Trajectory Analysis for Identifying Classes of Attention Deficit Hyperactivity Disorder (ADHD) in Children of the United States.

Abstract

Attention Deficit Hyperactivity Disorder (ADHD) is a mental health disorder that affects attention and behavior. People with ADHD frequently encounter challenges in social interactions, facing issues, like social rejection and difficulties in interpersonal relationships, due to their inattention, impulsivity, and hyperactivity. A National Longitudinal Survey of Youth (NLSY) database was employed to identify patterns of ADHD symptoms. The children who were born to women in the NLSY study between 1986 and 2014 were included. A total of 1,847 children in the NLSY 1979 cohort whose hyperactivity/inattention score was calculated when they were four years old were eligible for this study. A trajectory modeling method was used to evaluate the trajectory classes. Sex, baseline antisocial score, baseline anxiety score, and baseline depression score were adjusted to build the trajectory model. We used stepwise multivariate logistic regression models to select the risk factors for the identified trajectories. The trajectory analysis identified six classes for ADHD, including (1) no sign class, (2) few signs since preschool being persistent class, (3) few signs in preschool but no signs later class, (4) few signs in preschool that magnified in elementary school class, (5) few signs in preschool that diminished later class, and (6) many signs since preschool being persistent class. The sensitivity analysis resulted in a similar trajectory pattern, except for the few signs since preschool that magnified later class. Children's race, breastfeeding status, headstrong score, immature dependent score, peer conflict score, educational level of the mother, baseline antisocial score, baseline anxious/depressed score, and smoking status 12 months prior to the birth of the child were found to be risk factors in the ADHD trajectory classes. The trajectory classes findings obtained in the current study can (a) assist a researcher in evaluating an intervention (or combination of interventions) that best decreases the long-term impact of ADHD symptoms and (b) allow clinicians to better assess as to which class a child with ADHD belongs so that appropriate intervention can be employed.