|Title:||ADHD: Population-Based Estimates of Diagnosis, Treatments, and School Outcomes|
|Principal Investigator:||Morgan, Paul||Awardee:||Pennsylvania State University|
|Program:||Social and Behavioral Outcomes to Support Learning [Program Details]|
|Award Period:||7/1/2012-6/30/2014||Award Amount:||$694,704|
Co-Principal Investigators: Marianne Hillemeier; George Farkas (University of California-Irvine)
Purpose: ADHD is the most commonly diagnosed mental health disorder in school-aged children. Students with ADHD often engage in off-task and disruptive behaviors that reduce classroom engagement and, consequently, student learning. Students with ADHD are more likely to drop out of school, obtain a lower-level diploma, display low academic achievement, and fail to obtain a postsecondary education. Thus, identifying malleable and educationally relevant factors that decrease the impact of ADHD over time—particularly on student learning—is important.
The purpose of this study is to use data from the Early Childhood Longitudinal Study-Kindergarten Cohort (ECLS-K) to examine the following research questions: (a) What are the age- and grade-specific patterns of ADHD diagnosis among U.S. students in grades 1–8?; (b) Which population subgroups of students are more and less likely to receive a diagnosis and to experience different patterns of ADHD over time?; (c) Among students diagnosed with ADHD, which are more and less likely to receive treatment for this condition?; and (d) Is medication, special education and related services, grade retention, therapy, or combinations of these treatments effective in increasing behavioral, socio-emotional, and academic functioning of students diagnosed with ADHD? Which treatments are most effective for which students?
Project Activities: In the ECLS-K, a nationally representative sample of students entering kindergarten in the United States in the fall of 1998 were assessed in fall and spring of kindergarten, fall and spring of first grade, and each spring of third, fifth, and eighth grade. The researchers plan four sets of analyses on this dataset. First, the team will calculate ADHD diagnosis prevalence in grades 1, 3, 5, and 8, determine who is more and less likely to receive a diagnosis of ADHD, and determine who experiences different patterns of diagnosis of ADHD over time (e.g., early or late diagnosis). Second, the researchers will analyze the relations among student, family, school, and neighborhood characteristics and rates of ADHD diagnosis. Third, the team will create variables indicating whether students received various interventions for ADHD (i.e., special education or related services, grade retention, medication, therapy) in grades 1, 3, 5, and 8 to determine which students are more and less likely to receive services and which students are more and less likely to receive particular combinations of these treatments. Fourth, the research team will examine the relationship between the receipt of treatment(s) and behavioral, socio-emotional, and academic functioning in fifth and eighth grade.
Products: The expected products from this study include publications and presentations on research activities and findings that may serve as a basis for developing interventions for elementary students with ADHD.
Setting: The ECLS-K is a dataset with a nationally representative sample of children in kindergarten in 1998–99.
Sample: The ECLS-K includes data on children entering kindergarten in the United States in the fall of 1998. These children were assessed in fall and spring of kindergarten, fall and spring of first grade, and each spring of third, fifth, and eighth grade. Approximately 420 of the first-grade students had been diagnosed with ADHD. In subsequent years, approximately 520 third-grade students, 620 fifth-grade students, and 500 eighth-grade students were diagnosed with ADHD.
Intervention: The ECLS-K database indicates whether students received interventions, including special education and related services, grade retention, medication, behavior therapy, and individual and/or family therapy.
Research Design and Methods: This study involves secondary analyses of standardized direct measures and questionnaire data collected in the ECLS-K.
Control Condition: Due to the nature of the research design, there is no control condition.
Key Measures: In the ECLS-K study, parent surveys provided information regarding diagnosis and treatment of ADHD in grades 1, 3, 5, and 8. In addition, school records indicate whether the student received special education and received related services in these grades. Data on duration (e.g., five times or less) and location (e.g., in school) of treatment and who provided this therapy (e.g., counselor, psychologist, social worker) were also collected. Teacher surveys and rating scales provided information on student behavior. Student achievement in reading, mathematics, and science was measured via direct measures of student skills as well as teacher ratings of students' skill proficiency. Students also provided self-reports of socio-emotional functioning.
Data Analytic Strategy: For the first research question, the researchers will calculate ADHD diagnosis prevalence in grades 1, 3, 5, and 8. These variables will serve as dependent variables in logistic regression models estimating how a wide range of student, family, school, and neighborhood characteristics predict student diagnosis rates and patterns. ADHD rates and patterns will also be regressed on student, family, school, and neighborhood characteristics to identify how each diagnostic measure covaries with these characteristics. For the second research question, the team will estimate sequential logistic regression models to determine which characteristics of students, families, schools, and neighborhoods predict higher/lower rates of ADHD diagnosis. For the third research question, the team will conduct regression analyses to predict whether or not a student received a treatment of some type, and whether characteristics of students, families, schools, and neighborhoods predict receipt of treatment. The fourth research question will be answered with a combination of regression models that estimate the efficacy of treatments, as well as propensity score matching to create estimates of a treatment's impact on student learning and behavior.
Journal article, monograph, or newsletter
Lin, Y-C., Morgan, P.L., Hillemeier, M.H., Cook, M., and Maczuga, S. (2013). Reading, Mathematics, and Behavioral Difficulties Interrelate: Evidence From a Cross-Lagged Panel Design and Population-Based Sample of US Upper Elementary Students. Behavioral Disorders, 38(4): 212–227.
Morgan, P.L., Farkas, G., and Maczuga, S. (2015). Which Instructional Practices Most Help First-Grade Students With and without Mathematics Difficulties?. Educational Evaluation and Policy Analysis, 37(2): 184–205. doi:10.3102/0162373714536608
Morgan, P.L., Farkas, G., Hillemeier, M.H., Mattison, R., Maczuga, S., Li, H., and Cook, M. (2015). Minorities Are Disproportionately Underrepresented in Special Education: Longitudinal Evidence Across Five Disability Conditions. Educational Researcher, 44(5): 278–292. doi:10.3102/0013189X15591157?
Morgan, P.L., Farkas, G., Hillemeier, M.H., Mattison, R., Maczuga, S., Li, H., and Cook, M. (2015). Minorities Are Disproportionately Underrepresented in Special Education: Longitudinal Evidence Across Five Disability Conditions. Educational Researcher, 44(5): 278–292. doi:10.3102/0013189X15591157
Morgan, P.L., Hillemeier, M.M., Farkas, G., and Maczuga, S. (2014). Racial/Ethnic Disparities in ADHD Diagnosis by Kindergarten Entry. Journal of Child Psychology and Psychiatry, 55(8): 905–913. doi:10.1111/jcpp.12204
Morgan, P.L., Li, H., Cook, M., Farkas, G., Hillemeier, M.H., and Lin, Y-C. (2016). Which Kindergarten Children are at Greatest Risk for Attention-Deficit/Hyperactivity and Conduct Disorder Symptomatology as Adolescents?. School Psychology Quarterly, 31(1): 58–75. doi:10.1037/spq0000123
Morgan, P.L., Staff, J., Hillemeier, M.M., Farkas, G., and Maczuga, S. (2013). Racial and Ethnic Disparities in ADHD Diagnosis From Kindergarten to Eighth Grade. Pediatrics, 132: 85–93. doi:10.1542/peds.2012–2390 Full text