Does the Geographical Distribution of Facial Trauma Surgeons Correspond to Facial Trauma Burden? A Nationwide Population-Level Analysis.

ABSTRACT: It is unknown if craniofacial trauma services are inequitably distributed throughout the US. The authors aimed to describe the geographical distribution of craniofacial trauma, surgeons, and training positions nationwide. State-level data were obtained on craniofacial trauma admissions, surgeons, training positions, population, and income for 2016 to 2017. Normalized densities (per million population [PMP]) were ascertained. State/ regional-level densities were compared between highest/lowest. Risk-adjusted generalized linear models were used to determine independent associations. There were 790,415 craniofacial trauma admissions (x? = 2330.6 PMP), 28,004 surgeons (x? = 83.5 PMP), and 746 training positions (x? = 1.9 PMP) nationwide. There was significant state-level variation in the density PMP of trauma (median 1999.5 versus 2983.5, P   <  0.01), surgeon (70.8 versus 98.8, P  < 0.01), training positions (0 versus 3.4, P  < 0.01) between lowest/highest quartiles. Surgeon distribution was positively associated with income and training positions density ( P  < 0.01). Subanalysis revealed that there was an increase of 6.7 plastic and reconstructive surgeons/PMP for every increase of 1000 trauma admissions/PMP ( P  < 0.01). There is an uneven state-level distribution of facial trauma surgeons across the US associated with income. Plastic surgeon distribution corresponded closer to craniofacial trauma care need than that of ENT and OMF surgeons. Further work to close the gap between workforce availability and clinical need is necessary.

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.

Trauma patient transport times unchanged despite trauma center proliferation: A 10-year review.

INTRODUCTION: In certain regions of the United States, there has been a dramatic proliferation of trauma centers. The goal of our study was to evaluate transport times during this period of trauma center proliferation. METHODS: Aggregated data summarizing level I trauma center admissions in Arizona between 2009 and 2018 were provided to our institution by the Arizona Department of Health Services. We evaluated patient demographics, transport times, and injury severity for both rural and urban injuries. RESULTS: Data included statistics summarizing 266,605 level I trauma admissions in the state of Arizona. The number of state-designated trauma centers during this time increased from 14 to 47, with level I centers increasing from 8 to 13. Slight decreases in mean Injury Severity Score (rural, 9.4 vs. 8.4; urban, 7.9 vs. 7.0) were observed over this period. Median transport time for cases transported from the injury scene directly to a level I center remained stable in urban areas at 0.9 hours in both 2009 and 2018. In rural areas, transport times for these cases were approximately double but also stable, with median times of 1.8 and 1.9 hours. Transport times for cases requiring interfacility transfer before admission at a level I center increased by 0.3 hours for urban injuries (5.3-5.6 hours) and 0.9 hours for rural injuries (5.6-6.5 hours). CONCLUSION: Despite the threefold increase in the number of state-designated trauma centers, transport time has not decreased in urban or rural areas. This finding highlights the need for regulatory oversight regarding the number and geographic placement of state-designated trauma centers. LEVEL OF EVIDENCE: Care management, level IV, Epidemiological, level III.

Road Traffic Accidents and Disparities in Child Mortality.

BACKGROUND AND OBJECTIVES: Road traffic accidents are a leading cause of child deaths in the United States. Although this has been examined at the national and state levels, there is more value in acquiring information at the county level to guide local policies. We aimed to estimate county-specific child mortality from road traffic accidents in the United States. METHODS: We queried the Fatality Analysis Reporting System database, 2010-2017, for road traffic accidents that resulted in a death within 30 days of the auto crash. We included all children <15 years old who were fatally injured. We estimated county-specific age- and sex-standardized mortality. We evaluated the impact of the availability of trauma centers and urban-rural classification of counties on mortality. RESULTS: We included 9271 child deaths. Among those, 45% died at the scene. The median age was 7 years. The overall mortality was 1.87 deaths per 100 000 children. County-specific mortality ranged between 0.25 and 21.91 deaths per 100 000 children. The availability of a trauma center in a county was associated with decreased mortality (adult trauma center [odds ratio (OR): 0.59; 95% credibility interval (CI), 0.52-0.66]; pediatric trauma center [OR: 0.56; 95% CI, 0.46-0.67]). Less urbanized counties were associated with higher mortality, compared with large central metropolitan counties (noncore counties [OR: 2.33; 95% CI, 1.85-2.91]). CONCLUSIONS: There are marked differences in child mortality from road traffic accidents among US counties. Our findings can guide targeted public health interventions in high-risk counties with excessive child mortality and limited access to trauma care.

