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The epidemiology of firearm injuries managed in US emergency departments

The epidemiology of firearm injuries managed in US emergency departments Background: Firearm-related injuries cause significant morbidity and mortality in the United States (US), consuming resources and fueling political and public health discourse. Most analyses of firearm injuries are based on fatality statistics. Here, we describe the epidemiology of firearm injuries presenting to US emergency departments (EDs). Methods: We performed a retrospective study of the Healthcare Cost and Utilization Program Nationwide Emergency Department Survey (NEDS) from 2009 to 2012. NEDS is the largest all-payer ED survey in the US containing approximately 30 million annual records. Results include survey-adjusted counts, proportions, means, and rates, and confidence intervals of age-stratified ED discharges for firearm injuries. Results: There were 71,111 (se = 613) ED discharges for firearm injuries in 2009; the absolute number increased 3. 9% (se = 1.2) to 75,559 (se = 610) in 2012. 18-to-44-year-olds accounted for the largest proportion of total injuries with 52,187 (se = 527) in 2009 and 56,644 (se = 528) in 2012—a 7.2% (se = 1.6) relative rate increase and an absolute increase of 3.3/100,000 (se = 0.7). Firearm injuries among children < 5-years-old increase 16%, and 19% among children 5-to-9-years-old. 136,112 (se = 761)—or 48.2%—of those injured were treated and discharged home without admission; 106,927 (se = 755) were admitted. Firearm deaths represented one-third of all trauma mortality. Three-quarters of those injured resided in neighborhoods with median incomes below $49,250. Conclusions: Firearm injuries increased from 2009 to 2012, driven by adults aged 18-to-44-years-old, and disproportionately impacting lower socioeconomic communities. Injuries also increased among young children. Firearm injuries remain a continued public health challenge, and a significant source of ED morbidity and mortality. Background country as a result of firearm injuries (Centers for Disease Firearms have long been the second leading cause of trau- Control and Prevention, 2017), which includes a mix of matic injury-related death in the United States (US) self-inflicted injuries, unintentional injuries, and assault- exceeded only by motor-vehicle crashes (MVC) (Agarwal, related injuries. 2015). Recently, MVC-associated deaths have declined Children and adolescents are particularly vulnerable. while firearm injuries have increased, narrowing this Collectively, traumatic injuries are the leading cause of mortality disparity. In fact, by 2014, the numbers of MVC death among US children and adolescents, with firearm and firearm-related deaths in the US were equivalent injuries alone the fourth-leading cause of mortality (Steinbrook et al., 2017). The US mortality rate from (Centers for Disease Control and Prevention, 2017). In firearm-related injuries is the highest in the developed addition, the US firearm-related fatality rate is 49 times world (Richardson & Hemenway, 2011)and, according to higher for those aged 15-to-24-years-old as compared the Centers for Disease Control and Prevention (CDC), an with those in other high-income countries (Grinshteyn average of over 90 people are killed each day in this & Hemenway, 2016). Despite the ubiquity of firearm injuries and its considerable impact on US health and trauma care, there has not been extensive research * Correspondence: Charles.DiMaggio@nyumc.org into this significant public health burden (Newgard et Department of Surgery, Division of Acute Care and Trauma Surgery, New al., 2016). York University School of Medicine, 462 First Avenue, 15NBV, New York, NY 10016, USA The objective of this study is to describe the epidemiology Department of Population Health, New York University School of Medicine, of firearm-related injuries in the US by examining patient New York, NY, USA © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Avraham et al. Injury Epidemiology (2018) 5:38 Page 2 of 6 data derived from emergency department (ED) visits. Our models not supported by “survey” and “sqlsvy,” with study analyzes the most recent 4 years (2009–2012) of robust co-variances specified to account for clustering by available data from the Agency for Healthcare Research and strata within the NEDS survey design. Quality’s (AHRQ) Healthcare Cost and Utilization Project Injury severity was quantified using the ICD-derived (HCUP) Nationwide Emergency Department Survey Injury Severity Score (ICISS) (Osler et al., 1996)and then (NEDS) database, the largest and most comprehensive categorized as severe vs. non-severe. Osler et al. first pro- publicly-available ED database. Our goal is to boost under- posed ICISS in 1996 as a means of estimating injury seve- standing of US firearm injuries in order to help inform rity using ICD codes in administratively collected hospital public policy. discharge data. It is calculated in two steps. First, survival risk ratios (SRRs) for each injury diagnosis in a data set are Methods “...calculated as the ratio of the number of times a given We queried the HCUP NEDS database for the years ICD-9 code occurs in (surviving patients) to the total num- 2009 to 2012, the most recent four-year period for which ber of occurrences of that code”. Second, the ICISS for an data were available (NEDS did not identify firearm-re- individual patient is calculated as “the product of all the lated injuries in a systematic way prior to 2009). NEDS survival risk ratios for each of an individual patient’sinjuries is constructed based upon a 20% stratified single-cluster (for as many as ten different injuries)” (Seguí-Gómez & sample of hospital-based EDs nationwide. The stratifica- Lopez-Valdes, 2012). The ICISS is then defined as the prob- tion variables included in the sampling strategy are ability of patient surviving their injuries and ranges from 0 intended to make the sample representative of all to 1. ICISS cut-off of less than 0.94 was used to categorize hospital-based EDs. As of 2013, the sample contained patients into those