Frequency and Predictors of Trauma Transfer Futility to a Rural Level I Trauma Center.

INTRODUCTION: Transfer of trauma patients whose injuries are deemed unsurvivable, often results in early death or transition to comfort care and could be considered misuse of health care resources. This is particularly true where tertiary care resources are limited. Identifying riskfactors for and predicting futile transfers could reduce this impact and help to optimize triage and management. METHODS: A retrospective study of interfacility trauma transfers to a single rural Level I rauma center from 2014 to 2019. Futility was defined as death, hospice, or declaration of comfort measures within 48 h of transfer without procedural or radiographic intervention at the accepting center. Multiple logistic regressions identified independent predictors of futile transfers. The predictive power of Mechanism,Glasgow coma scale, Age, and Arterial pressure (MGAP), an injury severity score based on Mechanism, Glasgow coma scale, Age, and systolic blood Pressure, were evaluated. RESULTS: Of the 3368 trauma transfers, 37 (1.1%) met criteria as futile. Futile transfers occurred among patients who were significantly older with falls as the most common mechanism. Age, Glasgow coma scale, systolic blood Pressure and Injury Severity Score were significant (P < 0.05) independent predictors of futile transfer. MGAP had a high predictive power area under the receiver operating characteristic (AUROC 0.864, 95% confidence interval 0.803-0.925) for futility. CONCLUSIONS: A small proportion (1.1%) of transfers to a rural Level I trauma center met criteria for futility. Predictive tools, such as MGAP scoring, can provide objective criteria for evaluation of transfer necessity and prompt care pathways that involve pre-transfer communications, telemedicine, and/or patient centered goals of care discussions. Such tools could be used in conjunction with a more granular assessment regarding potential operational barriers to reduce futile transfers and to enhance optimization of resource utilization in low-resource service areas.

Practice Variation in Vena Cava Filter Use Among Trauma Centers in the National Trauma Database.

BACKGROUND: Agreement regarding indications for vena cava filter (VCF) utilization in trauma patients has been in flux since the filter's introduction. As VCF technology and practice guidelines have evolved, the use of VCF in trauma patients has changed. This study examines variation in VCF placement among trauma centers. MATERIALS AND METHODS: A retrospective study was performed using data from the National Trauma Data Bank (2005-2014). Trauma centers were grouped according to whether they placed VCFs during the study period (VCF+/VCF-). A multivariable probit regression model was fit to predict the number of VCFs used among the VCF+ centers (the expected [E] number of VCF per center). The ratio of observed VCF placement (O) to expected VCFs (O:E) was computed and rank ordered to compare interfacility practice variation. RESULTS: In total, 65,482 VCFs were placed by 448 centers. Twenty centers (4.3%) placed no VCFs. The greatest predictors of VCF placement were deep vein thrombosis, spinal cord paralysis, and major procedure. The strongest negative predictor of VCF placement was admission during the year 2014. Among the VCF+ centers, O:E varied by nearly 500%. One hundred fifty centers had an O:E greater than one. One hundred sixty-nine centers had an O:E less than one. CONCLUSIONS: Substantial variation in practice is present in VCF placement. This variation cannot be explained only by the characteristics of the patients treated at these centers but could be also due to conflicting guidelines, changing evidence, decreasing reimbursement rates, or the culture of trauma centers.

Comparisons of New HIV Rapid Test Kit Performance.

The development of rapid point-of-care tests for HIV infection has greatly reduced the problem of failure to return for test results. Test manufacturers are now developing test kits that can test for two or even three diseases at the same time, multiple-disease test kits. This study reports on the sensitivity and specificity of HIV tests when included on multi-disease test kits. 1029 participants were recruited from 2011 to 2014. HIV test kit sensitivities ranged from 91.1 to 100%, and the HIV test kit specificities from 99.5 to 100%. The two HIV kits which used oral fluid instead of blood performed well.

Summary goodness-of-fit statistics for binary generalized linear models with noncanonical link functions.

