I ROWELL, TIFFANY A., M.A. MAY 2020 PSYCHOLOGICAL

ROWELL, TIFFANY A., M.A. MAY 2020 PSYCHOLOGICAL SCIENCES EXAMINING THE IMPACT OF PREGNANT BLACK WOMEN’S ADVERSE CHILDHOOD

EXPERIENCES THROUGH MATERNAL HEALTH AND BIRTH OUTCOMES (74 PP.)

Thesis Advisor: Angela Neal-Barnett, Ph.D.

In the United States, Black mothers and their infants are dying at an alarming rate compared to

White, Hispanic, Asian, and Native American infants. Psychosocial factors such as socioeconomic status, racism, and trauma have been cited as contributors to this health disparity.

In order to better understand potential risk factors that contribute to the rising infant mortality rate of Black babies, this study aims to examine the impact of adverse childhood experiences

(ACEs) on maternal health outcomes and infant birth outcomes. It was hypothesized that maternal ACEs would predict maternal distress, BMI, and blood pressure, as well as infant gestational age and birth weight. Additionally, it was hypothesized that maternal distress, BMI, and blood pressure would moderate the relationship between ACEs and birth outcomes. Results indicated that higher ACEs predicted lower levels of distress. There were no significant relationships between ACEs and maternal BMI, maternal, blood pressure, and infant birth outcomes. Lastly, the relationship between ACEs and birth outcomes was not moderated by maternal health variables. The findings from this study support an association between childhood trauma and distress that is likely found in trauma survivors who have difficulties with regulating their emotions and accurately conceptualizing their feelings of distress. Alternatively, the results may be explained by the resiliency of the mothers in this sample. Future work should continue to

examine the relationships between ACEs and maternal health and birth outcomes to understand risk and protective factors that influence the impact of trauma on maternal and infant health.

EXAMINING THE IMPACT OF PREGNANT BLACK WOMEN’S ADVERSE CHILDHOOD

EXPERIENCES THROUGH MATERNAL HEALTH AND BIRTH OUTCOMES

A thesis submitted To Kent State University in partial Fulfillment of the requirements for the Degree of Master of Arts

Tiffany A. Rowell

May, 2020 © Copyright

Except for previously published materials

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Thesis written by

Tiffany A. Rowell

B.A., University of North Carolina at Chapel Hill, 2017

M.A., Kent State University, 2020

Approved by

Angela Neal-Barnett, Ph.D. , Advisor

Maria S. Zaragoza, Ph.D. , Chair, Department of Psychological Sciences

James L. Blank, Ph.D. , Dean, College of Arts and Sciences

TABLE OF CONTENTS ...... v

LIST OF TABLES...... vi

ACKNOWLEDGEMENTS ...... vii

CHAPTERS

I. Introduction ...... 1

II. Methods...... 18

III. Results ...... 22

IV. Discussion ...... 25

REFERENCES ...... 32

LIST OF TABLES

Table 1. Descriptive Information and Bivariate Correlations Among Variables…………..………...55

Table 2. One-Way Analysis of Variance of BMI by Trimester and Games-Howell Post-Hoc

Comparisons…………………………………………………………………………………...……..56

Table 3. Regression Predicting Maternal Distress from ACEs………………………………..……..56

Table 4. Regression Predicting Maternal BMI from ACEs ……...... 57

Table 5. Regression Predicting Systolic Blood Pressure from ACEs…………………………...... 58

Table 6. Regression Predicting Diastolic Blood Pressure from ACEs……………………….……....58

Table 7. Regression Predicting Gestational Age from ACEs…………………………………...……59

Table 8. Regression Predicting Birth Weight from ACEs……………………...………………….…59

Table 9. Regression Predicting Gestational Age from the Interaction between ACEs and Distress…60

Table 10. Regression Predicting Gestational Age from the Interaction between ACEs and BMI.. …61

Table 11. Regression Predicting Gestational Age from the Interaction between ACEs and Systolic

Blood Pressure…………………………………………………………………………………...... 62

Table 12. Regression Predicting Gestational Age from the Interaction between ACEs and Diastolic

Blood Pressure…………………………………………………………………………………...... 63

Table 13. Regression Predicting Birth Weight from the Interaction between ACEs and Distress…..64

Table 14. Regression Predicting Birth Weight from the Interaction between ACEs and BMI………65

Table 15. Regression Predicting Birth Weight from the Interaction between ACEs and Systolic Blood

Pressure………………...………………………………………………………………………...... 66

Table 16. Regression Predicting Birth Weight from the Interaction between ACEs and Diastolic

Blood Pressure…………………………………………………………………………………...... 67

Acknowledgements

My completion of this thesis would not have been possible without the continual support of many individuals. First, I would like to thank my advisor, Dr. Angela Neal- Barnett, for her guidance and dedication to my growth as a researcher. I am also grateful for the feedback that I received from the members of my committee, Dr. Robert Stadulis, Dr. Josefina Grau, and Dr.

Jennifer Taber, who inspired me to think critically about this thesis. I am always thankful for my previous and current lab members, Dr. Martale Davis, Dana Pugh, Delilah Ellzey, Elizabeth

Jean, and Keaton Somerville, for their endless encouragement. To my family, Robert, Berdell, and Leslie, thank you for your unconditional love and support throughout the completion of this project. Lastly, I would like to thank the previous project coordinator, Jordan Lally, and research assistants, Aliyah Moyé, Bobbi Broom, and Erika Daniels, for their hard work in data collection and preparation.

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Introduction

Infant mortality is defined as the amount of infant deaths per 1,000 live births (Centers for Disease Control and Prevention [CDC], 2018). This rate was developed to measure a country’s health and progress, yet the United States continues to have one of the highest infant mortality rates among developed nations (Singh & Yu, 2019). One major area of concern focuses on the lives of Black infants. Black infants in the United States are far more likely to die before their first birthday compared to White, Hispanic, Asian, and Native American infants. In 2016, the infant mortality rate for Black infants was 11.4 deaths per 1,000 live births. This is greater than the 2017 national rate of 5.79 for all births (CDC, 2018).

The racial/ethnic divide for infant mortality has been present since the earliest collection of this data in 1850, when the rate was 216.8 for Whites and 340 for Blacks (Haines, 2008).

During this time, it was customary for infants to die due to weather, famine, war, and disease

(Caplow, Hicks, Wattenberg, 2001). It was not until the 1880s when there were attempts to improve public health by implementing changes relating to hygiene, nutrition, and living conditions (Caplow, Hicks, Wattenberg, 2001). From that point forward, sewers were installed in cities, central heating became more commonplace to protect infants from the cold, and eventually, antibiotics and vaccinations were created to prevent fatal diseases. The national infant mortality rate drastically dropped as a result of these changes (Caplow, Hicks, Wattenberg,

2001).

Despite the improvements in public health, Black infants were consistently dying at a higher rate compared to White infants (Haines, 2008; Singh & Yu, 2019). According to United

States National Vital Statistics System, between 1916 and 2017, the infant mortality rate declined annually by 3.1% for White infants and 2.6% for Black infants. This slow decline in

Black infant mortality has led to a disparity that has worsened over time. For example, this gap changed from 87% in 1916, to 64% in 1950, and 112% in 2017 (Singh & Yu, 1995; Singh & Yu,

2019). The CDC (2018) cites birth defects, preterm birth, low birth weight, maternal pregnancy complications, sudden infant death syndrome, and injuries as the current leading physical causes of infant mortality. While these physical factors explain why the infant died in that moment, there are still questions about what led to these complications at birth. Literature concerning the reproductive outcomes of Black infants posit that psychological distress stress stemming various social determinants also leads to infant mortality (Dominguez, Dunkel-Schetter, Glynn, Hobel, &

Sandman, 2008; Giurgescu et al., 2012; Lobel, Hamilton, & Cannella, 2008).