with the most severe injuries (Gedeborg, data on all visits from sampled hospitals, accounting for 2015). This indicator variable identifies patients with a 6% 66% of all ED visits in the 30 participating states. The or greater probability of dying, and has performed well in most recent NEDS core file contains approximately 30 previous analyses, returning an odds ratio of 6.75 (95% CI million annual ED records (Introduction to the Health- 6.48, 7.03) in a multivariate logistic regression analysis of care Cost and Utilization Project’s (HCUP) Nationwide trauma mortality (DiMaggio et al., 2016). Emergency Department Sample (NEDS), 2013, 2015). Descriptive statistical analyses consisted of survey-ad- Hospitals are defined as non-federal general and spe- justed counts, proportions, means, with associated stand- cialty hospitals, including public hospitals and academic ard errors. Annual total and age-specific rates were medical centers. calculated using US Census data obtained from AHRQ as Comma-separated core files for each study year were part of the HCUP family of data products. Age classifica- imported into an R data-frame (R: A language and envir- tions were chosen to both broadly reflect clinical popula- onment for statistical computing, 2015). Survey-adjusted tions and to be generally consistent with census point estimates and standard errors (se) for individual population categories (e.g., 18 to 44 years of age). Add- years were verified against estimates obtained from a itionally, we performed subgroup analyses of children publicly available HCUP online query system (HCUPnet, under 15 consistent with the American College of 2011). All traumatic injury discharges were identified Surgeons’ age criterion for pediatric trauma (American using principal or first-listed International Classification College of Surgeons, 2014). HCUP data also include me- of Diseases (ICD) 9th edition clinical modification (CM) dian household income level by zip code (Introduction to (American Medical Association, 2004) diagnosis codes the Healthcare Cost and Utilization Project’s(HCUP) Na- using previously published methodology (DiMaggio et tionwide Emergency Department Sample (NEDS), 2013, al., 2017), and those patients with firearm-related 2015), which we stratify by income quartile and assessed discharge diagnoses were identified and compared with respect to firearm injuries. Of note, due to the con- with the larger group of traumatic injury discharges. struct of the NEDS database, mortality information is As noted in the HCUP documentation, the available with respect to those patients who were admitted ICD-9-CM coding guidelines define principal diagno- from the ED to the same hospital, however, NEDS does sis as “that condition established after study to be not track information on patients transferred to other fa- chiefly responsible for occasioning the admission of cilities from the ED; additionally, the database excludes the patient to the hospital for care” (HCUP methods patients dead on arrival (DOA) to the ED (Healthcare cost series, 2011). Data were then stratified by age, and injury and utilization project, user support, 2017). intent was determined for each year of the study period. A complete set of notes and code to reproduce or All analyses were based on weighted data adjusted for the adapt the study methods are available from the authors complex survey design of HCUP NEDS using the R pack- on request. The study was approved by the New York ages “survey” (Lumley, 2004)and “sqlsurvey” (Lumley, University School of Medicine Institutional Review 2014). The R “rms” (Harrell, 2016) package was used for Board and conforms to accepted standards for the Avraham et al. Injury Epidemiology (2018) 5:38 Page 3 of 6 reporting of observational studies, excluding elements Table 2 Pediatric patients treated for firearm-related ED injuries by age: adjusted counts (SE) by year, 2009–2012, US hospitals not applicable to a retrospective, repeated cross-sec- tional study design (STROBE Statement, 2007). This Age (years) 2009 2010 2011 2012 study was funded in part by the National Institute of 0–4 309 (44) 306 (38) 385 (44) 364 (44) Child Health and Human Development of the National 5–9 311 (38) 284 (36) 296 (39) 368 (43) Institutes of Health, grant number R01-HD087460 10–14 1400 (87) 1238 (80) 1162 (76) 1483 (87) (DiMaggio). patients (92.2%) in the study group injured by firearms Results survived to discharge. Between 2009 and 2012, there were 101,966,038 (se = With respect to intent, unintentional injuries were 18,980) traumatic injury ED discharges in the US, of most common (55%), followed by assaults (41%) and which 282,542 (se = 1202) were firearm-related. 252,213 self-harm (4%). There was little annual variability. Table 3 (se = 1288), or 89.3%, of all firearm-related ED discharges displays proportions of injuries by intent. were male. In 2009, there were 71,111 (se = 613) total Injury severity proportions, as defined by the afore- firearm-related ED discharges from US hospitals, which, mentioned ICISS criteria, were calculated for each of the by 2012, increased to 75,559 (se = 610), representing a rate study years. Approximately 30% of firearm-related injur- increase of 3.9% (se = 1.2) from 23.2 [se = 0.2] per 100,000 ies were classified as severe. Table 4 displays proportion total US population in 2009 to 24.1 [se = 0.2] in 2012. of severely injured by year. Patients aged 18 to 44 accounted for the largest propor- In total, 99.6% of the 282,542 firearm-related ED tion of overall firearm-related ED discharges with 52,187 discharges had available destination data that identified the (se = 527) firearm-related ED visits (46.3 [se = 0.5] per place of discharge. 136,112 (se = 761) patients were 100,000) in 2009 and 56,644 (se = 528) (49.6 [se = 0.6] per discharged home directly from the ED (representing 48.2% 100,000) in 2012, representing a 7.2% (se = 1.6) relative rate of all firearm-related injuries). An additional 106,927 (se = increase and an absolute increase of 3.3 (se = 0.7) diagnoses 755) patients were admitted to the hospital to which they per 100,000. Table 1 displays overall firearm-related injury presented for their injuries (37.9% of all firearm-related in- counts and annual rates stratified by age. juries). 