Generalized linear models (GLM) with a canonical logit link function are the primary modeling technique used to relate a binary outcome to predictor variables. However, noncanonical links can offer more flexibility, producing convenient analytical quantities (e.g., probit GLMs in toxicology) and desired measures of effect (e.g., relative risk from log GLMs). Many summary goodness-of-fit (GOF) statistics exist for logistic GLM. Their properties make the development of GOF statistics relatively straightforward, but it can be more difficult under noncanonical links. Although GOF tests for logistic GLM with continuous covariates (GLMCC) have been applied to GLMCCs with log links, we know of no GOF tests in the literature specifically developed for GLMCCs that can be applied regardless of link function chosen. We generalize the Tsiatis GOF statistic originally developed for logistic GLMCCs, (TG), so that it can be applied under any link function. Further, we show that the algebraically related Hosmer-Lemeshow (HL) and Pigeon-Heyse (J(2) ) statistics can be applied directly. In a simulation study, TG, HL, and J(2) were used to evaluate the fit of probit, log-log, complementary log-log, and log models, all calculated with a common grouping method. The TG statistic consistently maintained Type I error rates, while those of HL and J(2) were often lower than expected if terms with little influence were included. Generally, the statistics had similar power to detect an incorrect model. An exception occurred when a log GLMCC was incorrectly fit to data generated from a logistic GLMCC. In this case, TG had more power than HL or J(2) .

A goodness-of-fit test for the proportional odds regression model.

We examine goodness-of-fit tests for the proportional odds logistic regression model-the most commonly used regression model for an ordinal response variable. We derive a test statistic based on the Hosmer-Lemeshow test for binary logistic regression. Using a simulation study, we investigate the distribution and power properties of this test and compare these with those of three other goodness-of-fit tests. The new test has lower power than the existing tests; however, it was able to detect a greater number of the different types of lack of fit considered in this study. Moreover, the test allows for the results to be summarized in a contingency table of observed and estimated frequencies, which is a useful supplementary tool to assess model fit. We illustrate the ability of the tests to detect lack of fit using a study of aftercare decisions for psychiatrically hospitalized adolescents. The test proposed in this paper is similar to a recently developed goodness-of-fit test for multinomial logistic regression. A unified approach for testing goodness of fit is now available for binary, multinomial, and ordinal logistic regression models.

Current patterns of trauma center proliferation have not led to proportionate improvements in access to care or mortality after injury: An ecologic study.

BACKGROUND: Timely access to high-level (I/II) trauma centers (HLTCs) is essential to minimize mortality after injury. Over the last 15 years, there has been a proliferation of HLTC nationally. The current study evaluates the impact of additional HLTC on population access and injury mortality. METHODS: A geocoded list of HLTC, with year designated, was obtained from the American Trauma Society, and 60-minute travel time polygons were created using OpenStreetMap data. Census block group population centroids, county population centroids, and American Communities Survey data from 2005 and 2020 were integrated. Age-adjusted nonoverdose injury mortality was obtained from CDC Wide-ranging Online Data for Epidemiologic Research and the Robert Wood Johnson Foundation. Geographically weighted regression models were used to identify independent predictors of HLTC access and injury mortality. RESULTS: Over the 15-year (2005-2020) study period, the number of HLTC increased by 31.0% (445 to 583), while population access to HLTC increased by 6.9% (77.5-84.4%). Despite this increase, access was unchanged in 83.1% of counties, with a median change in access of 0.0% (interquartile range, 0.0-1.1%). Population-level age-adjusted injury mortality rates increased by 5.39 per 100,000 population during this time (60.72 to 66.11 per 100,000). Geographically weighted regression controlling for population demography and health indicators found higher median income and higher population density to be positively associated with majority (≥50%) HLTC population coverage and negatively associated with county-level nonoverdose mortality. CONCLUSION: Over the past 15 years, the number of HLTC increased 31%, while population access to HLTC increased only 6.9%. High-level (I/II) trauma center designation is likely driven by factors other than population need. To optimize efficiency and decrease potential oversupply, the designation process should include population level metrics. Geographic information system methodology can be an effective tool to assess optimal placement. LEVEL OF EVIDENCE: Prognostic and Epidemiological; Level IV.

