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Adopted Adolescents' Overrepresentation in Mental Health Counseling: Adoptees' Problems or Parents' Lower Threshold for Referral?
Adopted Adolescents’ Overrepresentation in Mental Health Counseling: Adoptees’ Problems or Parents’ Lower Threshold for Referral? BRENT C. MILLER, PH.D., XITAO FAN, PH.D., HAROLD D. GROTEVANT, PH.D., MATHEW CHRISTENSEN, M.S., DIANA COYL, PH.D., AND MANFRED VAN DULMEN, M.S.
ABSTRACT A larger proportion of adopted adolescents receive mental health counseling than do their nonadopted peers. Adoptees might have more problems that require counseling, or their adoptive parents might have a lower threshold for referral (or both). Objective: To test the hypothesis that both the extent of adolescents’ problems and their adoption status would predict whether adolescents received psychological counseling, after controlling for family demographic characteristics. Method: Two large data sets collected from 1994 through 1996 by the National Longitudinal Study of Adolescent Health (Add Health) were used. In parallel analyses of the 2 data sets, hierarchical logistic regression models were implemented to assess the incremental effects of problem behaviors, family characteristics, and adoption status on adolescents receiving counseling. Results: Selected adolescents’ problems and family demographic characteristics were significant predictors for having received counseling, but, after controlling for these variables, adoptees were still about twice as likely as nonadoptees to have received counseling. Conclusions: Prevalence of problems, adoptive family characteristics, and adoption status must all be taken into account to understand why adoptees are more likely to receive counseling. Clinicians should be sensitive to issues that are especially salient in adoptive families. J. Am. Acad. Child Adolesc. Psychiatry, 2000, 39(12):1504–1511. Key Words: adolescents, adoption, counseling, mental health.
It has been widely reported that adopted adolescents are referred for psychological counseling and residential treatment more often than nonadopted adolescents. Approximately 2% to 3% of adolescents in the United States are adopted, but there is a much higher proportion of adopted adolescents in therapeutic settings. As summarized in recent comprehensive reviews (Haugaard, 1998; Ingersoll, 1997), the percentage of adopted adolescents who receive therapy ranges from 5% to 17%. Accepted July 11, 2000. Dr. Miller, Dr. Fan, Mr. Christensen, and Dr. Coyl are with Utah State University, Logan; Dr. Grotevant and Mr. van Dulmen are with the University of Minnesota, St. Paul. The authors gratefully acknowledge support provided by grant HD 36479 from the National Institute of Child Health and Human Development, to Brent C. Miller, Principal Investigator. Helpful suggestions about adjusting analyses for the complex sampling design were provided by Steven Heeringa, Director of Surveys and Technologies at the Institute of Social Research, University of Michigan. Reprint requests to Dr. Miller, Department of Family and Human Development, Utah State University, Logan, UT 84322-2905; e-mail: bcmiller@cc. usu.edu. 0890-8567/00/3912-1504䉷2000 by the American Academy of Child and Adolescent Psychiatry.
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Many studies indicate that adopted adolescents have more problems for which therapy might be needed. On the basis of an extensive meta-analysis, Wierzbicki (1993) concluded that there is a small to moderate effect size for being adopted, with adopted adolescents having schoolrelated and behavioral problems more often than nonadopted adolescents. Ingersoll (1997) also emphasized that adopted adolescents are in treatment more than nonadopted adolescents partly because they have more problems; specifically, they display higher rates of acting-out behaviors, which tend to be more distressing to parents compared with internalizing problems such as depression. Other scholars contend that adoptees do not have more problems (Benson et al., 1994; Borders et al., 1998; Finley, 1999). If adoptees do not have more problems than their peers, or if there are only small differences, what else might account for the substantially higher rates of mental health treatment among adopted adolescents? Ingersoll (1997) discussed alternative explanations for the higher rate of adopted adolescents in therapeutic settings, including an adoptive parent referral bias for psy-
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chological counseling. Higher socioeconomic status and education among adoptive parents might enable them to identify problems sooner and to have the financial resources for obtaining treatment, compared with less affluent parents. Ingersoll (1997) also hypothesized that adoptive parents might have higher parental anxiety than biological parents. Because most adoptive parents have had contact with social service agencies, they also might be more likely to use professional help than would parents who have not had prior contact with such agencies (National Council for Adoption, 1989). Warren (1992) conducted the only empirical analysis of this issue. She reported that adopted adolescents receive clinical services more often than nonadopted adolescents, both because they have more problems and because their adoptive parents have a lower threshold for referral. Warren’s (1992) analyses included variables that might be expected to account for the higher rate of treatment among adopted adolescents (e.g., school problems, running away, a behavior problem rating, and the parent’s level of education). Results showed that adopted adolescents did have more problems than nonadopted adolescents, and this predicted their receiving counseling. After controlling for the extent of problems, adopted adolescents still were more likely to receive psychological counseling compared with nonadopted adolescents. Warren (1992) concluded that both adoptee problems and adoptive parent referral bias are related to the higher proportion of adopted adolescents in therapy. Building on Warren’s (1992) research, this study tested the hypothesis that adopted adolescents have more psychological and behavior problems and that adoptive parents have a lower threshold for referral, both of which contribute to adopted adolescents being more likely to receive therapy than nonadopted adolescents. Because Warren (1992) is the only study to have examined this issue, our methodology was designed to be as similar as possible, so that previous findings might be replicated.
cents (N = 20,745) were interviewed in their homes. The Add Health self-administered questionnaire (SAQ) school sample allows comparisons between a large, representative sample of adopted and nonadopted adolescents. The home interview sample was selected from school rosters, so most adolescents who were interviewed at home had previously completed the school survey (Bearman et al., 1997). About half of the home interview was conducted by an interviewer; the most sensitive parts of the interview were conducted with computer-assisted audio self-interview technology. This method of hearing sensitive interview questions through earphones and entering responses on a laptop keyboard has been shown to produce much higher reports of some risky behaviors (Turner et al., 1998). Because school questionnaires and home interviews each included some measure of counseling, both samples were used in the present analysis.
METHOD
Home Interview Measures
Sample and Data The National Longitudinal Study of Adolescent Health (Add Health) is a large, nationally representative survey designed to measure social and familial contextual variables that influence the healthrelated behaviors of adolescents in grades 7 through 12 (Bearman et al., 1997). Add Health data were first collected in schools and later in homes. From September 1994 to April 1995, paper-and-pencil surveys were administered to 90,118 students at 129 schools in the United States. From April 1995 to December 1995, selected adoles-
School SAQ Measures Adoption Status. Whether or not a child was adopted was assessed by 2 questions in the school SAQ: “Are you adopted?” and a follow-up question, “Do you live with either of your biological parents?” Only adolescents who indicated that they were adopted and did not live with either biological parent were included in the adopted group (1.9%); all others were included in the nonadopted comparison group. Adolescents’ Problems. A group of problem behavior and positive behavior variables were constructed from multiple items. For example, “school troubles” included items like not getting along with teachers and students and not getting homework done, with answers ranging from “never” (0) to “every day” (4) since the beginning of the school year. “Emotional distress” was a frequency report of troubles eating and sleeping, feeling moody, depressed, etc., in the previous month, ranging from “never” (0) to “every day” (4). In constructing multiple-item composite scales, no score was computed if respondents had missing values on more than half of the items in the scale. For example, the composite emotional distress scale consisted of 7 items, but if a respondent had missing data on 4 or more of the 7 items, no score was calculated. Composite scales were based on both content analysis of the questionnaire items and the empirical results from exploratory factor analysis (see Miller et al., 2000, for more complete description of these measures). Received Counseling. In the school SAQ, the dependent variable about receipt of counseling was based on the question, “When did you last have counseling, psychological testing, or any other mental health or therapy service?” The 5 response options were (1) “within the last 12 months,” (2) “1 to 2 years ago,” (3) “more than 2 years ago,” (4) “I don’t remember,” or (5) “I’ve never had any of these services.” The way the question was written, option 4 meant that respondents received counseling but did not remember how long ago. Therefore, adolescents who responded with options 1 through 4 were combined into one group coded as having received counseling, to be compared with those who responded that they had never received these services (option 5).