Quantifying geographic barriers to trauma care: Urban-rural variation in prehospital mortality.

BACKGROUND: Few studies of trauma care access and quality account for prehospital injury mortality. Little is known about geographic variation in prehospital mortality or the impact of prehospital care on injury disparities. METHODS: Using the Centers for Disease Control Wide-ranging Online Data for Epidemiologic Research database, we queried county-level incidence of prehospital injury mortality from 1999 to 2016. We linked mortality incidence with county-level urban-rural classifications from the National Center for Health Statistics and population data from the US Census Bureau. We used negative binomial regression to estimate the relationship between rurality and prehospital injury mortality, adjusting for county-level distribution of race, sex, age, income, and insurance coverage. Models were then stratified by injury mechanism (motor vehicle traffic [MVT] vs. penetrating) to determine if prehospital mortality rates varied by type of injury. RESULTS: Prehospital injury mortality rates were elevated for all urban-rural county classes, relative to large central metro counties, with incidence rate ratios (IRR) ranging from 1.25 (95% confidence interval [CI], 1.16-1.35) for fringe metro counties to 1.69 (95% CI, 1.58-1.82) for noncore counties. For MVT injury, IRRs for urban-rural classes compared with large central metro counties ranged from 2.02 (95% CI, 1.85-2.21) for fringe metro counties to 3.02 (95% CI, 2.76-3.30) to noncore counties. Incidence of prehospital mortality from penetrating injury was 14% higher for noncore counties compared to large central metro counties (IRR, 1.14; 95% CI, 1.05-1.23). CONCLUSION: There is substantial geographic variation in prehospital injury mortality in the United States, with risk of prehospital death increasing with rurality. Patterns of prehospital death associated with penetrating and MVT injuries suggest that improvements to both trauma center access, prehospital care, and primary injury prevention are essential to reduce preventable injury deaths. LEVEL OF EVIDENCE: Retrospective ecological analysis, level III.

Perfil epidemiológico dos pacientes vítimas de queimaduras no estado da Bahia no período de 2009 a 2018 / Epidemiological profile of burn victims in the state of Bahia from 2009 to 2018 / Perfil epidemiológico de víctimas de quemaduras en el estado de Bahia de 2009 a 2018

Objetivo: Descrever o perfil epidemiológico das vítimas de queimaduras no estado da Bahia entre 2009 e 2018. Método: Estudo ecológico com levantamento de dados do período entre janeiro de 2009 e dezembro de 2018, por meio do Departamento de Informática do Sistema Único de Saúde (DATASUS), no ícone "Informações em saúde". A seleção nesta plataforma foi direcionada a "Epidemiológicas e Morbidade" e "procedimentos hospitalares do SUS". Os dados foram tabelados no programa Microsoft Excel, com posterior contagem absoluta e relativa utilizando estatística descritiva. Resultados: Foram analisados 18.490 pacientes, dos quais 630 (3,4%) foram a óbito. Dentre os pacientes internados, a maior parte era de adultos (43,0%), seguidos pelas crianças (36,2%). Quanto aos óbitos, 61,5% dos casos foram adultos, enquanto 5,5%, crianças. A maioria dos óbitos (96,3%) ocorreu nos pacientes que necessitaram de tratamentos cirúrgicos (90,4%). O somatório mensal de internamentos e óbitos, no período estudado, demonstrou picos de ocorrência nos meses de julho e setembro. No período estudado, o tempo médio de internamento foi de 6,9 dias no regime público e 5,6 dias no privado; já o valor médio de internamento de R$ 1.726,70 e R$ 666,80, respectivamente. Conclusão: Observou-se, no presente estudo, grande prevalência de internamentos e óbitos por queimadura no estado da Bahia. Dada a magnitude dessa condição na Bahia, esse estudo poderá servir como planejamento de políticas de saúde pública direcionadas à região estudada, com enfoque na prevenção primária e na utilização de tratamento custo-efetivo.

