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Preeclampsia and occurrence of neurological outcomes in the child: a meta-analysis

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Preeclampsia and occurrence of neurological outcomes in the child: a meta-analysis

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PREECLAMPSIA AND OCCURRENCE OF NEUROLOGICAL OUTCOMES IN THE
CHILD: A META-ANALYSIS

By

TIFFANY XIU ZHEN LIM

A Thesis Presented to the
FACULTY OF THE USC KECK SCHOOL OF MEDICINE
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfilment of the
Requirements for the Degree
MASTER OF SCIENCE
(APPLIED BIOSTATISTICS and EPIDEMIOLOGY)

MAY 2021

Copyright 2021 TIFFANY XIU ZHEN LIM
ii
ACKNOWLEDGEMENTS
As my time at USC comes to a close with the submission of my thesis, I am in awe of
and immensely grateful for all the of the people who have gotten me here to this point.
I am eternally thankful for Dr. Melissa Wilson, my thesis advisor. Without your
guidance, knowledge, and support, this thesis would not have come to fruition. You have
instilled in me the confidence and skills to conduct my own research. To Dr. Richard Lee and Dr.
Trevor Pickering, thank you for taking your time to provide guidance and expert advice and
serve as my thesis committee.
Finally, thank you, mom, for your unconditional love, support, generosity, and words of
encouragement as I endeavor toward my educational, personal, and life-long goals.

iii
TABLE OF CONTENTS
Acknowledgements……………………………………………………………………………….ii
List of Tables……………………………………………………………………………………..iv
List of Figures…………………………………………………………………………………..…v
Abstract…………………………………………………………………………………………...vi
Chapter 1: Introduction……………………….….….….….….….….….….….….….….…………
Hypertensive Disorders of Pregnancy………………………………………….…1
Attention-Deficit/Hyperactivity Disorders..………………………………….…...3
Autism Spectrum Disorders…………………………………………………….…4
Epilepsy……………………………………………………………………………6
Preeclampsia and neurological outcomes..…………………………………….….7
Chapter 2: Methods……………………….….….….….….….….….….….….….….……………..
Data sources and search strategy……………………………………………….…9
Inclusion and exclusion criteria………………………………………….………11
Preliminary screening………………………………………….………………...11
Full text data abstraction and quality assessment…………………………….….12
Statistical analysis………………………………………….…………………….13
Chapter 3: Results……………………….….….….….….….….….….….….….….………………
Search results ………………………………………….….….….….….….….…14
Characteristics of included studies………………………………………….……16
Primary analysis………………………………………….….….….….….….…..18
Bias and heterogeneity………………………………………….….….….….…..20
Sensitivity analysis………………………………………….….….….….….…...22
Discussion………………………………………….….….….….….….….….….….….….….…24
References………………………………………….….….….….….….….….….….….….….…27

iv
TABLES
Table 1: List of search terms……..……………………………………………………………9-11
Table 2: Characteristics of ADHD studies……………………………………………………….17
Table 3: Characteristics of ASD studies……….….….………………………………………….17
Table 4: Characteristics of epilepsy studies……………………..……………………………….17

v
FIGURES
Figure 1: Flow diagram…………………………………………………………………………..15
Figure 2: Forest plot….…………………………………………………………………………..19
Figure 3: Funnel plots for ADHD studies………………………………………………………..20
Figure 4: Funnel plots for ASD studies…...……………………………………………………..21
Figure 5: Funnel plot for epilepsy studies………………………………………………………..21
Figure 6: Forest plot for sensitivity analysis……………………………………………………..23
Figure 7: Funnel plot for ADHD sensitivity analysis..…………………………………………..23
Figure 8: Funnel plot for ASD sensitivity analysis..……………………………………………..23

vi
ABSTRACT
Background. Recently, there has been an increase in risk for preeclampsia among expectant
mothers due to the rise of associated risk factors such as obesity, chronic hypertension, diabetes
mellitus, and older maternal age. Moreover, there is a concern that there is an increased risk in
neuropathy in the offspring of preeclamptic mothers. Maternal complications due to
preeclampsia have been well-documented, as have cardiovascular complications among
offspring. However, there is still an uncertainty regarding neurodevelopmental outcomes in the
offspring. Neurodevelopmental outcomes are important to assess and study as it can impact the
physical, emotional, and mental wellbeing of the child.

Aim. The objective of this meta-analysis is to synthesize epidemiologic literature pertaining to
the association between preeclampsia, attention-deficit/hyperactivity disorder (ADHD), autism
spectrum disorder (ASD), and epilepsy.

Data sources. A systematic search of PubMed and Embase was performed supplemented by
hand-searching of reference lists of eligible studies.

Study selection. Two independent reviewers screened titles and abstracts following a prepared
protocol and screening form. One reviewer reviewed full texts for final assessments, data
extraction, and quality appraisal. Cohort and case-control studies in which preeclampsia and
ADHD, ASD, or epilepsy was reported were included in the meta-analysis.

Data extraction and analysis. Data extraction and quality appraisal using the Newcastle Ottawa
Scale were performed by an independent reviewer following a detailed abstraction form.
PRISMA guidelines were followed throughout. Random-effects meta-analyses were performed
and adjusted pooled Odds Ratios and their 95% confidence intervals were reported for
preeclampsia and ADHD, preeclampsia and ASD, and preeclampsia and epilepsy.

Results. 121 studies were identified from PubMed, Embase, and hand-searching. 15 unique
studies were eligible for inclusion. 6 reported adjusted estimates for ADHD, resulting in a pooled
odds ratio of 1.29 (95% CI: 1.20, 1.38). 8 reported adjusted estimates for ASD, resulting in a
pooled odds ratio of 1.27 (95% CI:1.22, 1.32). 3 reported adjusted estimates for epilepsy,
resulting in a pooled odds ratio of 1.35 (95% CI: 1.12, 1.63). Sensitivity analyses conducted for
the ADHD and ASD showed no significant differences in pooled odds ratios with the removal of
low-quality studies [ADHD OR: 1.24 (95% CI: 1.15, 1.34); ASD OR: 1.26 (95% CI: 1.21,
1.32)].