Adoption Status. In the home interview, adolescents were not directly asked whether or not they were adopted, but their adoption status was inferred from a question about their relationship with each parent figure in their home: “Which description best fits [ father’s or mother’s name] relationship to you?” Adolescents from 2-parent homes who chose both “adoptive father” and “adoptive mother,” along with adolescents from single-parent homes who chose either “adoptive father” or “adoptive mother,” were classified into the adopted group (2.7%). The proportion of adoptees in this sample was slightly higher because an oversample of nonrelated sibling pairs was included in the home interview. Adoles-
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cents who reported all other kinds of relationships to parents were put into the nonadopted group. Because adolescents’ living arrangements can be very complicated, resolving adoption status inconsistencies for the Add Health sample is the subject of another report (Miller et al., 1999). Received Counseling. Adolescents were asked, “In the past year, have you received psychological or emotional counseling?” They answered by choosing yes or no. Compared with the school survey measure of the dependent variable (ever received counseling or not), the definition of the dependent variable in the home interview was more specific (psychological or emotional counseling in clinical settings) and was limited to within the previous year. Control Variables Family income should be related to whether families can afford to pay for mental health services. Family income data were not available in the school survey data, so we decided to use parents’ education as the indicator of socioeconomic status in both data sets because there were relatively few cases with missing data on parent education. Adolescents who lived with their mother or father were asked, “How far in school did she (he) go?” If the adolescent was living with 2 parents who had differing levels of education, the parent with the higher education level was coded for analysis; if the adolescent was living with a single parent, that parent’s education was used. Five parent education levels were coded: (1) parents with less than a high school diploma, (2) parents with a high school diploma (or GED), (3) parents who had attended some college, (4) parents who had a college degree, and (5) parents with graduate training or degrees. Because race is related to use of mental health services, with whites being more likely to receive counseling (Garland and Besinger, 1997), it was included as a control variable. In addition to the variables of race and parent education, we reasoned that health insurance coverage would be a meaningful control variable for receiving counseling. Three categories of family health insurance status were constructed: those who reported having no insurance, those who reported having public insurance (Medicare, Medicaid), and those who reported having private insurance (e.g., Blue Cross/Blue Shield, health maintenance organization). The bottom row of Table 1 shows that the percentage of adolescents who reported they had received counseling was 4 times larger in the school SAQ data than in the home interview data. This was expected for 2 reasons. First, in the SAQ, a much broader definition of counseling was used, including psychological testing and school counseling, along with mental or emotional counseling. For the home interview data, only psychological or emotional counseling in clinical settings was defined as having received counseling. Second, the SAQ used a longer time frame for receiving counseling than the home interview. Thus, it was not surprising that a larger proportion of adolescents (about 37%) in SAQ data reported that they had (ever) received counseling, compared with the much smaller proportion of adolescents in the home interview data (9%) who reported receiving clinical services in the previous year. Table 1 also shows the expected bivariate relationships between control variables and receipt of counseling. In the home interview data, the effect of these variables on counseling is quite substantial. A larger proportion of white adolescents (10.29%) interviewed at home reported counseling than did nonwhite adolescents (6.46%), and low parent education level was related to a smaller proportion of adolescents having received counseling. Adolescents also were much more likely to report psychological counseling if their family was covered by health insurance, especially public health insurance (Medicare or
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TABLE 1 Percentages of Adolescents Who Reported Receiving Counseling School SAQ
Race White Nonwhite Parent education
Home Interview
Mean
(SE)
Mean
(SE)
36.45 38.23
(0.68) (0.96)
10.29 6.46
(0.50) (0.53)
40.16 35.90 35.60 34.25 36.63
(1.27) (0.76) (0.85) (0.69) (0.99)
6.35 8.67 9.20 10.27 9.54
(0.79) (0.63) (0.68) (0.83) (0.96)
5.63 14.16 8.92
(0.70) (1.59) (0.48)
— — — 52.26 36.70
(2.06) (0.60)
17.71 8.67
(3.18) (0.41)
37.29
(0.61)
8.95
(0.42)
Note: Percentages for receipt of counseling are so much larger in school SAQ than home interview data because the school SAQ measure of counseling was defined more broadly and was unlimited in time. See text for details. SAQ = self-administered questionnaire. Medicaid). Finally, the proportion of adopted adolescents (17.71%) who reported receiving counseling was approximately twice as large as the proportion among nonadopted adolescents (8.67%). In the SAQ data, the relationships between these variables and receipt of counseling was not as obvious, except with regard to adoption status. The difference between having received counseling among adopted (52.26%) and nonadopted (36.70%) adolescents was quite large, indicating a potential effect of adoption status on counseling. The lack of obvious relationship of the school SAQ variables to receipt of counseling may be due to the much broader definition of counseling used in the SAQ. The data presented in Table 1 indicate only a potential effect of these variables on receipt of counseling. For example, it is possible that the higher rate of counseling among adopted adolescents is the result of more problems among adoptees. To investigate the potential effect of these variables, the problems of adolescents were controlled in hierarchical models implemented specifically for this purpose. Analysis Plan Logistic regression analysis is well suited for using multiple independent variables to predict a dichotomous dependent variable, such as having received counseling (coded 1) or not (coded 0). In addition to fitting the dichotomous nature of the dependent variable, logistic regression analysis has other analytic advantages: it is relatively assumption-free, and a combination of continuous, ordinal, and categorical variables can be used as predictors. Furthermore, odds ratios are estimated for independent variables, which reflect the relative contributions of the predictors to the outcome of having received counseling (Cox and Snell, 1989; Deal and Anderson, 1995). Add Health data were collected using a school-based cluster sampling design; this poses some special challenges for data analyses
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because cluster sampling increases the standard error of a sample statistic (Heeringa and Liu, 1998; Kish, 1965). If the larger standard error from a cluster sampling design were ignored in analyses, erroneous decisions in inferential significance testing could result. Therefore, Stata (version 6) was used for statistical analyses because it includes procedures specifically designed for data collected through cluster sampling (Stata, 1999). Stata’s SVYLOGIT procedure was used to conduct the logistic regression analyses so that the cluster sampling design effect has been adjusted. RESULTS
Data in Table 2 reveal obvious differences in the set of problem behavior variables between those who had received counseling and those who had not. According to the rule of thumb about the magnitude of effect sizes (.2 = small; .5 = moderate; .8 = large) suggested by Cohen (1988), the majority of the effect sizes between the 2 groups can be characterized as being in the small-tomoderate range. The same pattern of results emerged from both the school SAQ data and the home interview
data: on positive variables for which higher values are desirable (e.g., school grades, self-worth), those who had not received counseling had higher scores; conversely, adolescents who reported receiving counseling always had higher scores on negative variables (e.g., school troubles, substance use). In school SAQ data, adolescents who had received counseling were most different from those who had not on the measure of emotional distress (effect size = .49). In home interview data, the measures of school trouble, drug use, suicide, depression, and emotional upheaval (all effect sizes ⬎ .60) most strongly differentiated adolescents who did or did not report that they had received mental health counseling in the previous year. Preliminary analysis indicated that some problem behavior variables were not statistically significant predictors of receiving counseling. To ensure that all regression models would be applied to the same sample, and given the large sample size, only observations with no missing values on any variables were used. With these restrictions,
TABLE 2 Variable Mean Scores for Adolescents Who Did and Did Not Receive Counseling School SAQ Counseling
School variables School grades + School trouble School feelings + Skipping school Suspension/expulsion Substance use Smoke Drink Drunk Drug use Well-being Self-worth + Suicide Emotional distress Depression Emotional upheaval Optimism + Relationships Family closeness + Closeness to others + Delinquency Fighting
Home Interview
No Counseling
Counseling
No Counseling
Mean
SD
Mean
SD
ESa
Mean
SD
Mean
SD
ESa
2.71 1.72 3.40 0.91 —
0.81 1.07 0.90 1.50 —
2.89 1.49 3.59 0.55 —
0.78 1.12 0.82 1.16 —
–.23 .21 –.23 .28 —
2.52 6.67 4.42 4.93 0.53
0.85 3.30 4.42 13.22 0.66
2.82 4.97 18.66 1.79 0.30
0.76 2.79 3.71 6.72 0.53
–0.40 .60 –.46 .42 .42
1.80 1.58 1.00 —
2.38 1.67 1.55 —
0.99 1.12 0.62 —
1.88 1.45 1.23 —
.40 .30 .29 —
1.16 1.71 0.67 1.46
0.87 1.33 0.92 2.34
0.73 1.22 0.40 0.57
0.81 1.24 0.71 1.35
.53 .39 .38 .61
3.74 — 1.28 — — 6.12
0.76 — 0.88 — — 1.34
3.95 — 0.90 — — 6.41
0.66 — 0.73 — — 1.16
–.30 — .49 — — –.24
23.18 0.52
4.35 0.92
24.82 0.15
3.44 0.48
–.47 .69
5.66 3.28 7.27
5.28 2.36 2.90
3.34 2.17 8.15
3.70 1.75 2.68
.60 .61 –.33
— —
— —
— —
— —
— —
18.20 16.23
4.12 2.59
20.06 17.03
3.39 2.19
–.54 –.36
0.96
1.22
0.64
0.64
.29
1.62
2.40
0.97
1.68
.37
Note: + = positive variables on which a higher value is desired; SAQ = self-administered questionnaire. a ES (effect size) = [mean (receiving counseling – mean (not) receiving counseling)]/SD pooled. The ES assumes simple random sampling, and these are therefore probably overestimates of effect sizes under Add Health Cluster sample design. Because design-corrected sample standard deviations are not available from Stata, design-corrected effect sizes cannot be computed.
