Diversity of adverse childhood experiences among adolescent mothers and the intergenerational transmission of risk to children's behavior problems

Journal Pre-proof Diversity of adverse childhood experiences among adolescent mothers and the intergenerational transmission of risk to Children's behavior problems Lauren E. Stargel, M. Ann Easterbrooks PII:

S0277-9536(20)30047-2

DOI:

https://doi.org/10.1016/j.socscimed.2020.112828

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SSM 112828

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Social Science & Medicine

Received Date: 25 July 2019 Revised Date:

27 November 2019

Accepted Date: 31 January 2020

Please cite this article as: Stargel, L.E., Easterbrooks, M.A., Diversity of adverse childhood experiences among adolescent mothers and the intergenerational transmission of risk to Children's behavior problems, Social Science & Medicine (2020), doi: https://doi.org/10.1016/j.socscimed.2020.112828. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd.

Diversity of Adverse Childhood Experiences Among Adolescent Mothers and the Intergenerational Transmission of Risk to Children’s Behavior Problems Lauren E. Stargela* and M. Ann Easterbrooksa Tufts University

Author Note a

Tufts Interdisciplinary Evaluation Research, Eliot-Pearson Department of Child Study and

Human Development, Tufts University, 574 Boston Ave., 111B, Medford, MA, 02155, U.S. *Correspondence concerning this article should be addressed to Lauren E. Stargel Email: [email protected]

Acknowledgements. The research for the study was supported by the Health Resources and Services Administration of the U.S. Department of Health and Human Services [grant number: X10MC29474] Maternal, Infant, and Early Childhood Home Visiting Grant Program.

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Introduction Adverse childhood experiences, more commonly called ACEs, comprise multiple types of trauma, such as maltreatment and family stressors, experienced by children and youth under the age of 18 (Centers for Disease Control and Prevention, 2010). According to a recent national study, just under half of children in the United States experience at least one ACE, and roughly 10% experience three or more (Sacks & Murphey, 2018). A large body of research has established links between early adversity and children’s later health and well-being, including their physical health and chronic illness (e.g., lung disease, liver disease, cancer; Felitti et al., 1998), behavioral and emotional problems (Herbers, Cutuli, Monn, Narayan, & Masten, 2014), later economic self-sufficiency (i.e., education, employment, poverty; Metzler, Merrick, Klevens, Ports, & Ford, 2017), and experiences of homelessness (Cutuli, Montgomery, EvansChase, & Culhane, 2017). Not only do children’s own early experiences play a substantial role in their later development and functioning but their caregivers’ early experiences can play an important role as well. Recently, researchers have sought to better understand the associations between caregivers’ childhood adversity and their children’s development (e.g., Folger et al., 2018; Letourneau et al., 2019; McDonald et al., 2019). Among a diverse sample of young mothers, the current study aims to (a) capture the heterogeneity in risk factors among children and families experiencing adversity; (b) include risk profiles that examine both mothers’ and children’s childhood experiences to incorporate an intergenerational approach; and (c) examine mechanisms through which maternal childhood risk might be linked with children’s later socio-emotional development. Heterogeneity in Adverse Experiences

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Many studies use a cumulative risk score of ACEs to address associations, either directly or indirectly, to later development, rather than teasing apart the different types of adversity. Using this cumulative risk model, researchers have tried to disentangle how risky environments (e.g., poverty, violent neighborhoods, history of abuse and neglect) are related to children’s emotional functioning, caregivers’ parenting capacities, and families’ future adverse experiences (Chang, Shelleby, Cheong, & Shaw, 2012; Cutuli et al., 2017). Within this model, the number of risk factors, or ACEs, are summed into a score, such that each point increase indicates one additional risk an individual has experienced in their life. One limitation of using a cumulative risk model is that it may not fully capture the nuances of children’s and families’ unique experiences with adversity. It may be the case that certain parental ACEs predict children’s socio-emotional functioning better than others (Schickedanz, Halfon, Sastry, & Chung, 2018), indicating a need to better understand the nuance of what types of adversity families experience and how these differential experiences relate to children’s development. In addition, some researchers argue that using a cumulative risk model is less meaningful when discussing the practical implications of working with families exposed to adversity as it assumes all risk indicators are linked to child and family outcomes in the same way, indicating any effective intervention will be beneficial for all families in the same way and that all risks should be weighted equally (Lanier, Maguire-Jack, Lombardi, Frey, & Rose, 2018; Merians, Baker, Frazier, & Lust, 2019). The current practice of precision medicine argues that interventions are more likely to be effective if they are matched to particular characteristics of a subgroup or individual. Although using the cumulative risk score easily allows clinicians to screen families for histories of adversity, a more in-depth look into exactly what types of adversity were experienced will help clinicians provide more targeted assistance. Furthermore,

MATERNAL ACES AND CHILDREN’S BEHAVIOR PROBLEMS families may differ in the types of adverse experiences that they encounter (e.g., economic hardship, exposure to substance abuse alongside mental health issues, witnessing domestic violence in the household without parental mental health issues), and there may be family variation in types of ACEs experienced according to socio-economic status, immigration status, welfare status, or maternal young age at childbirth (Lanier et al., 2018; Lew & Xian, 2019; Menard, Bandeen-Roche, & Chilcoat, 2004). Further, researchers have recently investigated the differential pathways from heterogeneous ACEs to a multitude of adolescent and adult outcomes, including mental health, substance and alcohol abuse, and incarceration (e.g., Cavanaugh, Petras, & Martins, 2015; Merians et al., 2019; Negriff, in press; Roos et al., 2016). Most of these studies have found evidence to support the idea that various ACEs map onto later outcomes differently (Negriff, in press). For example, one study found that maternal maltreatment was directly associated with children’s socio-emotional functioning in infancy, but that maternal histories of household dysfunction were only linked with children’s socio-emotional functioning in infancy through other biopsychosocial risk factors at birth (McDonnell & Valentino, 2016). Other studies have tried to tease apart different typologies, or subgroups of individuals, who experience the same types of adversity in childhood. A study examining experiences of family stresses found six typologies of adversity in childhood (i.e., six subgroups of participants who were clustered based on the type of adversity that they had experienced) ranging from a healthy environment, multiple traumatic incidents across a broad range of experiences, to a group more likely to experience parental suicidal behavior and substance abuse (Menard et al., 2004). In addition, when examining differing types of maltreatment, researchers found differing experiences of maltreatment; that is, some children experienced supervisory neglect along with