12,993 (se = 257) patients died in the ED (NB: this Among children under the age of 15, there were 7906 number excludes those who were DOA) representing 4.6% (se = 202) total firearm-related ED discharges in the US of all those with firearm-related injuries. Of note, 31.6% of between 2009 and 2012, representing 2.3% of all all patients who died in the ED from traumatic injuries suf- firearm-related injuries. While most of these injuries oc- fered firearm-related injuries. Table 5 displays complete dis- curred in children between 10 and 14 years old (66.8%), position counts both as a percentage of firearm-related there were significant increases in the number of fire- injuries and as a percentage of all traumatic injury patients arm-related injury discharges in younger children; in chil- discharged from the ED to that particular destination. dren younger than 5-years-old, firearm-related injuries The relationship between firearm-related ED discharges increased from 309 (se = 44.0) in 2009 to 364 (se = 44.0) in and median neighborhood income is shown in Fig. 2. 2012, a 15.8% increase (se = 17.4) in the number of inju- Approximately half (49.6%) of patients resided in neighbor- ries. In children age 5-to-9-years-old, such injuries in- hoods where the median income was in the lowest quartile creased from 311 (se = 37.8) in 2009 to 368 (se = 42.6) in of US neighborhoods, or below $39,750. An additional 2012, an increase of 19.0% (se = 19.2). Table 2 displays 26.4% of patients lived in neighborhoods where the median firearm-related injury counts by year and age group. income was between $39,750–$49,249. Overall, 7.8% of patients in the study population died of their injuries, with only small annual fluctuation Discussion (Fig. 1). 59% of mortalities occurred in the ED, and the According to a recent review, from 2003 to 2012 over remaining 41% in inpatient services. The majority of 313,000 individuals died of firearm-related injuries in the Table 1 Firearm-related ED injury counts (2009–2012) and adjusted annual rates per 100,000 population, US hospitals Age (years) Count (SE) 2009 (age specific rates) 2010 (age specific rates) 2011 (age-specific rates) 2012 (age-specific rates) 0–17 29,739 (388) 11.4 10.1 9.0 9.6 18–44 208,995 (1033) 46.3 47.0 41.5 49.6 45–64 34,504 (421) 10.7 10.7 9.9 10.9 65–84 7619 (202) 4.8 5.1 5.7 5.9 > 84 1686 (92) 5.3 5.6 8.7 10.1 Avraham et al. Injury Epidemiology (2018) 5:38 Page 4 of 6 Fig. 1 Firearm-related case fatality rates, 2009–2012, US hospitals US, outnumbering the combat fatalities in any one of care. This amount exploded to $221 million, $1.4 billion, our nation’s wars, and equaling almost half of battlefield and $41, respectively, when indirect costs such as work loss deaths in all prior US wars combined (US Department were included (Centers for Disease Control and Prevention, of Veterans Affairs, 2017); these injuries also cost an 2017). Much of these costs are absorbed by US taxpayers estimated $174 billion (Wintemute, 2015). Given the (Cook et al., 1999). prevalence of US firearm-related injuries, we sought to Discharge destination data can offer important insight report on recent epidemiologic trends, using a compre- into the nature of injuries. Here, we report that almost half hensive and representative database. of patients with firearm-related injuries who reach EDs During the 4 years from 2009 to 2012, we report that are discharged home without admission; we hypothesize 282,542 individuals were discharged from US EDs with that many of those injuries were likely either related to firearm-related injuries, an average of just over 70,000 non-vascular extremity wounds or torso injuries primarily per year. Overall firearm-related injury rates increased affecting soft tissue, or other non-life-threatening almost 4%, and over 7% in the 18-to-44-year-old demo- firearm-related injuries such as tinnitus or ricochet injur- graphic, a notable and concerning trend. Additionally, ies. This is supported by our finding that the majority of rates of firearm-related ED injuries also increased among firearm-related injuries in our study population resulted children. Despite these trends, the proportion of those from unintentional injuries, while very few resulted from severely injured did not change during the study period. self-harm. In fact, we identified only 21,945 firearm-re- Beyond the toll of human suffering, the cost of care to lated mortalities during the 4-year study period. By com- treat those presenting with firearm-related injuries consti- parison, the CDC reports that 128,933 people were killed tutes a significant economic burden; in 2010, the CDC esti- by firearms during the same period and, of those, 78,783 mated that over $85 million was directly spent on ED people—or 61%—died by suicide (Centers for Disease treat-and-release firearm-related injury care, $852 million Control and Prevention, 2017). This implies that on non-fatal care for those hospitalized with firearm-related self-harm is a highly lethal injury mechanism and, more- injuries, and $186 million on fatal firearm-related injury over, approximately 83% of firearm mortality is occurring Table 3 Proportion of firearm-related ED injuries by intent, Table 4 Proportion of those with firearm-related injuries 2009–2012, US hospitals classified as severe, 2009–2012, US hospitals Unintentional Assault Self-Harm Proportion Severely Injured SE 2009 .537 .420 .043 2009 .306 .004 2010 .557 .395 .048 2010 .309 .004 2011 .552 .401 .047 2011 .306 .004 2012 .0547 .414 .039 2012 .316 .004 Avraham et al. Injury Epidemiology (2018) 5:38 Page 5 of 6 Table 5 Destination of patients with firearm-related ED injuries, 2009–2012, US hospitals Destination Count (SE) Destination as % of all Destination as % of all ED firearm-related injuries patients with traumatic injury classification Routine (home) 136,107 (1073) 48.1% 0.15% Transfer, short term facility 20,159 (317) 7.1% 1.5% Transfer, other 2726 (111) 1.0% 0.3% Home health care 131 (25) 0.04% 0.1% Against medical advice 