scholarly journals Comparing Emergency Department Gunshot Wound Data with Mass Casualty Shooting Reports

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Andrew Walsh
Keyword(s):  
Public Health ◽  
Emergency Department ◽  
Syndromic Surveillance ◽  
Gunshot Wound ◽  
Gunshot Wounds ◽  
Mass Casualty ◽  
Gun Violence ◽  
Mass Shootings ◽  
Mass Shooting ◽  
Zip Code

ObjectiveTo determine whether mass casualty shooting events are capturedvia syndromic surveillance data.IntroductionShootings with multiple victims are a concern for public safetyand public health. The precise impact of such events and the trendsassociated with them is dependent on which events are counted. Somereports only consider events with multiple deaths, typically four ormore, while other reports also include events with multiple victimsand at least one death.1Underreporting is also a concern. Somecommonly cited databases for these events are based on media reportsof shootings which may or may not capture the complete set of eventsthat meet whatever criteria are being considered.Many gunshot wounds are treated in the emergency departmentsetting. Emergency department registrations routinely collected forsyndromic surveillance will capture all of those visits. Analysis ofthat data may be useful as a supplement to mass shooting databases byidentifying unreported events. In addition, clusters of gunshot woundincidents which are not the result of a single shooting event but stillrepresent significant public safety and public health concerns mayalso be identified.MethodsEmergency department registration data was collected fromhospitals via the EpiCenter syndromic surveillance system. Gunshot-related visits were identified based on chief complaint contentsusing EpiCenter’s regular expression-based classification system.The gunshot wound classifier attempts to exclude patients with pre-existing wounds and shooting incidents involving weapon classes thatare lesser concerns for public safety, such as nail guns and toy guns.Gunshot-related visits were clustered by day of registration andseparately by facility, by patient home zip code, and by patienthome county. The largest clusters of each type were compared viamanual search against media reports of shootings and against the GunViolence Archive mass shooting database.ResultsA total of 23,132 gunshot-related visits were identified from 635healthcare facilities from 2013 to 2015. From these, the five largestclusters by facility, by zip code, and by county were identified. Theclusters included 112 gunshot wounds in total, ranging in size from4 to 12 with a median of 7.Of the 5 facility clusters, 5 had a corresponding media story and 2were located in the shooting database. Of the 5 zip code clusters, 1 hada corresponding media story and none were located in the shootingdatabase. Of the 5 county clusters, 4 had a corresponding media storyand 1 was located in the shooting database.ConclusionsMultiple gunshot wound patients being treated on the same daywere not necessarily all shot during the same incident or by the sameshooter. The information available in a syndromic surveillance feeddoes not allow for direct identification of the shooter or shooters.Given that limitation, a complete correspondence between clustersidentified in syndromic surveillance data and mass shootings was notexpected. The strong correlation between clusters and media coverageindicates that the news is a reasonable source for shooting data. Thesmaller overlap with the mass shooting database is likely due to themore stringent criteria required for an incident to qualify as a massshooting.It is still notable that the majority of gunshot clusters were notassociated with any particular mass shooting incident. This serves asa reminder that mass shootings represent only a small portion of thetotal gun violence in the United States. Healthcare data representsa significant additional data source for understanding the completeimpact of gun violence on public health and safety.Weekly time series of gunshot-related emergency department visits

scholarly journals Use of Emergency Department Data to Monitor and Respond to an Increase in Opioid Overdoses in New Hampshire, 2011-2015

2017 ◽  
Vol 132 (1_suppl) ◽  
pp. 73S-79S ◽  
Author(s):  
Elizabeth R. Daly ◽  
Kenneth Dufault ◽  
David J. Swenson ◽  
Paul Lakevicius ◽  
Erin Metcalf ◽  
...  
Keyword(s):  
Public Health ◽  
Emergency Department ◽  
New Hampshire ◽  
Syndromic Surveillance ◽  
Situational Awareness ◽  
The United States ◽  
Chief Complaint ◽  
Public Health Response ◽  
Syndromic Surveillance System ◽  
Icd Codes