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the final sample size for hierarchical modeling with school SAQ data was 52,041 and the final sample size for home interview data was 12,537. To assess the unique contribution of adoption status to psychological counseling, while controlling for the effects of adolescent behaviors and background variables, we implemented 3 hierarchical logistic regression models in each sample. For school SAQ data, the incremental predictive effect of each of the 3 hierarchical models was statistically significant at a very stringent α level in predicting counseling, while controlling for the effect of the independent variables already included in the previous model(s). The first model tested only the effect of adolescent problem behaviors as predictors for receiving counseling; it was highly significant (p ⬍ .0001), verifying the expectation that adolescents’ problems predict whether or not they had received counseling. The second model, which added demographic variables (race and parent education), had an incremental predictive effect for receiving counseling that was also statistically significant (p ⬍ .0001), demonstrating that the demographic variables have some unique predictive value beyond what can be predicted by adolescents’ behavior problems. The significant incremental effect of model 3 (p ⬍ .0001) indicated that adoption status also contributed to receipt of counseling, beyond what was explained by adolescents’ problems and family demographic characteristics. The pattern of results was very similar for the 3 logistic regression models using home interview data: the incremental effects of model 1 (adolescents’ problems) and model 2 (demographic variables) were both statistically significant (p ⬍ .0001). The incremental effect for adoption status (model 3) also was significant (p = .0003) in home interview data, indicating that adopted children were more likely to have received counseling, even after controlling for their problems and family demographic characteristics (race, parent education, health insurance coverage). Table 3 presents the effects of the individual variables in the final logistic regression models predicting the dependent variable of adolescents having received counseling. The effect of an individual predictor on the dependent variable is represented by the odds ratio (OR) associated with each predictor, and the 95% confidence intervals for each OR. The OR in logistic regression analysis assesses the increase (or decrease) of risk of being in the higher category of the dependent variable (receiving counseling in this study) for a unit increase on the 1508
TABLE 3 Final Model Odds Ratios of Predictors for Adolescents’ Receipt of Counseling School SAQ Variable Problems School grades + School trouble School feeling + Skipping school Suspension/expulsion Smoke Drug use Self-worth + Suicide Emotional distress Depression Emotional upheaval Optimism + Family closeness + Fighting Demographic variables Race (white = 1, nonwhite = 0) Parent education Health insurance Private vs. none Public vs. none Adoption status
Home Interview
OR
95% CI
OR
95% CI
0.83 1.12 1.05 1.01 — 1.06 — 0.91 — 1.49 — — 0.94 — 1.17
(0.79–0.88) (1.09–1.15) (1.01–1.09) (0.99–1.04) — (1.04–1.08) — (0.87–0.96) — (1.43–1.56) — — (0.91–0.96) — (1.13–1.19)
0.77 — 1.01 1.02 1.56 1.16 1.08 — 1.44 — 1.02 1.17 — 0.96 —
(0.67–0.89) — (0.99–1.04) (1.01–1.03) (1.32–1.84) (0.99–1.34) (1.03–1.13) — (1.26–1.64) — (0.99–1.06) (1.12–1.23) — (0.93–0.99) —
1.04 (0.98–1.14) 1.09 (1.05–1.12)
1.81 (1.43–2.30) 1.25 (1.13–1.37)
— — — — 1.61 (1.36–1.92)
0.97 (0.83–1.13) 1.85 (1.49–2.30) 2.45 (1.52–3.95)
Note: + = positive variables on which a higher value is desired; SAQ = self-administered questionnaire; OR = odds ratio; CI = confidence interval.
scale of a predictor. If a predictor increases the risk of being in the higher category of the dependent variable, the OR will be larger than 1.0; if the reverse is true, then the OR will be smaller than 1.0. For example, in Table 3 the OR for school grades is smaller than 1.0 for both data sets, indicating that adolescents with higher grades are less likely to have received counseling. If the 95% confidence interval does not contain 1.00, it means that the OR for a predictor is statistically different from 1.00, indicating that the predictor variable increases (OR ⬎ 1.0) or decreases (OR ⬍ 1.0) the risk of counseling. More importantly, confidence intervals are used to provide information about the magnitude (the distance of the upper or lower limit from 1.00) and precision (the width of the confidence interval) of a sample estimate. For SAQ data, the variable emotional distress had the highest OR (1.49) among the adolescent problem variables (Table 3); those who had high emotional distress scores were more likely to have received counseling. Among the adolescent problem variables in the home interview data,
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the OR for suspension/expulsion (1.56) and suicide (1.44) stand out as increasing, with higher school grades decreasing (0.77), the odds of receiving counseling. The OR for race (0 = nonwhite, 1 = white) in home data interview data indicated that white adolescents were more likely to have received counseling than were nonwhite adolescents. That the OR for parent education was larger than 1 in both data sets indicates that children whose parents have higher educational levels were more likely to have received counseling. The larger OR for parent education in the home interview data makes intuitive sense because, as explained previously, the measure of counseling in home interview data was focused on clinical mental health services. Because counseling in clinical settings typically requires more resources from parents than does counseling in schools, factors such as parents’ education and race would be expected to be more strongly related to clinical than to school counseling. Finally, the ORs for adoption status in school SAQ (OR = 1.61) and home interview (OR = 2.45) analyses indicate that adopted adolescents were about 11⁄2 to 21⁄2 times as likely to have received counseling as nonadopted peers. These ORs provide supporting evidence that adopted adolescents are more likely to receive counseling, even after adjusting for other variables in the model. DISCUSSION Clinical Implications
There are several important implications for clinicians. First, given the referral bias toward adoptees, it is important to consider adoption issues on an ongoing basis when working with the family. Sensitivity about adoption status would help clinicians make diagnoses that take into account the relationship issues unique to adoptive families (Cohen et al., 1996; Grotevant and Kohler, 1999; Grotevant et al., 1999). Some theoretical perspectives (e.g., object relations) lend themselves more readily to tracking family of origin information than others (e.g., cognitive-behavioral); however, sensitivity to adoption issues is potentially important to all mental health professionals. Although this recommendation may seem obvious, our research in residential treatment centers (McRoy et al., 1988) revealed that clinicians were sometimes not even aware of children’s adoptive status, especially if the children were the same race and nationality as their adoptive parents. Second, the results suggest the importance of training clinicians in the dynamics of adoptive families (see
Grotevant and Kohler, 1999). These issues include acknowledgment of difference, the degree to which the adoptive family has acknowledged the unique features of being an adoptive rather than a biological family (Brodzinsky, 1987); control, especially with regard to parents’ sense of control over the degree to which the child’s birth family members are included in their family life (Grotevant and McRoy, 1998); entitlement, the parents’ sense of their right to be the child’s full parents (Cohen et al., 1996); loss, experienced by all members of the adoptive kinship network (adoptive parents’ loss of fertility; the child’s loss of his or her birth family; and birth parents’ loss of the child) (Brinich, 1990); adoptive identity, the adolescent’s emerging sense of self as an adopted person (Grotevant, 1997); and perceived compatibility of the child with the adoptive family, given the lack of genetic connection between them (Grotevant et al., 1999). Mental health providers might benefit from professional education about issues specific to adoptive families, enabling them to build on the strength and knowledge brought by the adopted adolescent’s parents and to develop a more informed and effective working alliance. Third, families in which the parents were more highly educated tended to make greater use of counseling than did families with less educated parents. Although adoptive parents have higher levels of education on average than do nonadoptive parents, the effect of education on counseling was evident even within the group of adoptive parents. Highly educated parents might be especially knowledgeable about the dynamics of adoptive families because most probably participated in a preadoption education program and possibly received postadoption services as well. Finally, clinicians knowledgeable about adoption practices and policies will be better prepared to assist adoptive families. Because adoption laws vary across the United States, clinicians should be aware of their state’s specific policies, for example, laws that might affect searching for birth parents, especially if there are medically indicated reasons to do so. It is also important for clinicians to be aware of postadoption services offered in their communities and to be aware of the many adoption resources on the Internet. Limitations
