Identification of Core Antenatal Depressive Symptoms: A Network Analysis

Abstract

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.