Stress and Black Maternal and Infant Health

For years, stress has proven to have harmful effects on the lives of Black women and their children (Hogue & Bremmer, 2005; Rich-Edwards et al., 2001; Rosenthal & Lobel, 2011;

Pieterse, Carter, & Ray, 2013; Wadhwa et al., 2001). Chronic stress is more common and detrimental in the daily lives of Black women compared to White women (Lu, 2010; Renae

Stancil, Hertz-Picciotto, Schramm, & Watt-Morse, 2000) and it can produce allostatic load, which is poor health outcomes due to stress (Beckie, 2012; Lupien et al., 2006; McEwen, 1998).

It also associated with higher rates of adult mortality (Borrell, Dallo, Nguyen, 2010; Howard &

Sparks, 2016) and lower birth weight in Blacks and Hispanics compared to Whites (Strutz et al.,

2014). Common chronic stressors for infant mortality include socioeconomic disadvantage, racism, and trauma.

Socioeconomic Status.

Poverty negatively impacts the lives of Black women and their children by subjecting them to financial struggles and interpersonal and community violence (Hardaway, McLoyd, &

Wood, 2012; McLoyd, Mistry, & Hardaway, 2014; Suglia et al., 2010). In many cases, poverty influences the national infant mortality rate (Komro, Livingston, Markowitz, & Wagenaar, 2016;

Olson, Diekma, Elliot, & Reiner, 2010; Singh & Yu, 2019), but this does not seem to be the driving force for Black infants. For example, in 2013, Black babies born to mothers who are highly educated and have middle-class backgrounds are more likely to die than babies born to lower-class White mothers with less than a high school education (Reeves & Matthew, 2016).

Moreover, Black individuals are subjected to disparities in access to health care opportunities and treatment that exist even when controlling for insurance status, income, age, and severity of condition (Nelson, 2002). Poverty plays a role in the chronic stress of Black women and ultimately the survival of their infant, but it cannot fully explain the nuances of these social problems.

Racism.

These issues of economic and health disparities can be better explained by structural and interpersonal racism (Brondolo et al., 2008; Kaholokula, 2016; Muennig & Murphy, 2011;

Phelan & Link, 2015; Willimas & Mohammad, 2013). Structural racism can be defined by a system where ideologies, institutions, and policies operate to produce racial and ethnic inequality

(Jones, 1991; Jones, 2000; Viruell-Fuentes, Miranda, Abdulrahim, 2012). This inequality creates a system that provides privilege and power to White individuals but harms minorities. Structural racism is difficult to pinpoint because it is comprised of societal, historical, cultural, political, and economic influences (Jones, 1991; Jones, 2000; Lawerence & Keleher, 2004). Relatedly,

interpersonal racism involves two or more people and it can be manifested through bigotry, bias, prejudice, and stereotyping (Jones, 1991; Jones, 2000; Nelson, 2002).

The weathering hypothesis is one theory that aims to highlight the relationship between racism and infant mortality in the Black population by focusing on culturally relevant biological, psychological, and social processes. At the time of its inception, there were reports of Black infants with teen mothers who were healthier than those with older mothers who were in their

20s and 30s, which is typically thought of as the optimal age range for pregnancy. In attempts to explain this phenomenon, Geronimus (1992) suggested that social inequities cause Black women’s health to deteriorate earlier in adulthood compared to White women. Further, the risk for mortality among Black babies increased with increasing maternal age, while it decreased for

White babies. Geronimus (1992) explains that prolonged exposure to social and environmental stressors such as structural and interpersonal racism and socioeconomic status can have negative consequences for Black women’s health and eventually, the health of the Black infant.

Earlier research in support of the weathering hypothesis demonstrated that Black women, especially those who lived in low-income areas, experienced poorer health between adolescence and young adulthood (Geronimus, 1996). For example, there was an increased risk of Black women experiencing pre-term delivery (Holzman et al., 2009) or having babies with low and very low birth weights (Geronimus, 1996) that was related to an increase in age. More recent studies have explored allostatic load and telomere length as biological/physiological mechanisms that explain the relationship between social and economic disadvantage to health disparities

(Forde, Crookes, Suglia, & Demmer, 2019). Blacks tend to experience higher levels allostatic load compared to Whites (Bird, 2010; Chyu, 2011; Kaestner, 2009; Peek, 2010), and Black women appear to the most affected (Geronimus, Hicken, Keene, & Bound, 2006). In regard to

telomere length, telomeres are caps on the ends of chromosomes that protect cells from aging

(Blasco, 2007). Telomere length can serve as a marker of biological age because telomeres shorten as one gets older and experiences stress (Blasco, 2007; Geronimus et al., 2015; von

Zglinicki, 2002). One study found that Black women had shorter telomere lengths than White women, with stress and socioeconomic disadvantage accounted for 27% of the difference

(Geronimus et al., 2010). Additionally, in a sample of Black men and women, those who reported more experiences with discrimination during adolescence experienced higher levels of depression, which was associated with cellular aging (Carter et al., 2019). This further supports the idea that stress can physically age an individual.

Research examining the relationship between racism and infant health outcomes has found that Black mothers’ reports of racial discrimination are negatively associated with birth weight (Domginguez et al., 2008). These accounts were also associated with the risk of uterine fibroid tumors (Wise et al., 2007), which can provide an explanation as to why many Black women have premature births (Ciavattini et al., 2015; Klatsky, Tran, Caughey, & Fujimoto,

2008).

Some studies posit that simply being a Black woman in the United States can produce deleterious generational effects, even when measures of perceived racism are not collected. It was found that American infants with Black African and Caribbean-born mothers had birth weights that were more similar to infants of White U.S.-born mothers than those of Black U.S.- born mothers (David & Collins, 1997; Pallotto, Collins, & Daivd, 2000; Singh & Yu, 1996).

Over two generations, there were marginal improvements in birth weight for the third generation descendants of Black U.S.-born women compared to White U.S.-born women. Surprisingly, this intergenerational improvement of birth weight was not present among descendants of Black

African and Caribbean-born immigrants. The third generation infants weighed less than their mothers who were the daughters of Black African and Caribbean-born immigrants and raised as

Americans (Collins, Wu, & David, 2002). These findings suggest that even the transition into becoming a Black American places these women at risk because of their exposure to inequities that are linked to race.

Trauma.

Research on racism as an antecedent for poor health outcomes in the Black community has been growing, but there is a need for a greater exploration of the early experiences with trauma as well. Previous research has found that adults with a history of maltreatment in childhood have greater activation of the hypothalamic-pituitary-adrenal (HPA) axis, which is an essential system for maintaining the balance of hormones. The potential for there to be overactivation of the HPA axis in these individuals is heightened and this can lead to the eventual allostatic load that has negative effects on overall physical health (Danese & McEwen,

2012). Considering that Black women are vulnerable to the consequences of allostatic load, adverse childhood experiences (ACEs) should be considered as a stress-inducing factor that impedes the health of Black mothers and their children.

Adverse Childhood Experiences

While racism and financial struggles can be the source of stress for so many Black women, trauma can add to their burden and produce medical and mental health problems.