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emotional maltreatment, and another group of children were more likely to experience sexual abuse along with both emotional neglect and maltreatment, which were differentially related to later developmental domains, such as cognition or internalizing and externalizing behaviors (Pears, Kim, & Fisher, 2008). Other researchers who have examined differential types of ACEs have found between four and five subgroups of participants (Cavanaugh et al., 2015; Merians et al., 2019), including differences in the severity of each adverse experience (Roos et al., 2016). In one study, authors found four groups—participants who were more likely to experience a high number of ACEs, participants who were more likely to have a moderate risk for experiencing nonviolent household dysfunction, participants who were at risk for emotional and physical abuse, and participants who had a low risk of experiencing any adversity in childhood (Merians et al., 2019). Due to the growing body of research showing that families experience differences in the types of adversity to which they are exposed, the current study aimed to incorporate this more person-centered approach to examining the associations of maternal histories of adversity not only with mothers’ own mental health but also with their children’s socio-emotional development. To do so, the current study examined differing subgroups of maternal ACEs using a latent class analysis approach, rather than through a single cumulative risk score. This approach builds upon the previous literature examining the intergenerational transmission of ACEs, which has mostly examined the cumulative impact of ACEs. Intergenerational Transmission of ACEs Parental experiences of adversity in childhood are linked to negative outcomes for children’s development, including their socio-emotional functioning, physical health, and risk for developmental delays. For example, maternal adversity can increase children’s developmental

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risks, including delays in the areas of language, motor skills, or socio-emotional behaviors (Sun et al., 2017), and there may be greater risk for these negative outcomes as the number of parental adverse experiences increases (Folger et al., 2018; McDonald et al., 2019). The associations between these early parental experiences of adversity and children’s development may be lasting, with links to children’s development in both infancy (Racine, Plamondon, Madigan, McDonald, & Tough, 2018) and in later development (Schickedanz et al., 2018). Mechanisms for intergenerational transmission of risk. Understanding the ways in which early maternal adversity is related to children’s later development as well as the mechanisms through which such intergenerational risk may be transmitted has important implications for supporting mothers, particularly prior to or during pregnancy, in order to intervene and provide early support. There are several processes that may contribute to the intergenerational transmission of risk, in which mothers’ experiences of maltreatment or adversity, more broadly, are transmitted to their children, including through connections to children’s brain development in utero (Buss et al., 2017), increases in the risk of maltreatment towards their own children (Bartlett, Kotake, Fauth, & Easterbrooks, 2017), and increases in mental health issues, such as depression (Spieker, Oxford, Fleming, & Lohr, 2018). Maternal history of ACEs is related to children’s development in infancy, including motor skills, communication, and social skills, through both psychosocial risk factors during pregnancy, such as lack of social support and poor mental health, as well as through maternal and infant physical health risks during pregnancy and birth (Racine et al., 2018). As such, maternal mental health is one mechanism that is not only important for children’s socio-emotional development, but may also be directly related to mothers’ own childhood experiences (e.g., McDonald et al., 2019; Plant, Jones, Pariante, & Pawlby, 2017; Sun

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et al., 2017). Indeed, maternal histories of ACEs are detrimental to their mental health, particularly depression; furthermore, these associations may be lasting, with depressive symptoms showing both prenatally and postnatally (McDonnell & Valentino, 2016). Maternal depression has negative implications for children’s socio-emotional development, particularly in the early years when children are learning to co-regulate their emotions along with their caregiver and when dyadic interactions between children and caregivers play a key role in the development of self-regulatory systems (Easterbrooks, Bartlett, Beeghly, & Thompson, 2013). For example, studies examining maltreatment as one indicator of early adversity have found indirect links to pre-adolescent children’s internalizing and externalizing behaviors through both prenatal and postnatal depression (Plant et al., 2017). Other types of adverse experiences, including maltreatment and household dysfunction, predict prenatal depression, and maltreatment may even be associated with a smaller magnitude in the decrease of maternal depression over time (McDonnell & Valentino, 2016). There is growing evidence that mothers who experience more ACEs are at a higher risk of experiencing postpartum depression than those mothers who experience less or no ACEs (McDonald et al., 2019). Current Study Hypotheses It is important to investigate the diversity and variability among ACEs and their implications within a sample, such as young mothers, who are likely to experience considerable adverse exposures during childhood as well as challenging environments following childbirth. Childbearing during the teenaged years is associated with adversities such as childhood history of maltreatment, family violence, parental mental illness, parental substance abuse, and parental incarceration (Hillis, Anda, Dube, Felitti, Marchbanks, & Marks, 2004; SmithBattle & Leonard, 2012). Similarly, young mothers are more likely than older parents to experience poverty,

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unemployment, housing instability, and other life stressors (for a review, see Easterbrooks, Menon, & Katz, 2019). Thus, this population allows for meaningful examination of a range of ACEs and their associations with later adaptation. In investigating eight categories of ACEs, including verbal abuse, sexual abuse, physical abuse, having a mentally ill household member, having an incarcerated household member, living with a household substance abuser, having separated or divorced parents, and witnessing domestic violence, we hypothesized that there would be variation among the types of ACEs that young mothers in our sample experienced in that not all mothers would experience all or the same types of ACEs. We expected that there would be a class of mothers who experienced few adversities in childhood and a class of mothers who experienced a high number of adversities in childhood. Due to the exploratory nature of latent class analysis methodology and to the inconsistent results of other studies, we did not develop specific hypotheses around the exact number of classes of ACEs that we would find or how the ACEs would cluster together across classes, other than a high- and low-risk class. We also hypothesized that there would be differences in both maternal mental health and children’s socio-emotional functioning across classes, with the class who experienced a high number of adversities fairing worse than other classes identified on maternal depression and child socioemotional functioning, but other class differences would arise depending on how ACEs clustered. Finally, we hypothesized that among those classes of ACEs in which mothers’ children experienced poor socio-emotional functioning, as characterized by internalizing and externalizing problems, more maternal depressive symptoms would predict worse socioemotional functioning. Method Participants and Procedure

