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Service Use Patterns for Adolescents with ADHD and Comorbid Conduct Disorder

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Abstract

Service use patterns and costs of youth diagnosed with attention-deficit/hyperactivity disorder (ADHD) and comorbid conduct disorder (CD) were assessed across adolescence (ages 12 through 17). Featured service sectors include mental health, school services, and the juvenile justice system. Data are provided by three cohorts from the Fast Track evaluation and are based on parent report. Diagnostic groups are identified through a structured assessment. Results show that public costs for youth with ADHD exceed $40,000 per child on average over a 6-year period, more than doubling service expenditures for a non-ADHD group. Public costs for children with comorbid ADHD and CD double the costs of those with ADHD alone. Varying patterns by service sector, diagnosis, and across time indicate different needs for youth with different conditions and at different ages and can provide important information for prevention and treatment researchers.

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Notes

  1. Racial breakdown varies substantially across sites as sample composition reflects the characteristics of the representative high-risk youth in the respective communities. The confounding of site with race is addressed in statistical models using covariates. Any further focus on varying service patterns across racial and/or regional differences is beyond the scope of this study.

  2. The Fast Track project assessed behavioral conditions at age 9, age 12, and age 15. The latter two measurement periods were used to assess ADHD and CD since they are proximal to the period of adolescence when service trends are examined, and also provide diagnoses based on behavior across a full year (the earlier assessment was only based on a 6-month period).

  3. These numbers are based on pre-imputed data. All figures in this section are unweighted N’s and percentages (except where noted). Weights were used in all cost calculations and significance tests presented in the “Results” section.

  4. The year 2000 was used as it represents a central timepoint between the earliest data collection for these outcomes (1997 for cohort 1) and the latest (2004 for cohort 3).

  5. Differences are marginally significant at age 17, p < 0.10.

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Acknowledgments

This work was supported by National Institute of Mental Health (NIMH) grants R18 MH48043, R18 MH50951, R18 MH50952, and R18 MH50953. The Center for Substance Abuse Prevention and the National Institute on Drug Abuse also have provided support for Fast Track through a memorandum of agreement with the NIMH. This work was also supported in part by the Department of Education grant S184U30002 and NIMH grants K05MH00797 and K05MH01027. The economic analysis of the Fast Track project is supported through R01MH62988. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute On Drug Abuse or the National Institutes of Health.

Author information

Authors and Affiliations

  1. Penn State University, University Park, PA, USA

    Damon E. Jones PhD

  2. University of North Carolina-Chapel Hill, Chapel Hill, NC, USA

    E. Michael Foster PhD

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Consortia

Conduct Problems Prevention Research Group

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Correspondence to Damon E. Jones PhD.

Additional information

Members of the Conduct Problems Prevention Research Group are (in alphabetical order) Karen L. Bierman, Department of Psychology, Penn State University; John D. Coie, Department of Psychology, Duke University; Kenneth A. Dodge, Center for Child and Family Policy, Duke University; Mark T. Greenberg, Department of Human Development and Family Studies, Penn State University; John E. Lochman, Department of Psychology, The University of Alabama; Robert J. McMahon, Department of Psychology, University of Washington; and Ellen E. Pinderhughes, Department of Child Development, Tufts University.

Appendices

Appendix A: Using multiple imputation to replace missing values

Missing data occurred in the Fast Track sample because of typical study attrition as well as because of different assessment schedules in earlier years (e.g., cohort 1 did not receive the SACA in year 8). Multiple imputation (MI) enables researchers to perform complete case analyses while acknowledging the variation inherent to accommodating missing data. Simulations have demonstrated that MI routines lead to valid analytic results given the assumption that data are conditionally missing-at-random (i.e., missingness is explained by non-missing covariates in the imputation model). Further details on the computer-intensive estimation processes involved in MI can be found elsewhere.40

Table 2 shows the actual total sample size for each year and source of data.

Table 2 Sample sizes providing data (N = 650 for entire sample)
Full size table

Outcomes were averaged across ten multiply imputed datasets, and variations in the imputed values were factored into significance tests. Imputation models included all of the service outcomes for all years, as well as diagnosis variables (conduct disorder, kindergarten risk status) and key demographic variables (race, gender, cohort, study site). Models were run using IVEWare,41 an imputation program that provides flexibility in estimating models with Poisson-distributed count variables.

Appendix B: Service expenditures

Per-unit expenditures were derived from two sources: estimates from previous research and estimates from record reviews of services being delivered to the Fast Track sample (more below). Table 3 provides the source for each cost estimate used. Total expenditures per child were calculated by multiplying the per-unit expenditure in Table 3 by the service amount number provided by the parent/caregiver.

Table 3 Cost estimates for per-unit services
Full size table

An agency review was instigated in year 9 of the Fast Track project (when most youth were in 8th grade) to obtain more detailed information on service delivery within the sample. Parents who reported service use in the past year were asked for written permission to follow up with facilities they could identify. Information recorded in these reviews included costs of services delivered. Estimates for expenditures per service from this supplemental data were used for sectors that did not have reliable estimates available from the literature (noted in Table 3). The following clarifies the identification of per-unit costs from the literature:

  • School services

School expenditures were comprised primarily of special education and grade retention. Special education expenditures were taken from Forness and Kavale32 (pp. 24–28) using data from 19 states. The overall average estimate for the excess cost of special education services was used ($5,435) as provided by these authors.

National Center for Education Statistics estimated the per pupil grade retention expenditure as $6,508 in school year 1998–1999.33 This expenditure included instructional services, support services, and non-instructional services.

  • Juvenile justice services

Juvenile justice arrest processing expenditure estimates are available in the literature based on the type of crime. Given the typical crime committed by this population, arrest expenditures were based on robbery-related offenses (in contrast to higher costs of arrest for such offenses as murder and rape).34 The average cost in 1987 dollars ($1,125) was adjusted to 2000 dollars for computations.

  • Medication

Using the MEPS, Zuvekas35 estimated spending for psychotropic drugs (antidepressants, antianxiety, antipsychotics, stimulants, and sedative hypnotics). Among youth aged from 6 to 17 who received these medications, related expenditures averaged $318 per person in 1996.

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Jones, D.E., Foster, E.M. & Conduct Problems Prevention Research Group. Service Use Patterns for Adolescents with ADHD and Comorbid Conduct Disorder. J Behav Health Serv Res 36, 436–449 (2009). https://doi.org/10.1007/s11414-008-9133-3

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