Preterm birth is the leading cause of perinatal mortality, increasing the risk of chronic medical conditions and developmental delays in children. In 2020, 13.4 million preterm births caused around 900,000 infant deaths globally, and in the U.S. alone, the preterm birth rate stood at 10.4%, leading to higher neonatal mortality, significant morbidity, and a minimum $25.2 billion economic cost. Subsequently, there is growing literature demonstrating health disparities in the U.S. in conjunction with pregnancy and its direct and mediated relationship with anxiety [1], [2]. Current efforts in creating large-scale, public health oriented data repositories, including NIH's AllofUs, have further energized research related to women's health, across many ethnic and socio-economic groups. Using the data extracted from AllofUS, we examine our hypothesis that having anxiety during pregnancy disproportionately affects pregnant women from minority groups and lower socio-economic backgrounds. This, in turn, leads to substantial health disparities in pregnancy outcomes, particularly premature delivery. In this work, we highlight two parallel modeling approaches for studying the dynamics of women's pregnancy, anxiety, and premature delivery, particularly among minorities and economically disadvantaged groups. Specifically, we we use ordinal logistic regression and cox proportional hazards models to identify parameters contributing to anxiety among pregnant women.

References: 

[1] Dario Elias, Hebe Campaña, Fernando A Poletta, Silvina L Heisecke, Juan A Gili, Julia Ratowiecki, Mariela Pawluk, Maria R Santos, Viviana Cosentino, Rocio Uranga, et al. Preterm birth etiological pathways: a bayesian networks and mediation analysis approach. Pediatric research, 91(7):1882–1889, 2022.

[2] Lianne Tomfohr-Madsen, Emily E Cameron, Christine Dunkel Schetter, Tavis Campbell, Maeve O’Beirne, Nicole Letourneau, and Gerald F Giesbrecht. Pregnancy anxiety and preterm birth: The moderating role of sleep. Health psychology, 38(11):1025, 2019.

Media is pervasive in children's lives, often beginning in infancy. Yet, little is understood about its early impact and the factors driving its use. Parental stress may play a role. Research that surveys mothers in predominantly middle-class white households links parental stress, care giving practices, and infant social development [1]. In previous literature, stress is associated with increased TV time, but newer media like tablets and smartphones are understudied. This analysis contributes to our comprehension of the relationship between parental stress, modern media, and child social-emotional development. The mothers and fathers surveyed were economically disadvantaged and came from culturally diverse backgrounds.

We employed a series of hierarchical linear mixed effects models to examine relationships between parental stress, media usage, and infant social-emotional issues. Data was collected when the infant was nine months old, eighteen months old, and twenty-four months old. Our findings revealed that heightened parental stress predicts increased media usage to soothe infants, which, in turn, is associated with higher social-emotional problems in infants. These difficulties further exacerbate parental stress. 

References: 

[1] Shin, E., Choi, K., Resor, J., & Smith, C. L. (2021). Why do parents use screen media with toddlers? The role of child temperament and parenting stress in early screen use. Infant Behavior and Development, 64, 101595.

Alzheimer’s disease (AD) is predicted to affect 13.8 million Americans by mid-century [1]. Additionally, AD is the fifth leading cause of death for those 65 and older in the United States [1]. Not only does Alzheimer’s disease cause stress to family members and caregivers, in 2022 the care given to AD patients costed about $339.5 billion [1]. Consequently, early detection of Alzheimer’s disease offers considerable emotional, physical, and financial gains (“Facts and Figures”).

In this project we explore a significantly large data repository, collected by the National Alzheimer's Coordinating Center (NACC). This data repository consists of a variety of data sets and variables including  demographic features, health history, medical scans, brain recordings, and cognitive tests associated with patients in varying socio-econimic groups. To build informative predictive engines for Alzheimer's disease diagnosis, we have employed a number of data wrangling and data manipulation techniques, followed by building machine learning models. Specifically, our team has developed an R package for data preparation and modeling. Our machine learning models can be utilized for prediction of the disease diagnosis with high accuracy. Moreover, these models can be utilized for establishing inferential mechanisms to identify and validate variables that can influence the diagnosis of the disease more strongly. 

