Violence-Related Traumatic Brain Injury in Justice-Involved Women

Abstract

The present study investigated gender differences in the prevalence and incidence of violence-related traumatic brain injury (TBI) among justice-involved individuals, as well as potential associations between violence-related TBI and select biopsychosocial variables among women in the sample. Data from 409 justice-involved individuals were analyzed, and men and women were compared for rate of violence-related TBI. Women were grouped by violence-related TBI history and compared on eight biopsychosocial variables. Gender was significantly associated with multiple TBIs and multiple violence-related TBIs. History of violence-related TBI in women was associated with physical health problems and incarceration history. This research revealed a high rate of violence-related multiple TBIs among justice-involved women. Violence-related TBIs were associated with more prevalent physical illness and increased incarceration times. Identification of justice-involved women with these injuries may help clinicians better tailor services to improve inmate outcomes and reduce cost burdens to justice systems.

Keywords

violence TBI women incarcerated probation justice-involved

Introduction

The prevalence and impact of traumatic brain injury (TBI) is a burgeoning area of research. The over-representation of TBI among justice-involved individuals has been an issue of particular interest in recent years; however, research specifically highlighting the prevalence and impact of TBI among justice-involved women remains limited (Allely, 2016; Mollayeva & Colantonio, 2017). Violence victimization is one factor that may increase risk of TBI in justice-involved women, with significant impact on recovery outcomes (Bushnik, Hanks, Kreutzner, & Rosenthal, 2003; Gagnon & DePrince, 2017; Jackson, Philp, Nuttall, & Diller, 2002; Valera & Berenbaum, 2003). The present study investigated sex differences in the prevalence, incidence, and features of violence-related TBI in a large sample of justice-involved individuals and provided a broad exploration of biopsychosocial variables potentially associated with violence-related TBI in justice-involved women.

TBI in Justice-Involved Individuals

TBI has been recognized as a significant public health problem, with an estimated 1.4 million TBIs occurring each year, resulting in 1.1 million Emergency Department visits, 235,000 hospitalizations, and 50,000 deaths (Centers for Disease Control and Prevention, 2016; Corrigan, Selassie, & Langois Orman, 2010; Langlois, Rutland-Brown & Wald, 2006). Incarcerated individuals are over-represented in these data. Although the prevalence of TBI in the general population is 8.5%, estimates for the prevalence of TBI among incarcerated individuals range from 41% to 60% (Centers of Disease Control and Prevention, 2015; Farrer & Hedges, 2011; Shiroma, Ferguson, & Pickelsimer, 2010).

Violence and Justice-Involved Individuals

Another characteristic of justice-involved individuals is an over-representation of exposure to violence across the lifespan. A comparison between rates of violence victimization in inmates (21%) and the general population (2%) reveals a rate 10 times higher in the justice-involved (Bureau of Justice Statistics, 2015; Wolff, Blitz, Shi, Siegel, & Bachman, 2007). Violence victimization among justice-involved individuals may be related to greater risk for TBIs due to that violence or violence-related TBI. Some evidence indicates that the prevalence of violence-related TBIs is 26 to 27 percentage points higher among justice-involved individuals than the general population (11% vs. 37%-38%; Corrigan et al., 2010; Schofield, et al., 2006; Wall, Gorgens, Yeo, & Alexander, 2016).

Gender Differences in TBI and Violence for Justice-Involved Individuals

Gender differences in TBI have been well documented. While men are twice as likely to incur a TBI during their lifetime relative to women (Langlois, Rutland-Brown, & Thomas, 2004), these gender differences are significantly minimized or disappear completely in the justice-involved population. Some research even suggests that the rate of TBI is 5 to 7 percentage points higher among incarcerated women compared with incarcerated men (Ferguson, Pickelsimer, Corrigan, Bogner, & Wald, 2012; Fishbein, Dariotis, Ferguson, & Pickelsimer, 2014; Shiroma, Ferguson, & Pickelsimer, 2010).

One reason for the increased rate of TBI among incarcerated women may be high rates of violence victimization. Prison wardens and health care workers estimate that 75% to 90% of incarcerated women have experienced intimate partner violence (IPV; Zust, 2009). In contrast, a national survey revealed that 23% of women in the general U.S. population report a lifetime history of IPV (Moracco, Runyan, Bowling, & Earp, 2007). The literature on IPV suggests that women who are exposed to violence are at greater risk for TBI. Reported estimates of victims of IPV who sustain TBIs range from 30% to 74% (Kwako et al., 2011; Valera & Berenbaum, 2003). Jackson et al. (2002) found that 92% of women in their study reported a history of being hit in the head or face during partner violence. In addition, 80% of identified victims of IPV reported a lifetime history of head injury, with 65% of those head injuries caused by violence (Gagnon & DePrince, 2017). Moreover, women who are victims of IPV may be at greater risk of sustaining multiple head injuries within a single violent episode and at greater risk of sustaining repeated injuries in close proximity (Murray, Lundgren, Olson, & Hunnicutt, 2016). For example, 51% of the women in one sample had sustained multiple brain injuries related to domestic violence (Valera & Berenbaum, 2003).

Given these findings, it is perhaps unsurprising that some researchers have reported assault to be the leading cause of injury among incarcerated women, responsible for anywhere from 35% to 47% of TBIs (Diamond, Harzke, Magaletta, Cummins, & Frankowski, 2007; Durand et al., 2017). In contrast, these same studies documented motor vehicle accidents as the leading cause of TBI among incarcerated men, at 36% and 27% of the samples, respectively. All told, incarcerated women have a high likelihood of having a history of violence victimization, which increases their risk of sustaining TBI.