One strength of this study, the large sample, is also a source of limitations. The school questionnaire provided a straightforward assessment of adoptive status but obtained no information about age of placement, preplacement his-
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tory, family income, or family health insurance. The home interview did not contain a direct question about adoption status, which had to be inferred from family living arrangements. Another limitation is related to the different measures of counseling in the 2 data sets, which raises the question of whether or not the analyses from the 2 data sets are parallel; however, similar findings were obtained for the effect of adoption on receipt of counseling in both data sets. In this sense, our study replicated findings for both the broader and the narrower definition of “counseling” and for the somewhat different assessments of adoption status. Conclusions
This research replicated Warren’s (1992) finding that adopted adolescents are more likely to receive mental health treatment than nonadoptees, even after controlling for adolescents’ problems and selected demographic variables. Conceptually, the hierarchical models implemented by Warren (1992) and in this research are similar, as the models were designed to test for the incremental effect of adoption status on the dependent variable of adolescents receiving counseling. Our results support Warren’s (1992) conclusion that adoption status is related to receipt of counseling. In 2 Add Health data sets (school SAQ and home interview data), adoption status predicts receipt of counseling, beyond what is explained by adolescents’ problems, race, parent education, and health insurance coverage. Our analyses, however, went beyond Warren’s (1992) research in several respects. First, our analyses were based on 2 large overlapping data sets in which the dependent variable was broadly defined (ever received school or psychological counseling or testing) in one data set, and more narrowly (received psychological counseling in clinical settings in the preceding 12 months) in the other. Hierarchical logistic regression models, which tested the effect of adoption status on counseling referral with both school SAQ data and home interview data, are consistent with Warren’s conclusion that there is a referral bias due to adoption status. Thus, our results indicate that adoption status contributes to adolescents’ receipt of counseling, even after adjusting for adolescents’ problems and demographic variables. As summarized in meta-analyses (Wierzbicki, 1993), adoptees and nonadoptees consistently show small to moderate average differences, in favor of nonadoptees, on a variety of psychological and behavioral variables. This is also the conclusion of recent empirical analyses (Miller 1510
et al., 2000; Sharma et al., 1996, 1998). Thus it appears that the greater prevalence of problems among adoptees, their adoptive families’ characteristics, and adoption status itself all predict the receipt of psychological or mental health counseling. This research is based on data from the Add Health project, a program project designed by J. Richard Udry (Principal Investigator) and Peter Bearman and funded by grant P01-HD31921 from the National Institute of Child Health and Human Development to the Carolina Population Center, University of North Carolina at Chapel Hill, with cooperative funding participation by the National Cancer Institute; National Institute on Alcohol Abuse and Alcoholism; National Institute on Deafness and Other Communication Disorders; National Institute on Drug Abuse; National Institute of General Medical Sciences; National Institute of Mental Health; National Institute of Nursing Research; Office of AIDS Research, NIH; Office of Research on Women’s Health, NIH; Office of Population Affairs, DHHS; National Center for Health Statistics, Centers for Disease Control and Prevention, DHHS; Office of the Assistant Secretary for Planning and Evaluation, DHHS; and National Science Foundation. Persons interested in obtaining data files from the National Longitudinal Study of Adolescent Health should contact Add Health Project, Carolina Population Center, 123 West Franklin Street, Chapel Hill, NC 27516-3997; e-mail: [email protected].
REFERENCES Bearman PS, Jones J, Udry RJ (1997), The National Longitudinal Study of Adolescent Health: Research Design [online] 1–5. Available: http://www.cpc. unc.edu/projects/Add Health/design.html. Accessed May 1999 Benson PL, Sharma AR, Roehlkepartain EC (1994), Growing Up Adopted: A Portrait of Adolescents and Their Families. Minneapolis: Search Institute Borders DL, Black LK, Pasley KB (1998), Are adopted children and their parents at greater risk for negative outcomes? Fam Relat 47:237–241 Brinich PM (1990), Adoption from the inside out: a psychoanalytic perspective. In: The Psychology of Adoption, Brodzinsky DM, Schechter MD, eds. New York: Oxford University Press, pp 42–61 Brodzinsky DM (1987), Adjustment to adoption: a psychosocial perspective. Clin Psychol Rev 7:25–47 Cohen J (1988), Statistical Power Analysis for the Behavioral Sciences, 2nd ed. Hillsdale, NJ: Erlbaum Cohen NJ, Coyne JC, Duvall JD (1996), Parent’s sense of “entitlement” in adoptive and nonadoptive families. Fam Process 35:441–456 Cox DR, Snell EJ (1989), The Analysis of Binary Data, 2nd ed. London: Chapman and Hall Deal JE, Anderson ER (1995), Reporting and interpreting results in family research. J Marriage Fam 57:1040–1048 Finley GE (1999), Children of adoptive families In: Developmental Issues in the Clinical Treatment of Children and Adolescents, Silverman WK, Ollendick TH, eds. Boston: Allyn & Bacon, pp 358–370 Garland AF, Besinger BA (1997), Racial/ethnic differences in court referred pathways to mental health services for children in foster care. Child Youth Serv Rev 19:651–666 Grotevant HD (1997), Coming to terms with adoption: the construction of identity from adolescence into adulthood. Adoption Q 1:3–27 Grotevant HD, Kohler JK (1999), Adoptive families. In: Parenting and Child Development in “Nontraditional” Families, Lamb ME, ed. Mahwah, NJ: Erlbaum, pp 161–190 Grotevant HD, McRoy RG (1998), Openness in Adoption: Connecting Families of Birth and Adoption. Newbury Park, CA: Sage Grotevant HD, Wrobel GM, van Dulmen MH, McRoy RG (1999), The emergence of psychosocial engagement in adopted adolescents: the family as a
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context over time. Paper presented at the Fourth Sundance Conference on Youth and Family, Sundance, UT Haugaard JJ (1998), Is adoption a risk factor for the development of adjustment problems? Clin Psychol Rev 18:47–69 Heeringa SG, Liu J (1998), Complex sample design effects and inference for mental health survey data. Int J Methods Psychiatr Res 7:56–65 Ingersoll BD (1997), Psychiatric disorders among adopted children: a review and commentary. Adoption Q 1:57–73 Kish L (1965), Survey Sampling. New York: Wiley McRoy RG, Grotevant HD, Zurcher LA (1988), Emotional Disturbance in Adopted Adolescents: Origins and Development. New York: Praeger Miller BC, Fan X, Christensen M et al. (2000), Comparisons of adopted and non-adopted adolescents in a large nationally representative sample. Child Dev 71:1458–1473 Miller BC, Fan X, Christensen M, Grotevant HD, Van Dulmen M (1999), Who is adopted? Measuring adoption status using national survey data. Presented at the International Conference on Adoption Research, Minneapolis, August 6
National Council for Adoption (1989), Adoption Factbook: United States Data, Issues, Regulations, and Resources. Washington, DC: National Council for Adoption Sharma AR, McGue MK, Benson PL (1996), The emotional and behavioral adjustment of adopted adolescents, part I: an overview. Child Youth Serv Rev 18:83–100 Sharma AR, McGue MK, Benson PL (1998), The psychological adjustment of United States adopted adolescents and their nonadopted siblings. Child Dev 69:791–802 Stata (1999), Stata User’s Guide. College Station, TX: Stata Corporation Turner CF, Ku L, Rogers LD, Lindberg JH, Pleck JH, Sonenstein FL (1998), Adolescent sexual behavior, drug use and violence: increased reporting with computer survey technology. Science 280:867–874 Warren SB (1992), Lower threshold for referral for psychiatric treatment for adopted adolescents. J Am Acad Child Adolesc Psychiatry 31:512–517 Wierzbicki M (1993), Psychological adjustment of adoptees: a meta-analysis. J Clin Child Psychol 22:447–454
Coming in February 2001 Special Section: ADHD Comorbidity and Treatment Outcomes in the MTA Guest Editor: Peter S. Jensen
Symptom Profiles in Children With ADHD: Effects of Comorbidity and Gender Jeffrey H. Newcorn et al. ◆
ADHD Comorbidity Findings From the MTA Study: Comparing Comorbid Subgroups Peter S. Jensen et al. ◆
Multimodal Treatment of ADHD in the MTA: An Alternative Outcome Analysis C. Keith Conners et al. ◆
Clinical Relevance of the Primary Findings of the MTA: Success Rates Based on Severity of ADHD and ODD Symptoms at the End of Treatment James M. Swanson et al. ◆
Impairment and Deportment Responses to Different Methylphenidate Doses in Children With ADHD: The MTA Titration Trial Laurence L. Greenhill et al. ◆
Methylphenidate Dosage for Children With ADHD Over Time Under Controlled Conditions: Lessons From the MTA Benedetto Vitiello et al.