Objectives: Opioid-related overdoses and deaths in New Hampshire have increased substantially in recent years, similar to increases observed across the United States. We queried emergency department (ED) data in New Hampshire to monitor opioid-related ED encounters as part of the public health response to this health problem. Methods: We obtained data on opioid-related ED encounters for the period January 1, 2011, through December 31, 2015, from New Hampshire’s syndromic surveillance ED data system by querying for (1) chief complaint text related to the words “fentanyl,” “heroin,” “opiate,” and “opioid” and (2) opioid-related International Classification of Diseases ( ICD) codes. We then analyzed the data to calculate frequencies of opioid-related ED encounters by age, sex, residence, chief complaint text values, and ICD codes. Results: Opioid-related ED encounters increased by 70% during the study period, from 3300 in 2011 to 5603 in 2015; the largest increases occurred in adults aged 18-29 and in males. Of 20 994 total opioid-related ED visits, we identified 18 554 (88%) using ICD code alone, 690 (3%) using chief complaint text alone, and 1750 (8%) using both chief complaint text and ICD code. For those encounters identified by ICD code only, the corresponding chief complaint text included varied and nonspecific words, with the most common being “pain” (n = 3335, 18%), “overdose” (n = 1555, 8%), “suicidal” (n = 816, 4%), “drug” (n = 803, 4%), and “detox” (n = 750, 4%). Heroin-specific encounters increased by 827%, from 4% of opioid-related encounters in 2011 to 24% of encounters in 2015. Conclusions: Opioid-related ED encounters in New Hampshire increased substantially from 2011 to 2015. Data from New Hampshire’s ED syndromic surveillance system provided timely situational awareness to public health partners to support the overall response to the opioid epidemic.

scholarly journals Validation of Emergency Department and Outpatient Data Using ILI syndrome classifiers

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Jenna Iberg Johnson ◽  
Komal Brown
Keyword(s):  
Public Health ◽  
Emergency Department ◽  
New Orleans ◽  
Information Exchange ◽  
Syndromic Surveillance ◽  
Health Information Exchange ◽  
International Classification Of Diseases ◽  
Outpatient Clinics ◽  
Classification Of Diseases

The goal of this analysis is to compare the results of influenza-like-illness (ILI) text and International Classification of Diseases (ICD) code classifiers applied to the Louisiana Office of Public Health (OPH) syndromic surveillance data reported by New Orleans area emergency departments and the Greater New Orleans Health Information Exchange (GNOHIE) data reported by New Orleans area outpatient clinics. This study adds to the evidence supporting the emerging use of outpatient data for syndromic surveillance.

scholarly journals The Difference Between Living and Dying: Victim Characteristics and Motive Among Nonfatal Shootings and Gun Homicides

2017 ◽  
Vol 32 (6) ◽  
pp. 977-997 ◽  
Author(s):  
Natalie Kroovand Hipple ◽  
Lauren A. Magee
Keyword(s):  
Public Health ◽  
Data Collection ◽  
Gunshot Wound ◽  
Gun Violence ◽  
Data Sources ◽  
Policy And Practice ◽  
Police Policy ◽  
The Difference ◽  
Sector Data ◽  
Gun Homicides

Using both official and unofficial data sources, researchers examined nonfatal (n = 617) and fatal shooting (n = 159) victim characteristics over an 18-month period in Indianapolis. This research revealed that the typical shooting victim was male, non-White, almost 29 years old, had been arrested prior to inclusion in this study, and had been shot more than once. Interestingly, this research supports the notion that nonfatal shooting and homicide victims are different, especially as they relate to victim age, gunshot wound severity, and shooting motive. It highlights the need for better gun violence data collection beyond what currently exists. Striving for improved, more comprehensive cross-sector data collection has implications beyond just police policy and practice to include public health and prevention efforts.

