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The Severity of Disparity:
Increasing Injury Intensity Accentuates Disparate Outcomes Following Trauma

Objective. Studies have shown disparities in mortality among racial groups and among those with differing insurance coverage. Our goal was to determine if injury severity affects these disparities. Methods. We classified patients from the 2003–2008 National Trauma Data Banks suffering moderate to severe injuries into six groups based on race/ethnicity and insurance, stratifying by injury severity. Logistic regression compared odds of death between races-ethnicities/insurance groups within these strata. We adjusted for age, gender, Injury Severity Score, Glasgow Coma Scale motor component, hypotension, and mechanism of injury. Results. Patients meeting inclusion criteria numbered 760,598. Disparities between races-ethnicities/insurance groups increased as injury severity worsened. Odds of death for uninsured Black patients compared with insured Whites increased from 1.82 among moderately injured patients to 3.14 among severely injured, hypotensive patients. A similar pattern was seen among uninsured Hispanic patients. Conclusions. Disparities in trauma mortality suffered by minority and uninsured patients, when compared with non-minority and insured patients, worsen with increasing injury.


Health care disparities, injuries, insurance, race/ethnicity, trauma severity indices

Trauma is a leading cause of death in the United States and worldwide. In the U.S., traumatic injury is the number one cause of death for people from the ages of one to 44, and is one of the top five causes of mortality overall.1 [End Page 308]

However, survival after similar traumatic injuries is not equal among all patients. It has been shown that racial and ethnic minorities have worse outcomes after trauma.2,3 The odds of mortality with moderate to severe injury are significantly worse for both Black and Hispanic patients after controlling for important potential confounders such as age, injury severity, and mechanism of injury.4 Similarly, patients without insurance have also been found to have an increased risk of dying. It has been suggested that lack of insurance is more strongly associated with mortality than race/ethnicity alone.4 Looking at approximately 9,000 motor vehicle crash victims treated in state-designated trauma centers, Tepas et al. demonstrated that being uninsured was significantly related to death within 24 hours of injury.5 Other studies have demonstrated that lack of health insurance increases a trauma patient’s adjusted odds of death by approximately 50% during their hospital stay,4 and up to two years post-injury.6

An important consideration in the analysis of these risk factors is the substantial interplay between race/ethnicity* and insurance status. While only 11% of White, non-Hispanic U.S. citizens were uninsured in 2008, 19% of Black and 31% of Hispanics were uninsured.7 Given the link between race/ethnicity and insurance coverage, it can be difficult to separate their individual effects on health outcomes fully.8 Therefore, it is important to assess the combined effect of race/ethnicity and insurance status when evaluating the increased risk of death these potentially vulnerable populations face.


Despite awareness of the increased risks to uninsured and minority populations, we have not yet made substantive progress in reducing them. A major cause for this appears to be the lack of knowledge regarding the underlying mechanisms that drive these inequities. Without developing an understanding of exactly what is driving these inequities, we cannot create effective solutions to eliminate them.

A first step in the process of elucidating these factors is determining if there are certain populations within these groups most at risk. One factor that is well known to directly affect risk of death is injury severity. It is expected that deaths increase as severity increases. However, we do not yet know if the risk of death increases proportionally for patients of all race/ethnicities and insurance statuses. If vulnerable groups can be identified, and if the mechanisms behind race/ethnicity and insurance related disparities can be thus discovered, there is a possibility of targeting policy interventions to reduce or eliminate them.


Our objective was to determine whether severity of injury affects disparities in in-hospital mortality related to race/ethnicity and insurance status. As mortality tends to increase with increasing injury severity, we hypothesize that disparities will become increasingly apparent among populations with worse injuries. Revealing the subset of individuals at the greatest disadvantage in this regard may help direct interventions at the groups most in need. [End Page 309]


Study design and setting

This study was a retrospective analysis of six years of patients included in the merged National Trauma Data Banks (NTDB) between 2003–2008. The NTDB is maintained by the American College of Surgeons-Committee on Trauma and contains approximately three million records from 1,051 participating trauma centers. The Johns Hopkins Hospital and Johns Hopkins School of Public Health Institutional Review Boards approved this study.

