Changes in Outpatient Healthcare Utilization and Costs Following Mild Traumatic Brain Injury Among Service Members in the Military Health System by Preexisting Behavioral Health Condition Status.

OBJECTIVE: To evaluate changes in healthcare utilization and cost following an index mild traumatic brain injury (mTBI) diagnosis among service members (SMs). We hypothesized that differences in utilization and cost will be observed by preexisting behavioral health (BH) diagnosis status. SETTING: Direct care outpatient healthcare facilities within the Military Health System. PARTICIPANTS: A total of 21 984 active-duty SMs diagnosed with an index mTBI diagnosis between 2017 and 2018. DESIGN: This retrospective study analyzed changes in healthcare utilization and cost in military treatment facilities among SMs with an index mTBI diagnosis. Encounter records 1 year before and after mTBI were assessed; preexisting BH conditions were identified in the year before mTBI. MAIN MEASURES: Ordinary least squares regressions evaluated difference in the average change of total outpatient encounters and costs among SMs with and with no preexisting BH conditions (eg, posttraumatic stress disorder, adjustment disorder). Additional regressions explored changes in utilization and cost within clinic types (eg, mental health, physical rehabilitation). RESULTS: There was a 39.5% increase in overall healthcare utilization during the following year, representing a 34.8% increase in total expenditures. Those with preexisting BH conditions exhibited smaller changes in overall utilization (ß, -4.9; [95% confidence interval (CI), -6.1 to -3.8]) and cost (ß, $-1873; [95% CI, $-2722 to $-1024]), compared with those with no BH condition. The greatest differences were observed in primary care clinics, in which those with prior BH conditions exhibited an average decreased change of 3.2 encounters (95% CI, -3.5 to -3) and reduced cost of $544 (95% CI, $-599 to $-490) compared with those with no prior BH conditions. CONCLUSION: Despite being higher utilizers of healthcare services both pre- and post-mTBI diagnosis, those with preexisting BH conditions exhibited smaller changes in overall cost and utilization. This highlights the importance of considering prior utilization and cost when evaluating the impact of mTBI and other injury events on the Military Health System.

Intensive Outpatient Program Response Among Service Members With Mild Traumatic Brain Injury: Change Between Distinct Post-Concussive Symptom Subgroups.

OBJECTIVE: Among service members (SMs) with mild traumatic brain injury (mTBI) admitted to an intensive outpatient program (IOP), we identified qualitatively distinct subgroups based on post-concussive symptoms (PCSs) and characterized changes between subgroups from admission to discharge. Further, we examined whether co-morbid posttraumatic stress disorder (PTSD) influenced changes between subgroups. DESIGN: Quasi-experimental. Latent transition analysis identified distinctive subgroups of SMs and examined transitions between subgroups from admission to discharge. Logistic regression examined the effect of PTSD on transition to the Minimal subgroup (low probability of any moderate-very severe PCS) while adjusting for admission subgroup designation. SETTING: National Intrepid Center of Excellence (NICoE) at Walter Reed National Military Medical Center. PARTICIPANTS: 1141 active duty SMs with persistent PCS despite prior treatment (N=1141). INTERVENTIONS: NICoE 4-week interdisciplinary IOP. MAIN OUTCOME MEASURE(S): Subgroups identified using Neurobehavioral Symptom Inventory items at admission and discharge. RESULTS: Model fit indices supported a 7-class solution. The 7 subgroups of SMs were distinguished by diverging patterns of probability for specific PCS. The Minimal subgroup was most prevalent at discharge (39.4%), followed by the Sleep subgroup (high probability of sleep problems, low probability of other PCS; 26.8%). 41% and 25% of SMs admitted within the Affective (ie, predominantly affective PCS) and Sleep subgroups remained within the same group at discharge, respectively. The 19% of SMs with co-morbid PTSD were less likely to transition to the Minimal subgroup (odds ratio=0.28; P<.001) and were more likely to remain in their admission subgroup at discharge (35.5% with PTSD vs 22.2% without). CONCLUSIONS: Most of SMs achieved symptom resolution after participation in the IOP, with most transitioning to subgroups characterized by reduced symptom burden. SMs admitted in the Affective and Sleep subgroups, as well as those with PTSD, were most likely to have continuing clinical needs at discharge, revealing priority targets for resource allocation and follow-up treatment.