scholarly journals Establishing a nationwide emergency department-based syndromic surveillance system for better public health responses in Taiwan

2008 ◽  
Vol 8 (1) ◽  
Author(s):  
Tsung-Shu Joseph Wu ◽  
Fuh-Yuan Frank Shih ◽  
Muh-Yong Yen ◽  
Jiunn-Shyan Julian Wu ◽  
Shiou-Wen Lu ◽  
...  
Keyword(s):  
Public Health ◽  
Emergency Department ◽  
Surveillance System ◽  
Syndromic Surveillance ◽  
Syndromic Surveillance System

scholarly journals Development and validation of an automated emergency department-based syndromic surveillance system to enhance public health surveillance in Yukon: a lower-resourced and remote setting

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Etran Bouchouar ◽  
Benjamin M. Hetman ◽  
Brendan Hanley
Keyword(s):  
Public Health ◽  
Emergency Department ◽  
Surveillance System ◽  
Syndromic Surveillance ◽  
Validation Dataset ◽  
Free Text ◽  
Mass Gathering ◽  
Syndromic Surveillance System ◽  
Development And Validation ◽  
Remote Setting

Abstract Background Automated Emergency Department syndromic surveillance systems (ED-SyS) are useful tools in routine surveillance activities and during mass gathering events to rapidly detect public health threats. To improve the existing surveillance infrastructure in a lower-resourced rural/remote setting and enhance monitoring during an upcoming mass gathering event, an automated low-cost and low-resources ED-SyS was developed and validated in Yukon, Canada. Methods Syndromes of interest were identified in consultation with the local public health authorities. For each syndrome, case definitions were developed using published resources and expert elicitation. Natural language processing algorithms were then written using Stata LP 15.1 (Texas, USA) to detect syndromic cases from three different fields (e.g., triage notes; chief complaint; discharge diagnosis), comprising of free-text and standardized codes. Validation was conducted using data from 19,082 visits between October 1, 2018 to April 30, 2019. The National Ambulatory Care Reporting System (NACRS) records were used as a reference for the inclusion of International Classification of Disease, 10th edition (ICD-10) diagnosis codes. The automatic identification of cases was then manually validated by two raters and results were used to calculate positive predicted values for each syndrome and identify improvements to the detection algorithms. Results A daily secure file transfer of Yukon’s Meditech ED-Tracker system data and an aberration detection plan was set up. A total of six syndromes were originally identified for the syndromic surveillance system (e.g., Gastrointestinal, Influenza-like-Illness, Mumps, Neurological Infections, Rash, Respiratory), with an additional syndrome added to assist in detecting potential cases of COVID-19. The positive predictive value for the automated detection of each syndrome ranged from 48.8–89.5% to 62.5–94.1% after implementing improvements identified during validation. As expected, no records were flagged for COVID-19 from our validation dataset. Conclusions The development and validation of automated ED-SyS in lower-resourced settings can be achieved without sophisticated platforms, intensive resources, time or costs. Validation is an important step for measuring the accuracy of syndromic surveillance, and ensuring it performs adequately in a local context. The use of three different fields and integration of both free-text and structured fields improved case detection.

scholarly journals Using Syndromic Surveillance Alert Protocols for Epidemiologic Response in Georgia

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Rene Borroto ◽  
Bill Williamson ◽  
Patrick Pitcher ◽  
Lance Ballester ◽  
Wendy Smith ◽  
...  
Keyword(s):  
Public Health ◽  
Emergency Department ◽  
Health Care Providers ◽  
Syndromic Surveillance ◽  
Emergency Department Patient ◽  
Chief Complaint ◽  
Care Providers ◽  
Public Health Response ◽  
Presumptive Diagnosis ◽  
Notifiable Diseases

