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Health Equity/Community Health

Poster Session 1

(249) Assessing the social network structure of high risk patients: A novel research method in obstetrics

Tuesday, February 13, 2024

10:30 AM – 12:00 PM

Location: Prince George's Exhibit Hall CDE, Lower Atrium Level

Primary & Presenting Author(s)

Rachel Meislin, MD

Assistant Professor, Maternal Fetal Medicine
Icahn School of Medicine at Mount Sinai
New York, NY, United States

Coauthor(s)

AB

Angela Bianco, MD

Attending Physician
Mount Sinai Medical Center
New York, NY, United States
CH

Corrine Hanson, PhD

Professor
University of Nebraska Medical Center
Omaha, NE, United States
RP

Rachel Potter, MS

Icahn School of Medicine at Mount Sinai
New York, NY, United States
JP

Jana Ponce, PhD

Assistant Professor
University of Nebraska
Omaha, NE, United States
CJ

Courtney Juliano, MD

Icahn School of Medicine at Mount Sinai
New York, NY, United States
AD

Amar Dhand, MD, PhD

Brigham and Women's Hospital, Harvard Medical School
Boston, MA, United States
SB

Sonali Bose, MD, MPH

Associate professor
Icahn School of Medicine at Mount Sinai
New York, NY, United States

Objective: Maternal psychosocial health has been associated with adverse pregnancy outcomes including preterm birth. Quantitative tools to measure an individual’s social network have been demonstrated to predict health outcomes (e.g. stroke) in nonpregnant patients. The Surgeon General’s 2023 advisory on the loneliness epidemic highlights the research gap in social connectivity as a possible determinant of obstetric outcomes. The objective of this study was to investigate the applicability of a novel quantitative approach to assess social networks within a population of high risk pregnant patients.

Study Design: Patients with asthma participating in a longitudinal pregnancy cohort were interviewed in their second or third trimester of pregnancy using an established social network analysis instrument (PERSNET). PERSNET measures components of one’s social network including density (how each tie in the network is related to all ties), constraint (closeness of people to the participant and each other), and size (number of ties). Statistical and graphical analyses were generated using R software.

Results: Eighteen participants completed the PERSNET survey with 100% participation rate. All reported active asthma with medication use in the last three months. The mean age of participants was 28.7 years (SD 3.73). The majority (n=16, 88.9%) reported non-white race. Black race was reported by n=7 (38.8%) and Hispanic ethnicity by n=7 (38.8%). Thirteen (72.2%) completed some college or less. Network structures are depicted in Figure 1. Mean (SD) network density was 0.82 (0.253), mean (SD) constraint was 0.67 (0.22), and the range of network size was 2-12 people [median (IQR) 4, 3.75].

Conclusion: Social network structure among a high risk pregnant cohort was highly variable in size, density, and interconnectedness. Smaller networks tended to be more interconnected. Because social capacity may influence health behaviors, these findings represent a rigorous way to capture a critical part of the prenatal social environment. Future research is needed to explore how social network structures may impact reproductive outcomes.