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Data for the current study were drawn from a longitudinal, randomized controlled trial evaluation of Healthy Families Massachusetts, a universal statewide home visiting program for first-time, young parents under the age of 21 across the state of Massachusetts (Jacobs et al., 2016). Mothers were randomly assigned to program (n = 517) or control groups (n = 320). Program group participants were offered home visits from pregnancy until their firstborn child’s third birthday, and control group participants were offered information about child development and referrals to services only. Eligibility criteria included being female, 16 years or older, an English or Spanish speaker, cognitively able to provide informed consent, and no prior program participation. A total of 704 mothers participated in evaluation activities, which included, at a minimum, an agency data release or an initial (Time 1 [T1]) phone interview. Data were collected at six time points (T1-T6) from 2008 to 2017, through in-person interviews and observations, and semi-structured interviews conducted over the phone, collecting information such as mothers’ residential circumstances, maternal and child well-being, maternal parenting practices, use of public assistance, and demographic information. In addition, data were collected from multiple state agencies. Analyses for the current study were restricted to a subsample of mother-child dyads (n = 407) with data at T6, when children’s socio-emotional functioning was assessed at around age 8. Approximately 60% of dyads in the final analytic sample were program participants, and 40% were from the control group. Demographic information was collected through maternal interviews. Measures Adverse Childhood Experiences (ACEs). The Adverse Childhood Experiences questionnaire (ACES; Centers for Disease Control and Prevention, 2010) is a survey used to assess childhood (before age 18) experiences of maltreatment and family dysfunction. The

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questionnaire was adopted from the ACEs survey administered by the CDC and does not include questions on physical or emotional neglect, which were included in the original study (Felitti et al., 1998). The questionnaire includes items such as “Did you live with anyone who was depressed, mentally ill, or suicidal?”, “Did you live with anyone who used illegal street drugs or who abused prescription medications?”, and “How often did your parents or adults in your home ever slap, hit, kick, punch, or beat each other up?”. Mothers completed the questionnaire retrospectively at T5. The eight binary (0 = ACE not present, 1 = ACE present) indicators used in the latent class analysis were created from the survey questions to represent eight categories of adversities, including verbal abuse, sexual abuse, physical abuse, having a mentally ill household member, having an incarcerated household member, living with a household substance abuser, having separated or divorced parents, and witnessing domestic violence. The items in this sample had good internal consistency (α = 0.76). Maternal depression. Maternal depression was assessed at T1-T3 using the Center for Epidemiological Studies Depression Scale (CES-D; Radloff, 1977). The scale is a 20-item selfreport questionnaire designed to capture depressive symptoms in the general population. Mothers in the study responded to items such as “I felt that I could not shake off the blues even with help from my family or friends” and “I was bothered by things that usually don't bother me” using a four-point Likert scale (0 = not at all to 3 = a lot) indicating how frequently they experienced a particular depressive symptom in the last week. An overall scale score was created by summing the 20 items, where a higher score represented more depressive symptoms. The CES-D is not intended for diagnosis, but individuals with scores of 16 or higher are considered of clinical significance for depression. Reliability across the three time points was high (α = 0.74; α = 0.80; α = 0.78; respectively). In the current sample, mother-reported symptoms of depression ranged

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from 0 to 55, and decreased over time, with average scores of 15 at T1, 13 at T2, and 12 at T3. Scores of depressive symptoms at the three time points were significantly correlated with each other, ranging from r = 0.43 to r = 0.51. Children’s socio-emotional functioning. Children’s socio-emotional functioning was assessed at T6 when children were around 8 years of age (M = 7.80, SD = 0.58) using the 25item Strengths and Difficulties Questionnaire (SDQ; Bourdon, Goodman, Rae, Simpson, & Koretz, 2005). Mothers reported on children’s emotional problems (e.g., nervous or clingy in new situations, easily loses confidence), conduct problems (e.g., often loses temper), hyperactivity (e.g., constantly fidgeting or squirming), and peer problems (e.g., gets along better with adults than with other children) using a Likert scale (0 = not true to 2 = certainly true). The externalizing behaviors subscale consisted of the conduct problems and hyperactivity items, and a sum score was created, ranging from a possible 0 to 20, where a higher score indicated more externalizing problems. The internalizing behaviors subscale combined the emotional symptoms and peer problems items. A sum score was created, with a possible range of 0 to 20, where a higher score indicated more internalizing behaviors. Finally, a total difficulties subscale was created by summing all of the subscales together, resulting in a possible range of 0 to 40, with higher values indicative of more difficulties. Overall reliability for the questionnaire was good (α = 0.72). In the current sample, scores ranged from 0 to 35 for the total difficulties scale and 0 to 18 for the two subscales. Overall, mothers reported children’s total difficulties to be around nine, on average, close to four on the internalizing behaviors subscale, and approximately six on the externalizing behaviors subscale. Analytic Approach

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Latent class analysis (LCA; Raykov, 2018) is a person-centered technique that is used to identify mutually exclusive and exhaustive subgroups of participants within the population of interest based on similar patterns of responses to indicator variables. LCA was used to identify subgroups of mothers based on probable class membership on the eight categorical risk indicators from the ACEs questionnaire. Each maternal adverse childhood experience was used as an indicator of the latent classes. The modified Bolck, Croon, and Hagenaars (BCH; Asparouhov & Muthen, 2018) method was then used to estimate distal outcomes; the BCH method allows for examination of the relationship between class membership and additional observed variables, using weights to take into account the probability of class membership while avoiding unwanted shifts in the latent classes. Specifically, mean estimates of children’s socioemotional functioning at T6 using scores on the SDQ as well as maternal depression at T1-T3 were assessed for differences across identified classes. Subsequently, in order to examine maternal depression as a mechanism through which maternal ACEs are associated with children’s socio-emotional functioning and continue considering the weighted probabilities of class membership, a secondary model of class-specific regression analyses, in which children’s socio-emotional functioning was regressed on all three time points of maternal depression jointly, were conducted. Maternal age at childbirth, maternal race/ethnicity (Non-Hispanic White, Black/African American, Hispanic, or other), program status (0 = control participant, 1 = program participant), and whether mothers had graduated from high school or completed their GED (0 = dropped out, 1 = completed HS/GED) were included in the regression analyses as covariates. Program status was included as a control variable to consider any associations the program may have had in supporting both mothers’ and their children’s mental health and wellbeing. All analyses were conducted using MPlus 8 (Muthen & Muthen, 2017). Full Information

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Maximum Likelihood (FIML) was used to handle missing data. Missingness ranged from 10 to 16% in the overall sample, with less than 5% missingness in the analytic sample for all variables used in the LCA. Missingness among the auxiliary variables ranged from 1 to 14% in the overall sample. Results Sample Characteristics As Table 1 summarizes, on average, mothers were 18.8 years old (SD = 1.3) at the birth of their child and 26.58 years old (SD = 1.37) at T6. Children in the sample were 7.8 years (SD = 0.58) at T6, and a little over half (52.3%) were male. Mothers were of diverse ethnic backgrounds, with approximately 35% of mothers self-identifying as Non-Hispanic White, 36% as Hispanic, 23% as Black or African American, and 6% as other, and most were born in the U.S. (82.3%). The majority of mothers (81.1%) received a high school degree or completed a GED program by T6. Mothers who participated in the study at T6 differed significantly from mothers who dropped out of the study after T1 with respect to a few demographic indicators; mothers in the T6 sample were more likely to identify as non-Hispanic Black and less likely to have dropped out of high school than mothers who were not retained in the study. Over half of mothers in the sample (55%) reported experiencing three or more ACEs in their childhood, with approximately 12% experiencing two ACEs, about 18% experiencing one ACE, and 14% reported never having experienced an ACE.

