The Influence of Children's Cognitive Delay and Behavior Problems on Maternal Depression

Abstract

Objective To determine the impact of children’s cognitive delay and behavior on maternal depressive symptoms using a large, national cohort of US families. Study design Data were from two waves of the nationally-representative Early Childhood Longitudinal Study, Birth Cohort (n=7,550). Cognitive delay was defined at 24 months by the lowest 10th percentile of the Bayley Short Form-Research Edition. At age 4 years, child behavior was assessed by the Preschool and Kindergarten Behavior Scales, administered to mothers and primary non-parental child care providers, and maternal depressive symptoms with the Center for Epidemiologic Studies Depression Scale. Weighted generalized estimating equation models examined whether children’s behavior mediated the relation between their cognitive delay status at 24 months and four-year maternal depressive outcomes. Results At age 4 years, 26.9% of mothers children with cognitive delay reported high depressive symptoms compared with 17.4% of mothers of typically developing children (p<.0001). When children’s behavior was accounted for, the effect of cognitive delay on maternal depressive symptoms decreased by 36% (p<.0001). Findings remained significant when children’s behaviors were assessed by their primary non-parental care providers. Conclusions Caring for a child with a cognitive delay influences maternal depressive symptoms in part through children’s behavior problems. Preventive interventions to ameliorate adverse outcomes for children with cognitive delay and their families should consider the impact of children’s behavior.

Associations Among Mothers’ Depression, Emotional and Learning‐Material Support to Their Child, and Children's Cognitive Functioning: A 16‐Year Longitudinal Study

“…It may be that mothers emotionally and verbally disengage with less cognitively advanced children which, in turn, contribute to mothers’ depressive symptoms. Studies show that mothers of children with a cognitive delay report lower responsiveness (Guralnick, Neville, Hammond, & Connor, ), as well as high levels of depression, stress, and difficulties coping and managing such children (Cheng, Palta, Poehlmann‐Tynan, & Witt, ). The children in the current study tested broadly in the normal intelligence range.…”

Screening for maternal postpartum depression during infant hospitalizations

“…Perhaps our population of older and mainly full‐term infants makes consequences of neurologic insult more obvious and affects mothers more significantly. Cheng et al reported that 26.9% of mothers of children with cognitive delay reported high depressive symptoms, compared with 17.4% of mothers of typically developing children at 4 years of age . Another body of evidence suggests maternal emotional state during pregnancy influences neurodevelopmental outcome in the child.…”

“…Cheng et al reported that 26.9% of mothers of children with cognitive delay reported high depressive symptoms, compared with 17.4% of mothers of typically developing children at 4 years of age. 33 Another body of evidence suggests maternal emotional state during pregnancy influences neurodevelopmental outcome in the child. Maternal anxiety or depression has been associated with altered placental function, reduced infant gray matter density, and worse cognitive function.…”

Modelling correlated data: Multilevel models and generalized estimating equations and their use with data from research in developmental disabilities

“…For example, MLMs have been used to analyse data clustered because of nesting of parents in couples (Garcia-Lopez et al, 2016;Hartley & Schultz, 2015;Jones et al, 2014;Langley et al, 2017;Pottie et al, 2009), families in households (Pottie et al, 2009), individuals with ID in community homes (Qian et al, 2015), support staff in organizations (Knotter et al, 2016). GEEs have been used to account for clustering caused by carers nesting within households (Totsika et al, 2017), multiple births nesting within women (Brown et al, 2016), or twins nesting within families (Cheng et al, 2015). A key difference in these two approaches reflected in the studies is that those studies that modelled their data using MLMs wanted to describe how much of the outcome variance could be attributed to the factors causing the clustering, whereas in GEE analyses this was not a main consideration, but a characteristic of the design that had to be controlled for prior to the interpretation of estimated parameters.…”