ObjectiveDescribe how the Georgia Department of Public Health (DPH) usessyndromic surveillance to initiate review by District Epidemiologists(DEs) to events that may warrant a public health response (1).IntroductionDPH uses its State Electronic Notifiable Disease SurveillanceSystem (SendSS) Syndromic Surveillance (SS) Module to collect,analyze and display results of emergency department patient chiefcomplaint data from hospitals throughout Georgia.MethodsDPH prepares a daily SS report, based upon the analysis ofdaily visits to 112 Emergency Department (EDs). The visits areclassified in 33 syndromes. Queries of chief complaint and dischargediagnosis are done using the internal query capability of SendSS-SSand programming in SAS/BASE. Charting of the absolute countsor percentage of ED visits by syndromes is done using the internalcharting capability of SendSS-SS. A daily SS report includes thefollowing sections:Statewide Emergency Department Visitsby Priority Syndromes(Bioterrorism, BloodyRespiratory,FeverRespiratory, FeverChest, FeverFluAdmit, FeverFluDeaths,VeryIll, andPoxRashFever, Botulism, Poison, BloodyDiarrhea,BloodyVomit, FeverGI, ILI, FeverFlu, RashFever, Diarrhea,Vomit).Statewide Flag Analysis: Is intended to detect statewideflags, by using theChartscapability in SendSS SS.Possible caseswith presumptive diagnosis of potentially notifiable diseases: Isintended to provide early-warning to the DEs of possible cases thatare reportable to public health immediately or within 7 days usingqueries in the Chief Complaint and Preliminary Diagnosis fields ofSendSS-SS.Possible clusters of illness: Since any cluster of illnessmust be reported immediately to DPH, this analysis is aimed atquerying and identifying possible clusters of patients with similarsymptoms (2).Possible travel-related illness: Is intended to identifypatients with symptoms and recent travel history.Other events ofinterest: Exposures to ill patients in institutional settings (e.g. chiefcomplaint indicates that other children in the daycare have similarsymptoms).Trend Analysis: Weekly analysis of seasonality andtrends of 14 syndromes. Finally, specific events are notified to andreviewed by the 18 DEs, who follow up by contacting the InfectionPreventionists of the hospitals to identify the patients using medicalrecords or other hospital-specific identification numbers and followup on the laboratory test results.ResultsSince 05/15/2016, 12 travel-related illnesses, 29 vaccine-preventable diseases, 14 clusters, and 3 chemical exposures havebeen notified to DEs. For instance, a cluster of chickenpox in childrenwas identified after the DE contacted the Infection Preventionist ofa hospital, who provided the DE with the laboratory results and thephysician notes about the symptoms of the patients. These actionshave resulted in earlier awareness of single cases or cluster of illness,prompt reporting of notifiable diseases, and successful interactionbetween DEs and health care providers. In addition, SS continues totrack the onset, peak, and decline of seasonal illnesses.ConclusionsThe implementation of SS in the State of Georgia is helping withthe timely detection and early responses to disease events and couldprove useful in reducing the disease burden caused by a bioterroristattack.

scholarly journals A Bird’s Eye View on Firearm Injuries Lady Reading Hospital MTI, Peshawar

2021 ◽  
Vol 15 (5) ◽  
pp. 954-958
Author(s):  
Almas Afridi ◽  
Hamid Shahzad ◽  
S H Shirin ◽  
M Nouman ◽  
J Akhtar ◽  
...  
Keyword(s):  
Public Health ◽  
Emergency Department ◽  
Domestic Violence ◽  
Health Management ◽  
Randomized Study ◽  
Age Groups ◽  
Public Health Problem ◽  
Gunshot Wounds ◽  
Health Management Information System ◽  
Firearm Injuries