2392 (110) 0.87% 0.4% Admitted to inpatient service of hospital 106,927 (755) 37.9% 2.1% Died in ED 12,993 (257) 4.6% 31.6% Destination unknown 1102 (76) 0.39% 0.66% Includes Skilled Nursing Facilities and Intermediate Care Facilities Does not include patients who were dead on arrival These patients were not admitted to the hospital outside the reach of US hospitals, highlighting a limitation Furthermore, those relative increases were based on of healthcare systems in addressing this crisis. Our data a population that represents only 2.3% of firearm-re- do, however, underscore an important fact with respect to lated ED discharges. Yet, 9 out of 10 children under traumatic injury deaths occurring in our nation’sEDs: age15who arekilledbyfirearms reside in theUS firearm-related injuries alone are responsible for nearly (Grinshteyn & Hemenway, 2016). one-third of all ED mortality in our trauma population, Overall, our results indicate that, across the US, there and the majority of firearm injuries were unintentional. are over 190 daily ED discharges for firearm-related in- This emphasizes both the acute lethality of firearm injur- juries. A number of studies suggest that these injuries ies—even when hospital providers initiate resuscitative ef- disproportionately occurs in African-American and forts—and suggests an important role for primary other minority communities, irrespective of socioeco- prevention initiatives (e.g. firearm safety education). nomic standing (Kalesan et al., 2016; Kalesan et al., Although children under age 5 and children aged 2014; Srinivasan et al., 2014). We report that nearly half 5–9 sustained a 16% and 19% respective increase in of all such injuries impact those who reside in predom- firearm-related ED discharges, there the data showed inantly poor neighborhoods. In fact, over three-quarters significant annual variability for those age groups. of those sustaining firearm-related injuries live in neigh- borhoods where the median household income is below $49,250, further highlighting the social and economic inequalities of this injury mechanism. Limitations There are limitations to using cross-sectional observa- tional data to capture injury trends. For example, NEDS firearm data are available from 2009, so long-term trends analysis is not yet possible. Furthermore, the dataset relies on administrative ICD-9-CM codes that, while reliable indicators of injury classification in hos- pital admissions (LeMier et al., 2001), have not been widely validated with respect to ED visits. Data may also be subject to individual coding variations and coder error (Hirshon et al., 2009). Also, NEDS lacks longitu- dinal data, so the rate at which patients re-presented with firearm-related injuries could not be determined (i.e., trauma recidivism). Additionally, NEDS does not contain information on the outcomes of patients trans- Fig. 2 Patients treated for firearm-related injuries as a function of ferred to other facilities from the ED or who arrived as median neighborhood income, 2009–2012, US hospitals. $USD DOAs. Avraham et al. Injury Epidemiology (2018) 5:38 Page 6 of 6 Conclusions boston2009/boston2009_proceedings.htm - proceeding_07 Published Dec 18, 2009. Updated Nov 6, 2015. Accessed Sept 15, 2016. Firearm-related injuries treated in US EDs have increased Grinshteyn E, Hemenway D. Violent death rates: the US compared with other over the study period primarily driven by injuries to young high- income OECD countries, 2010. Am J Med. 2016;129(3):266–73. adults. Such firearm injuries are a significant cause of ED Frank E Harrell Jr. RMS: Regression Modeling Strategies. R package version 4.5–0. 2016. https://CRAN.R-project.org/package=rms. Accessed 15 Sept 2016. mortality in the trauma population and disproportionately HCUP methods series: special study on the meaning of the first listed diagnosis impact lower socioeconomic communities. The public on emergency department and ambulatory surgery records. Agency for health significance of these firearm injuries is profound, Healthcare Research and Quality. https://www.hcup-us.ahrq.gov/reports/ methods/2011_03.pdf Published 2011. Accessed 28 Sept 2015. both in terms of its clinical and social implications. Fur- HCUPnet. Healthcare Cost and Utilization Project, 2006–2009. 2011. Agency for ther research must be conducted to inform policy strat- Healthcare Research and Quality. n.d. http://hcupnet.ahrq.gov Accessed Sept egies that will combat this persistent public health crisis. 2015. Healthcare cost and utilization project, user support. Personal correspondence. Abbreviations Communication July 25, 2017. AHRQ: Agency for healthcare research and quality; CDC: Centers for disease Hirshon JM, Warner M, Irvin CB, Niska RW, et al. Research using emergency control and prevention; ED: Emergency department; HCUP: Healthcare cost department–related data sets: current status and future directions. Acad and utilization project; MVC: Motor vehicle crash; NEDS: Nationwide Emerg Med. 2009;16(11):1103–9. emergency department sample; SE: Standard error; US: United States Introduction to the Healthcare Cost and Utilization Project’s (HCUP) Nationwide Emergency Department Sample (NEDS), 2013. Agency for Healthcare Funding Research and Quality. https://www.hcup-us.ahrq.gov/db/nation/neds/ Funding provided by: Grant 1 R01-HD087460, National Institute of Child NEDS2013Introduction.pdf. Published 2015. Accessed 15 Sept 2016. Health and Human Development of the National Institutes of Health. The Kalesan B, Vasan S, Mobily ME, Villarreal MD, et al. State-specific, racial and ethnic funding body had no role in the design of the study, data collection, heterogeneity in trends of firearm-related fatality rates in the USA from 2000 analysis, interpretation or writing of this manuscript. to 2010. BMJ Open. 2014;4(9):e005628. Kalesan B, Vyliparambil MA, Bogue E, Villarreal MD, et al. 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The epidemiology of firearm injuries managed in US emergency departments

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Springer Journals
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2018 The Author(s).