NSI and PCL-5 Normative Tables for Active Duty Service Members Affected by Traumatic Brain Injury.

INTRODUCTION: Many service members (SMs) have been diagnosed with traumatic brain injury. Currently, military treatment facilities do not have access to established normative tables which can assist clinicians in gauging and comparing patient-reported symptoms. The aim of this study is to provide average scores for both the Neurobehavioral Symptom Inventory (NSI) and Posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5) for active duty SMs based upon varying demographic groups. METHODS: Average scores were calculated for both the NSI and PCL-5 surveys from SMs who attended a military outpatient traumatic brain injury clinic. For this analysis, only the initial surveys for each SM were considered. The identifying demographics included age group, gender, grade, and race. RESULTS: Four normative tables were created to show the average scores of both the NSI and PCL-5 surveys grouped by demographics. The tables are grouped by Age Group/Gender/Race and Grade/Gender/Race. CONCLUSION: Clinicians and healthcare administrators can use the scores reported in this study to determine where SM NSI or PCL-5 scores fall within the average for their demographic group.

Traumatic Brain Injury Classification Variability During the Afghanistan/Iraq Conflicts: Surveillance, Clinical, Research, and Policy Implications.

OBJECTIVE: Challenges associated with case ascertainment of traumatic brain injuries (TBIs) sustained during the Afghanistan/Iraq military operations have been widespread. This study was designed to examine how the prevalence and severity of TBI among military members who served during the conflicts were impacted when a more precise classification of TBI diagnosis codes was compared with the Department of Defense Standard Surveillance Case-Definition (DoD-Case-Definition). SETTING: Identification of TBI diagnoses in the Department of Defense's Military Health System from October 7, 2001, until December 31, 2019. PARTICIPANTS: Military members with a TBI diagnosis on an encounter record during the study window. DESIGN: Descriptive observational study to evaluate the prevalence and severity of TBI with regard to each code set (ie, the DoD-Case-Definition and the more precise set of TBI diagnosis codes). The frequencies of index TBI severity were compared over time and further evaluated against policy changes. MAIN MEASURES: The more precise TBI diagnosis code set excludes the following: (1) DoD-only extender codes, which are not used in other healthcare settings; and (2) nonprecise TBI codes, which include injuries that do not necessarily meet TBI diagnostic criteria. RESULTS: When comparing the 2 TBI classifications, the DoD-Case-Definition captured a higher prevalence of TBIs; 38.5% were classified by the DoD-Case-Definition only (>164 000 military members). 73% of those identified by the DoD-Case-Definition only were diagnosed with nonprecise TBI codes only, with questionable specificity as to whether a TBI occurred. CONCLUSION: We encourage the field to reflect on decisions made pertaining to TBI case ascertainment during the height of the conflicts. Efforts focused on achieving consensus regarding TBI case ascertainment are recommended. Doing so will allow the field to be better prepared for future conflicts, and improve surveillance, screening, and diagnosis in noncombat settings, as well as our ability to understand the long-term effects of TBI.

Correlations Between the Neurobehavioral Symptom Inventory and Other Commonly Used Questionnaires for Traumatic Brain Injury.