Model Selection

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To determine the best-fitting solution, models with increasing numbers of classes were compared up to a total of five latent class models. A high number of initial random starts (2000) was used to avoid local solutions (i.e., a false maximum likelihood). Class selection was based on substantive interpretation of the classes and examination of several goodness of fit indices, including the Bayesian Information Criterion (BIC) and the sample size adjusted BIC, the Approximate Weight of Evidence Criterion (AWE), the Lo-Mendell-Ruben adjusted likelihood ratio test (LRT) and the bootstrap LRT, along with entropy values. Smaller values on the BIC, adjusted BIC, and the AWE are representative of better fitting models. The likelihood ratio tests compare models with k and k-1 (H0) classes, and a significant test on the LRT indicates that the estimated model is more desirable than the model with one fewer class. Entropy values close to one are indicative of classes with better separability. The four-class model showed the lowest BIC and adjusted BIC. The AWE value for a two-class solution was the lowest. In addition, although entropy was not the highest for the four-class solution, it still showed acceptable separability (0.6-0.8). Furthermore, both the LRT and the bootstrap LRT indicated that a fourclass model was a better fitting model than a three-class option. Starting with the five-class option, the BIC and sample-size adjusted BIC began to increase, and the LRT comparing the four-class (H0) to the five-class model was retained, indicating the five-class solution was not a better fitting model than the four-class solution. Taken together, a four-class model provided an optimal solution (see Table 2). Class Characteristics Labels were assigned to each class to best represent the unique patterns of responses to the risk indicators (see Figure 1). Class One was characterized as High Risk for Household

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Dysfunction with Moderate Other Risk, and 26.3% of the sample comprised it. Mothers in this class had a high probability of living with a household member during childhood who abused substances or who had a mental illness, as well as to having separated or divorced parents. In addition, the risk for other ACEs was moderate. Around 16.4% of the sample fell into Class Two, which was characterized as High Risk for Abuse and Moderate Other Risk. This class had a high probability of experiencing verbal or physical abuse during childhood and a moderate risk of experiencing other adverse childhood experiences, including sexual abuse. The largest class, with 40% of the sample, was Class Three, characterized as Low Risk, with a low probability for experiencing any ACE, with the exception of having separated or divorced parents. Finally, 17.3% of the sample were in Class Four, which was characterized as High, Multiple Risk, with a high probability of mothers’ experiencing all or most of the eight ACEs. All classes had a moderate to high risk of having separated or divorced parents during childhood. See Table 3 for additional demographic information about each class.

Auxiliary Models Auxiliary models were conducted to compare mean levels of children’s socio-emotional functioning and maternal depression across the four classes (see Figure 2). Subsequent analyses examined class specific regression analyses (see Table 4).

High-Risk for Household Dysfunction and Moderate Other Risk class. Children of mothers in the High-Risk for Household Dysfunction and Moderate Other Risk class showed significantly less internalizing behaviors than children of mothers in the High, Multiple-Risk

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class. Further, at T1, mothers in this class showed average levels of depression at or above 16, which is indicative of being at risk for clinical levels of depression. Finally, maternal depression at T3 was significantly and positively associated with children’s later internalizing behavior in that when mothers showed higher levels of depression, their children showed more internalizing problems. High-Risk for Abuse and Moderate Other Risk class. Children of mothers in the HighRisk for Abuse and Moderate Other Risk class showed significantly less externalizing behaviors than children in the High, Multiple-Risk class. In addition, at no time point was maternal depression significantly associated with children’s socio-emotional functioning. Low-Risk class. Children of mothers in the Low-Risk class scored significantly better than children of mothers in the High, Multiple-Risk class across all indicators of socio-emotional functioning, showing significantly less total difficulties and less externalizing and internalizing behaviors. In addition, mothers in the Low-Risk class showed significantly less depressive symptoms than mothers in the High, Multiple-Risk class and in the High-Risk for Household Dysfunction and Moderate Other Risk class at both T1 and T2. Furthermore, mothers in the Low-Risk class continued to show less depressive symptoms than both of these classes as well as the High-Risk for Abuse and Moderate Other Risk class by T3. Among dyads in the Low-Risk class, maternal depression at T3 was significantly associated with children’s’ socio-emotional functioning across all indicators, with an increase in depressive symptoms predicting more total difficulties as well as internalizing and externalizing problems. High, Multiple-Risk class. Mothers in the High, Multiple Risk class showed average levels of depression above 16 at both T1 and T2, which is indicative of being at risk for clinical

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levels of depression. Among dyads in this class, increased maternal depressive symptomology at T2 was significantly predictive of more total difficulties for children.

Discussion The current study extends the literature in several ways. First, the results build upon the recently expanding research which captures a nuanced picture of what types of adversity individuals experience in childhood. By utilizing latent class analysis, we determined that there were different patterns of adverse childhood experiences, including those characterized by few ACEs, ACEs that focused on dysfunctional family circumstances, ACEs that focused on child abuse, and a group in which risk for multiple ACEs was high. Further, the results extend the literature on intergenerational impacts of childhood adversity by using the subgroups identified through LCA to examine how each subgroup was differentially associated with mothers’ own and their children’s development, in comparison to most studies of the transmission of childhood risk, which have used parents’ cumulative risk score. The first aim of the study was to capture the heterogeneity in risk factors for children and families experiencing adversity. Four classes consisting of different types of risk experienced in mothers’ childhoods were identified (see Figure 1). One class was characterized by mothers having a high probability of experiencing household dysfunctional adversity, such as living with a household member who abused substances or had a mental illness, or having separated/divorced parents, and a moderate probability of experiencing any of the other adversities. In another class, mothers had a high probability of experiencing verbal and physical abuse and a moderate probability of experiencing the other types of adversity; mothers in a third class had a low probability of experiencing any adversity; and mothers in the last group had a

