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Recording available 2/16-5/2
Recording available 2/16-5/2
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Poster Session 1

Category: Epidemiology

Poster Session 1

(389) Additive Bayesian Network Analysis of Social Determinants of Health and Adverse Pregnancy Outcomes

Wednesday, February 11, 2026
10:30 AM - 12:00 PM  

    Submitting Author and Presenting Author(s)

  • UB

    Ummayhany Bharmal, MBBS (she/her/hers)

    Medical College Baroda
    Vadodara, Gujarat, India

    • Coauthor(s)

  • GS

    Grace Spencer, BS, MS

    Eastern Virginia Medical School at Old Dominion University
    EVMS OBGYN, Virginia Health Sciences at Old Dominion University, Virginia, United States

  • Tetsuya Kawakita, MD, MS (he/him/his)

    Associate Professor
    Eastern Virginia Medical School Department of Obstetrics and Gynecology, Macon & Joan Brock Virginia Health Sciences at Old Dominion University
    Norfolk, Virginia, United States

Objective:
The relationship between Social Determinants of Health (SDoH) and Adverse Pregnancy Outcomes (APO) has been well established, yet these factors interact in complex and non-linear ways. Our objective was to use Additive Bayesian Networks to examine interrelationships between SDoH variables and APO.

Study Design:

This was a secondary analysis of data from the Nulliparous Pregnancy Outcomes Study: Mothers-to-Be (nuMoM2b). Individuals with missing data or delivery < 23 weeks were excluded. The primary outcome was a composite of APOs, including preterm births < 37 weeks, hypertensive disorders of pregnancy, small for gestational age (< 5th percentile), stillbirth, and gestational diabetes mellitus. We examined direct and indirect associations between APOs and SDoH as well as clinical factors, including body mass index (BMI), pregestational diabetes mellitus (PDM), and chronic hypertension (CHTN), using additive Bayesian network analysis. A final Directed Acyclic Graph (DAG) was derived from 1,000 resamples created using non-parametric bootstrapping. Results are reported as odds ratios (OR) along with their 95% credibility intervals (CrI).

Results:

Of 7,089, 1,686 (23.7%) had APOs. The optimal model allowed a maximum of six parent nodes and initially identified 44 arcs; 40 were retained after bootstrapping in the final DAG (Figure 1). APOs were directly associated with BMI [logOR: 1.33; 95%CrI:1.26,1.41], race [logOR: 1.39; 95%CrI: 1.17, 1.64], and PDM [logOR: 3.29; 95%CrI: 2.08, 5.23]. Race was influenced by emotional support, health literacy, employment, perceived stress, anxiety, and social support. BMI mediated the effects of race, smoking history, and anxiety, and its effects on APO were also partly mediated by PDM. Associations between various variables are presented in Table 1.

Conclusion:

A complex interplay of social, behavioral, and psychosocial factors contributes to the development of adverse outcomes. Integration of social and clinical interrelationships into risk models and intervention is necessary to predict adverse pregnancy outcomes.