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Assessing the Impact of Childhood Interventions
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Barbara J. Burns, Ph.D.
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Service Use Among Adolescents With Comorbid Mental Health and Substance Use Disorders

Paul E. Greenbaum

Part 3: Method

Links to other parts of this paper:


Method

Participants. Participants were a subset of 668 children, 75.4% males (n = 504) and 24.6% females (n = 164) females, and their families who were enrolled in the National Adolescent and Treatment Study (NACTS). Approximately half of the sample was enrolled in mental health residential facilities (47.5%) and the other half was in community-based special education programs (52.5%). NACTS was a comprehensive 7-year longitudinal study of 812 children identified with serious emotional disturbance, who, when the study began, were either in a residential mental health facility or a community-based special education program (Greenbaum, Dedrick, Friedman, Kutash, Brown, Lardieri, & Pugh, 1996). Inclusion criteria for the present study was that a participant had to have a DSM-III Axis I diagnosis (i.e., Anxiety, Depression, Conduct Disorder, Attention Deficit, Schizophrenia) when assessed at either Wave 1 (n = 557) or Wave 4 (n = 483). At the start of the study, the children were aged 9-17 years of age (M = 13.44, SD = 2.27) with 72% white, 19.9% African American, and 8.1% Hispanic or other ethnicity (e.g., Asian American, Native American).

Procedure. During Waves 1 through 6 of NACTS, each caregiver (e.g., parent, relative, professional caregiver) was asked to specify what services their child had received during the year. Receipt of 11 different services from the following five service areas were queried: (a) mental health (i.e., psychological testing, individual counseling, family counseling, group therapy, alcohol and drug counseling, psychotropic medication), (b) education (i.e., special education classes, speech therapy), (c) vocational rehabilitation, (d) nonroutine health care (e.g., doctor visits, emergency room), and (e) criminal justice (i.e., police contacts).

Measures. Rates of service utilization were measured in three ways. First, an aggregate measure of service use based on whether a child ever received a service during the entire 6-year period was calculated by assigning a value of 1 if a child received that service at any time during the 6-year period and an 0 if not. Second, for an 18-month period starting at Wave 1, a child was assigned a value of 1 or 0 representing whether or not a service was received during that period. Similarly, for an 18-month period starting at Wave 4, a child was assigned a value of 1 or 0 representing whether or not a service was received. Use of these three measures provided one global and two relatively short-term measures of service use. The two short-term measures also provided some degree of comparability (i.e., both were for the same length of time) and, therefore, information on stability of service use patterns. Moreover, these measures were concurrent with when the DSM-III diagnoses were obtained. Therefore, these measures were most informative about determining the short-term relationship between service needs and service use, whereas the 6-year aggregate measure provided a long-term perspective on how children’s service needs were being met.

DSM-III (American Psychiatric Association, 1980) diagnoses were derived from the Diagnostic Interview Schedule for Children (DISC-C; Costello, Edelbrock, Dulcan, Kalas, & Klaric, 1984, revised June 1985), a structured instrument administered during Waves 1 and 4 of the study. Derived diagnoses included 33 Axis I DSM-III diagnoses including alcohol and marijuana abuse/dependency. Other types of psychoactive drug use (e.g., opiates, cocaine, amphetamines, barbiturates, heroin, hallucinogens, inhalants) also were assessed. However, as pathological use, impairment, and tolerance/withdrawal symptoms for these drugs were not part of the DISC-C, no diagnoses for other drug disorders were available.

Analyses. For each type of service, comparisons of whether a child ever used that service during each of the three selected time periods were made between NACTS participants who had a comorbid MH and SU diagnoses versus those who had only an MH disorder. In this way, it was possible to determine if any significant differences existed in service use between the two groups of participants. Control variables also were entered into the analyses, which included child’s age, gender, race/ethnicity and facility (residential mental health /community-based special education). These control variables were included for two reasons. First, they represented the variables that formed the stratified sampling design of the study, so by inclusion in the analyses, any group differences in service use would not be confounded with sampling differences. Second, these design variables have been identified previously as either predictors or potential predictors of the variables of interest in this study (e.g., service use, comorbidity, mental health and substance use problems). By including these variables in the analyses, any differences in service use that could be attributed to the control variables will have been removed from the test of differences between comorbid and noncomorbid groups, thereby reducing spurious results. For all three service-use measures, which were dichotomous, logistic regression analysis was the statistical technique used to test for differences in the proportion of children in each group who ever used a service during the specified time period. In conjunction with reporting statistical significance (alpha = .05), adjusted odds ratios (AOR) also are reported. The odds ratio describes the ratio between the odds of one group (e.g., co-occurring) receiving an outcome (e.g., a specific service) relative to the odds of another group (e.g., only an MH disorder) receiving the same outcome. An odds ratio of 1.00 indicates no relationship, whereas, values of 2.00 or more are interpreted as indicating a meaningful difference. Adjusted odds ratios (AOR) are odds ratios that control for the shared variance with other predictors (i.e., control variables) that have been included in the logistic regression analyses.

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