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ABSTRACT A larger proportion of adopted adolescents receive mental health counseling than do their nonadopted peers. Adoptees might have more problems that require counseling, or their adoptive parents might have a lower threshold for referral (or both). Objective: To test the hypothesis that both the extent of adolescents’ problems and their adoption status would predict whether adolescents received psychological counseling, after controlling for family demographic characteristics. Method: Two large data sets collected from 1994 through 1996 by the National Longitudinal Study of Adolescent Health (Add Health) were used. In parallel analyses of the 2 data sets, hierarchical logistic regression models were implemented to assess the incremental effects of problem behaviors, family characteristics, and adoption status on adolescents receiving counseling. Results: Selected adolescents’ problems and family demographic characteristics were significant predictors for having received counseling, but, after controlling for these variables, adoptees were still about twice as likely as nonadoptees to have received counseling. Conclusions: Prevalence of problems, adoptive family characteristics, and adoption status must all be taken into account to understand why adoptees are more likely to receive counseling. Clinicians should be sensitive to issues that are especially salient in adoptive families. J. Am. Acad. Child Adolesc. Psychiatry, 2000, 39(12):1504–1511. Key Words: adolescents, adoption, counseling, mental health.
It has been widely reported that adopted adolescents are referred for psychological counseling and residential treatment more often than nonadopted adolescents. Approximately 2% to 3% of adolescents in the United States are adopted, but there is a much higher proportion of adopted adolescents in therapeutic settings. As summarized in recent comprehensive reviews (Haugaard, 1998; Ingersoll, 1997), the percentage of adopted adolescents who receive therapy ranges from 5% to 17%. Accepted July 11, 2000. Dr. Miller, Dr. Fan, Mr. Christensen, and Dr. Coyl are with Utah State University, Logan; Dr. Grotevant and Mr. van Dulmen are with the University of Minnesota, St. Paul. The authors gratefully acknowledge support provided by grant HD 36479 from the National Institute of Child Health and Human Development, to Brent C. Miller, Principal Investigator. Helpful suggestions about adjusting analyses for the complex sampling design were provided by Steven Heeringa, Director of Surveys and Technologies at the Institute of Social Research, University of Michigan. Reprint requests to Dr. Miller, Department of Family and Human Development, Utah State University, Logan, UT 84322-2905; e-mail: bcmiller@cc. usu.edu. 0890-8567/00/3912-1504䉷2000 by the American Academy of Child and Adolescent Psychiatry.
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Many studies indicate that adopted adolescents have more problems for which therapy might be needed. On the basis of an extensive meta-analysis, Wierzbicki (1993) concluded that there is a small to moderate effect size for being adopted, with adopted adolescents having schoolrelated and behavioral problems more often than nonadopted adolescents. Ingersoll (1997) also emphasized that adopted adolescents are in treatment more than nonadopted adolescents partly because they have more problems; specifically, they display higher rates of acting-out behaviors, which tend to be more distressing to parents compared with internalizing problems such as depression. Other scholars contend that adoptees do not have more problems (Benson et al., 1994; Borders et al., 1998; Finley, 1999). If adoptees do not have more problems than their peers, or if there are only small differences, what else might account for the substantially higher rates of mental health treatment among adopted adolescents? Ingersoll (1997) discussed alternative explanations for the higher rate of adopted adolescents in therapeutic settings, including an adoptive parent referral bias for psy-
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chological counseling. Higher socioeconomic status and education among adoptive parents might enable them to identify problems sooner and to have the financial resources for obtaining treatment, compared with less affluent parents. Ingersoll (1997) also hypothesized that adoptive parents might have higher parental anxiety than biological parents. Because most adoptive parents have had contact with social service agencies, they also might be more likely to use professional help than would parents who have not had prior contact with such agencies (National Council for Adoption, 1989). Warren (1992) conducted the only empirical analysis of this issue. She reported that adopted adolescents receive clinical services more often than nonadopted adolescents, both because they have more problems and because their adoptive parents have a lower threshold for referral. Warren’s (1992) analyses included variables that might be expected to account for the higher rate of treatment among adopted adolescents (e.g., school problems, running away, a behavior problem rating, and the parent’s level of education). Results showed that adopted adolescents did have more problems than nonadopted adolescents, and this predicted their receiving counseling. After controlling for the extent of problems, adopted adolescents still were more likely to receive psychological counseling compared with nonadopted adolescents. Warren (1992) concluded that both adoptee problems and adoptive parent referral bias are related to the higher proportion of adopted adolescents in therapy. Building on Warren’s (1992) research, this study tested the hypothesis that adopted adolescents have more psychological and behavior problems and that adoptive parents have a lower threshold for referral, both of which contribute to adopted adolescents being more likely to receive therapy than nonadopted adolescents. Because Warren (1992) is the only study to have examined this issue, our methodology was designed to be as similar as possible, so that previous findings might be replicated.
cents (N = 20,745) were interviewed in their homes. The Add Health self-administered questionnaire (SAQ) school sample allows comparisons between a large, representative sample of adopted and nonadopted adolescents. The home interview sample was selected from school rosters, so most adolescents who were interviewed at home had previously completed the school survey (Bearman et al., 1997). About half of the home interview was conducted by an interviewer; the most sensitive parts of the interview were conducted with computer-assisted audio self-interview technology. This method of hearing sensitive interview questions through earphones and entering responses on a laptop keyboard has been shown to produce much higher reports of some risky behaviors (Turner et al., 1998). Because school questionnaires and home interviews each included some measure of counseling, both samples were used in the present analysis.
METHOD
Home Interview Measures
Sample and Data The National Longitudinal Study of Adolescent Health (Add Health) is a large, nationally representative survey designed to measure social and familial contextual variables that influence the healthrelated behaviors of adolescents in grades 7 through 12 (Bearman et al., 1997). Add Health data were first collected in schools and later in homes. From September 1994 to April 1995, paper-and-pencil surveys were administered to 90,118 students at 129 schools in the United States. From April 1995 to December 1995, selected adoles-
School SAQ Measures Adoption Status. Whether or not a child was adopted was assessed by 2 questions in the school SAQ: “Are you adopted?” and a follow-up question, “Do you live with either of your biological parents?” Only adolescents who indicated that they were adopted and did not live with either biological parent were included in the adopted group (1.9%); all others were included in the nonadopted comparison group. Adolescents’ Problems. A group of problem behavior and positive behavior variables were constructed from multiple items. For example, “school troubles” included items like not getting along with teachers and students and not getting homework done, with answers ranging from “never” (0) to “every day” (4) since the beginning of the school year. “Emotional distress” was a frequency report of troubles eating and sleeping, feeling moody, depressed, etc., in the previous month, ranging from “never” (0) to “every day” (4). In constructing multiple-item composite scales, no score was computed if respondents had missing values on more than half of the items in the scale. For example, the composite emotional distress scale consisted of 7 items, but if a respondent had missing data on 4 or more of the 7 items, no score was calculated. Composite scales were based on both content analysis of the questionnaire items and the empirical results from exploratory factor analysis (see Miller et al., 2000, for more complete description of these measures). Received Counseling. In the school SAQ, the dependent variable about receipt of counseling was based on the question, “When did you last have counseling, psychological testing, or any other mental health or therapy service?” The 5 response options were (1) “within the last 12 months,” (2) “1 to 2 years ago,” (3) “more than 2 years ago,” (4) “I don’t remember,” or (5) “I’ve never had any of these services.” The way the question was written, option 4 meant that respondents received counseling but did not remember how long ago. Therefore, adolescents who responded with options 1 through 4 were combined into one group coded as having received counseling, to be compared with those who responded that they had never received these services (option 5).