While the over-representation of violence-related TBI among justice-involved women is concerning enough, research also suggests that TBI outcomes may vary significantly by gender, as well as whether violence was involved in the TBI. While the research is mixed, several studies suggest that women report significantly more postconcussive symptoms and have a higher rate of long-term disability (Bazarian, Blyth, Mooerjee, He, & McDermott, 2010; Corrigan et al., 2010). A meta-analysis of eight studies found that women demonstrated poorer outcomes on 85% of 20 measured variables (Farace & Alves, 2000).

Regarding outcomes of violence-related TBI, Bruns and Hauser (2003) and Wenden, Crawford, Wade, King, and Moss (1998) reported that TBIs from assault result in more severe injuries than many other etiologies, which may contribute to those poor outcomes. Violent TBI survivors complain of more symptoms, do not reintegrate into the community as well, show poorer social productivity, and have higher rates of public sector income sources compared with survivors of nonviolent TBI (Gerhardt, Mellick, & Weintraub, 2003; Hanlon, Demery, Martinovich & Kelly, 1999; Schopp et al., 2006; Wenden et al., 1998). Finally, Bushnik et al. (2003) documented that, at 1 year post-injury, a violently injured group’s unemployment rate and divorce rate increased more than any other group with TBI. This suggests that even those who were engaged in productive activity prior to their injuries showed greater environmental, social, and economic instability post-TBI. These outcome data present a serious concern for women with violence-related TBI, even before considering that the multiple TBIs often sustained by women in IPV situations may even further increase risk for long-term cognitive deficits, poor physical health outcomes, poor mental health outcomes, and increased risk of substance abuse (Murray et al., 2016; Valera & Berenbaum, 2003).

Implications

These TBIs may also contribute to future violence and criminality in women. According to Colantonio et al. (2014), incarcerated women with histories of TBI were more likely than incarcerated men with histories of TBI to have sustained their TBI prior to their first criminal offense (54.3% vs. 31.7%). Other studies have also demonstrated correlations between violence and criminality in women following TBI (Brewer-Smyth, Burgess, & Shults, 2004; Shiroma, Pickelsimer, et al., 2010). Brewer-Smyth et al. (2004) reported that TBIs with loss of consciousness (LOC), along with suicide attempts, recent physical abuse, and low cortisol levels, were correlated with conviction for violent crimes, with the number of TBIs with LOC representing the strongest correlation. In fact, the authors note, “(t)he number of traumatic brain injuries with LOC was strikingly high in all subjects and significantly higher in those currently convicted of a violent crime” (p. 28). In addition, Shiroma, Pickelsimer, et al. (2010) reported that, in both genders, inmates with medically attended TBI had a higher rate of prison behavioral infractions than inmates without a history of TBI. More specifically, women with a TBI history had a rate of violent infractions that was 144% higher than women without TBI.

Present Study

The purpose of the present study was to test previously reported research suggesting an increased rate of violence-related TBI among justice-involved women. This research describes the characteristics of justice-involved women who sustain these injuries. In addition, we identify additional vulnerabilities associated with TBIs among justice-involved women by investigating the physical health, mental health, and criminal behavior characteristics that may be associated with violence-related TBIs in these women. A better understanding of the vulnerabilities associated with these injuries will help correctional mental health providers better tailor and deliver services to mitigate the adverse outcomes associated with these injuries in this at-risk population.

Method

Research Design

The TBI Implementation Grant database, DU institutional review board (IRB) Protocol No. 674,894-2, was originally developed as a research and program development partnership between the University of Denver, the Colorado Department of Human Services Brain Injury Program, and multiple county jails and probation systems in the Front Range area of Colorado. The program identifies inmates and probationers who have a TBI history, provides cognitive screens to identify strengths and deficits, and offers intervention services and resources to qualifying participating individuals during probation or after incarceration, in addition to developing intervention programs designed for use during incarceration and providing a research database to better understand the needs and vulnerabilities of justice-involved individuals with TBI.

Data Collection

Assessments were completed at five county jails, one juvenile probation program, and four adult probation programs in Colorado. Upon entry into the jails or probation systems, individuals were screened for TBI history by correctional personnel trained to administer the Ohio State University TBI Identification Method (OSU-TBI-ID; Corrigan & Bogner, 2007). Individuals who met the OSU-TBI-ID criteria were offered cognitive screening as described below. Individuals were asked for consent to allow their data to be coded for research purposes. Individuals were offered screening and case management services regardless of research participation. The research participation rate was 79%. Data entered through November 4, 2016, were included in these analyses. The sample consisted of a total of 409 individuals: 274 men and 135 women. Participant demographics, including setting of data collection, are presented in Table 1. Study data were collected and managed using the REDCap (Research Electronic Data Capture) electronic data capture tools hosted at the University of Denver (Harris et al., 2009). REDCap is a secure, web-based application designed to support data capture for research studies, providing (a) an intuitive interface for validated data entry, (b) audit trails for tracking data manipulation and export procedures, (c) automated export procedures for seamless data downloads to common statistical packages, and (b) procedures for importing data from external sources.

