After reading all the articles and lectures, answer the following using at least 500 word.

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1. What were the two key findings of the Duffy article? (10 points)

2. What could explain increased maltreatment in single-mother households? How do these things explain increased maltreatment? Don't just list them out--think about it. Explain. (10 points)

3. How is society affected by child maltreatment? Think about how child maltreatment affects an individual and how that individual then affects society. Also, think about the costs (both monetary and not) to society. (10 points)

4. Ask a question to your classmates. This can be a question meant to garner better understanding of the material or just a question about the material.

5- Response to other student (150 words)- for this Q, I will send it to you after you are done with first 4 questions because it will be visible after I post the answers of the first four questions.

Article The Interrelatedness of Adverse Childhood Experiences Among High-Risk Juvenile Offenders Youth Violence and Juvenile Justice 2016, Vol. 14(3) 179-198 ª The Author(s) 2015 Reprints and permission: sagepub.com/journalsPermissions.nav DOI: 10.1177/1541204014566286 yvj.sagepub.com Michael T. Baglivio1 and Nathan Epps1 Abstract The interrelatedness of adverse childhood experiences (ACEs) in 64,329 juvenile offenders was examined. ACEs include childhood abuse (physical, emotional, and sexual), neglect (physical and emotional), and household dysfunction (family violence, family substance use, family mental illness, separation/divorce, and family incarceration). Prevalence ranged from 12% to 82% for each ACE. Of youth experiencing one ACE 67.5% reported four or more additional exposures and 24.5% exposure to six or more additional ACEs. Females have higher prevalence and multiple exposures. ACEs are interrelated, necessitating assessment of multiple ACEs rather than one or a few. ACE exposure differs by gender and race/ethnicity. Keywords adverse childhood experiences, ACE, trauma, abuse, juvenile offenders Introduction Adverse childhood experiences (ACEs) as a composite score were first described in 1998 in the seminal study ‘‘Relationship of childhood abuse and dysfunction to many of the leading causes of death in adults: The Adverse Childhood Experiences (ACE) Study’’ (Felitti et al., 1998). The ACE concept acknowledges the complex and cumulative nature of risk factors through the process of summing risk factors and associating the composite score with relevant outcomes developed by Rutter (1983). Through a prospective study including 17,421 insured, well-educated, adult patients, Felitti and colleagues identified 10 negative childhood events that positively correlate with chronic disease in adulthood. The 10 adverse experiences are emotional abuse, physical abuse, sexual abuse, emotional neglect, physical neglect, violent treatment toward mother, household substance abuse, household mental illness, parental separation or divorce, and having an incarcerated household member. Additional work has shown even higher prevalence of ACEs in special populations, such 1 Florida Department of Juvenile Justice, Bureau of Research and Planning, Tallahassee, FL, USA Corresponding Author: Michael T. Baglivio, Florida Department of Juvenile Justice, Bureau of Research and Planning, 2737 Centerview Drive, Tallahassee, FL 32399, USA. Email: Michael.Baglivio@djj.state.fl.us 180 Youth Violence and Juvenile Justice 14(3) as children of alcoholics (Dube, Anda, Felitti, Croft, et al., 2001) and juveniles with justice system involvement (Baglivio, Epps, Swartz, Huq, & Hardt, 2014), than those found for the mostly middleclass original ACE Study population. A person’s cumulative ACE score is expressed as the total number of the reported 10 ACEs. Each ACE is measured in a binary yes–no fashion such that an affirmative response to each ACE ‘‘counts’’ as 1 point. For example, a positive response for sexual abuse would score 1 point whether there were 1 or 100 incidents and regardless of the duration or severity of the abuse. The ACE score ranges from 0 (not having been exposed to any of the traumas/abuses) to 10 (having been exposed to all of them). Prior ACE studies have indicated a dose–response relationship between ACE scores and negative outcomes, with higher ACE scores correlating most strongly with negative outcomes (Brown et al., 2009; Felitti et al., 1998). The concept of a composite ACE score is central to the understanding of the effect of ACEs, as it is now clearly evident from empirical evaluation that ACEs are common, highly interrelated, and exert a powerful cumulative impact on human development (Anda, Butchart, Felitti, & Brown, 2010; Dong et al., 2004). This ‘‘cumulative stressor approach’’ based on the cooccurrence and cumulative impact of these exposures necessitates the examination of them as a collective composite. The customary approach of examining one or only a few adverse exposures misses the broader interrelated context in which they occur. The use of the ACE score as a measure of the cumulative traumatic stress exposure during childhood is consistent with the latest understanding of the effects of traumatic stress on neurodevelopment (Anda et al., 2006, 2010). Life Course Perspective Sampson and Laub (1990) describe trajectories and transitions as two central concepts of the life course perspective. Trajectories are defined as pathways or lines of development over the life span, referring to long-term patterns of behavior. These trajectories are marked by the sequencing of meaningful life events and transitions, with transitions being specific life events that develop over shorter time spans (such as single to married or getting arrested). Transitions may typically result in a change in status or social identity. How an individual adapts to and copes with life events is crucial to the ultimate outcome where different adaptations and coping styles may lead to different life trajectories. Sampson and Laub (1990, p. 610) note the life course perspective ‘‘implies both a strong connection between childhood events and experiences in young adulthood, and that transitions or turning points can modify life trajectories – they can ‘‘redirect paths.’’ Life course criminology, then, is principally concerned with pathways, and how transitions affect trajectories with respect to offending, and/or in the lives of offending populations. Prevalence studies, briefly reviewed below, showing at-risk and offending youth having more abuse/neglect exposure, place ACE in the risk factor prevention paradigm of developmental and life course criminology (Farrington, 2003). As such, risk factor research’s primary focus is not on establishing causality and explanations but on finding correlations (Farrington, 2000). From a life course perspective, implications of high ACE scores on both proximal and distal negative outcomes are well documented in the medical literature (Anda et al, 2006, 2010). Higher ACE scores are associated with significantly increased odds of developing some of the leading causes of death in adulthood, such as heart disease, cancer, chronic lung disease, skeletal fractures, and liver disease. Prior studies have shown the odds of having one of those above-mentioned negative health outcomes in adulthood are up to 12 times higher for children who have experienced four or more ACEs, in comparison to children without such exposure (Felitti et al., 1998). Examining more proximal outcomes, higher cumulative ACE scores have been shown to increase the odds of smoking, heavy drinking, intravenous drug use, incarceration, and morbid obesity, with Baglivio and Epps 181 greater risk also for poor educational and employment outcomes and recent involvement in violence (Bellis, Lowey, Leckenby, Hughes, & Harrison, 2014). ACEs account for a 20–70% increased likelihood of mid-adolescence alcohol use initiation (Dube et al., 2006), and a dose–response relationship has been found between ACE score and a history of suicide attempts (Dube, Anda, Felitti, Chapman, et al., 2001). High ACE scores have been linked to a number of sexual risk behaviors such as having 50 or more sexual partners, intercourse before age 15 (Hillis, Anda, Felitti, & Marchbanks, 2001), and becoming pregnant as a teenager (Hillis et al., 2004). ACE scores have more recently been identified with immediate negative consequences such as chromosome damage (Shalev et al., 2013) and functional changes to the developing brain (Anda et al., 2010; Cicchetti, 2013; Danese & McEwen, 2012; Teicher et al., 2003). However, there is a gap in the literature with regard to the prevalence and interrelatedness of ACEs in offending populations, specifically a juvenile offending population. The prior work reviewed earlier illustrates the ACE cumulative stressor composite score concept has implications over a person’s entire life course. Maltreated children are vulnerable to a range of complications over the life course, including social, health-related, and behavioral problems, including criminal offending. Furthermore, studies reporting gender and racial differences in ACEs for special populations, such as juvenile offenders, are also scarce. We attempt to address that gap by examining the interrelatedness of ACEs in a diverse sample of over 60,000 of the highest risk juvenile offenders across an entire diverse state. To that end, this study is structured as follows: first, we very briefly describe the prior work on individual ACEs and justice-involved youth and the limited examination of cumulative ACE scores with that population. Next, we examine the prevalence of ACEs in the current sample of juveniles as well as the prevalence of additional ACEs, given the presence of any specific ACE. Third, we present the odds of having each additional ACE, given the presence of any specific ACE. Finally, we examine the prevalence of ACEs by gender and race/ethnicity, followed by a discussion of the findings, policy implications, and directions for future work. Adverse Childhood Experiences and Adolescents Recent studies have begun to examine the impact of childhood abuse/maltreatment (in a cumulative ACE score context) on behaviors in adolescent samples as opposed to retrospective recall of childhood abuse in adult samples. Examining the effects of maltreatment on early alcohol use in seventh through twelfth graders, Hamburger and colleagues found students witnessing domestic violence, having a history of physical abuse, and sexual abuse were up to 3 times more likely to have early alcohol use initiation (Hamburger, Leeb, & Swahn, 2008). Examining six types of ACEs on over 130,000 students, Duke and colleagues found each additional type of reported ACE increased the risk of violence perpetration by 35–144% (Duke, Pettingell, McMorris, & Borowsky, 2010). Interestingly, these results included interpersonal violence (including delinquency, weapon-carrying, fighting, bullying, and dating violence) as well as self-directed violence (attempted suicide, selfmutilation). A dose–response relationship was found between ACE score and both learning/behavior problems and obesity in a retrospective chart review of high-risk urban pediatric patients (Burke, Hellman, Scott, Weems, & Carrion, 2011). Childhood Abuse, Neglect, Household Dysfunction, and Justice-Involved Youth Higher prevalence rates of adversity and trauma for justice system-involved youth in comparison to the general population have been revealed in prior work (Dierkhising et al., 2013). Youth with juvenile justice system histories have been found more likely to have experienced multiple forms of trauma (Abram et al., 2004), with one third reporting exposure to multiple types of trauma each year (Dierkhising et al., 2013). Among offenders, experiencing childhood physical abuse and other forms 182 Youth Violence and Juvenile Justice 14(3) of maltreatment leads to higher rates of self-reported total offending, violent offending, and property offending, even after controlling for prior delinquent behavior. (Teague, Mazerolle, Legosz, & Sanderson, 2008). In a comparison of over 90,000 officially delinquent youth with an equal number of comparison youth, placement in Child Protective Services due to parental maltreatment, as well as foster care placement, has been shown to make unique contributions to the risk for delinquency (Barrett, Katsiyannis, Zhang, & Zhang, 2014). A prior meta-analysis has documented parental divorce to have a strong association with delinquency, showing moderate effect sizes (Amato, 2001). The differences in delinquency between youth exposed to parental divorce and those from intact families have not decreased, despite increased social acceptability and prevalence of divorce in recent decades (Amato, 2001; D’Onofrio et al., 2005). Examining adoptive and biological families, Burt, Barnes, McGue, and Iacono (2008) demonstrated the association with delinquency was driven by the parental divorce experience, rather than being mediated by common genes. Exposure to parental incarceration has also demonstrated association to delinquency and maladaptive behaviors (Geller, Garfinkel, Cooper, & Mincy, 2009; Murray & Farrington, 2005; Parke & Clarke-Stewart, 2002). In a longitudinal study following over 400 males, Murray and Farrington (2005) show parental imprisonment predicted antisocial and delinquent outcomes (beyond that of other types of parental separation) up to age 32, even after controlling for other childhood risk factors. Herrera and McCloskey (2001) found witnessing marital violence in childhood uniquely contributes to later behavioral problems and/or delinquency and predicted referral to juvenile court. These findings support prior research, including meta-analytic work, indicating that exposure to domestic violence leads to a range of internalizing and externalizing behavior problems (Evans, Davies, & DiLillo, 2008; Moylan et al., 2010). In perhaps the first investigation of ACE with a juvenile offender sample, Tacoma Urban Network and Pierce County Juvenile Court used risk assessment instrument data to measure ACE prevalence among juvenile offenders (Grevstad, 2010). Grevstad (2010) found prevalence rates of ACEs 3 times higher than those reported by Felitti and Anda. Furthermore, youth with higher ACE scores had more substance abuse, self-harm behaviors, and school-related problems such as disruptive behaviors, substandard performance, and truancy. By deriving ACE scores from the standardized risk assessment tool used within the Florida Department of Juvenile Justice (FDJJ), prior work has demonstrated increased ACE scores correlate with increased risk to reoffend (Baglivio, Epps, et al., 2014). Juvenile offenders were 13 times less likely to report zero ACES and 4 times more likely to report ACE scores of 4 or more compared to Felitti and Anda’s private-insured population of mostly college-educated adults (Baglivio, Epps, et al., 2014). This study attempts to extrapolate from the Baglivio, Epps, and colleagues (2014) findings to examine the interrelatedness of each individual ACE with one another and the likelihood of having multiple other ACEs’, given the presence of any specific ACE. Gender and Adverse Childhood Experiences Justice system-involved females report higher levels of exposure to sexual assault and interpersonal victimization, while males report higher rates of witnessing violence (Cauffman, Feldman, Waterman, & Steiner, 1998; Ford, Chapman, Hawker, & Albert, 2007; Wood, Foy, Layne, Pynoos, & James, 2002). Similar rates of exposure to each of 19 different trauma types were found in other studies of juveniles in the juvenile justice system, although females have higher rates of sexual abuse and sexual assault (Dierkhising et al., 2013). Males experiencing maltreatment have been shown to be prone to violent behavior and delinquency (Chen, Propp, deLara, & Corvo, 2011; Mass, Herrenkohl, & Sousa, 2008; Yu-Ling Chiu, Ryan, & Herz, 2011). Additionally, others have found significantly more maltreated females (including all forms of abuse) committed violent offenses as Baglivio and Epps 183 juveniles or adults than nonmaltreated females, while by contrast, there were no significant differences in prevalence rates of violent offending for maltreated versus nonmaltreated males (Herrera & McCloskey, 2001; Widom & Maxfield, 2001). Others have found no sex differences for heightened risk of violent offending when examining an offending population and physical abuse in particular (Teague et al., 2008). The first study to assess gender differences in ACE composite scores with juvenile justice youth found the ACE rank order by prevalence across gender is similar, except for sexual abuse (Baglivio, Epps, et al., 2014). However, females had a higher prevalence on every single ACE indicator, although effect sizes revealed that the majority of the differences are small (Cohen’s d less than .5 with the exception of sexual abuse at .92 and the ACE composite score the second largest at a moderate .59). These results are consistent with prior findings that the main gender difference in this population is in experiencing sexual abuse (Cauffman et al., 1998; Dierkhising et al., 2013). Interrelatedness of ACEs The seminal work on the interrelatedness of ACEs was conducted by Dong and colleagues (2004) with over 8500 men and women from the original ACE study (Wave II). They found individual ACE indicator prevalence rates between 6% to just over 28%. Additionally, if an individual had experienced one ACE, 86.5% reported exposure to at least one additional ACE and 38.5% exposure to four or more ACEs on average (Dong et al., 2004). Comparing individuals with and without exposure to each individual ACE, they found the odds of having at least one other of the nine remaining ACEs were 2 to 17.7 times higher for those who had experienced any given ACE (Dong et al., 2004). Mirroring previous ACE findings, Dong and colleagues concluded ACEs should not be assumed to be isolated events and that both the negative short- and long-term influences of ACEs on health and behaviors is a cumulative, dose–response relationship (2004; see also Andaet al., 1999; Dietz et al., 1999; Dong, Dube, Felitti, Giles, & Anda, 2003; Dube, Anda, Felitti, Chapman, et al., 2001). Community Positive Achievement Change Tool The FDJJ implemented statewide in 2006 the Community Positive Achievement Change Tool (C-PACT), a fourth-generation risk/needs assessment. A primary purpose of the C-PACT is to classify youth according to their risk to reoffend. There are