Population studied

The population studied comprised trauma patients aged 18–64 years contained within the NDTB and suffering blunt injury with an Injury Severity Score (ISS) ≥ 9 on admission to the emergency department who were White, Black, or Hispanic (Figure 1). Patients from facilities that did not submit any data on insurance status were excluded.* Patients with missing insurance data from facilities that did not systematically fail to report insurance data were included in the final analysis and their insurance data were imputed (see below). Those patients who died on or prior to arrival in the emergency department were also excluded, as they were not at risk for in-hospital mortality. Elderly patients were excluded due to differences in injury mechanism and comorbid disease prevalence.9,10 Pediatric patients were excluded due to differences in injury patterns, pathophysiology, and treatment needs.11 Patients of “other” race/ethnicity or ethnicities were excluded due to anticipated small cell size, as our primary covariate was categorical. Only patients with blunt injuries were included in light of the known differences in mortality between blunt, penetrating, and burn injury mechanisms, so that a more homogenous patient population could be analyzed.12,13

Primary data analysis

Demographic data on age, gender, and insurance status were assessed. Insurance status was classified as insured (private/commercial insurance, public insurance (Medicaid) or other governmental insurance) or uninsured. After testing for and finding an interaction between race/ethnicity and insurance status, patients were stratified by race/ethnicity and insurance status into White insured, Black insured, Hispanic insured, White uninsured, Black uninsured, and Hispanic uninsured. Univariate analysis was undertaken using Pearson’s chi squared test for categorical variables and student’s t test for continuous variables. Crude mortality rates were also calculated within each race-ethnicity/insurance strata for the different injury levels.

Multivariable logistic regression was employed to compare adjusted odds of in-hospital mortality for the six race-ethnicities/insurance groups with White insured patients as the reference. The multivariate model controlled for patient-level characteristics known to predict mortality after trauma including age, gender, injury severity score (ISS), Glasgow Coma Scale motor score (GCS-M),14 presence of hypotension (systolic blood pressure <90) on arrival to the emergency department,15 year of admission, and mechanism of injury. To statistically account for inter-hospital variations in mortality, cluster-correlated robust estimate of variance that adjusted for within-hospital cluster correlation was used.16 In order to investigate potential differences in magnitude of disparities between different injury severity groups, patients were further stratified [End Page 310] into three different degrees of injury: a moderately injured group consisting of patients with ISS 9–15 on presentation to the emergency department, a normotensive severely injured group with ISS > 15 and systolic blood pressure (SBP) > 90, and a hypotensive severely injured group with ISS > 15 and SBP < 90.

Given the importance of co-morbidity in predicting mortality outcomes, we performed a subset analysis focusing on younger patients aged 18–40 who are presumed to have few co-morbidities. We chose to perform this subset analysis instead of including comorbidity variables withinin the NTDB, because of considerable inconsistency in reporting. Missing data have also been a matter of concern in trauma injury analysis. In order to combat this, in all analyses we utilized multiple imputation, which has been shown to be a valid tool to account for missing values within the dataset.17 We imputed the following variables: death, insurance, race/ethnicity, type of injury, gender, and presence of hypotension, as they had the most missing data among variables considered for the analysis. We imputed our dataset five times and specified initial values for our random number seeds in order to allow for reproducibility. We then compared the point estimates from our regression model from the imputed dataset to the point estimates from our original dataset. Statistical analyses were performed using Stata MP Statistical Software: Release 11, StataCorp, College Station, TX, 2009. Statistical significance was set at p < .05.


There were no qualitative differences in regression output between the original and the imputed datasets (Supplemental Table 2, available upon request from the authors), and we were therefore retained patients who were missing variables in the original data-set. The percentages of patients missing data for the following variables are shown in parentheses: death (2.5%), insurance (16%), race/ethnicity (6%), type of injury (1%), gender (<1%), and presence of hypotension (5.5%). There were 2,996,725 patient cases collected into the NTDB from the years 2003–2008. Of these, 1,673,707 were between the ages of 18–64 and either White, Black, or Hispanic. The final analysis was limited to 760,598 patients with ISS ≥ 9 and blunt injuries only (Figure 1). The study population was 71% men, 15% Black, 11% Hispanic, and 18% uninsured. The overall median age was 39 years, although uninsured and minority patients were younger (Table 1).

Crude mortality for all injury levels ranged from 4.1% for White insured patients, to 6.6% and 6.0% for uninsured Black and Hispanic patients. The race-ethnicity/insurance composition was comparable across the three injury levels, with 44–48% White insured patients in each category. There were slightly more uninsured and minority patients who were the most severely injured: moderately injured 11.2% White uninsured, 3.3% Black uninsured, 3.5% Hispanic uninsured vs. hypotensive severely injured 13.4% White uninsured, 4.0% Black uninsured, 3.7% Hispanic uninsured (p < .001).

Unadjusted mortality had a six-fold difference between injury levels, and the difference between the races-ethnicities/insurance groups also enlarged dramatically as the severity of the injury increased (Figure 2).

Adjusted analysis also revealed a greater difference in mortality disparities between races-ethnicities/insurance groups as the degree of injury worsened (Table 2). Among [End Page 311]

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Figure 1.