Table 1:

Participant Demographics by Gender, Including TBIs and Site

Demographics	Women (n = 135)	Men (n = 274)	Total (N = 409)
Total TBIs	603	1,085	1,688
	M (SD)	M (SD)	M (SD)
Age	36.68 (10.82)	38.65 (11.18)	38.00 (11.09)
Education	11.54 (2.40)	11.77 (2.11)	11.69 (2.22)
TBIs per participant	4.47 (3.09)	3.96 (3.70)	4.13 (3.52)
	n (%)	n (%)	n (%)
Race/Ethnicity
White	68 (50.37)	139 (50.73)	207 (50.61)
Hispanic	34 (25.19)	57 (20.80)	91 (22.25)
Black	17 (12.59)	46 (16.79)	63 (15.40)
Native American/Alaskan Native	8 (5.93)	11 (4.01)	19 (4.65)
Hawaiian/Pacific Islander	1 (0.74)	0 (0.00)	1 (0.24)
More than one race	5 (3.70)	14 (5.11)	19 (4.65)
Not reported	2 (1.48)	7 (2.55)	8 (1.96)
Site type
Jail	95 (70.37)	199 (72.63)	294 (71.88)
Probation	40 (29.63)	64 (23.36)	104 (25.43)
Juvenile probation	0 (0.00)	9 (3.28)	9 (0.02)
Missing data	0 (0.00)	2 (0.73)	2 (<0.01)

Note. n = number of individuals in the demographic category; % = percentage of the subgroup (women, men, or total) in the demographic category; TBI = traumatic brain injury.

Measures

The OSU-TBI-ID is a standardized structured interview procedure designed to elicit reports of lifetime TBI histories from participants. It utilizes interview techniques designed to reduce the weaknesses inherent in self-report methods (Corrigan & Bogner, 2007). The OSU-TBI-ID has been demonstrated to have high inter-rater reliability, as well as acceptable-to-high test–retest reliability (Bogner & Corrigan, 2009; Corrigan & Bogner, 2007). This research used a modified version of the tool, which eliminated the “recent” and “other” indices while retaining the “first,” “worst,” and “multiple” indices (Glover et al., 2018). A main objective for the implementation partnership is to create an efficient, cost-effective, and sustainable program to identify individuals at risk for poor long-term outcomes due to TBI; provide cognitive screening; and offer support services to individuals with identified deficits. The “recent” and “other” indices were eliminated from the OSU-TBI-ID form to streamline the program by reducing false positives (individuals with a recent TBI who are not at risk for long-term cognitive deficits) and removing an index with reduced sensitivity and specificity in a population where substance abuse and mental illness are over-represented (Glover et al., 2018). It is noted that this modification does not alter the OSU-TBI-ID interview process, but considered individuals meeting only the “recent” or “other” criteria as negative screens for TBI history. Participants were considered to have a significant history of TBI if they reported a “first” TBI with LOC before age 15, a “worst” TBI with LOC longer than 30 min, or a “multiple” TBI event, defined as “a period where three or more blows to the head caused altered consciousness OR two or more TBIs with LOC within a 3-month period” (Glover et al., 2018, p. 16). A semi-structured clinical interview was also conducted to gather comprehensive self-reported participant histories, including developmental history, adverse experiences, and mental health, substance use, self-harm, legal, and medical histories. Participants were queried specifically about whether they met developmental milestones at appropriate times, age of first substance use, age of first incarceration, total aggregate length of lifetime incarceration, whether they had ever been diagnosed with a mental health or physical health condition, substance use history including length of use and substances used, and history of suicide or self-harm attempt. While these data are self-reported, an attempt was made to increase the objectivity of the data by asking whether participants had ever been diagnosed with or treated for a mental health or medical condition.

The Automated Neuropsychological Assessment Metrics (Version 4) Core Battery (ANAM, 2016; Reeves et al., 1992) or Neuropsychological Assessment Battery–Screening Module (NAB-SM; Stern & White, 2003), along with the Rey 15 Item Test (FIT; Rey, 1964) and Trail Making Test B:A Ratio (Martin, Hoffman, & Donders, 2003), was administered to participants with a significant history of TBI to screen for gross neuropsychological deficits and assess performance validity. Participants given the NAB-SM (Stern & White, 2003) were also administered either the Miller Forensic Assessment of Symptoms Test (M-FAST; Miller, 2001) or the Validity Indicator Profile (VIP; Frederick, 1997) as a third measure of effort to ensure adequate assessment of validity. Although the M-FAST is the only measure used that has been validated specifically in a forensic context, all measures administered are considered well-validated tests for the detection of inadequate effort and/or neurocognitive impairment, and all are widely used clinically for those purposes (Bleiberg, Kane, Reeves, Garmoe, & Halpern, 2000; Frederick & Crosby, 2000; Jones, Loe, Krach, Rager, & Jones, 2008; Kabat, Kane, Jefferson, & DiPino, 2001; Martin et al., 2003; Reznek, 2005; Roebuck-Spencer, Vincent, Gilliland, Johnson, & Cooper, 2013; Temple et al., 2009; Zgaljardic & Temple, 2010).