Comparison of hospital mortality rates after burn injury in New York State: a risk-adjusted population-based observational study.

BACKGROUND: Severity-adjusted mortality is an unequivocal measure of burn care success. Hospitals can be compared on this metric using administrative data because information required for calculating statistically adjusted risk of mortality is routinely collected on hospital admission. METHODS: The New York State Department of Health provided information on all 13,113 thermally injured patients hospitalized at 1 of 194 hospitals between 2004 and 2008. We compared hospital survival rates using a random effects logistic model of mortality that incorporated age and several predictors that were present on admission and captured as International Classification of Diseases-9 codes: burn surface area, inhalation injury, three measures of physiologic compromise, and four medical comorbidities. Hospitals were compared on the adjusted odds of death and the number of excess deaths. RESULTS: Overall mortality was 3.2%. Nine high-volume hospitals (>100 patients/year) cared for 83% of patients with burn injuries. Overall variability of the odds of mortality among these high-volume centers was modest (median odds ratio=1.2) and we found little evidence for differences in the adjusted odds of mortality. A secondary analysis of the 185 low-volume hospitals that cared for 2,235 patients disclosed only 24 deaths. When examined in aggregate, these hospitals had better than predicted risk-adjusted mortality; a logical explanation is judicious case selection. CONCLUSIONS: Administrative hospital discharge data are extensive and comparably enough collected to allow comparison of the performance of burn centers. Risk-adjusted models show that patients have statistically indistinguishable risk-adjusted odds of mortality regardless of which hospital in New York State cared for them.

Administration of epinephrine by advanced emergency medical technicians for out-of-hospital cardiac arrest in a rural emergency medical services system.

OBJECTIVE: Epinephrine in out-of-hospital cardiac arrest (OHCA) remains controversial and understudied in rural emergency medical services (EMS) systems. We evaluated the effects of allowing advanced emergency medical technicians (AEMTs) to administer epinephrine during OHCA in a rural EMS system. METHODS: An interrupted time series study was conducted using statewide EMS electronic records. Patients with OHCA before (phase I) and after (phase II) a protocol change expanding the AEMT scope of practice to include epinephrine for OHCA were identified. Number and timing of initial epinephrine administration, return of spontaneous circulation, and 30-day survival rates were compared using descriptive statistics, logistic regression, regression discontinuity, and propensity score matching. RESULTS: A total of 1037 OHCAs met the inclusion criteria. In phase 1 compared with phase 2, 275 (56.12%) patients received epinephrine versus 624 (83.53%; P < 0.001). The mean time to first administration of epinephrine for unwitnessed and bystander-witnessed OHCA were 11.73 minutes versus 8.17 minutes (P < 0.001) and 11.59 minutes versus 8.85 minutes (P < 0.01), respectively. Unadjusted analysis showed a decrease in 30-day survival rates among patients receiving epinephrine from 18.01% to 12.66% (P < 0.05). Adjusted analysis showed an increase in 30-day survival with decreased time to first epinephrine dose(OR 0.960, 1.005; 95% confidence interval, 0.929, 0.992). CONCLUSION: Adding epinephrine for OHCA to the AEMT scope of practice was associated with an increased percentage of patients receiving epinephrine and decreased time to first administration of epinephrine for patients with unwitnessed OHCA. Unadjusted analysis showed a decrease in 30-day survival rates among patients receiving epinephrine. Adjusted analysis found that earlier administrationof epinephrine was associated with increased ROSC and 30-day survival.

Geographic Coverage and Verification of Trauma Centers in a Rural State: Highlighting the Utility of Location Allocationfor Trauma System Planning.

