10/17/2024: 11:45 AM - 1:15 PM EDT
Panel
Room: Spruce Oak
Maternal health research in the United States plays a pivotal role in safeguarding both maternal and infant well-being. In 2021, the US fertility rate was up 1% from the previous year at 56.3 births per 1000 women aged 15-44 years old. Still, infertility affects approximately 16% of US couples, and there is a pressing need to identify couples who are at-risk for infertility. Predictive modeling offers hope to couples struggling with conception. There are additional concerns in ensuring the welfare of unborn and newborn babies. For example, a key measurement on unborn babies is gestational age, which must be estimated under complex circumstances. Estimating gestational age accurately is crucial for ensuring appropriate prenatal care and timely interventions to mitigate potential risks during pregnancy. Other key measures are related to anemia, which is a concern for very low birthweight infants in the neonatal intensive care unit. Often, measures like hemoglobin levels cannot be obtained, leading researchers to predict them from prognostic values. This session, sponsored by the Caucus for Women in Statistics, brings together women in our field to discuss how their research is often "more than statistics," as they strive to improve maternal and infant health through the use of data.
Organizer
Sarah Lotspeich, Wake Forest University
Target Audience
Mid-Level
Tracks
Knowledge
Women in Statistics and Data Science 2024
Presentations
Monitoring for anemia is an important part of clinical care for very low birth weight (VLBW) infants in the neonatal intensive care unit. We will present a novel analysis of a longitudinal study of VLBW infants from three Atlanta-area hospitals to evaluate the prognostic value of mothers' and infants' characteristics for predicting infant's hemoglobin levels and need for transfusion. We will discuss statistical challenges and complexities in analyzing such data. We will conclude the talk with sensible interpretation of our analysis results.
Speaker
Amita Manatguna, Emory University
Infertility affects approximately 16% of couples in the United States. It is estimated that roughly one-third of infertility is caused by female disorders, and one-third by combined male and female disorders. Motivated by concerns of identifying couples' at-risk for infertility, our goal has been to build a dynamic risk predictor of infertility based on biological processes, example menstrual cycle length and behavior, intercourse pattern of couples and other well-known risk factors to develop personalized risk prediction for couples. Here, we will focus on joint analysis and prediction of multivariate longitudinal processes, like menstrual cycle lengths (a skewed longitudinal process), intercourse pattern (a binary longitudinal process), cycle-specific peaks of reproductive hormonal profiles within woman with time-to-pregnancy (a discrete survival time). We will present a rigorous formulation for the joint modeling of these processes with time to event under a shared parameter framework. Our proposed approach will be investigated through simulations and application to multiple real data studies including Oxford Conception Study and the LIFE Study.
Speaker
Rajeshwari Sundaram, National Institute of Child Health and Human Development
The talk will present work on estimating gestational age in situations where ultrasound metrics are not available. Instead, we use repeated maternal anthropometry measures such as fundal height and cross-sectional neonatal anthropometry measures (at birth) to estimate the date of conception. We conduct a comprehensive simulation study to assess robustness of the method to model specification and illustrate this procedure using data from the National Institute of Child Health and Human Development fetal growth studies.
Speaker
Ana Maria Ortega-Villa, National Institutes of Health