Background There is a concern that firearm injuries are very common reported at lady Reading hospital Peshawar MTI, but the there are no contemporary studies. Gunshot wound are persistent burden on community as well as on hospitals Aim: To evaluate trends of firearm injuries reporting to LRH their anatomical distributions and their outcomes. Methods: This prospective randomized study (a pilot project for public health alert) was conducted in Accident & Emergency Department of Lady Reading hospital MTI, Peshawar KPK, from 1st May, 2020 to 1st October, 2020. Hundred patients with firearm injuries to different region shot were included in this study. Data collected on predesigned proforma and entries in HMIS used after stabilizing the patient. A prospective pilot study done over a period of six months from 1st May, 2020 to 1st October, 2020 in trauma section of Accident Emergency department of Lady Reading hospital MTI Peshawar. Results: Patients of all age groups with firearm injuries were included in this study from May 2020 to October 2020. Data was collected on predesigned proforma as well as HMIS (health management information system) of ED department. A total of 100 patients presented to LRH with firearm injuries during six months. Males outnumbered female by 6:1 accounting for 100(85%) of injured. : Domestic violence was found to be the most common reason for the firearm use 44(44%), Don’t know or refuse to share was 29(29%), rivalry 12(12%), land and property conflict 9(9%), street fight 3(3%) accidental gunshots 2%, (n=25) and robbery 1.1% (n=1).The most common firearm used for inflicting injury was pistol (70%), shot gun (2.2%), curiously 16% were either reluctant or were never knowing about the weapon Conclusion: Firearm injuries are common public health problem globally, in our set up situation is more grievous and less highlighted, the most common cause being domestic violence, lack of education, easy availability of arms, illegal or legal weapon ownership making situation worst in the form of premature deaths and disability. Understanding the nature of problem can prevent this violence Keywords: Firearms, violence, gunshot wounds, homicides, suicides

scholarly journals Syndrome Development to Assess IDU, HIV, and Homelessness in MA Emergency Departments

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Stefanie P. Albert ◽  
Rosa Ergas ◽  
Sita Smith ◽  
Gillian Haney ◽  
Monina Klevens
Keyword(s):  
Public Health ◽  
Emergency Department ◽  
Drug Use ◽  
Hiv Infection ◽  
Injection Drug Use ◽  
Syndromic Surveillance ◽  
Injection Drug ◽  
Free Text ◽  
Increased Risk ◽  
Ed Visits