BACKGROUND: Care at verified trauma centers has improved survival and functional outcomes, yet determining the appropriate location of potential trauma centers is often driven by factors other than optimizing system-level patient care. Given the importance of transport time in trauma, we analyzed trauma transport patterns in a rural state lacking an organized trauma system and implemented a geographic information system to inform potential future trauma center locations. STUDY DESIGN: Data were collected on trauma ground transport during a 3-year period (2014 through 2016) from the Statewide Incident Reporting Network database. Geographic information system mapping and location-allocation modeling of the best-fit facility for trauma center verification was computed using trauma transport patterns, population density, road network layout, and 60-minute emergency medical services transport time based on current transport protocols. RESULTS: Location-allocation modeling identified 2 regional facilities positioned to become the next verified trauma centers. The proportion of the Vermont population without access to trauma center care within 60 minutes would be reduced from the current 29.68% to 5.81% if the identified facilities become verified centers. CONCLUSIONS: Through geospatial mapping and location-allocation modeling, we were able to identify gaps and suggest optimal trauma center locations to maximize population coverage in a rural state lacking a formal, organized trauma system. These findings could inform future decision-making for targeted capacity improvement and system design that emphasizes more equitable access to trauma center care in Vermont.

Simplified estimates of the probability of death after burn injuries: extending and updating the baux score.

BACKGROUND: : Generations of clinicians have used the Baux score, defined as the sum of age in years and percent body burn, to predict percent mortality after trauma, but advances in burn care have rendered the predictions of this score too pessimistic. Additionally, this score does not include the effects of inhalation injury. METHODS: : We revised the Baux score to include inhalation injury and recalibrated its predictions using a single-term logistic regression model developed using data on 39,888 burned patients provided by the national burn repository. We compared this revised Baux score to a more complex logistic regression model derived from the same data set and predictors. RESULTS: : A preliminary logistic regression model showed that age and percent burn contribute almost equally to mortality and further that the presence of inhalation injury added the equivalent of 17 years (or 17% burn). These observations suggested a revised Baux Score:Age + Percent Burn + 17 * (Inhalation Injury, 1 = yes, 0 = no)A logistic model based on the Revised Baux Score performed well, but a more complex model obtained using modern statistical model building tools had better discrimination and calibration. CONCLUSIONS: : Our proposed revised Baux score is simple enough for mental calculation, and its inverse logit transformation (provided with a calculator or nomogram) can provide precise predictions of mortality. Better predictions can be obtained using our more complex statistical model. Burn surgeons and nurses accustomed to using the original Baux score may welcome an updated version.

A trauma mortality prediction model based on the ICD-10-CM lexicon: TMPM-ICD10.

BACKGROUND: Outcome prediction models allow risk adjustment required for trauma research and the evaluation of outcomes. The advent of ICD-10-CM has rendered risk adjustment based on ICD-9-CM codes moot, but as yet no risk adjustment model based on ICD-10-CM codes has been described. METHODS: The National Trauma Data Bank provided data from 773,388 injured patients who presented to one of 747 trauma centers in 2016 with traumatic injuries ICD-10-CM codes and Injury Severity Score (ISS). We constructed an outcome prediction model using only ICD-10-CM acute injury codes and compared its performance with that of the ISS. RESULTS: Compared with ISS, the TMPM-ICD-10 discriminated survivors from non-survivors better (ROC TMPM-ICD-10 = 0.861 [0.860-0.872], ROC [reviever operating curve] ISS = 0.830 [0.823-0.836]), was better calibrated (HL [Hosmer-Lemeshow statistic] TMPM-ICD-10 = 49.01, HL ISS = 788.79), and had a lower Akaike information criteria (AIC TMPM-ICD10 = 30579.49; AIC ISS = 31802.18). CONCLUSIONS: Because TMPM-ICD10 provides better discrimination and calibration than the ISS and can be computed without recourse to Abbreviated Injury Scale coding, the TMPM-ICD10 should replace the ISS as the standard measure of overall injury severity for data coded in the ICD-10-CM lexicon. LEVEL OF EVIDENCE: Prognostic/Epidemiologic, level II.

Variation in readmission rates among hospitals following admission for traumatic injury.

