Elderly trauma associated with high-risk recreational activity: A population-based study, United States, 2010 through 2016.

BACKGROUND: Understanding trends in prevalence and etiology is critical to public health strategies for prevention and management of injury related to high-risk recreation in elderly Americans. METHODS: The National Emergency Department Sample from 2010 through 2016 was queried for patients with a principal diagnosis of trauma (ICD-9 codes 800.0-959.9) and who were 55 years and older. High-risk recreation was determined from e-codes a priori. Primary outcome measures were mortality and total hospital charges. RESULTS: Of the 29,491,352 patient cohort, 458,599 (1.56%) engaged in high-risk activity, including those age 85 and older. High-risk cases were younger (median age 61 vs 70) and majority male (71.87% vs 39.24%). The most frequent activities were pedal cycling (45.81%), motorcycling (29.08%), and off-road vehicles (9.13%). Brain injuries (8.82% vs 3.88%), rib/sternal fractures (13.35% vs 3.53%), and cardiopulmonary injury (5.25% vs 0.57%) were more common among high-risk cases. Mortality (0.75% vs 0.40%) and total median hospital charges ($3,360 vs $2,312) were also higher for high-risk admissions, where the odds of mortality increased exponentially per year of age (odds ratio, 1.06; 99.5% CI, 1.05-1.08). High-risk recreation was associated with more than $1 billion in total hospital charges and more than 100 deaths among elderly Americans per year. CONCLUSION: Morbidity, mortality, and resource utilization due to high-risk recreation extend into the ninth decade of life. The patterns of injury described here offer opportunities for targeted injury prevention education to minimize risk among this growing segment of the United States population.

Prevalence and hospital charges from firearm injuries treated in US emergency departments from 2006 to 2016.

BACKGROUND: Age- and intent-related differences in the burden and costs of firearm injury treated in emergency departments are not well-documented. METHODS: We performed a serial cross-sectional study of the Healthcare Cost and Utilization Program Nationwide Emergency Department Survey from 2006 to 2016. We used International Classification of Diseases diagnoses codes revisions 9 and 10 to identify firearm injuries. We calculated survey-weighted counts, proportions, means, and rates and confidence intervals of national, age-specific (0-4, 5-9, 10-14, 15-17, 18-44, 45-64, 65-84, >84) and intent-specific (assault, unintentional, suicide, undetermined) emergency department discharges for firearm injuries. We used survey-weighted regression to assess temporal trends. RESULTS: There was a total of 868,483 (25.5 per 100,000) emergency department visits for firearm injuries from 2006 to 2016, and 7.8% died in the emergency department. Overall, firearm injury rates remained steady (P = .78). The largest burden was among those 25 to 44 years of age, but their rates remained stable (10.8 per 100,000). Overall assault injuries declined from 39.7% to 36.4%, and overall unintentional injuries increased from 46.4% to 54.7%. Legal-intervention injuries declined from 0.6 to 0.3 per 100,000. The charges (total $4,059,070,364, $369,006,396/year) increased across time in age and intent groups. The mean predicted charges increased from $1,922 to $3,348 in those alive versus $3,741 to $6,515 among those who died. CONCLUSION: Interventions and programs to manage the consequences of firearm injury in persons who live with ongoing morbidity and economic burden are warranted.

The effect of the Affordable Care Act on rates on inpatient rehabilitation hospital admission in a mature trauma system.