OBJECTIVE: To evaluate the correlations between the Neurobehavioral Symptom Inventory (NSI) and other questionnaires commonly administered within military traumatic brain injury clinics. SETTING: Military outpatient traumatic brain injury clinics. PARTICIPANTS: In total, 15,428 active duty service members who completed 24,162 NSI questionnaires between March 2009 and May 2020. DESIGN: Observational retrospective analysis of questionnaires collected as part of standard clinical care. MAIN MEASURES: NSI, Post-Traumatic Stress Disorder Checklist for DSM-5 and Military Version, Patient Health Questionnaire (PHQ), Generalized Anxiety Disorder, Headache Impact Test (HIT-6), Insomnia Severity Index (ISI), Epworth Sleepiness Scale (ESS), Activities-Specific Balance Confidence Scale (ABC), Dizziness Handicap Inventory (DHI), Alcohol Use Disorders Identification Test (AUDIT), and the World Health Organization Quality of Life Instrument-Abbreviated Version. Only questionnaires completed on the same date as the NSI were examined. RESULTS: The total NSI score was moderately to strongly correlated with all questionnaires except for the AUDIT. The strongest correlation was between the NSI Affective Score and the PHQ9 (r = 0.86). The NSI Vestibular Score was moderately correlated with the ABC (r = -0.55) and strongly correlated with the DHI (r = 0.77). At the item level, the HIT-6 showed strong correlation with NSI headache (r = 0.80), the ISI was strongly correlated with NSI difficulty sleeping (r = 0.63), and the ESS was moderately correlated with NSI fatigue (r = 0.39). CONCLUSION: Clinicians and healthcare administrators can use the correlations reported in this study to determine if questionnaires add incremental value for their clinic as well as to make more informed decisions regarding which questionnaires to administer.

Risk Factors Associated With the Prescription of Opioids Among Service Members Following a First Mild Traumatic Brain Injury.

OBJECTIVE: To evaluate factors impacting opioid receipt among active-duty service members (SMs) following a first mild traumatic brain injury (mTBI). SETTING: Active-duty SMs receiving care within the Military Health System. PARTICIPANTS: In total, 14 757 SMs who have sustained an initial mTBI, as documented within electronic health records (EHRs), between 2016 and 2017. DESIGN: A retrospective analysis of EHR metadata. MAIN MEASURES: Multivariable logistic regression assessed factors impacting opioid receipt and initiation. Factors include demographics, military characteristics, and preexisting clinical conditions, including prior opioid prescription. RESULTS: Of the sample population, 33.4% (n = 4927) were prescribed opioids after their initial mTBI, of which, 60.6% (n = 2985) received opioids for the first time following injury. Significant risk factors associated with the increased probability of opioid receipt included age, gender, and preexisting behavioral health and musculoskeletal conditions. Military characteristics also exhibited changes in the probability of opioid receipt, both among initiation and new prescription. No changes were observed among race, nor among those with preexisting headaches or migraines. CONCLUSION: Despite concerns about the negative impact on recovery, the prescribing of opioids is common in this population of active-duty SMs first diagnosed with an mTBI. As several demographic and preexisting health conditions are factors in the receipt of opioids post-mTBI, the entire medical history of these patients should be considered prior to prescription. Understanding these factors may further inform policy for opioid use in the Military Health System.

Burden of Behavioral Health Comorbidities on Outpatient Health Care Utilization by Active Duty Service Members With a First Documented mTBI.

OBJECTIVE: More than 280,000 Active Duty Service Members (ADSMs) sustained a mild traumatic brain injury (mTBI) between 2000 and 2019 (Q3). Previous studies of veterans have shown higher utilization of outpatient health clinics by veterans diagnosed with mTBI. Additionally, veterans with mTBI and comorbid behavioral health (BH) conditions such as post-traumatic stress disorder, depression, and substance use disorders have significantly higher health care utilization than veterans diagnosed with mTBI alone. However, few studies of the relationship between mTBI, health care utilization, and BH conditions in the active duty military population currently exist. We examined the proportion of ADSMs with a BH diagnosis before and after a first documented mTBI and quantified outpatient utilization of the Military Health System in the year before and following injury. MATERIALS AND METHODS: Retrospective analysis of 4,901,840 outpatient encounters for 39,559 ADSMs with a first documented diagnosis of mTBI recorded in the Department of Defense electronic health record, subsets of who had a BH diagnosis. We examined median outpatient utilization 1 year before and 1 year after mTBI using Wilcoxon signed rank test, and the results are reported with an effect size r. Outpatient utilization is compared by BH subgroups. RESULTS: Approximately 60% of ADSMs experience a first mTBI with no associated BH condition, but 17% of men and women are newly diagnosed with a BH condition in the year following mTBI. ADSMs with a history of a BH condition before mTBI increased their median outpatient utilization from 23 to 35 visits for men and from 32 to 42 visits for women. In previously healthy ADSMs with a new BH condition following mTBI, men more than tripled median utilization from 7 to 24 outpatient visits, and women doubled utilization from 15 to 32 outpatient visits. CONCLUSIONS: Behavioral health comorbidities affect approximately one-third of ADSMs following a first mTBI, and approximately 17% of previously healthy active duty men and women will be diagnosed with a new BH condition in the year following a first mTBI. Post-mTBI outpatient health care utilization is highly dependent on the presence or absence of BH condition and is markedly higher is ADSMs with a BH diagnosis in the year after a first documented mTBI.