The ANAM (Version 4) is a validated measure of several neuropsychological constructs (Bleiberg et al., 2000; Jones et al., 2008; Kabat et al., 2001). Subtests include the Core Battery tests of Reaction Time (administered twice), Learning, Attention/Processing Speed, Working Memory, Spatial Working Memory, Delayed Memory, and Inhibition. The ANAM also includes a clinically validated embedded performance validity measure (Roebuck-Spencer et al., 2013)

The NAB-SM measures five domains of neuropsychological functioning: Attention, Executive Functions, Language, Spatial, and Memory (Stern & White, 2003). The test has demonstrated reliability and validity, including ecological validity in two TBI clinical population samples (Temple et al., 2009; Zgaljardic & Temple, 2010). The FIT is a test of visual memory malingering (Rey, 1964). At the traditional cutoff listed in Strauss, Sherman, and Spreen (2006), the FIT has been shown to have high specificity in identifying unfeigned performance (Reznek, 2005). The Trail Making Test B:A ratio (Martin et al., 2003) has been demonstrated to have a sensitivity of 68% and specificity of 57% in detecting feigned impairment (Egeland & Langfjaeran, 2007). Egeland and Langfjaeran (2007) noted that these levels of sensitivity and specificity are too low to be useful as a sole measure of performance validity, but that the measure gains clinical utility as one of several measures of performance validity.

The M-FAST is a test to detect strategies of malingering symptoms among forensic populations (Miller, 2001). In a correctional population seeking mental health services, the M-FAST was found to have high reliability and good validity as measured by high correlation with a previously accepted measure of malingering (Guy & Miller, 2004). The VIP is a measure of performance validity using verbal and nonverbal forced choice tests (Frederick, 1997). In a cross-validation sample, the VIP achieved a classification rate of 80% for the nonverbal subtest and 76% for the verbal subtest (Frederick & Crosby, 2000).

Analyses

All traditional statistical analyses were conducted using IBM SPSS Student BASE Version 24 software. Normality was tested using the Kolmogorov–Smirnov (K-S) test and evaluations of skewness and kurtosis. All continuous variables in these analyses were significant at p < .05. In addition, skewness and kurtosis values for all variables were unacceptably high. As data were not normally distributed, nonparametric tests were used for all analyses.

The present study sought to determine whether the rate of violence-related TBI is higher than would be expected among justice-involved women compared with justice-involved men and whether the nature of violence-related TBI among justice-involved women differed from justice-involved men. Because prior research indicates women in the general U.S. population have a significantly lower prevalence and incidence of violence-related TBI compared with men, and the literature suggests that there is a significantly higher prevalence and incidence among women in this population such that their rates are equivalent to or greater than the rate in men (Diamond et al., 2007; Durand et al., 2017), traditional statistical methods of rejecting the null (that there are not group differences) are not well suited to this research question. Instead, we sought to determine the probability of the observed data given group differences versus no group differences, thus allowing us to determine whether the prevalence and incidence of violent TBI in the groups were definitively statistically equivalent, and when not statistically equivalent, to determine which gender had the higher prevalence and incidence. Bayesian analyses were thus used to test research questions related to gender differences. The open-source software JASP Version 0.8.5 was used to complete these analyses, using default priors.

We also investigated whether a history of violence-related TBI was associated with other vulnerabilities, such as physical illness, mental illness, substance use history, suicide attempt, and neuropsychological deficits. In addition, we examined whether a history of violence-related TBI was associated with certain criminal behavior characteristics such as convictions for violent crime and longer length of incarceration. Finally, this study explored whether rates of comorbid vulnerabilities and criminal behavior characteristics were affected by the nature of injury (i.e., whether women with violence-related multiple TBI had higher rates of comorbidities than women with a single violence-related TBI and whether both groups had higher rates of comorbidities relative to women with nonviolence-related multiple or a single TBI). These analyses were conducted using data from a large program development and data collection project. As such, the specific variables recorded have evolved over time, and data for some cases were incomplete. Cases with missing data for a particular variable were excluded for the relevant analyses and included where data were complete enough to allow for analysis. As such, the number of cases in each analysis varies.

The prevalence and incidence rates of violence-related TBI among women and men in this sample were calculated and reported. Bayesian frequency tables were used to determine the probability of the observed data, given a gender difference in the overall prevalence of violence-related TBI, multiple TBIs, and multiple violence-related TBI (H₁) versus no gender difference in these variables (H₀). For analyses where the data were deemed statistically more likely given a gender difference model, log odds ratios (ORs) with credibility intervals are provided; traditional ORs were derived using antilog transformations to aid interpretation and data reporting. Bayesian independent-samples t tests were used to determine the probability of the observed data, given a gender difference in incidence, age of first TBI, and total number of TBIs (H₁) versus no gender difference in these variables (H₀).

For the investigation of biopsychosocial vulnerabilities and criminal behavior characteristics, women were grouped by violence-related TBI history, and group differences were evaluated using chi-square tests of independence for reported mental illness, physical health problems, substance abuse, previous suicide attempt, neuropsychological deficits, and conviction for violent crime. Group differences were evaluated using Mann–Whitney U tests for median age of first substance use and median length of incarceration. Then, women were grouped by history of violence-related multiple TBI, violence-related single TBI, and TBI not related to violence. Group differences were then evaluated for all variables. Chi-square analyses were used to evaluate group differences on medical illness, physical illness, history of substance use, suicide attempt, neuropsychological deficits, and convictions for violent crime. A Kruskal–Wallis one-way analysis of variance (ANOVA) by ranks was used to evaluate group differences on median age of first substance use and median length of incarceration. Post hoc analyses to compute achieved power were completed using G*Power open-source software, Version 3.1 (Faul, Erdfelder, Lang, & Buchner, 2007).

