Timing of Traumatic Brain Injury as a Predictor of Dual Systems Development: Testing for Moderation Effects of Concurrent Age

Timing of Traumatic Brain Injury as a Predictor of Dual Systems Development: Testing for Moderation Effects of Concurrent Age | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Timing of Traumatic Brain Injury as a Predictor of Dual Systems Development: Testing for Moderation Effects of Concurrent Age Thomas Wojciechowski This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3988657/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract There is limited work examining the developmental timing of traumatic brain injuries for influencing development of sensation-seeking and impulse control. Further, there is a dearth of research which examines whether the manifestation of effects of earlier traumatic brain injury appear at later ages via moderation by age. A subsample of participants from the Pathways to Desistance dataset was analyzed (N = 393). This subsample was comprised of all justice-involved youth in the sample who reported ever experiencing traumatic brain injury prior to baseline measurements. Mixed effects modeling was used to examine direct and moderated effects of interest. Results indicated that earlier age at first TBI was a significant predictor of lower impulse control, but not sensation-seeking. There were no significant moderation effects. Dual Systems Model Development Traumatic Brain Injury Introduction Traumatic brain injury (TBI) is characterized by any kind of injury to the head resulting in brain dysfunction that generally results from a violent blow or jolt to the head. Such injuries can vary in their severity, from relatively minor cases to cases requiring hospitalization and/or long-term care. TBI is an incredibly important category of injury because of the impact that it can have on functioning, emotion, cognition, and behavior. For example, prior research has indicated that TBI is a risk factor for dysfunctional cognitive development (Gerard-Morris et al., 2010; Jonsson et al., 2013 ; Kohler et al., 2020 ). While prior research has established the relevance of TBI for understanding cognitive development, there important gaps in our understanding. One area in need of continued investigation relates to the developmental timing of TBI experiences for predicting cognitive development. There is a dearth of research that has examined age at first TBI as being relevant for understanding cognitive development pertaining to the dual systems model specifically. The dual systems model focuses on explaining involvement in risky behaviors due to imbalance in the development of impulse control and sensation-seeking during adolescence (Steinberg et al., 2008 ; Steinberg, 2010 ). As such, this framework has risen to prominence because of its capacity to explain increased risk of involvement in such behaviors that is generally observed during adolescence due to differential development of brain regions governing these constructs. That said, developmental timing has yet to be examined as a predictor of dual systems development. This is to say that the age at which an individual first experiences TBI may have relevance for just how much dual systems model development is impacted. Beyond this, there is there must also be concern over when variation in development driven by earlier or later TBI actually manifests. This is to say that alterations to cognitive development may not be immediately present and instead may become apparent at different ages. The present study sought to address these limitations of prior research by examining age at first TBI as a predictor of dual systems development and ageas a moderator of this relationship among a sample of justice-involved youth (JIY) who reported experiencing TBI in their lifetimes prior to baseline measurements. The Dual Systems Model The dual systems model is a relevant developmental framework centered on the role that differential development of sensation-seeking and impulse control plays for understanding increased prevalence of involvement in risky behavior during adolescence (Steinberg et al., 2008 ; Steinberg, 2010 ). Sensation-seeking refers to the drive to seek out novel and thrilling experiences and is governed by the socioemotional network in the brain. The socioemotional network is comprised, in part, with the dopaminergic system which is responsible for the release of dopamine in response to stimulus. Dopamine is a neurotransmitter that stimulates feelings of pleasure and is deeply involved in the learning, reward, and reinforcement process (Berke, 2018 ; Walsh, 2009 ). When individuals are exposed to a particularly rewarding stimulus, dopamine is released that reinforces that experience and should increase drive towards continued and/or re-experience of that stimulus. Following puberty, the socioemotional network experiences rapid development that results in the increased capacity for release and receipt of dopamine in response to novel and thrilling stimuli. Development of these systems then tends to plateau and may even decline in salience as individuals enter into adulthood. Impulse control refers to the capacity that individuals possess (or don’t possess) to stop and consider consequences before taking action. Impulse control is governed by the cognitive control network, with the prefrontal cortex as the primary brain region involved with this network. The cognitive control network is relevant because it facilitates communication across various regions of the brain, thus, creating the capacity for individuals to engage impulse control and stop and think before acting. However, unlike the socioemotional network, the prefrontal cortex is one of the final brain regions to fully mature. The prefrontal cortex tends to develop slowly and steadily during adolescence and only reaching full maturity in the mid-20s or later (Spencer-Smith & Anderson, 2009 ). These varying developmental patterns of brain regions governing impulse control and sensation-seeking creates an imbalance between levels of these constructs during adolescence that is particularly conducive for involvement in risky behaviors. Adolescents then tend to develop a strong drive toward stimulating behaviors long before they develop the capacity to stop and think about the possible risks involved with engagement. The analogy of giving adolescents the keys and accelerator of a car long before they develop the steering wheel and brakes has been utilized to explain this differential development (Gopnik, 2012 ). While the original dual systems model framework focuses on understanding typical developmental patterns associated with sensation-seeking and impulse control (Steinberg et al., 2008 ; Steinberg, 2010 ), there remains a need to better understand drivers of atypical development of these constructs. A great deal of research has indicated that variance in these constructs during salient periods of the life-course is relevant for predicting involvement in a variety of risky behaviors (Armstrong et al., 2020 ; Duell et al., 2016 ; Wojciechowski, 2020a ). As such, it makes sense to better understand what causes dysfunctional development of these constructs since this should lead to increased risk for involvement in risky behaviors. Age at first TBI is one predictor that may be relevant in this regard. Traumatic Brain Injury as a Predictor of Dysfunctional Cognitive Development within Developmental Context As noted above, TBI is a risk factor for dysfunctional cognitive development (Gerard-Morris et al., 2010; Jonsson et al., 2013 ; Kohler et al., 2020 ). Research has also indicated that early experiences with TBI often are more salient for interrupting cognitive development that may have lasting effects on this development across the life-course (Babikian et al., 2015 ; Beauchamp & Anderson, 2013 ; Jacobson & Gerner, 2021 ). This would seem to indicate that the younger the age that an individual experiences TBI, the greater impact that this should have on cognitive development across the life-course. However, other research does indicate that experiences at older ages can have a more pervasive impact, particularly among the elderly (Mosenthal et al., 2002 ; Sariaslan et al., 2016 ; Senathi-Raja et al., 2010 ). In the case of impulse control and sensation-seeking, it may also be important to remember that dual systems development is particularly salient during the adolescent years of the life-course (Steinberg, 2010 ). This is to say that development of impulse control and sensation-seeking systems accelerates during adolescence and this may create a particularly sensitive period of the life-course for development. It may be that development related to sensation-seeking and impulse control may be disrupted to a greater extent if experienced during the adolescent years and that it may not be as simple as earlier TBI always resulting in greater dysfunction. It may be that experiencing TBI early sets off a longer-term chain reaction of neurological development that hinders dual systems model development later in life even if the sensitive period has yet to begin. In this way, TBI experienced during childhood may result in a priming effect that has long-term effects on the socioemotional and/or cognitive control networks even before any real development of these systems begin. It may also be that the impact of developmental timing of TBI may be dependent on the specific construct of interest. While rapid development of sensation-seeking is generally observed during adolescence, the development of impulse control occurs in a much steadier manner. For this reason, it may be that TBI during adolescence may be particularly relevant for sensation-seeking, but potentially not as much for impulse control given the rapid vs. steady paces of development during this period of the life-course. However, again, there are reasons to believe that being younger at first experienced TBI may have a greater impact on later development regardless of outcome. For these reasons, it is difficult to predict specifically how timing of first TBI may be relevant in general and across dual systems model outcomes. Another important consideration of the role that TBI plays for predicting dysfunctional cognitive development pertains to the timing at which dysfunctional development stemming from TBI actually manifests. For example, it may be that TBI experienced earlier in life does indeed cause major issues with the developing brain, but it may also be that these issues do not manifest in noticeable alterations to cognition until many years later. In this example, the average effects of age at first TBI may be obscured by a lack of change occurring during the years directly following the TBI. Alternatively, it may be that the greatest dysfunction in development following TBI may occur directly following the experience and cognitive development may normalize later in life for many participants, thus, indicating a lack of lasting influence of the experience. In both cases, it is also important to again note that the timing in the life-course that TBI is experienced may also matter a great deal for understanding these effects. This is to say that earlier or later TBI may lead to differences in both the impact of TBI overall, but also in the timing at which these effects are experienced. Despite the multifaceted ways in which TBI may impact cognitive development, there remains a dearth of research in these areas. This gap in the literature highlights the need for examination of the potential moderating role that age plays on the impact of time of first TBI and dual systems model development. TBI has been identified as a robust risk factor for dysfunctional cognitive development and behavioral issues (Gerard-Morris et al., 2010; Jonsson et al., 2013 ; Kohler et al., 2020 ; Moore et al., 2014 ; Wiliams et al., 2018). That said, there remains limited understanding of how the timing of experiencing TBI is associated with cognitive development pertaining to the dual systems model. This is problematic, as a better understanding of TBI timing may act as a driver of differential development of sensation-seeking and impulse control may aid in reducing behavioral problems during the adolescent period of the life-course and beyond. Further, there also remains a dearth of research focused on the timing at which dysfunction in cognitive development occurs following TBI. It may be that the effects of TBI wane across time or it may be that noticeable effects of TBI on cognitive development may not occur until later in the life-course. Again though, there remains a dearth of research which has examined the role of developmental timing in this regard. This study sought to address these gaps in the literature by answering the following research questions: RQ1: Does developmental timing of first TBI matter for the development of impulse control and sensation-seeking? RQ2: Do the effects of age at first TBI on cognitive development manifest differently depending on concurrent age? Methods Data The present study utilized data from all 11 waves of the Pathways to Desistance study. While the complete dataset comprises the responses of 1,354 JIY who were adjudicated for a serious offense just prior to baseline measurements, the present study only utilized data from participants who reported experiencing a lifetime TBI prior to baseline. This resulted in a final sample size of 393 participants for this study. Serious offenses which qualified participants for inclusion in the study consisted of all felony offenses and also misdemeanor sexual assault and weapons-related offenses. Participants were recruited for the study from study sites located in Maricopa County, Arizona and Philadelphia, Pennsylvania. Recruitment occurred from 2000 to 2003, with the entire study period spanning from 2000 to 2010. This resulted in 11 waves of data for participants collected across seven years post-adjudication. Observation