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Primary Submission Category: Reproductive health
Early Detection of Health-Related Social Needs During Pregnancy Using Routinely Collected Data
Authors: Runhan Chen, Alexander Azan, Radhika Gore, Min Wu, Brita Roy, Rumi Chunara,
Presenting Author: Jessica Gjonaj*
Background: Health-related social needs (HRSNs) during pregnancy are associated with adverse maternal and infant outcomes, yet they are often identified only after needs escalate. Routine screening can help identify HRSNs, but implementing universal screening requires substantial resources and responses may be incomplete. Early detection using existing clinical data may enable efficient targeting of screening and referral in clinical settings.
Methods: We conducted a retrospective cohort study using electronic health record data from 3,829 deliveries (Jan 2022–Dec 2024) among a predominantly Hispanic/Latino-serving obstetric population receiving care at the Family Health Centers at NYU Langone, a Federally Qualified Health Center. HRSNs included financial, food, and housing insecurity, as reported through the Accountable Health Communities HRSN screener administered at each patient’s first prenatal visit. Descriptive analyses characterized the distribution of HRSNs. Predicted risk was derived from demographic and clinical characteristics; individual predictors are not emphasized given the model’s screening-oriented design and limited specificity. Data were split randomly 70/30 into training and test sets and logistic regression (with class weighting due to imbalance) was used to estimate the probability of HRSNs.
Results: Overall prevalence of HRSNs was 17.7%, and 80.3% of those patients reported multiple needs. Observed HRSN incidence increased from 6.1% in the lowest predicted-risk decile to 30.2–37.4% in the top two risk deciles. Patients in these highest risk groups were approximately 1.6–2.0 times more likely than average to experience HRSNs.
Implications: Findings suggest routinely collected data can support early detection of maternal social risk. Such approaches require clinical oversight for reliability. Next steps will focus on improving model performance and clinician-partnered implementation to support equitable, trusted use in practice.