Objective: To describe the epidemiological profile of burn victims in the state of Bahia between 2009 and 2018. Methods: Ecological study with data collection for the period between January 2009 and December 2018, through the Department of Informatics of the Unified Health System (DATASUS), under the icon "Information in health". The selection on this platform was directed to "Epidemiological and Morbidity" and "SUS hospital procedures". The data were tabulated in the Microsoft Excel program, with subsequent absolute and relative counting using descriptive statistics. Results: 18,490 patients were analyzed, of which 630 (3.4%) died. Among inpatients, most were adults (43.0%), followed by children (36.2%). As for deaths, 61.5% of the cases were adults while 5.5% were children. Most deaths (96.3%) occurred in patients who needed surgical treatment (90.4%). The monthly sum of hospitalizations and deaths, in the studied period, showed peaks of occurrence in the months of July and September. During the studied period, the average length of stay was 6.9 days in the public regime and 5.6 days in the private regime; the average hospital stay was R$ 1,726.70 and R$ 666.80, respectively. Conclusion: In the present study, there was a high prevalence of hospitalizations and deaths from burns in the state of Bahia. Given the magnitude of this condition in Bahia, this study may serve as a planning for public health policies aimed at the region studied, with a focus on primary prevention and the use of cost-effective treatment.

Objetivo: Describir el perfil epidemiológico de las víctimas de quemaduras en el estado de Bahía entre 2009 y 2018. Metodo: Estudio ecológico con recolección de datos para el período comprendido entre enero de 2009 y diciembre de 2018, a través del Departamento de Informática del Sistema Único de Salud (DATASUS), bajo el ícono "Información en salud". La selección en esta plataforma se dirigió a "Epidemiología y morbilidad" y "Procedimientos hospitalarios del SUS". Los datos se tabularon en el programa Microsoft Excel, con el recuento absoluto y relativo posterior utilizando estadísticas descriptivas. Resultados: Se analizaron 18.490 pacientes, de los cuales 630 (3,4%) fallecieron. Entre los pacientes hospitalizados, la mayoría eran adultos (43,0%), seguidos de niños (36,2%). En cuanto a las muertes, el 61,5% de los casos eran adultos y 5,5% eran niños. La mayoría de las muertes (96,3%) ocurrieron en pacientes que necesitaban tratamiento quirúrgico (90,4%). La suma mensual de hospitalizaciones y muertes mostró picos de ocurrencia en los meses de julio y septiembre. Durante el período estudiado, la duración promedio de la estadía fue de 6.9 días en el régimen público y 5.6 días en el régimen privado; la estancia hospitalaria promedio fue de R$ 1726.70 y R$ 666.80, respectivamente. Conclusión: Hubo una alta prevalencia de hospitalizaciones y muertes en el estado. Dada la magnitud de esta condición, este estudio puede servir como una planificación para las políticas de salud pública dirigidas a la región estudiada, con un enfoque en la prevención y el uso de tratamientos rentables.

The Black Cloud Phenomenon in Hand Surgery.