BACKGROUND: The beneficial effects of acute rehabilitation for trauma patients are well documented but can be limited because of insurance coverage. The Patient Protection and Affordable Care Act (ACA) went into effect on March 23, 2010. The ACA allowed patients who previously did not have insurance to be fully incorporated into the health system. We sought to analyze the likelihood of discharge to rehab for trauma patients before and after the implementation of the ACA. We hypothesized that there would be a higher rate of inpatient rehabilitation hospital (IRH) admission after the ACA was put into effect. METHODS: The Pennsylvania Trauma Outcome Study (PTOS) database was retrospectively queried from 2003 to 2017 for all trauma patients admitted to accredited trauma centers in Pennsylvania, who also had a functional status at discharge (FSD). Admission to an IRH was determined using discharge destination. Two categories were created to represent periods before and after ACA was implemented, 2003 to 2009 (pre-ACA) and 2010-2017 (post-ACA). A multilevel mixed-effects logistic regression model controlling for demographics, injury severity, and FSD assessed the adjusted impact of ACA implementation on IRH admissions. RESULTS: From the Pennsylvania Trauma Outcome Study query, 341,252 patients had FSD scores and of these patients, 47,522 (13.9%) were admitted to IRH. Patients who were severely injured were more likely to be admitted to IRH. Compared with FSD scores signifying complete independence at discharge, those with lower FSD had significantly increased odds of IRH admission. The odds of IRH admission post-ACA implementation significantly increased when compared with pre-ACA years (adjusted odds ratio, 1.14; 95% confidence interval, 1.12-1.17; p < 0.001; area under the receiver operating curve, 0.818). CONCLUSION: The implementation of the ACA significantly increased the likelihood of discharge to IRH for trauma patients. LEVEL OF EVIDENCE: Care management, level III.

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.

Multidimensional Benefits of Improved Sanitation: Evaluating 'PEE POWER®' in Kisoro, Uganda.

With 2.3 billion people around the world lacking adequate sanitation services, attention has turned to alternative service provision models. This study suggests an approach for meeting the sanitation challenge, especially as expressed in Sustainable Development Goal 6.2, using a toilet technology system, such as Pee Power® that generates electricity using diverted urine as a fuel. A field trial was carried out in a girls' school in Kisoro, Uganda, where the generated electricity was used to light the existing toilet block. The trial was evaluated in terms of social acceptability and user experience using a multidimensional assessment protocol. The results of our assessment show that users felt safer when visiting the toilets at night. Lights provided from the technology also helped with the perceived cleanliness of the toilets. The technology was well accepted, with 97% of the respondents saying that they liked the idea of the Pee Power® technology and 94% preferring it over other facilities on site. This shows how the technology helps meet SDG target 6.2, with its particular focus on vulnerable populations.

The geriatric trauma patient: A neglected individual in a mature trauma system.

BACKGROUND: Those older than 65 years represent the fastest growing demographic in the United States. As such, their care has been emphasized by trauma entities such as the American College of Surgeons Committee on Trauma. Unfortunately, much of that focus has been of their care once they reach the hospital with little attention on the access of geriatric trauma patients to trauma centers (TCs). We sought to determine the rate of geriatric undertriage (UT) to TCs within a mature trauma system and hypothesized that there would be variation and clustering of the geriatric undertriage rate (UTR) within a mature trauma system because of the admission of geriatric trauma patient to nontrauma centers (NTCs). METHODS: From 2003 to 2015, all geriatric (age >65 years) admissions with an Injury Severity Score of greater than 9 from the Pennsylvania Trauma Systems Foundation (PTSF) registry and those meeting trauma criteria (International Classification of Diseases, Ninth Revision: 800-959) from the Pennsylvania Health Care Cost Containment Council (PHC4) database were included. Undertriage rate was defined as patients not admitted to TCs (n = 27) divided by the total number of patients as from the PHC4 database. The PHC4 contains all inpatient admissions within Pennsylvania (PA), while PTSF reports admissions to PA TCs. The zip code of residence was used to aggregate calculations of UTR as well as other aggregate patient and census demographics, and UTR was categorized into lower, middle box, and upper quartiles. ArcGIS Desktop: Version 10.7, ESRI, Redlands, CA and GeoDa: Version 1.14.0, Open source license were used for geospatial mapping of UT with a spatial empirical Bayesian smoothed UTR, and Stata: Version 16.1, Stata Corp., College Station TX was used for statistical analyses. RESULTS: Pennsylvania Trauma Systems Foundation had 58,336 cases, while PHC4 had 111,626 that met the inclusion criteria, resulting in a median (Q1-Q3) smoothed UTR of 50.5% (38.2-60.1%) across PA zip code tabulation areas. Geospatial mapping reveals significant clusters of UT regions with high UTR in some of the rural regions with limited access to a TC. The lowest quartile UTR regions tended to have higher population density relative to the middle or upper quartile UTR regions. At the patient level, the lowest UTR regions had more racial and ethnic diversity, a higher injury severity, and higher rates of treatment at a TC. Undertriage rate regions that were closer to NTCs had a higher odds of being in the upper UTR quartile; 4.48 (2.52-7.99) for NTC with less than 200 beds and 8.53 (4.70-15.47) for NTC with 200 beds or greater compared with zip code tabulation areas with a TC as the closest hospital. CONCLUSION: There are significant clusters of geriatric UT within a mature trauma system. Increased emphasis needs to focus prehospital on identifying the severely injured geriatric patient including specific geriatric triage protocols. LEVEL OF EVIDENCE: Epidemiological, Level III.