Conclusions. Intrauterine exposure to preeclampsia increased the risk of ADHD, ASD, and
epilepsy. This is the first meta-analysis to quantify the association between preeclampsia and
epilepsy. Early detection and management of preeclampsia is necessary to limit adverse events in
the mother and especially the offspring due to the poor understanding of how preeclampsia
develops. Further, the results of this meta-analysis can inform preventive measures and routine
screening of children born to preeclamptic mothers for early identification of these neurological
disorders.
1
INTRODUCTION
I. Hypertensive disorders of pregnancy
Hypertensive disorders of pregnancy (HDP) are one of the most common feto-maternal
complication worldwide. HDP affects 5-8% of all pregnancies and the rates of complications
have been steadily increasing over the years (High Blood Pressure During Pregnancy 2019).
These disorders are characterized into four distinct categories: gestational hypertension,
preeclampsia-eclampsia, chronic hypertension, and chronic hypertension with preeclampsia
(Wilkerson & Ogunbodede, 2019).
Gestational hypertension is diagnosed in women who experiences new onset
hypertension after 20 weeks of gestation. Preeclampsia-eclampsia, comparable to gestational
hypertension, is the elevation of blood pressure after 20 weeks of gestation coupled with the
presence of proteinuria or end-organ dysfunction (Mammaro et al., 2009). Chronic hypertension
is classified by presence of elevated blood pressure prior to conception, before 20 weeks of
gestation, or hypertension prolonging past 12 weeks after delivery (Hypertension in Pregnancy
2013). Finally, chronic hypertension with superimposed preeclampsia occurs in hypertensive
pregnant women who are diagnosed with preeclampsia. Of these disorders, preeclampsia is the
most severe, accounting for over 70,000 maternal and 500,000 fetal deaths (Rana, Lemoine,
Granger & Karumanchi, 2019).
Understanding the pathology, challenges, and management of these disorders, especially
preeclampsia, are of utmost importance as it affects both the mothers and the fetus. Rates of
preeclampsia diagnosis are on the rise worldwide due to the increase in maternal age and obesity
(Bartsche, Medcalf, Park, & Ray, 2016). Other risk factors include nulliparity, diabetes mellitus,
antiphospholipid syndrome, chronic kidney disease, and history of preeclampsia. Moreover,
2
there is evidence of genetic susceptibility. Several genome-wide association studies have found
two susceptibility loci, rs4769613 and rs9478812, both of which are associated with blood
pressure regulation (Gray et al., 2013; McGinnis et al., 2017).
Preeclampsia is diagnosed by two or more subsequent readings of elevated systolic blood
pressure of 140mmHg or 90mmHg diastolic and proteinuria or presence of severe features
after 20 weeks of gestation. Proteinuria is characterized by a concentration of 300mg within a
24-hour period, a protein to creatinine ratio of 0.3 or a dipstick reading of 1+. Severe features
include thrombocytopenia, systolic 160mmHg, diastolic 110mmHg, serum creatinine
>1.1mg/dL, impaired liver function, visual changes, headaches and altered mental status, among
others (Hypertension in pregnancy 2013).
Management of preeclampsia is oftentimes a tug of war between lessening deleterious
outcomes for the mother and prematurity of the neonate and associated complications.
Preeclamptic mothers are at an increased risk of kidney failure, cerebral hemorrhage, placental
abruption, and disseminated intravascular coagulation (Bokslag, van Weissenbruch, Mol, & de
Groot, 2016). Likewise, the fetus is at an increased risk of intrauterine growth restriction and
fetal death; however, early delivery and preterm birth increases complications for respiratory
distress syndrome, sepsis, hemorrhage, and neurodevelopmental disabilities (Saigal & Doyle,
2008).
Preeclampsia is characterized as a maternal disease during pregnancy, however risk of
adverse outcomes later in life affect both the mother and the neonate. One of the most researched
and prominent issues seen later in life in both mother and baby are cardiovascular events, such as
chronic hypertension, heart disease, and myocardial infarctions. Neurological outcomes due to
preeclampsia are not as heavily studied, especially in the neonate.
3
II. ADHD
Attention deficit hyperactivity disorder (ADHD) is one the two most common
neurodevelopmental disorders in children worldwide. Worldwide, 5% of children and 2.5% of
adults are affected (Faraone et al., 2015). Among children between the ages of 4 and 17 in the
United States, 10.2% of them are living with ADHD (Xu et al., 2018). Moreover, boys have an
approximate two-fold risk of ADHD than girls. ADHD is characterized by a pattern and
persistence of inattention, impulsivity, and hyperactivity (Attention-Deficit/Hyperactivity
Disorder 2020). Inattention refers to disorganization, difficulty keeping focused, continually
strays from tasks and inconstancy. Impulsivity is portrayed by thoughtless actions for instant
rewards. Hyperactivity displays as continuous movement, restlessness and constant activity.
There are three types of ADHD: predominantly inattentive, predominantly hyperactive-
impulsive, and combined presentation. Differentiation between the three depends on which
component of ADHD manifests the strongest in an individual.
Risk factors for ADHD are not well known. It is theorized that there is a multitude of
genetic factors combined with genetic-environment interactions that predisposes one to the
disease. Various family studies have shown a greater than five-time increased risk among first
degree relatives and heritability estimates of about 71-90% among twins (Faraone, Biederman, &
Monuteaux, 2000; Thapar, Cooper, Eyre, & Langley, 2012). While the mechanisms involved
with onset of preeclampsia are not fully understood, various environmental factors increase the
risk for ADHD, including childhood eczema, childhood asthma, childhood/adolescent head
trauma, and preterm birth (Kim et al., 2020). Additionally, pre- and perinatal correlates of
preeclampsia include hypertensive disorders of pregnancy, maternal obesity, maternal
acetaminophen use, maternal smoking, and prenatal alcohol exposure.
4
Diagnosis of ADHD is complex in childhood because multiple other neurological and
psychological disorders and issues, such as depression and learning disabilities, present similarly
to ADHD. The DSM-5 diagnostic criteria are mainly used and requires thorough medical,
familial, and environmental histories from those close to the child. The DSM-5 criteria are
broken down into 3 parts. To be diagnosed with ADHD, one must have six or more symptoms of
inattention and six or more symptoms of hyperactivity/impulsivity along with symptom
presentation before age of 12, symptoms present in more than two different environmental
settings, and evidence of reduction in functioning (Symptoms and Diagnosis of ADHD 2020).
ADHD is typically treated using behavioral therapy and medications. Additionally, parents
undergo management training to aid their child as they grow up.
Though there are therapies and medications to help treat ADHD, it is a lifelong complex
disorder. Over one’s lifespan, there is an increased risk of developing other psychological and
mental disorders. Around 60-75% of those with ADHD have one or more comorbid disorder
(Banachewski et al., 2017). Common comorbidities of ADHD are executive dysfunction,
emotional dysfunction, and impaired social functioning (Tarver, Daley, & Sayal, 2014). Due to
the lifelong effects of ADHD and association with comorbidities, ADHD is an important
neurodevelopmental disorder to study, diagnose, and manage to improve the quality of life of
those afflicted.
III. ASD
Alongside ADHD, autism spectrum disorder (ASD) is the most common neurobehavioral
disorder: 1 in 54 children are diagnosed, identified, and living with ASD currently in the United
States (U.S. Department of Human and Health Services, 2016). Among children under the age of
8, boys are four times as likely to have ASD than girls, and there are no differences in prevalence
5
rates across different racial and ethnic groups. Broadly, ASD encompasses mild to severe
behavioral and communication dysfunction and is frequently diagnosed among children very
early in life, usually within 3 years of age. Signs and symptoms of ASD include limited eye
contact, speech impediments, distress by routine changes, overreaction to sensory signals,
repetitive movements, and strong attachments (Autism Spectrum Disorder 2016). Later in life,
common medical conditions that are commonly diagnosed are ADHD, gastrointestinal disorders,
epilepsy, social anxiety disorder among others. The diagnostic criteria for ASD, as outlined in
the DSM-5, focuses on two specific aspects that separates ASD from other neurobehavioral
disorders: communication and restricted repetitive behaviors.
Risk factors for ASD are both environmental interaction and genetic predisposition.
Studies have shown that ASD may arise due to a single penetrant mutation among low-risk
alleles such as the mutation of SHANK3, rare gene variants amplifying pleiotropic effects, as
well as systemic vulnerabilities that contributes to deleterious molecular mechanisms that affect
language and social behaviors (Durand et al., 2007; Kim & State, 2014; Wen, Alshikho, &
Herbert, 2016). Significant environmental risk factors are prenatal influences, socioeconomic
status, and exposure to drugs and toxins (Chaste & Leboyer, 2012). Maternal gestational
hypertension, diabetes, multiparity, and maternal medications are prenatal risk factors that are
noteworthy (Gardener, Spiegelman, & Buka, 2009). Understanding risk factors that can be
manipulated and changed will hopefully reduce the number of ASD cases.
Though ASD is a lifelong condition, recent developments in treatment have conferred
positive outcomes, such as the oxytocin nasal spray (Yatawarra, Einfeld, Hickie, Davenport, &
Guastella, 2016; McPheeters et al., 2011). Currently, there are various pharmacological therapies
that targets hyperactivity, psychosis, aggression, depression, and irritability (Sharma, Gonda, &
6
Tarazi, 2018). However, most of these drugs are mainly used to target comorbid diseases of
ASD. Non-pharmacological therapies include music therapy, cognitive-behavioral therapy, and
social behavioral therapy. Though there is an abundance of therapy courses one can take,
benefits of these treatments are minimal unless the regimen began very early in life (McPheeters
et al., 2011).
IV. Epilepsy
Out of all the known neurological problems, epilepsy is the fourth common disorder in
the United States: each year, 150,000 people will be diagnosed and there is a 1 in 26 lifetime risk
(Epilepsy Statistics 2020). According to the CDC, 1.2% of the current US population are living
with epilepsy – over 470,000 children and 3 million adults (2020). The disease burden
worldwide is roughly ten times that of the United States, with over 20 million people living with
epilepsy (GBD 2016 Epilepsy Collaborators, 2019).
Epilepsy is characterized by two or more seizures with similar features. These features
depend on onset of the seizure, whether awareness is affected, and whether other symptoms are
involved (Epileptic seizures 2021). Depending on where the seizures originate in the brain, they
can be described as focal, generalized or unknown onset (U.S. Department of Human and Health
Services, 2020). Focal seizures, also known as partial seizures, are primarily located in one area
of the brain and can either affect a small portion of the brain, alter consciousness, or spread to
both sides of the brain. Petit mal and grand mal seizures are considered generalized seizures, and
they affect both sides of the brain. Generalized seizures causes changes in consciousness,
movement, balance, and can induce jerks and spasms. Typically, seizures can last anywhere
between seconds to a few minutes.
7
Risk factors for epilepsy remain largely unknown since more than 50% of the cases
worldwide have unknown causes. The underlying genetic mechanism remains a huge focus in
the etiology of epilepsy – over fifteen genes have been associated with risk of epilepsy and
further research is continually done to identify single nucleotide polymorphisms (Shorvon,
2011). Other risk factors linked to an increased risk of epilepsy are prenatal and perinatal issues
that leads to brain damage, congenital abnormalities, head injuries, history of stroke, brain
infections, and brain tumors (World Health Organization, 2019).
Much like other neurological and neurobehavioral disorders, epilepsy is a lifelong
disease; however, early diagnosis, treatment and management can confer seizure-free lives.
Treatment and management options are bountiful. There are a multitude of antiepileptic drugs
that target the different types of seizures such as valproate carbamazepine, and lamotrigine
(Wheless, Clarke, Arzimanoglou, & Carpenter, 2007). Though these drugs may not cure
epilepsy, there is evidence of long-term remission. However, Mohanraj and Brodie showed that
the chance of long-term remission decreases and odds of becoming drug-resistant increases in
those who did not become seizure-free in the first two years of treatment (2013). In cases where
two antiepileptic therapies have failed, resective surgery and radiosurgery can be considered.
About 50% of adult and 86% of pediatric epilepsy surgeries resulted in sustained remission
without the need for continuing antiepileptic drugs (Manford, 2017).
V. Preeclampsia and Neurological Outcomes
ADHD, ASD, and epilepsy among children are a group of neurological disorders that are
highly prevalent in the population. These disorders are characterized by deficits in
communication, social behaviors, and quality of life that extends into the adult years. Risk
factors for neurological disorders have mainly been thought to be primarily genetics but more
8
recently, emphasis has been placed on risk as a genetic and environmental interaction
(Akutagava-Martins, Rohde, & Hutz, 2018; Buchmayer et al., 2009; Engel, & Daniels, 2011).
Environmental risk factors during the developmental period plays a large role. Specifically,
certain perinatal factors have been linked to an increased risk for these neurological outcomes
(Nomura et al., 2017).
Preeclampsia, one of the most common perinatal conditions, affects 2% to 8% of all
pregnancies worldwide (Kramer et al., 2013). It is a multi-organ disease that creates adverse in
utero environments that have long-lasting effects on the offspring’s cognitive, cardiovascular,
physical, and psychological health (Pinheiro et al., 2016). The association between preeclampsia
and cardiovascular health has been well described (Bokslag et al., 2016; Andraweera, & Lassi,
2019). Conversely, the effects of intrauterine exposure to hypertension and neurological
outcomes have been inconsistent. A few studies have found preeclampsia to be a protective
factor against adverse neurological outcomes, others have shown preeclampsia to be a risk
factor. However, recent meta-analyses have found there to be no association between
preeclampsia and ADHD and ASD (Maher et al., 2018). Moreover, there are few studies that aim
to look at the association between preeclampsia and epilepsy. With the rising prevalence of
preeclampsia, understanding the long-term effects on the offspring is of utmost importance. The
objective of this meta-analysis is to synthesize epidemiologic literature pertaining to the
association between preeclampsia, ADHD, ASD, and epilepsy.