periods were six months in length for the first 36 months that participants were involved in the study and shifted to 12 months in length for the remainder of their time in the study following that point. Attrition peaked at the final wave, with 16.2% of the original sample no longer providing data for the study. Of all participants approached regarding their interest in the study, 20% declined the opportunity to take part. A cap was also applied to the total number of male drug offenders who were included in the sample at 15% of the total baseline sample. This was done to ensure heterogeneity on these sample characteristics at baseline. All data that were used in this study were collected via participant self-report interviews. Interviews were held in locations that were convenient for participants, including, but not limited to: participants’ homes, libraries, and criminal justice facilities. Participants were provided with laptop computers to manually input their responses to survey prompts during the interviews while a member of the research team sat with them. Measures Dual Systems Model Constructs The key dependent variables examined in this study were the dual systems model constructs of impulse control and sensation-seeking. Impulse control was measured at each wave using the Weinberger Adjustment Inventory (Weinberger & Schwartz, 1990 ). This instrument assessed impulse control using a series of ordinal items that asked participants to report how accurate a set of given statements were of their own attitudes/behaviors pertaining to impulse control (e.g., "I say the first thing that comes into my mind without thinking enough about it"). Seven of these eight items were reverse coded so that higher scores corresponded to higher levels of impulse control. A mean score was then computed so that all participants had a single impulse control score at every wave. Sensation-seeking was measured at each wave using the Youth Psychopathic Traits Inventory (Andershed et al., 2002 ). This instrument measured sensation-seeking using a series of ordinal items that asked participants to rate their degree of agreement with various statements pertaining to their own attitudes or behaviors regarding sensation-seeking (e.g., "I like to be where exciting things happen"). An additive index was then computed from the individual ordinal scores ranging from 4–20 so that all participants had a single sensation-seeking score at each wave. Age at First TBI One of the key independent variables examined in this study was the age at which participants reported experiencing their first TBI that resulted in a loss of consciousness. This was then a variable which assessed this age in single-year intervals and measured at baseline. Age The other key independent variable examined in this study was age at each wave. Age was measured at each wave in single-year intervals. Control Variables Several control variables were also included in these analyses in order to mitigate bias in estimation of effects of interest. Gender was the first of these control variables, as prior research has indicated gender differences in dual systems development (Hannula, 2019 ). Gender was measured using a binary variable which delineated male and female participants at baseline (0 = Male; 1 = Female). Race was also included as a control variable, as prior research has indicated racial variation in patterns of cognitive development (Wojciechowski, 2020b ). Race was measured as a four-category nominal variable at baseline with the following response options: Black, Hispanic, White, and Other Race. A series of dummy variables was then coded from this original nominal variable, with each delineating participants in one of the four race categories from all other participants (e.g., 1 = Black; 0 = All other participants). The dummy variable which corresponded to White participants was then omitted from the models in order to allow White participants to be the reference group to interpret coefficients in relation to. Another control variable included in analyses was socioeconomic status (SES) at baseline, as prior research has indicated that healthy cognitive development is dependent upon access to resources under the umbrella of the concept of SES (Seidler & Ritchie, 2018 ). Parental SES was measured at baseline using Hollingshead’s ( 1957 ) two-factor index of social position. This instrument measured SES as a weighted score comprised of educational attainment and occupational prestige scores for participants’ parents. If both parents were able to provide data for the study, then a mean score was computed from the two individual scores so that every participant had a single SES score at baseline. It was also necessary to control for depression at each wave, as past research has indicated that cognitive development of individuals with depression differs from that of otherwise healthy individuals (Ahern & Semskova, 2017). Depressive symptoms were measured at each wave using the Brief Symptom Inventory (Derogatis & Melisaratos, 1983 ). This instrument measured depression via a series of ordinal items that asked participants to report how bothered they had been by various symptoms of depression during the prior two weeks (e.g., "Feeling no interest in things"), with higher scores indicating greater depressive pathology. A mean score was then computed from these individual ordinal scores so that every participant had a single depression score at each wave. A one observation period lag was applied to this variable in analyses so that temporal ordering was established between this independent variable and the dependent variables examined here. Social support has also been identified as a promotive factor related to healthy cognitive development (Kurtz & Zavala, 2017 ), indicating the need to control this variable in analyses also. Social support was measured at each wave using the Contact with Caring Adults Inventory (Nakkula et al., 1990 ). The measure used in analyses was a measure of social support depth, as it provided a count of the number of unique adults who participants reported provided them with social support in at least two distinct domains, thus, eschewing more superficial support relationships. A one-observation period lag was applied to this variable in analyses. Recent research has also indicated that degree of missingness is correlated with outcomes of interest in the Pathways to Desistance study (Kijowski & Wilson, 2022 ). This indicates the need to control for individual participant attrition in order to avoid bias in estimation. As such, a variable which provided a count of the total number of follow-up waves for which participants provided valid data across the entire study period was included in analyses as a control variable also. The final control variable included in these analyses was a lagged measure of each of the dependent variables included in their corresponding set of models (e.g., lagged measure of impulse control included in models where impulse control is the dependent variable). This was done to ensure that any observed relationships were not simply confounded by continuity in levels of the dependent variables from wave to wave. Both sensation-seeking and impulse control were measured analogously to how they were measured as dependent variables, but with a one-observation period lag applied to establish the appropriate temporal ordering. Analytic Strategy The present study utilized a series of mixed effects models to examine the direct and moderated effects of time at first TBI on the development of dual systems model constructs. The longitudinal nature of the repeated measures data violated assumptions of independence in measurements, necessitating a modeling strategy that can account for this issue. Mixed effects modeling does this by separating fixed and random effects portions of the model. Random intercepts were modeled at the individual participant level to address this issue of a lack of independence in measurement characterized by repeated measures being nested within individual participants. Because the hypothesized interactions dealt with examining whether there was significant variance in the impact of age at first TBI by chronological age during the study period, random slopes were then modeled for age. All other covariates were then included in the fixed effects portion of the model in order to obtain unstandardized coefficients as they pertain to each dependent variable. Two models were estimated for each dependent variable. Model 1 for each dependent variable examined the direct effects of age at first TBI on the specific form of cognition of interest. Model 2 for each dependent variable then included a cross-level interaction term to model the interaction between time at first TBI and chronological age during the study period to determine whether the salience of time at first TBI resulted in different manifestations of this effect at various ages. Because both the age and age at first TBI variables were both continuous, it was necessary to ensure that collinearity did not impact analyses when the hypothesized interactions were calculated. For this reason, age and age at first TBI variables were mean-centered for all of the interactions assessed in these analyses. Full-information maximum likelihood estimation was used to manage missing data in the analyses. Results Table 1 provides descriptive statistics for all of the variables that were examined in analyses. Table 2 then provides Model 1 and Model 2 results for the impulse control analyses. Table 3 provides these same results for the sensation-seeking outcome. A preliminary analytic step entailed determination of the appropriate growth patterns used to model each dependent variable. This entailed examining differences in model fit when estimating models for each dependent variable with an unaltered age variable used to approximate linear change across time and age 2 and age 3 variables as the only predictors in the models. Likelihood ratio tests were then used to determine whether inclusion of age variables of increasing complexity significantly improved model fit in a stepwise manner. For impulse control, these analyses indicated that inclusion of an age 2 variable significantly improved model fit compared to a model with only the unaltered age variable included as a predictor, but inclusion of an age 3 variable did not significantly improve model fit. This indicated that a quadratic growth pattern best fit the impulse control data. As such, age and age 2 variables were included in the main analyses as fixed effects and random slopes nested at the individual participant level were modeled for each of these age variables also. For sensation-seeking, these analyses indicated that neither age 2 nor age 3 variables significantly improved model fit over just having the unaltered age variable in the model. This indicated that a linear growth function best described the sensation-seeking data. For this reason, only the unaltered age variable was modeled as a fixed effect and with random slopes nested at the individual participant level. Model 1 results pertaining to the impulse control outcome indicated a positive relationship between age at first TBI and impulse control (Coefficient=.012, p<.045). This indicated that experiencing TBI at younger ages was associated with diminished impulse control on average across the study period. Neither the age nor age 2 variables were significant predictors of impulse control in this model. Greater deviant peer association, lower SES, weaker social support, and lower impulse control during the prior observation period were also all significant predictors of lower impulse control in this model. Compared to White participants, Black and Hispanic participants reported significantly greater impulse control on average across the study period. Model 2 results indicated that inclusion of the hypothesized interaction terms impacted the direct effect of age at first TBI on impulse control, as younger age at first TBI remained a significant predictor of lower impulse control (Coefficient=.014, p<.030). Neither age variables were significant predictors of impulse control in this model, nor were either of the proposed interaction effects significant. Lower impulse control during the prior observation period, greater deviant peer association, and weaker social support all significantly predicted lower impulse control at follow-up in this model. Black and Hispanic participants also reported significantly greater impulse control on average than White participants in this model. Model 1 results for the sensation-seeking model indicated that age at first TBI was not a significant predictor of sensation-seeking. Getting older was associated with diminishing sensation-seeking in this model (Coefficient=-.103, p<.001). Greater deviant peer association, being male, greater depressive symptoms, and greater sensation-seeking during the prior observation period were also all associated within increases in sensation-seeking at follow-up. Black and Hispanic participants reported significantly lower sensation-seeking than White participants on average in this model also. Model 2 results indicated that inclusion of the hypothesized interaction term did little to change the direct effects of age at first TBI and current age on sensation-seeking, as age at first TBI remained a nonsignificant predictor of sensation-seeking and getting older remained a significant predictor of decreases in sensation-seeking (Coefficient=-.102, p<.001). The hypothesized interaction between age and age and first TBI was nonsignificant. Being male, greater deviant peer association, weaker social support, greater depressive symptoms, and greater sensation-seeking during the prior observation period were also all associated with increased sensation-seeking at follow-up. Hispanic and Black participants reported lower average sensation-seeking scores than White