Exposure to trauma can lead to long term consequences such as PTSD, anxiety, depression, psychotic symptoms, poor health behaviors, and poor health outcomes (Bonomi, Anderson,

Rivara, & Thompson, 2007; Breslau, 2009; Freeman & Fowler, 2009; Kaltman, Green, Mete,

Shara, & Miranda, 2010; Messina & Grella, 2006; O’Toole & Catts, 2008; Schnurr & Green,

2004. Suglia and colleagues (2010) found that among Black women, cumulative stress defined by interpersonal violence, discrimination, negative life events, and community violence is associated with lower morning cortisol levels. This is an indication of the disruption of normal hypothalamic-pituitary-adrenal (HPA) axis functioning, which is a pattern that can sometimes be found in PTSD and related stress disorders (Freidenberg et al., 2010; McFarlane, Barton,

Yehuda, & Wittert, 2011; Suglia et al., 2010).

Trauma at any point in life is damaging; however, it can be far more detrimental during childhood because children have fewer coping and defense skills compared to adults (Brodsky,

2016; Shonkoff et al., 2012). Children and adolescents who have been exposed to trauma experience deficits in psychological, cognitive, and behavioral areas of functioning (Armsworth

& Holaday, 1993; Cook et al., 2017; De Bellis, Spratt, & Hooper, 2011). Those who were exposed to physical and sexual abuse and physical neglect in childhood were more likely to develop symptoms of PTSD compared to those who did not experience such maltreatment

(Briere, Elliott, Harris, & Cotman, 1995; Epstein, Saunders, & Kilpatrick, 1997; Widom, 1999).

They may also experience a multitude of psychopathology such as anxiety, depression, bipolar disorder, schizophrenia, and suicidal ideation (Alvarez et al., 2011; Brodsky, 2016). In a longitudinal study that observed the consequences of childhood sexual abuse in Black women, it was found that these individuals presented with higher levels of anxiety, depression, dissociation, sexual concerns, intrusive symptoms, and in impaired sense of self (Baynard, Williams, and

Siegel, 2001). Regarding cognitive development, some studies have reported that children exposed to trauma were likely to be diagnosed with learning disabilities, receive poor grades in school, have lower IQs, and experience developmental delays in language and communication skills (Armsworth & Holaday, 1993; Bücker et al., 2012). Trauma during childhood can also lead

to behavioral issues such as disruptive behavior, withdrawal and isolation, and reenactment of the event (Armsworth & Holaday, 1993; Wilkinson, 2016). It appears that the experience of being abused or neglected by individuals who a child typically trusts can lead to neurobiological changes that can inhibit one’s ability to adequately respond to stressful situations and lead to developmental difficulties (De Bellis, Spratt, & Hooper, 2011; Teicher & Samson, 2016).

Given that adversity during childhood has a long-lasting impact, Felitti and colleagues

(1998) conducted the ACE Study to examine the impact of exposure to adverse events such as abuse and household dysfunction in childhood. Through a survey of 19,000 adults, they found that 52% of all participants experienced at least one type of ACE. Those who identified as Black and female were more likely to report experiencing more than one type of ACE compared to

White, Hispanic, and Asian counterparts. There was as dose-response relationship between number of ACEs and risk for health behaviors including alcoholism, drug abuse, depression, smoking, sexually transmitted infections, physical inactivity and obesity. Additionally, there was a graded relationship between number of ACEs and diseases such as ischemic heart disease, cancer, chronic lung disease, and liver disease (Felitti et al., 1998). This study expanded the understanding of how social, emotional, and medical problems are linked throughout an individual’s lifespan. The authors proposed that ACEs lead to social, emotional, and cognitive impairment, which can then increase the risk of adopting risky health behaviors that will likely cause disease, disability, social problems, and eventual early death.

Multiple studies have supported this model that explains the association between childhood trauma and health outcomes. For example, people who reported experiencing more

ACEs reported having poorer self-rated health, depressive symptoms, anxiety, and drug use

(Burke Harris, 2018; Danese et al., 2009; Dube, Cook, & Edwards, 2010; Mersky, Topitzes, &

Reynolds, 2013).

ACES Impacting Maternal Health

When narrowing the scope on pregnant women, ACEs predicted maternal physical health and psychosocial outcomes. For example, mothers who experienced physical and/or emotional abuse or household dysfunction during childhood were more likely to begin pregnancy with a chronic health condition such as diabetes, heart disease, hypertension, or obesity. Additionally, these mothers were more likely to report psychosocial difficulties such as low social support, history of mental health difficulty, high stress, and symptoms of anxiety and depression during pregnancy (Racine, Madigan, Plamondon, McDonald, & Tough, 2018). A separate study that examined the relationship between ACEs and risky health behaviors found that there was a higher prevalence of smoking, alcohol, and illicit drug use among expectant mothers who were exposed to at least one ACE compared to those who were not (Chung et al., 2010). ACEs not only impact the physical health of these mothers, but also their health behaviors, both of which have direct consequences for unborn children. For these reasons, it is essential to examine the influence of ACEs on health outcomes such as blood pressure, body mass index (BMI), and psychological distress when researching infant mortality.

Blood Pressure.

Blood pressure is a variable of interest when examining ACEs and health outcomes in

Black expectant mothers because it is predictive of various cardiac health-related conditions such as heart attacks and strokes that can be life threatening to the mother and her infant. In diverse populations, ACEs negatively impacted blood pressure. There appears to be a dose-response relationship between ACEs and blood pressure, such that in a sample of women, more reports of

a history of childhood physical and sexual abuse were associated with diagnoses of hypertension

(Riley, Wright, Jun, Hibert, & Rich-Edwards, 2010). Relatedly, adults who reported multiple traumatic events during their childhood tended to have hypertension and faster rises in blood pressure than those without a history of childhood adversity (Stein et al., 2010; Su et al., 2015).

When controlling for other cardiovascular risk factors such as age, BMI, and pharmacology use, the resting systolic and diastolic blood pressure was higher in individuals who experienced their first episode of schizophrenia and reported a history of general trauma, physical punishment, emotional abuse, and/or sexual abuse (Misiak, Kiejna, & Frydecka, 2015). In attempts to understand the link between childhood trauma and high blood pressure, Norman and colleagues

(2013) examined individuals’ perceived isolation because isolation and withdrawal are recurrent outcomes of trauma. In those who had higher levels of perceived isolation, there was a significant positive relationship between trauma and pulse pressure, which is systolic blood pressure minus diastolic blood pressure. The authors suggested that the quality of interpersonal relationships in adulthood may be a mediator between ACEs and pulse pressure.

BMI.

Although waist-to-height ratio has been shown to be a more reliable measure of obesity

(Ashwell & Hsieh, 2005), BMI is still commonly utilized in the ACE literature to assess weight- related problems. In some cases, these issues can arise during childhood. For example, children in grades 6 to 7 who reported four or more ACEs were more likely to have higher heart rates, waist circumference, and BMI. When controlling for potential extraneous variables such as family education, income, age, sex, physical activity, and parental history of hypertension, the experience of four or more ACEs was associated with clinical obesity (Pretty, O’Leary, Cairney,

& Wade, 2013).