9
METHODS
I. Data Sources and Search Strategy
This meta-analysis was conducted following the PRISMA 2009 checklist (Moher et al.,
2009). PubMed and Embase databases were used to conduct the search for relevant studies from
the time of database inception until December 1, 2020. The search strategy included the
following keywords: “preeclampsia,” “offspring,” “adhd,” “autism,” and “epilepsy.” Relevant
MeSH terms and Emtree terms were used and is detailed in Table 1.
Studies from PubMed were yielded from the following search strategy: (“Pre-
eclampsia”[Mesh]) AND ((“Adult Children”[Mesh]) OR (“Child”[Mesh]) OR
(“Adolescent”[Mesh]) OR (“Infant”[Mesh]) AND ((“Attention Deficit Disorder with
Hyperactivity”[Mesh]) OR (“Epilepsy”[Mesh]) OR (“Autism Spectrum Disorder”[Mesh])).
Studies from Embase were a result of the following search strategy: (‘Preeclampsia’/exp)
AND (‘progeny’/exp) AND ((‘epilepsy’/exp) OR (‘attention deficit disorder’/exp) OR
(‘autism’/exp)).
After relevant studies were identified, the reference lists of those studies were hand-
searched for relevant articles. Potential articles from reference searching underwent abstract
screening to determine eligibility.
Table 1. List of the MeSH and Emtree terms for the search strategy.
Key Term MeSH Terms
Preeclampsia 1. Preeclampsia
2. Preeclampsia
3. Pregnancy toxemia
4. Pregnancy toxemias
5. Edema proteinuria hypertension gestosis
6. Toxemia of pregnancy
7. Toxemia of pregnancies
8. EPH complex
9. EPH complexes
10. EPH toxemia
11. EPH toxemias
12. EPH gestosis
10
13. Preeclampsia eclampsia
14. Pregnancy induced hypertension
15. Gestational hypertension
16. Pregnancy transient hypertension
17. Eclampsia
18. Eclampsias
19. Toxemia
20. Toxemias
Offspring 1. Offspring
2. Offspring, adult
3. Adult son
4. Adult sons
5. Children, adult
6. Adult daughter
7. Adult daughters
8. Adult offspring
9. Adult, young
10. Adults, young
11. Young adults
12. Young adult
13. Child
14. Children
15. Adolescent
16. Adolescents
17. Adolescence
18. Teen
19. Teens
20. Teenager
21. Teenagers
22. Youth
23. Youths
24. Adolescents, female
25. Female adolescent
26. Female adolescents
27. Adolescent, male
28. Male adolescent
29. Male adolescents
30. Infant
31. Infants
ADHD 1. ADHD
2. Attention deficit disorders with hyperactivity
3. Attention deficit hyperactivity disorder
4. Attention deficit hyperactivity disorders
5. Hyperkinetic syndrome
6. Hyperkinetic syndromes
7. ADDH
8. Attention deficit disorder
9. Attention deficit disorders
10. Minimal brain dysfunction
11. Attention deficit and disruptive behavior disorder
12. Attention deficit and disruptive behavior disorders
13. Oppositional defiant disorder
14. Disruptive behavior disorder
15. Chronic brain damage
16. Chronic encephalopathy
ASD 1. Autism spectrum disorders
11
2. Autistic spectrum disorder
3. Autistic spectrum disorders
4. Autistic disorder
5. Infantile autism
6. Autism
7. Early infantile autism
Epilepsy 1. Epilepsy
2. Epilepsies
3. Seizure disorder
4. Seizure disorders
5. Awakening epilepsy
6. Cryptogenic epilepsy
7. Cryptogenic epilepsies
8. Aura
9. Auras