participants in this model also. Sensitivity analyses examined unaltered age and age at first TBI variables to model the interaction between these variables for predicting dual systems model development. This differed from the main analyses which examined mean-centered versions of these variables for modeling the interactions of interest. Results observed here were analogous to those observed in the main analyses, indicating robustness of the nonsignificant interactions between age and age at first TBI for predicting dual systems model development. Discussion The present study provided an important examination of age of experiencing TBI as a predictor of dysfunctional cognitive development. Findings indicated that experiencing TBI at earlier ages was associated with diminished impulse control, but was unrelated to sensation-seeking. This study also examined age as a moderator of the relationship between age at first TBI and cognitive development in order to determine whether or not sensitive periods of the life-course for manifestations of early TBI existed. No significant interactions were identified when examining the unaltered age variable as a moderator nor when years since first TBI was examined as a moderator in sensitivity analyses. There are a number of important implications of these findings for understanding cognitive development and providing treatment for JIY who have experienced TBI. The main significant finding observed in these analyses indicated that JIY who experienced TBI at younger ages reported lower impulse control compared to JIY who experienced TBI at older ages. This indicates that those JIY who have experienced TBI at younger ages should be prioritized for treatment in order to mitigate these damaging effects to cognition. Considering that low impulse control is a risk factor associated with recidivism (Petrich & Sullivan, 2020 ; Ray et al., 2016 ; Wojciechowski, 2020b ), targeting these JIY may aid in reducing strain on the juvenile justice system from repeat offenders and may set these youth up for a more prosocial future. In terms of specific services in this regard, some research has indicated that certain interventions show some mixed evidence for improving impulse control among people who have experienced TBI that is having a deleterious effect on this aspect of cognition. That said, there remains a relative dearth of research which has demonstrated strong evidence of effectiveness in this regard. For example, several studies have discussed the role that memantine, a drug used to treat Alzheimer’s disease, may play for improving cognitive processes among individuals who have experienced TBI, but limited evidence of effectiveness was demonstrated (Hammond, 2017 ; Litvinenko et al., 2010 ). One study examined the potential role that cannabis could play in improving impulse control among individuals who have experienced TBI, with results indicating that participants in the group receiving medical cannabis showed improved impulse control (Gruber et al., 2016 ). That said, this study was conducted using a very small sample, with only 11 participants in the treatment group, so findings should be interpreted with great caution. Beyond this, there appears to be a dearth of research focused on the role that psychosocial programming may play for improving impulse control among individual who have experienced TBI specifically, indicating that this should potentially be a direction for future research. There also is a lack of research which has focused on treatment for TBI to improve impulse control among JIY specifically. This is particularly problematic given the fact that such youth are already at elevated risk for involvement in antisocial behavior, but also because of the particular salience that TBI may have on the developing adolescent brain. These issues highlight the need for continued research focused on understanding how impulse control issues can be improved among JIY who have experienced TBI in order to best mitigate the risk that TBI will result in increased recidivism risk due to diminished impulse control. It was also important to note that age at first TBI was not significantly related to the development of sensation-seeking among JIY in this sample, nor was there a significant interaction between TBI and age. While it is unclear exactly why this dimension of cognition was not impacted by TBI, there are potentially several explanations. It may be that the specific locations of TBIs among participants in the sample were not centered in brain regions governing sensation-seeking, at least not to the same degree as brain regions governing impulse control. It may also be that other factors correlated with TBI and sensation-seeking washed this relationship out through mediation processes. In any case, these findings indicate that treatment for JIY who have experienced TBI at younger ages should be prioritized to target impulse control and that age at first TBI should be a lesser concern related to sensation-seeking. That said, it may still be that TBI in general impacts the development of sensation-seeking when compared to JIY who have not experienced TBI. This indicates the continued need to examine these relationships to better understand how TBI impacts the developing brain and how impacts on sensation-seeking may lead to increased recidivism risk among JIY. There are several important limitations of these findings. The first limitation relates to the potential limited generalizability of results. The sample used here consisted only of JIY who reported experiencing a TBI prior to baseline measurements. As such, it is unclear whether or not these findings are generalizable beyond this population. Further, these data were collected using a purposive sampling strategy, thus, their generalizability beyond this specific sample is also unclear. These limitations highlight the need for continued work in this area to better understand how TBI may influence dual systems development. Future research should seek to examine these relationships in a general population sample and also using probability sampling methodologies with both general population and JIY samples in order to determine the robustness of these findings. Another limitation of this study pertains to the limited nature of the TBI measure used as an independent variable. While this variable provided utility as a general measure of age at first TBI, this measure was limited in that information about the location of the TBI on participants’ heads was unknown. This is to say that it isn’t clear whether or not any given TBI measured a blow to the front, back, top, etc. of the head. Because different regions of the brain are responsible for distinct dimensions of cognition, this may have led to measurement error due to the coarseness of the TBI measure in terms of location of the TBI. This may have contributed to null effects observed for sensation-seeking and/or the significant effects observed for impulse control if participants with injuries to regions of the brain important for these dimensions of cognition were under/oversampled. This indicates the need for future research to continue studying these relationships using a finer measure of TBI with additional information about distinct brain region injured in the event. Doing so would provide a more accurate understanding of how TBI and age at first TBI impact dual systems model development. Declarations Ethical Approval: The original Pathways to Desistance data collection received ethical approval from the University of Pittsburgh institutional review board. The present study was exempt from full ethical review because it entailed secondary data analysis of a publicly available dataset. Funding: Not applicable. Author Contribution The sole author conducted all analyses and writing for the project focused on secondary data analysis. Availability of Data and Materials: The Pathways to Desistance dataset is publicly available via the Inter-university Consortium of Political and Social Research. Code will be made available upon request. References Ahern, E., & Semkovska, M. (2017). Cognitive functioning in the first-episode of major depressive disorder: A systematic review and meta-analysis. Neuropsychology , 31 (1), 52. 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Clinical features of the formation and possibilities of treatment of posttraumatic cognitive disturbances. Zhurnal nevrologii i psikhiatrii imeni SS Korsakova , 110 (12), 60-66. Moore, E., Indig, D., & Haysom, L. (2014). Traumatic brain injury, mental health, substance use, and offending among incarcerated young people. The Journal of head trauma rehabilitation , 29 (3), 239-247. Mosenthal, A. C., Lavery, R. F., Addis, M., Kaul, S., Ross, S., Marburger, R., ... & Livingston, D. H. (2002). Isolated traumatic brain injury: age is an independent predictor of mortality and early outcome. Journal of Trauma and Acute Care Surgery , 52 (5), 907-911. Nakkula, M. J., Way, N., Stauber, H. Y., & London, P. (1990). Teenage risk prevention questionnaire and interview: An integrative assessment of adolescent high-risk behavior. Piscataway, NJ: Rutgers University, Graduate School of Applied and Professional Psychology . Petrich, D. M., & Sullivan, C. J. (2020). Does future orientation moderate the relationship between impulse control and offending? Insights from a sample of serious young offenders. Youth Violence and Juvenile Justice , 18 (2), 156-178. Ray, J. V., Thornton, L. C., Frick, P. J., Steinberg, L., & Cauffman, E. (2016). Impulse control and callous-unemotional traits distinguish patterns of delinquency and substance use in justice involved adolescents: Examining the moderating role of neighborhood context. Journal of abnormal child psychology , 44 , 599-611. Sariaslan, A., Sharp, D. J., D’Onofrio, B. M., Larsson, H., & Fazel, S. (2016). Long-term outcomes associated with traumatic brain injury in childhood and adolescence: a nationwide Swedish cohort study of a wide range of medical and social outcomes. PLoS medicine , 13 (8), e1002103. Seidler, A. L., & Ritchie, S. J. (2018). The association between socioeconomic status and cognitive development in children is partly mediated by a chaotic home atmosphere. Journal of Cognition and Development , 19 (5), 486-508. Senathi-Raja, D., Ponsford, J., & Schönberger, M. (2010). Impact of age on long-term cognitive function after traumatic brain injury. Neuropsychology , 24 (3), 336. Spencer-Smith, M., & Anderson, V. (2009). Healthy and abnormal development of the prefrontal cortex. Developmental Neurorehabilitation , 12 (5), 279-297. Steinberg, L., Albert, D., Cauffman, E., Banich, M., Graham, S., & Woolard, J. (2008). Age differences in sensation seeking and impulsivity as indexed by behavior and self-report: evidence for a dual systems model. Developmental psychology , 44 (6), 1764. Steinberg, L. (2010). A dual systems model of adolescent risk‐taking. Developmental Psychobiology: The Journal of the International Society for Developmental Psychobiology, 52(3), 216-224. Walsh, A. (2009). Biology and criminology: The biosocial synthesis . Routledge. Weinberger, D.A., and Schwartz, G.E. (1990). Distress and restraint as superordinate dimensions of self-reported adjustment: a typological perspective. Journal of Personality , 58(2), 381-417. Williams, W. H., Chitsabesan, P., Fazel, S., McMillan, T., Hughes, N., Parsonage, M., & Tonks, J. (2018). Traumatic brain injury: a potential cause of violent crime?. The Lancet Psychiatry , 5 (10), 836-844. Wojciechowski, T. (2020a). Relevance of routine activities for understanding the impact of the dual systems model on binge drinking among college students. Drug and alcohol dependence , 216 , 108233. Wojciechowski, T. (2020b). The relevance of the dual systems model of self-control for age-related deceleration in offending variety among juvenile offenders. Journal of criminal justice , 70 , 101716. Tables Table 1 Descriptive Statistics Mean Standard Deviation Minimum Maximum Impulse Control (Pooled) 3.197 .969 1 5 Sensation-Seeking (Pooled) 12.734 3.421 4 20 Age at First TBI 10.936 4.070 0 17 Age (Pooled) 18.898 2.453 14 26 Age 2 (Pooled) 365.622 94.781 196 625 Gender Male=86.4% Female=13.6% Race Black=41.4% Hispanic=33.5% White=20.2% Other Race=4.8% Socioeconomic Status at Baseline 51.409 12.299 11 77 Exposure to Violence (Pooled) No=45.56% Yes=54.44% Social Support (Pooled) .923 .713 0 6 Deviant Peer Association (Pooled) Depression (Pooled) .871 .514 .863 .708 0 0 4 4 Number of Waves with Valid Data 8.941 2.014 0 10 Table 2 Mixed Effects Regression Modeling of Covariate Effects on Impulse Control (N=2,180) Model 1 Model 2 Coefficient p-Value 95% Confidence Interval Coefficient p-Value 95% Confidence Interval Fixed Effects Age at First TBI .012 .045 <.001 .024 .014 .030 .001 .027 Age -.058 .513 -.231 .115 -.065 .466 -.238 .109 Age 2 .002 .368 -.002 .006 .002 .332 -.002 .007 Age at First TBI X Age --- --- --- -.022 .402 -.075 .030 Age at First TBI X Age 2 --- --- --- .001 .429 -.001 .002 Gender (0=Male; 1=Female) .142 .096 -.025 .310 .143 .096 -.025 .311 Race (reference=White) Black Hispanic Other Race .352 .227 .232 <.001 .001 .076 .225 .478 .095 .360 -.024 .488 .352 .228 .235 -.001 -.005 .031 -.009 >-.001 Social Support (Lagged) .059 .004 .019 .099 .058 .004 .018 .098 Exposure to Violence (0=No; 1=Yes) (Lagged) .023 .489 -.041 .086 .023 .482 -.041 .087 Deviant Peer Association (Lagged) -.079 <.001 -.116 -.041 -.079 <.001 -.116 -.041 Depression (Lagged) -.028 .210 -.071 .016 -.027 .225 -.070 .017 Number of Waves with Valid Data -.030 .087 -.064 .004 -.030 .084 -.064 .004 Impulse Control (Lagged) .315 <.001 .277 .353 .314 <.001 .276 .352 Constant 2.481 .006 .717 4.245 2.540 .005 .771 4.309 Random Effects Standard Deviation: Age .009 .009 Standard Deviation: Age 2 <.001 <.001 Standard Deviation: Constant .347 .352 Table 3 Mixed Effects Regression Modeling of Covariate Effects on Sensation-Seeking (N=2,180) Model 1 Model 2 Coefficient p-Value 95% Confidence Interval Coefficient p-Value 95% Confidence Interval Fixed Effects Age at First TBI -.018 .448 -.065 .029 -.021 .390 -.068 .027 Age -.103 <.001 -.156 -.050 -.102 <.001 -.154 -.049 Age at First TBI X Age --- --- --- .026 .182 -.012 .065 Gender (0=Male; 1=Female) -1.157 .001 -1.823 -.490 -1.164 .001 -1.831 -.497 Race (reference=White) Black Hispanic Other Race -1.218 -.744 -.079 <.001 .005 .879 -1.716 -.721 -1.269 -.220 -1.087 .930 -1.229 -.755 -094 <.001 .005 .856 -1.727 -.731 -1.281 -.230 -1.104 .916 Socioeconomic Status at Baseline .009 .314 -.008 .025 .008 .335 -.008 .025 Social Support (Lagged) -.220 .051 -.441 <.001 -.221 .049 -.442 -.001 Exposure to Violence (0=No; 1=Yes) (Lagged) .044 .736 -.211 .298 .046 .725 -.209 .300 Deviant Peer Association (Lagged) .304 <.001 .146 .463 .307 <.001 .149 .466 Depression (Lagged) .200 .030 .019 .381 .203 .028 .022 .384 Number of Waves with Valid Data -.028 .709 -.177 .120 -.021 .780 -.170 .128 Sensation-Seeking (Lagged) .228 <.001 .185 .271 .227 <.001 .184 .271 Constant 13.896 <.001 11.737 16.055 13.859 <.001 11.698 16.020 Random Effects Standard Deviation: Age <.001 <.001 Standard Deviation: Constant 1.501 1.503 Additional Declarations No competing interests reported. 