Identification of Core Antenatal Depressive Symptoms: A Network Analysis.

Background: The limited identification of core symptoms of antenatal depression is currently impeding the development of targeted interventions, with scant research utilizing network analysis to identify these core symptoms.

Objective: We aimed to construct a network of depressive symptoms using data from a sample of pregnant women in the United States, identifying the core symptoms using centrality indices within the network.

Methods: We conducted a secondary analysis of data from the National Health and Nutrition Examination Survey (2003-2020). Depressive symptoms were measured with the Patient Health Questionnaire-9. Pregnancy status was evaluated via self-report, urine, and serum testing. Centrality analysis was then used to examine the centralities in depressive symptoms and their relationships with each other. A case-dropping bootstrap procedure and a bootstrapped difference test were used to assess the accuracy and stability of the network.

Results: The final analysis included 669 pregnant women. A sad mood (r_s = 6.444, r_c = 0.091, r_b = 12), guilt (r_s = 6.260, r_c = 0.088, r_b = 10), and anhedonia (r_s = 5.141, r_c = 0.088, r_b = 12) had the largest values in terms of strength, closeness, and betweenness. Sad mood-guilt had the highest edge weight. Although fatigue was the most severe depressive symptom among pregnant women, the centrality of fatigue was lower than the other depressive symptoms.

Conclusions: Sadness, guilt, and anhedonia can be identified as core antenatal depressive symptoms. Targeting these core symptoms could improve the precision and effectiveness of interventions aimed at pregnant women with depression.