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.

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.

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.

No Disparity for American Indians in Surgery for Pelvis/Lower Extremity Fractures: a Cohort Study of the National Trauma Data Bank (NTDB).

BACKGROUND: Racial/ethnic disparities in trauma care have been reported. The American Indian/Alaska Native (AI/AN) population faces a twofold to fourfold increase of risk for traumatic injury. We hypothesized that surgical intervention and time to surgery were associated with race/ethnicity, specifically AI/AN compared to other race/ethnicity groups with open pelvic and lower extremity fractures (OPLEFx). METHODS: Non-AI/AN racial/ethnic groups were compared to AI/ANs among adults aged 15 years and older using the National Trauma Data Bank for 2008-2012. OPLEFx were identified via ICD-9-CM. Predictors of surgery and time to surgery were modeled via logistic regression and survival analyses. RESULTS: AI/AN patients (2.7 %, n = 206) were younger (36 ± 16 versus 41 ± 18 years, p < 0.001) and more likely to have Medicaid and other government insurance. There were no differences in AI/ANs versus non-AI/ANs undergoing surgery (88.4 versus 86.8 %, respectively) or time to surgery (11.7 ± 25.3 versus 12.0 ± 22.5 h, respectively). Injury severity was predictive of surgery in all six models (OR = 0.04 to 0.32). A race-gender interaction increased odds of surgery in the AI/AN versus all other races model (OR = 3.58, 95 % CI 1.18-10.84) and in three of five pairwise models. Median time to surgery varied by race, favoring AI/ANs with least preoperative time. CONCLUSION: The AI/AN population experienced no disparities in rate of, or time to, OPLEFx surgery. Race-specific predictors for surgery included gender, probability of death, and multiple fractures. More study is warranted to ameliorate trauma care disparities and achieve reasonably equitable care as demonstrated in AI/ANs with OPLEFx.

Trauma patient readmissions: Why do they come back for more?

BACKGROUND: Hospital readmissions are a frequent challenge. Speculation exists that rates of readmission following traumatic injury will be publicly disclosed. The primary aim of this study was to characterize and model 1-year readmission patterns to multiple institutions among patients originally admitted to a single, urban Level I trauma center. Additional analyses within the superutilizers subgroup identified predictors of 30-day readmissions as well as patient loyalty for readmission to their index hospital. We hypothesized that hospital readmission among trauma patients would be associated with socioeconomic, demographic, and clinical features and superutilizers would be identifiable during initial hospitalization. METHODS: Data were retrospectively gathered for 2,411 unique trauma patients admitted to a Level I American College of Surgeons-certified trauma center over 1 year, with readmissions identified 1 year after index admission. A regional hospital database was queried for readmissions. Outcomes of all readmission encounters were analyzed using a binary logistic regression model including demographic, diagnoses, Injury Severity Score (ISS), procedures, Elixhauser comorbidities, insurance, and disposition data. Subset analysis of superutilizers was also performed to examine patterns among superutilizers. RESULTS: A total of 434 patients (21%) were readmitted during the study period, accounting for 720 readmission encounters. Sixty-three patients accounting for 269 encounters were identified as superutilizers (3+ readmissions). A total of 136 patients (6%) were readmitted within 30 days of initial discharge. Fifty-seven percent of readmissions returned to the originating hospital. CONCLUSION: Complications including comorbid disease (diabetes and congestive heart failure), septicemia, weight loss, and trauma recidivism distinguish the superutilizer trauma patient. Having Medicaid funding increased the odds of readmission by 274%. It is imperative that interventions be developed and targeted toward those at high risk of superutilization of health care resources to curb spending. These results strongly support continuation of longitudinal readmission research in trauma patients conducted in multicenter settings. LEVEL OF EVIDENCE: Epidemiologic study, level III.