II. Inclusion and Exclusion Criteria
Prospective or retrospective cohort and case-control studies were eligible for inclusion
into the study if the primary association of interest was risk of neuropathy (ADHD, ASD, or
epilepsy) in the offspring and maternal preeclampsia exposure. The exposure of interest were
women with preeclampsia who gave birth to live children. Studies whose primary exposure was
gestational hypertension, hypertensive disorders of pregnancy, and toxemia were also eligible.
The main outcome of interest was incidence of ADHD, ASD, and/or epilepsy among offspring of
preeclamptic mothers; studies with a composite outcome were excluded. Articles originally
written in non-English languages were ineligible for inclusion.
III. Preliminary Screening
All citations for studies identified through PubMed and Embase were exported into
Mendeley (Elsevier, 2008). The citations were numbered alphabetically and shared with two
independent reviewers, TL and IH. Research Electronic Data Capture (REDCap), hosted by
University of Southern California, was used to collect and manage data for this meta-analysis.
Both reviewers used a screening form populated in REDCap to determine eligibility based on the
abstracts available (Harris et al., 2009). Data in the screening form were: PMID, title of paper,
12
journal name, first author, year of publication, article type, study design, exposure definition,
outcome definition, estimates presented, article language, geographic location where study was
conducted, and whether the article qualified for inclusion.
Disagreements between the two reviewers were adjudicated by a third reviewer, MW,
after discussion with TL and IH. Studies that qualified for inclusion or required additional
information for determination were advanced to full-text review.
IV. Full-text Data Abstraction and Quality Assessment
Full-text reviews, data abstraction, and quality assessment was performed by a single
reviewer, TL and reviewed by MW. Articles that did not meet the inclusion criteria after full-text
review were removed from the list of included studies and no data abstraction or quality
assessment were performed. A data abstraction form was created in REDCap and used for each
included study. The data compiled in this form were: study design, data source, exclusions
identified, primary and secondary aims, exposure definition and assessment, outcome definition
and assessment, racial groups included, maternal age range, offspring age range, sample size,
estimates reported (OR, RR, HR, 95% confidence intervals, and p-values), covariates, and
limitations of the study.
The Newcastle Ottawa Scale (NOS) for cohort and case-control studies was used to
assess the quality for all included studies. Each study was awarded stars based on criteria for 3
different sections: selection of the study population, comparability to other studies, and exposure
ascertainment. The two main criteria for comparability are adjustment for parity and maternal
age. The maximum number of stars available for selection, comparability, and exposure are 4, 2,
and 3, respectively. Studies were considered to be high quality if they receive 7 stars and low
quality if they receive <7 stars.
13
V. Statistical Analysis
All statistical analyses were performed using Stata/IC 16.1 and RStudio (StataCorp,
2019; RStudio Team, 2020). Studies with stratified estimates were eligible for inclusion and
required synthesis of a pooled estimate. Estimates were pooled using a fixed effects meta-
analysis in Stata using the meta command for stratified estimates that used different controls.
Studies where the same referent group was used required a dose-response variance-covariance
matrix to weight the stratified estimates. The dosresmeta package in RStudio was used for these
studies. Hazard ratios and rate ratios were transformed into odds ratios using the following
equation: 𝑂𝑅 =
(1−𝑝 )∗𝑅𝑅
1−(𝑅𝑅 ∗𝑝 )
where p equaled the event rate among the unexposed, non-
preeclamptic, group (Zhang & Yu, 1998).
Primary analyses of the association between preeclampsia and each outcome of interest –
ADHD, ASD, and epilepsy – were performed using a random effects model in Stata using the
meta command. A random effects model was used due to the different populations under
examine. Forest plots were generated to show risk estimates and their 95% confidence intervals
for all studies included, as well as indicate their respective weights in the calculation of the
pooled estimate.
Heterogeneity was evaluated using the Cochran’s Q statistic and I
2
test. Low, medium,
and high heterogeneity are indicated by I
2
values of 25%, 50%, and 75%, respectively. To
explore causes of heterogeneity, when found, we stratified on factors believed to be the cause
such as study design, exposure definition, or publication year, as permitted by the number of
available studies. Publication bias was evaluated visually by symmetry of the funnel plots and
Egger’s test. Lastly, sensitivity analysis will be conducted with the removal of low-quality
studies to determine if their exclusion substantially changed the model results.
14
RESULTS
I. Search Results
After performing the literature search using the search strategy detailed above, 92 studies
were identified from PubMed and Embase. After removal of duplicates, 88 studies were
available for title and abstract screening. 12 studies were identified for full-text review and 8
studies were eligible for inclusion. The reference list for the eight studies were hand-searched
and an additional 33 studies were screened. From these 33 studies, the full-text review yielded 7
additional studies for inclusion. Overall, 15 studies were eligible, and data were abstracted: six
studies assessed the association between preeclampsia and ADHD, eight studies assessed
preeclampsia and risk of ASD, and three studies looked at the association between preeclampsia
and epilepsy. One study presented effect estimates for all three outcomes (Figure 1).
Of the 99 studies deemed ineligible during the screening phase, 49 studies did not have
the exposure of interest (preeclampsia), 24 were not original research (case reports, letters,
comments, guidelines, opinion-editorials, meta-analyses, protocols, case series, and other forms
of reviews), 14 were non-English studies, 5 did not have any of the outcomes of interest, 1 only
had an abstract available, and 1 was a non-human study. In the eligibility phase, an additional 7
studies were excluded because they did not present effect estimates for the exposure or outcome
of interest, or effect estimates were not given and unable to be calculated using the information
presented.
15

