Evaluating Outcomes of Nonaccidental Trauma in Military Children.

BACKGROUND: Nonaccidental trauma (NAT), or child abuse, is a leading cause of childhood injury and death in the US. Studies demonstrate that military-affiliated individuals are at greater risk of mental health complication and family violence, including child maltreatment. There is limited information about the outcomes of military children who experience NAT. This study compares the outcomes between military-dependent and civilian children diagnosed with NAT. STUDY DESIGN: A single-institution, retrospective review of children admitted with confirmed NAT at a Level I trauma center was performed. Data were collected from the institutional trauma registry and the Child Abuse Team's database. Military affiliation was identified using insurance status and parental or caregiver self-reported active-duty status. Demographic and clinical data including hospital length of stay (LOS), morbidity, specialty consult, and mortality were compared. RESULTS: Among 535 patients, 11.8% (n = 63) were military-affiliated. The median age of military-associated patients, 3 months (interquartile range [IQR] 1 to 7), was significantly younger than civilian patients, 7 months (IQR 3 to 18, p < 0.001). Military-affilif:ated patients had a longer LOS of 4 days (IQR 2 to 11) vs 2 days (IQR 1 to 7, p = 0.041), increased morbidity or complication (3 vs 2 counts, p = 0.002), and a higher mortality rate (10% vs 4%, p = 0.048). No significant difference was observed in the number of consults or injuries, trauma activation, or need for surgery. CONCLUSIONS: Military-affiliated children diagnosed with NAT experience more adverse outcomes than civilian patients. Increased LOS, morbidity or complication, and mortality suggest military-affiliated patients experience more life-threatening NAT at a younger age. Larger studies are required to further examine this population and better support at-risk families.

Application of a Machine Learning Algorithm in Prediction of Abusive Head Trauma in Children.

PURPOSE: We explored the application of a machine learning algorithm for the timely detection of potential abusive head trauma (AHT) using the first free-text note of an encounter and demographic information. METHODS: First free-text physician notes and demographic information were collected for children under 5 years of age at a Level 1 Trauma Center. The control group, which included patients with head/neck injury, was compared to those with AHT diagnosed by the Child Protective Team. Differential scores accounted for words overrepresented in AHT patient vs. control notes. Sentiment scores were reflective of note positivity/negativity and subjectivity scores accounted for note subjectivity/objectivity. The composite scores reflected the patient's differential score modified by the subjectivity score. Composite, sentiment, and subjectivity scores combined with demographic information trained a Random Forest (RF) machine learning algorithm to predict AHT. RESULTS: Final composite scores with demographic information were highly associated with AHT in a test dataset. The control group included 587 patients and the test group included 193 patients. Combining composite scores with demographic information into the RF model improved AHT classification area under the curve (AUC) from 0.68 to 0.78, with an overall accuracy of 84%. Feature importance analysis of our RF model revealed that composite score, sentiment, age, and subjectivity were the most impactful predictors of AHT. The sentiment was not significantly different between control and AHT notes (p = 0.87), while subjectivity trended higher for AHT notes (p = 0.081). CONCLUSION: We conclude that a machine learning algorithm can recognize patterns within free-text notes and demographic information that aid in AHT detection in children. LEVEL OF EVIDENCE: III.

Minimizing Health-Compromising Behaviors via School-Based Programs: An Optimization Approach.

School health programs are united by their desire to promote health and health-related outcomes among youth. They are also united by the fact that their expected effects are contingent on successful program implementation, which is often impeded by a multitude of real-world barriers. Techniques used in management science may help optimize school-based programs by accounting for implementation barriers. In this exploratory study, we present a detailed example of the first known application of linear programming (LP), which is an optimization technique, to Positive Action (PA). PA is a social emotional and character development program that includes a six-unit, teacher-delivered, classroom curriculum. We specify how we used LP to calculate the optimal levels of program implementation needed to minimize substance use, subject to known levels of implementation barriers (e.g., disruptive behavior, teacher education, teacher attitudes towards character development, school resources, and school safety). We found that LP is a technique that can be applied to data from a school health program. Specifically, we were able to develop a model that calculated the number of lessons that should be taught to minimize a specific health-compromising behavior, given expected levels of predetermined implementation barriers. Our findings from this exploratory study support the utility of applying LP during the program planning and implementation processes of school health programs.

Racial/ethnic variation in mental health correlates of substance use among college students.

This study investigated mental health indicators, substance use, and their relationships, by race/ethnicity. A probability sample of 1,053 students at two California universities self-reported their frequency of substance use and rated their experience with indicators of mental health. One-way analysis of variance (ANOVA), chi-square tests, and multivariate censored regression models were estimated to examine which indicators of mental health were associated with each substance use form by race/ethnicity. Results from the one-way ANOVA and chi-square tests showed differences in substance use prevalence and mental health by race/ethnicity. For example, students who identified as White demonstrate a higher prevalence for every form of substance use in comparison to the Asian, Latino, and "All other" categories. Results from the regression showed, among Whites, inattention was associated with prescription stimulant misuse, and psychological distress was associated with marijuana use. Among Latinos, inattention was associated with cocaine and prescription stimulant use. Among Asians, psychological distress was associated with tobacco use and the misuse of prescription painkillers. Findings highlight the need to ensure subpopulations receive needed services.