Depressive symptoms, stress, fatigue, and lack of sleep are often experienced by women in the perinatal period and are potential contributors to adverse maternal and child health outcomes. To explore the evolution of symptoms and identify groups of women of similar severity and patterns, we utilized clustering of multidimensional symptom trajectories. In an observational study data were collected from pregnant women in the 3rd trimester (36 weeks prenatal) and in the postnatal period at weeks 1 and 2 as well as at 1-, 2-, 3-, and 6-months postpartum. Depressive symptoms and maternal stress were measured using the Edinburg Postnatal Depression Scale (EPDS) and the Perceived Stress Scale (PSS), respectively. Self-reported duration of sleep and levels of fatigue also were collected. A model-based clustering approach was used to classify women by their symptom severity. The sample included 151 pregnant women with a 6-month follow-up. Two clusters were identified. Cluster 1 (n = 43) comprised women with fewer depressive symptoms, less perceived stress, lower likelihood of being fatigued, increased sleep duration and a negative trend in EPDS (β = -0.05, CI [-0.09, -0.001]), and PSS (β = -0.09, CI [-0.17, -0.01]). Cluster 2 (n = 108) comprised women with higher EPDS and PSS scores, increased likelihood of fatigue and lower sleep duration with a positive trend in sleep hours (β = -0.02, CI [0.01, 0.03]). Pro-inflammatory markers interleukin-6 and tumor necrosis factor-α were associated with longer sleep duration and fewer depressive symptoms, respectively. Using this methodology in maternal and child health research can potentially predict women's risk of developing severe symptoms and help clinicians provide timely interventions.
Keywords: model-based clustering; multivariate; symptoms; trajectories.
© 2019 Wiley Periodicals, Inc.