Search

Background: Variations in adjusted costs have been observed among trauma centres in the United States but patient outcomes were not better in centres with higher costs. Attempts to improve injury care efficiency are hampered by insufficient patient-level information on resource use and on the drivers of resource use intensity.Objectives: To estimate patient-level resource use for injury admissions, identify determinants of resource use intensity, and evaluate inter-hospital variations in resource use.Methods: We conducted a retrospective cohort study including ≥16-year-olds admitted to adult trauma centres in a mature, inclusive Canadian trauma system between 2014 and 2016. We extracted data from the trauma registry and hospital financial reports. We estimated resource use with activity-based costs, identified determinants of resource use intensity using a multilevel linear model and assessed the relative importance of each determinant with Cohen's f2. We evaluated inter-provider variations with intraclass correlation coefficients (ICC) and 95% confidence intervals.Results: We included 32,411 patients. Median costs per admission were $4857 (Quartiles 1 and 3 2961-8448). The most important contributors to total resource use were the medical ward (57%), followed by the operating room (OR; 23%) and the intensive care unit (13%). The strongest determinant of resource use intensity was discharge destination (Cohen's f2 = 7%). The most resource intense patient group was spinal cord injuries with $11,193 (7115-17,606) per admission. While resource use increased with increasing age for the medical ward, it decreased with increasing age for the OR. Resource use was 18% higher in level I centres compared to level IV centres and we observed significant variations in resource use across centres (ICC = 5% [4-6]), particularly for the OR (28% [20-40]).Conclusions: Resource use for acute injury care in Quebec is not solely due to the clinical status of patients. We identified determinants of resource use that can be used to establish evidence-based resource allocations and improve injury care efficiency. The method we developed for estimating patient-level, in-hospital resource use for injury admissions and identifying related determinants could be reproduced using local trauma registry data and our unit costs or unit costs specific to each setting. [ABSTRACT FROM AUTHOR]

Background: Improving care is a key strategy for reducing the burden of injuries, but it is unknown whether the use of quality indicators (QI) is associated with patient outcomes. We sought to evaluate the association between the use of QIs by trauma centers and outcomes in adult injury patients.Methods: We identified consecutive adult patients (n=223,015) admitted to 233 verified trauma centers January 1, 2007 to December 31, 2010 that contributed data to the National Trauma Data Bank and participated in a survey of QI practices. Generalized Linear Mixed Models were employed to evaluate the association between the intensity (number of QIs) and nature (report cards, internal and external benchmarking) of QI use and survival to hospital discharge, adjusting for patient and hospital characteristics.Results: There were no significant differences in the odds of survival to trauma center discharge according to the number of QIs measured (quartiles; odds ratio{OR} [95% confidence interval{CI}] 1.00 vs. 1.08 [0.90-1.31] vs. 1.00 [0.82-1.22] vs. 1.21 [0.99-1.49]), or whether centers used reports cards (OR 1.07, 95%CI 0.94-1.23), internal (OR 1.06, 95%CI 0.89-1.26) or external (OR 1.09, 95%CI 0.92-1.31) benchmarking. The duration (geometric mean) of mechanical ventilation (4.0days), ICU stay (4.6days), hospital stay (7.7days) and proportion of patients with a complication (13.6%) did not significantly differ according to the intensity or nature of QI use.Conclusions: The intensity and nature of the QIs used by trauma centers was not associated with outcomes of patient care. Alternative quality improvement strategies may be needed. [ABSTRACT FROM AUTHOR]

Objectives. We evaluated the effect of North American public bicycle share programs (PBSPs), which typically do not offer helmets with rentals, on the occurrence of bicycle-related head injuries. Methods. We analyzed trauma center data for bicycle-related injuries from 5 cities with PBSPs and 5 comparison cities. We used logistic regression models to compare the odds that admission for a bicycle-related injury would involve a head injury 24 months before PBSP implementation and 12 months afterward. Results. In PBSP cities, the proportion of head injuries among bicycle-related injuries increased from 42.3% before PBSP implementation to 50.1% after (P < .01). This proportion in comparison cities remained similar before (38.2%) and after (35.9%) implementation (P = .23). Odds ratios for head injury were 1.30 (95% confidence interval = 1.13, 1.67) in PBSP cities and 0.94 (95% confidence interval = 0.79, 1.11) in control cities (adjusted for age and city) when we compared the period after implementation to the period before. Conclusions. Results suggest that steps should be taken to make helmets available with PBSPs. Helmet availability should be incorporated into PBSP planning and funding, not considered an afterthought following implementation. [ABSTRACT FROM AUTHOR]