Adoption Status. In the home interview, adolescents were not directly asked whether or not they were adopted, but their adoption status was inferred from a question about their relationship with each parent figure in their home: “Which description best fits [ father’s or mother’s name] relationship to you?” Adolescents from 2-parent homes who chose both “adoptive father” and “adoptive mother,” along with adolescents from single-parent homes who chose either “adoptive father” or “adoptive mother,” were classified into the adopted group (2.7%). The proportion of adoptees in this sample was slightly higher because an oversample of nonrelated sibling pairs was included in the home interview. Adoles-
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cents who reported all other kinds of relationships to parents were put into the nonadopted group. Because adolescents’ living arrangements can be very complicated, resolving adoption status inconsistencies for the Add Health sample is the subject of another report (Miller et al., 1999). Received Counseling. Adolescents were asked, “In the past year, have you received psychological or emotional counseling?” They answered by choosing yes or no. Compared with the school survey measure of the dependent variable (ever received counseling or not), the definition of the dependent variable in the home interview was more specific (psychological or emotional counseling in clinical settings) and was limited to within the previous year. Control Variables Family income should be related to whether families can afford to pay for mental health services. Family income data were not available in the school survey data, so we decided to use parents’ education as the indicator of socioeconomic status in both data sets because there were relatively few cases with missing data on parent education. Adolescents who lived with their mother or father were asked, “How far in school did she (he) go?” If the adolescent was living with 2 parents who had differing levels of education, the parent with the higher education level was coded for analysis; if the adolescent was living with a single parent, that parent’s education was used. Five parent education levels were coded: (1) parents with less than a high school diploma, (2) parents with a high school diploma (or GED), (3) parents who had attended some college, (4) parents who had a college degree, and (5) parents with graduate training or degrees. Because race is related to use of mental health services, with whites being more likely to receive counseling (Garland and Besinger, 1997), it was included as a control variable. In addition to the variables of race and parent education, we reasoned that health insurance coverage would be a meaningful control variable for receiving counseling. Three categories of family health insurance status were constructed: those who reported having no insurance, those who reported having public insurance (Medicare, Medicaid), and those who reported having private insurance (e.g., Blue Cross/Blue Shield, health maintenance organization). The bottom row of Table 1 shows that the percentage of adolescents who reported they had received counseling was 4 times larger in the school SAQ data than in the home interview data. This was expected for 2 reasons. First, in the SAQ, a much broader definition of counseling was used, including psychological testing and school counseling, along with mental or emotional counseling. For the home interview data, only psychological or emotional counseling in clinical settings was defined as having received counseling. Second, the SAQ used a longer time frame for receiving counseling than the home interview. Thus, it was not surprising that a larger proportion of adolescents (about 37%) in SAQ data reported that they had (ever) received counseling, compared with the much smaller proportion of adolescents in the home interview data (9%) who reported receiving clinical services in the previous year. Table 1 also shows the expected bivariate relationships between control variables and receipt of counseling. In the home interview data, the effect of these variables on counseling is quite substantial. A larger proportion of white adolescents (10.29%) interviewed at home reported counseling than did nonwhite adolescents (6.46%), and low parent education level was related to a smaller proportion of adolescents having received counseling. Adolescents also were much more likely to report psychological counseling if their family was covered by health insurance, especially public health insurance (Medicare or
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TABLE 1 Percentages of Adolescents Who Reported Receiving Counseling School SAQ
Race White Nonwhite Parent education
Home Interview
Mean
(SE)
Mean
(SE)
36.45 38.23
(0.68) (0.96)
10.29 6.46
(0.50) (0.53)
40.16 35.90 35.60 34.25 36.63
(1.27) (0.76) (0.85) (0.69) (0.99)
6.35 8.67 9.20 10.27 9.54
(0.79) (0.63) (0.68) (0.83) (0.96)
5.63 14.16 8.92
(0.70) (1.59) (0.48)
— — — 52.26 36.70
(2.06) (0.60)
17.71 8.67
(3.18) (0.41)
37.29
(0.61)
8.95
(0.42)
Note: Percentages for receipt of counseling are so much larger in school SAQ than home interview data because the school SAQ measure of counseling was defined more broadly and was unlimited in time. See text for details. SAQ = self-administered questionnaire. Medicaid). Finally, the proportion of adopted adolescents (17.71%) who reported receiving counseling was approximately twice as large as the proportion among nonadopted adolescents (8.67%). In the SAQ data, the relationships between these variables and receipt of counseling was not as obvious, except with regard to adoption status. The difference between having received counseling among adopted (52.26%) and nonadopted (36.70%) adolescents was quite large, indicating a potential effect of adoption status on counseling. The lack of obvious relationship of the school SAQ variables to receipt of counseling may be due to the much broader definition of counseling used in the SAQ. The data presented in Table 1 indicate only a potential effect of these variables on receipt of counseling. For example, it is possible that the higher rate of counseling among adopted adolescents is the result of more problems among adoptees. To investigate the potential effect of these variables, the problems of adolescents were controlled in hierarchical models implemented specifically for this purpose. Analysis Plan Logistic regression analysis is well suited for using multiple independent variables to predict a dichotomous dependent variable, such as having received counseling (coded 1) or not (coded 0). In addition to fitting the dichotomous nature of the dependent variable, logistic regression analysis has other analytic advantages: it is relatively assumption-free, and a combination of continuous, ordinal, and categorical variables can be used as predictors. Furthermore, odds ratios are estimated for independent variables, which reflect the relative contributions of the predictors to the outcome of having received counseling (Cox and Snell, 1989; Deal and Anderson, 1995). Add Health data were collected using a school-based cluster sampling design; this poses some special challenges for data analyses
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because cluster sampling increases the standard error of a sample statistic (Heeringa and Liu, 1998; Kish, 1965). If the larger standard error from a cluster sampling design were ignored in analyses, erroneous decisions in inferential significance testing could result. Therefore, Stata (version 6) was used for statistical analyses because it includes procedures specifically designed for data collected through cluster sampling (Stata, 1999). Stata’s SVYLOGIT procedure was used to conduct the logistic regression analyses so that the cluster sampling design effect has been adjusted. RESULTS
Data in Table 2 reveal obvious differences in the set of problem behavior variables between those who had received counseling and those who had not. According to the rule of thumb about the magnitude of effect sizes (.2 = small; .5 = moderate; .8 = large) suggested by Cohen (1988), the majority of the effect sizes between the 2 groups can be characterized as being in the small-tomoderate range. The same pattern of results emerged from both the school SAQ data and the home interview
data: on positive variables for which higher values are desirable (e.g., school grades, self-worth), those who had not received counseling had higher scores; conversely, adolescents who reported receiving counseling always had higher scores on negative variables (e.g., school troubles, substance use). In school SAQ data, adolescents who had received counseling were most different from those who had not on the measure of emotional distress (effect size = .49). In home interview data, the measures of school trouble, drug use, suicide, depression, and emotional upheaval (all effect sizes ⬎ .60) most strongly differentiated adolescents who did or did not report that they had received mental health counseling in the previous year. Preliminary analysis indicated that some problem behavior variables were not statistically significant predictors of receiving counseling. To ensure that all regression models would be applied to the same sample, and given the large sample size, only observations with no missing values on any variables were used. With these restrictions,
TABLE 2 Variable Mean Scores for Adolescents Who Did and Did Not Receive Counseling School SAQ Counseling
School variables School grades + School trouble School feelings + Skipping school Suspension/expulsion Substance use Smoke Drink Drunk Drug use Well-being Self-worth + Suicide Emotional distress Depression Emotional upheaval Optimism + Relationships Family closeness + Closeness to others + Delinquency Fighting
Home Interview
No Counseling
Counseling
No Counseling
Mean
SD
Mean
SD
ESa
Mean
SD
Mean
SD
ESa
2.71 1.72 3.40 0.91 —
0.81 1.07 0.90 1.50 —
2.89 1.49 3.59 0.55 —
0.78 1.12 0.82 1.16 —
–.23 .21 –.23 .28 —
2.52 6.67 4.42 4.93 0.53
0.85 3.30 4.42 13.22 0.66
2.82 4.97 18.66 1.79 0.30
0.76 2.79 3.71 6.72 0.53
–0.40 .60 –.46 .42 .42
1.80 1.58 1.00 —
2.38 1.67 1.55 —
0.99 1.12 0.62 —
1.88 1.45 1.23 —
.40 .30 .29 —
1.16 1.71 0.67 1.46
0.87 1.33 0.92 2.34
0.73 1.22 0.40 0.57
0.81 1.24 0.71 1.35
.53 .39 .38 .61
3.74 — 1.28 — — 6.12
0.76 — 0.88 — — 1.34
3.95 — 0.90 — — 6.41
0.66 — 0.73 — — 1.16
–.30 — .49 — — –.24
23.18 0.52
4.35 0.92
24.82 0.15
3.44 0.48
–.47 .69
5.66 3.28 7.27
5.28 2.36 2.90
3.34 2.17 8.15
3.70 1.75 2.68
.60 .61 –.33
— —
— —
— —
— —
— —
18.20 16.23
4.12 2.59
20.06 17.03
3.39 2.19
–.54 –.36
0.96
1.22
0.64
0.64
.29
1.62
2.40
0.97
1.68
.37
Note: + = positive variables on which a higher value is desired; SAQ = self-administered questionnaire. a ES (effect size) = [mean (receiving counseling – mean (not) receiving counseling)]/SD pooled. The ES assumes simple random sampling, and these are therefore probably overestimates of effect sizes under Add Health Cluster sample design. Because design-corrected sample standard deviations are not available from Stata, design-corrected effect sizes cannot be computed.