Evaluation of Machine Learning Techniques to Predict the Likelihood of Mental Health Conditions Following a First mTBI.

OBJECTIVE: Limited research has evaluated the utility of machine learning models and longitudinal data from electronic health records (EHR) to forecast mental health outcomes following a traumatic brain injury (TBI). The objective of this study is to assess various data science and machine learning techniques and determine their efficacy in forecasting mental health (MH) conditions among active duty Service Members (SMs) following a first diagnosis of mild traumatic brain injury (mTBI). MATERIALS AND METHODS: Patient demographics and encounter metadata of 35,451 active duty SMs who have sustained an initial mTBI, as documented within the EHR, were obtained. All encounter records from a year prior and post index mTBI date were collected. Patient demographics, ICD-9-CM and ICD-10 codes, enhanced diagnostic related groups, and other risk factors estimated from the year prior to index mTBI were utilized to develop a feature vector representative of each patient. To embed temporal information into the feature vector, various window configurations were devised. Finally, the presence or absence of mental health conditions post mTBI index date were used as the outcomes variable for the models. RESULTS: When evaluating the machine learning models, neural network techniques showed the best overall performance in identifying patients with new or persistent mental health conditions post mTBI. Various window configurations were tested and results show that dividing the observation window into three distinct date windows [-365:-30, -30:0, 0:14] provided the best performance. Overall, the models described in this paper identified the likelihood of developing MH conditions at [14:90] days post-mTBI with an accuracy of 88.2%, an AUC of 0.82, and AUC-PR of 0.66. DISCUSSION: Through the development and evaluation of different machine learning models we have validated the feasibility of designing algorithms to forecast the likelihood of developing mental health conditions after the first mTBI. Patient attributes including demographics, symptomatology, and other known risk factors proved to be effective features to employ when training ML models for mTBI patients. When patient attributes and features are estimated at different time window, the overall performance increase illustrating the importance of embedding temporal information into the models. The addition of temporal information not only improved model performance, but also increased interpretability and clinical utility. CONCLUSION: Predictive analytics can be a valuable tool for understanding the effects of mTBI, particularly when identifying those individuals at risk of negative outcomes. The translation of these models from retrospective study into real-world validation models is imperative in the mitigation of negative outcomes with appropriate and timely interventions.

The Community Balance and Mobility Scale: Detecting Impairments in Military Service Members With Mild Traumatic Brain Injury.

INTRODUCTION: The objective of this study was to determine the utility of the Community Balance and Mobility scale (CB&M) among service members presenting with mild traumatic brain injury (mTBI), to compare the results against well-established balance assessments, and to find a new military-specific CB&M cut score to help differentiate those with and without mTBI. MATERIALS AND METHODS: The setting was a 4-week, intensive-outpatient, interdisciplinary program for active duty service members with mTBI. This was a nonrandomized, cross-sectional design that compared multiple measures between two groups: active duty service members with (n = 45) and without (n = 45) mTBI. The assessments, including the Activities-Specific Balance Confidence Scale, gait speed (comfortable and fast), the Functional Gait Assessment, and the CB&M, were provided to both sample groups. RESULTS: The mTBI group performed significantly worse (P ≤ 0.01) across all measures. A higher cut score for the CB&M of 81.5 is suggested. The CB&M demonstrated the best sensitivity (78%) and specificity (91%) ratio, as well as the largest effect size and area under the curve(0.92). CONCLUSION: All objective measures distinguish participants with mTBI from controls, ranging from fair to excellent. The recommended CB&M cut score of 81.5 allows for good variance, standard deviation, and reduced risk of ceiling or floor effects. Further examination of the recommended CB&M cut score is warranted for use in the mTBI civilian populations.