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Such injuries can vary in their severity, from relatively minor cases to cases requiring hospitalization and/or long-term care. TBI is an incredibly important category of injury because of the impact that it can have on functioning, emotion, cognition, and behavior. For example, prior research has indicated that TBI is a risk factor for dysfunctional cognitive development (Gerard-Morris et al., 2010; Jonsson et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Kohler et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). While prior research has established the relevance of TBI for understanding cognitive development, there important gaps in our understanding. One area in need of continued investigation relates to the developmental timing of TBI experiences for predicting cognitive development. There is a dearth of research that has examined age at first TBI as being relevant for understanding cognitive development pertaining to the dual systems model specifically. The dual systems model focuses on explaining involvement in risky behaviors due to imbalance in the development of impulse control and sensation-seeking during adolescence (Steinberg et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Steinberg, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). As such, this framework has risen to prominence because of its capacity to explain increased risk of involvement in such behaviors that is generally observed during adolescence due to differential development of brain regions governing these constructs. That said, developmental timing has yet to be examined as a predictor of dual systems development. This is to say that the age at which an individual first experiences TBI may have relevance for just how much dual systems model development is impacted. Beyond this, there is there must also be concern over when variation in development driven by earlier or later TBI actually manifests. This is to say that alterations to cognitive development may not be immediately present and instead may become apparent at different ages. The present study sought to address these limitations of prior research by examining age at first TBI as a predictor of dual systems development and ageas a moderator of this relationship among a sample of justice-involved youth (JIY) who reported experiencing TBI in their lifetimes prior to baseline measurements.\u003c/p\u003e \u003cp\u003e \u003cem\u003eThe Dual Systems Model\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe dual systems model is a relevant developmental framework centered on the role that differential development of sensation-seeking and impulse control plays for understanding increased prevalence of involvement in risky behavior during adolescence (Steinberg et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Steinberg, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Sensation-seeking refers to the drive to seek out novel and thrilling experiences and is governed by the socioemotional network in the brain. The socioemotional network is comprised, in part, with the dopaminergic system which is responsible for the release of dopamine in response to stimulus. Dopamine is a neurotransmitter that stimulates feelings of pleasure and is deeply involved in the learning, reward, and reinforcement process (Berke, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Walsh, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). When individuals are exposed to a particularly rewarding stimulus, dopamine is released that reinforces that experience and should increase drive towards continued and/or re-experience of that stimulus. Following puberty, the socioemotional network experiences rapid development that results in the increased capacity for release and receipt of dopamine in response to novel and thrilling stimuli. Development of these systems then tends to plateau and may even decline in salience as individuals enter into adulthood. Impulse control refers to the capacity that individuals possess (or don\u0026rsquo;t possess) to stop and consider consequences before taking action. Impulse control is governed by the cognitive control network, with the prefrontal cortex as the primary brain region involved with this network. The cognitive control network is relevant because it facilitates communication across various regions of the brain, thus, creating the capacity for individuals to engage impulse control and stop and think before acting. However, unlike the socioemotional network, the prefrontal cortex is one of the final brain regions to fully mature. The prefrontal cortex tends to develop slowly and steadily during adolescence and only reaching full maturity in the mid-20s or later (Spencer-Smith \u0026amp; Anderson, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). These varying developmental patterns of brain regions governing impulse control and sensation-seeking creates an imbalance between levels of these constructs during adolescence that is particularly conducive for involvement in risky behaviors. Adolescents then tend to develop a strong drive toward stimulating behaviors long before they develop the capacity to stop and think about the possible risks involved with engagement. The analogy of giving adolescents the keys and accelerator of a car long before they develop the steering wheel and brakes has been utilized to explain this differential development (Gopnik, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). While the original dual systems model framework focuses on understanding typical developmental patterns associated with sensation-seeking and impulse control (Steinberg et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Steinberg, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), there remains a need to better understand drivers of atypical development of these constructs. A great deal of research has indicated that variance in these constructs during salient periods of the life-course is relevant for predicting involvement in a variety of risky behaviors (Armstrong et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Duell et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wojciechowski, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e). As such, it makes sense to better understand what causes dysfunctional development of these constructs since this should lead to increased risk for involvement in risky behaviors. Age at first TBI is one predictor that may be relevant in this regard.\u003c/p\u003e \u003cp\u003e \u003cem\u003eTraumatic Brain Injury as a Predictor of Dysfunctional Cognitive Development within Developmental Context\u003c/em\u003e \u003c/p\u003e \u003cp\u003eAs noted above, TBI is a risk factor for dysfunctional cognitive development (Gerard-Morris et al., 2010; Jonsson et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Kohler et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Research has also indicated that early experiences with TBI often are more salient for interrupting cognitive development that may have lasting effects on this development across the life-course (Babikian et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Beauchamp \u0026amp; Anderson, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Jacobson \u0026amp; Gerner, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This would seem to indicate that the younger the age that an individual experiences TBI, the greater impact that this should have on cognitive development across the life-course. However, other research does indicate that experiences at older ages can have a more pervasive impact, particularly among the elderly (Mosenthal et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Sariaslan et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Senathi-Raja et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In the case of impulse control and sensation-seeking, it may also be important to remember that dual systems development is particularly salient during the adolescent years of the life-course (Steinberg, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This is to say that development of impulse control and sensation-seeking systems accelerates during adolescence and this may create a particularly sensitive period of the life-course for development. It may be that development related to sensation-seeking and impulse control may be disrupted to a greater extent if experienced during the adolescent years and that it may not be as simple as earlier TBI always resulting in greater dysfunction. It may be that experiencing TBI early sets off a longer-term chain reaction of neurological development that hinders dual systems model development later in life even if the sensitive period has yet to begin. In this way, TBI experienced during childhood may result in a priming effect that has long-term effects on the socioemotional and/or cognitive control networks even before any real development of these systems begin. It may also be that the impact of developmental timing of TBI may be dependent on the specific construct of interest. While rapid development of sensation-seeking is generally observed during adolescence, the development of impulse control occurs in a much steadier manner. For this reason, it may be that TBI during adolescence may be particularly relevant for sensation-seeking, but potentially not as much for impulse control given the rapid vs. steady paces of development during this period of the life-course. However, again, there are reasons to believe that being younger at first experienced TBI may have a greater impact on later development regardless of outcome. For these reasons, it is difficult to predict specifically how timing of first TBI may be relevant in general and across dual systems model outcomes.\u003c/p\u003e \u003cp\u003eAnother important consideration of the role that TBI plays for predicting dysfunctional cognitive development pertains to the timing at which dysfunctional development stemming from TBI actually manifests. For example, it may be that TBI experienced earlier in life does indeed cause major issues with the developing brain, but it may also be that these issues do not manifest in noticeable alterations to cognition until many years later. In this example, the average effects of age at first TBI may be obscured by a lack of change occurring during the years directly following the TBI. Alternatively, it may be that the greatest dysfunction in development following TBI may occur directly following the experience and cognitive development may normalize later in life for many participants, thus, indicating a lack of lasting influence of the experience. In both cases, it is also important to again note that the timing in the life-course that TBI is experienced may also matter a great deal for understanding these effects. This is to say that earlier or later TBI may lead to differences in both the impact of TBI overall, but also in the timing at which these effects are experienced. Despite the multifaceted ways in which TBI may impact cognitive development, there remains a dearth of research in these areas. This gap in the literature highlights the need for examination of the potential moderating role that age plays on the impact of time of first TBI and dual systems model development.\u003c/p\u003e \u003cp\u003eTBI has been identified as a robust risk factor for dysfunctional cognitive development and behavioral issues (Gerard-Morris et al., 2010; Jonsson et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Kohler et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Moore et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Wiliams et al., 2018). That said, there remains limited understanding of how the timing of experiencing TBI is associated with cognitive development pertaining to the dual systems model. This is problematic, as a better understanding of TBI timing may act as a driver of differential development of sensation-seeking and impulse control may aid in reducing behavioral problems during the adolescent period of the life-course and beyond. Further, there also remains a dearth of research focused on the timing at which dysfunction in cognitive development occurs following TBI. It may be that the effects of TBI wane across time or it may be that noticeable effects of TBI on cognitive development may not occur until later in the life-course. Again though, there remains a dearth of research which has examined the role of developmental timing in this regard. This study sought to address these gaps in the literature by answering the following research questions:\u003c/p\u003e \u003cp\u003eRQ1: Does developmental timing of first TBI matter for the development of impulse control and sensation-seeking?