Performance-Based Assessment of Trauma Systems: Estimates for the State of Ohio.

OBJECTIVES: There are no widely accepted metrics to determine the optimal number and geographic distribution of trauma centers (TCs). We propose a Performance-based Assessment of Trauma System (PBATS) model to optimize the number and distribution of TCs in a region using key performance metrics. METHODS: The proposed PBATS approach relies on well-established mathematical programming approach to minimize the number of level I (LI) and level II (LII) TCs required in a region, constrained by prespecified system-related under-triage (srUT) and over-triage (srOT) rates and TC volume. To illustrate PBATS, we collected 6002 matched (linked) records from the 2012 Ohio Trauma and EMS registries. The PBATS-suggested network was compared to the 2012 Ohio network and also to the configuration proposed by the Needs-Based Assessment of Trauma System (NBATS) tool. RESULTS: For this data, PBATS suggested 14 LI/II TCs with a slightly different geographic distribution compared to the 2012 network with 21 LI and LII TC, for the same srUT≈.2 and srOT≈.52. To achieve UT ≤ .05, PBATS suggested 23 LI/II TCs with a significantly different distribution. The NBATS suggested fewer TCs (12 LI/II) than the Ohio 2012 network. CONCLUSION: The PBATS approach can generate a geographically optimized network of TCs to achieve prespecified performance characteristics such as srUT rate, srOT rate, and TC volume. Such a solution may provide a useful data-driven standard, which can be used to drive incremental system changes and guide policy decisions.

Disparities in Insurance Status Are Associated With Outcomes but Not Timing of Trauma Care.

INTRODUCTION: Patient factors influence outcomes after injury. Delays in care have a crucial impact. We investigated the associations between patient characteristics and timing of transfer from the emergency department to definitive care. METHODS: This was a review of adult trauma patients treated between January 1, 2016, and December 31, 2018. Bivariate analyses were used to build Cox proportional hazards models. We built separate logistic and negative binomial regression models for secondary outcomes using mixed-step selection to minimize the Akaike information criterion c. RESULTS: A total of 1219 patients were included; 68.5% were male, 56.8% White, 11.2% Black, and 7.8% Asian/Pacific Islander. The average age was 51 ± 21 y. Overall, 13.7% of patients were uninsured. The average length of stay was 5 d and mortality was 5.9%. Shorter transfer time out of the emergency department was associated with higher tier of activation (relative risk [RR] 1.39, 95% confidence interval [CI] 1.09-1.77; P = 0.0074), Injury Severity Score between 16 and 24 points (RR 1.57, 95% CI 1.04-2.32; P = 0.0307) or ≥25 (RR 3.85, 95% CI 2.45-5.94; P = 0.0001), and penetrating injury. Longer time to event was associated with Glasgow coma scale score ≥14 points (RR 0.47, 95% CI 0.27-0.85; P = 0.0141). Uninsured patients were less likely to be admitted (odds ratio 0.29, 95% CI 0.17-0.48; P = 0.0001) and more likely to experience shorter length of stay (incidence rate ratio 0.34, 95% CI 0.24-0.51; P = 0.0001). CONCLUSIONS: Injury characteristics and insurance status were associated with patient outcomes in this retrospective, single-center study. We found no disparity in timing of intrafacility transfer, perhaps indicating that initial management protocols preserve equity.

Development of a geospatial approach for the quantitative analysis of trauma center access.

INTRODUCTION: Decisions around trauma center (TC) designation have become contentious in many areas. There is no consensus regarding the ideal number and location of TC and no accepted metrics to assess the effect of changes in system structure. We aimed to develop metrics of TC access, using publicly available data and analytic tools. We hypothesize that geospatial analysis can provide a reproducible approach to quantitatively asses potential changes in trauma system structure. METHODS: A region in New York State was chosen for evaluation. Geospatial data and analytic tools in ArcGIS Online were used. Transport time polygons were created around TC, and the population covered was estimated by summing the census tracts within these polygons. Transport time from each census tract to the nearest TC was calculated. The baseline model includes the single designated TC. Model 1 includes one additional TC, and Model 2 includes two additional TC, chosen to maximize coverage. The population covered, population-weighted distribution of transport times, and population covered by a specific TC were calculated for each model. RESULTS: The baseline model covered 1.12 × 10 people. The median transport time was 19.2 minutes. In Model 1, the population covered increased by 14.4%, while the population catchment, and thus the estimated trauma volume, of the existing TC decreased by 12%. Median transport time to the nearest TC increased to 20.4 minutes. Model 2 increased coverage by 18% above baseline, while the catchment, and thus the estimated trauma volume, of the existing TC decreased by 22%. Median transport time to the nearest TC decreased to 19.6 minutes. CONCLUSIONS: Geospatial analysis can provide objective measures of population access to trauma care. The analysis can be performed using different numbers and locations of TC, allowing direct comparison of changes in coverage and impact on existing centers. This type of data is essential for guiding difficult decisions regarding trauma system design. LEVEL OF EVIDENCE: Care management, level IV.

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.

An assessment of the impact of trauma systems consultation on the level of trauma system development.

BACKGROUND: Studies have shown that trauma systems decrease morbidity and mortality after injury. Despite these findings, overall progress in system development has been slow and inconsistent. The American College of Surgeons Committee on Trauma (COT) has developed a process to provide expert consultation to facilitate regional trauma system development. This study evaluated the progress that occurred after COT consultation visits in six regional systems. STUDY DESIGN: All six trauma systems undergoing COT consultation between January 1, 2004 and September 1, 2006 were included in the study. Using a set of 16 objective indicators, preconsultation status was retrospectively assessed by members of the original consultation team using data from the final consultation reports. Postconsultation status was assessed by directed telephone conference, conducted by members of the original consultation team with current key representatives from each system. Progress was assessed by comparing changes in both aggregate and individual indicator scores. RESULTS: This study showed a statistically significant increase in aggregate indicator scores after consultation. The largest gains were seen in systems with the longest time interval between the two assessments. Individual indicators related to system planning and quality assurance infrastructure showed the most improvement. Little or no change was seen in indicators related to system funding. CONCLUSIONS: The COT consultation process appears to be effective in facilitating regional trauma system development. In this short-term followup study, progress was seen primarily in areas related to planning and system design. Consultation was not effective in helping systems secure stable funding.

Risk adjustment for injured patients using administrative data.

BACKGROUND: Risk adjustment methods are needed for population-based studies of injured patients. METHODS: Data were obtained from National Hospital Discharge Surveys, 1996 to 2000. International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnoses were used to categorize Abbreviated Injury Scale score, Injury Severity Score, ICD-9-CM Injury Severity Score, injury mechanisms, and comorbidities. Regression models for weighted survey data were constructed from combinations of these classifications, plus age and sex, to predict mortality, length of stay (LOS), or discharge to long-term care (LTC). RESULTS: Increased Abbreviated Injury Scale score, increased Injury Severity Score, or decreased ICD-9-CM Injury Severity Score were similarly associated with mortality, prolonged LOS, or more frequent LTC, as was increased age. Penetrating or burn mechanisms were associated with mortality and longer LOS; penetrating or vehicle mechanisms were associated with less frequent LTC. Different comorbidities affected LOS and LTC. Men had shorter LOS and less frequent LTC than women. CONCLUSION: Hospital outcomes after injury are predictable from age, sex, and standard diagnosis groupings. Anatomic scales gave similar results when adjusted for other factors.