Background: Trauma center profiling is commonly performed with Standardized Mortality Ratios (SMRs). However, comparison of SMRs across trauma centers with different case mix can induce confounding leading to biased trauma center ranks. We hypothesized that Regression-Adjusted Mortality (RAM) estimates would provide a more valid measure of trauma center performance than SMRs. Objective: Compare trauma center ranks generated by RAM estimates to those generated by SMRs. Materials and Methods: The study was based on data from a provincial Trauma Registry (1999-2006; n = 88,235). SMRs were derived as the ratio of observed to expected deaths using: (1) the study population as an internal standard, (2) the US National Trauma Data Bank as an external standard. The expected death count was calculated as the sum of mortality probabilities for all patients treated in a hospital conditional on the injury severity score, the revised trauma score, and age. RAM estimates were obtained directly from a hierarchical logistic regression model. Results: Crude mortality was 5.4% and varied between 1.3% and 13.5% across the 59 trauma centers. When trauma center ranks from internal SMRs and RAM were compared, 49 out of 59 centers changed rank and six centers changed by more than five ranks. When trauma center ranks from external SMRs and RAM were compared, 55 centers changed rank and 17 changed by more than five ranks. Conclusions: The results of this study suggest that the use of SMRs to rank trauma centers in terms of mortality may be misleading. RAM estimates represent a potentially more valid method of trauma center profiling. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to develop and evaluate the content validity of quality criteria for providing patient- and family-centered injury care., Background: Quality criteria have been developed for clinical injury care, but not patient- and family-centered injury care., Methods: Using a modified Research AND Development Corporation (RAND)/University of California, Los Angeles (UCLA) Appropriateness Methodology, a panel of 16 patients, family members, injury and quality of care experts serially rated and revised criteria for patient- and family-centered injury care identified from patient and family focus groups. The criteria were then sent to 384 verified trauma centers in the United States, Canada, Australia, and New Zealand for evaluation., Results: A total of 46 criteria were rated and revised by the panel over 4 rounds of review producing 14 criteria related to clinical care (n = 4; transitions of care, pain management, patient safety, provider competence), communication (n = 3; information for patients/families; communication of discharge plans to patients/families, communication between hospital and community providers), holistic care (n = 4; patient hygiene, kindness and respect, family access to patient, social and spiritual support) and end-of-life care (n = 3; decision making, end-of-life care, family follow-up). Medical directors, managers, or coordinators representing 254 trauma centers (66% response rate) rated 12 criteria to be important (95% of responses) for patient- and family-centered injury care. Fewer centers rated family access to the patient (80%) and family follow-up after patient death (65%) to be important criteria., Conclusions: Fourteen-candidate quality criteria for patient- and family-centered injury care were developed and shown to have content validity. These may be used to guide quality improvement practices.

Background: Like most trauma registries, the National Trauma Data Bank (NTDB) is limited by the problem of missing physiologic data. Multiple imputation (MI) has been proposed to simulate missing Glasgow Coma Scale (GCS) scores, respiratory rate (RR), and systolic blood pressure (SBP). The aim of this study was to develop an MI model for missing physiologic data in the NTDB and to provide guidelines for its implementation., Study Design: The NTDB 7.0 was restricted to patients admitted in 2005 with at least one anatomic injury code. A series of auxiliary variables thought to offer information for the imputation process was selected from the NTDB by literature review and expert opinion. The relation of these variables to physiologic variables and to the fact that they were missing was examined using logistic regression. The MI model included all auxiliary variables that had a statistically significant association with physiologic variables or with the fact that they were missing (Bonferroni-corrected p value <0.05)., Results: The NTDB sample included 373,243 observations. Glasgow Coma Scale, respiratory rate, and systolic blood pressure were missing for 20.3%, 3.9%, and 8.5% of data observations, respectively. The MI model included information on the following: gender, age, anatomic injury severity, transfer status, injury mechanism, intubation status, alcohol and drug test results, emergency department disposition, total length of stay, ICU length of stay, duration of mechanical ventilation, and discharge disposition. The MI model offered good discrimination for predicting the value of physiologic variables and the fact that they were missing (areas under the receiver operating characteristic curve between 0.832 and 0.999)., Conclusions: This article proposes an MI model for imputing missing physiologic data in the NTDB and provides guidelines to facilitate its use. Implementation of the model should improve the quality of research involving the NTDB. The methodology can also be adapted to other trauma registries.