Date of this Version
Neurotoxicol Teratol. 2020 ; 81: 106915. doi:10.1016/j.ntt.2020.106915.
Objective—Precise phenotypic characterization of prenatal tobacco exposure (PTE) −related disruptive behavior (DB) that integrates nuanced measures of both exposures and outcomes is optimal for elucidating underlying mechanisms. Using this approach, our goals were to identify dimensions of DB most sensitive to PTE prior to school entry and assess contextual variation in these dimensions.
Methods—A community obstetric sample of N=369 women (79.2% lifetime smokers; 70.2% pregnancy smokers) from two Midwestern cities were assessed for PTE using cotinine-calibrated interview-based reports at 16, 28, and 40 weeks of gestation. A subset of n=244 who completed observational assessments with their 5-year-old children in a subsequent preschool follow-up study constitute the analytic sample. Using two developmentally-meaningful dimensions previously associated with emergent clinical risk for DB—irritability and noncompliance—we assessed children with 2 parent-report scales: the Multidimensional Assessment Profile of Disruptive Behavior (MAP-DB) and the Early Childhood Inventory (ECI). We also assessed children by direct observation across 3 interactional contexts with the Disruptive Behavior Diagnostic Observation Schedule (DB-DOS). We used generalized linear models to examine between-child variability across behavioral dimensions, and mixed effects models to examine directly observed within-child variability by interactional context.
Results—Increasing PTE predicted increasing impairment in preschoolers’ modulation of negative affect (irritability), but not negative behavior (noncompliance) across reported (MAP-DB) and observed (DB-DOS) dimensional measures. Moreover, children’s PTE-related irritability was more pronounced when observed with parents than with the examiner. The ECI did not detect PTE-related irritability nor noncompliance.
Conclusions—Nuanced, dimension- and context-specific characterization of PTE-related DB described can optimize early identification of at-risk children.