ObjectiveWe sought to measure the burden of emergency department (ED) visits associated with injection drug use (IDU), HIV infection, and homelessness; and the intersection of homelessness with IDU and HIV infection in Massachusetts via syndromic surveillance data.IntroductionIn Massachusetts, syndromic surveillance (SyS) data have been used to monitor injection drug use and acute opioid overdoses within EDs. Currently, Massachusetts Department of Public Health (MDPH) SyS captures over 90% of ED visits statewide. These real-time data contain rich free-text and coded clinical and demographic information used to categorize visits for population level public health surveillance.Other surveillance data have shown elevated rates of opioid overdose related ED visits, Emergency Medical Service incidents, and fatalities in Massachusetts from 2014-20171,2,3. Injection of illicitly consumed opioids is associated with an increased risk of infectious diseases, including HIV infection. An investigation of an HIV outbreak among persons reporting IDU identified homelessness as a social determinant for increased risk for HIV infection.MethodsTo accomplish our objectives staff used an existing MDPH SyS IDU syndrome definition4, developed a novel syndrome definition for HIV-related visits, and adapted Maricopa County's homelessness syndrome definition. Syndromes were applied to Massachusetts ED data through the CDC’s BioSense Platform. Visits meeting the HIV and homelessness syndromes were randomly selected and reviewed to assess accuracy; inclusion and exclusion criteria were then revised to increase specificity. The final versions of all three syndrome definitions incorporate free-text elements from the chief complaint and triage notes, as well as International Statistical Classification of Diseases and Related Health Problems, 9th (ICD-9) and 10th Revision (ICD-10) diagnostic codes. Syndrome categories were not mutually exclusive, and all reported visits occurring at Massachusetts EDs were included in the analysis.Syndromes CreatedFor the HIV infection syndrome definition, we incorporated the free-text term “HIV” in both the chief complaint and triage notes. Visit level review demonstrated that the following exclusions were needed to reduce misspellings, inclusion of partial words, and documentation of HIV testing results: “negative for HIV”, “HIV neg”, “negative test for HIV”, “hive”, “hivies”, and “vehivcle”. Additionally, the following diagnostic codes were incorporated: V65.44 (Human immunodeficiency virus [HIV] counseling), V08 (asymptomatic HIV infection status), V01.79 (contact with or exposure to other viral diseases), 795.71 (nonspecific serologic evidence of HIV), V73.89 (special screening examination for other specified viral diseases), 079.53 (HIV, type 2 [HIV-2]), Z20.6 (contact with and (suspected) exposure to HIV), Z71.7 (HIV counseling), B20 (HIV disease), Z21 (asymptomatic HIV infection status), R75 (inconclusive laboratory evidence of HIV), Z11.4 (encounter for screening for HIV), and B97.35 (HIV-2 as the cause of diseases classified elsewhere).Building on the Maricopa County homeless syndrome definition, we incorporated a variety of free-text inclusion and exclusion terms. To meet this definition visits had to mention: “homeless”, or “no housing”, or, “lack of housing”, or “without housing”, or “shelter” but not animal and domestic violence shelters. We also selected the following ICD-10 codes for homelessness and inadequate housing respectively, Z59.0 and Z59.1.We analyzed MDPH SyS data for visits occurring from January 1, 2016 through June 30, 2018. Rates per 10,000 ED visits categorized as IDU, HIV, or homeless were calculated. Subsequently, visits categorized as IDU, HIV, and meeting both IDU and HIV syndrome definitions (IDU+HIV) were stratified by homelessness.ResultsSyndrome Burden on EDThe MDPH SyS dataset contains 6,767,137 ED visits occurring during the study period. Of these, 82,819 (1.2%) were IDU-related, 13,017 (0.2%) were HIV-related, 580 (<0.01%) were related to IDU + HIV, and 42,255 visits (0.6%) were associated with homelessness.The annual rate of IDU-related visits increased 15% from 2016 through June of 2018 (from 113.63 to 130.57 per 10,000 visits); while rates of HIV-related and IDU + HIV-related visits remained relatively stable. The overall rate of visits associated with homelessness increased 47% (from 49.99 to 73.26 per 10,000 visits).Rates of IDU, HIV, and IDU + HIV were significantly higher among visits associated with homelessness. Among visits that met the homeless syndrome definition compared to those that did not: the rate of IDU-related visits was 816.0 versus 118.03 per 10,000 ED visits (X2= 547.12, p<0. 0001); the rate of visits matching the HIV syndrome definition was 145.54 versus 18.44 per 10,000 ED visits (X2= 99.33, p<0.0001); and the rate of visits meeting the IDU+HIV syndrome definition was 15.86 versus 0.76 per 10,000 visits (X2= 13.72, p= 0.0002).ConclusionsMassachusetts is experiencing an increasing burden of ED visits associated with both IDU and homelessness that parallels increases in opioid overdoses. Higher rates of both IDU and HIV-related visits were associated with homelessness. An understanding of the intersection between opioid overdoses, IDU, HIV, and homelessness can inform expanded prevention efforts, introduction of alternatives to ED care, and increase consideration of housing status during ED care.Continued surveillance for these syndromes, including collection and analysis of demographic and clinical characteristics, and geographic variations, is warranted. These data can be useful to providers and public health authorities for planning healthcare services.References1. Vivolo-Kantor AM, Seth P, Gladden RM, et al. Vital Signs: Trends in Emergency Department Visits for Suspected Opioid Overdoses — United States, July 2016–September 2017. MMWR Morbidity and Mortality Weekly Report 2018; 67(9);279–285 DOI: http://dx.doi.org/10.15585/mmwr.mm6709e12. Massachusetts Department of Public Health. Chapter 55 Data Brief: An assessment of opioid-related deaths in Massachusetts, 2011-15. 2017 August. Available from: https://www.mass.gov/files/documents/2017/08/31/data-brief-chapter-55-aug-2017.pdf3. Massachusetts Department of Public Health. MA Opioid-Related EMS Incidents 2013-September 2017. 2018 Feb. Available from: https://www.mass.gov/files/documents/2018/02/14/emergency-medical-services-data-february-2018.pdf4. Bova, M. Using emergency department (ED) syndromic surveillance to measure injection-drug use as an indicator for hepatitis C risk. Powerpoint presented at: 2017 Northeast Epidemiology Conference. 2017 Oct 18 – 20; Northampton, Massachusetts, USA.