Figure 1. Flow diagram of studies searched and assessed for eligibility. Studies sum to more
than 15 since one study reported for all three outcomes.

Included Eligibility Screening Identification
16
II. Studies Included in the Meta-Analysis
After screening and full-text reviews, a total of 15 studies (12 cohort studies and 3 nested
case-control studies) were included in this meta-analysis. Six reported on the association
between preeclampsia and ADHD (Table 2), eight studied the risk of preeclampsia on ASD
(Table 3), and three assessed preeclampsia and epilepsy (Table 4). Seven studies were conducted
in the United States of America, three studies used a United Kingdom-based cohort, two were
based in Australia, and the three remaining studies were conducted in Denmark, Norway, and
Sweden. The sample size ranged between 867 children in a nested case-control study to
2,842,230 children in a population-based cohort study. The total sample size for the ADHD,
ASD, and epilepsy cohorts are 1,211,497 children, 4,771,424 children, and 2,613,216 children,
respectively. 13 studies used preeclampsia as the exposure definition, one used toxemia, and
another used pregnancy hypertension. For ascertainment of the outcome 8 studies used ICD
codes, two used DSM criteria, three relied on self-report from the parent, one used pharmacy
record linkage, and one used the Development and Wellbeing Assessment (DAWBA). All 15
studies reported adjusted estimates. Twelve studies were deemed to be high quality and three
were low quality based on the Newcastle Ottawa Scale.
The two most important covariates were maternal age and parity – 14 studies adjusted for
maternal age and 11 studies adjusted for parity. Other confounding factors identified include
maternal chronic hypertension, alcohol use or smoking during pregnancy, gestational weight
gain, maternal body mass index, maternal genitourinary infection during pregnancy, prenatal
care, maternal psychosocial disorders, gestational diabetes, marital status, parental education,
parental birth country or immigration, socioeconomic status, child’s gender, race and ethnicity,
year of outcome diagnosis, child’s birth year, birth order, child’s birthweight, and APGAR score.
17

