Factors affecting hospital charges and length of stay from teenage motor vehicle crash-related hospitalizations among United States teenagers, 2002-2007
ABSTRACT Motor vehicle crashes are the leading cause of death for all teenagers, and each year a far greater number of teens are hospitalized with non-fatal injuries. This retrospective cohort study used the National Inpatient Sample data to examine hospitalizations from the years 2002 to 2007 for 15-18-year-old teenagers who had been admitted due to a motor vehicle crash. More than 23,000 teens were hospitalized for motor vehicle-related crash injuries each year, for a total of 139,880 over the 6-year period. Total hospital charges exceeded $1 billion almost every year, with a median hospital charge of more than $25,000. Older teens, boys, those with fractures, internal injuries or intracranial injuries, and Medicaid/Medicare as a payer were associated with higher hospital charges and longer lengths of stay. These high charges and hospitalization periods pose a significant burden on teens, their families, and the health care system.
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ABSTRACT: The adolescent Latino male mortality profile is an anomaly when compared to an otherwise more favorable overall U.S. Latino population mortality profile. Motor vehicle crash fatalities bear a considerable proportion of mortality burden in this vulnerable population. Friend influence and relational connection are two contextual domains that may mediate crash injury risk behavior in these adolescents. Our study goal was to assess the role of friend influence over time and relational connections associated with crash injury risk behavior (CIRB) in adolescent Latino males. Waves I and II data from the National Longitudinal Study of Adolescent Health were used. Scale of CIRB, and three relational connections; school connectedness, parent connectedness, and expectation of academic success were developed and tested. Friend nomination data were available and the index student responses were linked to friend responses. Linear regression was used to assess the relationship of relational connections and friend CIRB on index student CIRB at wave I and II. Longitudinal analysis did not show significant evidence for friend influence among adolescent Latino males on CIRB. The best predictor of CIRB at wave II for adolescent Latino males was their CIRB at wave I. Relational connections were important yet exaggerated cross-sectionally but their effect was substantially attenuated longitudinally. The lack of friend influence on CIRB for adolescent Latino males may be specific to this demographic group or characteristic of the sample studied. Prevention strategies that focus on modulating friend influence in adolescent Latino males may not yield the desired prevention effects on CIRB.Annals of advances in automotive medicine 01/2011; 55:41-50.
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ABSTRACT: Emergency department (ED) and hospital charges obtained from administrative data sets are useful descriptors of injury severity and the burden to EDs and the health care system. However, charges are typically positively skewed due to costly procedures, long hospital stays, and complicated or prolonged treatment for few patients. The median is not affected by extreme observations and is useful in describing and comparing distributions of hospital charges. A least-squares analysis employing a log transformation is one approach for estimating median hospital charges, corresponding confidence intervals (CIs), and differences between groups; however, this method requires certain distributional properties. An alternate method is quantile regression, which allows estimation and inference related to the median without making distributional assumptions. The objective was to compare the log-transformation least-squares method to the quantile regression approach for estimating median hospital charges, differences in median charges between groups, and associated CIs. The authors performed simulations using repeated sampling of observed statewide ED and hospital charges and charges randomly generated from a hypothetical lognormal distribution. The median and 95% CI and the multiplicative difference between the median charges of two groups were estimated using both least-squares and quantile regression methods. Performance of the two methods was evaluated. In contrast to least squares, quantile regression produced estimates that were unbiased and had smaller mean square errors in simulations of observed ED and hospital charges. Both methods performed well in simulations of hypothetical charges that met least-squares method assumptions. When the data did not follow the assumed distribution, least-squares estimates were often biased, and the associated CIs had lower than expected coverage as sample size increased. Quantile regression analyses of hospital charges provide unbiased estimates even when lognormal and equal variance assumptions are violated. These methods may be particularly useful in describing and analyzing hospital charges from administrative data sets.Academic Emergency Medicine 07/2012; 19(7):866-75. DOI:10.1111/j.1553-2712.2012.01388.x · 2.20 Impact Factor
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ABSTRACT: Hospital expenses are a major cost driver of healthcare systems in Europe, with motor injuries being the leading mechanism of hospitalizations. This paper investigates the injury characteristics which explain the hospitalization of victims of traffic accidents that took place in Spain. Using a motor insurance database with 16,081 observations a generalized Tobit regression model is applied to analyse the factors that influence both the likelihood of being admitted to hospital after a motor collision and the length of hospital stay in the event of admission. The consistency of Tobit estimates relies on the normality of perturbation terms. Here a semi-parametric regression model was fitted to test the consistency of estimates, concluding that a normal distribution of errors cannot be rejected. Among other results, it was found that older men with fractures and injuries located in the head and lower torso are more likely to be hospitalized after the collision, and that they also have a longer expected length of hospital recovery stay.Accident; analysis and prevention 11/2012; 49:512-9. DOI:10.1016/j.aap.2012.03.025 · 1.65 Impact Factor