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the final sample size for hierarchical modeling with school SAQ data was 52,041 and the final sample size for home interview data was 12,537. To assess the unique contribution of adoption status to psychological counseling, while controlling for the effects of adolescent behaviors and background variables, we implemented 3 hierarchical logistic regression models in each sample. For school SAQ data, the incremental predictive effect of each of the 3 hierarchical models was statistically significant at a very stringent α level in predicting counseling, while controlling for the effect of the independent variables already included in the previous model(s). The first model tested only the effect of adolescent problem behaviors as predictors for receiving counseling; it was highly significant (p ⬍ .0001), verifying the expectation that adolescents’ problems predict whether or not they had received counseling. The second model, which added demographic variables (race and parent education), had an incremental predictive effect for receiving counseling that was also statistically significant (p ⬍ .0001), demonstrating that the demographic variables have some unique predictive value beyond what can be predicted by adolescents’ behavior problems. The significant incremental effect of model 3 (p ⬍ .0001) indicated that adoption status also contributed to receipt of counseling, beyond what was explained by adolescents’ problems and family demographic characteristics. The pattern of results was very similar for the 3 logistic regression models using home interview data: the incremental effects of model 1 (adolescents’ problems) and model 2 (demographic variables) were both statistically significant (p ⬍ .0001). The incremental effect for adoption status (model 3) also was significant (p = .0003) in home interview data, indicating that adopted children were more likely to have received counseling, even after controlling for their problems and family demographic characteristics (race, parent education, health insurance coverage). Table 3 presents the effects of the individual variables in the final logistic regression models predicting the dependent variable of adolescents having received counseling. The effect of an individual predictor on the dependent variable is represented by the odds ratio (OR) associated with each predictor, and the 95% confidence intervals for each OR. The OR in logistic regression analysis assesses the increase (or decrease) of risk of being in the higher category of the dependent variable (receiving counseling in this study) for a unit increase on the 1508
TABLE 3 Final Model Odds Ratios of Predictors for Adolescents’ Receipt of Counseling School SAQ Variable Problems School grades + School trouble School feeling + Skipping school Suspension/expulsion Smoke Drug use Self-worth + Suicide Emotional distress Depression Emotional upheaval Optimism + Family closeness + Fighting Demographic variables Race (white = 1, nonwhite = 0) Parent education Health insurance Private vs. none Public vs. none Adoption status
Home Interview
OR
95% CI
OR
95% CI
0.83 1.12 1.05 1.01 — 1.06 — 0.91 — 1.49 — — 0.94 — 1.17
(0.79–0.88) (1.09–1.15) (1.01–1.09) (0.99–1.04) — (1.04–1.08) — (0.87–0.96) — (1.43–1.56) — — (0.91–0.96) — (1.13–1.19)
0.77 — 1.01 1.02 1.56 1.16 1.08 — 1.44 — 1.02 1.17 — 0.96 —
(0.67–0.89) — (0.99–1.04) (1.01–1.03) (1.32–1.84) (0.99–1.34) (1.03–1.13) — (1.26–1.64) — (0.99–1.06) (1.12–1.23) — (0.93–0.99) —
1.04 (0.98–1.14) 1.09 (1.05–1.12)
1.81 (1.43–2.30) 1.25 (1.13–1.37)
— — — — 1.61 (1.36–1.92)
0.97 (0.83–1.13) 1.85 (1.49–2.30) 2.45 (1.52–3.95)
Note: + = positive variables on which a higher value is desired; SAQ = self-administered questionnaire; OR = odds ratio; CI = confidence interval.
scale of a predictor. If a predictor increases the risk of being in the higher category of the dependent variable, the OR will be larger than 1.0; if the reverse is true, then the OR will be smaller than 1.0. For example, in Table 3 the OR for school grades is smaller than 1.0 for both data sets, indicating that adolescents with higher grades are less likely to have received counseling. If the 95% confidence interval does not contain 1.00, it means that the OR for a predictor is statistically different from 1.00, indicating that the predictor variable increases (OR ⬎ 1.0) or decreases (OR ⬍ 1.0) the risk of counseling. More importantly, confidence intervals are used to provide information about the magnitude (the distance of the upper or lower limit from 1.00) and precision (the width of the confidence interval) of a sample estimate. For SAQ data, the variable emotional distress had the highest OR (1.49) among the adolescent problem variables (Table 3); those who had high emotional distress scores were more likely to have received counseling. Among the adolescent problem variables in the home interview data,
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the OR for suspension/expulsion (1.56) and suicide (1.44) stand out as increasing, with higher school grades decreasing (0.77), the odds of receiving counseling. The OR for race (0 = nonwhite, 1 = white) in home data interview data indicated that white adolescents were more likely to have received counseling than were nonwhite adolescents. That the OR for parent education was larger than 1 in both data sets indicates that children whose parents have higher educational levels were more likely to have received counseling. The larger OR for parent education in the home interview data makes intuitive sense because, as explained previously, the measure of counseling in home interview data was focused on clinical mental health services. Because counseling in clinical settings typically requires more resources from parents than does counseling in schools, factors such as parents’ education and race would be expected to be more strongly related to clinical than to school counseling. Finally, the ORs for adoption status in school SAQ (OR = 1.61) and home interview (OR = 2.45) analyses indicate that adopted adolescents were about 11⁄2 to 21⁄2 times as likely to have received counseling as nonadopted peers. These ORs provide supporting evidence that adopted adolescents are more likely to receive counseling, even after adjusting for other variables in the model. DISCUSSION Clinical Implications
There are several important implications for clinicians. First, given the referral bias toward adoptees, it is important to consider adoption issues on an ongoing basis when working with the family. Sensitivity about adoption status would help clinicians make diagnoses that take into account the relationship issues unique to adoptive families (Cohen et al., 1996; Grotevant and Kohler, 1999; Grotevant et al., 1999). Some theoretical perspectives (e.g., object relations) lend themselves more readily to tracking family of origin information than others (e.g., cognitive-behavioral); however, sensitivity to adoption issues is potentially important to all mental health professionals. Although this recommendation may seem obvious, our research in residential treatment centers (McRoy et al., 1988) revealed that clinicians were sometimes not even aware of children’s adoptive status, especially if the children were the same race and nationality as their adoptive parents. Second, the results suggest the importance of training clinicians in the dynamics of adoptive families (see
Grotevant and Kohler, 1999). These issues include acknowledgment of difference, the degree to which the adoptive family has acknowledged the unique features of being an adoptive rather than a biological family (Brodzinsky, 1987); control, especially with regard to parents’ sense of control over the degree to which the child’s birth family members are included in their family life (Grotevant and McRoy, 1998); entitlement, the parents’ sense of their right to be the child’s full parents (Cohen et al., 1996); loss, experienced by all members of the adoptive kinship network (adoptive parents’ loss of fertility; the child’s loss of his or her birth family; and birth parents’ loss of the child) (Brinich, 1990); adoptive identity, the adolescent’s emerging sense of self as an adopted person (Grotevant, 1997); and perceived compatibility of the child with the adoptive family, given the lack of genetic connection between them (Grotevant et al., 1999). Mental health providers might benefit from professional education about issues specific to adoptive families, enabling them to build on the strength and knowledge brought by the adopted adolescent’s parents and to develop a more informed and effective working alliance. Third, families in which the parents were more highly educated tended to make greater use of counseling than did families with less educated parents. Although adoptive parents have higher levels of education on average than do nonadoptive parents, the effect of education on counseling was evident even within the group of adoptive parents. Highly educated parents might be especially knowledgeable about the dynamics of adoptive families because most probably participated in a preadoption education program and possibly received postadoption services as well. Finally, clinicians knowledgeable about adoption practices and policies will be better prepared to assist adoptive families. Because adoption laws vary across the United States, clinicians should be aware of their state’s specific policies, for example, laws that might affect searching for birth parents, especially if there are medically indicated reasons to do so. It is also important for clinicians to be aware of postadoption services offered in their communities and to be aware of the many adoption resources on the Internet. Limitations