Race and rehabilitation following spinal cord injury: equality of access for American Indians/Alaska Natives compared to other racial groups.

BACKGROUND: Representing 2 % of the general population, American Indians/Alaska Natives (AIs/ANs) were associated with 0.5 % (63) of the estimated 12,500 new cases of spinal cord injury (SCI) reported to the National Spinal Cord Injury Statistic Center in 2013. To date, the trend in health care disparities among AIs/ANs in the SCI community has not been examined. We sought to compare the rate of discharge to rehabilitation facilities (DRF) following traumatic SCI among adult AIs/ANs to other racial/ethnic groups for patients 15 to 64 years old. METHODS: Utilizing data from the National Trauma Data Bank (NTDB), we performed a retrospective analysis of SCI cases occurring between January 1, 2008 and December 31, 2012. SCI injuries were identified by International Classification of Diseases 9th Revision-Clinical Modification (ICD-9) codes or Abbreviated Injury Scale (AIS) scores. Injury severity was determined using the Trauma Mortality Prediction Model (TMPM) which empirically estimates each patient's probability of death given their individual complement of injuries. A series of seven logistic regression models were used to predict DRF between racial groups. RESULTS: Among the 29,443 patients in our cohort, 52.4 % were discharged to rehabilitation facilities. AIs/ANs comprised 1.1 % of the population, with 63.8 % dismissed to rehabilitation. AIs/ANs were significantly younger, had a higher probability of death, had longer hospital length of stay (HLOS), and were proportionately more likely to be discharged to rehabilitation compared to non-AIs. Regression models demonstrated increased odds of DRF for AIs/ANs compared to Hispanic and Asian racial/ethnic groups. CONCLUSIONS: American Indians/Alaska Natives who sustain SCI access rehabilitative care at a rate equitable to or greater than other races when multiple factors are taken into account. Further research is needed to assess the effect of those patient, physician, and health care system determinants as they relate to a patient's ability to access post-trauma rehabilitative care. Recommendations include advancing the level of racial, insurance, and geographic data necessary to adequately explore disparities related to such ubiquitously life-altering conditions as SCI.

Analysis of referrals to a multi-disciplinary breast cancer genetics clinic: practical and economic considerations.

Analysis of activity was undertaken in an established regional clinic providing risk assessment, counselling, screening and management for women with a family history of breast or ovarian cancer. The objectives were to determine: (1) how closely the route and pattern of referrals matched official guidelines (2) whether the previously recorded socio-economic imbalance among clinic clientele persisted and (3) the economic and practical consequences of committing resources to verification and extension of reported family histories. The findings were: (1) after some years of operation, the proportion of referrals direct from primary care had increased from less than 50% to over 75%, with a concomitant slight decrease in overall referral rate; (2) the socio-economic distribution of patients referred had become less selective and (3) extension and verification of reported family histories led to a redistribution of risk categories, increasing the proportion of referrals judged to be in the "low risk" category, from 25% (based on referral letter alone) to 41% (at the end of the process). The costs associated with this approach are offset by the savings generated and it allows specialised counselling and screening services to be targeted more efficiently.