Background: The term black cloud for a surgeon is generally used to describe someone who is unusually busy compared with his or her counterparts, and it is a superstition that tends to pervade the medical world. The purpose of this study is to investigate whether black clouds exist in hand surgery. Methods: We examined one academic year's worth of hand surgery-specific call at a level I trauma center and tabulated the number of hand-related patient transfers and add-on cases per surgeon. Each surgeon was given a black cloud rating by the fellows who were in training that year. Correlations were made between the black cloud rating and the surgeons' call volume. Results: There were 12 surgeons who shared 365 days of hand call, and 5 of them are hand surgery fellowship trained. Those 5 surgeons tended to be busier on their call days, with more cases added on overnight and the next day, and also had worse black cloud ratings than the 7 non-hand fellowship trained surgeons. Conclusions: In regard to hand surgery, while true emergencies occur and require emergent intervention, how busy hand surgeons may be during call may be influenced by a variety of factors not related to their patients' problems but rather their daily schedules, their hospitals' ability to facilitate add-on cases, and their rapport with their fellow surgeons to share case loads.

Functional inclusivity of trauma networks: a pilot study of the North West London Trauma Network.

BACKGROUND: Metrics exist to assess and validate trauma system outcomes; however, these are clinically focused and do not evaluate the appropriateness of admission patterns, relative to geography and triage category. We propose the term "functional inclusivity", defined as the number and proportion of triage-negative, and/or nonseverely injured patients, who were injured in proximity to a level II/III trauma center but admitted to a level I facility. The aim of this study was to evaluate this metric in the North West London Trauma Network. METHODS: Retrospective, geospatial, observational analysis of registry data from the North West London Trauma Network. We included all adult (≥16 years) patients transported to the level I trauma center at St. Mary's Hospital between 1/1/13-31/12/16. Incident location data were geocoded into longitude/latitude, and drive times were calculated from incident location to each hospital in London's Trauma System, using Google Maps. RESULTS: Of 2051 patients, 907 (44%) were severely injured (injury severity score [ISS] ≥15), and 1144 (56%) were nonseverely injured (ISS 1-15). Seven hundred ninety five of the 1144 nonseverely injured patients (69%) were injured in proximity to a level II/III but taken to the level I facility. A total of 488 (24%) patients were triage-negative, and 229 (47%) of these were injured in proximity to a level II/III, but taken to the level I trauma center. CONCLUSIONS: This study has demonstrated the concept of functional inclusivity in characterizing trauma system performance. Further work is required to establish what constitutes an acceptable level of functional inclusivity and what the denominator should be, as well as validating and further evaluating the concept of functional inclusivity.

Rural Level III centers in an inclusive trauma system reduce the need for interfacility transfer.

BACKGROUND: Development of Level III trauma centers in a regionalized system facilitates early stabilization and prompt transfer to a higher level center. The resources to care for patients at Level III centers could also reduce the burden of interfacility transfers. We hypothesized that the development and designation of Level III centers in an inclusive trauma system resulted in lower rates of transfer, with no increase in morbidity or mortality among the non-transferred patients. METHODS: State trauma registry data from January 2009 through September 2015 were examined from five rural hospitals that transfer patients to our highest (Level II) trauma center and resource hospital. These five rural hospitals began receiving state support in 2010 to develop their trauma programs and were subsequently verified and designated Level III centers (three in 2011, two in 2013). Multivariate logistic regression was used to examine the adjusted odds of patient transfers and adverse outcomes, while controlling for age, gender, penetrating mechanism, presence of a traumatic brain injury, arrival by ambulance, and category of Injury Severity Score. The study period was divided into "Before" Level III center designation (2009-2010) and "After" (2011-2015). RESULTS: 7,481 patient records were reviewed. There was a decrease in the proportion of patients who were transferred After (1,281/5,737) compared to Before (516/1,744) periods (22% vs. 30%, respectively). After controlling for the various covariates, the odds of patient transfer were reduced by 32% (p < 0.0001) during the After period. Among non-transferred patients, there were no significant increases in adjusted odds of mortality, or hospitalizations of seven days or more, Before versus After. CONCLUSIONS: Development of rural Level III trauma centers in a regionalized system can significantly reduce the need for transfer to a remote, higher level trauma center. This may benefit the patient, family, and trauma system, with no adverse effect upon patient outcome. LEVEL OF EVIDENCE: Epidemiological, level III.