BMI is an important variable to examine in relation to maternal and infant health because excessive gestational weight gain can lead to pregnancy complications and increases in the risk for childhood obesity. (Ranchod et al., 2016). Among pregnant Black women, obesity can be even more prevalent because they are more likely to experience food insecurity (Laraia, Siega-

Riz, Gundersen, & Dole, 2006). Individuals who are food insecure typically come from low- income areas and have limited access to a sufficient amount of nutritious food. This means that low-income pregnant Black women typically consume foods high in sodium and fat that are linked to obesity, hypertension, preeclampsia, and the delivery of a low birth weight baby

(Borders, Grobman, Amsden, & Holl, 2007; Siega-Riz & Laraia, 2006). An additional explanation for high weight gain in Black women may be the relationship between exposure trauma, binge eating, and the “Strong Black Woman” (SBW) ideology (Harrington, Crowther, &

Shipherd, 2010). SBW ideology developed in response to common negative stereotypical images of Black women that portrayed them as caretakers or hypersexualized and lazy individuals (e.g., the Mammy, Jezebel, and Welfare Queen). Black women wanted to be viewed as a positive symbol in their community and SBW ideology permitted them to do so (Beauboeuf-Lafontant,

2003; Harris-Lacewell, 2001; Woods-Giscombé, 2010). However, this can result in negative consequences when Black female trauma survivors internalize the thought that they must exude strength and be self-reliant, even while facing hardships (Beauboeuf-Lafontant, 2003; Harris-

Lacewell, 2001; Woods-Giscombé, 2010). As a result, these women likely have emotional inhibition and regulation problems that are associated with binge eating (Harrington, Crowther,

& Shipherd, 2010). Given that binge eating is linked to obesity (Leehr et al., 2015; Schag,

Schönleber, Teufel, Zipfel, & Giel, 2013) this culturally specific model for binge eating may be appropriate for some Black women.

In a study that examined the association between childhood adversity and pregnancy- related weight gain, it was found that exposure to physical abuse during childhood was associated with a 60% increase in the risk of pregnancy obesity. Additionally, childhood exposure to household alcohol abuse and a close relative with a mental health disorder was associated with a 30% increase in risk pre-pregnancy obesity. Physical abuse and household alcohol abuse were associated with a 20% increase in the risk of excessive gestational weight gain (Ranchod et al., 2016).

Psychological Distress.

The term psychological distress broadly encompasses perceived negative emotional experiences. Researchers often utilize symptoms of psychiatric disorders, specifically anxiety and depression to measure an individual’s level of distress. In a systematic review that evaluated

124 studies about ACEs, there were significant relationships between physical abuse, emotional abuse, and neglect and a range of psychological difficulties, including depressive disorders and suicide attempts (Norman, et al., 2012). Additionally, ACEs are associated with distress, anxiety, depressive, and somatic symptoms, PTSD, substance abuse, concentration problems, and sleep difficulties (Beutel et al., 2017; Bruskas & Tessin, 2013; Mersky, Janczewski, Nitkowski, 2018;

Kalmakis & Chandler, 2014; Widom, DuMont, Czaja, 2007). Given that distress has such negative consequences for an individuals’ wellbeing, its influence on maternal and infant health outcomes should be explored further.

To understand the underlying mechanisms that drive the relationship between ACEs and distress, Wright, Crawford, and Del Castillo (2009) considered schemas of vulnerability to harm, shame, and self-sacrifice as possible mediators. Both emotional abuse and neglect were associated with symptoms of anxiety and depression through these three schemas. Neglect alone

was associated with dissociative symptoms and this was mediated by schemas of shame and vulnerability to harm. Other researchers have examined emotion regulation strategies as potential mediators of the effect of ACEs on distress. Deficiencies in adaptive strategies such as cognitive reappraisal and the use of expressive suppression, a less adaptive strategy, can lead to increased levels of distress in adolescents who have experienced adversity in their childhood (Boyes,

Hasking, & Martin, 2015). In support of this finding, an intervention that focused on improving emotion regulation skills was successful in decreasing perceived stress, negative mood, and depressive symptoms and increasing positive mood and use of adaptive emotion regulation strategies in a sample of adults with an ACE history (Cameron, Carroll, & Hamilton, 2018).

While the evidence for the association between ACEs and psychiatric disorders has been extensive, there has been criticism about the magnitude of this relationship. In one study, the presence of psychiatric disorders had a weaker relationship with ACEs that were obtained through records from the childhood protection agency compared to ACEs that were obtained through self-report (Widom, Weiler, and Cottler, 1999). The reliability and validity of self- reported ACEs have been questioned because of evidence of nonreporting (Widom & Morris,

1997) and instability of reports of abuse (Fergusson, Horwood, & Woodward, 2000). Recent findings from Scott, Smith, and Ellis (2010) suggest that retrospective reports can be valid and reliable. In their study, reports from the childhood protection agency were associated with PTSD, mood disorders, anxiety disorders, and substance use disorders, even when retrospectively reported ACEs were removed from analyses.

Maternal Health Impacting Infant Health

Blood Pressure.

Blood pressure levels are not only indicative of maternal health, but of infant health as well. The risk for fetal and neonatal mortality and in-hospital mortality increased in hypertensive mothers compared to non-hypertensive mothers (Gilbert, Young, Danielson, 2007). Preeclampsia is a complication that typically arises after 20 weeks of pregnancy and is often caused by gestational hypertension. It is characterized by high blood pressure and protein in the urine.

Preeclampsia can detrimentally affect the mother and the infant so much so that in cases of extreme preeclampsia, preterm delivery is necessary. While early labor protects the infant from the risks associated with the preeclampsia in the womb, the infant is still at risk for morbidity and mortality because of poor fetal development (Backes et al., 2011; Bakker, Steegers, Hofman, &

Jaddoe, 2011).

The extent to which gestational hypertension influences birth outcomes can be dictated by trimester. Among women in their third trimester, high blood pressure is associated with low fetal weight and small fetal head circumference and fetal femur length. Additionally, rises in blood pressure between the second and third trimesters are related to increased probability of having infants who are preterm, low in birth weight, and small for their gestational age (Bakker,

Steegers, Hofman, & Jaddoe, 2011). The risk for these outcomes increases for Black infants because non-Hispanic Black women are more likely to have gestational hypertension and eventually poor outcomes for birth weight and preterm birth compared to non-Hispanic White and Hispanic women (Miranda et al., 2010). It is this disparity in maternal hypertension that may be one of the contributing factors to the rising mortality in Black infants.

BMI.

It is evident that ACEs affect maternal weight before and during pregnancy, to which the latter can be riskier for the mother and the child (Ranchod et al., 2016). For example, in extremely obese women, there was increased risk for low birth weight, NICU admission, and still birth (Crane, Murphy, Burrage, & Hutchens, 2013). Similar results were found in a population of Black women, such that those who were overweight and obese were more likely to have a spontaneous preterm birth (Wise, Palmer, Heffner, & Rosenberg, 2010).

Psychological Distress.

Similar to the aforementioned studies, ACEs influence maternal psychological distress.

They are specifically associated with antepartum depressive symptoms, postpartum depressive symptoms, suicidal ideation, and negative life events (Barrios et al., 2015; Benedict, Paine,

Paine, Brandt, & Stallings, 1999; Choi et al., 2017; Chung, Matthew, Elo, Coyne, Culhane, 2008;

Leeners et al., 2014; Mahenge, Stöckl, Mizinduko, Mazalale, & Jahn, 2018; McDonnell &

Valentino, 2016). These high levels of distress can then cause preterm births and lower birth weights, as well as poor fetal development and socioemotional development (Dole et al., 2003;

Lobel, Hamilton, Cannella, 2008).

ACES and Birth Outcomes

When examining the birth outcomes, a majority of the literature focuses on birth weight and gestational age because they are reliable predictors of an infant’s health. Birth weight not only predicts infant mortality (CDC, 2018; McCormick, 1985) but it also predicts developmental outcomes throughout life such as coronary heart disease, type 2 diabetes, and hypertension in adulthood (Barker, Erikson, Forsén, & Osmond, 2002). Lower gestational ages and preterm births are linked to poor health outcomes in childhood and (Boyle et al., 2012),

neurodevelopmental disabilities (Allen, 2008). There is often a focus on how maternal physical health influences these outcomes; however, environmental factors such as exposure to ACEs may also play an important role because they have been linked to increased fetal death after the first pregnancy (Hillis et al., 2004).