two versions of the C-PACT, namely, the Pre-Screen, with 46 items, and the Full Assessment, consisting of 126 items. Both versions produce identical overall risk to reoffend classifications (low, moderate, moderate–high, and high). The overall risk score is based on a matrix of the criminal history and social history subscores (see Baglivio, 2009, for further explanation of C-PACT scoring). The criminal history component is a scale of the extent of prior offending and prior justice system placements. The social history score is a combined scale of school, peer, family/home circumstances, substance use, and trauma/abuse and mental health history. The C-PACT assesses both static and dynamic risk and protective factors, rank orders criminogenic needs, which are automated into a case plan, and requires reassessments to gauge rehabilitative progress. The Pre-Screen and Full Assessment both produce a criminal history subscore (extent and seriousness of prior offending/justice system placements) and a social history subscore (individual, family, and environmental risk factors). The overall risk score and the criminal and social history subscores for an individual youth are always identical for the Pre-Screen and the Full Assessment, as only the questions in the Pre-Screen used for scoring are used in the Full Assessment for scoring (e.g., if the same youth was administered a Pre-Screen and a Full Assessment, the overall risk score, criminal history score, and social history score would be identical). Each of the Full Assessment domains produces a risk score, and most have a protective score. The C-PACT domains are 184 Youth Violence and Juvenile Justice 14(3) reflective of the ‘‘Central Eight’’ risk factors espoused by Andrews and Bonta (2003). Full Assessment domains include Criminal history, school, leisure/free time, employment, relationships, family/living situation, alcohol/drugs, mental health, attitudes/behaviors, aggression, and social skills. The current policy of the FDJJ is to assess each youth entering the system using the PACT PreScreen. Youth scoring at moderate–high or high risk to reoffend on the Pre-Screen are then administered the Full Assessment. The PACT Full Assessment is then repeated every 90 days for youth under FDJJ supervision who initially scored at moderate–high or high risk to reoffend. Youth on probation supervision who score at low or moderate risk to reoffend are reassessed every 180 days using the Pre-Screen. Any time a youth’s score indicates moderate–high or high risk, reassessment is performed using the Full Assessment. Any youth placed in a residential commitment facility, a day treatment program, or the FDJJ’s Redirection Program (intensive community-based family therapy, predominately Multisystemic Therapy, Functional Family Therapy, or Brief Strategic Family Therapy) is also assessed using the Full Assessment. This practice results in thousands of low and moderate risk youth being assessed using the Full Assessment as well. Multiple evaluations have examined the predictive validity and reliability of the C-PACT for all juvenile offenders in Florida, including across gender, race/ethnicity, and dispositions/placements (such as diversion, probation supervision, and day treatment centers), with a cumulative ‘‘N’’ in excess of 130,000 youth. These evaluations include two peer-reviewed publications (Baglivio, 2009; Baglivio and Jackowski, 2013), one National Council on Crime and Delinquency (NCCD) multiassessment comparison report (Baird et al., 2013), and one independent research agency report (Winokur-Early, Hand, & Blankenship, 2012). Integral for this study, data collected by the C-PACT assessment for the purpose of predicting the likelihood of reoffense and the identification of intervention alternatives for the screened population includes information reflecting each domain examined in the original ACE study. Current Focus The purpose of this study was to replicate the Dong and colleagues (2004) interrelatedness of ACEs study using a high-risk juvenile offending population. Additionally, we examined differences in ACE prevalence across gender and race/ethnicity. As males and minority groups (Black youth in particular) are disproportionately represented in the juvenile justice system (Office of Juvenile Justice and Delinquency Prevention [OJJDP], 2012), including in Florida (FDJJ, 2011), differences in cumulative ACE scores are integral to examining those phenomena. To that aim, we addressed the following research questions: (1) what are the prevalence rates of each of the ten ACE indicators, and what is the proportion of youth who have each maltreatment that has additional ACE indicators?; (2) do the odds of having each additional ACE maltreatment increase for youth with exposure to any given ACE versus youth without exposure to that indicator?; and (3) does the cumulative ACE score differ between males and females, or by race/ethnicity? Methods The data for this study are inclusive of all youth within Florida with a history of an arrest who turned 18 between January 1, 2007, and December 31, 2012, and who were assessed using the Full C-PACT risk/needs assessment. Using only Full Assessment data biases the sample toward higher risk youth, which is the intent of this study. Only youth who had ‘‘aged out’’ of the juvenile justice system (turned 18, the age of majority in Florida) were included so as to capture the full range of ACEs and delinquency referrals (arrests) for each individual. This resulted in a final sample of 64,329 unduplicated youth who were assessed with the PACT Full Assessment and had turned 18 between January 1, 2007, and December 31, 2012. Baglivio and Epps 185 This sampling strategy does not specifically isolate only youth for whom interventions targeted to trauma history may be most appropriate; those being youth who present with multiple ACEs in early adolescence. The strategy does, however, allow for examining the complete range of ACEs juvenile offenders in Florida will present with prior to young adulthood, allowing for the aim of this study of examining interrelatedness of those ACEs. Allowing youth who have not ‘‘aged out’’ in the sample would set up the scenarios by which perhaps not all of the ACEs a given youth will experience prior to age 18 would be captured, and certainly not all of that youth’s offending history. However, it should be noted that any youth who first presented with multiple ACEs at an early age has been included (provided that youth has since ‘‘aged out’’); however, those youth have not been isolated for the purposes of this study. C-PACT and ACE Score C-PACT data were used to create ACE scores for each youth. In contrast to ACE studies with adults, this study suffered less from the challenges of retrospective recall of childhood events, as these events were much more recent for the current sample. In keeping with prior ACE studies, we ascertained the following 10 ACEs: emotional abuse, physical abuse, sexual abuse, emotional neglect, physical neglect, family violence, household substance abuse, household mental illness, parental separation or divorce, and household member incarceration. The exact items, responses, and coding used to create ACE scores from C-PACT data have been reported elsewhere (Baglivio, Epps, et al., 2014). Each exposure was binary (yes/no) and exposures were summed for a cumulative ACE score ranging from 0 (unexposed) to 10 (exposed to all categories). C-PACT reassessments every 90 days are part of FDJJ protocol for all youth who receive the Full Assessment. Therefore, the vast majority of the youth in the sample had multiple assessments. A positive indication of an ACE on any of the C-PACT assessments for a given youth was coded as a ‘‘yes’’ for that exposure, even if previous or future assessments did not indicate that exposure (in essence any ‘‘yes’’ response was carried forward). A brief description of each ACE and responses indicating being exposed are: Emotional abuse: Parents/caretakers were hostile, berating, and/or belittling to youth. Physical abuse: The youth reported being a victim of physical abuse was victimized or physically abused by a family member. Sexual abuse: The youth reported being the victim of sexual abuse/rape. Emotional neglect: The youth reported no support network, little or no willingness to support the youth by the family, youth does not feel close to any family member. Physical neglect: The youth has a history of being a victim of neglect (includes a negligent or dangerous act or omission that constitutes a clear and present danger to the child’s health, welfare, or safety, such as: failure to provide food, shelter, clothing, nurturing, or health care). Family violence: The level of conflict between parents included verbal intimidation, yelling, heated arguments, threats of physical abuse, domestic violence, or the youth has witnessed violence at home or in a foster/group home. Household substance abuse: Problem history of parents and/or siblings in the household includes alcohol or drug problems. Household mental illness: Problem history of parents and/or siblings in the household includes mental health problems. Parental separation/divorce: Youth does not live with both mother and father. Incarceration of household member: There is a jail/prison history of family members. 186 Youth Violence and Juvenile Justice 14(3) Participants This sample represents the entire population of juveniles who were arrested since the 2006 implementation of the C-PACT in Florida, who have since reached the age of at least 18, and who had been assessed with the C-PACT Full Assessment. However, because the C-PACT Full Assessment is the only tool ascertaining all 10 ACEs (the Pre-Screen does not assess all 10), there is a bias toward oversampling more serious delinquents. The final sample of 64,329 youth was 21.7% female, 15.4% Hispanic, and 42.9% Black. Of these 64,329 youth, 18,835 were low risk (29.3%), 10,043 moderate risk (15.6%), 13,931 moderate–high risk (21.7%), and 21,520 high risk to reoffend (33.5%). Analysis First, the prevalence rates of each ACE exposure were examined as well as the proportion of the study group that had each exposure that also had additional ACEs. For example, the prevalence of youth with emotional abuse was examined, as well as the proportion that had 0, 1, etc. additional ACEs. That analysis yields an initial examination of the interrelatedness of ACE exposures. Next, youth who had a given exposure were compared to youth without that exposure on the likelihood of having each additional ACE. Multivariate logistic regressions were conducted to investigate whether exposure to each category of ACE was significantly associated with the risk of having other types of ACE exposures, after controlling for gender and race/ethnicity. This provided adjusted odds ratios (ORs) indicating whether the odds of having each ACE were increased or decreased by the presence of each other ACE. To assess whether youth exposed to one type of ACE have elevated adjusted mean ACE scores (here the adjusted ACE score is the sum of the additional nine ACEs), multiple linear regressions were conducted. In each of these regressions, the ACE score (the sum of the nine additional ACES) was the dependent variable, and the yes–no response to a given ACE was the independent. Gender and race/ethnicity were entered as controls. This resulted in 10 multiple linear regressions conducted (one for each of the ten ACE exposures, with the 0–9 adjusted ACE score being the dependent variable). Finally, the prevalence of ACE exposure was examined for males and females with chi-square tests indicating if those prevalence rates were significantly different. The same prevalence and chi-square testing was conducted across race/ethnicity as well. Results The prevalence rates of each ACE are listed in Table 1. Also included are the percentage with each ACE exposure that have additional ACE exposures. As indicated, prevalence rates ranged from a low of 9% for family mental illness to a high of 82% for family violence. The percentage of youth who had an exposure to any ACE that did not have exposure to any other ACE ranged from 0% to 8%, depending on the given ACE. Depending on the given ACE, the percentage of youth with exposure to at least one additional ACE ranged from 93% to 100%. Using the median value, Table 1 shows if a respondent had experienced one category of ACE, 100% of the youth reported having been exposed to at least one additional ACE, 67.5% reported four or more additional exposures, and 24.5% exposure to six or more additional ACEs. Interrelatedness of ACEs Tables 2 and 3 present the probability that youth exposed to a given ACE were also exposed to another ACE (based on logistic regressions). Youth exposed to one type of ACE were compared to youth not exposed to that ACE on the odds of having each additional ACE, controlling for gender Baglivio and Epps 187 Table 1. Prevalence of Each Adverse Childhood Experience and Occurrence of Additional ACEs. Additional ACEs (%) ACE Category Abuse Emotional Physical Sexual Neglect Emotional Physical Household dysfunction Family violence Family substance abuse Family mental illness Separation/divorce Family incarceration Median Range N Prevalence (%) 0 1 2 3 4 5 6 20,928 18,969 7,665 32.5 29.5 11.9 0 0 0 100 100 100 97 99 98 83 90 93 58 70 80 34 45 59 18 24 36 11,610 8,542 18.0 13.3 1 0 99 100 93 99 84 95 65 84 43 65 25 41 52,715 16,102 5,678 50,828 42,370 81.9 25.0 8.8 79.0 65.9 3 97 84 61 38 21 10 0 100 97 89 70 46 25 0 100 98 93 82 62 39 8 93 78 56 36 20 10 2 98 88 66 43 24 12 0 100 97.0 86.5 67.5 44.0 24.5 0–3 93–100 78–99 56–95 36–84 20–65 10–41 Note. ACE ¼ adverse childhood experience. Percentages in additional ACEs columns rounded to the nearest whole percentage. and race/ethnicity. When these youth were compared, the adjusted OR of having at least one of the other nine types of ACE ranged from 1.0 (having the same odds as youth without that ACE) to 1286.2 (having over 1000 times higher odds than youth without that ACE). The median OR was 2.3 indicating youth who have a given ACE are 2.3 times more likely to have any other given exposure than youth who did not have that ACE. Having a given ACE exposure significantly predicted each other ACE exposure for all relationships between categories of ACEs (p < .001), with the exception of the relationship between sexual abuse and emotional abuse (and emotional abuse and sexual abuse) which was not significant (OR of 1.0). To provide an example, the prevalence of physical abuse was 36% among youth who were exposed to emotional abuse, compared to 26% for youth not exposed to emotional abuse (with an adjusted OR of 1.5%; see Table 2). As an additional example, youth having been exposed to family incarceration have 6.8 times the odds of being exposed to family substance abuse as youth without family incarceration exposure, as 7.7% of youth without family incarceration exposure have family substance abuse exposure, compared to 34% of those with family incarceration exposure (see Table 3). The ORs based on the relationship between abuse (emotional and physical) and family violence were by far the largest (see Appendix A for the correlation matrix among each ACE indicator and the ACE composite score). Bivariate correlations lend further support of the strength between the abuse and the family violence measures, as they are the strongest relationships between any ACE indicators, with the exception of sexual abuse with physical abuse. The Pearson correlation between physical abuse and family violence and emotional abuse and family violence are .325 and .301, respectively (p < .001). Specific ACE Exposure and Adjusted Cumulative ACE Score Next, 10 multiple linear regressions were conducted to examine whether youth exposed to a given ACE have higher adjusted cumulative ACE scores (here the adjusted ACE score is the sum of the 188 Youth Violence and Juvenile Justice 14(3) Table 2. Prevalence (%) and Adjusted Odds of Abuse and Neglect by Presence or Absence of Each ACE. Abuse Emotional Outcome (ACE Category) Abuse Emotional Physical Sexual Neglect Emotional Physical Household dysfunction Family violence Physical a % N % 43,401 20,928 45,360 18,969 56,664 7,665 — — 29.5 39.7 32.1 35.9 — — 1.0 1.5** 1.0 1.0 26.4 36.0 — — 22.7 79.9 No 52,719 28.5 Yes 11,610 50.7 No 55,787 30.6 Yes 8,542 45.1 1.0 2.5** 1.0 1.8** 26.7 42.1 24.3 63.4 No Yes No Yes No Yes No Yes Family substance abuse No Yes Family mental illness No Yes Separation/divorce No Yes Family incarceration No Yes 11,614 52,715 48,227 16,102 58,651 5,678 13,501 50,828 21,959 42,370 OR Neglect OR Sexual a % OR Emotional a % a OR 1.0b 11.3 1.0 13.2 1.0 1.5** 13.2 1.0 28.1 2.5** — 3.4 1.0 14.8 1.0 — 32.3 12.8** 25.8 1.9** 1.0 — — 16.6 1.0 12.8** — — 28.5 1.8** 1.0 10.4 1.9** 18.8 1.0 9.5 5.3** 28.0 0.1 1.0 0.2 1.0 2.3 39.7 1286.2** 35.9 237.3** 14.0 28.1 1.0 24.4 1.0 9.7 45.8 2.1** 44.8 2.4** 18.5 30.9 1.0 27.1 1.0 10.6 49.1 2.1** 54.5 3.0** 25.1 31.1 1.0 24.8 1.0 9.1 32.9 1.1** 30.7 1.4** 12.7 24.6 1.0 18.5 1.0 8.3 36.7 1.8** 35.2 2.5** 13.8 Physical % ORa 10.8 18.4 6.9 28.6 10.9 31.2 1.0 1.8** 1.0 5.3** 1.0 3.5** 1.0 — — 11.1 1.0 1.8** — — 23.2 2.3** 1.0 16.0 1.0 — — 3.5** 31.5 2.3** — — 1.0 7.5** 1.0 1.9** 1.0 2.5** 1.0 1.4** 1.0 1.9** 10.0 19.8 16.3 23.1 17.3 25.7 13.4 19.3 15.3 19.5 1.0 2.2** 1.0 1.5** 1.0 1.6** 1.0 1.5** 1.0 1.3** 3.2 15.5 9.0 26.1 11.7 29.5 6.5 15.1 5.3 17.4 1.0 5.5** 1.0 3.5** 1.0 3.0** 1.0 2.6** 1.0 3.8** Note. ACE ¼ adverse childhood experience; OR ¼ odds ratio. a Odds Ratio from a logistic model adjusting for gender and race. bThe referent group for all results are persons without the ACE. *p < .05, **p < .001. remaining nine ACEs). Each binary ACE exposure was entered as an independent variable, along with gender and race/ethnicity, in a model predicting the adjusted ACE score of the other nine ACE exposures. For example, an indicator of physical abuse exposure (no or yes) was entered with gender and race/ethnicity to predict the adjusted ACE score of the other nine exposures. This was repeated for all 10 ACEs, resulting in 10 models. The unstandardized coefficients (B) from the 10 models ranged from a low of 2.961 for Separation or Divorce to a high of 5.073 for Physical Neglect (results not shown for brevity). Each ACE was a significant predictor of the adjusted ACE scores (p < .001 in all instances). Essentially, youth exposed to a given ACE have between three and five more exposures than youth not exposed to that given ACE. ACE Prevalence Rates by Gender and Race/Ethnicity Tables 4 and 5 provide the ACE exposure prevalence rates across gender and race/ethnicity (respectively). Table 4 indicates only 3% of the male youth, and only 2% of the female youth had no ACE exposures. At the opposite end of the ACE scale, 29% of female youth have six or more ACE exposures, more than twice the proportion of male youth (14%). The relationship between ACE and gender is significant (w2¼ 2504.96; p < .001) indicating females are more likely to have more ACE Baglivio and Epps 189 Table 3. Prevalence (%) and Adjusted Odds of Growing Up in a Dysfunctional Household by Presence or Absence of Each ACE. Household Dysfunction Violence Outcome (ACE Category) N Abuse Emotional No Yes No Yes No Yes Physical Sexual Neglect Emotional Household dysfunction Family violence No Yes Family substance abuse No Yes Family mental illness No Yes Separation/divorce No Yes Family incarceration No Yes ORa ORa % Mental Illness % 43,401 73.3 1.0 20.1 1.0 6.7 20,928 100 1284.5** 35.3 2.1** 13.3 45,360 74.5 1.0 19.6 1.0 5.7 18,969 99.8 236.1** 38.0 2.4** 16.3 56,664 80.0 1.0 23.2 1.0 7.5 7,665 96.5 7.4** 38.8 1.9** 18.6 No 52,719 Yes 11,610 No 55,787 Yes 8,542 Physical % Substance Abuse 11,614 52,715 48,227 16,102 58,651 5,678 13,501 50,828 21,959 42,370 Separation/ Divorce Incarceration ORa % ORa % ORa 1.0 2.1** 1.0 3.0** 1.0 2.5** 78.6 79.9 77.6 82.3 78.3 84.0 1.0 1.1** 1.0 1.4** 1.0 1.4** 61.8 74.2 60.6 78.5 64.5 76.1 1.0 1.8** 1.0 2.5** 1.0 1.9** 80.2 89.9 79.9 95.6 1.0 2.2** 1.0 5.5** 23.5 32.1 21.3 49.2 1.0 8.0 1.0 1.5** 12.5 1.6** 1.0 7.2 1.0 3.5** 19.6 3.0** 77.8 84.4 77.4 89.8 1.0 1.5** 1.0 2.6** 64.7 71.1 62.7 86.4 1.0 1.3** 1.0 3.8** — — 78.2 93.2 80.7 94.6 77.6 83.1 69.4 88.4 — — 1.0 4.0** 1.0 4.3** 1.0 1.4** 1.0 3.3** 9.4 28.5 — — 22.0 56.4 24.8 25.1 7.7 34.0 1.0 2.6 1.0 4.0** 10.2 4.3** — 5.1 1.0 — 19.9 4.4** 1.0 — — 4.4** — — 1.0 8.4 1.0 1.1** 8.9 1.1** 1.0 3.7 1.0 6.8** 11.5 3.6** 73.9 80.1 79.0 79.2 78.9 80.0 — — 73.4 81.9 1.0 1.4** 1.0 1.1** 1.0 1.1** — — 1.0 1.5** 42.2 71.1 58.0 89.5 63.9 85.8 56.8 68.3 — — 1.0 3.3** 1.0 6.8** 1.0 3.6** 1.0 1.5** — — Note. ACE ¼ adverse childhood experience; OR ¼ odds ratio. a Odds ratio from a logistic model adjusting for gender and race. bThe referent group for all results are persons without the ACE. *p < .05, **p < .001. Table 4. ACE Score by Gender. ACE Score (%) Gender N 0 1 2 3 4 5 6 Males Females w2 50,391 13,938 2504.96 3 2 p < .001 97 98 87 91 69 79 47 62 27 45 14 29 Note. ACE ¼ adverse childhood experience. Percentages in ACE score columns rounded to the nearest whole percentage. exposures. ACE exposure by race/ethnicity is displayed in Table 5. As shown, only 1% of Black youth report no ACE exposure, compared to 4% of White youth, 5% of Hispanic youth, and 3% of youth classified as ‘‘Other.’’ Over 50% of White youth and 50% of Black youth report exposure to 4 or more ACEs, compared to 40% of Hispanic youth and 39% of youth classified as ‘‘Other.’’ At the extreme upper end, a higher proportion of White youth report 6 or more ACEs (22%) than any 190 Youth Violence and Juvenile Justice 14(3) Table 5. ACE Score by Race/Ethnicity. ACE Score (%) Race White Black Hispanic Other w2 N 0 1 2 3 4 5 6 24,595 27,583 9,887 2,264 2424.19 4 1 5 3 p < .001 96 99 95 97 86 91 81 83 72 75 61 64 55 50 40 39 38 29 22 21 22 15 11 9 Note. ACE ¼ adverse childhood experience Percentages in ACE score columns rounded to the nearest whole percentage. Table 6. Gender and Race/Ethnicity Predicting Cumulative ACE Score. Measure Unstandardized B Standard Error b t Female White Hispanic ‘‘Other’’ Constant .774 .159 .491 .500 3.524 .018 .016 .022 .040 .012 .168 .041 .094 .049 43.7** 9.8** 22.6** 12.4** 299.5** Note. ACE ¼ adverse childhood experience. *p < .05, **p < .001. other race/ethnicity classification. The differences in ACE exposure between race/ethnicity classifications are significant (w2 ¼ 2424.19; p < .001). The final step in the gender and race/ethnicity analysis was to use linear regression to examine whether gender and race/ethnicity predict the ACE composite score (0–10). Being a female (female ¼ 1), White (White youth ¼ 1), Hispanic (Hispanic youth ¼ 1), and ‘‘Other’’ (youth classified as ‘‘Other’’ ¼ 1) were entered into a regression model with the ACE score as the dependent measure. Table 6 provides the results. As shown, being female increases the ACE composite score by .774 (substantively 1 ACE exposure). Being White increases the ACE score by .159, being Hispanic decreases the ACE score by .49, and being classified as ‘‘Other’’ is equated to a .500 decrease in the ACE score. All of the independent measures are significant predictors (p < .001). Discussion The primary purpose of this study was to examine the interrelatedness of ACE exposures in a high-risk population of juveniles with a history of arrest. This study attempted to replicate earlier work conducted using the original ACE study sample (Dong et al., 2004) on a high-risk sample. The prevalence rates of exposure ranged from a low of 9% for family mental illness to a high of 82% for family violence. If a youth had an exposure to one ACE, the likelihood of having another was as large as 1286 times higher than youth reporting no ACEs. Of those youth experiencing at least one ACE, 67.5% reported four or more additional exposures, and 24.5% exposure to six or more additional ACEs. These interrelatedness results echo those examining the original ACE study sample (Dong et al., 2004) in that exposure to multiple ACEs was extremely common. With regard to prevalence, only 3% of males and only 2% of females lacked exposure to any abuse/neglect type. Forty-seven percent of males, and 62% of females had ACE scores of 4 or more. These prevalence rates stand in stark contrast to the ACE score rates of the original ACE study, where 36% of the sample had ACE scores of 0 and only 13% had ACE scores of 4 or higher (Felitti et al., 1998). The higher risk juvenile offenders examined Baglivio and Epps 191 in this study are 13 times less likely to have exposure to zero ACEs and 4 times more likely to have ACE scores of 4 or above. Again, this highlights the importance of examining abuse/neglect through a ‘‘cumulative stressor’’ approach, such as the ACE composite score, rather than the customary practice of examining them singularly, or only a few categories of abuse in a given population. In regard to gender and race/ethnicity, female youth have higher prevalence rates of exposure as well as multiple exposures. Black youth are the least likely to report no exposure to any ACE category, although White youth are the most likely to report exposure to four or more ACE categories. These results mirror those of prior studies suggesting a ‘‘gender paradox’’ where certain risks for deviant outcomes, especially mental health-related risks/diagnoses, are found to be higher in girls than boys although male delinquency is more prevalent (Loeber & Keenan, 1994; Tiet, Wasserman, Loeber, McReynolds, & Miller, 2001). Similar circumstances have been suggested in multi-level models examining immigration and crime where immigrants, although possessing more risk factors and residing in areas of higher prevalence of economic disadvantage, are less likely to offend (referred to as the ‘‘immigrant paradox’’) (Martinez, Stowell, & Lee, 2010; Ousey & Kubrin, 2009; Wright & Rodriguez, 2012). The results of ACE prevalence found in this study are, however, in contrast to a plethora of research showing males and minorities more likely to be serious, violent, and chronic (SVC) offenders (Baglivio, Jackowski, Greenwald, and Howell, 2014; Huizinga, Loeber, Thornberry, & Cothern, 2000; Kempf-Leonard, Tracy, & Howell, 2001; Loeber & Farrington, 1998a, 1998b; Vaughn, Salas-Wright, DeLisis, & Maynard, 2014). Future research should attempt to replicate our findings by examining prevalence of ACE scores by gender and race/ethnicity. It must be reiterated that we are examining a juvenile offending population and that perhaps these results highlight issues surrounding disproportionate minority contact (DMC) and reducing ethnic disparities (RED)—the system is less sensitive to white youths’ misbehavior, meaning that, on average, White youth are more likely to have higher ACE scores before they are noticed by the JJ system. Hispanic youth and youth classified as ‘‘Other’’ were less likely to experience higher ACE exposures. Over 50% of White and Black youth have ACE scores of 4 or more. We hypothesize the same logic may apply to the gender differences found in that females are more likely to have higher ACE scores before they are ‘‘noticed’’ by the juvenile justice system. Cycle of Violence research has indicated abuse and neglect are both related to increased risk of arrest for violent crime for males (in terms of frequency) and females (in terms of participation in violent crime), with more deleterious repercussions for Black youth (Widom & Maxfield, 2001). Many prior ACE studies either mention controlling for gender and race or just mention demographics of the sample. We chose to explicitly present prevalence rates separately for males and females and across race/ethnicity. Family mental illness had the lowest reported prevalence (9%) of any ACE examined. However, youth with this ACE exposure evidenced 3.6–4.4 times the odds of exposure to household member incarceration, household substance abuse, and household violence than youth not presenting with family mental illness. The household dysfunction ACE indicators, with the exception of parental separation/divorce, were highly interrelated with one another such that exposure to one increased the odds of exposure to another 3.3–6.8 times. Being that ACEs are highly interrelated, as we have attempted to show in the current study, which mimics prior findings (Dong et al., 2004), having one exposure greatly increases the odds of having additional exposures. This is troubling from a public policy standpoint in that the number of different exposures (the ACE composite score) has been found predictive of a host of mental health and psychosocial behaviors and outcomes. As discussed earlier, higher ACE scores have been shown to increase the odds of smoking, drug use, incarceration, obesity, poor education and employment, risky sexual behavior, teenage pregnancy, and suicide attempts (Bellis et al., 2014; Dube, Anda, Felitti, Chapman, et al., 2001; Dube et al., 2006; Hillis et al., 2001, 2004). Service providers, practitioners, and policy makers would be remiss to ignore the interrelatedness of childhood maltreatment types. These findings are particularly salient in light of the ‘‘Cycle of 192 Youth Violence and Juvenile Justice 14(3) Violence’’ research showing ‘‘If violence is begotten by not only violence, but also by neglect, far more attention needs to be devoted to families of children who are abandoned and severely malnourished’’ (Widom & Maxfield, 2001, p. 1). The results of this study would argue for more attention to include children who have any indication of household dysfunction. ACE studies using retrospective recall of adults to report abuse/neglect during childhood may have questionable reliability. Dube and colleagues have discussed three reliability and validity concerns of such studies (Dube, Williamson, Thompson, Felitti, & Anda, 2004). Potential concerns include the time lapse between the events in question and the survey assessing those events and the difficulty recalling such experiences (Della Femina, Yeager, & Lewis, 1990). Additionally, the sensitive nature of reporting abuse/neglect could lead to variability in responding (Dube et al., 2004). Finally, recall impairments due to the stressful nature of the exposures may occur (Bremner, 1999; Williams, 1995). Examining test–retest reliability of the ACE sample specifically has found good/ moderate to substantial agreement (Dube et al., 2004). However, they note that analysis compared agreement between responses at two different times during adulthood. They could not examine whether reported exposures changed over the decades between the abuse during childhood and adulthood. In contrast, examining ACE exposure more proximal to the event, such as the approach used in the current study, has less difficulty with such recall issues. Limitations, Implications, and Future Directions Perhaps the most constraining limitation of this study lies in generalizability and sampling bias. Only the C-PACT full assessment contained appropriate items to capture all 10 ACEs. Therefore, only youth who were administered the full assessment were included in this study. Youth whose scores indicate they are at low or moderate risk to reoffend may not receive the Full Assessment. Most youth who score at low or moderate risk to reoffend and who receive the Full Assessment are those whose treatment plan includes placement in resource-intensive services such as day treatment or residential commitment programs. While 64,329 youth who turned 18 during the study period were assessed with the PACT Full Assessment, an additional 136,691 youth who turned 18 during that time were only assessed with the PACT Pre-Screen, prohibiting the creation of ACE scores for those youth. While we captured ACE scores for all youth receiving a Full Assessment (approximately 32% of all juvenile offenders), caution should be used in generalizing the results to all juvenile offenders in Florida. This sample bias toward higher risk youth, limits generalizability to all juvenile offenders in Florida. However, it should be noted that 45% of the sample used were classified as low or moderate risk to reoffend. Nonetheless, we argue a useful model to couch ACE work may be the Risk-Need-Responsivity (RNR) framework espoused by Andrews and Bonta (2003). Through the RNR paradigm, it is precisely the group of high risk youth (the risk principle) that have the most to gain from policies and procedures that address trauma exposure as a responsivity factor serving as a roadblock (responsivity principle) to providing effective intervention (needs principle). Along those lines, future endeavors should include examining whether youth with higher ACE scores evidence worse recidivism outcomes controlling for services provided and individual risk factors, and whether providing services to address traumatic exposure (addressing the responsivity factor) can mitigate any recidivism differences found between higher and lower ACE score youth. The question becomes whether trauma serves as a responsivity factor in being a roadblock to providing services targeted at more criminogenic needs, and whether trauma-informed services reduces that barrier allowing for effective interventions to be delivered to reduce future delinquency. In essence, can programming and intervention to reduce delinquency be delivered effectively without first addressing more immediate concerns such as safety, security, and stability and working with the youth to address the traumatic exposure? Such research would empirically validate childhood abuse/neglect as a responsivity Baglivio and Epps 193 factor, and lead to additional policy implications in terms of providing trauma-informed care and treatment to optimize the effectiveness of other delinquency prevention/intervention services. We would be remiss to not express that this matching of youth to appropriate services (the ‘‘Need Principle’’ in RNR) is precisely where the ‘‘evidence-based practices’’ movement has seemed to stall. We have seen the proliferation in research on risk factors of offending, and in the use of risk assessments over the last decade. Where we as a field have fell short is in empirical research showing the benefits of matching youth to services (for a notable exception see Luong & Wormith, 2011). Fortunately, FDJJ has begun to collect ‘‘dosage’’ data showing which interventions each youth receives, which can be matched to assessment data. This will permit future efforts to examine our proposition to investigate whether services aimed at addressing traumatic exposure mitigate recidivism differences across ACE scores. Additional policy implications include the need for universal screening. The population of high-risk juvenile offenders clearly experiences ACEs at heightened rates from previously examined nonoffending populations. The types of exposure are clearly interrelated. Practitioners are doing a disservice to these youth if neglecting to screen for, and provide or refer for services to address, the multiple exposures experienced. With respect to prevention, future research should examine whether risk or protective factors of ACE exposure differ by gender or race/ethnicity. Although this study adds to the literature on prevalence of ACE exposure, and examines a previously neglected high-risk population, few ACE studies have done much in the realm of examining what can be done to (a) mitigate the effects of ACE exposure once they have occurred, or (b) whether ACE exposures differ based on sociological contexts (such as living in a disadvantaged neighborhood or areas with heightened crime rates). More thorough examination of the risk factors for higher ACE exposure and the resiliency factors that can mitigate, or can be leveraged to mitigate those effects through intervention programs is clearly warranted. Although the ACE concept argues for binary summation of exposure types, regardless of frequency or severity of exposure, additional child abuse/neglect research (such as Smith & Thornberry, 1995) argues those concepts essential. Future research should examine how timing, frequency, duration, and severity of exposure to all 10 ACEs replicate or add to study findings. Additionally, future work should examine the processes by which gender and race/ethnicity may mediate the relationship between maltreatment and offending. To the extent that ACE are risk factors for future delinquency, and Black youth have elevated exposure to specific ACEs, policies and prevention efforts surrounding RED and DMC are warranted. If childhood maltreatment is a risk factor of delinquency as found in prior research (such as Smith & Thornberry, 1995; Teague et al., 2008), if abuse/neglect types co-occur and are interrelated, and if juvenile offenders have a high prevalence of maltreatment exposure, as has been shown in the current study, then ACE composite scores can make a significant contribution to life course perspectives. We argue that exposure to ACEs represents a significant transition in the developmental pathway. We argue that the higher the ACE composite score, the more substantial the transition and the increased likelihood of that exposure being a turning point which may negatively alter a youth’s current criminal trajectory. Future research should strive to test this theoretical framework examining the impact of ACE scores on trajectories. Perhaps more important, from a juvenile justice system policy perspective, is the provision of trauma-informed services to ACE-exposed juvenile offenders a meaningful life event/transition that can ‘‘redirect paths’’ on a more positive trajectory? Additional work could examine the implications of whether trauma-informed intervention can alter the course of trajectories for higher ACE scoring youth. Implications for developmental/life course criminology will surely avail themselves, as such frameworks are important for understanding and/or possibly preventing the onset and escalation of offending, and negative life outcomes associated with childhood maltreatment. 