Access to trauma centers for road crashes in the United States.

INTRODUCTION: Existing research indicates that around 90% of all U.S. residents have access to at least one level I or II trauma center within 60min. However, a limitation of these estimates lies in that they are based on where people live and not where people are injured, which may overestimate the access to trauma centers for seriously injured patients in fatal crashes. METHOD: In this study, the Fatality Analysis Reporting System (FARS) data between 2013 and 2014 were collected and analyzed to quantify the access of injured patients to trauma centers for fatal crashes across states. Two types of distance, linear distance and route distance, were calculated using ArcGIS. The estimated transport time to the nearest level I/II trauma center was also calculated and compared to the recorded on-scene and transport time. RESULTS AND CONCLUSIONS: The Northeast region had the nearest average linear and route distance between fatal crash and trauma center (25.3km and 31.7km, respectively), followed by the Midwest (44.4km and 54.1km), the South (47.3km and 57.0km), and the West (50.9km and 67.5km). The comparison between the estimated and actual transport time revealed that the different states adopted different trauma triage protocols, resulting in different utilization rates of the level I/II trauma center among states. A linear regression analysis demonstrated that the longer the average route distance, the less the seriously injured patients in fatal crashes were taken to level I/II trauma center directly. Practical applications: These findings may help to identify the access to trauma centers for road crashes and the variation of delivery ratio to trauma center among the states, therefore a better utilization of trauma centers for road crashes can be achieved for the emergency medical services (EMS) systems.

Level I Trauma Centers: More Is Not Necessarily Better.

The optimal number of level I trauma centers (L1TCs) in a region has not been elucidated. To begin addressing this, we compared mortalities for patients treated in counties or regions with 1 L1TC to those with >1 L1TC across Ohio. Ohio Trauma Registry data from 2010 to 2012 were analyzed. Patients with age ≥15 from counties/regions with L1TC were included. Region was defined as a L1TC containing county and its neighboring counties. Two analyses were performed. In the county analysis, counties containing 1 L1TC were compared with counties with multiple L1TCs. This comparison is repeated on a regional level for the regional analysis. Subgroup analyses were performed. 38,661 and 55,064 patients were in the county and regional analysis, respectively. Patients treated in counties or regions with multiple L1TCs were significantly younger (P < 0.001). Despite this, the mortality was similar for the two groups in the county analysis and significantly higher for regions with multiple L1TCs (P < 0.001). Multivariate logistic regression demonstrated that having multiple L1TC coverage in a region was an independent predictor for death (odds ratios: 1.17; 1.07-1.28; P = 0.001). Subgroup analyses showed that mortality in counties and regions with multiple L1TCs was not lower in any subgroups but was higher in patients with age ≥65 and patients with blunt injuries (P < 0.05). Having multiple L1TCs in a county was associated with increased mortality in certain patient subgroups. Having multiple L1TCs in a region was an independent predictor for death. These results should be considered carefully when designing future regionalized trauma networks. More L1TCs is not necessarily better.

Adult and elderly population access to trauma centers: an ecological analysis evaluating the relationship between injury-related mortality and geographic proximity in the United States in 2010.

Background: Ongoing development and expansion of trauma centers in the United States necessitates empirical analysis of the effect of investment in such resources on population-level health outcomes. Methods: Multiple linear regressions were performed to predict state-level trauma-related mortality among adults and the elderly across 50 US states in 2010. The number of trauma centers per capita in each state and the percentage of each state's population living within 45-min of a trauma center served as the key independent variables and injury-related mortality served as the dependent variable. All analyses were stratified by age (adult versus elderly; elderly ≥ 65 years old) and were performed in SPSS. Results: The proportion of a population with geographic proximity to a trauma center demonstrates a consistent inverse linear relationship to injury-related mortality. The relationship reliably retains its significance in models including demographic covariates. Interestingly, access to Levels I and II trauma centers demonstrates a stronger correlation with mortality than was observed with Level III centers. Conclusion: Trauma center access is associated with reduced trauma-related mortality among both adults and the elderly as measured by state reported mortality rates. Ongoing efforts to designate and verify new trauma centers, particularly in poorly-served 'trauma deserts', could lead to lower mortality for large populations.

