# Peripartum Depression Prevention: Algorithmic Identification and Digital Solutions

> **NIH NIH R34** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2024 · $186,933

## Abstract

Project Summary/Abstract
Background: Depression during pregnancy and the postpartum period affects up to 15% of US
mothers, imposing costs on mother, child, and society. Significantly more Black individuals meet
the criteria for depression than white individuals in the US, yet they are less likely to receive
mental health care, highlighting disparities in diagnosis and treatment. A United States
Preventive Services Task Force recommendation suggests that pregnant people at risk for
depression be proactively engaged in behavioral health services. For any depression prevention
approach to be scalable and sustainable, those at risk of depression must be accurately identified
prior to depression onset and subsequently connected to an evidence-based treatment that is
feasible, acceptable, and usable.
Methods Aim 1 will apply the PC Kernel Conditional Independence algorithm to two large
prospective observational datasets. The output will be models of the potential causal pathways of
perinatal depression onset that can be used to predict individual patient depression risk based on
factors that can 1) be queried from the existing electronic health record (EHR), and 2) can be
combined with EHR data to more precisely predict subsequent maternal depression above and
beyond standard of care screeners. This will create a minimal set of data needed for risk prediction
and intervention. Aim 2 will convene an expert panel of 5 OB/GYN providers and two community
engagement studios, one with Black pregnant individuals and one with white pregnant
individuals, to obtain feedback on the implementation needs and acceptability of the risk
prediction algorithms. Aim 3 will randomize 60 participants (50% Black individuals) who are at-
risk for future depression in the first trimester of pregnancy to a digital CBT (treatment) or usual
care (control), using a non-traditional, highly scalable approach to trial enrollment.
Potential Impact: This work will identify the data and processes necessary for a subsequent
randomized controlled trial of an acceptable, scalable, and largely digital strategy for depression
detection and prevention among pregnant people. It is highly innovative as it will be the first study
of its kind to identify risk prior to depression onset using a scalable approach to engaging a
diverse population of pregnant patients paired with digital mental health care provision,
incorporating the perspectives and experiences of the patient population for whom the model is
serving into the development process.

## Key facts

- **NIH application ID:** 10874562
- **Project number:** 5R34MH130950-03
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Tamar Krishnamurti
- **Activity code:** R34 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $186,933
- **Award type:** 5
- **Project period:** 2022-08-15 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10874562

## Citation

> US National Institutes of Health, RePORTER application 10874562, Peripartum Depression Prevention: Algorithmic Identification and Digital Solutions (5R34MH130950-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10874562. Licensed CC0.

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