Neuropsychological deficits were defined as a score of “clearly below average” on any neuropsychological domain on the ANAM or a score of “mildly impaired” or below on any domain of the NAB-SM. Consistent with the findings of Meyers and Volbrecht (2003), individuals failing all three performance validity measures were identified as providing suboptimal effort and were eliminated from the neuropsychological deficit analysis. In addition, individuals failing the embedded performance validity measure on the ANAM were eliminated because response inconsistency invalidates the ANAM administration.

Results

An alpha level of .05 was used for all traditional statistical tests. For Bayesian analyses, Bayes factors less than 1 favor the null hypothesis (H₀), whereas values greater than 1 favor the alternative hypothesis (H₁). The strength of evidence for these analyses was determined using the interpretive guidelines proposed by Jeffreys (as cited in Wetzels et al., 2011). The sample consisted of 409 participants; 274 men and 135 women. Demographic data, including total number of TBIs and average number of TBIs per participant, are presented in Table 1. The prevalence rate of violence-related TBI was 66.7% for women (n = 90) and 62.8% for men (n = 127). The incidence rate of violence-related TBI was 38.4% for women and 36.6% for men. The Bayesian analysis of history of violence-related TBI (prevalence) indicated a slightly greater relative probability of the observed data under the assumption of no gender difference (BF₁₀ = 0.82). The strength of evidence is considered anecdotal; thus, no reliable conclusions can be drawn about the relationship between gender and prevalence of violence-related TBI. Bayesian analysis also indicated a greater probability of the data under the no gender differences model (BF₁₀ = 0.17), which is considered substantial (or moderate) evidence that there is equal incidence of violence-related TBI between men and women in this sample. The frequency table for this analysis is represented in Table 2.

Table 2:

Bayes Frequency Table, Gender Versus History of V-TBI

Gender	V-TBI	No V-TBI	Total
Women	90	21	111
Men	172	69	241
Total	262	90	352

Note. V-TBI = violence-related TBI; TBI = traumatic brain injury.

Data from 348 participants were included in the analysis of gender difference in multiple TBI history. Bayesian analysis provided strong evidence that the observed data are more probable, given the model that gender differences exist (BF₁₀ = 26.04; logOR = 0.76; 95% confidence interval (CI) = [0.30, 1.25]). The OR was calculated and revealed that women were twice as likely as men to sustain multiple TBIs (OR = 2.15, 95% CI = [1.34, 3.48]). Forty-nine participants had responses recorded for cause of multiple TBI. The Bayesian analysis indicated a higher probability of the data, given the model that gender differences exist in violence-related multiple TBI, with strength of evidence bordering between substantial and strong (BF₁₀ = 10.37; logOR = 1.81, 95% CI = [0.44, 3.49]). The OR was calculated and revealed women were six times more likely than men to sustain violence-related multiple TBIs (OR = 6.10, 95% CI = [1.55, 32.69]). Frequency tables for these analyses are presented in Tables 3 and 4. A Bayesian independent-samples t test found a greater probability of the data, given no gender differences in total number of TBIs; however, strength of evidence was anecdotal (p = .054, n = 409, BF₁₀ = 0.36). A Bayesian independent-samples t test examining age of first TBI supported the no gender differences model with substantial evidential strength (p = .294, n = 409, BF₁₀ = 0.26).

Table 3:

Bayes Frequency Table, Gender Versus Multiple TBI

Gender	Mult. TBI	No mult. TBI	Total
Women	81	33	114
Men	124	110	234
Total	205	143	348

Note. TBI = traumatic brain injury; Mult. = multiple.

Table 4:

Bayes Frequency Table, Gender Versus Multiple V-TBI

Gender	Mult. NV-TBI	Mult. V-TBI	Total
Female	2	21	23
Male	11	15	26
Total	13	36	49

Note. TBI = traumatic brain injury; V-TBI = violence-related TBI; Mult. = Multiple; NV-TBI = nonviolence-related TBI.

Of the sample of 135 women, all available data were analyzed. A chi-square test of independence comparing prevalence of physical health diagnoses between women with a history of violence-related TBI and no history of violence-related TBI indicated a moderately strong association, with acceptable statistical power (Table 5; 1 – β = .80). Women with a history of violence-related TBI were four times more likely to have a physical health diagnosis than women without a history of violent TBI (OR = 4.03, 95% CI = [1.46, 11.11]). The frequency table for this analysis is presented in Table 6. Violence-related TBI among women was not significantly associated with mental health diagnoses, history of substance use, suicide attempt, neuropsychological deficits, and conviction of violent crime (Table 5). A Mann–Whitney U test indicated there was no difference in the median age of first substance use between women with a history of violence-related TBI and women with no history of violence-related TBI (p = .722, n = 105, median for both groups = 14 years); however, length of incarceration (in months) was significantly greater for women with a history of violence-related TBI than those without a history of violence-related TBI (p = .022, r = .23, n = 97); however, this test had unacceptably low statistical power (1 – β = .22). The median length of incarceration for those with violence-related TBI was 6 months, compared with 2 months for those without violence-related TBI.