The American College of Surgeons Needs-Based Assessment of Trauma Systems: Estimates for the State of California.

BACKGROUND: In 2015, the American College of Surgeons Committee on Trauma convened a consensus conference to develop the Needs-Based Assessment of Trauma Systems (NBATS) tool to assist in determining the number of trauma centers required for a region. We tested the performance of NBATS with respect to the optimal number of trauma centers needed by region in California. METHODS: Trauma center data were obtained from the California Emergency Services Authority Information Systems (CEMSIS). Numbers of admitted trauma patients (ISS > 15) were obtained using statewide nonpublic admissions data from the California Office of Statewide Health Planning and Development (OSHPD), CEMSIS, and data from local emergency medical service agency (LEMSA) directors who agreed to participate in a telephone survey. Population estimates per county for 2014 were obtained from the U.S. Census. NBATS criteria used included population, transport time, community support, and number of discharges for severely injured patients (ISS > 15) at nontrauma centers and trauma centers. Estimates for the number of trauma centers per region were created for each of the three data sources and compared to the number of existing centers. RESULTS: A total of 62 state-designated trauma centers were identified for California: 13 (21%) Level I, 36 (58%) Level II, and 13 (11%) Level III. NBATS estimates for the total number of trauma centers in California were 27% to 47% lower compared to the number of trauma centers in existence, but this varied based on urban/rural status. NBATS estimates were lower than the current state in 70% of urban areas but were higher in almost 90% of rural areas. All data sources (OSHPD, CEMSIS, local data) produced similar results. CONCLUSION: Estimates from the NBATS tool are different from what is currently in existence in California, and differences exist based on whether the region is rural or urban. Findings from the current study can help inform future iterations of the NBATS tool. LEVEL OF EVIDENCE: Economic, level V.

Results of a Nationwide Capacity Survey of Hospitals Providing Trauma Care in War-Affected Syria.

IMPORTANCE: The Syrian civil war has resulted in large-scale devastation of Syria's health infrastructure along with widespread injuries and death from trauma. The capacity of Syrian trauma hospitals is not well characterized. Data are needed to allocate resources for trauma care to the population remaining in Syria. OBJECTIVE: To identify the number of trauma hospitals operating in Syria and to delineate their capacities. DESIGN, SETTING, AND PARTICIPANTS: From February 1 to March 31, 2015, a nationwide survey of 94 trauma hospitals was conducted inside Syria, representing a coverage rate of 69% to 93% of reported hospitals in nongovernment controlled areas. MAIN OUTCOMES: Identification and geocoding of trauma and essential surgical services in Syria. RESULTS: Although 86 hospitals (91%) reported capacity to perform emergency surgery, 1 in 6 hospitals (16%) reported having no inpatient ward for patients after surgery. Sixty-three hospitals (70%) could transfuse whole blood but only 7 (7.4%) could separate and bank blood products. Seventy-one hospitals (76%) had any pharmacy services. Only 10 (11%) could provide renal replacement therapy, and only 18 (20%) provided any form of rehabilitative services. Syrian hospitals are isolated, with 24 (26%) relying on smuggling routes to refer patients to other hospitals and 47 hospitals (50%) reporting domestic supply lines that were never open or open less than daily. There were 538 surgeons, 378 physicians, and 1444 nurses identified in this survey, yielding a nurse to physician ratio of 1.8:1. Only 74 hospitals (79%) reported any salary support for staff, and 84 (89%) reported material support. There is an unmet need for biomedical engineering support in Syrian trauma hospitals, with 12 fixed x-ray machines (23%), 11 portable x-ray machines (13%), 13 computed tomographic scanners (22%), 21 adult (21%) and 5 pediatric (19%) ventilators, 14 anesthesia machines (10%), and 116 oxygen cylinders (15%) not functional. No functioning computed tomographic scanners remain in Aleppo, and 95 oxygen cylinders (42%) in rural Damascus are not functioning despite the high density of hospitals and patients in both provinces. CONCLUSIONS AND RELEVANCE: Syrian trauma hospitals operate in the Syrian civil war under severe material and human resource constraints. Attention must be paid to providing biomedical engineering support and to directing resources to currently unsupported and geographically isolated critical access surgical hospitals.