Following the trend in previous ACE literature, there is a dose-response relationship between ACEs and birth outcomes, such that the increase in endorsement of one ACE decreased birth weight by 16.33 grams and gestational age by 0.063 weeks (Smith, Gotman, & Yonkers,

2016). Similarly, reports of exposure to 2 or more ACEs was associated with a two-fold risk of preterm birth and increases in ACEs led to increase in risk for preterm birth by 18% (Christiaens,

Hegadore, & Olson, 2015). When controlling for smoking status and adult social class, childhood adversity is still associated with low birth weight and preterm birth (Harville,

Boynton-Jarrett, Power, & Hyppönen, 2010). Racine, Plamondon, Madigan, McDonald, and

Tough (2018) proposed a similar model to Felitti and colleagues (1998) concerning these associations between ACEs and birth outcomes. They posited that mothers who experience more

ACEs in childhood were more likely to experience health risks and complications during pregnancy that influenced the infant’s health at birth and at 12 months of age. This model supports the hypothesis that there may be a link between ACEs and birth outcomes that can be explained by maternal health factors such as blood pressure, BMI, and psychological distress.

The Present Study

Currently, there is a small body of literature that contains a sample of Black women and considers their previous trauma and current health as contributing factors in birth outcomes.

ACEs are associated with multiple health-related outcomes such as high blood pressure, obesity, and significant levels of distress that may negatively influence infant birth weight and gestational

age. For these reasons, this thesis aims to examine the relationship between pregnant Black women’s ACEs, blood pressure, BMI, and distress and infant birth outcomes in order to understand potential risk factors contributing to the rising infant mortality rate for Black babies in the United States. Specific hypotheses include:

1. Maternal ACEs will predict maternal psychosocial and physical markers of health such as

distress, BMI, and blood pressure such that participants with higher ACEs will be more

likely to have higher distress, BMI, and blood pressure.

2. Maternal ACEs will predict birth outcomes, such that participants who reported

experiencing more ACEs will have babies with lower gestational ages and birth weights.

3. Maternal distress, BMI, and blood pressure will moderate the relationship between

maternal ACEs and birth outcomes, such that the relationship between ACEs and birth

outcomes will be stronger and negative for mothers who have higher distress, BMI, and

blood pressure.

Method

Participants

The sample consisted of 31 Black expectant mothers from an urban city in the Midwest who were a part of a perinatal support program. Participants were enrolled in a larger study that examined the effectiveness of a 7-session culturally relevant anxiety intervention that highlighted topics related to overall stress, race-related stressors, and pregnancy-related anxiety (Manns-

James & Neal-Barnett, 2019; Neal-Barnett et al., 2011). All participants identified as Black and ranged in age from 17 to 41-years-old. Eighteen participants had a household income of less than

$10,000, seven had a household income between $10,000 and $15,000, and six had a household income greater than $25,000. Additionally, two participants were in their first trimester at the time of the study, seventeen were in their second trimester, and twelve were in their third trimester. Participants were compensated with a $40 gift card, food storage items, and a yoga mat. This study was approved by the Kent State University Institutional Review Board.

Measures

Adverse Childhood Experiences Questionnaire (Felitti et al., 1998). Maternal ACEs were measured with the Adverse Childhood Experiences Questionnaire that was developed from the ACE study by Felitti and colleagues (1998). The questionnaire is composed of 10 dichotomous items that required participants to disclose their experiences with household dysfunction or childhood maltreatment. There are five items concerning household dysfunction that included questions about exposure to household substance abuse, parental separation or

divorce, domestic violence, a family member with a mental illness, or a family member with criminal behavior. The other five items target the indicators of childhood maltreatment, such as emotional abuse, physical abuse, sexual abuse, physical neglect, and emotional neglect. The total score of ACEs can range from 0 to 10. This scale has shown to be a reliable and valid measure for childhood abuse and neglect in Black populations (Dong et al., 2004; Dube, Williamson,

Thompson, Felitti, & Anda, 2004; Murphy et al., 2014; Pinto, Correia, & Maia, 2014). The

Adverse Childhood Experiences Questionnaire also demonstrated good reliability in this study’s sample (α = .80).

Kessler Distress Scale (Kessler et al., 2002). Distress was measured with the Kessler

Psychological Distress Scale, which is a 10-item scale that broadly assesses the level of distress that an individual has experienced within the past 30 days. The items target symptoms related to anxiety (e.g., Did you feel nervous?) and depression (e.g. Did you feel so sad that nothing could cheer you up?). Item responses existed on a Likert scale ranging from 1 (none of the time) to 5

(all of the time). Composite scores consisted of the total sum of item responses and scores can range from 10 to 50. The measure has shown to be reliable and valid in African American men and women (Krieger, Smith, Naishadham, Hartman, & Barbeau, 2005) and in this sample (α =

.86).

Maternal BMI. Participants’ height was measured with a stadiometer (seca model 213) in centimeters and their weight was measured in kilograms. Height was measured twice, weight was measured once, and the means of these values were used to calculate BMI calculated with an online calculator provided by the National Heart, Lung, and Blood Institute (2006) that converted centimeters to meters and used the following equation: kg/m2. The test-retest reliability of height was good for this study (r = .99, p < .001). Additionally, BMI has been

shown to be a valid and reliable measure of body fatness (Deurenberg, Weststrate, Seidell, &

1991; Prentice & Jeb, 2001).

Maternal Blood Pressure. Systolic and diastolic blood pressure were measured and recorded once with an automated upper arm blood pressure monitor (Omron model HEM-

907XL). The circumference of participants’ upper arm was utilized to determine appropriate cuff size. The Omron model HEM-907XL has been shown to be a valid and reliable measure of blood pressure (Ostchega, Nwankwo, Sorlie, Wolz, & Zipf, 2010).

Birth Outcomes. Birth weight was measured in grams and gestational age was measured in weeks. According to the World Health Organization (2014), babies who weigh less than 2,500 grams at birth and are born before 37 weeks of gestation are classified as having a low birth weight and being premature. Given that birth weight and gestational age exist on a continuum, this study will focus on the continuous outcomes of these variables.

Procedure

In the second session of the intervention, participants were administered the Kessler

Distress Scale. After establishing rapport with the participants, their height, weight, and blood pressure were collected in the third session by a certified doctoral-level nurse-midwife. Given the sensitive nature of the Adverse Childhood Experiences Questionnaire, this scale was administered in the fifth session of the intervention where the participants learn about relaxation techniques. Infant birth weight and gestational age were collected by perinatal support persons

(doulas) who were present at the delivery.

Data Analysis

Data were collected with self-report paper measures and entered into SPSS Program

(Version 25) for analysis. To address hypotheses one and two, linear regressions were used to

assess the ability of the frequency of ACEs to predict maternal physical and psychosocial health, as well as birth weight and gestational age. The interaction terms between ACEs and maternal distress, BMI, and blood pressure were added to the linear regression analyses to address hypothesis three, which aims to examine whether measures of maternal health moderate the relationship between frequency of ACEs and birth outcomes. In order to reduce nonessential multicollinearity between the predictors, ACEs, maternal distress, BMI, and blood pressure were centered before performing the linear regression analyses with interaction terms by subtracting each variable’s mean from all of its values.

Statistical Power

An a priori power analysis for a linear regression with one tested predictor was conducted to determine a sample size of 55 with an alpha of 0.05, a power of 0.80, a medium effect size (f 2

= .15) (Faul et al., 2013). When an additional predictor variable and interaction term was added to the model, the desired sample size was 77. The obtained sample size was 31 and 46 additional participants were needed to reach the desired power for all analyses.