194 Youth Violence and Juvenile Justice 14(3) This study shows juveniles with histories of criminal offending are indeed an extremely high-risk population with respect to exposure to ACEs. The prevalence rates of ACE exposure surpass those reported for the original ACE study population. Given that the negative life and health outcomes found related to ACE exposure is now a massive body of research, the expected outcomes for these youth is an obvious concern. Considering there were over 1,642,600 arrests of juveniles during 2010 alone (Puzzanchera, 2013), this study serves as a warning sign about the high level of ACEs likely to exist in that arrested population. If this population carries into the future the same devastating health risks found in the original ACE study, the potential health crisis and financial strain in the decades to come is staggering to comprehend. Appendix A. Correlations of ACEs to One Another and the ACE Composite Score 1 Emotional abuse 2 Physical abuse 3 Sexual abuse 4 Emotional neglect 5 Physical neglect 6 Family violence 7 Family substance abuse 8 Family mental illness 9 Separation/divorce 10 Family incarceration 11 ACE composite score 1 2 3 4 5 6 .099** .027** .182** .105** .325** .164** .110** .015** .122** .486** .406** .130** .291** .301** .194** .171** .053** .173** .599** .100** .194** .139** .117** .127** .045** .080** .435** .137** .098** .076** .062** .062** .052** .394** .139** .218** .149** .103** .170** .502** .170** .103** .058** .234** .550** 7 8 9 10 .226** .002 .008* .288** .131** .098** .532** .386** .309** .531* Note. ACE ¼ adverse childhood experience. Pearson correlations reported. 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An examination of the relationships between violence exposure, posttraumatic stress symptomatology, and delinquent activity: An ‘‘ecopathological’’ model of delinquent behavior among incarcerated adolescents. Journal of Aggression, Maltreatment, Trauma, 6, 127–147. Wright, K. A., & Rodriguez, N. (2012). A closer look at the paradox: Examining immigration and youth reoffending in Arizona. Justice Quarterly, 31, 882–904. doi:10.1080/07418825.2012.700057 Yu-Ling Chiu, Y. L., Ryan, J. P., & Herz, D. C. (2011). Allegations of maltreatment and delinquency: Does risk of juvenile arrest vary substantiation status? Children and Youth Services Review, 33, 855–860. Author Biographies Michael T. Baglivio, PhD, currently works for the Bureau of Research and Planning at the Florida Department of Juvenile Justice (FDJJ). Michael serves as a member of FDJJ’s Juvenile Justice System Improvement Project team, a grant initiative administered by Georgetown University’s Center for Juvenile Justice Reform. 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Child Abuse & Neglect 44 (2015) 184–193 Contents lists available at ScienceDirect Child Abuse & Neglect Child maltreatment and risk patterns among participants in a child abuse prevention program夽 Jennifer Y. Duffy a , Marcia Hughes b , Andrea G. Asnes a , John M. Leventhal a,∗ a b Yale School of Medicine, 333 Cedar Street, New Haven, CT 06520, USA Center for Social Research, University of Hartford, 260 Girard Ave, Hartford, CT 06105, USA a r t i c l e i n f o Article history: Received 12 February 2014 Received in revised form 29 October 2014 Accepted 5 November 2014 Available online 4 December 2014 Keywords: Child abuse and neglect Prevention Substantiation CPS report a b s t r a c t The relationship between risk factors and Child Protective Services (CPS) outcomes in families who participate in home visiting programs to prevent abuse and neglect and who are reported to CPS is largely unknown. We examined the relationship between parental risk factors and the substantiation status and number of CPS reports in families in a statewide prevention program. We reviewed CPS reports from 2006 to 2008 for families in Connecticut’s child abuse prevention program. Six risk factors (histories of CPS, domestic violence [DV], mental health, sexual abuse, substance abuse, and criminal involvement) and the number of caregivers were abstracted to create risk scores for each family member. Maltreatment type, substantiation, and number of reports were recorded. Odds ratios were calculated. Of 1,125 families, 171 (15.6%) had at least one CPS report, and reports of 131 families were available for review. Families with a substantiated (25.2%) versus unsubstantiated (74.8%) first report had a high number of paternal risk factors (OR = 6.13, 95% CI [1.89, 20.00]) and were more likely to have a history of maternal DV (OR = 8.47, 95% CI [2.96, 24.39]), paternal DV (OR = 11.23, 95% CI [3.33, 38.46]), and maternal criminal history (OR = 4.55; 95% CI [1.32, 15.60]). Families with >1 report (34.4%) versus 1 report (65.6%) were more likely to have >3 caregivers, but this was not statistically significant (OR = 2.53, 95% CI [0.98, 6.54]). In a prevention program for first-time families, DV, paternal risk, maternal criminal history, and an increased number of caregivers were associated with maltreatment outcomes. Targeting parental violence may impact child abuse prevention. © 2014 Elsevier Ltd. All rights reserved. Introduction Home visiting programs have been developed in an attempt to prevent child abuse and neglect through home-based parenting programs, parenting curricula, emotional support, and linking families to community services (Avellar & Supplee, 2013; Donelan-McCall, Eckenrode, & Olds, 2009; MacMillan et al., 2005). These programs have traditionally targeted families that are at high-risk of perpetrating child maltreatment. These high-risk families are generally identified by the presence of certain sociodemographic characteristics, such as poverty or young maternal age, which have been shown to be associated with an increased risk of child maltreatment (Brown, Cohen, Johnson, & Salzinger, 1998). Several outcome measures have been used to assess program efficacy including parental reports of behaviors toward the child (DuMont et al., 2008), the 夽 This study was supported by the Child Abuse funds of the Department of Pediatrics and by a grant from the Doris Duke Foundation to Yale Medical School. ∗ Corresponding author. http://dx.doi.org/10.1016/j.chiabu.2014.11.005 0145-2134/© 2014 Elsevier Ltd. All rights reserved. J.Y. Duffy et al. / Child Abuse & Neglect 44 (2015) 184–193 185 occurrence of injuries resulting in visits to the emergency department or hospitalizations (Kitzman et al., 1997; Matone, O’Reilly, Luan, Localio, & Rubin, 2012), and reports to Child Protective Services (CPS) (Duggan et al., 2004, 2007; Gonzalez & MacMillan, 2008; Olds, Henderson, Kitzman, & Cole, 1995). In this article, we focus on a different aspect of home visiting programs by examining families that were enrolled in a state-wide home visiting program to prevent abuse and neglect and that were reported to CPS. Specifically, we examined the risk factors in those reported to CPS and the associations between risk factors and substantiation and between risk factors and more than one report to CPS. Risk Factors for Abuse and Neglect There is a comprehensive literature on the risk factors for abuse and neglect, and four major domains of risk have been identified: (a) child characteristics (e.g., prematurity, disability), (b) parental characteristics (e.g., mental health problems, substance use, maltreatment during childhood), (c) family characteristics (e.g., DV, absent father), and (d) social characteristics (e.g., poverty, violent neighborhoods; Belsky, 1980). Studies in the United States and the United Kingdom have examined these risk factors by following families longitudinally. For example, in the United States, Brown et al. (1998) examined the outcomes of abuse and neglect of children who were enrolled in a longitudinal study at ages 1–10 years in 1975 and were followed with periodic interviews. In 1991–1993, data about child maltreatment were obtained from interviews of those over 18 years of age and records from the state’s CPS agency. Many risk factors were associated with the occurrence of abuse or neglect, and children with at least four risk factors were 8 times more likely to have experienced maltreatment compared to those with no risk factors. Risk factors with an odds ratio of greater than 2.5 included parental characteristics: maternal sociopathy, which included drug, alcohol, or police involvement (OR = 4.91), maternal dissatisfaction (3.15), and low maternal education (3.09); family characteristics: early separation from mother (2.80), low father involvement (3.14), and low father warmth (2.57); and social characteristics: being on welfare (5.14) and low income (3.02) (Brown et al., 1998). In a more recent study from the United Kingdom, Sidebotham, Heron, and ALSPAC Study Team (2006) used data from the Avon Longitudinal Study of Parents and Children (ALSPAC) to examine risk factors for children who were reported for suspected maltreatment or placed on the local CPS registry by 6 years of age. Data were obtained on 14,256 children whose mothers were enrolled prenatally; 259 children were reported for suspected maltreatment, and 115 of these were placed on the local registry. Risk factors for being placed on the registry included child characteristics: low birth weight (OR = 2.23) and few positive attributes reported (1.97); parental characteristics: low educational achievement (4.96), young age (3.41), history of psychiatric illness (2.82), and history of childhood abuse (1.86); family characteristics: single mother (2.64) and reordered family (2.58); and social characteristics: social deprivation (11.02) and poor social network (1.93). The odds ratio for the risk factor of DV was elevated at 1.60, but this result was not statistically significant. The risk factors for reporting suspected maltreatment were similar to those for being listed on the child abuse registry (Sidebotham et al., 2006). Based on these studies of risk factors, concerns about the links among family violence, substance abuse, and maltreatment (Connelly et al., 2006; Dubowitz et al., 2011; Laslett, Room, Dietze, & Ferris, 2012; U.S. Department of Health and Human Services, 2013), and data that were available in our study about parental characteristics, we focused on six risk factors: (a) parent’s CPS history as a child, (b) DV history, (c) mental health history, (d) sexual abuse history, (e) substance abuse history, and (f) criminal history. Reports to CPS as the Outcome of Child Maltreatment The accuracy of using CPS reports to approximate abuse and neglect has been debated for several reasons. First, CPS reports may underestimate and overestimate the actual rates of abuse and neglect. Underestimation may occur if levels of maltreatment exist that do not lead to a CPS report, but, in fact, may cause harm to the child. Overestimation can result from the fact that the families in prevention programs may have greater contact with mandated reporters than non-participating families, thus leading to surveillance bias in the reporting of maltreatment in the families with home visitors (Chaffin & Bard, 2006). Second, it is unknown whether the severity of child maltreatment is more accurately measured by the frequency with which maltreatment occurs (estimated by the number of maltreatment reports) or by whether a report is substantiated (meaning that there is sufficient evidence to indicate that abuse has occurred; Drake, Jonson-Reid, Way, & Chung, 2013; Hussey et al., 2005). This debate arises, in part, from the fact that both substantiated and unsubstantiated maltreatment can result in negative consequences for the child (Jonson-Reid, Kohl, & Drake, 2012; Smith, Ireland, & Thornberry, 2005). Although substantiation is meant to reflect that maltreatment has occurred, substantiation of a CPS report may be more reflective of the process and biases inherent in the CPS investigation rather than the severity of the maltreatment (Drake, 1996). Therefore, it has been suggested that the recidivism of maltreatment (represented by the presence of multiple CPS reports) may be a more appropriate way to risk stratify families involved with CPS (Hussey et al., 2005; Jonson-Reid et al., 2012). 186 J.Y. Duffy et al. / Child Abuse & Neglect 44 (2015) 184–193 Limitations of Previous Studies and Purpose of Current Study Studies of home visiting programs have usually focused on the frequency of the outcomes in the families in the prevention program compared to the same outcomes in a comparison group who have not received the intervention. For example, several studies have examined the outcome of reports to CPS, and some of these studies have used a randomized clinical trial to examine this outcome. Duggan et al. have conducted two randomized trials – in Hawaii and Alaska – of home visiting programs and have found no statistically significant differences in the percentages of children reported to CPS in the treatment versus the control groups (Duggan et al., 2004, 2007). Such studies, however, have not gone further to examine two important aspects of the children and families who have received services in the prevention program and who have experienced the outcome. First, what is the risk profile of these children and families, and how does this profile differ from the children and families who have not experienced the outcome? Second, of the children reported to CPS, are there differences in the risk profile for those cases that are substantiated versus those that are not, and are there differences in those children who experienced a single report versus those who went on to experience subsequent CPS reports? In the current study, we propose to close this gap by examining the detailed risk-profiles of first-time families who participated in a state-wide home visiting program aimed at preventing child abuse and neglect and who were reported to CPS for suspected maltreatment. We used the data collected from the structured CPS investigation to examine three specific questions: (a) What are the risk profiles of the reported cases? (b) Are the risk profiles different for those cases that were substantiated versus those that were not? (c) Are the risk profiles different for those with a single report to CPS versus those with at least two reports? To answer questions 2 and 3, we conducted two case control studies where cases were families with the outcome of interest and controls were families without that outcome. Methods Study Sample The sample was drawn from a group of families who participated in Connecticut’s child abuse and neglect prevention program, the Nurturing Families Network (NFN), and who were reported to Connecticut’s CPS agency. NFN is a program that screens all first-time births in Connecticut using a 17-item questionnaire (Revised Early Identification – REID screen) that solicits information about a mother’s level of risk based on the presence or absence of 17 factors (e.g., young age, single, and history of psychiatric care). The identified high-risk group is predominately single mothers living in poverty. Eligible families are offered weekly home visits by a paraprofessional visitor until the child’s fifth birthday. Services offered through the home visiting program include an evidence-based parenting curriculum (Zigler, Pfannenstiel, & Seitz, 2008), advice about common parenting problems, modeling effective parenting, screening for developmental problems, and linkage to community resources. Since NFN’s inception in 1997, over 50,000 first-time mothers have been screened. Of these, 15,300 families have been identified as high-risk, and approximately 5,300 individuals ultimately accepted home visiting services. The average length of program participation is 22 months. To determine whether a child has been reported to the state’s Child Protective Service agency for suspected abuse or neglect, NFN obtains annual written consent from participating families to access their records from Child Protective Services. Roughly 50–70% of these families grant this permission each year. This study, therefore, focused on a group of 1,125 families who were involved in NFN between 2006 and 2008 and gave NFN consent to access their CPS reports. This study was granted exemption status by Yale Medical School’s IRB because all data were de-identified prior to review by the investigators. Demographic Questionnaires We collected basic demographic information, including date of birth, race, and gender of the first-born child and date of birth and race for each parent. We also recorded each family’s primary language and town or region of the state. This information was obtained from the CPS reports or from the intake questionnaire that each family completed upon enrolling in NFN’s home visiting program. CPS Reports Full-length narrative CPS reports were reviewed. Each report was written by the CPS investigative social worker and was based on interviews of family members, collateral reports, criminal background checks, medical checks, and a search of CPS’s electronic records. The full-length report consisted of a multi-page narrative that chronicled the circumstances surrounding a report to CPS of suspected maltreatment, the subsequent CPS investigation, and the final result of the investigation J.Y. Duffy et al. / Child Abuse & Neglect 44 (2015) 184–193 187 (whether the case was substantiated). The report also contained information about family dynamics, risk factors present in all individuals involved in the child’s life, and the number of caregivers (full-time and partial caregivers). For each family, we recorded the number of reports made to CPS for suspected maltreatment over a two-year period. Although each report pertained to a single alleged maltreatment event, a report may have contained multiple allegations, either because there were multiple