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high probability of experiencing all adversities. These findings are similar to those classes found in another study that utilized LCA with a sample that was characterized as low risk and identified mostly as White, in which there was a high-risk class, a class with a moderate risk of nonviolent household dysfunction, a class with a high risk for emotional and physical abuse, and a low-risk class (Merians et al., 2019). In contrast, the current study sample is a diverse group of young mothers who were characterized as more high risk (over half of the sample experienced 3 or more ACEs in childhood) than the sample in the study by Merians and colleagues. The similarities in the results of both studies indicate that there may be certain typologies of adversity that tend to group together (such as abuse or household dysfunction), despite other risk factors. Other studies have similarly found classes of high-risk and low-risk classes, with additional classes that differ across samples (e.g., Cavanaugh et al., 2015; Menard et al., 2004). The second aim of our study was to incorporate an intergenerational approach. As such, we examined differences in children’s socio-emotional functioning (i.e., total difficulties, externalizing behaviors, internalizing behaviors) depending on classes of mothers’ childhood adversity (see Figure 2). When looking at the combined total difficulties for children, children of mothers in the Low-Risk class scored significantly better than those children whose mothers were in the High, Multiple-Risk class, but there were no differences between children from the Low-Risk class and children from the High-Risk for Abuse nor the High-Risk for Household Dysfunction groups. These results are similar to those findings of Merians et al. (2019), who only found differences between the high-risk and low-risk classes, consistent with a cumulative risk score approach. Yet, more nuanced differences emerged when examining externalizing and internalizing behaviors separately. In addition to the differences between the Low-Risk class and the High, Multiple-Risk class, children from the High, Multiple-Risk group scored significantly

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worse than children from High-Risk for Household Dysfunction group in terms of their internalizing behavior, while children in the High, Multiple-Risk group did significantly worse than the High-Risk for Abuse group with regard to their externalizing behavior. It may be the case that maternal histories of household dysfunction, such as mental health, substance abuse or incarceration, play a bigger role in their children’s externalizing behaviors rather than their internalizing behaviors. For example, mothers who have experienced household dysfunction in their own childhoods might exhibit harsh or punitive parenting practices, contributing to the development of externalizing behaviors (Leve, Kim, & Pears, 2005). Alternatively, children’s socio-emotional functioning was assessed when they were 8 years of age, and it may be that the impact on children’s internalizing behaviors from maternal histories of household dysfunction have not manifested yet, as externalizing behaviors sometimes decrease over time, with internalizing behaviors manifesting later in development and subsequently increasing (Gilliom & Shaw, 2004). The final aim of the study was to examine a potential mechanism through which maternal childhood risk might be connected to children’s later socio-emotional development—namely maternal depression in early childhood. To capture a robust picture, maternal depression was examined at three times points, when mothers were prenatal or early infancy, when children were around 12 months of age, and again at around 24 months of age; on average, mothers’ depressive symptoms in all four classes decreased over time. This decrease over time concurs with other studies that have found that although depression tends to be higher postpartum for adolescent mothers than for older mothers, trajectories decline over time (Schmidt, Wiemann, Rickert, & Smith, 2006). Mothers in the High-Risk for Household Dysfunction class were, on average, at the clinical cutoff point for depression at the first time point; mothers in the High, Multiple-Risk

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class were, on average, above the clinical cutoff point at T1 and T2 and fell below by T3. Despite decreases in all four classes across time on average scores of maternal depression, the Low-Risk group did significantly better than both the High-Risk group and the High-Risk for Household Dysfunction group at T1 and T2 (see Figure 2). Overall, adversity in childhood was found to be associated with later maternal depression, yet certain types of adversity put mothers in the sample at a higher risk for such mental health challenges. Studies utilizing a cumulative risk score have found that mothers who experienced higher number of ACEs (regardless of type) experienced higher rates of postpartum depression (McDonald et al., 2019), which aligns with our finding that the High, Multiple-Risk class showed higher levels of depression across all three time points. Other studies have confirmed that both cumulative risk and certain types of risk increase the risk for later psychosocial problems such as depression (Madigan, Wade, Plamondon, Maguire, & Jenkins, 2017). Our findings indicate that mothers who experience multiple adversities in childhood as well as those mothers who are exposed to household dysfunction may need additional mental health supports during the postpartum time. Parental mental health or substance abuse issues in childhood may negatively influence parent emotional and physical availability, parent-child interaction, and the parent-child relationship during periods that are sensitive for both brain development and the development of attachment and socio-emotional functioning. Intervening during pregnancy or the early postpartum period may be critical for supporting these mothers’ mental health, and their obstetrician or their child’s pediatrician may be one gateway to resources (Hodgkinson, Beers, Southammakosane, & Lewin, 2014). Preparing medical professionals to engage in more in-depth conversations around what type of adversity mothers have experienced will help illuminate such exposure, leading to more effective prevention and intervention efforts.

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To capture whether these differences in maternal depression played a role in children’s socio-emotional functioning, we conducted class-specific regression analyses (see Table 4). For children from the High-Risk for Household Dysfunction class, maternal depression at T3 was a significant predictor of children’s internalizing behaviors. For children from the Low-Risk class, maternal depression played a role in children’s socio-emotional problems across indicators, highlighting the role that depression can play in children’s development, regardless of other risk factors. For the High, Multiple-Risk class, maternal depression at T2 was a significant predictor of children’s total difficulties. Yet, effect sizes for each of the significant models was small to moderate, ranging from 10% to 37% of the variance explained in socio-emotional functioning, indicating that additional factors, such as parenting (e.g., Schickedanz et al., 2018) may be important as well; this reasoning may be particularly true for the High-Risk for Abuse class, in which maternal depression unexpectantly did not predict children’s socio-emotional functioning. For individuals who experience maltreatment in childhood, the impact of depression may not make an additive contribution beyond the adversity already experienced. Alternatively, some studies have found that multiple psychosocial risk factors, such as anxiety, being a single parent, or low economic resources, along with depression, rather than depression alone, play a role in children’s externalizing and internalizing behaviors (Letourneau et al., 2019; Madigan et al., 2017). Future studies should consider mental health within a larger context of psychosocial risk when looking at mechanisms of intergenerational transmission of adversity. Limitations There are several limitations that warrant discussion. First, the current study utilized the ACEs questionnaire retrospectively, asking mothers the extent to which they had been exposed to any of the ACEs prior to turning 18. Although using the ACEs questionnaire in this format is