\u003c/p\u003e \u003cp\u003eRQ2: Do the effects of age at first TBI on cognitive development manifest differently depending on concurrent age?\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e \u003cem\u003eData\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe present study utilized data from all 11 waves of the Pathways to Desistance study. While the complete dataset comprises the responses of 1,354 JIY who were adjudicated for a serious offense just prior to baseline measurements, the present study only utilized data from participants who reported experiencing a lifetime TBI prior to baseline. This resulted in a final sample size of 393 participants for this study. Serious offenses which qualified participants for inclusion in the study consisted of all felony offenses and also misdemeanor sexual assault and weapons-related offenses. Participants were recruited for the study from study sites located in Maricopa County, Arizona and Philadelphia, Pennsylvania. Recruitment occurred from 2000 to 2003, with the entire study period spanning from 2000 to 2010. This resulted in 11 waves of data for participants collected across seven years post-adjudication. Observation periods were six months in length for the first 36 months that participants were involved in the study and shifted to 12 months in length for the remainder of their time in the study following that point. Attrition peaked at the final wave, with 16.2% of the original sample no longer providing data for the study. Of all participants approached regarding their interest in the study, 20% declined the opportunity to take part. A cap was also applied to the total number of male drug offenders who were included in the sample at 15% of the total baseline sample. This was done to ensure heterogeneity on these sample characteristics at baseline.\u003c/p\u003e \u003cp\u003eAll data that were used in this study were collected via participant self-report interviews. Interviews were held in locations that were convenient for participants, including, but not limited to: participants\u0026rsquo; homes, libraries, and criminal justice facilities. Participants were provided with laptop computers to manually input their responses to survey prompts during the interviews while a member of the research team sat with them.\u003c/p\u003e \u003cp\u003e \u003cem\u003eMeasures\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eDual Systems Model Constructs\u003c/span\u003e \u003c/p\u003e \u003cp\u003eThe key dependent variables examined in this study were the dual systems model constructs of impulse control and sensation-seeking. Impulse control was measured at each wave using the Weinberger Adjustment Inventory (Weinberger \u0026amp; Schwartz, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). This instrument assessed impulse control using a series of ordinal items that asked participants to report how accurate a set of given statements were of their own attitudes/behaviors pertaining to impulse control (e.g., \"I say the first thing that comes into my mind without thinking enough about it\"). Seven of these eight items were reverse coded so that higher scores corresponded to higher levels of impulse control. A mean score was then computed so that all participants had a single impulse control score at every wave.\u003c/p\u003e \u003cp\u003eSensation-seeking was measured at each wave using the Youth Psychopathic Traits Inventory (Andershed et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). This instrument measured sensation-seeking using a series of ordinal items that asked participants to rate their degree of agreement with various statements pertaining to their own attitudes or behaviors regarding sensation-seeking (e.g., \"I like to be where exciting things happen\"). An additive index was then computed from the individual ordinal scores ranging from 4\u0026ndash;20 so that all participants had a single sensation-seeking score at each wave.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAge at First TBI\u003c/span\u003e \u003c/p\u003e \u003cp\u003eOne of the key independent variables examined in this study was the age at which participants reported experiencing their first TBI that resulted in a loss of consciousness. This was then a variable which assessed this age in single-year intervals and measured at baseline.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAge\u003c/span\u003e \u003c/p\u003e \u003cp\u003eThe other key independent variable examined in this study was age at each wave. Age was measured at each wave in single-year intervals.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eControl Variables\u003c/span\u003e \u003c/p\u003e \u003cp\u003eSeveral control variables were also included in these analyses in order to mitigate bias in estimation of effects of interest. Gender was the first of these control variables, as prior research has indicated gender differences in dual systems development (Hannula, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Gender was measured using a binary variable which delineated male and female participants at baseline (0\u0026thinsp;=\u0026thinsp;Male; 1\u0026thinsp;=\u0026thinsp;Female).\u003c/p\u003e \u003cp\u003eRace was also included as a control variable, as prior research has indicated racial variation in patterns of cognitive development (Wojciechowski, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e). Race was measured as a four-category nominal variable at baseline with the following response options: Black, Hispanic, White, and Other Race. A series of dummy variables was then coded from this original nominal variable, with each delineating participants in one of the four race categories from all other participants (e.g., 1\u0026thinsp;=\u0026thinsp;Black; 0\u0026thinsp;=\u0026thinsp;All other participants). The dummy variable which corresponded to White participants was then omitted from the models in order to allow White participants to be the reference group to interpret coefficients in relation to.\u003c/p\u003e \u003cp\u003eAnother control variable included in analyses was socioeconomic status (SES) at baseline, as prior research has indicated that healthy cognitive development is dependent upon access to resources under the umbrella of the concept of SES (Seidler \u0026amp; Ritchie, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Parental SES was measured at baseline using Hollingshead\u0026rsquo;s (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1957\u003c/span\u003e) two-factor index of social position. This instrument measured SES as a weighted score comprised of educational attainment and occupational prestige scores for participants\u0026rsquo; parents. If both parents were able to provide data for the study, then a mean score was computed from the two individual scores so that every participant had a single SES score at baseline.\u003c/p\u003e \u003cp\u003eIt was also necessary to control for depression at each wave, as past research has indicated that cognitive development of individuals with depression differs from that of otherwise healthy individuals (Ahern \u0026amp; Semskova, 2017). Depressive symptoms were measured at each wave using the Brief Symptom Inventory (Derogatis \u0026amp; Melisaratos, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1983\u003c/span\u003e). This instrument measured depression via a series of ordinal items that asked participants to report how bothered they had been by various symptoms of depression during the prior two weeks (e.g., \"Feeling no interest in things\"), with higher scores indicating greater depressive pathology. A mean score was then computed from these individual ordinal scores so that every participant had a single depression score at each wave. A one observation period lag was applied to this variable in analyses so that temporal ordering was established between this independent variable and the dependent variables examined here.\u003c/p\u003e \u003cp\u003eSocial support has also been identified as a promotive factor related to healthy cognitive development (Kurtz \u0026amp; Zavala, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), indicating the need to control this variable in analyses also. Social support was measured at each wave using the Contact with Caring Adults Inventory (Nakkula et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). The measure used in analyses was a measure of social support depth, as it provided a count of the number of unique adults who participants reported provided them with social support in at least two distinct domains, thus, eschewing more superficial support relationships. A one-observation period lag was applied to this variable in analyses.\u003c/p\u003e \u003cp\u003eRecent research has also indicated that degree of missingness is correlated with outcomes of interest in the Pathways to Desistance study (Kijowski \u0026amp; Wilson, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This indicates the need to control for individual participant attrition in order to avoid bias in estimation. As such, a variable which provided a count of the total number of follow-up waves for which participants provided valid data across the entire study period was included in analyses as a control variable also.\u003c/p\u003e \u003cp\u003eThe final control variable included in these analyses was a lagged measure of each of the dependent variables included in their corresponding set of models (e.g., lagged measure of impulse control included in models where impulse control is the dependent variable). This was done to ensure that any observed relationships were not simply confounded by continuity in levels of the dependent variables from wave to wave. Both sensation-seeking and impulse control were measured analogously to how they were measured as dependent variables, but with a one-observation period lag applied to establish the appropriate temporal ordering.\u003c/p\u003e \u003cp\u003e \u003cem\u003eAnalytic Strategy\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe present study utilized a series of mixed effects models to examine the direct and moderated effects of time at first TBI on the development of dual systems model constructs. The longitudinal nature of the repeated measures data violated assumptions of independence in measurements, necessitating a modeling strategy that can account for this issue. Mixed effects modeling does this by separating fixed and random effects portions of the model. Random intercepts were modeled at the individual participant level to address this issue of a lack of independence in measurement characterized by repeated measures being nested within individual participants. Because the hypothesized interactions dealt with examining whether there was significant variance in the impact of age at first TBI by chronological age during the study period, random slopes were then modeled for age. All other covariates were then included in the fixed effects portion of the model in order to obtain unstandardized coefficients as they pertain to each dependent variable. Two models were estimated for each dependent variable. Model 1 for each dependent variable examined the direct effects of age at first TBI on the specific form of cognition of interest. Model 2 for each dependent variable then included a cross-level interaction term to model the interaction between time at first TBI and chronological age during the study period to determine whether the salience of time at first TBI resulted in different manifestations of this effect at various ages. Because both the age and age at first TBI variables were both continuous, it was necessary to ensure that collinearity did not impact analyses when the hypothesized interactions were calculated. For this reason, age and age at first TBI variables were mean-centered for all of the interactions assessed in these analyses. Full-information maximum likelihood estimation was used to manage missing data in the analyses.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTable 1 provides descriptive statistics for all of the variables that were examined in analyses. Table 2 then provides Model 1 and Model 2 results for the impulse control analyses. Table 3 provides these same results for the sensation-seeking outcome.\u003c/p\u003e\n\u003cp\u003eA preliminary analytic step entailed determination of the appropriate growth patterns used to model each dependent variable. This entailed examining differences in model fit when estimating models for each dependent variable with an unaltered age variable used to approximate linear change across time and age\u003csup\u003e2 \u003c/sup\u003eand age\u003csup\u003e3\u003c/sup\u003e variables as the only predictors in the models. Likelihood ratio tests were then used to determine whether inclusion of age variables of increasing complexity significantly improved model fit in a stepwise manner. For impulse control, these analyses indicated that inclusion of an age\u003csup\u003e2\u003c/sup\u003e variable significantly improved model fit compared to a model with only the unaltered age variable included as a predictor, but inclusion of an age\u003csup\u003e3\u003c/sup\u003e variable did not significantly improve model fit. This indicated that a quadratic growth pattern best fit the impulse control data. As such, age and age\u003csup\u003e2\u003c/sup\u003e variables were included in the main analyses as fixed effects and random slopes nested at the individual participant level were modeled for each of these age variables also. For sensation-seeking, these analyses indicated that neither age\u003csup\u003e2\u003c/sup\u003e nor age\u003csup\u003e3\u003c/sup\u003e variables significantly improved model fit over just having the unaltered age variable in the model. This indicated that a linear growth function best described the sensation-seeking data. For this reason, only the unaltered age variable was modeled as a fixed effect and with random slopes nested at the individual participant level.