alleged perpetrators or a single alleged perpetrator was accused of committing multiple types of maltreatment. Therefore, we recorded information on each allegation contained within each report, including: the alleged perpetrator, the alleged victim, the final CPS classification of maltreatment (physical abuse, sexual abuse, emotional maltreatment, physical neglect, medical neglect, educational neglect), and whether the allegation was substantiated. Risk Factor Assessment We abstracted risk information from the full-length CPS reports using a coding scheme to classify the types and extent of risk factors in household members. The abstraction of all data was done by the first author (JYD); a random selection of 15 cases was also abstracted by a second reviewer to ensure that there was agreement between the abstractors. The full-length reports provided systematic information on six risk factors that we used as our variables of interest based on a literature review of factors known to increase the risk of child maltreatment: (a) parent’s CPS history as a child, (b) DV history, (c) mental health history, (d) sexual abuse history, (e) substance abuse history, and (f) criminal history. This information was recorded on each individual listed in the CPS report, including immediate family members, non-immediate family members, and non-related individuals living in the child’s home. In addition, for each individual mentioned in each CPS report, we recorded the extent of the person’s involvement as a caregiver to the index child (full-time caregiver, part-time caregiver, non-caregiver living in the home with child, or not involved). Development of Risk Scores Based on the six risk factors, each individual (e.g., mother, father, grandparent) was given a risk score of 0–6. Using these individual risk scores, an additional risk score was derived. The Environmental Risk Score was the sum of the risk scores of all individuals involved in the child’s life, excluding the mother and father. In the analyses, the risk scores were initially utilized as continuous variables. To conserve power and protect against the impact of outliers in the small sample, risk indices were then used to create risk groups based on a median split of the continuous variable. For example, the maternal risk score was used to create a high risk group (>2 risk factors) and low risk group (0–2 risk factors). These risk groups were created for maternal, paternal, and environmental risk scores and were separately considered in the analyses. Data about risk factors were based on information contained in the first CPS report, as we felt that this information was most relevant to the question of maltreatment prevention. Additionally, because families with multiple reports may have had a greater amount of available information, we did not want to bias the results by including risk information obtained from subsequent CPS reports. Definitions of Outcome Measures For the families with CPS reports, we used two outcomes: the result of the CPS investigation and the number of CPS reports. We defined the result of the CPS investigation according to the substantiation status, meaning that at the end of the CPS investigation there was sufficient evidence for CPS to conclude that maltreatment had occurred. Our second outcome was maltreatment recidivism, based on whether the family had a single or multiple CPS reports. Our study period reviewed all reports generated for a two-year period. Case–Control Analyses Using the risk factor and caregiver data, we conducted two case–control studies involving the families with CPS reports. In this standard epidemiological methodology, we divided the sample based on the occurrence of the outcome: cases were those with the outcome present and controls were those who did not have the outcome of interest (Schlesselman, 1982). The first case–control study compared families with substantiated versus not substantiated reports. The second compared families with multiple versus those with single reports. For each of these studies, we examined differences in risk factors and caregiver information between cases and controls. Univariate logistic regression and multivariate logistic regression analyses controlling for the mother’s age, race, and language and the child’s gender were used to calculate odds ratios. Two sub-analyses were also conducted. First, because of the heterogeneity of paternal involvement in the sample and because the level of paternal involvement may impact the level of information available to prevention programs and CPS investigators, we conducted a sub-analysis in which uninvolved fathers (e.g., non-caregivers) were removed from the sample. This sub-analysis allowed us to examine the relationship between caregiver risk and child maltreatment without consideration of the risk associated with non-caregivers. Second, the sample contained a subset of families who continued to participate in the prevention program beyond our study dates. For these families, we, therefore, could not ascertain whether they truly had a single report during their program 188 J.Y. Duffy et al. / Child Abuse & Neglect 44 (2015) 184–193 Table 1 Characteristics of 131 families reported to CPS while involved in NFN home visiting. Characteristic % Mother’s race (%) Caucasian African-American/Black Hispanic/Latino Biracial Other Mother’s age at child’s birth (years, median; range) Child’s age at 1st report (months, median; range) Child’s gender (%) Male Female Primary household language (%) English Spanish Bilingual Location (%) Urban Non-urban Number of caregivers 1 2 3 4+ 42.0% 14.5% 35.1% 3.1% 5.3% 20; 13–44 5; 0–42 51.2% 48.8% 84.7% 6.9% 8.4% 50.4% 49.6% 6.9% 41.2% 35.1% 16.8% Table 2 Maltreatment allegations in sample. Type of maltreatment Total (N = 333) Neglect Physical neglect Emotional neglect Medical neglect Educational neglect Total neglect 70.0% 15.0% 2.1% 0.6% 87.7% Abuse Physical abuse Emotional abuse Sexual abuse Total abuse 6.0% 3.9% 2.4% 12.3% participation or whether additional reports occurred during their time in the prevention program, but after our study period. Therefore, for the analyses in which we used the number of reports as a binary outcome, an additional sub-analysis only included families who had exited the prevention program before the end of the study period. This approach allowed us to confidently place each of these families in the correct group – single or multiple reports. Results Description of Families, Reports, and Risk Between 2006 and 2008, approximately 65% or 1,125 families who were receiving home visiting services gave written consent to have their CPS records reviewed. Of these families, 175 families (15.6%) had one or more CPS reports, and 950 (84.4%) did not have any CPS reports. When compared on the basis of their demographics, the group with CPS reports as compared to those without CPS reports had a larger percentage of Caucasians (39.6% versus 23.3%) and a smaller percentage of Hispanic families (34.0% versus 46.3%) (p < 0.05). Of the 175 families with CPS reports, 131 (76.2%) had a detailed CPS report that could be reviewed; 44 (23.8%) of the reports were not available because they had been misplaced prior to the initiation of our study. The demographic characteristics of these 131 families are shown in Table 1. Of the mothers, 58.0% were non-white, and there were equal numbers of families living in urban and non-urban settings. The median age of the mothers at the time of the child’s birth was 20 years. There were no demographic differences between these 131 families and the other 44 families for whom no CPS report was available for review. Of the 131 families, 45 (34.4%) had more than one CPS report, which yielded a total of 210 CPS reports. The child’s median age at the time of the first report was 5 months (range 0–42 months). Each report contained one or more allegations of J.Y. Duffy et al. / Child Abuse & Neglect 44 (2015) 184–193 189 Fig. 1. Risk factors in caregiving mothers and fathers (“father” includes all biological fathers in sample for whom information is available, including both fathers in a caretaking role and fathers not in a caretaking role) in 131 families with CPS reports. White bars represent n individuals with available data; dark bars represent n individuals with each risk factor. Paternal Sexual Abuse risk estimates include 5 fathers (8% of available sample) who were perpetrators of sexual abuse and 3 fathers (5% of available sample) who were victims of sexual abuse. maltreatment, resulting in 333 allegations of maltreatment. Of these 333 allegations, 291 (87.7%) were because of neglect (Table 2). As shown in Fig. 1, there were high levels of maternal and paternal risk in the 131 families: the highest levels of risk for mothers were prior CPS involvement (46.4%) and mental health problems (47.4%) and for fathers were DV (39.3%) and criminal history (58.9%). Both caregiving and non-caregiving parents are included in this figure, and the percentages are based on the number of individuals for whom data were available rather than the sample size of 131. For example, prior CPS history was known for 127/131 mothers in the sample (96.9%). Of these 127 mothers, 59 (46.4%) had been involved with CPS as a child. In contrast, prior CPS history was only known for 87/131 fathers (66.4%); of these 87 fathers, 17 (19.5%) had been involved with CPS as a child. Families with a “Substantiated Report” versus “Unsubstantiated Report” The first case–control analysis compared the families based on substantiation status of their first DCF report. Of the 131 families, 33 (25.2%) had their first report to CPS that was substantiated, and 98 (74.8%) had an unsubstantiated first report. Table 3 Unadjusted odds ratios for parental and environmental risk predictors of “substantiated” versus “not substantiated” CPS reports. Maternal risk Maternal risk groupa Maternal CPS history Maternal DV history Maternal MH history Maternal sexual abuse history Maternal criminal history Maternal substance abuse history Paternal risk Paternal risk groupb Paternal CPS history Paternal DV history Paternal MH history Paternal sexual abuse history Paternal criminal history Paternal substance abuse history Environmental risk Environmental risk groupb Number of caregivers Caregiver groupc a b c * ** *** OR CI 2.19 0.93 5.11*** 0.87 0.84 2.86* 0.71 0.97–5.00 0.41–2.10 2.12–12.32 0.37–2.03 0.30–2.38 1.10–7.42 0.27–1.82 4.16** 1.21 5.71*** 1.89 0.79 1.45 1.84 1.57–11.01 0.29–5.18 2.06–15.80 0.59–5.99 0.19–3.31 0.57–3.71 0.64–5.31 0.67 1.17 1.14 0.16–2.64 0.77–1.77 0.41–3.20 Defined as having 0–2 risk factors versus 3 or more risk factors. Defined as having 0–1 risk factors versus 2 or more risk factors. Defined as having 0–3 caregivers versus 4 or more caregivers. DV, DV; MH, mental health. p < 0.05. p < 0.01. p < 0.001. 190 J.Y. Duffy et al. / Child Abuse & Neglect 44 (2015) 184–193 Table 4 Frequencies of maternal risk factors and adjusted odds ratios for “substantiated” versus “not substantiated” CPS reports. Maternal risk Risk groupa CPS history DV history MH history Sexual abuse history Criminal history Substance abuse history Mean total risk score SUBS (% with risk) – 45.2 66.7 44.8 22.2 32.3 21.9 2.12 NOT SUBS (% with risk) – 46.9 28.1 48.3 25.3 14.3 28.4 1.78 OR 2.45 0.99 8.47*** 2.07 0.64 4.55* 0.64 – CI 0.90–6.67 0.92–1.06 2.96–24.39 0.68–2.05 0.19–2.17 1.32–15.60 0.20–2.03 – a Defined as having 0 to 2 risk factors versus 3 or more risk factors. MH, mental health. SUBS, at least one substantiated allegation (case); NOT SUBS, no substantiated allegations. * p < 0.05. *** p < 0.001. Table 5 Frequencies of paternal risk factors and adjusted odds ratios for “substantiated” versus “not substantiated” CPS reports. Paternal risk Risk groupa CPS history DV history MH history Sexual abuse history Criminal history Substance abuse history Mean total risk score SUBS (% with risk) – 13.0% 68.0% 35.0% 15.8% 65.4% 34.8% 2.10 NOT SUBS (% with risk) – 10.9% 27.1% 22.2% 19.1% 56.5% 22.4% 1.52 OR 6.13** 0.88 11.23*** 2.22 0.91 1.83 2.58 – CI 1.89–20.00 0.17–4.65 3.33–38.46 0.56–8.77 0.17–4.90 0.62–5.41 0.69–9.72 – a Defined as having 0–1 risk factors versus 2 or more risk factors. MH, mental health; SUBS, at least one substantiated allegation (case), NOT SUBS, no substantiated allegations. ** p < 0.01. *** p < 0.001. Table 3 shows the unadjusted odds ratios for the likelihood of substantiation based on maternal, paternal, and environmental risk factors. Tables 4 and 5 show the results for the maternal and paternal risk factors, respectively; each table shows the percentage of the risk factors and the risk scores in the substantiated versus not substantiated families and the adjusted ORs and 95% CI for the likelihood of substantiation. Maternal DV history (OR = 8.47) and criminal history (OR = 4.55) and paternal DV history (OR = 11.23) were each significantly associated with report substantiation. In addition, the paternal risk group score (the presence of at least two or more risk factors) was significantly associated with report substantiation (OR = 6.13). To determine if the occurrence of the risk factors for the mothers was affected by whether risk factor information was available for fathers, we stratified mothers by whether the father data were available. We then compared these two groups of mothers and found that the level of risk was not different between the two groups (data not shown), suggesting that the level of maternal risk was not higher when father data were absent. When paternal risk factors were examined after stratifying by the level of paternal involvement, 55 families (42%) had no paternal involvement and were classified as non-caregivers. The removal of these non-caregiving fathers resulted in a significant relationship between paternal criminal history and report substantiation (OR = 4.57, 95% CI [1.03, 20.41]). There were no other substantial differences between this sub-analysis and the analysis in which fathers without parental involvement were included in the sample. Families with “Multiple Reports” versus a “Single Report” Of the 131 families, 45 (34.4%) had more than one report, and 86 (65.6%) had a single report. The median time between the first two reports was 7 months (range: 1–44). None of the maternal or paternal risk factors or maternal or paternal risk scores was significantly associated with the outcome of two or more reports. The variable – number of caregivers (considered as 0–3 caregivers versus >3 caregivers) approached statistical significance (p = .05) with an odds ratio of 2.53 (95% CI [0.98, 6.54]). The results were similar in the sub-analysis in which we analyzed only the families who exited the program prior to the study completion. Discussion This study is the first to focus on families that were identified as “high-risk” prior to program entry, participated in a home visiting program intended to prevent child maltreatment, and were reported to CPS. There were two key findings: first, we found very high levels of parental risk in the sample, and second, we found that DV and criminal history (in the mothers’ and fathers’ lives), paternal risk score, and complex caregiver networks were related to maltreatment outcomes. J.Y. Duffy et al. / Child Abuse & Neglect 44 (2015) 184–193 191 The levels of parental risk in this population were substantial and highlight the challenges of providing preventive services to this group of fist-time mothers and their partners. Almost half of the mothers had a history of mental health problems (47%) and had previous involvement with CPS (46%); when violence was examined, over a third had a criminal history (37%), and 25% had a history of sexual abuse. Data were available for fewer fathers, and the levels of risk were lower than for mothers except for criminal history, which was noted for 59% of the fathers with available data. The percentage of fathers with each risk factor may have been higher if information were available on all fathers since it is likely that CPS workers were not able to collect data from the highest risk fathers. In the case–control study focusing on substantiation, we found that several risk factors were correlated with the substantiation of CPS reports, including paternal risk group score, maternal and paternal DV, and maternal criminal history. These results suggest that substantiation may depend not just on the extent of maltreatment, but also on the levels of risk in the family and, in particular, on the risk factors of DV and criminal history. In terms of paternal risk, prior studies have proposed a relationship between decreased paternal involvement as a caregiver and an increased risk of child maltreatment (Guterman & Lee, 2005). This relationship may be linked to the economic hardship experienced in single-mother households or to the fact that paternal absence increases the likelihood that there will be a surrogate father figure (e.g., maternal boyfriend), which may be associated with child maltreatment (Radhakrishna, Bou-Saada, Hunter, Catellier, & Kotch, 2001). Although these studies have focused on paternal involvement, few have focused on the impact of paternal risk on child maltreatment. One study by Berger et al., however, used paternal risk as a covariate in the investigation of the relationship between paternal involvement and maltreatment. This study found that the level of paternal risk did not attenuate the relationship between paternal involvement and maltreatment. The authors noted, however, that there was little variance in paternal risk in their sample, so it is possible that their analyses were not able to detect the effect of paternal risk factors on child maltreatment (Berger, Paxson, & Waldfogel, 2009). The strong association between DV (either maternal or paternal) and report substantiation is consistent with the results of previous studies. For example, one study found that DV was implicated in 20% of the cases that were reported to CPS, and that among reports that were classified as “high-risk” by CPS investigators, reports involving DV were more likely to remain open for services or have a child removed from the home than the reports in which DV was not a factor (English, Edleson, & Herrick, 2005). Another study with findings similar to ours by Kohl et al. reported a significant association between report substantiation and DV: in families with active DV, the substantiation rate was 52% compared to 29% in families with a past history of DV and 22% in families with no DV history (Kohl, Edleson, English, & Barth, 2005). The relationship between parental criminal history and