Table 2. Characteristics of ADHD studies included in the meta-analysis (n=6).
Study Country Design Sample size
(# PE, # ADHD)
Exposure ADHD
criteria
Age Range
(years)
Risk measure Adjusted or
Crude
Risk Estimate Quality
Mann &
McDermott
(2011)
USA Cohort 84,721
(4674, 7911)
Preeclampsia ICD-9 <15 Odds Ratio Adjusted 1.19 (1.03,
1.37)
High (8
stars)
Getahun et
al. (2013)
USA Nested case-
control
81,678
(17492, 13613)
Preeclampsia ICD-9 5-11 Odds Ratio Adjusted 1.34 (1.25,
1.44)
Low (6
stars)
Silva et al.
(2014)
Australia Nested case-
control
43,062
(3183, 12991)
Preeclampsia Pharmacy
record linkage
10-24 Odds Ratio Adjusted 1.23 (0.84,
1.62)
High (7
stars)
Böhm et al.
(2019)
UK Cohort 13,500
(1069, 166)
Preeclampsia Self-reported <7 Odds Ratio Adjusted 1.78 (1.03,
3.07)
High (7
stars)
Dachew et al.
(2019)
UK Cohort 7,976\
(281, 123)
Preeclampsia DAWBA 7-10 Odds Ratio Adjusted 2.77 (1.42,
5.38)
High (9
stars)
Sun et al.
(2020)
Norway Cohort 980,560
(28068, 8617)
Preeclampsia ICD-10 5-23 Odds Ratio Adjusted 1.23 (1.12,
1.36)
High (9
stars)

Table 3. Characteristics of ASD studies included in the meta-analysis (n=8).
Study Country Design Sample size
(# PE, # ASD)
Exposure ADHD
criteria
Age Range
(years)
Risk measure Adjusted or
Crude
Risk Estimate Quality
Mann et al.
(2010)
USA Cohort 87,677
(5531, 472)
Preeclampsia ICD-9 7-14 Odds Ratio Adjusted 1.69 (1.26,
2.28)
High (9
stars)
Lyall et al.
(2012)
USA Cohort 66,445
(5968, 793)
Toxemia Self-reported >2 Odds Ratio Adjusted 1.36 (1.04,
1.79)
Low (6
stars)
Langridge et
al. (2013)
Australia Cohort 378,887
(N/A, 1179)
Pregnancy
Hypertension
DSM 6-21 Odds Ratio Adjusted 1.03 (0.83,
1.27)
High (9
stars)
Walker et al.
(2015)
USA Nested case-
control
867
(41, 517)
Preeclampsia ICD-9 with
validation
<7 Odds Ratio Adjusted 2.36 (1.18,
4.68)
Low (6
stars)
Curran et al.
(2017)
UK Cohort 13,098
(1037, 199)
Preeclampsia Self-reported <7 Odds Ratio Adjusted 2.10 (1.20,
3.70)
High (7
stars)
Getahun et
al. (2017)
USA Cohort 401,660
(16693, 6255)
Preeclampsia DSM-IV-TR 4-22 Hazard Ratio Adjusted 1.26 (1.13,
1.41)
High (9
stars)
Maher et al.
(2020)
Sweden Cohort 2,842,230
(77600, 2024)
Preeclampsia ICD-9, ICD-
10
10-34 Hazard Ratio Adjusted 1.25 (1.19,
1.30)
High (8
stars)
Sun et al.
(2020)
Norway Cohort 980,560
(28068, 3548)
Preeclampsia ICD-10 5-23 Odds Ratio Adjusted 1.31 (1.12,
1.52)
High (9
stars)

Table 4. Characteristics of epilepsy studies included in the meta-analysis (n=3).
Study Country Design Sample size Exposure ADHD
criteria
Age Range
(years)
Risk measure Adjusted or
Crude
Risk Estimate Quality
Wu et al.
(2007)
Denmark Cohort 1,537,206
(45288, 20762)
Preeclampsia ICD-8, ICD-
10
1-27 Incidence Rate
Ratio
Adjusted 1.17 (1.09,
1.26)
High (9
stars)
Mann &
McDermott
(2011)
USA Cohort 95,450
(5460, 1106)
Preeclampsia ICD-9 8-14 Odds Ratio Adjusted 1.46 (1.17,
1.82)
High (9
stars)
Sun et al.
(2020)
Norway Cohort 980,560
(28068, 1492)
Preeclampsia ICD-10 5-23 Odds Ratio Adjusted 1.55 (1.25,
1.92)
High (9
stars)

18

III. Primary Analysis
a. ADHD
In this meta-analysis, 6 studies reported on the association between preeclampsia and
ADHD: 5 studies reported a statistically significant positive association, and one reported a
statistically nonsignificant positive association. These 6 studies contributed a total sample size of
1,211,497 children. Figure 2 depicts the adjusted odds ratios for all six unique studies and the
resulting pooled estimate. The pooled odds ratio indicates a statistically significant elevated risk
of ADHD among children born to preeclamptic mothers (pooled OR: 1.29; 95% CI: 1.20, 1.38).
Additionally, there was evidence of low heterogeneity (I
2
= 19.37%) among these six studies.
b. ASD
Seven of the eight unique ASD studies found a statistically significant elevated risk of
ASD among those with in utero preeclampsia exposure. These eight studies contributed a total
sample size of 6,282,921 children. The pooled effect of these studies indicates a statistically
significant positive association between preeclampsia and ASD (pooled OR: 1.27; 95% CI: 1.22,
1.32) and there seems to be very low heterogeneity (I
2
= 0.00%; Figure 2).
c. Epilepsy
Three studies, with a total sample size of 2,513,216, resulted in a statistically significant
higher risk of epilepsy among children exposed prenatally to preeclampsia (pooled OR: 1.35;
95% CI: 1.12, 1.63; Figure 2). All three studies showed a statistically significant positive
association individually. However, there is evidence of moderate heterogeneity (I
2
= 73.18%)
and this could be due to the limited number of studies available and the large range of sample
size (95,450 – 1,537,206). All three were retrospective cohort studies but were done in different
countries (USA, Norway, and Denmark) and this could have influenced heterogeneity.
19

Figure 2. Forest plot of the association between preeclampsia and ADHD, ASD, and epilepsy.