\u003c/p\u003e\n\u003cp\u003eModel 1 results pertaining to the impulse control outcome indicated a positive relationship between age at first TBI and impulse control (Coefficient=.012, p\u0026lt;.045). This indicated that experiencing TBI at younger ages was associated with diminished impulse control on average across the study period. Neither the age nor age\u003csup\u003e2\u003c/sup\u003e variables were significant predictors of impulse control in this model. Greater deviant peer association, lower SES, weaker social support, and lower impulse control during the prior observation period were also all significant predictors of lower impulse control in this model. Compared to White participants, Black and Hispanic participants reported significantly greater impulse control on average across the study period. Model 2 results indicated that inclusion of the hypothesized interaction terms impacted the direct effect of age at first TBI on impulse control, as younger age at first TBI remained a significant predictor of lower impulse control (Coefficient=.014, p\u0026lt;.030). Neither age variables were significant predictors of impulse control in this model, nor were either of the proposed interaction effects significant. Lower impulse control during the prior observation period, greater deviant peer association, and weaker social support all significantly predicted lower impulse control at follow-up in this model. Black and Hispanic participants also reported significantly greater impulse control on average than White participants in this model.\u003c/p\u003e\n\u003cp\u003eModel 1 results for the sensation-seeking model indicated that age at first TBI was not a significant predictor of sensation-seeking. Getting older was associated with diminishing sensation-seeking in this model (Coefficient=-.103, p\u0026lt;.001). Greater deviant peer association, being male, greater depressive symptoms, and greater sensation-seeking during the prior observation period were also all associated within increases in sensation-seeking at follow-up. Black and Hispanic participants reported significantly lower sensation-seeking than White participants on average in this model also. Model 2 results indicated that inclusion of the hypothesized interaction term did little to change the direct effects of age at first TBI and current age on sensation-seeking, as age at first TBI remained a nonsignificant predictor of sensation-seeking and getting older remained a significant predictor of decreases in sensation-seeking (Coefficient=-.102, p\u0026lt;.001). The hypothesized interaction between age and age and first TBI was nonsignificant. Being male, greater deviant peer association, weaker social support, greater depressive symptoms, and greater sensation-seeking during the prior observation period were also all associated with increased sensation-seeking at follow-up. Hispanic and Black participants reported lower average sensation-seeking scores than White participants in this model also.\u003c/p\u003e\n\u003cp\u003eSensitivity analyses examined unaltered age and age at first TBI variables to model the interaction between these variables for predicting dual systems model development. This differed from the main analyses which examined mean-centered versions of these variables for modeling the interactions of interest. Results observed here were analogous to those observed in the main analyses, indicating robustness of the nonsignificant interactions between age and age at first TBI for predicting dual systems model development.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study provided an important examination of age of experiencing TBI as a predictor of dysfunctional cognitive development. Findings indicated that experiencing TBI at earlier ages was associated with diminished impulse control, but was unrelated to sensation-seeking. This study also examined age as a moderator of the relationship between age at first TBI and cognitive development in order to determine whether or not sensitive periods of the life-course for manifestations of early TBI existed. No significant interactions were identified when examining the unaltered age variable as a moderator nor when years since first TBI was examined as a moderator in sensitivity analyses. There are a number of important implications of these findings for understanding cognitive development and providing treatment for JIY who have experienced TBI.\u003c/p\u003e \u003cp\u003eThe main significant finding observed in these analyses indicated that JIY who experienced TBI at younger ages reported lower impulse control compared to JIY who experienced TBI at older ages. This indicates that those JIY who have experienced TBI at younger ages should be prioritized for treatment in order to mitigate these damaging effects to cognition. Considering that low impulse control is a risk factor associated with recidivism (Petrich \u0026amp; Sullivan, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ray et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wojciechowski, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e), targeting these JIY may aid in reducing strain on the juvenile justice system from repeat offenders and may set these youth up for a more prosocial future. In terms of specific services in this regard, some research has indicated that certain interventions show some mixed evidence for improving impulse control among people who have experienced TBI that is having a deleterious effect on this aspect of cognition. That said, there remains a relative dearth of research which has demonstrated strong evidence of effectiveness in this regard. For example, several studies have discussed the role that memantine, a drug used to treat Alzheimer\u0026rsquo;s disease, may play for improving cognitive processes among individuals who have experienced TBI, but limited evidence of effectiveness was demonstrated (Hammond, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Litvinenko et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). One study examined the potential role that cannabis could play in improving impulse control among individuals who have experienced TBI, with results indicating that participants in the group receiving medical cannabis showed improved impulse control (Gruber et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). That said, this study was conducted using a very small sample, with only 11 participants in the treatment group, so findings should be interpreted with great caution. Beyond this, there appears to be a dearth of research focused on the role that psychosocial programming may play for improving impulse control among individual who have experienced TBI specifically, indicating that this should potentially be a direction for future research. There also is a lack of research which has focused on treatment for TBI to improve impulse control among JIY specifically. This is particularly problematic given the fact that such youth are already at elevated risk for involvement in antisocial behavior, but also because of the particular salience that TBI may have on the developing adolescent brain. These issues highlight the need for continued research focused on understanding how impulse control issues can be improved among JIY who have experienced TBI in order to best mitigate the risk that TBI will result in increased recidivism risk due to diminished impulse control.\u003c/p\u003e \u003cp\u003eIt was also important to note that age at first TBI was not significantly related to the development of sensation-seeking among JIY in this sample, nor was there a significant interaction between TBI and age. While it is unclear exactly why this dimension of cognition was not impacted by TBI, there are potentially several explanations. It may be that the specific locations of TBIs among participants in the sample were not centered in brain regions governing sensation-seeking, at least not to the same degree as brain regions governing impulse control. It may also be that other factors correlated with TBI and sensation-seeking washed this relationship out through mediation processes. In any case, these findings indicate that treatment for JIY who have experienced TBI at younger ages should be prioritized to target impulse control and that age at first TBI should be a lesser concern related to sensation-seeking. That said, it may still be that TBI in general impacts the development of sensation-seeking when compared to JIY who have not experienced TBI. This indicates the continued need to examine these relationships to better understand how TBI impacts the developing brain and how impacts on sensation-seeking may lead to increased recidivism risk among JIY.\u003c/p\u003e \u003cp\u003eThere are several important limitations of these findings. The first limitation relates to the potential limited generalizability of results. The sample used here consisted only of JIY who reported experiencing a TBI prior to baseline measurements. As such, it is unclear whether or not these findings are generalizable beyond this population. Further, these data were collected using a purposive sampling strategy, thus, their generalizability beyond this specific sample is also unclear. These limitations highlight the need for continued work in this area to better understand how TBI may influence dual systems development. Future research should seek to examine these relationships in a general population sample and also using probability sampling methodologies with both general population and JIY samples in order to determine the robustness of these findings. Another limitation of this study pertains to the limited nature of the TBI measure used as an independent variable. While this variable provided utility as a general measure of age at first TBI, this measure was limited in that information about the location of the TBI on participants\u0026rsquo; heads was unknown. This is to say that it isn\u0026rsquo;t clear whether or not any given TBI measured a blow to the front, back, top, etc. of the head. Because different regions of the brain are responsible for distinct dimensions of cognition, this may have led to measurement error due to the coarseness of the TBI measure in terms of location of the TBI. This may have contributed to null effects observed for sensation-seeking and/or the significant effects observed for impulse control if participants with injuries to regions of the brain important for these dimensions of cognition were under/oversampled. This indicates the need for future research to continue studying these relationships using a finer measure of TBI with additional information about distinct brain region injured in the event. Doing so would provide a more accurate understanding of how TBI and age at first TBI impact dual systems model development.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthical Approval:\u003c/strong\u003e \u003cp\u003e The original Pathways to Desistance data collection received ethical approval from the University of Pittsburgh institutional review board. The present study was exempt from full ethical review because it entailed secondary data analysis of a publicly available dataset.\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe sole author conducted all analyses and writing for the project focused on secondary data analysis.\u003c/p\u003e\u003ch2\u003eAvailability of Data and Materials:\u003c/h2\u003e \u003cp\u003eThe Pathways to Desistance dataset is publicly available via the Inter-university Consortium of Political and Social Research. Code will be made available upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhern, E., \u0026amp; Semkovska, M. (2017). 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Traumatic brain injury, mental health, substance use, and offending among incarcerated young people. \u003cem\u003eThe Journal of head trauma rehabilitation\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(3), 239-247.\u003c/li\u003e\n\u003cli\u003eMosenthal, A. C., Lavery, R. F., Addis, M., Kaul, S., Ross, S., Marburger, R., ... \u0026amp; Livingston, D. H. (2002). Isolated traumatic brain injury: age is an independent predictor of mortality and early outcome. \u003cem\u003eJournal of Trauma and Acute Care Surgery\u003c/em\u003e, \u003cem\u003e52\u003c/em\u003e(5), 907-911.\u003c/li\u003e\n\u003cli\u003eNakkula, M. J., Way, N., Stauber, H. Y., \u0026amp; London, P. (1990). Teenage risk prevention questionnaire and interview: An integrative assessment of adolescent high-risk behavior. \u003cem\u003ePiscataway, NJ: Rutgers University, Graduate School of Applied and Professional Psychology\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003ePetrich, D. M., \u0026amp; Sullivan, C. J. (2020). Does future orientation moderate the relationship between impulse control and offending? Insights from a sample of serious young offenders. \u003cem\u003eYouth Violence and Juvenile Justice\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(2), 156-178.\u003c/li\u003e\n\u003cli\u003eRay, J. V., Thornton, L. C., Frick, P. J., Steinberg, L., \u0026amp; Cauffman, E. (2016). Impulse control and callous-unemotional traits distinguish patterns of delinquency and substance use in justice involved adolescents: Examining the moderating role of neighborhood context. \u003cem\u003eJournal of abnormal child psychology\u003c/em\u003e, \u003cem\u003e44\u003c/em\u003e, 599-611.\u003c/li\u003e\n\u003cli\u003eSariaslan, A., Sharp, D. J., D\u0026rsquo;Onofrio, B. M., Larsson, H., \u0026amp; Fazel, S. (2016). Long-term outcomes associated with traumatic brain injury in childhood and adolescence: a nationwide Swedish cohort study of a wide range of medical and social outcomes. \u003cem\u003ePLoS medicine\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(8), e1002103.