Results

Preliminary Analyses

Normality testing indicated that the data was normally distributed, thus no transformations were needed. The absolute z-scores for skewness and kurtosis of each variable were less than 1.96, meaning that they were in an acceptable range (Kim, 2013). Bivariate correlations were conducted between the study’s variables. Maternal age was included in the analyses to examine its potential of being a confounding variable. The bivariate correlations demonstrated a negative relationship between maternal ACEs and distress (r (29) = -.38, p = .04;

Table 1), such that participants who reported more adverse childhood experiences had lower levels of distress. The associations between ACEs and systolic blood pressure (r (29) = .29, p =

.11), diastolic blood pressure (r (29) = .02, p = .90), BMI (r (29) = .01, p = .97, gestational age (r

(29) = .18, p = .34,), birth weight (r (29) = .11, p = .57), and maternal age (r (29) = .04, p = .82) were not significant. Lastly, there were negative relationships between systolic blood pressure and distress (r (29) = -.46, p = .01) and BMI (r (29) = -.42, p = .02). Considering these findings, maternal age was not utilized as a control variable for any regression analyses.

Given that maternal trimester can influence BMI (Campbell et al., 2016; Fontaine,

Hellerstedt, Dayman, Wall, & Sherwood, 2012), an ANOVA was conducted to determine if there were systematic differences in BMI for each trimester (Table 2). Levene’s Test of Homogeneity of Variances showed that the variances for BMI were not equal (F (2, 28) = 4.74, p = .017), thus the Welch test was more appropriate in comparing the means of BMI among the trimesters.

Results indicated that mothers in their first trimester (M = 28.25, SD = 1.63) had the lowest BMI

compared to mothers in their second (M = 35.96, SD = 7.18) and third trimesters (M = 31.02, SD

= 3.80) (F (2, 6.08) = 6.20, p = .034). Further investigation with the Games-Howell post-hoc test revealed a significant difference between the BMI of mothers in their first and second trimesters.

For this reason, maternal trimester was included as a control variable when assessing the relationship between BMI and ACEs, birth weight, and gestational age. Additionally, two dummy coded variables were created for maternal trimester by coding the trimester of interest as

1 and all other trimesters as 0 because it is a categorical variable with three levels.

ACEs and Maternal and Infant Health Outcomes

To address hypotheses one and two, several linear regressions were conducted with

ACEs as the predictor variable and maternal and infant health outcomes as the dependent variable. The first model investigated the relationship between ACEs and maternal distress. It was found that greater reports of ACEs were associated with lower levels of distress, β = -.38, t

(29) = -2.19, p = .04. This model explained 14.2% of the variance in distress, R2 = .142, F (1, 29)

= 4.80, p = .04 (Table 3). In separate models, ACEs were not significantly associated with BMI

(R2 = .00, F (1, 29) = .002, p = .97; β = .008, t (29) = .05, p = .97; Table 4), systolic blood pressure (R2 = .086, F (1, 29) = 2.72, p = .11; β = .29, t (29) = 1.65, p = .11; Table 5), and diastolic blood pressure (R2 = .001, F (1, 29) = .017, p = .90; β = .02, t (29) = .13, p = .90; Table

6). The results were similar for birth outcomes, such that ACEs were not associated with gestational age (R2 = .032, F (1, 29) = .958, p = .34; β = .18, t (29) = .98, p = .34; Table 7) or birth weight (R2 = .011, F (1, 29) = .335, p = .57; β = .11, t (29) = .58, p = .57; Table 8).

Moderation with ACEs and Maternal Health and Birth Outcomes

A series of linear regressions were conducted to test the possible moderation of maternal health variables between ACEs and birth outcomes. The first regression model examined ACEs,

distress, and their interaction as predictors of gestational age. Before including the interaction term in the regression equation, both ACEs (β= .27, t (28) = 1.38, p = .18) and distress (β= .24, t

(28) = 1.23, p = .23) were not significant unique predictors of the outcome. This model did not account for a significant amount of the variance in gestational age, R2 = .08, F (2, 28) = 1.25, p =

.30. The addition of the interaction between ACEs and distress (β= -.02, t (27) = -.06, p = .95) did not significantly improve the fit of the model, R2 = .08, F (3, 27) = .80, p = .50 (Table 9).

Three additional analyses were conducted with BMI, systolic blood pressure, and diastolic blood pressure replacing distress. The results indicate that BMI, systolic blood pressure, diastolic blood pressure, and the interactions between these variables and ACEs were not significant predictors of gestational age (Tables 10, 11, 12).

Four regression analyses were conducted with distress, BMI, systolic blood pressure, diastolic blood pressure, and each variable’s interaction with ACEs as predictors of birth weight.

Similar to the previous results in this study, distress, BMI, systolic blood pressure, and diastolic blood pressure did not moderate the relationship between ACEs and birth weight (Tables 13, 14,

15, 16).

Discussion

The present study found that in a community sample of expectant Black mothers, ACEs predicted maternal distress, such that those who endured more ACEs reported experiencing less distress within the past month. Results of the direction of this relationship did not support the first hypothesis that more ACEs would predict higher levels of distress. One explanation for this finding could be that the women in this study have emotion regulation difficulties and experience emotional numbing that likely suppresses their subjective reports of distress. Previous research has found that individuals who have experienced trauma during their early childhood have trouble regulating, labeling, and expressing emotions (Briere & Jordan, 2009; Ehring & Quack,

2010; Frewen & Lanius, 2006). This is related to emotional numbing in that it is a common automatic process associated with trauma exposure that includes a loss of interest in activities, disengagement from others, and a restricted range of emotional affect (Feeny, Zoellner,

Fitzgibbons, & Foa, 2000; Tull & Roemer, 2002). Trauma survivors who have poor emotion regulation skills and a low self-awareness of emotions are susceptible to experiencing limited affect and having difficulty accurately expressing their internal experiences (Ehring & Quack,

2010; Presseau, Contractor, Reddy, & Shea, 2018). In support of these hypotheses, it was found that those with childhood trauma tended to have more problems with emotion regulation compared to those who experienced trauma later in life (Ehring & Quack, 2010). In a separate study, veterans who experienced childhood trauma did not necessarily report more distress after being deployed, but they did endorse more symptoms of emotional numbing (Presseau,

Contractor, Reddy, & Shea, 2018). If the current study’s participants did in fact have poor

emotion regulation skills after enduring childhood trauma, then it is conceivable to believe that they would demonstrate emotional numbing through their low reports of distress. This interpretation may also be supported by the negative relationship that was found between systolic blood pressure and distress. It is possible that the mothers who reported lower levels of distress on the Kessler Distress Scale exhibited higher levels of distress on an objective measure such as blood pressure, meaning that they likely had difficulty identifying and labeling their true internal experiences.

The literature concerning low awareness of emotion in traumatized individuals is related to an additional hypothesis that this study’s unexpected relationship between ACEs and distress was impacted by the participants’ conceptualization of stress. Previous literature posits that

Black women appraise stressors differently than what would be normally expected. Instead of viewing common stressors as distressing, Black women tend to perceive stress as a normal part of their lives (Neal-Barnett & Crowther, 2000; Ward, Clark, & Heidrich, 2009; West, Donovan,

& Daniel, 2016). In support of this theory, Vines, Esserman, and Baird (2009) found that Black women reported feeling less stressed compared to White women, even when all women reported experiencing similar amounts of stressful events. SBW ideology may explain why Black women have difficulty identifying their stressful experiences as distressing. As stated earlier, SBW ideology suggests that Black women must display strength and care for their families in the face of adversity. When Black women internalize this ideology, they may underreport, minimize, or suppress their emotional difficulties and rarely conceptualize their actual internal experiences as distress (Beauboeuf-Lafontant, 2003; Harrington, Crowther, & Shipherd, 2010). This is often because SBW ideology prevents Black women from displaying certain negative emotions and vulnerability, which can lead to interpersonal difficulties, stress, and poor health-related

behaviors related to sleep, eating, and self-care (Harris-Lacewell, 2001; Woods-Giscombé,

2010). If the women in this study’s sample adopted and internalized SBW ideology, their reports of distress were likely lower than their actual experiences.

