An application of growth mixture modeling in MCH: Unintended pregnancy and perinatal depression trajectories among Hispanic immigrants

Background: Growth mixture modeling (GMM) is a relatively new method that identifies distinct trajectory patterns in longitudinal data. Typical methods of longitudinal modeling can obscure high-risk patterns by identifying only mean trends over time. GMM is useful for identifying high-risk subgroups in multiple MCH public health contexts. This paper demonstrates GMM using an example analysis that identifies distinct perinatal depression trajectories among Hispanic immigrants and tests the association between unintended pregnancy and depression trajectory. Methods: Depressive symptoms were collected from low-income Hispanic immigrants (n= 215) at 5 time points from early pregnancy to 12 months postpartum. The sample was selected to be at high-risk for perinatal depression. GMM was used to describe distinct trajectories of depressive symptoms over the perinatal period. Multinomial logistic regression was conducted to examine the association between unintended pregnancy and the depression trajectory patterns. Results: Three distinct patterns of depressive symptoms were identified: high during pregnancy with a postpartum decrease (“Pregnancy High”: 9.8%); borderline during pregnancy, with a postpartum increase (“Postpartum High”: 10.2%); and low throughout pregnancy and postpartum (“Perinatal Low”: 80.0%). Unintended pregnancy was associated with a 3- to 4-fold increase in risk of the “Postpartum High” pattern in depressive symptoms (unadjusted OR: 3.79, p<0.05; adjusted OR: 3.95, p<0.10). Conclusion: Unintended pregnancy is associated with postpartum depressive symptoms. Routine depression screening should occur postpartum and referral for culturally appropriate treatment should follow positive screening results. GMM is a tool that MCH researchers can use to identify high-risk patterns within longitudinal data.