The Texas driver responsibility program: A preliminary analysis of the impact on impaired driving and trauma system funding

Objective. In 2003, the State of Texas instituted the Driver Responsibility Program (TDRP), a program consisting of a driving infraction point system coupled with a series of graded fines and annual surcharges for specific traffic violations such as driving while intoxicated (DWI). Approximately half of the revenues generated are earmarked to be disbursed to the state's trauma system to cover uncompensated trauma care costs. This study examined initial program implementation, the impact of trauma system funding, and initial impact on impaired driving knowledge, attitudes and behaviors. A model for targeted media campaigns to improve the program's deterrence effects was developed. ^ Methods. Data from two independent driver survey samples (conducted in 1999 and 2005), department of public safety records, state health department data and a state auditor's report were used to evaluate the program's initial implementation, impact and outcome with respect to drivers' impaired driving knowledge, attitudes and behavior (based on constructs of social cognitive theory) and hospital uncompensated trauma care funding. Survey results were used to develop a regression model of high risk drivers who should be targeted to improve program outcome with respect to deterring impaired driving. ^ Results. Low driver compliance with fee payment (28%) and program implementation problems were associated with lower surcharge revenues in the first two years ($59.5 million versus $525 million predicted). Program revenue distribution to trauma hospitals was associated with a 16% increase in designated trauma centers. Survey data demonstrated that only 28% of drivers are aware of the TDRP and that there has been no initial impact on impaired driving behavior. Logistical regression modeling suggested that target media campaigns highlighting the likelihood of DWI detection by law enforcement and the increased surcharges associated with the TDRP are required to deter impaired driving. ^ Conclusions. Although the TDRP raised nearly $60 million in surcharge revenue for the Texas trauma system over the first two years, this study did not find evidence of a change in impaired driving knowledge, attitudes or behaviors from 1999 to 2005. Further research is required to measure whether the program is associated with decreased alcohol-related traffic fatalities. ^

Trauma care capacity and performance in Texas: Regional comparisons

Objective. The study reviewed one year of Texas hospital discharge data and Trauma Registry data for the 22 trauma services regions in Texas to identify regional variations in capacity, process of care and clinical outcomes for trauma patients, and analyze the statistical associations among capacity, process of care, and outcomes. ^ Methods. Cross sectional study design covering one year of state-wide Texas data. Indicators of trauma capacity, trauma care processes, and clinical outcomes were defined and data were collected on each indicator. Descriptive analyses were conducted of regional variations in trauma capacity, process of care, and clinical outcomes at all trauma centers, at Level I and II trauma centers and at Level III and IV trauma centers. Multilevel regression models were performed to test the relations among trauma capacity, process of care, and outcome measures at all trauma centers, at Level I and II trauma centers and at Level III and IV trauma centers while controlling for confounders such as age, gender, race/ethnicity, injury severity, level of trauma centers and urbanization. ^ Results. Significant regional variation was found among the 22 trauma services regions across Texas in trauma capacity, process of care, and clinical outcomes. The regional trauma bed rate, the average staffed bed per 100,000 varied significantly by trauma service region. Pre-hospital trauma care processes were significantly variable by region---EMS time, transfer time, and triage. Clinical outcomes including mortality, hospital and intensive care unit length of stay, and hospital charges also varied significantly by region. In multilevel regression analysis, the average trauma bed rate was significantly related to trauma care processes including ambulance delivery time, transfer time, and triage after controlling for age, gender, race/ethnicity, injury severity, level of trauma centers, and urbanization at all trauma centers. Transfer time only among processes of care was significant with the average trauma bed rate by region at Level III and IV. Also trauma mortality only among outcomes measures was significantly associated with the average trauma bed rate by region at all trauma centers. Hospital charges only among outcomes measures were statistically related to trauma bed rate at Level I and II trauma centers. The effect of confounders on processes and outcomes such as age, gender, race/ethnicity, injury severity, and urbanization was found significantly variable by level of trauma centers. ^ Conclusions. Regional variation in trauma capacity, process, and outcomes in Texas was extensive. Trauma capacity, age, gender, race/ethnicity, injury severity, level of trauma centers and urbanization were significantly associated with trauma process and clinical outcomes depending on level of trauma centers. ^ Key words: regionalized trauma systems, trauma capacity, pre-hospital trauma care, process, trauma outcomes, trauma performance, evaluation measures, regional variations ^

Data quality in trauma transfusion studies and the impact of missing data on predicting massive transfusion

Maximizing data quality may be especially difficult in trauma-related clinical research. Strategies are needed to improve data quality and assess the impact of data quality on clinical predictive models. This study had two objectives. The first was to compare missing data between two multi-center trauma transfusion studies: a retrospective study (RS) using medical chart data with minimal data quality review and the PRospective Observational Multi-center Major Trauma Transfusion (PROMMTT) study with standardized quality assurance. The second objective was to assess the impact of missing data on clinical prediction algorithms by evaluating blood transfusion prediction models using PROMMTT data. RS (2005-06) and PROMMTT (2009-10) investigated trauma patients receiving ≥ 1 unit of red blood cells (RBC) from ten Level I trauma centers. Missing data were compared for 33 variables collected in both studies using mixed effects logistic regression (including random intercepts for study site). Massive transfusion (MT) patients received ≥ 10 RBC units within 24h of admission. Correct classification percentages for three MT prediction models were evaluated using complete case analysis and multiple imputation based on the multivariate normal distribution. A sensitivity analysis for missing data was conducted to estimate the upper and lower bounds of correct classification using assumptions about missing data under best and worst case scenarios. Most variables (17/33=52%) had <1% missing data in RS and PROMMTT. Of the remaining variables, 50% demonstrated less missingness in PROMMTT, 25% had less missingness in RS, and 25% were similar between studies. Missing percentages for MT prediction variables in PROMMTT ranged from 2.2% (heart rate) to 45% (respiratory rate). For variables missing >1%, study site was associated with missingness (all p≤0.021). Survival time predicted missingness for 50% of RS and 60% of PROMMTT variables. MT models complete case proportions ranged from 41% to 88%. Complete case analysis and multiple imputation demonstrated similar correct classification results. Sensitivity analysis upper-lower bound ranges for the three MT models were 59-63%, 36-46%, and 46-58%. Prospective collection of ten-fold more variables with data quality assurance reduced overall missing data. Study site and patient survival were associated with missingness, suggesting that data were not missing completely at random, and complete case analysis may lead to biased results. Evaluating clinical prediction model accuracy may be misleading in the presence of missing data, especially with many predictor variables. The proposed sensitivity analysis estimating correct classification under upper (best case scenario)/lower (worst case scenario) bounds may be more informative than multiple imputation, which provided results similar to complete case analysis.^