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typical in the literature due to its practicality (e.g., Folger et al., 2018; Schickedanz et al., 2018), a prospective and longitudinal measure of childhood adversity would likely capture a more complete picture of adversity. In addition, both the timing of when each adverse experience occurred as well as the intensity or duration likely impact the associations between early adversity and later outcomes, none of which is captured through this measure retrospectively. Further, analyses for the current study are drawn from a larger randomized controlled trial of a home visiting program with aims of supporting maternal and child health and well-being. Although we have controlled for program status in our analyses, these home visit aims are directly related to the maternal and child outcomes of interest. Finally, our measure of ACEs did not include the two items capturing childhood neglect. Although neglect is seen in many cases of maltreatment (U.S. Department of Health and Human Services, 2019) and is an important adversity to try to unpack, it is difficult to self-report on instances of neglect (Stoltenborgh, Bakermans-Kranenburg, & van IJzendoorn, 2013) and is often under-reported by individuals who have experienced it (McGee, Wolfe, Yuen, Wilson, & Carnochan, 1995). Nevertheless, it can be difficult to make comparisons across multiple studies when different indicators from the ACEs questionnaire are examined. Conclusions Understanding the differences in what risks individuals experience in childhood has important implications for not only how we research the impact of risk but also in regard to the practicalities of providing trauma-informed care. Some argue that the ease of implementation of the cumulative ACEs score as a screener for clinicians to use lends itself to be a more useful approach than LCA. Yet, results of this study, and others that have utilized LCA, finding multiple classes of individuals who have a higher probability of experiencing certain ACEs than

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others, supports the need for a more detailed look at how we use the ACEs questionnaire, both in research and in practice. In concert with previous work, the current study found that the High, Multiple-Risk for adversity class scored significantly worse on all child and maternal indicators than the Low-Risk for adversity class, but there were additional differences across classes that focused on type of adversity. The ACEs screener may be an initial tool for clinicians to use as the introduction to a more in-depth conversation about childhood adversity, but understanding the impact of certain ACEs beyond a sum score may help clinicians build a trusting relationship with mothers that will lead to more targeted, “precision” treatment approach. Having a more nuanced picture of childhood risk will allow clinicians to provide more targeted resources and referrals to programs, including resources for mental health. In addition, identifying potential risk during pregnancy allows for early intervention, which may be one avenue for breaking the intergenerational impact of ACEs (Racine et al., 2018; Sun et al., 2017). Further, assessing for childhood adversity during pregnancy may optimize on a time when mothers are currently seeking information around health and child development (Roggman, Cook, Peterson, & Raikes, 2008; Tandon, Parillo, Mercer, Keefer, & Duggan, 2008) and may be open to accessing needed resources.

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outcomes. Child Maltreatment, 21(4), 317–326. https://doi.org/10.1177/1077559516659556 McGee, R. A., Wolfe, D. A., Yuen, S. A., Wilson, S. K., & Carnochan, J. (1995). The measurement of maltreatment: A comparison of approaches. Child Abuse & Neglect, 19(2), 233–249. https://doi.org/10.1016/0145-2134(94)00119-F Menard, C. B., Bandeen-Roche, K. J., & Chilcoat, H. D. (2004). Epidemiology of multiple childhood traumatic events: Child abuse, parental psychopathology, and other familylevel stressors. Social Psychiatry and Psychiatric Epidemiology, 39(11), 857–865. https://doi.org/10.1007/s00127-004-0868-8 Merians, A. N., Baker, M. R., Frazier, P., & Lust, K. (2019). Outcomes related to adverse childhood experiences in college students: Comparing latent class analysis and cumulative risk. Child Abuse & Neglect, 87, 51–64. https://doi.org/10.1016/j.chiabu.2018.07.020 Metzler, M., Merrick, M. T., Klevens, J., Ports, K. A., & Ford, D. C. (2017). Adverse childhood experiences and life opportunities: Shifting the narrative. Children and Youth Services Review, 72, 141–149. https://doi.org/10.1016/j.childyouth.2016.10.021 Muthen, L. K., & Muthen, B. O. (2017). Mplus User’s Guide (6th ed.). Los Angeles: Muthen & Muthen. Negriff, S. (in press). ACEs are not equal: Examining the relative impact of household dysfunction versus childhood maltreatment on mental health in adolescence. Social Science & Medicine.

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Pears, K. C., Kim, H. K., & Fisher, P. A. (2008). Psychosocial and cognitive functioning of children with specific profiles of maltreatment. Child Abuse & Neglect, 32(10), 958–971. https://doi.org/10.1016/j.chiabu.2007.12.009 Plant, D. T., Jones, F. W., Pariante, C. M., & Pawlby, S. (2017). Association between maternal childhood trauma and offspring childhood psychopathology: Mediation analysis from the ALSPAC cohort. British Journal of Psychiatry, 211(3), 144–150. https://doi.org/10.1192/bjp.bp.117.198721 Racine, N., Plamondon, A., Madigan, S., McDonald, S., & Tough, S. (2018). Maternal adverse childhood experiences and infant development. Pediatrics, 141(4), e20172495. https://doi.org/10.1542/peds.2017-2495 Radloff, L. S. (1977). The CES-D Scale: A self-report depression scale for research in the general population. Applied Psychological Measurement, 1(3), 385-401. https://doi.org/10.1177/014662167700100306 Raykov, T. (2018). Latent Class Analsis in Social Science Research. Course Book. InterUniversity Consortium for Political and Social Research (ICPSR). Berkeley, CA: University of California at Berkeley. Roggman, L. A., Cook, G. A., Peterson, C. A., & Raikes, H. H. (2008). Who drops out of early head start home visiting programs? Early Education and Development, 19(4), 574–599. https://doi.org/10.1080/10409280701681870 Roos, L. E., Afifi, T. O., Martin, C. G., Pietrzak, R. H., Tsai, J., & Sareen, J. (2016). Linking typologies of childhood adversity to adult incarceration: Findings from a nationally representative sample. American Journal of Orthopsychiatry, 86(5), 584–593. https://doi.org/10.1037/ort0000144

