Peripartum Depression Prevention: Algorithmic Identification and Digital Solutions

项目总结/摘要 背景：妊娠期和产后期抑郁症影响高达15%的美国人 母亲，给母亲、孩子和社会带来成本。更多的黑人在一起 抑郁症的标准比美国的白色人，但他们不太可能接受 精神卫生保健，突出了诊断和治疗方面的差距。一架美国 预防服务工作组的建议表明，有风险的孕妇 抑郁症应积极参与行为健康服务。对于任何抑郁症的预防 为了使这种方法具有可扩展性和可持续性，必须准确识别那些有抑郁风险的人 在抑郁症发作之前，随后与循证治疗相关联， 可行的，可接受的，可用的。 方法Aim 1将PC核条件独立性算法应用于两个大型 前瞻性观察数据集。输出将是潜在因果途径的模型， 围产期抑郁症发作，可用于预测个体患者的抑郁症风险， 可以1）从现有电子健康记录（EHR）查询的因素，以及2）可以 结合EHR数据，更准确地预测随后的母亲抑郁症， 超出了标准的护理筛查。这将创建风险预测所需的最小数据集 和干预。目标2将召集一个由5个OB/GYN提供者和两个社区组成的专家小组 订婚工作室，一个与黑人怀孕的人，一个与白色怀孕的人 个人，以获得关于实施需求和风险可接受性的反馈 预测算法目标3将随机抽取60名参与者（50%为黑人），他们的年龄在- 未来抑郁症的风险在怀孕的头三个月数字CBT（治疗）或常规 护理（对照），使用非传统的、高度可扩展的方法进行试验招募。 潜在影响：这项工作将确定后续评估所需的数据和流程。 一项可接受的、可扩展的、主要是数字化的抑郁症治疗策略的随机对照试验 孕妇的检测和预防。这是第一个具有高度创新性的研究 在抑郁症发作之前，使用可扩展的方法来识别风险， 不同人群的怀孕患者与数字化精神卫生保健服务相结合， 结合患者群体的观点和经验， 服务于发展进程。

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.