20
IV. Bias and Heterogeneity
The overall pooled risk of ADHD, ASD, and epilepsy was almost 30% higher among
children exposed in utero to preeclampsia compared to controls (pooled OR: 1.29; 95% CI: 1.23,
1.35) and there is low evidence for heterogeneity (I
2
= 38.55%; Q = 0.48, p = 0.79) across all 15
studies. There is no evidence of heterogeneity among the ASD subgroup (I
2
= 0.00%, Q = 13.99,
p = 0.051) and evidence of low heterogeneity among the ADHD subgroup (I
2
= 19.37%, Q =
9.73, p = 0.08). However, there is evidence of moderate heterogeneity among the epilepsy
subgroup (I
2
= 73.18%, Q = 8.59, p = 0.01). Due to the limited number of studies within the
epilepsy cohort, further subgroup analyses were not possible and therefore potential causes of
heterogeneity such as study design and country were not assessed.
Visual inspection of the funnel plots in Figure 3 indicates potential publication bias in the
ADHD subgroup. Conversely, the Egger’s test was not statistically significant (z = 1.78, p =
0.07) and trim-and-fill analysis identified one additional study. The addition of the imputed study
resulted in a pooled OR of 1.28 (n = 7, 95% CI: 1.19, 1.37) which is similar to the original
pooled estimate of 1.29 (n = 6, 95% CI: 1.20, 1.38).

Figure 3. Left: funnel plot of the association between preeclampsia and ADHD. Right: funnel
plot of the association between preeclampsia and ADHD after trim-and-fill analysis.

21
Figure 4 shows evidence of asymmetry indicating potential publication bias among
studies reporting the association between preeclampsia and risk of ASD. In addition, the Egger’s
test is statistically significant suggestive of presence of small-study effects (z = 2.22, p = 0.03)
and trim-and-fill analysis identified three additional studies (trim-and-fill OR: 1.24; 95% CI:
1.11, 1.40; n = 11). The trim-and-fill OR is similar to that of the original pooled estimate with
eight studies (pooled OR: 1.27; 95% CI: 1.22, 1.32).

Figure 4. Left: funnel plot of the association between preeclampsia and ASD. Right: funnel plot
of the association between preeclampsia and ASD after trim-and-fill analysis.

There is evidence of publication bias due to the clear asymmetry of the funnel plot of
studies that reports on the association between preeclampsia and risk of epilepsy (Figure 5).
Furthermore, the Egger’s test is highly statistically significant, indicating presence of small-study
effects (p = 0.004). However, trim-and-fill analysis did not impute any additional studies.

Figure 5. Funnel plot of the association between preeclampsia and ASD.
22
V. Sensitivity Analysis
Sensitivity analyses were conducted for the ADHD and ASD groups and results are
presented in Figure 6. In the ADHD group, one study identified as having low quality (6 stars)
per the Newcastle Ottawa Scale was removed and analysis was repeated. The adjusted pooled
estimate indicates exposure to preeclampsia increases the risk of ADHD by 24% (95% CI: 1.14,
1.34; n=5). Compared to the original OR of 1.29 (95% CI: 1.20, 1.38), removal of the single
low-quality study did not change the estimates considerably. The I
2
value decreased substantially
to 0% from 19.37% indicating no evidence of heterogeneity among the five studies (Cochran’s Q
= 7.63, p=0.11). However, there is evidence of small-study effects and publication bias from
visual inspection of the funnel plot (Egger’s p=0.03) (Figure 7).
In the ASD analysis, two studies were removed due to low quality per the assessment
yielding a pooled estimate of 1.26 (95% CI: 1.21, 1.32; n=6). Exclusion of the two studies did
not change the odds significantly as the original estimate was 1.27 (95% CI: 1.22, 1.32; n=8).
Similar to the original analysis, there is no evidence of heterogeneity (I
2
= 0.0%; Q = 10.60,
p=0.06) or bias (Egger’s p=0.12; Figure 8). This time, the trim-and-fill analysis did not impute
any additional studies.
Taking into account all the available estimates after removal of low-quality studies for
ADHD, ASD, and epilepsy, the overall adjusted pooled estimate indicates a 27% higher risk
among children exposed to preeclampsia compared to controls (95% CI: 1.21, 1.33) and there is
moderate evidence for heterogeneity (I
2
= 30.37%; Q = 0.70, p=0.704).
23

Figure 6. Forest plot of the sensitivity analysis for the association between preeclampsia and
ADHD, ASD, and epilepsy with the removal of low-quality studies.

Figure 7. Funnel plot of the association
between preeclampsia and ADHD.

