Peripartum Depression Prevention: Algorithmic Identification and Digital Solutions

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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.