Table 5:

Chi-Square Tests of Independence, Women, V-TBI Versus NV-TBI

Biopsychosocial variable	n	df	χ²	p	phi
Physical health diagnosis	106	1	7.9	.005*	0.27
Mental health diagnosis	109	1	1.47	.225	ns
Suicide attempt	103	1	2.07	.151	ns
History of substance use	109	1	4.49	.183	ns
Neuropsych. deficit	89	1	2.37	.124	ns
Violent crime conviction	100	1	1.69	.193	ns

Note. TBI = traumatic brain injury; V-TBI = violence-related TBI; NV-TBI = nonviolence-related TBI; Neuropsych. = neuropsychological.

p < .05

Table 6:

Chi-Square Frequency Table, Women, V-TBI Versus Phys. Health Diagnosis

V-TBI history	No phys. health dx	Phys. health dx	Total
V-TBI	20	66	86
NV-TBI	11	9	20
Total	31	75	106

Note. TBI = traumatic brain injury; V-TBI = violence-related TBI; phys. = physical; dx = diagnosis; NV-TBI = nonviolence-related TBI.

When women were considered in three groups, violence-related multiple TBI, violence-related single TBI, and nonviolence-related TBI, chi-square tests of independence indicated no significant differences in the variables physical health diagnoses, mental health diagnoses, history of substance abuse, suicide attempt, neuropsychological deficits, or convictions for violent crime (Table 7). A Kruskal–Wallis analysis found no significant difference between the groups on age of first substance use (p = .978, df = 2, n = 107). The groups were significantly different only in their length of incarceration (p = .001, df = 2, n = 81). Post hoc Mann–Whitney U tests with Bonferroni correction were used to analyze significant group comparisons. The significance level with Bonferroni correction was p < .017. There was a large difference in the median length of incarceration between the violence-related multiple TBI group (n = 17, median = 21.3) and the nonviolence-related TBI group (n = 14, median = 2.0; U = 60, p = .001, r = .55, 1 – β = .42). In addition, there was a small difference in the median length of incarceration between the violence-related single TBI group (n = 50, median = 6.0) and the nonviolence-related TBI group (n = 14, median = 2.0; U = 321, p = .013, r = .29, 1 – β = .26); however, both analyses had unacceptably low statistical power. There was no significant difference between the violence-related multiple TBI group (n = 17, median = 21.3) and the violence-related single TBI group (n = 50, median = 6.0; U = 402, p = .018).

Table 7:

Chi-Square Tests of Independence, Women, Mult. V-TBI Versus Single V-TBI Versus NV-TBI

Biopsychosocial variable	n	df	χ²	p	phi
Physical health diagnosis	109	2	4.46	.108	ns
Mental health diagnosis	112	2	0.16	.923	ns
Suicide attempt	106	2	2.58	.275	ns
History of substance use	112	2	5.27	.072	ns
Neuropsych. deficit	92	2	3.11	.212	ns
Violent crime conviction	102	2	0.8	.670	ns

Note. Mult. = multiple; TBI = traumatic brain injury; V-TBI = violence-related TBI; NV-TBI = nonviolence-related TBI; Neuropsych. = neuropsychological.

p < .05

Discussion

These findings suggest equal incidence of violence-related TBI between justice-involved men and women in this sample of justice-involved individuals in the Front Range area of Colorado. This represents a significant deviation from the trend in the general population, suggesting that justice-involved women sustain violence-related TBIs at a much higher rate than women in the general population, a rate comparable to justice-involved men. The analysis of the TBI Implementation Grant data was inconclusive with regard to gender differences in the prevalence of violence-related TBI in this data set, meaning we are unable to say whether a comparable proportion of justice-involved women and men have histories of violence-related TBI. Although the observed data appear to support equivalent prevalence, the strength of evidence was weak.

These analyses also revealed several significant differences between justice-involved men and women on the frequency of multiple TBIs and violence-related multiple TBIs, suggesting that the features and quality of TBI in this population varied significantly by gender. Women were twice as likely as men to incur multiple TBIs of any kind and six times more likely to have multiple TBIs related to violence. Women were overwhelmingly more likely to have two or more violence-related injuries within close proximity to each other, which can increase acute medical risk and may increase the risk of poor long-term effects due to TBI, depending on the frequency and severity of injuries (Shively, Scher, Perl, & Diaz-Arrastia, 2012; Theadom et al., 2015).

The research regarding the long-term implications of multiple injuries over periods of years remains incomplete (Solomon & Zuckerman, 2015). That said, the injury patterns implicated as increasing the risk for long-term neuropathology are clearly demonstrated in this population. Despite the relevance of the research into the cumulative effects of TBI to justice-involved individuals, there are no studies to date on the impact of cumulative injuries in this population (O’Rourke, Linden, Lohan, & Bates-Gatson, 2016), and our research further highlights the need for more research in justice-involved women specifically.

Overall, data regarding gender differences in mechanism of TBI in both the general population and the justice-involved population remain limited, and thus difficult to compare using rigorous statistical methods. To our knowledge, this study is the most comprehensive examination to date focusing specifically on gender differences in violence-related TBI rates among incarcerated individuals. Further study of gender differences in the mechanisms of TBI is needed, for both general and correctional populations.

The present research revealed significant differences in physical health problems among justice-involved women based on violence-related TBI history, similar to the findings of Durand et al. (2017). These findings have significant implications for clinical practice with justice-involved women. First, this research highlights the importance of screening for history of violence-related TBI in this population. Not only do women with this history appear to represent a significant proportion of justice-involved women, this research suggests that these violently injured women are at higher risk for physical health problems. Identifying women who have violence-related TBIs in the justice system can help clinicians more quickly and accurately identify individuals who may benefit from health interventions. Clinicians should provide health improvement and health education programming to help reduce systemic costs and burden created by these health problems. Future research should investigate categories of physical and mental illness to explore whether violence-related TBIs are associated with specific physical illnesses, such as epilepsy, as suggested by Durand et al. (2017).