\u003c/li\u003e\n\u003cli\u003eSeidler, A. L., \u0026amp; Ritchie, S. J. (2018). The association between socioeconomic status and cognitive development in children is partly mediated by a chaotic home atmosphere. \u003cem\u003eJournal of Cognition and Development\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(5), 486-508.\u003c/li\u003e\n\u003cli\u003eSenathi-Raja, D., Ponsford, J., \u0026amp; Sch\u0026ouml;nberger, M. (2010). Impact of age on long-term cognitive function after traumatic brain injury. \u003cem\u003eNeuropsychology\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(3), 336.\u003c/li\u003e\n\u003cli\u003eSpencer-Smith, M., \u0026amp; Anderson, V. (2009). Healthy and abnormal development of the prefrontal cortex. \u003cem\u003eDevelopmental Neurorehabilitation\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(5), 279-297.\u003c/li\u003e\n\u003cli\u003eSteinberg, L., Albert, D., Cauffman, E., Banich, M., Graham, S., \u0026amp; Woolard, J. (2008). Age differences in sensation seeking and impulsivity as indexed by behavior and self-report: evidence for a dual systems model. \u003cem\u003eDevelopmental psychology\u003c/em\u003e, \u003cem\u003e44\u003c/em\u003e(6), 1764.\u003c/li\u003e\n\u003cli\u003eSteinberg, L. (2010). A dual systems model of adolescent risk‐taking. Developmental Psychobiology: \u003cem\u003eThe Journal of the International Society for Developmental Psychobiology,\u003c/em\u003e 52(3), 216-224.\u003c/li\u003e\n\u003cli\u003eWalsh, A. (2009). \u003cem\u003eBiology and criminology: The biosocial synthesis\u003c/em\u003e. Routledge.\u003c/li\u003e\n\u003cli\u003eWeinberger, D.A., and Schwartz, G.E. (1990). Distress and restraint as superordinate dimensions of self-reported adjustment: a typological perspective. \u003cem\u003eJournal of Personality\u003c/em\u003e, 58(2), 381-417.\u003c/li\u003e\n\u003cli\u003eWilliams, W. H., Chitsabesan, P., Fazel, S., McMillan, T., Hughes, N., Parsonage, M., \u0026amp; Tonks, J. (2018). Traumatic brain injury: a potential cause of violent crime?. \u003cem\u003eThe Lancet Psychiatry\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(10), 836-844.\u003c/li\u003e\n\u003cli\u003eWojciechowski, T. (2020a). Relevance of routine activities for understanding the impact of the dual systems model on binge drinking among college students. \u003cem\u003eDrug and alcohol dependence\u003c/em\u003e, \u003cem\u003e216\u003c/em\u003e, 108233.\u003c/li\u003e\n\u003cli\u003eWojciechowski, T. (2020b). The relevance of the dual systems model of self-control for age-related deceleration in offending variety among juvenile offenders. \u003cem\u003eJournal of criminal justice\u003c/em\u003e, \u003cem\u003e70\u003c/em\u003e, 101716.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 \u003cstrong\u003eDescriptive Statistics\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.121794871794872%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.55128205128205%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.596153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard Deviation\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.621794871794872%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMinimum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.108974358974358%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaximum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.121794871794872%\" valign=\"top\"\u003e\n \u003cp\u003eImpulse Control (Pooled)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.55128205128205%\" valign=\"top\"\u003e\n \u003cp\u003e3.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.596153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e.969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.621794871794872%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.108974358974358%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.121794871794872%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.55128205128205%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.596153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.621794871794872%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.108974358974358%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.121794871794872%\" valign=\"top\"\u003e\n \u003cp\u003eSensation-Seeking (Pooled)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.55128205128205%\" valign=\"top\"\u003e\n \u003cp\u003e12.734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.596153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e3.421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.621794871794872%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.108974358974358%\" valign=\"top\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.121794871794872%\" valign=\"top\"\u003e\n \u003cp\u003eAge at First TBI\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.55128205128205%\" valign=\"top\"\u003e\n \u003cp\u003e10.936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.596153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e4.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.621794871794872%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.108974358974358%\" valign=\"top\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.121794871794872%\" valign=\"top\"\u003e\n \u003cp\u003eAge (Pooled)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.55128205128205%\" valign=\"top\"\u003e\n \u003cp\u003e18.898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.596153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e2.453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.621794871794872%\" valign=\"top\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.108974358974358%\" valign=\"top\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.121794871794872%\" valign=\"top\"\u003e\n \u003cp\u003eAge\u003csup\u003e2\u003c/sup\u003e (Pooled)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.55128205128205%\" valign=\"top\"\u003e\n \u003cp\u003e365.622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.596153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e94.781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.621794871794872%\" valign=\"top\"\u003e\n \u003cp\u003e196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.108974358974358%\" valign=\"top\"\u003e\n \u003cp\u003e625\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.121794871794872%\" valign=\"top\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003cp\u003eMale=86.4%\u003c/p\u003e\n \u003cp\u003eFemale=13.6%\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.55128205128205%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.596153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.621794871794872%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.108974358974358%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.121794871794872%\" valign=\"top\"\u003e\n \u003cp\u003eRace\u003c/p\u003e\n \u003cp\u003eBlack=41.4%\u003c/p\u003e\n \u003cp\u003eHispanic=33.5%\u003c/p\u003e\n \u003cp\u003eWhite=20.2%\u003c/p\u003e\n \u003cp\u003eOther Race=4.8%\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.55128205128205%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.596153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.621794871794872%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.108974358974358%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.121794871794872%\" valign=\"top\"\u003e\n \u003cp\u003eSocioeconomic Status at Baseline\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.55128205128205%\" valign=\"top\"\u003e\n \u003cp\u003e51.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.596153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e12.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.621794871794872%\" valign=\"top\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.108974358974358%\" valign=\"top\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.121794871794872%\" valign=\"top\"\u003e\n \u003cp\u003eExposure to Violence (Pooled)\u003c/p\u003e\n \u003cp\u003eNo=45.56%\u003c/p\u003e\n \u003cp\u003eYes=54.44%\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.55128205128205%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.596153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.621794871794872%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.108974358974358%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.121794871794872%\" valign=\"top\"\u003e\n \u003cp\u003eSocial Support (Pooled)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.55128205128205%\" valign=\"top\"\u003e\n \u003cp\u003e.923\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.596153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e.713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.621794871794872%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.108974358974358%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.121794871794872%\" valign=\"top\"\u003e\n \u003cp\u003eDeviant Peer Association (Pooled)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eDepression (Pooled)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.55128205128205%\" valign=\"top\"\u003e\n \u003cp\u003e.871\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e.514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.596153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e.863\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e.708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.621794871794872%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.108974358974358%\" valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.121794871794872%\" valign=\"top\"\u003e\n \u003cp\u003eNumber of Waves with Valid Data\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.55128205128205%\" valign=\"top\"\u003e\n \u003cp\u003e8.941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.596153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e2.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.621794871794872%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.108974358974358%\" valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2 \u003cstrong\u003eMixed Effects Regression Modeling of Covariate Effects on Impulse Control (N=2,180)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u003cu\u003eCoefficient\u003c/u\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u003cu\u003ep-Value\u003c/u\u003e\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u003cu\u003e\u0026nbsp;\u003c/u\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u003cu\u003e95% Confidence Interval\u003c/u\u003e\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u003cu\u003e\u0026nbsp;\u003c/u\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u003cu\u003eCoefficient\u003c/u\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.653846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u003cu\u003ep-Value\u003c/u\u003e\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u003cu\u003e\u0026nbsp;\u003c/u\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u003cu\u003e95% Confidence Interval\u003c/u\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u003cu\u003eFixed Effects\u003c/u\u003e\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u003cu\u003e\u0026nbsp;\u003c/u\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.653846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eAge at First TBI\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001 \u0026nbsp; .024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.653846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e.001 \u0026nbsp; .027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e-.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e.513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e-.231 \u0026nbsp; .115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e-.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.653846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e.466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e-.238 \u0026nbsp; .109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.653846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eAge\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e.368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e-.002 \u0026nbsp; .006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.653846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e.332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e-.002 \u0026nbsp; .007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eAge at First TBI X Age\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e-.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.653846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e.402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e-.075 \u0026nbsp; .030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eAge at First TBI X Age\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.653846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e.429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e-.001 \u0026nbsp; .002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eGender (0=Male; 1=Female)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e-.025 \u0026nbsp; .310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.653846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e-.025 \u0026nbsp; .311\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eRace (reference=White)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Black\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Hispanic\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Other Race\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e.352\u003c/p\u003e\n \u003cp\u003e.227\u003c/p\u003e\n \u003cp\u003e.232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003cp\u003e.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e.225 \u0026nbsp; .478\u003c/p\u003e\n \u003cp\u003e.095 \u0026nbsp; .360\u003c/p\u003e\n \u003cp\u003e-.024 \u0026nbsp; .488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e.352\u003c/p\u003e\n \u003cp\u003e.228\u003c/p\u003e\n \u003cp\u003e.235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.653846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003cp\u003e.