perpetration of child maltreatment is less thoroughly described; the relationship between violence and child maltreatment, however, is well known (Belsky, 1980; Thornberry & Henry, 2013). In our study sample, we did not have the details of the parental criminal records. Thus, it is possible that the crimes in our sample were violent in nature and, therefore, the correlation of criminal history with report substantiation is a reflection of the relationship between violence and maltreatment. Additionally, there is evidence for a relationship between childhood maltreatment and a propensity to commit crimes as adults (Nikulina, Widom, & Czaja, 2011; Olds et al., 1998; Reavis, Looman, Franco, & Rojas, 2013; Widom & Maxfield, 1996). Approximately 40% of the parents in our sample had a history of maltreatment during their childhood, and therefore it is possible that the high level of parental criminal activity is reflective of this cycle of violence. In our study, we identified a relationship between the likelihood of substantiation and paternal risk score and DV. In the stratified sub-analysis when we only considered caregiving fathers, the father’s criminal history was also identified as a significant risk factor. Although the sample size in this sub-analysis was small, this result suggests that paternal risk bears an important relationship to substantiation of child maltreatment, regardless of the level of paternal involvement in these high-risk families. Regarding the outcome of maltreatment recidivism, only a single risk factor approached statistical significance, namely an increased number of caregivers. Prior studies have shown several factors to be associated with recidivism, including family poverty, a caregiver’s history of abuse, a caregiver’s substance abuse, and neglect as the maltreatment type (Connell, Bergeron, Katz, Saunders, & Tebes, 2007; Dakil, Sakai, Lin, & Flores, 2011; Hindley, Ramchandani, & Jones, 2006). In our study that focused on a selected, socially high-risk sample, no parental risk factors were independently associated with having multiple reports. Our results are consistent with Thompson et al. who followed a group of 149 high-risk, racially diverse infants for upwards of eleven years to investigate the possible predictors of maltreatment recidivism. This study found that only non-modifiable factors (maltreatment type and substantiation) were associated with maltreatment recidivism; in contrast, modifiable risk factors (e.g., substance abuse, DV) were not associated with multiple reports (Thompson & Wiley, 2009). None of the studies cited above has investigated the role of caregiver networks or family dynamics in predicting the likelihood of recidivism, although a recent study did highlight the relationship between caregiver instability and child maltreatment (Casanueva et al., 2014). Our finding about the number of caretakers needs to be examined in a larger sample. If this finding is confirmed, we postulate that the number of caregivers may serve as a proxy for instability in the home environment. The presence of multiple caregivers may indicate that the care of the child changes frequently, thus exposing the child to the risk of abuse and neglect. In addition, many of these caregivers were non-parental and, therefore, may not have had an attachment relationship with the child, which has been shown to increase the risk of child maltreatment (Morton & Browne, 1998; Stronach et al., 2011). It is also possible that the more caregivers responsible for the care of a child, 192 J.Y. Duffy et al. / Child Abuse & Neglect 44 (2015) 184–193 the greater the points of contact that a child may have with society, and the greater the number of people who may report an incident or concern to CPS. There are several limitations of the study. First, the sample sizes were relatively small, and thus important risk factors may have been missed because the odds ratios were not significant. Second, only 65% of the families signed consent to allow the review of CPS records, and those who refused consent may have been at higher risk than those who allowed the review. By not including these higher risk families, we may have underestimated the presence of some of the risk factors and the associations that were found. Third, we had CPS reports on 76.2% of the subjects who were reported to CPS, resulting in missing data, which contributed to the small sample size and potentially introduced bias. There were, however, no statistically significant demographic differences between the families whose reports we reviewed and the other 23.8% of families that were reported to CPS, but for whom no CPS reports were available. Fourth, the study relied on the reports of CPS social workers, which resulted in incomplete data regarding the six risk factors. The absence of data occurred more frequently for fathers than mothers and likely reflects the approach used by CPS in its investigation of reports of suspected maltreatment. By using CPS data, however, we were able to use information directly from the field, and these data were not biased by parental self-reports on questionnaires. Despite these limitations, our study is unique in its ability to investigate both substantiated reports and maltreatment recidivism in the same population, specifically socially high-risk families who were participating in a prevention program. By doing so, we were able to identify risk factors and social network data related to both types of outcomes and to identify risks that may be targeted for prevention efforts. Although our results do not point to a single risk factor that is common to both outcomes, high levels of paternal risk, maternal and paternal DV and criminal history were associated with the likelihood of substantiation. We cannot, however, determine from these strong associations, the extent to which these risk factors were predictive of more serious maltreatment, thus leading to substantiation or influenced the actual decision by CPS about substantiation. The only risk factor that was of borderline significance for the outcome of recidivism was the presence of many caregivers; this potentially important finding about the caregiving environment in these high-risk families needs further study. If, as prior studies suggest, the number of reports and report substantiations should both be considered as indicators of the severity of abuse, then, perhaps, it is important to target both the level of risk and the chaotic nature of caregiver networks with future prevention efforts. Recent attention has focused on DV in high-risk families as an important target for prevention efforts (Jack et al., 2012). The results of our study support the idea that because DV may correlate with report substantiation (as a marker of abuse severity), targeting DV may be a promising strategy for preventing child maltreatment. Conclusions In summary, in socially high-risk families who were receiving services in a state-wide child abuse prevention program and who were reported to CPS, the levels of risk was high in parents, and maternal and paternal histories of DV and criminal history, fathers with at least two of six risk factors, and the presence of multiple caretakers were linked to the outcomes of maltreatment, either substantiation or recidivism. Increased efforts to mitigate the effects of these risk factors on parenting or to reduce risk factors, such as DV, may be necessary to decrease the occurrences of maltreatment. References Avellar, S. A., & Supplee, L. H. (2013). 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Journal of Adolescent Health 57 (2015) 164e168 www.jahonline.org Original article More Than Poverty: The Effect of Child Abuse and Neglect on Teen Pregnancy Risk Sarah K. Garwood, M.D. a, *, Lara Gerassi, M.S.W. b, Melissa Jonson-Reid, Ph.D. b, Katie Plax, M.D. a, and Brett Drake, Ph.D. b a b Division of Adolescent Medicine, Department of Pediatrics, Washington University School of Medicine, St Louis, Missouri Brown School of Social Work, Washington University, St. Louis, Missouri Article history: Received January 22, 2015; Accepted May 5, 2015 Keywords: Unplanned pregnancy; Child abuse and neglect; Health disparity; Prospective study; Poverty A B S T R A C T Purpose: The purpose of the study was to compare risk for teen pregnancies between children living in poverty with no child protective services (CPS) report history and those in poverty with a history of CPS report. Methods: Children selected from families in poverty, both with and without CPS report histories were prospectively followed from 1993 to 2009 using electronic administrative records from agencies including CPS, emergency departments, Medicaid services, and juvenile courts. A total of 3,281 adolescent females were followed until the age of 18 years. Results: For teens with history of poverty only, 16.8% had been pregnant at least once by the age of 17 years. In teens with history of both poverty and report of child abuse or neglect, 28.9% had been pregnant at least once by the age of 17 years. Although multivariate survival analyses revealed several other significant factors at the family and youth services levels, a report of maltreatment remained significant (about a 66% higher risk). Conclusions: Maltreatment is a significant risk factor for teen pregnancy among low income youth even after controlling for neighborhood disadvantage, other caregiver risks and indicators of individual emotional and behavioral problems. Ó 2015 Published by Elsevier Inc. on behalf of Society for Adolescent Health and Medicine. Teen birth rates reached a 40-year low in 2013, with a rate of 26.6 births per 1,000 for females aged 15 through 19 years. Despite this progress, the United States continues to have the highest teen birth rate of any developed country [1]. Reduction in teen pregnancy rates remains a priority for multiple reasons. The public cost of teen pregnancy amounted to $9.4 billion in 2010 alone [2]. Evidence shows both infants and their teenage mothers have increased risk of poorer health and well-being [3]. Adolescent mothers are more likely to leave school and less likely * Address correspondence to: Sarah K. Garwood, M.D., Division of Adolescent Medicine, Department of Pediatrics, Washington University School of Medicine, Campus Box 800, St Louis, MO 63110. E-mail address: garwood_s@kids.wustl.edu (S.K. Garwood). IMPLICATIONS AND CONTRIBUTION This study supports initiatives to target pregnancy prevention for youth who have experienced childhood abuse and/or neglect. The increased risk associated with runaway history suggests that screening for sexual risk behaviors as a part of juvenile court or shelter processes followed by effective intervention may be another target of opportunity. to attend secondary education, which impacts economic opportunity [4]. Certain subpopulations of youth with histories of trauma seem to be at increased risk of pregnancy. For example, youth in foster care have persistently higher rates of adolescent pregnancy, as much as twice that of the general population [5]. Retrospective findings suggest that even youth suspected of being victims of maltreatment face increased risk. In a study using linked birth and child protective services (CPS) records in California, PutnamHornstein et al. [6] demonstrated that adolescent mothers had higher rates of both alleged and substantiated maltreatment reports. Studies show that a range of childhood adversities significantly contribute to the risk of teen pregnancy, abortion, and rapid repeat pregnancy [7e9]. Males with adverse childhood 1054-139X/Ó 2015 Published by Elsevier Inc. on behalf of Society for Adolescent Health and Medicine. http://dx.doi.org/10.1016/j.jadohealth.2015.05.004 S.K. Garwood et al. / Journal of Adolescent Health 57 (2015) 164e168 experiences are more likely to father children born to teenage mothers; this association was found over four successive birth cohorts [10]. Thus far, however, there is little prospective work to guide our understanding of the unique role of adversity in the context of other behavioral and environmental factors that may moderate or mediate the association between parenthood and child abuse and neglect (CAN). In contrast, the association of poverty with teenage pregnancy has been well described. Poverty has been identified as both an outcome and a correlate of teen pregnancies [11] and is associated with higher rates of multiple child maltreatment reports [12]. Teen pregnancy risks are complex and multifactorial. Although the federal government could spend up to eight times current spending levels to break even with the costs of teen pregnancy, targeted programs addressing teens with the greatest risk factors would have the highest yield [13]. This study helps to fill the gaps in our understanding of the prospective relationship between child maltreatment and later teen pregnancy taking into account poverty and the other indicators of nonsexual risk behaviors that can be used to better target prevention and intervention. Methods Study sample Data for this analysis were drawn from a larger longitudinal administrative data study that tracked a range of service system involvement and outcomes for children with histories of poverty or poverty and maltreatment during childhood. The larger study consisted of three groups of participants (one child randomly selected per family) born 1980e1994: those with a report of CAN, children with families who receive Aid to Families with Dependent Children (AFDC), and children with both CAN and AFDC (n ¼ 12,409). The sampling window was 1993e1994. All children from birth through the age of 11 years with a first report of alleged child abuse or neglect were matched to contemporary AFDC files. This created a group with a recent history of family poverty and also a report of maltreatment. One child was randomly selected per family and matched by birth year and city or county residence to children with similar histories of family poverty but no report of maltreatment. It should be noted that data were also available before the sampling period for (1) the index child’s birth; (2) parental arrest and corrections from the late 1970s onward; (3) previous Medicaid files from 1987 to 1994 for the parent and the child; and (4) parent history of Medicaid reimbursed mental health (87e94). At the close of the parent study, subjects ranged in age from 16 to 27 years. The present study was restricted to female youth who were aged 17 years by June 2009 to insure complete coverage of health records of pregnancy before adulthood (n ¼ 4,935). The present analyses are limited to the AFDC and the CAN and AFDC groups (n ¼ 3,337). Finally, a small number of subjects had records of pregnancy before the age of 10 years. Although technically possible, this is both outside the range of statistical reports for teen births and less likely to be associated with contact outside the family; so, these subjects and any subject who died before the age of 10 years were also dropped from analyses (n ¼ 56) for a final sample size of 3,281. 165 (AFDC then TANF); (2) children’s division (includes CAN reports, report disposition, record of in-home services, records of foster care); (3) Missouri Medicaid 1993 onward; (4) all emergency room records not limited by payment type (1997 onward); (5) juvenile court (1993 onward); (6) highway patrol; (7) births; (8) death; (9) special education (matched in 2003 and again in 2006); and (10) department of mental health for parent and child (1999 onward). Case file data were included from the three largest providers of runaway services in 2006. Addresses at baseline were geocoded and linked to census data at the tract level. There are no gaps in coverage of data with the exception of the runaway shelters where we only have occurrence in 2006 or before. Although data are collected retrospectively, exact dates associated with system contacts with the child protection system, health, income maintenance, juvenile justice, mental health, runaway shelters, and special education are used. Data were linked using a common state level identifier when possible, with matching on identifiers used and crosschecked with other data as well as any estimates of overlap available in the literature. Data cleaning was done by comprehensive review of data entry procedures and uses for each contributing agency (Department of Health, Mental Health, Social Services, Juvenile Court, Special Education) as well as reference to existing literature. Social services data included addresses which were geocoded to link to tract level U.S. Census information. All identifying information was removed before providing the data for analysis. Furthermore, all results are aggregated at a sufficient level to provide an additional protection against accidental identification. Human subject approval was granted by XXX (removed for blind review) and each participating agency. Variables Dependent variable. The dependent variable for the present study is a record of health care provided for pregnancy and/or a record of live birth before the age of 18 years. Independent variable. The independent variable for this study is subject’s history of childhood maltreatment. Childhood victimization of maltreatment was indicated by any report (substantiated or unsubstantiated) of child abuse or neglect before the age of 17 years. This is common practice because of the number of studies showing that unsubstantiated and substantiated cases are at similar risk of negative future outcomes [14e16]. Control variables. Control variables included family and community and subject demographic variables. Subject demographic variables included age and race (recoded as “white” vs. “nonwhite” because the demographics of the region at the time of sampling did not allow for more detailed categories). Family variables included information regarding caregiver’s high-school graduation at study start, mother’s age at the birth of the child, parent’s history of mental health treatment, and period of receipt of starting income assistance (family poor at subject’s birth but no income assistance later, childhood only not poor at birth, both [AFDC and later temporary assistance for needy families]). Community variables examined included % of children in tract who were below poverty level from the 1990 U.S. Census data. Data sources All children were followed prospectively through 2009 using electronic administrative records from (1) income maintenance Potential moderating variables. Moderating variables are conceptualized as indicators of behaviors or special needs that may impact