The finding of longer incarceration histories has limited interpretative value due to the lack of sufficient statistical power achieved; however, this suggests both a direction of future research and an area of potential intervention. This analysis should be repeated with more robust data to determine whether there truly is a significant difference in length of incarceration. Longer periods of incarceration may result either from multiple rearrests or from arrests for more serious crimes with longer sentences. The findings of this research, that women with violence-related TBIs do not have a higher rate of conviction for violent crimes, suggest that if these women have longer incarceration histories, it may be due to rearrests. More research is needed to investigate this question. If women with violence-related TBI are at higher risk of developing histories of frequent or chronic incarceration, then early identification of these women may allow for earlier enrollment in programs designed to reduce recidivism risk and reintegrate individuals into the community. For example, provision of therapeutic interventions such as CBT may help reduce likelihood of rearrest (Landenberger & Lipsey, 2005) and may therefore reduce the length of incarceration over the lifespan. In combination with other targeted services such as vocational and educational programs, the reduction in recidivism (and therefore lifetime length of incarceration) may be even more significant (Landenberger & Lipsey, 2005). Furthermore, identifying these women as having a history of TBI can help direct even more specialized services, such as cognitive rehabilitation and accommodations, that may help women benefit more fully from targeted health and vocational programs (Cicerone et al., 2005).

Limitations

There are several limitations to this research. Because these data were collected in jails and probation systems only along the Front Range of Colorado, they may not fully represent the larger population of incarcerated or justice-involved individuals. Particularly, the demographic makeup of these study participants was different from the national incarcerated population as a whole. According to the Prison Policy Initiative, White individuals comprise 39% of the national incarcerated population, Black individuals comprise 40%, and Hispanic individuals comprise 19%, with no data provided for other racial groups (Sakala, 2014). White participants were over-represented in these data by more than 10 percentage points, whereas Black participants were under-represented by more than 25 percentage points. This research should be replicated with justice-involved individuals in other areas to ensure a more complete representation of the entire population of justice-involved women, particularly with more representative racial demographics. In addition, racial differences in rates of violence-related TBI have not been thoroughly explored in men or women and could be investigated in future research (O’Rourke et al., 2016).

A limitation is noted regarding the use of a modified version of the OSU-TBI-ID for the TBI Implementation Grant Project as a whole. The modification of the OSU-TBI-ID to eliminate the “recent” and “other” indices may limit comparison to other studies using this tool. While the OSU-TBI-ID was administered using the standardized method (Ohio Valley Center for Brain Injury Prevention and Rehabilitation, 2017), the prevalence and incidence of TBI identified using the modified tool may be lower than studies using the full criteria.

Another important limitation involves the inclusion criteria and injury reporting. The original purpose of the data collection was to identify and treat individuals who may be vulnerable to complications resulting from TBI. While the severity of injury is one factor involved in determining risk, the addition of other factors such as younger age of first TBI and multiple TBIs resulted in a mixed sample that included the entire range of injury severity, although individuals who incurred only mild TBIs as an adult were screened out of this sample. This limits the comparison between these results and the results of other research, such as the prevalence and incidence rates presented by the CDC, which include all reported TBI injuries regardless of severity (CDC, 2015).

Several statistical analyses were limited by small cell sizes and low statistical power. Of several analyses considered statistically significant in these data, post hoc power analysis demonstrated adequate power in only one analysis. While the other significant analyses are interesting and provide direction for future research with more robust data, no consequential conclusions can be drawn from them for the purposes of this research. Of the nonsignificant analyses, small cell sizes further limit the usefulness of the analyses. Analysis of mental health and substance use variables were particularly limited. Only 10 women of 135 reported no history of mental illness, and only one woman reported no history of substance use. Mental health and substance use histories were so prominent that analyses of these variables were likely not meaningful in this data set. Given previous research documenting the significant association between these conditions and TBI, and among women specifically (Colantonio et al., 2014; Ray, Sapp, & Kincaid, 2014), these variables clearly warrant further investigation with more robust, heterogeneous data.

We sought to investigate patterns of vulnerabilities between women with multiple violence-related TBIs, single violence-related TBI, multiple nonviolence-related TBI, and single nonviolence-related TBI, but the limited availability of multiple nonviolence-related TBI among women participants prevented that analysis. Only two of 49 women had a history of multiple TBI not related to violence, which may reflect the prevalence of both violence victimization and multiple TBIs among women exposed to violence, as previously documented in the literature (Murray et al., 2016; Zust, 2009). Those two cases were consolidated into the nonviolence group. A larger group of women with nonviolence-related multiple TBI would allow meaningful comparison of women with similar injury patterns with and without the presence of violence.

Regarding the neuropsychological data, two neurocognitive screening methods were used. We attempted to compare the data by setting similar cutoff scores and evaluating only the presence or absence of impairment in any domain. Future research should revisit the question of group differences in overall neuropsychological performance as well as domain-specific deficits using a consistent assessment method.