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10.1186/s40621-018-0168-5
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Abstract

Background: Firearm-related injuries cause significant morbidity and mortality in the United States (US), consuming resources and fueling political and public health discourse. Most analyses of firearm injuries are based on fatality statistics. Here, we describe the epidemiology of firearm injuries presenting to US emergency departments (EDs). Methods: We performed a retrospective study of the Healthcare Cost and Utilization Program Nationwide Emergency Department Survey (NEDS) from 2009 to 2012. NEDS is the largest all-payer ED survey in the US containing approximately 30 million annual records. Results include survey-adjusted counts, proportions, means, and rates, and confidence intervals of age-stratified ED discharges for firearm injuries. Results: There were 71,111 (se = 613) ED discharges for firearm injuries in 2009; the absolute number increased 3. 9% (se = 1.2) to 75,559 (se = 610) in 2012. 18-to-44-year-olds accounted for the largest proportion of total injuries with 52,187 (se = 527) in 2009 and 56,644 (se = 528) in 2012—a 7.2% (se = 1.6) relative rate increase and an absolute increase of 3.3/100,000 (se = 0.7). Firearm injuries among children < 5-years-old increase 16%, and 19% among children 5-to-9-years-old. 136,112 (se = 761)—or 48.2%—of those injured were treated and discharged home without admission; 106,927 (se = 755) were admitted. Firearm deaths represented one-third of all trauma mortality. Three-quarters of those injured resided in neighborhoods with median incomes below $49,250. Conclusions: Firearm injuries increased from 2009 to 2012, driven by adults aged 18-to-44-years-old, and disproportionately impacting lower socioeconomic communities. Injuries also increased among young children. Firearm injuries remain a continued public health challenge, and a significant source of ED morbidity and mortality. Background country as a result of firearm injuries (Centers for Disease Firearms have long been the second leading cause of trau- Control and Prevention, 2017), which includes a mix of matic injury-related death in the United States (US) self-inflicted injuries, unintentional injuries, and assault- exceeded only by motor-vehicle crashes (MVC) (Agarwal, related injuries. 2015). Recently, MVC-associated deaths have declined Children and adolescents are particularly vulnerable. while firearm injuries have increased, narrowing this Collectively, traumatic injuries are the leading cause of mortality disparity. In fact, by 2014, the numbers of MVC death among US children and adolescents, with firearm and firearm-related deaths in the US were equivalent injuries alone the fourth-leading cause of mortality (Steinbrook et al., 2017). The US mortality rate from (Centers for Disease Control and Prevention, 2017). In firearm-related injuries is the highest in the developed addition, the US firearm-related fatality rate is 49 times world (Richardson & Hemenway, 2011)and, according to higher for those aged 15-to-24-years-old as compared the Centers for Disease Control and Prevention (CDC), an with those in other high-income countries (Grinshteyn average of over 90 people are killed each day in this & Hemenway, 2016). Despite the ubiquity of firearm injuries and its considerable impact on US health and trauma care, there has not been extensive research * Correspondence: Charles.DiMaggio@nyumc.org into this significant public health burden (Newgard et Department of Surgery, Division of Acute Care and Trauma Surgery, New al., 2016). York University School of Medicine, 462 First Avenue, 15NBV, New York, NY 10016, USA The objective of this study is to describe the epidemiology Department of Population Health, New York University School of Medicine, of firearm-related injuries in the US by examining patient New York, NY, USA © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Avraham et al. Injury Epidemiology (2018) 5:38 Page 2 of 6 data derived from emergency department (ED) visits. Our models not supported by “survey” and “sqlsvy,” with study analyzes the most recent 4 years (2009–2012) of robust co-variances specified to account for clustering by available data from the Agency for Healthcare Research and strata within the NEDS survey design. Quality’s (AHRQ) Healthcare Cost and Utilization Project Injury severity was quantified using the ICD-derived (HCUP) Nationwide Emergency Department Survey Injury Severity Score (ICISS) (Osler et al., 1996)and then (NEDS) database, the largest and most comprehensive categorized as severe vs. non-severe. Osler et al. first pro- publicly-available ED database. Our goal is to boost under- posed ICISS in 1996 as a means of estimating injury seve- standing of US firearm injuries in order to help inform rity using ICD codes in administratively collected hospital public policy. discharge data. It is calculated in two steps. First, survival risk ratios (SRRs) for each injury diagnosis in a data set are Methods “...calculated as the ratio of the number of times a given We queried the HCUP NEDS database for the years ICD-9 code occurs in (surviving patients) to the total num- 2009 to 2012, the most recent four-year period for which ber of occurrences of that code”. Second, the ICISS for an data were available (NEDS did not identify firearm-re- individual patient is calculated as “the product of all the lated injuries in a systematic way prior to 2009). NEDS survival risk ratios for each of an individual patient’sinjuries is constructed based upon a 20% stratified