teenage pregnancy separate from or combined with 166 S.K. Garwood et al. / Journal of Adolescent Health 57 (2015) 164e168 maltreatment. Service contact variables included information on various kinds of services the subjects received for school, health, mental health, or behavioral concerns before the age of 18 years. Variables included receipt of Special Education services by disability type, other health records indicated cognitive delay, mental health intervention as noted by International Classification of Disease-9 code for mental health (Department of Mental Health or Health records), record of juvenile court status offense petition, illicit substances, or delinquency, runaway, health care record for a sexually transmitted infection(proxy for high risk sexual behavior). Dates of contact were used to identify service contacts that occurred before early pregnancy or the end of the study for females who did not become pregnant. Data analysis All data cleaning and analyses were completed using SAS 9.4. Descriptive analyses included chi-square and bivariate survival analyses. Life tables and survival curve analyses were used both to suggest important variables for multivariate analyses and to help assess for proportionality issues. Time was programmed in years since birth to event (early pregnancy) or end of the study period (nonevent). An interaction term between a nonproportional variable and time was created if needed to adjust for nonproportionality in the multivariate model [17]. For multivariate analyses, Cox regression models using the SURVEYPHREG option to control for clustering by geographic unit. Terms which were significant or nonsignificant but impacted the overall model fit were retained in the final model. Significant risk ratios larger than one indicate increased risk, and those less than one indicate decreased risk of the outcome. Results The final sample consisted of 3,281 young women; of whom, 1,343 (40.9%) had a history of poverty only, and 1938 (59.1%) had histories of both CAN and poverty. Among subjects with a history of at least one report of abuse or neglect, 28.9% had a record of at least one pregnancy from ages 10 to 17 years compared with 16.8% for the poverty only group. This difference remained significant in bivariate analyses controlling for time from birth to the end of the study period (see Table 1). Mean age at first pregnancy was 14.9 years. The mean age at first pregnancy did not vary by history of a maltreatment report. Bivariate analyses indicated significant differences between females who became pregnant at the individual, family, and community levels (see Table 1). The youth service variables were used as predictors rather than comorbid factors; so, each was adjusted to occur before the event of interest or the end of the study period. Interactions between time of first delinquency and time of first sexually transmitted disease (STD) treatment were significant indicating a change in risk during the early teen years. An interaction between time and cognitive delay was retained because of its impact on model fit and the main effect but was not significant. Variables were entered in three stages to check for indications of mediation. Because there were no significant changes in effects by model, only the final model is discussed here. It should be noted, however, that there was a significant improvement in model fit as indicated by the Wald (sandwich) chi-square values (see Table 2). White females were about 20% less likely to be among the early pregnancy females compared with black Table 1 Bivariate results for teenage pregnancy by subject, family, and youth services Variable Subject Race Nonwhite White Childhood history of maltreatment None known At least one report Subject’s family during childhood Lived in high poverty census tract <40% child poverty 40% or more child poverty Poverty (government paid birth) no Poverty at birth Poverty (government assistance) early only Adolescence Caregiver history of mental health treatment Yes Caregiver HS graduate Yes Subject’s child/adolescent service system records Mental health treatment (Including emotional disturbance special education) Yes Health therapy for sexually transmitted disease Yes Drug arrest or drug therapy Yes Delinquency record (not drug) Yes Runaway record Yes Other disability (cognitive health or special education) Yes N size Pregnancy Log-rank (n ¼ 3,281) (n ¼ 906), % statistic (p value) .0002 2,602 679 25.4 18.4 1,343 1,938 16.8 28.9 1,703 1,578 1,697 1,584 1,607 27.0 21.7 27.8 19.9 15.8 1,674 3,063 31.8 22.9 218 1,494 1,787 38.5 29.7 19.9 <.0001 2,475 24.3 NS 806 3,118 22.9 23.7 NSa 163 3,083 198 2,638 643 3,197 84 2,623 28.2 23.6 29.8 21.2 34.5 23.4 45.1 23.0 658 27.8 <.0001 .002 <.0001a <.0001 <.0001a NSa <.0001a <.0001a .006 HS ¼ high school; NS ¼ not significant. a Bivariate analyses indicated potential issues with proportionality. females in our sample. Poverty in the community and childhood periods of family receipt of income maintenance along with a history of maltreatment was associated with increased risk of later early pregnancy. Females with a history of at least one report of maltreatment had about a 66% increased risk of being among the early pregnancy group. Females in families that received income maintenance in childhood but not at birth had about a 20% increased risk compared to those with records of poverty at birth only. Females born into poverty with continued record of poverty in later childhood were over 40% more likely to have an early pregnancy. Having had a caregiver who completed high school decreased the risk of later pregnancy by nearly 25%. Youth service contacts Females who also had a record of treatment or service for a mental health disorder were less likely to be among those pregnant (about 36% less likely), whereas the opposite was true for those with a history of runaway (88% greater risk). Females with records of cognitive delay or learning disability had more than 60% higher risk of early pregnancy. The effects of S.K. Garwood et al. / Journal of Adolescent Health 57 (2015) 164e168 167 Table 2 Cox regression model of early pregnancy Model 1 Model 2 Model 3 HR (95% CI) HR (95% CI) HR (95% CI) Race (black) White .78b (.64e.96) Child poverty in tract Per % unit increase 1.01b (1.0e1.01) Wald chi-square ¼ 24.9 (2); p < .0001 Report of maltreatment (none) Yes Caregiver HS graduate (none) Caregiver MH therapy (none) Family government assistance (per increase compared with birth only) Child and teen All stages Wald chi-square ¼ 158.13 (6); p < .0001 Youth MH therapy (none during risk period) Youth runaway history (none during risk period) Youth therapy for sexually transmitted disease (none during risk period) Youth delinquency history (none during risk period) Youth cognitive delay or disability (none known) Time interactions Youth delinquency dtime at risk (per year after the age of 14 years) Youth sexually transmitted disease dtime at risk (per year after the age of 15 years) Youth delay dtime at risk (continuous) Wald chi-square ¼ 281.80 (14); p < .0001 .75b (.61e.92) .79c (.64e.97) 1.004c (1.0e1.01) 1.004c (1.0e1.01) 1.65a (1.41e1.94) .76b (.66e.88) 1.43b (1.14e1.80) 1.66a (1.41e1.96) .76b (.66e.88) 1.46c (1.15e1.84) 1.23a (1.14e1.34) 1.21a (1.11e1.32) .73b (.62e.87) 1.89b (1.33e2.69) .44b (.25e.77) NS 1.69c (1.01e2.84) 2.55a (1.79e3.61) 3.21a (1.65e6.25) NS CI ¼ confidence interval; HR ¼ hazard ratio; HS ¼ high school; NS ¼ not significant. Comparison groups are identified in italics and HR for these ¼ 1.0. a p < .0001; b .0001 < p < .01; c .01 < p  .05; d Time is measured in years since birth. delinquency or treatment for an STD cannot be interpreted without considering the timing of these events. Subjects with delinquency records before the age of 14 years had no higher or lower risk of pregnancy, but those with first delinquency records after the age of 14 years were much more likely to have a record of pregnancy before the age of 18 years (about 2.5 times higher each year). A similar pattern existed for STD treatment although the higher risk emerged a year later (about age 15 years). In other words, females who began treatment for an STD before the age of 15 years did not appear more likely to have an early pregnancy, but the risk associated with diagnosis escalated sharply each year after the age of 15 years. Discussion This study contributes to our understanding of why some youth continue to face higher pregnancy rates while the general population pregnancy rate declines. With a growing body of literature linking adversity in childhood to poor adult outcomes, studies such as this one that determines relative risks of adversity exposures are important. This study adds to the understanding of adolescent pregnancy risk by controlling for poverty as a confounder. The prospective data findings that even a single maltreatment report independent of poverty is associated with higher risk of pregnancy are consistent with the retrospective findings reported by Putnam-Hornstein [6]. Older teens living in high poverty areas with histories of maltreatment may be a particularly essential target for pregnancy prevention efforts. This clearly underscores the importance of preventative measures for child abuse and maltreatment but also the important role of CPS and other interventions in addressing pregnancy prevention with families regardless of income. Although this is important for all children, maltreated populations should be given particular support with regard to screenings and intervention. Even within a low income sample, females who resided with a more functional caregiver (higher education level and no record of MH disorder) during childhood fared better in our study. This finding is consistent with literature demonstrating the multitude of causal factors involved in intergenerational poverty transmission [18]. Support services provided to parents or caregivers and the female children themselves residing with less functional caregivers should be explored further, as increased stability to young females may holistically improve outcomes. Although CPS and other services may provide support for families generally, specific policies targeting maltreated children and young adults should also reflect the higher risk of females who live with less functional parents or caregivers. Supportive services and policies must extend throughout young women’s lives to improve outcomes for young women as they age. The authors are unaware of literature that links services to mothers to improve education and mental health outcomes to later pregnancy in offspring. Future research should explore this outcome. Although record of STD treatment was conceptualized as a proxy for high risk sexual behaviors, the interaction with time suggests an interesting possibility. It is possible that females who are treated for STDs at a younger age receive services that may offset risk of continued unprotected sex. Similarly, females who have known and treated mental health disorders may have improved outcomes compared with those with undiagnosed conditions. These ideas are not testable with the data available but may be promising areas of investigation related to timing and service platforms for pregnancy prevention efforts. The association of developmental delay and runaway behavior with higher risk of pregnancy is consistent with the literature on other high risk sexual behaviors [19,20]. 168 S.K. Garwood et al. / Journal of Adolescent Health 57 (2015) 164e168 Limitations Funding Sources There are several limitations to consider. The use of administrative data does not take into account all the relevant behaviors that may have occurred in the study population such as substance abuse, mental health issues, or risk behaviors that were not identified by public services. Although services may be protective, diagnosis or system contact alone is not an indicator of the quality or type of service provided. For example, in the parent study from which data were obtained, data sharing agreements did not allow for obtaining prescription information. Nor is it possible to identify protective factors such as school performance. At the time the study was conducted, there was no centralized data system that collected high-school graduation information at the individual level. It was not feasible to attempt to obtain transcripts for each student. It is also not possible to measure compliance behaviors with any treatment provided for health or mental health issues. Nor is it possible to know whether the early STDs may have been associated with sexual abuse although the STD treatment records did not include a notation of abuse in the diagnostic codes. The use of administrative data also does not account for maltreatment and trauma that may have been occurring in the study population but not documented. This study also does not differentiate between types of child maltreatment. This may be relevant based on a recent meta-analysis that showed increased risk for pregnancy with history of physical and sexual abuse but not neglect or emotional abuse [21]. Our sample population reflected the demographics of Missouri with only Caucasian and AfricanAmerican subjects because of small sample size among other racial groups. Results may not be generalizable to other races and ethnicities as well as other regions of the country. We did not include males because at the time, there was no requirement to list fathers on birth records. Despite its limitations, this study has multiple implications for prevention of pregnancy in high risk populations. Our study supports initiatives to target and enhance pregnancy prevention for youth who have experienced childhood abuse and/or neglect. In addition, it is important for interventions to address cognitive delays and learning disabilities within this at risk population. The increased risk associated with runaway history suggests that screening for sexual risk behaviors as a part of juvenile court or shelter processes followed by effective intervention may be another target of opportunity. This study reinforces the importance of access to health care for children in foster care, a point which is especially salient given the findings in the Office of Inspector General’s 2015 report which demonstrated that one third of children in foster care who were enrolled in Medicaid did not receive at least one required health screening [22]. Finally, this study supports the growing body of evidence regarding the implications of child abuse over one’s lifespan and the importance of child abuse prevention through investment in evidencebased interventions such as the nurseefamily partnership [23]. This study was supported by Centers for Disease Control and Prevention (CE001190) and National Institute of Mental Health (2R01 MH061733). References [1] Hamilton BE, Martin JA, Osterman MJ, Curtin S. Births: Preliminary data for 2013. Natl Vital Stat Rep 2014;63. [2] National Campaign to Prevent Teen and Unplanned Pregnancy. Counting it up: Key data. Available at: https://thenationalcampaign.org/resource/counting-itkey-data-2013; 2014. Accessed July 22, 2014. [3] Paranjothy S, Broughton H, Adappa R, Fone D. Teenage pregnancy: Who suffers? Arch Dis Child 2009;94:239e45. [4] Hofferth SL, Reid L, Mott FL, et al. The effects of early childbearing on schooling over time. Family Planning Perspectives 2014;33:259e67. [5] Courtney M, Dworsky A, Lee J, Raap M. Midwest evaluation of the adult functioning of former foster youth: Outcomes at ages 23 and 24. Chicago, IL: Chapin Hall at the University of Chicago. Available at: http:// fosteringmediaconnections.org/wp-content/uploads/2010/08/MW-Wave-4full-report1.pdf; 2010. Accessed April 17, 2015. [6] Putnam-Hornstein E, Cederbaum JA, King B, et al. A population based examination of maltreatment history among adolescent mothers in California. J Adolesc Health 2013;53:794e7. [7] Noll JG, Shenk CE. Teen birth rates in sexually abused and neglected females. Pediatrics 2013;131:e1181e7. [8] Noll JG, Shenk CE, Putnam KT. Childhood sexual abuse and adolescent pregnancy: A meta-analytic update. J Pediatr Psychol 2009;34:366e78. [9] Zapataa LB, Kissina DM, Bogoliubovab O, et al. Orphaned and abused youth are vulnerable to pregnancy and suicide risk. Child Abuse Negl 2012;37:310e9. [10] Anda RF, Chapman DP, Felitti VJ, et al. Adverse childhood experiences and risk of paternity in teen pregnancy. Obstet Gynecol 2002;100:37e45. [11] Penman-Aguilar A, Carter M, Snead MC, Kourtis AP. Socioeconomic disadvantage as a social determinant of teen childbearing in the U.S. Public Health Rep 2013;128(Suppl 1):5e22. [12] Drake B, Pandey S. Understanding the relationship between neighborhood poverty and specific types of child maltreatment. Child Abuse Negl 1996; 20:1003e18. [13] Sawhill IV. (2001) What can be done to reduce teen pregnancy and out-ofwedlock births? (Brookings Policy Brief 8). Available at http://www. brookings.edu/research/papers/2001/10/childrenfamilies-sawhill. Accessed June 30, 2015. [14] Kohl PL, Jonson-Reid M, Drake B. Time to leave substantiation behind: Findings from a national probability study. Child Maltreat 2009;14:17e26. [15] Drake B. Unraveling “Unsubstantiated”. Child Maltreat 1996;1:261e71. [16] Hussey JM, Marshall JM, English DJ, et al. Defining maltreatment according to substantiation: Distinction without a difference? Child Abuse Negl 2005; 29:479e92. [17] Allison PD. Survival analysis. In: Hancock GR, Mueller RO, eds. The reviewer’s guide to quantitative methods in the social sciences. New York, NY: Routledge, Taylor and Francis Group; 2010:413e24. [18] Harper C, Marcus R, Moore K. Enduring poverty and the conditions of childhood: Lifecourse and intergenerational poverty transmissions. World Dev 2003;31:535e54. [19] Sullivan PM, Knutson JF. The prevalence of disabilities and maltreatment among runaway children. Child Abuse Negl 2000;24:1275e88. [20] Osgood DW, Foster EM, Courtney ME. Vulnerable populations and the transition to adulthood. Future Child 2010;20:209e29. [21] Madigan S, Wade M, Tarabulsy G, et al. Association between abuse history and adolescent pregnancy: A meta-analysis. J Adolesc Health 2014;55:151e9. [22] Daniel R. Levinson IG. Not all children in foster care who were enrolled in Medicaid received required health screenings. Available at: https://oig.hhs. gov/oei/reports/oei-07-13-00460.pdf. Accessed April 17, 2015. [23] Olds DL. The nurse-family partnership. In: Lester BM, Sparrow JD, eds. Nurturing children and families: Building on the legacy of T. Berry Brazelton. Hoboken, NJ: Wiley-Blackwell; 2010:192e203.