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Sacks, V., & Murphey, D. (2018). The prevalence of adverse childhood experiences, nationally, by state, and by race or ethnicity. Child Trends. Schickedanz, A., Halfon, N., Sastry, N., & Chung, P. J. (2018). Parents’ adverse childhood experiences and their children’s behavioral health problems. Pediatrics, 142(2), e20180023. https://doi.org/10.1542/peds.2018-0023 Schmidt, R., Wiemann, C., Rickert, V., & Smith, E. (2006). Moderate to severe depressive symptoms among adolescent mothers followed four years postpartum. Journal of Adolescent Health, 38(6), 712–718. https://doi.org/10.1016/j.jadohealth.2005.05.023 SmithBattle, L., & Leonard, V. (2012). Inequities compounded: Explaining variations in the transition to adulthood for teen mothers’ offspring. Journal of Family Nursing, 18(3), 409-431. https://doi.org/10.1177/1074840712443871 Spieker, S. J., Oxford, M. L., Fleming, C. B., & Lohr, M. J. (2018). Parental childhood adversity, depressive symptoms, and parenting quality: Effects on toddler self-regulation in child welfare services involved families. Infant Mental Health Journal, 39(1), 5–16. https://doi.org/10.1002/imhj.21685 Stoltenborgh, M., Bakermans-Kranenburg, M. J., & van IJzendoorn, M. H. (2013). The neglect of child neglect: A meta-analytic review of the prevalence of neglect. Social Psychiatry and Psychiatric Epidemiology, 48(3), 345–355. https://doi.org/10.1007/s00127-0120549-y Sun, J., Patel, F., Rose-Jacobs, R., Frank, D. A., Black, M. M., & Chilton, M. (2017). Mothers’ adverse childhood experiences and their young children’s development. American Journal of Preventive Medicine, 53(6), 882–891. https://doi.org/10.1016/j.amepre.2017.07.015

MATERNAL ACES AND CHILDREN’S BEHAVIOR PROBLEMS Tandon, S. D., Parillo, K., Mercer, C., Keefer, M., & Duggan, A. K. (2008). Engagement in paraprofessional home visitation. Women’s Health Issues, 18(2), 118–129. https://doi.org/10.1016/j.whi.2007.10.005 U.S. Department of Health and Human Services, Administration for Children and Families, Administration on Children, Youth and Families, Children’s Bureau. (2019). Child Maltreatment, 2017.

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MATERNAL ACES AND CHILDREN’S BEHAVIOR PROBLEMS Table 1 Background and Demographic Information for Overall Sample n M (SD) Range Maternal age at child birth 407 18.80 (1.28) 15.84-21.43 Maternal race/ethnicity 407 Non-Hispanic White Black/African American Hispanic Other Born in the U.S. 407 Completed high school or GED T6 407 Child age at T6 407 7.78 (0.58) 6.34-9.43 Child sex (male) 407 Adverse childhood experiences (ACEs) 299 Experienced 0 ACEs Experienced 1 ACE Experienced 2 ACEs Experienced 3 or more ACEs Indicator Variables Verbal abuse 360 Physical abuse 353 Sexual abuse 352 Witnessing domestic violence 351 Substance abusing household 365 member Mentally ill household member 365 Separated or divorced parents 345 Incarcerated household member 343 Distal Outcomes Maternal depression T1 403 14.63 (10.44) 0-55 Maternal depression T2 374 13.27 (11.04) 0-52 Maternal depression T3 395 12.36 (9.68) 0-51 Total difficulties 349 9.10 (5.98) 0-35 Internalizing behaviors 361 3.51 (2.95) 0-18 Externalizing behaviors 367 5.62 (3.94) 0-18 Note. Table presents M (SD) for continuous variables and % for categorical variables. T1, T2, T3, T6 indicate wave of data collection.

31

%

34.9% 22.6% 36.1% 6.4% 82.3% 81.1% 52.3% 14% 18.4% 12.4% 55.2% 48% 38% 30% 33% 42% 39% 61% 28%

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Table 2 Goodness of Fit Indices for Model Selection Lo-Mendell Sample-Size Rubin Adj Bootstrap BIC Adj BIC AWE Entropy LRT LRT 1 Class 18705.08 18641.62 18885.30 2 Class 3330.21 3276.28 3481.51 .81 p < .0001 p < .0001 3 Class 3321.44 3238.95 3552.84 .79 p = 0.02 p < .0001 4 Class 3320.61 3209.57 3632.11 .75 p = 0.01 p < .0001 5 Class 3353.79 3214.20 3745.38 .80 p = 0.006 p = 0.10 Note. BIC = Bayesian Information Criterion, AWE = Approximate Weight of Evidence, LRT = Likelihood Ratio Test

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Table 3 Adverse Childhood Experiences and Maternal Demographic Characteristics by Class Membership Class 1 Class 2 Class 3 Class 4 Adverse Childhood Experiences, n = 299a Experienced 0 ACEs 43.3% 0.0% 0.0% 0.0% Experienced 1 ACE 56.7% 0.0% 0.0% 0.0% Experienced 2 ACEs 0.0% 0.0% 55.2% 0.0% Experienced 3 or more ACEs 0.0% 100.0% 44.8% 100.0% Class 1 Class 2 Class 3 Class 4 Demographic Characteristics, n = 365 Maternal age at child birth 18.97* 18.38 18.83 18.99* Maternal race/ethnicity * * Non-Hispanic White 43.9% 18.3% 28.8% 43.5% Black/African American 10.3% 20.8% 32.0% 17.9% Hispanic 41.3% 59.0% 31.7% 27.4% Other 5.6% 1.9% 7.6% 11.1% Completed high school or GED 88.9% 79.6% 9.4% 86.4% Note. Table presents percentage of participants within each class who experienced certain numbers of adversities in childhood. In Latent class analysis, class membership of participants in the sample is unknown, and as such, proposed class memberships are based upon the class in which participants had the highest probability of being a member. a The discrepancy in sample size is due to the use of the sum score, which requires no missing to be included, rather than individual indicators, which were used in the primary analyses. *significantly different compared to class 2 at p < .05

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Table 4 Regression Analyses Examining Associations between Maternal Depression and Children’s Socio-Emotional Functioning Across Classes Total Difficulties Externalizing Behaviors Internalizing Behaviors 2 2 b SE 95% CI p R b SE 95% CI p R b SE 95% CI p

R2

High Risk for Household Dysfunction T1 Depression 0.18 T2 Depression 0.05 T3 Depression 0.07 Program Status -0.22 Age at childbirth 2.20