single-cluster (for as many as ten different injuries)” (Seguí-Gómez & sample of hospital-based EDs nationwide. The stratifica- Lopez-Valdes, 2012). The ICISS is then defined as the prob- tion variables included in the sampling strategy are ability of patient surviving their injuries and ranges from 0 intended to make the sample representative of all to 1. ICISS cut-off of less than 0.94 was used to categorize hospital-based EDs. As of 2013, the sample contained patients into those with the most severe injuries (Gedeborg, data on all visits from sampled hospitals, accounting for 2015). This indicator variable identifies patients with a 6% 66% of all ED visits in the 30 participating states. The or greater probability of dying, and has performed well in most recent NEDS core file contains approximately 30 previous analyses, returning an odds ratio of 6.75 (95% CI million annual ED records (Introduction to the Health- 6.48, 7.03) in a multivariate logistic regression analysis of care Cost and Utilization Project’s (HCUP) Nationwide trauma mortality (DiMaggio et al., 2016). Emergency Department Sample (NEDS), 2013, 2015). Descriptive statistical analyses consisted of survey-ad- Hospitals are defined as non-federal general and spe- justed counts, proportions, means, with associated stand- cialty hospitals, including public hospitals and academic ard errors. Annual total and age-specific rates were medical centers. calculated using US Census data obtained from AHRQ as Comma-separated core files for each study year were part of the HCUP family of data products. Age classifica- imported into an R data-frame (R: A language and envir- tions were chosen to both broadly reflect clinical popula- onment for statistical computing, 2015). Survey-adjusted tions and to be generally consistent with census point estimates and standard errors (se) for individual population categories (e.g., 18 to 44 years of age). Add- years were verified against estimates obtained from a itionally, we performed subgroup analyses of children publicly available HCUP online query system (HCUPnet, under 15 consistent with the American College of 2011). All traumatic injury discharges were identified Surgeons’ age criterion for pediatric trauma (American using principal or first-listed International Classification College of Surgeons, 2014). HCUP data also include me- of Diseases (ICD) 9th edition clinical modification (CM) dian household income level by zip code (Introduction to (American Medical Association, 2004) diagnosis codes the Healthcare Cost and Utilization Project’s(HCUP) Na- using previously published methodology (DiMaggio et tionwide Emergency Department Sample (NEDS), 2013, al., 2017), and those patients with firearm-related 2015), which we stratify by income quartile and assessed discharge diagnoses were identified and compared with respect to firearm injuries. Of note, due to the con- with the larger group of traumatic injury discharges. struct of the NEDS database, mortality information is As noted in the HCUP documentation, the available with respect to those patients who were admitted ICD-9-CM coding guidelines define principal diagno- from the ED to the same hospital, however, NEDS does sis as “that condition established after study to be not track information on patients transferred to other fa- chiefly responsible for occasioning the admission of cilities from the ED; additionally, the database excludes the patient to the hospital for care” (HCUP methods patients dead on arrival (DOA) to the ED (Healthcare cost series, 2011). Data were then stratified by age, and injury and utilization project, user support, 2017). intent was determined for each year of the study period. A complete set of notes and code to reproduce or All analyses were based on weighted data adjusted for the adapt the study methods are available from the authors complex survey design of HCUP NEDS using the R pack- on request. The study was approved by the New York ages “survey” (Lumley, 2004)and “sqlsurvey” (Lumley, University School of Medicine Institutional Review 2014). The R “rms” (Harrell, 2016) package was used for Board and conforms to accepted standards for the Avraham et al. Injury Epidemiology (2018) 5:38 Page 3 of 6 reporting of observational studies, excluding elements Table 2 Pediatric patients treated for firearm-related ED injuries by age: adjusted counts (SE) by year, 2009–2012, US hospitals not applicable to a retrospective, repeated cross-sec- tional study design (STROBE Statement, 2007). This Age (years) 2009 2010 2011 2012 study was funded in part by the National Institute of 0–4 309 (44) 306 (38) 385 (44) 364 (44) Child Health and Human Development of the National 5–9 311 (38) 284 (36) 296 (39) 368 (43) Institutes of Health, grant number R01-HD087460 10–14 1400 (87) 1238 (80) 1162 (76) 1483 (87) (DiMaggio). patients (92.2%) in the study group injured by firearms Results survived to discharge. Between 2009 and 2012, there were 101,966,038 (se = With respect to intent, unintentional injuries were 18,980) traumatic injury ED discharges in the US, of most common (55%), followed by assaults (41%) and which 282,542 (se = 1202) were firearm-related. 