Inequalities in Specialist Hand Surgeon Distribution across the United States.

BACKGROUND: Unequal access to hospital specialists for emergency care is an issue in the United States. The authors sought to describe the geographic distribution of specialist hand surgeons and associated factors in the United States. METHODS: Geographic distributions of surgeons holding a Subspecialty Certificate in Surgery of the Hand and hand surgery fellowship positions were identified from the American Board of Medical Specialties Database and the literature (2013), respectively. State-level population and per capita income were ascertained using U.S. Census data. Variations in hand trauma admissions were determined using Healthcare Cost and Utilization Project national/state inpatient databases. Risk-adjusted generalized linear models were used to assess independent association between hand surgeon density and hand trauma admission density, fellowship position density, and per capita income. RESULTS: Among 2019 specialist hand surgeons identified, 72.1 percent were orthopedic surgeons, 18.3 percent were plastic surgeons, and 9.6 percent were general surgeons. There were 157 hand surgery fellowship positions nationwide. There were 149,295 annual hand trauma admissions. The national density of specialist hand surgeons and density of trauma admission were 0.6 and 47.6, respectively. The density of specialist hand surgeons varied significantly between states. State-level variations in density of surgeons were independent and significantly associated with median per capita income (p < 0.001) and with density of fellowships (p = 0.014). CONCLUSIONS: Specialist hand surgeons are distributed unevenly across the United States. State-level analyses suggest that states with lower per capita incomes may be particularly underserved, which may contribute to regional disparities in access to emergency hand trauma care.

Geographic Distribution of Trauma Burden, Mortality, and Services in the United States: Does Availability Correspond to Patient Need?

BACKGROUND: The association between the need for trauma care and trauma services has not been characterized previously. We compared the distribution of trauma admissions with state-level availability of trauma centers (TCs), surgical critical care (SCC) providers, and SCC fellowships, and assessed the association between trauma care provision and state-level trauma mortality. STUDY DESIGN: We obtained 2013 state-level data on trauma admissions, TCs, SCC providers, SCC fellowship positions, per-capita income, population size, and age-adjusted mortality rates. Normalized densities (per million population [PMP]) were calculated and generalized linear models were used to test associations between provision of trauma services (higher-level TCs, SCC providers, and SCC fellowship positions) and trauma burden, per-capita income, and age-adjusted mortality rates. RESULTS: There were 1,345,024 trauma admissions (4,250 PMP), 2,496 SCC providers (7.89 PMP), and 1,987 TCs across the country, of which 521 were Level I or II (1.65 PMP). There was considerable variation between the top 5 and bottom 5 states in terms of Level I/Level II TCs and SCC surgeon availability (approximately 8.0/1.0), despite showing less variation in trauma admission density (1.5/1.0). Distribution of trauma admissions was positively associated with SCC provider density and age-adjusted trauma mortality (p ≤ 0.001), and inversely associated with per-capita income (p < 0.001). Age-adjusted mortality was inversely associated with the number of SCC providers PMP. For every additional SCC provider PMP, there was a decrease of 618 deaths per year. CONCLUSIONS: There is an inequitable distribution of trauma services across the US. Increases in the density of SCC providers are associated with decreases in mortality. There was no association between density of trauma admissions and location of Level I/Level II TCs. In the wake of efforts to regionalize TCs, additional efforts are needed to address disparities in the provision of quality care to trauma patients.