References: 

[1] "2023 Alzheimer's Disease Facts and Figures." Alzheimer's & Dementia, vol. 19, no. 4, 2023, pp. 1598-695, doi:https://doi.org/10.1002/alz.13016.

Studies have shown the negative impact television exposure during infancy has on a child’s cognitive development [1]. However, there are few studies showing the effects of smartphones and/or tablets on children. With the ubiquity of interactive media, it is beneficial to explore the effects of the association between media exposure and children's language development. We focus on the association between interactive media usage as a distraction, reward, or punishment(behavior management) and their percentile ranking of their Preschool Language Scale (PLS), a standardized test for assessing infant language development. To show this association, we built a linear mixed effects model with PLS percentile rank as a response and behavior management score as our predictor of interest while controlling for demographic covariates in accordance with domain expertise. Then, we explore how well this model predicts a child’s percentile ranking by their behavior management score, which is reported by parents. We found that the more a parent uses media to evoke a certain behavior on their infant, the language percentile ranking of the infant will decrease over time. 

References: 

[1] Thompson, A. L., Adair, L. S., & Bentley, M. E. (2013). Maternal characteristics and perception of temperament associated with infant TV exposure. Pediatrics, 131(2), e390-e397.

U-RISE Thesis Defense at California State University, Fullerton under PI, Dr. Sam Behseta

Title: Exploring the Effect of a Group of Predictors on Alzheimer’s Disease Progression Using Statistical Machine Learning Techniques

The American Association for the Advancement of Science - Pacific Division Annual Conference, at University of San Diego under PI, Dr. Stephanie Reich

Title: Stress, Screens, and Crying Scenes: Analyzing Cyclical Concerns in Child Development

SIAM Mini Poster Session at California State Polytechnic University, Pomona. under PI, Dr. Sam Behseta

Title: Exploring the effect of a group of predictors on Alzheimer’s disease progression using statistical machine learning techniques

Student Creative Activities and Research Day in Fullerton under PI, Dr. Stepanie Reich

Title: Stress, Screens, and Crying Scenes: Analyzing Cyclical Concerns in Child Development

Second CSU Mathematical Conference at California State University, Bakersfield under PI, Dr. Sam Behseta

Title: Exploring the Effect of a Group of Predictors on Alzheimer’s Disease Progression Using Statistical Machine Learning Techniques

Electronic Undergraduate Statistics Research Conference under PI, Dr. Stephanie Reich

Title: Stress, Screens, and Crying Scenes: Analyzing Cyclical Concerns in Child Development

National Diversity in STEM Conference (SACNAS) in Portland under PI, Dr. Stephanie Reich

Title:  Stress, Screens, and Crying Scenes: Analyzing Cyclical Concerns in Child Development

Orange County Biostatistics Program in Irvine under PI, Dr. Stephanie Reich

Title: Stress, Screens, and Crying Scenes: Analyzing Cyclical Concerns in Child Development

CSUF Summer Research Symposium in Fullerton under PI, Dr. Stephanie Reich

Title: Screen Time Struggles: An Exploration of Infant Media Usage and Parental Stress Levels Over Time

Student Creative Activities and Research Day in Fullerton under PI, Dr. Babak Shahbaba

Title: How Smart Phones Affect Infant Language Development

First CSU Mathematical Conference at California State University, Northridge under PI, Dr. Babak Shahbaba

Title: How Smart Phones Affect Infant Language Development

SIAM Mini Poster Session at California State University, Fullerton under PI, Dr. Babak Shahbaba

Title: How Smart Phones Affect Infant Language Development

CSUF Summer Research Symposium at California State University, Fullerton under PI, Dr. Babak Shahbaba

Title: How Smart Phones Affect Infant Language Development

SoCal Data Science Research Symposium at University of California, Irvine under PI, Dr. Babak Shahbaba

Title: How Smart Phones Affect Infant Language Development