INTRODUCTION: Readmission following hospital discharge is both common and costly. The Hospital Readmission Reduction Program (HRRP) financially penalizes hospitals for readmission following admission for some conditions, but this approach may not be appropriate for all conditions. We wished to determine if hospitals differed in their adjusted readmission rates following an index hospital admission for traumatic injury. PATIENTS AND METHODS: We extracted from the AHRQ National Readmission Dataset (NRD) all non-elderly adult patients hospitalized following traumatic injury in 2014. We estimated hierarchal logistic regression models to predicted readmission within 30 days. Models included either patient level predictors, hospital level predictors, or both. We quantified the extent of hospital variability in readmissions using the median odds ratio. Additionally, we computed hospital specific risk-adjusted rates of readmission and number of excess readmissions. RESULTS: Of the 177,322 patients admitted for traumatic injury 11,940 (6.7%) were readmitted within 30 days. Unadjusted hospital readmission rates for the 637 hospitals in our study varied from 0% to 20%. After controlling for sources of variability the range for hospital readmission rates was between 5.5% and 8.5%. Only 2% of hospitals had a random intercept coefficient significantly different from zero, suggesting that their readmission rates differed from the mean level of all hospitals. We also estimated that in 2014 only 11% of hospitals had more than 2 excess readmissions. Our multilevel model discriminated patients who were readmitted from those not readmitted at an acceptable level (C = 0.74). CONCLUSIONS: We found little evidence that hospitals differ in their readmission rates following an index admission for traumatic injury. There is little justification for penalizing hospitals based on readmissions after traumatic injury.

Population-Based Analysis of Firearm Injuries among Young Children in the United States, 2010-2015.

Firearm violence in the United States knows no age limit. This study compares the survival of children younger than five years to children and adolescents of age 5-19 years who presented to an ED for gunshot wounds (GSWs) in the United States to test the hypothesis of higher GSW mortality in very young children. A study of GSW patients aged 19 years and younger who survived to reach medical care was performed using the Nationwide ED Sample for 2010-2015. Hospital survival and incidence of fatal and nonfatal GSWs in the United States were the study outcomes. A multilevel logistic regression model estimated the strength of association among predictors of hospital mortality. The incidence of ED presentation for GSW is as high as 19 per 100,000 population per year. Children younger than five years were 2.7 times as likely to die compared with older children (15.3% vs 5.6%). Children younger than one year had the highest hospital mortality, 33.1 per cent. The mortality from GSW is highest among the youngest children compared with older children. This information may help policy makers and the public better understand the impact of gun violence on the youngest and most vulnerable Americans.

Oestrogen treatment for tall stature in girls: estimating the effect on height and the error in height prediction.

OBJECTIVE: To determine the effect of oestrogen treatment on attenuating the growth of tall girls after adjusting for error in height prediction. DESIGN: Retrospective cohort study. PATIENTS: Tall girls assessed by Australian paediatric endocrinologists between 1959 and 1993. A total of 279 girls received oestrogen treatment (diethylstilboestrol or ethinyl oestradiol) and 367 girls were assessed but not treated. MEASUREMENTS: Estimated mature height (EMH) was calculated using radiographic assessment of bone age in adolescence. Final adult height was self-reported at follow-up. To control for error in the EMH predictions and their different distributions by treatment status, pairs of treated and untreated girls, matched on EMH within 1 cm, were selected for analysis. Covariate adjusted estimates of treatment effect (final height - EMH) were calculated. RESULTS In the sample of 108 matched pairs, the mean difference between the final height and EMH was -1.4 cm (SE 0.29) in the treated group and 1.1 cm (SE 0.23) in the untreated group, giving an unadjusted treatment effect of -2.5 cm (95% CI -3.2 to 1.8). A regression model based on 107 pairs of treated and untreated girls contained a significant interaction between bone age at treatment initiation and treatment, which estimated an approximately 1 cm per year decrease in treatment effect. The treatment effect was greatest in those commencing treatment at an early bone age and was significant if initiated before a bone age of 15 years. CONCLUSIONS: On average, oestrogen treatment resulted in an adult height that was less than predicted. Although treatment was more effective in the least mature girls, the mean height difference was relatively modest for most treated girls.