252,213 self-harm (4%). There was little annual variability. Table 3 (se = 1288), or 89.3%, of all firearm-related ED discharges displays proportions of injuries by intent. were male. In 2009, there were 71,111 (se = 613) total Injury severity proportions, as defined by the afore- firearm-related ED discharges from US hospitals, which, mentioned ICISS criteria, were calculated for each of the by 2012, increased to 75,559 (se = 610), representing a rate study years. Approximately 30% of firearm-related injur- increase of 3.9% (se = 1.2) from 23.2 [se = 0.2] per 100,000 ies were classified as severe. Table 4 displays proportion total US population in 2009 to 24.1 [se = 0.2] in 2012. of severely injured by year. Patients aged 18 to 44 accounted for the largest propor- In total, 99.6% of the 282,542 firearm-related ED tion of overall firearm-related ED discharges with 52,187 discharges had available destination data that identified the (se = 527) firearm-related ED visits (46.3 [se = 0.5] per place of discharge. 136,112 (se = 761) patients were 100,000) in 2009 and 56,644 (se = 528) (49.6 [se = 0.6] per discharged home directly from the ED (representing 48.2% 100,000) in 2012, representing a 7.2% (se = 1.6) relative rate of all firearm-related injuries). An additional 106,927 (se = increase and an absolute increase of 3.3 (se = 0.7) diagnoses 755) patients were admitted to the hospital to which they per 100,000. Table 1 displays overall firearm-related injury presented for their injuries (37.9% of all firearm-related in- counts and annual rates stratified by age. juries). 12,993 (se = 257) patients died in the ED (NB: this Among children under the age of 15, there were 7906 number excludes those who were DOA) representing 4.6% (se = 202) total firearm-related ED discharges in the US of all those with firearm-related injuries. Of note, 31.6% of between 2009 and 2012, representing 2.3% of all all patients who died in the ED from traumatic injuries suf- firearm-related injuries. While most of these injuries oc- fered firearm-related injuries. Table 5 displays complete dis- curred in children between 10 and 14 years old (66.8%), position counts both as a percentage of firearm-related there were significant increases in the number of fire- injuries and as a percentage of all traumatic injury patients arm-related injury discharges in younger children; in chil- discharged from the ED to that particular destination. dren younger than 5-years-old, firearm-related injuries The relationship between firearm-related ED discharges increased from 309 (se = 44.0) in 2009 to 364 (se = 44.0) in and median neighborhood income is shown in Fig. 2. 2012, a 15.8% increase (se = 17.4) in the number of inju- Approximately half (49.6%) of patients resided in neighbor- ries. In children age 5-to-9-years-old, such injuries in- hoods where the median income was in the lowest quartile creased from 311 (se = 37.8) in 2009 to 368 (se = 42.6) in of US neighborhoods, or below $39,750. An additional 2012, an increase of 19.0% (se = 19.2). Table 2 displays 26.4% of patients lived in neighborhoods where the median firearm-related injury counts by year and age group. income was between $39,750–$49,249. Overall, 7.8% of patients in the study population died of their injuries, with only small annual fluctuation Discussion (Fig. 1). 59% of mortalities occurred in the ED, and the According to a recent review, from 2003 to 2012 over remaining 41% in inpatient services. The majority of 313,000 individuals died of firearm-related injuries in the Table 1 Firearm-related ED injury counts (2009–2012) and adjusted annual rates per 100,000 population, US hospitals Age (years) Count (SE) 2009 (age specific rates) 2010 (age specific rates) 2011 (age-specific rates) 2012 (age-specific rates) 0–17 29,739 (388) 11.4 10.1 9.0 9.6 18–44 208,995 (1033) 46.3 47.0 41.5 49.6 45–64 34,504 (421) 10.7 10.7 9.9 10.9 65–84 7619 (202) 4.8 5.1 5.7 5.9 > 84 1686 (92) 5.3 5.6 8.7 10.1 Avraham et al. Injury Epidemiology (2018) 5:38 Page 4 of 6 Fig. 1 Firearm-related case fatality rates, 2009–2012, US hospitals US, outnumbering the combat fatalities in any one of care. This amount exploded to $221 million, $1.4 billion, our nation’s wars, and equaling almost half of battlefield and $41, respectively, when indirect costs such as work loss deaths in all prior US wars combined (US Department were included (Centers for Disease Control and Prevention, of Veterans Affairs, 2017); these injuries also cost an 2017). Much of these costs are absorbed by US taxpayers estimated $174 billion (Wintemute, 2015). Given the (Cook et al., 1999). prevalence of US firearm-related injuries, we sought to Discharge destination data can offer important insight report on recent epidemiologic trends, using a compre- into the nature of injuries. Here, we report that almost half hensive and representative database. of patients with firearm-related injuries who reach EDs During the 4 years from 2009 to 2012, we report that are discharged home without admission; we hypothesize 282,542 individuals were discharged from US EDs with that many of those injuries were likely either related to firearm-related injuries, an average of just over 70,000 non-vascular extremity wounds or torso injuries primarily per year. Overall firearm-related injury rates increased affecting soft tissue, or other non-life-threatening almost 4%, and over 7% in the 18-to-44-year-old demo- firearm-related injuries such as tinnitus or ricochet injur- graphic, a notable and concerning trend. Additionally, ies. This is supported by our finding that the majority of rates of firearm-related ED injuries also increased among firearm-related injuries in our study population resulted children. Despite these trends, the proportion of those from unintentional injuries, while very few resulted from severely injured did not change during the study period. self-harm. In fact, we identified only 21,945 firearm-re- Beyond the toll of human suffering, the cost of care to lated mortalities during