Patient Inclusion Criteria.

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Table 1.

Baseline Characteristics by Race/Insurance Group

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Figure 2.

Crude mortality by race and insurance for injury levels.

moderately injured patients, odds of death were significantly higher for Black patients and White uninsured patients, but no statistically significant difference in mortality was found between Hispanic and White patients. However, for normotensive severely injured patients, odds of death for all uninsured and minority patients were significantly higher than for the insured White reference group. These disparities were even greater for severely injured patients who presented with hypotension. [End Page 314]

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Table 2.

Imputed Dataset: Adjusted Odds of Death for Race/Insurance Groups Stratified by Injury Levela

The sensitivity analysis that was performed on patients under the age of 40 also revealed an increase in the odds of death for higher injury levels (Supplemental Table 1, available from the authors upon request).


This study demonstrates that disparities in trauma mortality affecting uninsured and minority patients when compared with non-minority and insured patients increase as severity increases. A severely injured, uninsured Black patient in this study was 3.2 times more likely to die than a similarly injured insured White patient. However, significant race-ethnicity/insurance-based inequities in survival were not found in moderately injured patients.

Several previous studies have demonstrated similar race/ethnicity and insurance-based disparities in trauma outcomes;26 however, the causal factors that drive these disparities, and the populations and subpopulations at highest risk, are still largely unknown. This study found that among trauma victims, the patients most affected by disparities are those who suffer the most severe injuries. In order to understand better the mechanisms driving these disparities, it is important to examine which factors might create differences in outcomes for severely injured patients that would not exist, or exist to a lesser extent, for only moderately injured people.

As injury severity increases, immediate, effective and intensive medical management become increasingly important. One potential driver of disparities between uninsured/minority and insured/non-minority patients may be that they are unable to access the [End Page 315] increased level of care required to treat severe injuries. More specifically, uninsured/minority patients may experience differences in the timeliness of and appropriate delivery of emergent life-saving procedures. Black patients have been shown to have longer emergency department wait times than White patients,18 and are less likely to obtain a CT scan for headache.19 There is evidence to suggest that uninsured trauma patients are more likely to be transferred to another hospital, resulting in a delay in treatment.2023 White and colleagues reported that uninsured patients who present With equally high acuity are less likely to receive radiographic imaging than insured patients.24 Weitzman et al. showed that Black patients with chest pain are half as likely to receive thrombolysis for myocardial infarction.25 Further, Cuthbert et al. reported that ethnic minorities are more likely to be discharged directly home after moderate to severe traumatic brain injury.26 It is possible that these or other subtle differences in care may matter more for severely injured patients, who are in greater need of their timely provision. Due to the retrospective nature of this study, however, we were limited to the variables contained within the dataset and were not able to evaluate further these potential causes within this study. Further research is needed in order to assess in detail the contribution of these factors.

Another potential contributing factor to the worsening of gap in mortality between insured or non-minority patients and uninsured or minority patients may be that the latter present predominately to hospitals that lack sufficient resources to handle severely injured patients. It has been shown that hospitals predominantly serving minority patients have worse outcomes after trauma.27 However, there are varying data concerning the quality of care provided by hospitals caring predominately for the under-served in the U.S. Safety-net hospitals have been shown to have equivalent outcomes to non-safety net hospitals, for both traumatic28 and non-traumatic care.29 Part of this uncertainty may be due to differing definitions in what qualifies as a safety-net hospital. McHugh et al. showed that varying definitions of safety-net hospital capture different hospitals and yield differing relative outcomes.30 None of the definitions of safety-net hospitals reviewed by the authors were based on serving a majority of ethnic or racial minorities, and none looked at the breakdown of severe versus moderate injury. It is possible that safety-net hospitals that specifically care for minority populations do not have the resources to care for severely injured patients, even though their outcomes for moderately injured patients may be equivalent to other hospitals. More work is needed to understand the composition of patient populations in safety-net hospitals and the quality and extent of care that is provided at hospitals serving these populations.