Gunshot wounds resulting in hospitalization in the United States: 2004-2013.

INTRODUCTION: The United States (US) leads all high income countries in gunshot wound (GSW) deaths. However, as a result of two decades of reduced federal support, study of GSW has been largely neglected. In this paper we describe the current state of GSW hospitalizations in the US using population-based data. PATIENTS AND METHODS: We conducted an observational study of patients hospitalized for GSW in the National (Nationwide) Inpatient Sample (NIS) 2004 -2013. Our primary outcome is mortality after admission and we model its associations with gender, race, age, intent, severity of injury and weapon type, as well as providing temporal trends in hospital charges. RESULTS: Each year approximately 30,000 patients are hospitalized for GSW, and 2500 die in hospital. Men are 9 times as likely to be hospitalized for GSW as women, but are less likely to die. Twice as many blacks are hospitalized for GSW as non-Hispanic whites. In-hospital mortality for blacks and non-Hispanic whites was similar when controlled for other factors. Most GSW (63%) are the result of assaults which overwhelmingly involve blacks; accidents are also common (23%) and more commonly involve non-Hispanic whites. Although suicide is much less common (8.3%), it accounts for 32% of all deaths; most of which are older non-Hispanic white males. Handguns are the most common weapon reported, and have the highest mortality rate (8.4%). During the study period, the annual rate of hospitalizations for GSW remained stable at 80 per 100,000 hospital admissions; median inflation-adjusted hospital charges have steadily increased by approximately 20% annually from $30,000 to $56,000 per hospitalization. The adjusted odds for mortality decreased over the study period. Although extensively reported, GSW inflicted by police and terrorists represent few hospitalizations and very few deaths. CONCLUSIONS: The preponderance GSW hospitalizations resulting from assaults on young black males and suicides among older non-Hispanic white males have continued unabated over the last decade with escalating costs. As with other widespread threats to the public wellbeing, federally funded research is required if effective interventions are to be developed.

Failure to get into substance abuse treatment.

Among substance abusers in the US, the discrepancy in the number who access substance abuse treatment and the number who need treatment is sizable. This results in a major public health problem of access to treatment. The purpose of this study was to examine characteristics of Persons Who Use Drugs (PWUDs) that either hinder or facilitate access to treatment. 2646 participants were administered the Risk Behavior Assessment (RBA) and the Barratt Impulsiveness Scale. The RBA included the dependent variable which was responses to the question "During the last year, have you ever tried, but been unable, to get into a drug treatment or detox program?" In multivariate analysis, factors associated with being unable to access treatment included: Previously been in drug treatment (OR=4.51), number of days taken amphetamines in the last 30days (OR=1.18), traded sex for drugs (OR=1.53), homeless (OR=1.73), Nonplanning subscale of the Barratt Impulsiveness Scale (OR=1.19), age at interview (OR=0.91), and sexual orientation, with bisexual men and women significantly more likely than heterosexuals to have tried but been unable to get into treatment. The answers to the question on "why were you unable to get into treatment" included: No room, waiting list; not enough money, did not qualify, got appointment but no follow through, still using drugs, and went to jail before program start. As expected, findings suggest that limiting organizational and financial obstacles to treatment may go a long way in increasing drug abuse treatment accessibility to individuals in need. Additionally, our study points to the importance of developing approaches for increasing personal planning skills/reducing Nonplanning impulsivity among PWUDs when they are in treatment as a key strategy to ensure access to additional substance abuse treatment in the future.

Factors associated with health service utilization patterns in low-income women.

