive Statistics
Full Sample Chart Review Descriptive Statistics
Non-Reporting Group Chart Review Descriptive Statistics
Reporting Group Chart Review Descriptive Statistics
Reporting Status
Simple Logistic Regressions
Location Sensitivity: 100m
Location Sensitivity: 500m
Location Sensitivity: 1000m
Multivariable Logistic Regression
Summary
CHAPTER 5
Discussion
Overall Summary
Summary of Findings
Implications
Hospital Implications
Public Health Implications
Law Enforcement Implications
Limitations
Future Directions
Conclusion
About this Master's Thesis
| School | |
|---|---|
| Department | |
| Degree | |
| Submission | |
| Language |
|
| Research Field | |
| Palavra-chave | |
| Committee Chair / Thesis Advisor | |
| Committee Members |
Primary PDF
| Thumbnail | Title | Date Uploaded | Actions |
|---|---|---|---|
|
|
Utilizing Emergency Department Data to Understand Violent Injuries and Reporting Status () | 2021-05-03 08:42:38 -0400 |
|
Supplemental Files
| Thumbnail | Title | Date Uploaded | Actions |
|---|