Figure 8. Funnel plot of the association
between preeclampsia and ASD.
24
DISCUSSION
The primary aim of this meta-analysis is to synthesize existing epidemiologic literature
pertaining to the association between maternal preeclampsia and offspring’s risk of attention-
deficit/hyperactivity disorder, autism spectrum disorder, and epilepsy and to provide more
precise estimates of the effect size. The findings show that intrauterine exposure to preeclampsia
increased the risk of ADHD by 29% (pooled OR of 1.29; 95% CI: 1.20, 1.38). Similarly, the risk
of ASD is 1.27 (95% CI: 1.22, 1.32) among children born to preeclamptic mothers. Lastly, the
synthesis suggests that among children exposed in utero to preeclampsia are 1.35 times as likely
to have epilepsy compared to children born to healthy mothers (95% CI: 1.12, 1.6). Overall, we
found that all but epilepsy did not show significant heterogeneity and there is little evidence to
indicate publication bias in this meta-analysis.
The findings are somewhat consistent with existing literature. A meta-analysis performed
by Maher et al. indicated a statistically significant 35% increase in risk of ASD and 29%
increased risk of ADHD among children exposed to hypertensive disorders of pregnancy.
However, their subgroup analyses limited to preeclampsia only did not meet significance (Maher
et al., 2018). Additionally, another meta-analysis performed by Xu et al. found that preeclampsia
is significantly associated with a higher risk of ADHD compared to healthy pregnancies (2017).
Results by Dachew et al. and Jenabi et al. suggest a 32-36% increase in risk of ASD associated
with preeclampsia (2017; 2018). Although pooled estimates show a statistically significant
association between preeclampsia and neuropathy, individual studies are somewhat still
inconsistent and confidence intervals around the estimates are wide.
As a whole, the literature examining the association between preeclampsia and ADHD
and ASD find a significant increase in risk (Buchmayer et al., 2009; Getahun et al., 2013; Mann
25
et al., 2010; Mann & McDermott, 2012; Walker et al., 2015). However, there are studies who
find that the increase in risk is not statistically significant (Burstyn et al., 2010; Ketzer et al.,
2012; Larsson et al., 2005; Mrozek-Budzyn et al., 2013). Interestingly, a few studies found
preeclampsia to be a protective factor reducing the risk of ADHD by over 25% (Amiri et al.,
2012; Çak & Gokler, 2013) and reducing the risk of ASD by more than 10% (Deykin &
MacMahon, 1980; Lyall et al., 2012). However, only one study found preeclampsia to be a
statistically significant protective factor for ASD (Langridge et al., 2013). Only a handful of
studies have been published thus far regarding the association between preeclampsia and risk of
epilepsy in the offspring, all of which indicate a statistically significant increase in risk. Because
of this, it is difficult to determine whether preeclampsia is a true risk factor or whether it plays a
role as an effect modifier in its association with neurological disorders. Therefore, results from
this meta-analysis indicated further research in this area is required.
To our knowledge, this is the first meta-analysis that included epilepsy as a primary
outcome. The adjusted pooled odds ratio of 1.35 (95% CI: 1.12, 1.63) aligns with the three
studies included in the meta-analysis. However, only a handful of studies have been published
and as evident in Figure 5 there seems to be publication bias; therefore, more research must be
done before preeclampsia can be further assessed as a risk factor for epilepsy.
Though the exact causes of preeclampsia and how it leads to adverse neurological
outcomes are unknown, there are a few plausible mechanisms that have been identified.
Placental disruptions due to preeclampsia leads to inadequate serotonin sources and increased
monoamine oxidase activity, both can restrict fetal growth due to low activation of cell
proliferation regulators (Gujrati et al., 1996). Moreover, maternal inflammation is thought to play
a key role in this process. Increased IL-17 concentrations in turn increases L-NAME and MCP-1
26
concentrations, all of which promotes intrauterine growth restriction, further inflammation
processes, and placental oxidative stress (Cornelius et al., 2016). Coupled with changes to the
fetal brain induced by the hostile intrauterine environment, early delivery as a management
course can limit the fetal brain development. However, all but one study in this meta-analysis
adjusted for preterm birth (indicated as <37 weeks of gestation) or gestational age; therefore, the
29% increase in risk of ADHD, ASD, and epilepsy altogether is not mediated by gestational age.
Further, Wu et al. did not find gestational age to be a significant confounder in the preeclampsia-
neuropathy association (2009).
This meta-analysis had several limitations and strengths. PubMed and Embase were the
only two literature databases that were searched, leaving room for studies published elsewhere
and non-English studies to be excluded. However, we included studies published from any year
and any country. Moreover, four additional studies published in since the last meta-analysis were
included – two ADHD and one epilepsy study, giving additional power to the adjusted pooled
estimates calculated. Though this is the first meta-analysis to include epilepsy as a subgroup,
only three studies were identified. Publication bias and moderate heterogeneity was evident in
the epilepsy subgroup signifying further research is required. A substantial strength of our meta-
analysis is the large sample size available for statistical analysis and that all effect estimates
reported were adjusted for important confounders such as parity, maternal age, gestational age at
delivery, and socioeconomic status.
Despite the few limitations of this meta-analysis, our findings suggest that there is an
increased risk of ADHD, ASD, and epilepsy in offspring exposed to preeclampsia in utero.

27
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Abstract (if available)

Abstract Background. Recently, there has been an increase in risk for preeclampsia among expectant mothers due to the rise of associated risk factors such as obesity, chronic hypertension, diabetes mellitus, and older maternal age. Moreover, there is a concern that there is an increased risk in neuropathy in the offspring of preeclamptic mothers. Maternal complications due to preeclampsia have been well-documented, as have cardiovascular complications among offspring. However, there is still an uncertainty regarding neurodevelopmental outcomes in the offspring. Neurodevelopmental outcomes are important to assess and study as it can impact the physical, emotional, and mental wellbeing of the child. ❧ Aim. The objective of this meta-analysis is to synthesize epidemiologic literature pertaining to the association between preeclampsia, attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and epilepsy. ❧ Data sources. A systematic search of PubMed and Embase was performed supplemented by hand-searching of reference lists of eligible studies. ❧ Study selection. Two independent reviewers screened titles and abstracts following a prepared protocol and screening form. One reviewer reviewed full texts for final assessments, data extraction, and quality appraisal. Cohort and case-control studies in which preeclampsia and ADHD, ASD, or epilepsy was reported were included in the meta-analysis. ❧ Data extraction and analysis. Data extraction and quality appraisal using the Newcastle Ottawa Scale were performed by an independent reviewer following a detailed abstraction form. PRISMA guidelines were followed throughout. Random-effects meta-analyses were performed and adjusted pooled Odds Ratios and their 95% confidence intervals were reported for preeclampsia and ADHD, preeclampsia and ASD, and preeclampsia and epilepsy. ❧ Results. 121 studies were identified from PubMed, Embase, and hand-searching. 15 unique studies were eligible for inclusion. 6 reported adjusted estimates for ADHD, resulting in a pooled odds ratio of 1.29 (95% CI: 1.20, 1.38). 8 reported adjusted estimates for ASD, resulting in a pooled odds ratio of 1.27 (95% CI:1.22, 1.32). 3 reported adjusted estimates for epilepsy, resulting in a pooled odds ratio of 1.35 (95% CI: 1.12, 1.63). Sensitivity analyses conducted for the ADHD and ASD showed no significant differences in pooled odds ratios with the removal of low-quality studies [ADHD OR: 1.24 (95% CI: 1.15, 1.34)

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Asset Metadata

Creator Lim, Tiffany Xiu Zhen (author)

Core Title Preeclampsia and occurrence of neurological outcomes in the child: a meta-analysis

School Keck School of Medicine

Degree Master of Science

Degree Program Applied Biostatistics and Epidemiology

Publication Date 03/31/2021

Defense Date 03/29/2021

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag ADHD,ASD,attention deficit/hyperactivity disorder,autism,autism spectrum disorder,epilepsy,hypertensive disorders of pregnancy,OAI-PMH Harvest,offspring,preeclampsia,Pregnancy

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Wilson, Melissa (committee chair), Lee, Richard (committee member), Pickering, Trevor (committee member)

Creator Email lim.tiffanyx@gmail.com,txlim@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-434089

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Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

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