Little is known about the relationship of demographic, health, and psychosocial factors with health services utilization over time among low-income female heads of household. In a case-control study conducted between 1991 and 1997, 273 homeless and low-income housed mothers living in Worcester, Massachusetts were interviewed at baseline and at a two-year follow-up. The use of outpatient and emergency department care in this study sample was examined. High usage patterns were found at baseline. At the two-year follow-up, poor health status, non-White race, and fewer supportive relationships were significantly associated with frequent emergency department visits. Women with higher rates of ongoing outpatient medical care visits were more likely to have fewer social supports and more bodily pain than women with lower rates of ambulatory care usage. Mental health and victimization were not associated with service use patterns. In order to address poor women's needs in a more effective and potentially less costly way, health programs must understand their need for social support and culturally responsive services.

The differential mortality of Glasgow Coma Score in patients with and without head injury.

IMPORTANCE: The GCS was created forty years ago as a measure of impaired consciousness following head injury and thus the association of GCS with mortality in patients with traumatic brain injury (TBI) is expected. The association of GCS with mortality in patients without TBI (non-TBI) has been assumed to be similar. However, if this assumption is incorrect mortality prediction models incorporating GCS as a predictor will need to be revised. OBJECTIVE: To determine if the association of GCS with mortality is influenced by the presence of TBI. DESIGN/SETTING/PARTICIPANTS: Using the National Trauma Data Bank (2012; N=639,549) we categorized patients as isolated TBI (12.8%), isolated non-TBI (33%), both (4.8%), or neither (49.4%) based on the presence of AIS codes of severity 3 or greater. We compared the ability GCS to discriminate survivors from non-survivors in TBI and in non-TBI patients using logistic models. We also estimated the odds ratios of death for TBI and non-TBI patients at each value of GCS using linear combinations of coefficients. MAIN OUTCOME MEASURE: Death during hospital admission. RESULTS: As the sole predictor in a logistic model GCS discriminated survivors from non-survivors at an acceptable level (c-statistic=0.76), but discriminated better in the case of TBI patients (c-statistic=0.81) than non-TBI patients (c-statistic=0.70). In both unadjusted and covariate adjusted models TBI patients were about twice as likely to die as non-TBI patients with the same GCS for GCS values<8; for GCS values>8 TBI and non-TBI patients were at similar risk of dying. CONCLUSIONS: A depressed GCS predicts death better in TBI patients than non-TBI patients, likely because in non-TBI patients a depressed GCS may simply be the result of entirely reversible intoxication by alcohol or drugs; in TBI patients, by contrast, a depressed GCS is more ominous because it is likely due to a head injury with its attendant threat to survival. Accounting for this observation into trauma mortality datasets and models may improve the accuracy of outcome prediction.

Trauma care does not discriminate: The association of race and health insurance with mortality following traumatic injury.

BACKGROUND: Previous studies have reported that black race and lack of health insurance coverage are associated with increased mortality following traumatic injury. However, the association of race and insurance status with trauma outcomes has not been examined using contemporary, national, population-based data. METHODS: We used data from the National Inpatient Sample on 215,615 patients admitted to 1 of 836 hospitals following traumatic injury in 2010. We examined the effects of race and insurance coverage on mortality using two logistic regression models, one for patients younger than 65 years and the other for older patients. RESULTS: Unadjusted mortality was low for white (2.71%), black (2.54%), and Hispanic (2.03%) patients. We found no difference in adjusted survival for nonelderly black patients compared with white patients (adjusted odds ratio [AOR], 1.04; 95% confidence interval [CI], 0.90-1.19; p = 0.550). Elderly black patients had a 25% lower odds of mortality compared with elderly white patients (AOR, 0.75; 95% CI, 0.63-0.90; p = 0.002). After accounting for survivor bias, insurance coverage was not associated with improved survival in younger patients (AOR, 0.91; 95% CI, 0.77-1.07; p = 0.233). CONCLUSION: Black race is not associated with higher mortality following injury. Health insurance coverage is associated with lower mortality, but this may be the result of hospitals' inability to quickly obtain insurance coverage for uninsured patients who die early in their hospital stay. Increasing insurance coverage may not improve survival for patients hospitalized following injury. LEVEL OF EVIDENCE: Epidemiologic and prognostic study, level III.