Another important explanation to consider is whether lacking health insurance lowers baseline health, creating poorer physiologic reserves that are unable to withstand severe injury. This is especially important to consider given that insurance status appears to be an even greater predictor of mortality than even race/ethnicity.4 However, given the relatively young median age, especially in the uninsured minority populations, this is unlikely to be the sole contributing factor. Additionally, the subset analysis performed on patients under the age of 40, who are unlikely to have multiple co-morbidities, shows an even greater disparity between insured or non-minority patients and uninsured or minority patients among severely injured patients. [End Page 316]

There are several threats to the internal validity of this study based on our study design and available data. First, this study is retrospective and cross-sectional and therefore cannot be used to demonstrate causality, but rather to highlight associations of interest. Future studies should investigate the effects of injury severity on disparities after trauma in a prospective fashion. One limitation of the NTDB is that data reporting is voluntary and certain data are not consistently reported. In order to address this issue, we were able to impute missing data for several variables. Reassuringly, the analysis of the original, non-imputed dataset was reliably consistent with the imputed analysis.17 However, the substantial inconsistency in reporting of comorbid conditions within the NTDB precluded analysis of the effects of comorbidity as a potential confounder. In an attempt to address this limitation, we did perform a subset analysis of patients aged 18–40 years in whom the prevalence of comorbid conditions and the consequences of those comorbid conditions should be much less. This method has been carried out in several other studies throughout the trauma literature.28,31 This examination was reassuring, as it demonstrated no qualitative differences between the age-restricted analysis and that carried out on the entire study population. While we attempted to address these known confounders, there are likely other confounding variables that are unmeasured or unknown that may have biased our results.

This study focused on the adult trauma population, thus limiting its generalizability to the elderly and pediatric populations. Additional studies are needed to investigate the association between injury severity and disparities at the extremes of age. Although it has been shown that insurance coverage is a strong predictor of outcomes for patients suffering both blunt and penetrating injury,32 this study analyzed only patients with blunt injuries. The results from this study thus may not be generalizable to patients with other mechanisms of injury such as penetrating injuries and burns. Additional studies are needed to elucidate the relationship between trauma disparities and injury severity for these groups.

Uninsured minority patients suffer greater odds of death than insured and White patients after a similar injury. The results of this study revealed this disparity to be the greatest among those patients who have the most severe injuries and are most likely to die. Understanding insurance and race/ethnicity dependent differences is an essential step toward eliminating health care disparities. The exact mechanisms that lead to the observed higher mortality rates need further investigation. Potential contributors, such as differences in timeliness and appropriateness of emergent care, quality of care in hospitals that treat uninsured and minority patients, pre-hospital care, time to operation, co-morbidities, impact of income level and other disparities that may also affect survival all warrant careful scrutiny. Understanding the underlying cause of the disparities will permit the creation of programs and policies to close the gap between patients of different races/ethnicities and insurance statuses.

Lia I. Losonczy, P. Logan Weygandt, Cassandra V. Villegas, Erin C. Hall, Eric B. Schneider, Lisa A. Cooper, Edward E. Cornwell, Elliott R. Haut, David T. Efron, and Adil H. Haider

The authors are affiliated with the Center for Surgical Trials and Outcomes Research, Department of Surgery, Johns Hopkins University School of Medicine [LIL, PLW, ECH, EBS, ERH, DTE, AHH]; Highland General Hospital, Department of Emergency Medicine, in Oakland, California [LIL]; Northwestern Memorial Hospital, Department of Emergency Medicine, in Chicago, IL [PLW]; the University of Arizona, Department of Surgery, in Tucson [CVV]; Johns Hopkins University School of Medicine, Department of Medicine [LAC]; and Howard University College of Medicine, Department of Surgery [EEC].


Meetings: This research has been presented as an oral presentation at the 7th Annual Academic Surgical Congress, Las Vegas, N.V., 2012 and a poster presentation at 6th [End Page 317] Annual Johns Hopkins Department of Surgery Research Poster Session, Baltimore, M.D., 2011. This research has also been presented as a poster at the NIH/NIMHD 2012 Science of Eliminating Health Disparities Summit in Washington, D.C. in November of 2012.

Conflicts of Interest and Grant Support: Financial support provided to Adil Haider for this work was provided by: National Institutes of Health/NIGMS K23GM093112-01 and American College of Surgeons C. James Carrico Fellowship for the study of Trauma and Critical Care. Lia Losonczy received a 5 TL1 RR 25007-4 NIH grant. Cassandra Villegas received a Johns Hopkins University School of Medicine Pre-Doctoral Fellowship grant. P Logan Weygandt received a NIH 5TL1RR025007 grant as part of the Johns Hopkins University School of Medicine Pre-Doctoral Clinical Research Training Program. Elliot Haut has a pending AHRQ grant for work outside of this manuscript. Lisa Cooper has a K24 grant from NHLBI. Otherwise, the authors have no relevant financial disclosures or conflicts of interest.


* We use “race/ethnicity” as a term covering the groups Black, White, and Hispanic, under the assumption that Black and White are categorized as races and Hispanic as an ethnicity. When the term appears in combination with “insurance status,” it is written this way: race-ethnicity/insurance status.

* Data-reporting to the NTDB is voluntary and some institutions systematically do not submit patient insurance.

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