Contrary to hypothesis one, ACEs were not found to be significant predictors of other variables of maternal health such as BMI and blood pressure. It was also hypothesized that ACEs would predict birth outcomes; however, this was not supported. Additionally, the results did not support the final hypothesis that the relationship between ACEs and birth outcomes would be moderated by maternal health variables. Even when controlling for maternal trimester, there were still no significant relationships between BMI, ACEs, and birth outcomes, meaning that trimester did not appear to be confounding variable that could have influenced the analyses that included BMI. Maternal age did not appear to be a confounding variable for all analyses as well.

One explanation for these null findings could be that this was an underpowered study for correlation and regression analyses, which made it difficult to detect significant relationships between this study’s variables. Another interpretation of the results was that the participants were experiencing the psychological benefits from being a part of a perinatal support program.

Although the study’s limited power likely reduced the probability of observing trends that would be in support of all three hypotheses, there is also the possibility that the women in our sample had high levels of resilience and protective factors that buffered the effects of ACEs on the outcomes of their own and their infant’s health. This could explain why ACEs were not predictive of BMI, blood pressure, gestational age, and birth weight in this sample compared to other studies (Felitti et al., 1998; Harville, Boynton-Jarrett, Power, & Hyppönen, 2010; Norman, et al., 2012; Pretty, O’Leary, Cairney, & Wade, 2013; Racine, Madigan, Plamondon, McDonald,

& Tough, 2018; Ranchod et al., 2016; Riley, Wright, Jun, Hibert, & Rich-Edwards, 2010; Smith,

Gotman, & Yonkers, 2016; Stein et al., 2010; Su et al., 2015). Specific protective factors such as nurturing relationships with peers and parental involvement have been linked to better physical and mental health outcomes in children and adults who have experienced trauma early in life

(Bellis et al., 2018; Crouch, Radcliff, Strompolis, & Srivastav, 2019; Woods-Jaeger, Cho,

Sexton, Slagel, & Goggin, 2018; Youssef et al., 2017). In pregnant women, social support moderated the relationship between ACEs and measures of antepartum health risk such as maternal weight, diabetes, and heart disease (Racine et al., 2018). Social support also mediated the relationship between ACEs and prenatal depression, such that the association between childhood adversity and depressed mood was non-significant when mother’s reported high levels of instrumental and emotional support (Howell, Miller-Graff, Schaefer, & Scrafford, 2017).

Similarly, Young-Wollf and colleagues (2019) found that the relationship between ACEs and mental and behavioral health conditions was the strongest among pregnant women who reported lower levels of resilience. It is important to note that the perinatal support program that the participants were enrolled in assigned each woman to a doula who attended doctors’ appointments and encouraged them to participate in group classes that targeted various topics such as depression, parenting, and goal setting. This program provided these women with support and a sense of community that could have mitigated the negative effects of previous trauma and hardships. If this is in fact true, then it would further support the idea that the women in this study could be more resilient to distressing situations than previously hypothesized.

Limitations and Future Directions

There are limitations of this study that should be highlighted. As noted earlier, the small sample size of 31 limited this study’s power. Power analyses revealed that a larger sample size of

77 would likely alleviate this problem. If more women were added to the study, the likelihood of

finding relationships between the study’s variables would have increased. Additionally, the smaller sample size may have contributed to the finding of a negative relationship between BMI and systolic blood pressure that has not been supported by the literature because of reduced variability (Drøyvold, Midthjell, Nilsen, & Holmen, 2005; Savitri et al., 2016; Teng, Yan, Dong,

& Lai, 2010). It is important to note that working with a majority low-income community sample comes with many challenges that can limit sample size (Waheed et al., 2015; Williams,

Beckmann-Mendez, & Turkheimer, 2013). The women in this study often had occupational and familial responsibilities that made it difficult for them to participate in the intervention and complete the required assessments. Their demanding schedules still influenced attendance after providing them with a culturally-relevant intervention, free childcare, and transportation.

Additionally, participation could have been impacted by the low level of trust between Black

Americans and health care professionals and researchers that prevents these individuals from sharing their internal experiences with “outsiders” (Heurtin-Roberts, Snowden, & Miller, 1997;

Neal & Turner, 1991; Waheed, Hughes-Morley, Woodham, Allen, & Bower, 2015; Williams,

Beckmann-Mendez, & Turkheimer, 2013). This study attempted to alleviate this problem by utilizing Black research assistants and doulas to collect the data. Even with this solution, some of the mothers could have been cautious about joining the intervention and being vulnerable with others due to early trauma that often left them feeling rejected and unheard by friends, family members, and health care providers. Additionally, many researchers and community programs have overlooked a majority of these women because they are low-income or experience homelessness.

Nonetheless, it was still necessary to explore the impact of ACEs in this population because of their vulnerability to the current maternal and infant mortality crisis. Black mothers

and infants who live in the neighborhoods that we work in are dying at an alarming rate and should continue to be at the center of maternal and infant health research. ACEs are an important tool in the clinical assessment of potential psychological and medical health concerns that contribute to poor birth outcomes that are related to infant mortality. Although this study demonstrated a negative relationship between ACEs and distress and no relationship between

ACEs and BMI, blood pressure, gestational age, and birth weight, this does not mean that we should stop exploring this area of research. Future studies should focus on the longitudinal effects of the relationship between high ACEs and low distress in expectant mothers that could possibly lead to problems such as postpartum depression and PTSD.

The current study only examined distress as a mental health variable. Given that ACEs are predictive of other psychological disorders, it would be of interest to examine if measures of other related outcomes of ACEs such as anxiety, depression, social support, posttraumatic distress, and suicidal ideation would support the findings in the literature. Additionally, an assessment of protective factors such as social support would be beneficial for understanding how certain individuals are more resilient to the negative consequences of ACEs. Future studies should also continue to gather information about maternal BMI and blood pressure as measures of physical health, as well as cortisol levels and history of health conditions. Lastly, given that the participants were a part of an intervention that focused on decreasing their stress and anxiety, there is a possibility that it could have affected participant’s reports of distress in the second session. If possible, future studies should conduct all measures of emotional states during the first day of data collection.

Conclusions

Regardless of the limitations, the present study utilized a community sample of expectant

Black mothers to investigate the relationship between ACEs and outcomes of maternal and infant health. This contribution to the literature about maternal health is important because it highlights the experiences of an underrepresented population. The findings from this study support an association between childhood trauma and distress that is likely found in trauma survivors who have difficulties with regulating their emotions and accurately conceptualizing their feelings of distress. Alternatively, the nonsignificant relationships between ACEs and maternal BMI, maternal blood pressure, and infant birth outcomes may be explained by the resiliency of the mothers in this sample. Future research should continue to examine the relationships between

ACEs and maternal health and birth outcomes to understand risk and protective factors that influence the impact of early trauma on the health of the mother and her infant.

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Table 1.