Finally, as discussed by O’Rourke et al., TBI in the justice-involved population occurs alongside myriad other treatment issues that should be further explored. For violence-related TBI, this observation is even more true. Violence-related TBIs are inextricable from emotional trauma and the economic and sociological sequelae associated with domestic violence and criminal lifestyles. The complex interaction of these factors may actually impart additional vulnerability to justice-involved women with violent TBI histories. Violence-related TBI is a manifest variable that can alert clinicians to the complex histories and vulnerabilities of women requiring unique, focused intervention.

Conclusion

The body of research investigating the intersection of violence, gender differences, and justice-involvement suggests that TBI is a significant problem in justice-involved individuals, that violence-related TBI is common, and that these injuries may present special concern for justice-involved individuals’ abilities to function in the community (Bushnik et al., 2003; Shiroma, Ferguson, & Pickelsimer, 2010; Wall et al., 2016). In addition, a limited body of research suggests there are unique concerns regarding the injury histories and vulnerabilities of justice-involved women (O’Rourke et al., 2016). This research expanded on their work by exploring questions proposed by O’Rourke et al. (2016). Specifically, this study investigated the frequency and injury patterns of violence-related TBI among justice-involved women and explored the associations of these injuries to other vulnerabilities and criminal behavior characteristics.

The present study documents a lifetime history of violence-related TBI prevalence rate of 66.7% among this sample of justice-involved women. In addition, 38% of the TBIs sustained by women in this sample were violence-related, which is comparable to the results reported by Durand et al. (2017). These data reflect an over-representation of violence-related TBI among justice-involved women compared with women in the general U.S. population. Importantly, this research revealed differences in the pattern of violence-related injuries sustained by women and men, with women being six times more likely to experience violence-related multiple TBIs. The higher frequency of multiple injuries in these women may increase their risk of developing neuropathological conditions as they age (Shively et al., 2012). Finally, this research revealed differences in physical health between women with violence-related TBI and those without, where violently injured women are more likely to experience health problems. The present study suggests that violently injured women, particularly those with multiple violence-related TBI, could have longer incarceration histories than nonviolently injured women, but this finding needs to be confirmed with more robust analyses.

This work suggests many directions for future research, including the investigation of the impact of multiple injuries and specific vulnerabilities in greater detail. Specifically, future studies should further investigate whether specific physical health diagnoses are associated with violence-related TBIs, as Durand et al. (2017) have done. The vulnerabilities identified in this study, physical illness and incarceration length, can also serve as specific targets for programming and clinical intervention, as described above. The present research also has important implications for policy and clinical practice with justice-involved women. The staggering prevalence of these injuries and the related comorbidities underscore the importance of implementing standardized screening procedures to identify these women and provide opportunities for early intervention. TBI represents a significant concern among all justice-involved individuals. A specific focus on the needs of justice-involved women, with their unique injury patterns and vulnerabilities, will allow for targeted programs that can improve both personal and social outcomes.

Footnotes

Authors’ Note:

The authors would like to thank the Colorado Department of Human Services’ Brain Injury Program TBI Implementation Grant, funded by the Federal Health and Human Services, Administration for Community Living, and the State Office of Behavioral Health (OBH), and the Brain Injury Alliance of Colorado for their support and collaboration in providing the data for this research and providing services for justice-involved individuals with brain injuries. We also thank all the probation and jail systems who have participated in the implementation program, especially the Denver County Sheriff Department.

ORCID iD

Kim Gorgens

Kristi Wall completed her doctorate of psychology in clinical psychology, with a focus in neuropsychology at the University of Denver Graduate School of Professional Psychology in 2017. She is currently a resident in clinical neuropsychology with an emphasis in geropsychology at the Oklahoma City VA Medical Center and the University of Oklahoma Health Sciences Center, Department of Psychiatry and Behavioral Sciences.

Kim Gorgens is a clinical professor in the Graduate School of Professional Psychology at the University of Denver. She is board certified in rehabilitation psychology and is a fellow of the American Psychological Association’s Division 22, Rehabilitation Psychology.

Judy Dettmer has been working in the field of brain injury for more than 25 years. She is currently the director of MINDSOURCE—Brain Injury Network at the Colorado Department of Human Services. In this position, she oversees all activities related to both the Colorado TBI Trust Fund Program and a Federal grant funded through Health and Human Services, Administration for Community Living. She has worked extensively with adults, children, and family members of individuals with brain injury. She has provided direct and systems consultation to improve the lives of individuals with brain injury. She has also assisted with research efforts related to brain injury and has conducted countless presentations, classes, and seminars on brain injury both in the state of Colorado and nationally. She is currently the past president of the board and current chair of Public Policy Committee of the National Association of State Head Injury Administrator’s Board of Directors and she facilitates the National Collaborative on Children’s Brain Injury.

Terri M. Davis is associate professor and director of the PsyD Program in the Graduate School of Professional Psychology at the University of Denver. She is a member of the American Psychological Association Division 17, Society of Counseling Psychology.

Jennifer Gafford is a psychologist and the director of Correctional Behavioral Health for the Denver Sheriff Department and oversees all behavioral health services for the Downtown Detention Center and the Denver County Jail. She has worked for the Sheriff Department since 2008. She received her master’s degree in counseling psychology and counselor education from the University of Colorado at Denver and later her doctorate degree in counseling psychology from the University of Denver. She has been an adjunct professor for the psychology department at the University of Colorado at Denver and the counseling psychology department at the University of Denver. She is a member of the American Psychological Association, the American Psychology-Law Society, and the International Association of Correctional and Forensic Psychology.

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