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e.226 \u0026nbsp; .479\u003c/p\u003e\n \u003cp\u003e.095 \u0026nbsp; .361\u003c/p\u003e\n \u003cp\u003e-.022 \u0026nbsp; .492\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eSocioeconomic Status at Baseline\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e-.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e-.009 \u0026nbsp; \u0026gt;-.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e-.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.653846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e-.009 \u0026nbsp; \u0026gt;-.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eSocial Support (Lagged)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e.019 \u0026nbsp; .099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.653846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e.018 \u0026nbsp; .098\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eExposure to Violence (0=No; 1=Yes) (Lagged)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e.489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e-.041 \u0026nbsp; .086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.653846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e.482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e-.041 \u0026nbsp; .087\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eDeviant Peer Association (Lagged)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e-.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e-.116 \u0026nbsp; -.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e-.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.653846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e-.116 \u0026nbsp; -.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eDepression (Lagged)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e-.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e.210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e-.071 \u0026nbsp; .016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e-.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.653846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e-.070 \u0026nbsp; .017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eNumber of Waves with Valid Data\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e-.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e-.064 \u0026nbsp; .004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e-.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.653846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e-.064 \u0026nbsp; .004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eImpulse Control (Lagged)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e.315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e.277 \u0026nbsp; .353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e.314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.653846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e.276 \u0026nbsp; .352\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e2.481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e.717 \u0026nbsp; 4.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e2.540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.653846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e.771 \u0026nbsp; 4.309\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u003cu\u003eRandom Effects\u003c/u\u003e\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.653846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eStandard Deviation: Age\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.653846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eStandard Deviation: Age\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.653846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eStandard Deviation: Constant\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e.347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e.352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.653846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3 \u003cstrong\u003eMixed Effects Regression Modeling of Covariate Effects on Sensation-Seeking (N=2,180)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.076923076923077%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.153846153846153%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u003cu\u003eCoefficient\u003c/u\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u003cu\u003ep-Value\u003c/u\u003e\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u003cu\u003e\u0026nbsp;\u003c/u\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u003cu\u003e95% Confidence Interval\u003c/u\u003e\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u003cu\u003e\u0026nbsp;\u003c/u\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u003cu\u003eCoefficient\u003c/u\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.653846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u003cu\u003ep-Value\u003c/u\u003e\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u003cu\u003e\u0026nbsp;\u003c/u\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u003cu\u003e95% Confidence Interval\u003c/u\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u003cu\u003eFixed Effects\u003c/u\u003e\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u003cu\u003e\u0026nbsp;\u003c/u\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.653846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eAge at First TBI\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e-.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e.448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e-.065 \u0026nbsp; .029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e-.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.653846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e.390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e-.068 \u0026nbsp; .027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e-.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e-.156 \u0026nbsp; -.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e-.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.653846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e-.154 \u0026nbsp; -.049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.653846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eAge at First TBI X Age\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.653846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e-.012 \u0026nbsp; .065\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eGender (0=Male; 1=Female)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e-1.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e-1.823 \u0026nbsp; -.490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e-1.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.653846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e-1.831 \u0026nbsp; -.497\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eRace (reference=White)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Black\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Hispanic\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Other Race\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-1.218\u003c/p\u003e\n \u003cp\u003e-.744\u003c/p\u003e\n \u003cp\u003e-.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003cp\u003e.005\u003c/p\u003e\n \u003cp\u003e.879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-1.716 \u0026nbsp; -.721\u003c/p\u003e\n \u003cp\u003e-1.269 \u0026nbsp; -.220\u003c/p\u003e\n \u003cp\u003e-1.087 \u0026nbsp; .930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-1.229\u003c/p\u003e\n \u003cp\u003e-.755\u003c/p\u003e\n \u003cp\u003e-094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.653846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003cp\u003e.005\u003c/p\u003e\n \u003cp\u003e.856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-1.727 \u0026nbsp; -.731\u003c/p\u003e\n \u003cp\u003e-1.281 \u0026nbsp; -.230\u003c/p\u003e\n \u003cp\u003e-1.104 \u0026nbsp; .916\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eSocioeconomic Status at Baseline\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e.314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e-.008 \u0026nbsp; .025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.653846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e-.008 \u0026nbsp; .025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eSocial Support (Lagged)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e-.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e-.441 \u0026nbsp; \u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e-.221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.653846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e-.442 \u0026nbsp; -.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eExposure to Violence (0=No; 1=Yes) (Lagged)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e.736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e-.211 \u0026nbsp; .298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.653846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e.725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e-.209 \u0026nbsp; .300\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eDeviant Peer Association (Lagged)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e.304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e.146 \u0026nbsp; .463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e.307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.653846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e.149 \u0026nbsp; .466\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eDepression (Lagged)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e.019 \u0026nbsp; .381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e.203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.653846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e.022 \u0026nbsp; .384\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eNumber of Waves with Valid Data\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e-.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e.709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e-.177 \u0026nbsp; .120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e-.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.653846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e.780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e-.170 \u0026nbsp; .128\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eSensation-Seeking (Lagged)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e.185 \u0026nbsp; .271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.653846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e.184 \u0026nbsp; .271\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e13.896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e11.737 \u0026nbsp; 16.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e13.859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.653846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e11.698 \u0026nbsp; 16.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u003cu\u003eRandom Effects\u003c/u\u003e\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.653846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eStandard Deviation: Age\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.653846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.153846153846153%\" valign=\"top\"\u003e\n \u003cp\u003eStandard Deviation: Constant\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.461538461538462%\" valign=\"top\"\u003e\n \u003cp\u003e1.501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.615384615384615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26923076923077%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\" valign=\"top\"\u003e\n \u003cp\u003e1.503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.653846153846153%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Dual Systems Model, Development, Traumatic Brain Injury","lastPublishedDoi":"10.21203/rs.3.rs-3988657/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3988657/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThere is limited work examining the developmental timing of traumatic brain injuries for influencing development of sensation-seeking and impulse control. Further, there is a dearth of research which examines whether the manifestation of effects of earlier traumatic brain injury appear at later ages via moderation by age. A subsample of participants from the Pathways to Desistance dataset was analyzed (N\u0026thinsp;=\u0026thinsp;393). This subsample was comprised of all justice-involved youth in the sample who reported ever experiencing traumatic brain injury prior to baseline measurements. Mixed effects modeling was used to examine direct and moderated effects of interest. Results indicated that earlier age at first TBI was a significant predictor of lower impulse control, but not sensation-seeking. There were no significant moderation effects.\u003c/p\u003e","manuscriptTitle":"Timing of Traumatic Brain Injury as a Predictor of Dual Systems Development: Testing for Moderation Effects of Concurrent Age","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-07 09:24:29","doi":"10.21203/rs.3.rs-3988657/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b20fb43a-3755-4979-9376-c2f27d9014c5","owner":[],"postedDate":"March 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-12T22:08:15+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-07 09:24:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3988657","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3988657","identity":"rs-3988657","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}