Poster #56 - Examining Early Childhood Profiles That Reliably Differentiate Young Children at Highest Risk for Depression

• In Event: 3-159 - Poster Session 12
  In Poster Session: PS 12 Section - Developmental Psychopathology

Integrative Statement

There is growing recognition that major depression can occur early in life, starting as early as 3 years old (Domènech-Llaberia et al., 2009; Luby et al., 2009). Although few epidemiological studies have been conducted with young children, it is estimated that 0.5-2% of children ages 3-6 years experience major depression (Domènech-Llaberia et al., 2009; Centers for Disease Control and Prevention, 2013; Wichstrøm et al., 2012). The traditional approach to identifying depression in young children is based on discrete depressive behaviors, including sadness, inability to enjoy play activities, and decreases in activity level, adjusted for age (Luby et al., 2009). There is little information on the co-occurrence of depression in young children with other types of behaviors (e.g., irritability, temper loss) also linked to later outcomes.

Recent advances in research revealed that specific dimensions of disruptive behavior (e.g., irritability, temper loss) predict differential clinical courses and patterns of disruption in preschoolers (Wakschlag et al., 2015). Further research is needed to identify depression clusters at an early phase of the pathophysiological pathway. Clustering depression based on salient dimensions of behavior (e.g., temper loss, irritability) is important for generating an integrated profile of developmental risk. This study aims to 1) derive cluster‐analytic‐based profiles of preschool depression from irritability and disruptive behavior indicators at baseline, 2) determine if early life stressors can predict depression cluster membership, and 3) examine whether these patterns persist, using a nine-month follow-up.
 
This study leverages data from the Multidimensional Assessment of Preschoolers Study (MAPS), which examines behavioral and emotional problems in a diverse sample of preschoolers (N = 497; mean = 4.2 years; SD = 0.8). This study used the standardized Infant Toddler Social Emotional Assessment (ITSEA), designed to assess depression in young children; the Multidimensional Assessment Profile of Disruptive Behavior (MAP-DB), a novel measure used to model dimensional severity across developmental parameters (i.e., irritability, temper loss, and tantrums); and the Disruptive Behavior Diagnostic Observation Schedule (DB-DOS), a direct observation paradigm which systematically assesses contextual variation (in domains like anger modulation and behavior regulation). We use these measures to derive cluster-analytic-based profiles of preschool depression. Further, we used the Family Socialization Interview, a semi-structured interview for characterizing early life stress, to examine associations between profiles and early life stressors.

Table 1 presents descriptive statistics for measures of interest. Correlations between predictors and outcomes during baseline are presented in Table 2. Preschool depression was correlated with demographic characteristics (e.g., child race, parental education level/relationship status, and poverty) and externalizing characteristics (e.g., temper loss, tantrums, and irritable mood). To explore these correlations further, we will derive clusters of young children at risk for depression using aforementioned variables from the ITSEA, DB-DOS, and MAP-DB. Next, we will examine how these clusters are associated with individual characteristics, and whether experiences of early life stress predict membership in the clusters. Lastly, we will examine whether these profiles longitudinally predict differential patterns of depression and early life stress. Findings will create a framework for targeted prevention based on a holistic understanding of children.

Authors

• Jamilah Silver, Northwestern University
  Presenting Author

• Courtenay Kessler, Northwestern University
  Non-Presenting Author

• Terri Sabol, Northwestern University
  Non-Presenting Author

• Laurie S. Wakschlag, Northwestern University
  Non-Presenting Author