Child Physical Abuse Fact Sheet What is physical abuse? The precise definition of child physical abuse varies among states, the District of Columbia, and the US territories. All these entities agree that physical abuse occurs when a parent or caregiver commits an act that results in physical injury to a child or adolescent, such as red marks, cuts, welts, bruises, muscle sprains, or broken bones, even if the injury was unintentional. Physical abuse can occur when physical punishment goes too far or a parent lashes out in anger. Physical Abuse Myths and Facts Even forms of physical punishment that do not Myth: Child physical abuse is rare. result in physical injury are considered physical abuse and are outlawed in some states. For Fact: In 2007, there were approximately 149,000 cases of example, in Arkansas, Minnesota, and the child physical abuse reported in the District of Columbia, hitting a child with a closed 50 states, the District of Columbia, fist is considered physical abuse. In Arkansas, and Puerto Rico. Actual rates of child hitting a child on the face or head is also called physical abuse are probably higher, 1 physical abuse. (For more information on state since not every case is reported.2 laws, go to www.childwelfare.gov/systemwide/ laws_policies/statutes/defineall.pdf.) Who is physically abused? Children of all ages, races, ethnicities, and socioeconomic backgrounds are at risk for physical abuse. Physical abuse affects both boys and girls across neighborhoods, communities, and countries around the world. Children ages 4–7 and 12–15 are at the greatest risk of being physically abused. Very young children are most susceptible to receiving serious injuries.2 How can you tell if a child is being (or has been) physically abused? It can be difficult to determine from a child’s behavior or emotional state whether abuse has occurred. The best way to know if a child has been abused is if the child tells you. This project was funded by the Substance Abuse and Mental Health Services Administration (SAMHSA), US Department of Health and Human Services (HHS). The views, policies, and opinions expressed are those of the authors and do not necessarily reflect those of SAMHSA or HHS. There may also be physical signs, such as welts and bruises in various stages of healing, fingernail marks, human bite marks, burns, lacerations, abrasions in the pattern of an instrument, and missing, loose, or broken teeth. It is very possible for a child to be physically abused without anyone noticing if the child’s injuries are hidden by clothing. Physical Abuse Myths and Facts Myth: It’s only physical abuse if you mean to hurt your child. Fact: Even accidental injuries of a child are considered physical abuse if the act that injured the child was done intentionally as a form of punishment. There are several indicators that strongly suggest a child is being abused: ■■ Frequent physical injuries that are attributed to the child’s being clumsy or accident-prone ■■ Injuries that do not seem to fit the explanation given by the parents or child ■■ Conflicting explanations provided by child and/or caregivers, explanations that do not fit the injuries, or injuries attributed to accidents that could not have occurred given the child’s age (for example, an immersion burn on a child too young to walk or crawl) ■■ Habitual absence from or lateness to school without a credible reason. Parents may keep a child at home until physical evidence of abuse has healed. One should also be suspicious if a child comes to school wearing long-sleeved or high-collared clothing on hot days, since this may be an attempt to hide injuries ■■ Awkward movements or difficulty walking; this may suggest that the child is in pain or suffers from the aftereffects of repeated injuries What should you do if you suspect a child is being (or has been) physically abused? If you are a counselor, parent, teacher, or anyone else concerned about a child whom you suspect is being abused, the best way to begin is by talking to the child. ■■ Start with open-ended questions. Don’t assume that the child is being abused. There may be many explanations for why a child is behaving in a particular way or for how a child was injured. Some children have conditions, such as osteogenesis imperfecta or blood clotting disorders, that make them more vulnerable to bruising and/or broken bones. ■■ If the child has a visible injury, ask how the child was injured. Ask open-ended followup questions to look for inconsistencies if the explanation for the injury seems implausible or doesn’t match the injuries. Physical Abuse Myths and Facts Myth: Good parents don’t get frustrated or angry with their children’s behavior. Fact: All parents get angry at their children sometimes. It is okay to be angry, but it is not okay to hurt your children in anger. Angry feelings cannot get you into trouble but violent behavior can. It is important for parents to learn how to express and control their anger so that their children learn to do the same. 2 Child Physical Abuse Fact Sheet October 2009 What can you do if a child discloses physical abuse? Whether or not you are mandated to report child abuse to the child protection agency varies from state to state. In New Jersey, for example, every citizen who comes into contact with a child and observes behavior or conditions that might indicate abuse or neglect is required by law to report their suspicions. Even if you are If you know or suspect a child is being or not mandated to report abuse, there is no law has been physically abused, please call the ChildHelp-National Child Abuse Hotline against making an abuse report if you have at 1-800-4-A-Child (1-800-422-4453) or go a reasonable suspicion that a child is being to http://www.childhelp.org/get-help. abused. The identity of the person making the child abuse report is not shared with anyone If you need immediate assistance, call 911. other than child protection services workers. Some states also allow anonymous reporting. Why don’t children tell about physical abuse? There are many reasons why children don’t tell about physical abuse, including: ■■ Fear that their parents will be mad at them or will hurt them worse for telling ■■ Desire not to get their parents into trouble ■■ Fear of being removed from their homes ■■ A belief that it’s okay for their parents to hurt them ■■ Fear of not being believed ■■ Shame or guilt ■■ Belief that they deserve the abuse for their “bad” behavior What are the consequences of physical abuse for families? Children Experts in the field of child behavior believe that physical abuse teaches children to be submissive, fearful, and/or aggressive.3 It also teaches them that hitting is a way to control other people or solve problems. The attitudes, beliefs, and behaviors that grow out of physical abuse can cause a child to have problems at school, at home, and with friends.4 Sometimes children who have been hit don’t do well at making and keeping friends. They may not trust people in authority. Children may also become fearful of their parents. It can be confusing for children when a parent, the person they depend on and love the most, hurts them in some way. Physical Abuse Myths and Facts Myth: Physical punishment helps parents control their child’s behavior. Fact: Parents who use excessive punishment are not in control. Physical punishment does not teach children how to make good decisions, how to determine what is right and wrong, or how to control their own behavior. Instead, physical punishment makes children submissive, fearful, and/or aggressive. It also teaches them that hitting is a way to solve problems with other people.3 The National Child Traumatic Stress Network www.NCTSN.org 3 Being hit may make children feel angry, helpless, powerless, hostile, guilty, or ashamed. It may result in their becoming chronically anxious or depressed. All these negative feelings about themselves increase children’s stress levels and only make it harder for them to behave well. With therapy and support, children can overcome the effects of child physical abuse and go on to lead productive lives. Parents Physical Abuse Myths and Facts Myth: Parents who physically abuse their children are bad and unloving people. Fact: Most parents love their children and do not mean to hurt them. They discipline their children because they want them to behave well. Many parents feel frustrated with their children’s behavior and do not know any other way to discipline them, but are open to learning effective parenting strategies to reduce the risk of physical abuse in the future. When children’s behaviors get worse in response to being hit, parents may feel even more stress. When physical punishment does not create the results a parent seeks, the parent may escalate the punishment, and the child and parent may get locked in a vicious cycle of greater violence on the part of parents, and greater acting out on the part of the children. Many parents feel upset after hitting their children. They may also feel bad about themselves and their abilities to parent. Once the state’s child protection services agency becomes involved, parents may be arrested, may have to go to court, and may have their children removed from their care. There are alternatives to physical punishment. Don’t hesitate to contact a therapist in your area to assist you. References 1. Child Welfare Information Gateway. (2007). Definitions of child abuse and neglect: Summary of state laws. Washington, DC: US Department of Health and Human Services Administration for Children and Families. Available at http://www.childwelfare.gov/ systemwide/laws_policies/statutes/defineall.pdf 2. US Department of Health and Human Services, Administration on Children, Youth and Families (2009). Child Maltreatment 2007. Washington, DC: US Government Printing Office. 3. Gershoff, E.T. (2008). Report on physical punishment in the U.S.: What research tells us about its effects on children. Columbus, OH: Center for Effective Discipline. Available at http://www.phoenixchildrens.com/PDFs/principles_and_practices-of_effective_ discipline.pdf 4. Grogan-Kaylor, A. (2004). The effect of corporal punishment on anti-social behavior in children. Social Work Research, 28: 153–162. Established by Congress in 2000, the National Child Traumatic Stress Network (NCTSN) is a unique collaboration of academic and community-based service centers whose mission is to raise the standard of care and increase access to services for traumatized children and their families across the United States. Combining knowledge of child development, expertise in the full range of child traumatic experiences, and attention to cultural perspectives, the NCTSN serves as a national resource for developing and disseminating evidence-based interventions, trauma-informed services, and public and professional education. Suggested Citation: National Child Traumatic Stress, Network, Physical Abuse Collaborative Group. (2009). Child physical abuse fact sheet. Los Angeles, CA & Durham, NC: National Center for Child Traumatic Stress. 4 Child Physical Abuse Fact Sheet October 2009
Child Abuse and Maltreatment Caitlyn Meade Summer 2017 Important Stuff  Please keep in mind that this class cannot cover EVERYTHING about each topic covered. The material presented is what you will be tested on; however, if you are interested in knowing more/need more information for understanding, etc., I am happy to provide that.  If you have ever been a victim of maltreatment, there are lots of campus resources you can utilize if needed! I would be happy to point you to such services.  Also, remember that research and statistics presented here may be different than individual experiences. This does not discount the research OR your personal experiences. Also, we talk about these experiences and their relationships to crime—there are NO absolutes in the study of human behavior.  Each lecture in this class builds upon prior material. It is important to keep this in mind. Try to connect the dots between lectures. Roadmap How often does child maltreatment occur? How do we define abuse or neglect? What are the risk factors for experiencing child maltreatment? What are the long-term effects of experiencing child maltreatment? Prevalence of Child Maltreatment According to the CDC: At least one in four children have experienced child neglect or abuse (including physical, emotional, and sexual) at some point in their lives At least one in seven children experienced abuse or neglect in the last year. Reporting Maltreatment  Different definitions lead to differing prevalence rates Can’t provide very accurate nationwide statistics  Children don’t report for many reasons  Don’t know they are being maltreated  Too young to understand or report  Threats of bodily harm (to the child and/or the child’s family)  Fear of being removed from the home  Fear of not being believed  Shame or guilt Investigating Maltreatment Substantiated: An investigation disposition that concludes the allegation of maltreatment or risk of maltreatment was supported or founded by state law or policy. Unsubstantiated: An investigation disposition that concludes there was not sufficient evidence under state law to conclude or suspect that the child was maltreated or at-risk of being maltreated. Child Abuse Investigation Lingo  Indicated: A disposition that concludes maltreatment could not be substantiated under state law or policy, but there was a reason to suspect that at least one child may have been maltreated or was at-risk of maltreatment.  Intentionally false: A disposition that concludes the person who made the allegation of maltreatment knew that the allegation was not true.  Closed with no finding: A disposition that does not conclude with a specific finding because the CPS response could not be completed. This disposition is often assigned when CPS is unable to locate the alleged victim.  Other: States may use the category of “other” if none of the above is applicable. Several states use this disposition when the results of an investigation are uncertain, inconclusive, or unable to be determined. Child Abuse Investigation Lingo  Duplicate count of children: Counting a child each time he or she was the subject of a report. This count also is called a report-child pair.  Unique count of children: Counting a child once, regardless of the number times he or she was the subject of a report.  A victim is a child for whom the state determined at least one maltreatment was substantiated or indicated. This includes a child who died of child abuse and neglect. Florida Child Abuse  “Aggravated child abuse” occurs when a person:  Commits aggravated battery on a child;  Willfully tortures, maliciously punishes, or willfully and unlawfully cages a child  Knowingly or willfully abuses a child and in so doing causes great bodily harm, permanent disability, or permanent disfigurement to the child.  “Child abuse” means:  Intentional infliction of physical or mental injury upon a child;  An intentional act that could reasonably be expected to result in physical or mental injury to a child; or  Active encouragement of any person to commit an act that results or could reasonably be expected to result in physical or mental injury to a child. Neglect Defined  Federal definition  Failure of a parent or other person with responsibility for the child to provide needed food, clothing, shelter, medical care, or supervision to the degree that the child’s health, safety, and well-being are threatened with harm  Florida definition  A caregiver’s failure or omission to provide a child with the care, supervision, and services necessary to maintain the child’s physical and mental health, including, but not limited to, food, nutrition, clothing, shelter, supervision, medicine, and medical services that a prudent person would consider essential for the well-being of the child; or  A caregiver’s failure to make a reasonable effort to protect a child from abuse, neglect, or exploitation by another person. Impact of Neglect  Health and physical development  can suffer from untreated illnesses, malnourishment, etc.  Intellectual and cognitive development  Not stimulated in early childhood --> less development --> poor academic performance  Emotional and psychological development  Fear, anxiety, poor social skills  Social and behavioral development  Greater risk for developing conduct disorders and participating in delinquent behavior Mental/Emotional Abuse  Federal definition  “Injury to the psychological capacity or emotional stability of the child as evidenced by an observable or substantial change in behavior, emotional response, or cognition” and injury as evidenced by “anxiety, depression, withdrawal, or aggressive behavior.”  Florida definition  “Mental injury” means injury to the intellectual or psychological capacity of a child as evidenced by a discernible and substantial impairment in the ability of the child to function within the normal range of performance and behavior as supported by expert testimony. Physical Abuse  Federal Definition  “Any nonaccidental physical injury to the child” and can include striking, kicking, burning, or biting the child, or any action that results in a physical impairment of the child.  Some states include acts or circumstances that threaten the child with harm or create a substantial risk of harm to the child’s health or welfare  7 states include the crime of human trafficking, including labor trafficking, involuntary servitude, or trafficking of minors in the definition of child abuse Discipline or Abuse? Some forms of physical punishment are considered physical abuse Hitting with a closed hand Excessive/bizarre punishments can be determined to be abuse For example, kneeling on rice or withholding food/water as punishment Controversial How much control should state have in parenting decisions? Where is the line drawn between physical punishment and abuse? Impact of Physical Abuse Teaches children To be submissive, fearful, and/or aggressive. That hitting is a way to control other people or solve problems. Can cause a child to have problems at school, at home, and with friends. May not make/keep friends or trust authority figures Impact of Physical Abuse  May also become fearful of their parents.  May make children feel angry, helpless, powerless, hostile, guilty, or ashamed.  May result in their becoming chronically anxious or depressed.  All of these issues increase children’s stress levels and only make it harder for them to behave well.  How does the misbehaving resulting from abuse impact future abuse? Sexual Abuse  Federal definition  The employment, use, persuasion, inducement, enticement, or coercion of any child to engage in, or assist any other person to engage in, any sexually explicit conduct or simulation of such conduct for the purpose of producing a visual depiction of such conduct; or  The rape, and in cases of caretaker or interfamilial relationships, statutory rape, molestation, prostitution, or other form of sexual exploitation of children, or incest with children. Prevalence of Sexual Abuse  Child sexual abuse is not rare.  By age 18  1 out of 4 girls  1 out of 6 boys  Child sexual abuse is secretive--many cases never reported  Approximately 75% of reported cases committed by family members or other individuals who are considered part of the victim’s “circle of trust.”  23% percent of reported cases of child sexual abuse are perpetrated by individuals under the age of 18. Cycle of Violence  Neglected children may be at risk for repeating neglectful behavior to their own children  Approximately 1/3 of neglected children will maltreat their own children  High percentage of abusers report experiencing abuse in childhood  Sexually abused-abuser hypothesis  In one study, 33 percent of sex offenders against children were victims of a broad definition of sexual abuse.  By a more narrow definition of sexual abuse, 23 percent of sex offenders of children had experienced forced sexual contact with an adult.  Those who victimized boys were more likely to have experienced sexual victimization than those who victimized girls Effects of Abuse and Neglect on Justice Involvement  Running away  Academic problems → dropping out, suspension, etc  Serious, violent, and chronic offenders are significantly more likely than other juvenile offenders to have been victims of abuse, neglect, and other forms of trauma than less severe and nonoffending juveniles.  Each additional ACE increased the risk of SVC offending by more than 35% when controlling for other risk factors for criminal behavior. Duffy Article  Four major domains of risk  Child characteristics  Prematurity, disability  Low birth weight  Parental characteristics  Mental health problems, substance use, maltreatment during childhood  Low educational achievement  Young age  Family characteristics  Domestic violence  Absent father  Social characteristics  Poverty  Violent neighborhood  Social deprivation/poor social network Duffy Article  Issues with using CPS reports  Underestimation: maltreatment exists but is not reported  Overestimation: surveillance bias  Severity  Substantiated v unsubstantiated  Or frequency of referrals Duffy Article  What could explain increased maltreatment in single-mother households?  Economic hardship  Maternal boyfriends  What did the authors find in regards to caregiver networks?  Increased number of caregivers increased maltreatment recidivism  Instability  Non-parental caregivers with low attachment to the child  More points of contact with society Daily Review  What is child maltreatment?  How often does maltreatment occur?  What are the risk factors for perpetrating and experiencing maltreatment?  How does maltreatment translate to being at-risk?

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