the 4-year study period. By com- treat those presenting with firearm-related injuries consti- parison, the CDC reports that 128,933 people were killed tutes a significant economic burden; in 2010, the CDC esti- by firearms during the same period and, of those, 78,783 mated that over $85 million was directly spent on ED people—or 61%—died by suicide (Centers for Disease treat-and-release firearm-related injury care, $852 million Control and Prevention, 2017). This implies that on non-fatal care for those hospitalized with firearm-related self-harm is a highly lethal injury mechanism and, more- injuries, and $186 million on fatal firearm-related injury over, approximately 83% of firearm mortality is occurring Table 3 Proportion of firearm-related ED injuries by intent, Table 4 Proportion of those with firearm-related injuries 2009–2012, US hospitals classified as severe, 2009–2012, US hospitals Unintentional Assault Self-Harm Proportion Severely Injured SE 2009 .537 .420 .043 2009 .306 .004 2010 .557 .395 .048 2010 .309 .004 2011 .552 .401 .047 2011 .306 .004 2012 .0547 .414 .039 2012 .316 .004 Avraham et al. Injury Epidemiology (2018) 5:38 Page 5 of 6 Table 5 Destination of patients with firearm-related ED injuries, 2009–2012, US hospitals Destination Count (SE) Destination as % of all Destination as % of all ED firearm-related injuries patients with traumatic injury classification Routine (home) 136,107 (1073) 48.1% 0.15% Transfer, short term facility 20,159 (317) 7.1% 1.5% Transfer, other 2726 (111) 1.0% 0.3% Home health care 131 (25) 0.04% 0.1% Against medical advice 2392 (110) 0.87% 0.4% Admitted to inpatient service of hospital 106,927 (755) 37.9% 2.1% Died in ED 12,993 (257) 4.6% 31.6% Destination unknown 1102 (76) 0.39% 0.66% Includes Skilled Nursing Facilities and Intermediate Care Facilities Does not include patients who were dead on arrival These patients were not admitted to the hospital outside the reach of US hospitals, highlighting a limitation Furthermore, those relative increases were based on of healthcare systems in addressing this crisis. Our data a population that represents only 2.3% of firearm-re- do, however, underscore an important fact with respect to lated ED discharges. Yet, 9 out of 10 children under traumatic injury deaths occurring in our nation’sEDs: age15who arekilledbyfirearms reside in theUS firearm-related injuries alone are responsible for nearly (Grinshteyn & Hemenway, 2016). one-third of all ED mortality in our trauma population, Overall, our results indicate that, across the US, there and the majority of firearm injuries were unintentional. are over 190 daily ED discharges for firearm-related in- This emphasizes both the acute lethality of firearm injur- juries. A number of studies suggest that these injuries ies—even when hospital providers initiate resuscitative ef- disproportionately occurs in African-American and forts—and suggests an important role for primary other minority communities, irrespective of socioeco- prevention initiatives (e.g. firearm safety education). nomic standing (Kalesan et al., 2016; Kalesan et al., Although children under age 5 and children aged 2014; Srinivasan et al., 2014). We report that nearly half 5–9 sustained a 16% and 19% respective increase in of all such injuries impact those who reside in predom- firearm-related ED discharges, there the data showed inantly poor neighborhoods. In fact, over three-quarters significant annual variability for those age groups. of those sustaining firearm-related injuries live in neigh- borhoods where the median household income is below $49,250, further highlighting the social and economic inequalities of this injury mechanism. Limitations There are limitations to using cross-sectional observa- tional data to capture injury trends. For example, NEDS firearm data are available from 2009, so long-term trends analysis is not yet possible. Furthermore, the dataset relies on administrative ICD-9-CM codes that, while reliable indicators of injury classification in hos- pital admissions (LeMier et al., 2001), have not been widely validated with respect to ED visits. Data may also be subject to individual coding variations and coder error (Hirshon et al., 2009). Also, NEDS lacks longitu- dinal data, so the rate at which patients re-presented with firearm-related injuries could not be determined (i.e., trauma recidivism). Additionally, NEDS does not contain information on the outcomes of patients trans- Fig. 2 Patients treated for firearm-related injuries as a function of ferred to other facilities from the ED or who arrived as median neighborhood income, 2009–2012, US hospitals. $USD DOAs. Avraham et al. Injury Epidemiology (2018) 5:38 Page 6 of 6 Conclusions boston2009/boston2009_proceedings.htm - proceeding_07 Published Dec 18, 2009. Updated Nov 6, 2015. Accessed Sept 15, 2016. Firearm-related injuries treated in US EDs have increased Grinshteyn E, Hemenway D. Violent death rates: the US compared with other over the study period primarily driven by injuries to young high- income OECD countries, 2010. Am J Med. 2016;129(3):266–73. adults. Such firearm injuries are a significant cause of ED Frank E Harrell Jr. RMS: Regression Modeling Strategies. R package version 4.5–0. 2016. https://CRAN.R-project.org/package=rms. Accessed 15 Sept 2016. mortality in the trauma population and disproportionately HCUP methods series: special study on the meaning of the first listed diagnosis impact lower socioeconomic communities. The public on emergency department and ambulatory surgery records. 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