Descriptive Information and Bivariate Correlations Among Variables (N = 31) Variables 1 2 3 4 5 6 7 8

1. ACEs - -.38* .01 .29 .02 .18 .11 .04

2. Distress - -.05 -.46** -.17 .14 -.26 -.27

3. BMI - -.42* .13 -.04 .32 .16

4. Systolic BP - .38* .11 .13 -.04

5. Diastolic BP - .03 .11 -.04

6. Gestational - .32 -.01

Age

7. Birth - -.02

Weight

8. Maternal -

Age

M 4.03 20.26 33.55 116.58 72.19 38.84 3445.94 28.48

SD 2.80 7.22 6.37 8.47 6.33 1.30 546.99 5.97

Range 1 - 10 - 40 21.60 – 103 – 58 – 36 – 2466.41 – 17 – 41

10 49.90 135 82 41 4762.72

Note. *p < .05, **p < .01

Table 2.

One-Way Analysis of Variance of BMI by Trimester and Games-Howell Post-Hoc Comparisons (N = 31)

SS df MS F Welch F

Between 232.32 2 116.16 3.30 6.20*

Within 985.38 28 35.19

Total 1217.70 30

Post-Hoc Comparisons Mean Difference SE 95% CI

Trimester 1 Trimester 2 -7.71* 2.09 -13.65 – -1.78

Trimester 3 -2.77 1.59 -8.93 – 3.40

Trimester 2 Trimester 3 4.95 2.06 -.17 – 10.07

Note. *p < .05, **p < .01

Table 3.

Regression Predicting Maternal Distress from ACEs (N=31)

B SE B β t

ACEs -.97 .44 -.38* -2.19

R2 .142

F 4.80*

Note. *p < .05, **p < .01

Table 4.

Regression Predicting Maternal BMI from ACEs (N=31)

B SE B β t

Model 1 Trimester 1 -2.77 4.53 -.11 -.61

Trimester 2 4.95 2.24 .39 2.21

R2 .19

F 3.30

Model 2 Trimester 1 -2.79 4.62 -.11 -.61

Trimester 2 4.96 2.23 .39 2.18

ACEs .06 .39 .03 .16

R2 .19

F 2.13

Note. *p < .05, **p < .01, Trimester 1 and Trimester 2 are dummy coded variables

Table 5.

Regression Predicting Systolic Blood Pressure from ACEs (N=31)

B SE B β t

ACEs .89 .54 .29 1.65

R2 .09

F 2.72

Note. *p < .05, **p < .01

Table 6.

Regression Predicting Diastolic Blood Pressure from ACEs (N=31)

B SE B β t

ACEs .06 .42 .02 .13

R2 .00

F .02

Note. *p < .05, **p < .01

Table 7.

Regression Predicting Gestational Age from ACEs (N=31)

B SE B β t

ACEs .08 .09 .18 .98

R2 .03

F .96

Note. *p < .05, **p < .01

Table 8.

Regression Predicting Birth Weight from ACEs (N=31)

B SE B β t

ACEs 20.90 36.09 .11 .58

R2 .01

F .34

Note. *p < .05, **p < .01

Table 9.

Regression Predicting Gestational Age from the Interaction between ACEs and Distress (N=31)

B SE B β t

Model 1 ACEs .13 .09 .27 1.38

Distress .04 .04 .24 1.23

R2 .08

F 1.25

Model 2 ACEs .12 .11 .26 1.10

Distress .04 .05 .23 .92

ACEs x Distress -.001 .02 -.02 -.06

R2 .08

F .80

Note. *p < .05, **p < .01

Table 10.

Regression Predicting Gestational Age from the Interaction between ACEs and BMI (N=31).

B SE B β t

Model 1 Trimester 1 .29 1.04 .06 .28

Trimester 2 .51 .55 .20 .93

ACEs .09 .09 .19 .97

BMI -.02 .04 -.12 -.55

R2 .07

F .45

Model 2 Trimester 1 .36 1.00 .07 .36

Trimester 2 .47 .53 .18 .87

ACEs .13 .09 .28 1.48

BMI .00 .04 .002 .009

ACEs x BMI -.02 .01 -.36 -1.81

R2 .17

F 1.05

Note. *p < .05, **p < .01, Trimester 1 and Trimester 2 are dummy coded variables

Table 11.

Regression Predicting Gestational Age from the Interaction between ACEs and Systolic Blood

Pressure (N=31)

B SE B β t

Model 1 ACEs .08 .09 .16 .83

Systolic Blood Pressure .01 .03 .06 .33

R2 .04

F .52

Model 2 ACEs .07 .09 .15 .76

Systolic Blood Pressure .01 .03 .05 .26

ACES x Systolic Blood .003 .01 .06 .31

Pressure

R2 .04

F .37

Note. *p < .05, **p < .01

Table 12.

Regression Predicting Gestational Age from the Interaction between ACEs and Diastolic Blood

Pressure (N=31)

B SE B β t

Model 1 ACEs .08 .09 .18 .96

Diastolic Blood Pressure .01 .04 .02 .13

R2 .03

F .47

Model 2 ACEs .14 .09 .30 1.52

Diastolic Blood Pressure -.01 .04 -.06 -.29

ACES x Diastolic Blood Pressure -.03 .02 -.32 -1.58

R2 .11

F 1.16

Note. *p < .05, **p < .01

Table 13.

Regression Predicting Birth Weight from the Interaction between ACEs and Distress (N=31)

B SE B β t

Model 1 ACEs 1.88 38.48 .01 .05

Distress -19.58 14.93 -.26 -1.31

R2 .07

F 1.03

Model 2 ACEs -3.76 46.67 -.02 -.08

Distress -22.16 19.13 -.29 -1.16

ACES x Distress -1.61 7.25 -.05 -.22

R2 .07

F .68

Note. *p < .05, **p < .01

Table 14.

Regression Predicting Birth Weight from the Interaction between ACEs and BMI (N=31)

B SE B β t

Model 1 Trimester 1 -512.71 386.59 -.23 -1.33

Trimester 2 333.16 205.45 .31 1.62

ACEs 24.92 32.84 .13 .76

BMI 11.42 16.01 .13 .71

R2 .27

F 2.39

Model 2 Trimester 1 -494.30 380.97 -.23 -1.30

Trimester 2 319.36 202.60 .30 1.58

ACEs 37.44 33.65 .19 1.11

BMI 18.26 16.57 .21 1.10

ACEs x BMI -6.17 4.59 -.24 -1.34

R2 .32

F 2.33

Note. *p < .05, **p < .01, Trimester 1 and Trimester 2 are dummy coded variables

Table 15.

Regression Predicting Birth Weight from the Interaction between ACEs and Systolic Blood

Pressure (N=31)

B SE B β t

Model 1 ACEs 14.82 38.21 .08 .39

Systolic Blood Pressure 6.85 12.62 .11 .54

R2 .02

F .31

Model 2 ACEs 15.41 39.34 .08 .39

Systolic Blood Pressure 7.09 13.08 .11 .54

ACES x Systolic Blood -.35 3.47 -.02 -.10

Pressure

R2 .02

F .20

Note. *p < .05, **p < .01

Table 16.

Regression Predicting Birth Weight from the Interaction between ACEs and Diastolic Blood

Pressure (N=31)

B SE B β t

Model 1 ACEs 20.40 36.53 .10 .56

Diastolic Blood Pressure 9.19 16.16 .11 .57

R2 .02

F .33

Model 2 ACEs 30.55 39.99 .16 .76

Diastolic Blood Pressure 6.21 16.94 .07 .37

ACES x Diastolic Blood -4.52 6.85 -.14 -.66

Pressure

R2 .04

F .36

Note. *p < .05, **p < .01