One strength of this study, the large sample, is also a source of limitations. The school questionnaire provided a straightforward assessment of adoptive status but obtained no information about age of placement, preplacement his-
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tory, family income, or family health insurance. The home interview did not contain a direct question about adoption status, which had to be inferred from family living arrangements. Another limitation is related to the different measures of counseling in the 2 data sets, which raises the question of whether or not the analyses from the 2 data sets are parallel; however, similar findings were obtained for the effect of adoption on receipt of counseling in both data sets. In this sense, our study replicated findings for both the broader and the narrower definition of “counseling” and for the somewhat different assessments of adoption status. Conclusions
This research replicated Warren’s (1992) finding that adopted adolescents are more likely to receive mental health treatment than nonadoptees, even after controlling for adolescents’ problems and selected demographic variables. Conceptually, the hierarchical models implemented by Warren (1992) and in this research are similar, as the models were designed to test for the incremental effect of adoption status on the dependent variable of adolescents receiving counseling. Our results support Warren’s (1992) conclusion that adoption status is related to receipt of counseling. In 2 Add Health data sets (school SAQ and home interview data), adoption status predicts receipt of counseling, beyond what is explained by adolescents’ problems, race, parent education, and health insurance coverage. Our analyses, however, went beyond Warren’s (1992) research in several respects. First, our analyses were based on 2 large overlapping data sets in which the dependent variable was broadly defined (ever received school or psychological counseling or testing) in one data set, and more narrowly (received psychological counseling in clinical settings in the preceding 12 months) in the other. Hierarchical logistic regression models, which tested the effect of adoption status on counseling referral with both school SAQ data and home interview data, are consistent with Warren’s conclusion that there is a referral bias due to adoption status. Thus, our results indicate that adoption status contributes to adolescents’ receipt of counseling, even after adjusting for adolescents’ problems and demographic variables. As summarized in meta-analyses (Wierzbicki, 1993), adoptees and nonadoptees consistently show small to moderate average differences, in favor of nonadoptees, on a variety of psychological and behavioral variables. This is also the conclusion of recent empirical analyses (Miller 1510
et al., 2000; Sharma et al., 1996, 1998). Thus it appears that the greater prevalence of problems among adoptees, their adoptive families’ characteristics, and adoption status itself all predict the receipt of psychological or mental health counseling. This research is based on data from the Add Health project, a program project designed by J. Richard Udry (Principal Investigator) and Peter Bearman and funded by grant P01-HD31921 from the National Institute of Child Health and Human Development to the Carolina Population Center, University of North Carolina at Chapel Hill, with cooperative funding participation by the National Cancer Institute; National Institute on Alcohol Abuse and Alcoholism; National Institute on Deafness and Other Communication Disorders; National Institute on Drug Abuse; National Institute of General Medical Sciences; National Institute of Mental Health; National Institute of Nursing Research; Office of AIDS Research, NIH; Office of Research on Women’s Health, NIH; Office of Population Affairs, DHHS; National Center for Health Statistics, Centers for Disease Control and Prevention, DHHS; Office of the Assistant Secretary for Planning and Evaluation, DHHS; and National Science Foundation. Persons interested in obtaining data files from the National Longitudinal Study of Adolescent Health should contact Add Health Project, Carolina Population Center, 123 West Franklin Street, Chapel Hill, NC 27516-3997; e-mail: [email protected].
REFERENCES Bearman PS, Jones J, Udry RJ (1997), The National Longitudinal Study of Adolescent Health: Research Design [online] 1–5. Available: http://www.cpc. unc.edu/projects/Add Health/design.html. Accessed May 1999 Benson PL, Sharma AR, Roehlkepartain EC (1994), Growing Up Adopted: A Portrait of Adolescents and Their Families. Minneapolis: Search Institute Borders DL, Black LK, Pasley KB (1998), Are adopted children and their parents at greater risk for negative outcomes? Fam Relat 47:237–241 Brinich PM (1990), Adoption from the inside out: a psychoanalytic perspective. In: The Psychology of Adoption, Brodzinsky DM, Schechter MD, eds. New York: Oxford University Press, pp 42–61 Brodzinsky DM (1987), Adjustment to adoption: a psychosocial perspective. Clin Psychol Rev 7:25–47 Cohen J (1988), Statistical Power Analysis for the Behavioral Sciences, 2nd ed. Hillsdale, NJ: Erlbaum Cohen NJ, Coyne JC, Duvall JD (1996), Parent’s sense of “entitlement” in adoptive and nonadoptive families. Fam Process 35:441–456 Cox DR, Snell EJ (1989), The Analysis of Binary Data, 2nd ed. London: Chapman and Hall Deal JE, Anderson ER (1995), Reporting and interpreting results in family research. J Marriage Fam 57:1040–1048 Finley GE (1999), Children of adoptive families In: Developmental Issues in the Clinical Treatment of Children and Adolescents, Silverman WK, Ollendick TH, eds. Boston: Allyn & Bacon, pp 358–370 Garland AF, Besinger BA (1997), Racial/ethnic differences in court referred pathways to mental health services for children in foster care. Child Youth Serv Rev 19:651–666 Grotevant HD (1997), Coming to terms with adoption: the construction of identity from adolescence into adulthood. Adoption Q 1:3–27 Grotevant HD, Kohler JK (1999), Adoptive families. In: Parenting and Child Development in “Nontraditional” Families, Lamb ME, ed. Mahwah, NJ: Erlbaum, pp 161–190 Grotevant HD, McRoy RG (1998), Openness in Adoption: Connecting Families of Birth and Adoption. Newbury Park, CA: Sage Grotevant HD, Wrobel GM, van Dulmen MH, McRoy RG (1999), The emergence of psychosocial engagement in adopted adolescents: the family as a
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context over time. Paper presented at the Fourth Sundance Conference on Youth and Family, Sundance, UT Haugaard JJ (1998), Is adoption a risk factor for the development of adjustment problems? Clin Psychol Rev 18:47–69 Heeringa SG, Liu J (1998), Complex sample design effects and inference for mental health survey data. Int J Methods Psychiatr Res 7:56–65 Ingersoll BD (1997), Psychiatric disorders among adopted children: a review and commentary. Adoption Q 1:57–73 Kish L (1965), Survey Sampling. New York: Wiley McRoy RG, Grotevant HD, Zurcher LA (1988), Emotional Disturbance in Adopted Adolescents: Origins and Development. New York: Praeger Miller BC, Fan X, Christensen M et al. (2000), Comparisons of adopted and non-adopted adolescents in a large nationally representative sample. Child Dev 71:1458–1473 Miller BC, Fan X, Christensen M, Grotevant HD, Van Dulmen M (1999), Who is adopted? Measuring adoption status using national survey data. Presented at the International Conference on Adoption Research, Minneapolis, August 6
National Council for Adoption (1989), Adoption Factbook: United States Data, Issues, Regulations, and Resources. Washington, DC: National Council for Adoption Sharma AR, McGue MK, Benson PL (1996), The emotional and behavioral adjustment of adopted adolescents, part I: an overview. Child Youth Serv Rev 18:83–100 Sharma AR, McGue MK, Benson PL (1998), The psychological adjustment of United States adopted adolescents and their nonadopted siblings. Child Dev 69:791–802 Stata (1999), Stata User’s Guide. College Station, TX: Stata Corporation Turner CF, Ku L, Rogers LD, Lindberg JH, Pleck JH, Sonenstein FL (1998), Adolescent sexual behavior, drug use and violence: increased reporting with computer survey technology. Science 280:867–874 Warren SB (1992), Lower threshold for referral for psychiatric treatment for adopted adolescents. J Am Acad Child Adolesc Psychiatry 31:512–517 Wierzbicki M (1993), Psychological adjustment of adoptees: a meta-analysis. J Clin Child Psychol 22:447–454
Coming in February 2001 Special Section: ADHD Comorbidity and Treatment Outcomes in the MTA Guest Editor: Peter S. Jensen
Symptom Profiles in Children With ADHD: Effects of Comorbidity and Gender Jeffrey H. Newcorn et al. ◆
ADHD Comorbidity Findings From the MTA Study: Comparing Comorbid Subgroups Peter S. Jensen et al. ◆
Multimodal Treatment of ADHD in the MTA: An Alternative Outcome Analysis C. Keith Conners et al. ◆
Clinical Relevance of the Primary Findings of the MTA: Success Rates Based on Severity of ADHD and ODD Symptoms at the End of Treatment James M. Swanson et al. ◆
Impairment and Deportment Responses to Different Methylphenidate Doses in Children With ADHD: The MTA Titration Trial Laurence L. Greenhill et al. ◆
Methylphenidate Dosage for Children With ADHD Over Time Under Controlled Conditions: Lessons From the MTA Benedetto Vitiello et al.
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