Wound patterns in survivors of modern firearm related civilian Mass Casualty Incidents

2019 ◽  
Vol 14 (3) ◽  
pp. 175-180
Author(s):  
Chase Knickerbocker, MS ◽  
Mario F. Gomez, DO ◽  
Jose Lozada, MD ◽  
Jonathan Zadeh, MD ◽  
Eugene Costantini, MD ◽  
...  
Keyword(s):  
Trauma Center ◽  
Multidisciplinary Approach ◽  
Mass Casualty ◽  
Medical Examiner ◽  
Fatality Rate ◽  
Mass Shootings ◽  
Fort Lauderdale ◽  
Mass Shooting ◽  
Level 1 ◽  
Level 1 Trauma Center

Background: Civilian mass shooting events (CMSE) are occurring with increased frequency. Unfortunately, our knowledge of how to respond to these events is largely based on military experience and medical examiner data. While this translational knowledge has improved our basic response to such events, it is critical that we have a better understanding of the wound patterns observed and the resources utilized in civilian mass shootings. This will allow us to better prepare our systems for future events.Methods: Patients from two consecutive CMSEs presented to the same level 1 trauma center in Fort Lauderdale, Florida. The patients received by this center were studied for their wound patterns as well as the care they received while in the hospital. This included wound patterns and severity, subspecialty interventions, and hospitalization requirements.Results: Both events produced a total of 19 victims who were brought to the center as trauma activations. The events had a combined fatality rate of 55 percent. Fifty-five percent of patients also had at least one wound to an extremity, two with major vascular injuries who had field tourniquets applied. Sixty-three percent required an orthopedic intervention and 32 percent required intensive care unit (ICU) admission, half of these with prolonged ventilator support.Conclusions: Given the number of extremity wounds in these events, we should continue the efforts championed by the stop the bleed campaign. The variety and quantity of specialties involved in the care of these patients also highlights the importance of a multidisciplinary approach to preparation and implementation of care in mass shooting events.

Mass Shooting and Misogyny

2021 ◽  
pp. 48-73
Author(s):  
Perry L. Lyle ◽  
Ashraf Esmail ◽  
Lisa Eargle
Keyword(s):  
Gun Violence ◽  
Constitutional Rights ◽  
Gun Ownership ◽  
Mass Shootings ◽  
Self Defense ◽  
The World ◽  
Private Citizens ◽  
Mass Shooting ◽  
Per Capita ◽  
Privately Owned

It is the violent ideology Americans cannot ignore. This hate and extremism overwhelming reside in males. Disproportionately committed by males, gun violence, as shown by data, reveals that misogyny can be a precursor to other forms of extremism. Gun violence and particularly mass-shootings have once again seized Americans of all political stripes as the hot-topic debate of the day. American's fascination with gun ownership dates to the roots of independence from the British crown and why colonists insisted that protection to own and possess firearms be woven into the private citizens' constitutional rights. There are an estimated 393 million guns in America, almost one for each citizen but held by approximately 42% of the population. It makes America, per capita, the largest privately-owned gun-toting country in the world. Many of the population surveyed claim to own four or more weapons – hardly necessary for self-defense. This chapter explores mass shootings and misogyny.

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