0.10 0.15 0.11 2.02 0.67

[0.02, 0.34] [-0.19, 0.29] [-0.10, 0.25] [-3.53, 3.10] [1.11, 3.30]

.07+ .74 .48 .91 .001**

0.16 -0.02 -0.02 -0.62 1.30

0.07 0.10 0.08 1.47 0.45

[0.05, 0.28] [-0.18, 0.15] [-0.15, 0.12] [-3.04, 1.80] [0.55, 2.04]

.02* .88 .84 .67 .004**

0.01 0.08 0.10 0.55 0.81

0.06 0.06 0.05 0.86 0.37

[-0.09, 0.10] [-0.02, 0.19] [0.02, 0.18] [-0.87, 1.96] [0.20, 1.43]

.93 .21 .04* .53 .03*

Race/ethnicity HS/GED

0.75 2.05

[-1.35, 1.11] [-4.67, 2.07]

.87 .53

-0.35 -0.75

0.54 1.30

[-1.24, 0.53] [-2.89, 1.38]

.51 .56

0.28 -0.59

0.35 1.65

[-0.29, 0.86] [-3.29, 2.12]

.42 .72

High Risk for Abuse T1 Depression T2 Depression T3 Depression Program status Age at childbirth Race/ethnicity HS/GED Low Risk T1 Depression T2 Depression T3 Depression Program status Age at childbirth Race/ethnicity HS/GED

-0.12 -1.30

.30

.31

.37 0.19 -0.00 -0.09 2.42 -2.21 -3.42 -0.86

0.19 0.14 0.18 2.47 0.97 1.76 3.45

[-0.12, 0.50] [-0.24, 0.23] [-0.39, 0.21] [-1.65, 6.49] [-3.80, -0.62] [-6.31, -0.53] [-6.53, 4.82]

.32 .98 .61 .33 .02* .05+ .80

.26 0.02 -0.02 0.02 1.92 -1.19 -1.32 -1.32

0.10 0.10 0.10 1.37 0.55 1.00 1.83

[-0.14, 0.18] [-0.18, 0.14] [-0.14, 0.18] [-0.32, 4.17] [-2.09, -0.30] [-2.96, 0.32] [-4.34, 1.69]

.85 .84 .81 .16 .03* .19 .47

.12 -0.10 -0.01 0.21 -0.87 -0.28 -0.59 3.24

0.06 0.06 0.09 0.99 0.41 0.56 2.44

[-0.21, -0.00] [-0.11, 0.10] [0.05, 0.36] [-2.50, 0.76] [-0.96, 0.40] [-1.50, 0.33] [-0.77, 7.25]

.10 .92 .03* .38 .51 .29 .18

.29

.34 0.11 -0.00 -0.04 0.73 -0.63 -1.82 0.63

0.11 0.06 0.11 1.27 0.46 0.95 1.53

[-0.07, 0.29] [-0.10, 0.10] [-0.21, 0.14] [-1.35, 2.82] [-1.38, 0.13] [-3.38, -0.26] [-1.88, 3.15]

.32 .95 .73 .56 .17 .06+ .68

[-0.10, -0.00] [-0.04, 0.06] [0.03, 0.17] [-1.07, 0.54] [-0.36, 0.30] [-0.63, 0.21] [-0.67, 2.66]

+

.11 -0.08 0.01 0.12 -0.74 -0.13 -0.34 2.33

0.05 0.04 0.06 0.65 0.29 0.38 1.49

[-0.15, -0.00] [-0.06, 0.08] [0.02, 0.22] [-1.80, 0.32] [-0.61, 0.35] [-0.97, 0.28] [-0.12, 4.78]

.10 .81 .04* .25 .66 .37 .12

.10 -0.05 0.01 0.10 -0.26 -0.03 -0.21 1.00

0.03 0.03 0.04 .49 0.20 0.26 1.01

.09 .72 .02* .59 .88 .42 .33

MATERNAL ACES AND CHILDREN’S BEHAVIOR PROBLEMS High, Multiple Risk

.30

35 .26

T1 Depression -0.03 0.08 [-0.16, 0.09] .66 -0.10 0.06 [-0.20, -0.00] .10 0.08 0.04 [-0.09, 0.28] T2 Depression 0.27 0.13 [0.06, 0.48] .04* 0.11 0.09 [-0.03, 0.26] .19 0.13 0.07 [0.01, 0.24] T3 Depression -0.03 0.19 [-0.35, 0.28] .87 0.11 0.12 [-0.09, 0.31] .37 -0.09 0.08 [-0.22, 0.03] Program status 3.04 3.15 [-2.13, 8.22] .33 -0.20 2.07 [-3.61, 3.21] .92 2.33 1.45 [-0.06, 4.72] Age at childbirth 0.64 0.85 [-0.76, 2.04] .45 0.32 0.60 [-0.68, 1.31] .60 0.14 0.35 [-0.43, 0.70] Race/ethnicity 0.92 1.59 [-1.69, 3.52] .56 -0.24 0.95 [-1.80, 1.32] .80 0.94 0.65 [-0.13, 2.02] + HS/GED 5.84 4.04 [-0.80, 12.48] .15 4.91 2.85 [0.22, 9.60] .09 1.67 1.65 [-1.04, 4.38] Note. We present unstandardized regression coefficients and standard errors with 95% confidence intervals (CI) and R2 for each overall class model. T1, T2, T3 indicate wave of data collection. ** indicates significant at p < .01 * indicates significant at p < .05 + indicates significant at p < .10

.44 +

.06 .07+ .22 .11 .70 .15 .31

MATERNAL ACES AND CHILDREN’S BEHAVIOR PROBLEMS

Fig. 1. Probabilities of class membership for a four-class solution based on the Adverse Childhood Experiences indicators.

36

MATERNAL ACES AND CHILDREN’S BEHAVIOR PROBLEMS

37

Fig. 2. Mean differences in maternal depression at three time points and children’s socio-emotional functioning across classes. Significant mean differences at p < .05 are indicated with connecting lines to show comparisons across classes for each construct. T1, T2, T3 indicate wave of data collection.

Research Highlights


Person-centered analyses identify differences in maternal childhood adversity.




Maternal childhood adversity is associated with children’s behavior problems.




Maternal childhood adversity is associated with postpartum depression.




Postpartum depression partly links maternal adversity to child behavioral problems.

Lauren E Stargel: conceptualization, formal analysis, writing – original draft M. Ann Easterbrooks: funding acquisition, investigation, supervision, writing – review & editing