Predicting Diabetic Control from Competence, Adherence, Adjustment, and Psychopathology

Predicting Diabetic Control from Competence, Adherence, Adjustment, and Psychopathology W. BURLESON DAVISS, M.D., HILARY COON, PH.D., PAUL WHITEHEAD, M.D., KEN RYAN, M.D., MATT BURKLEY, M.D., AND WILLIAM McMAHON, M.D.

ABSTRACT

Objective: To determine what psychological and behavioral factors were most predictive of diabetic control. Method: Seventy-nine youths with diabetes were assessed cross-sectionally, using youths’ reports of self-esteem, anxiety, and attitudes about diabetes, and parents’ reports of competence and psychopathology (from the Child Behavior Checklist) and diabetic adherence as independent variables. Glycosylated hemoglobin A,, was the dependent variable, reflecting diabetic control. After the effects of several background variables were partialed out, a principal-components analysis grouped the substantive variables into three larger components. Results: Among the background variables, duration of illness and family size significantly predicted diabetic control. Among substantive components, Competence/Adherence (including Total Competence, dietary compliance, and frequency of blood glucose checks) was highly predictive of diabetic control, primarily due to the effect of Total Competence. Adjustment (including self-esteem, anxiety levels, and attitudes about diabetes) and Psychopathology were less predictive. A model was constructed showing the relationships between these predictive components and diabetic control. Conclusions: In this generally well-adjusted sample, that Total Competence, more than other measures, predicted diabetic control suggests it could be used by clinicians to anticipate diabetic youths at risk. J. Am. Acad. Child Adolesc. Psychiatry, 1995, 34, 12:1629-1636. Key Words: diabetes, competence, control, youths.

Among chronic illnesses of children, diabetes is unusual because the patient and family assume so much responsibility for its management. Required tasks include compliance with a diet, blood glucose monitoring, and taking insulin injections. There is increasing evidence that those who control the diabetes more tightly lower their risk of long-term complications such as retinopathy, nephropathy, and neuropathy (Kilo, 1985; Klein

Accepted January 19, 1995. From the Department of Psychiatry, University of Utah, Salt Lake City (Drs. Daviss, Coon, and McMahon); Yale University, New Haven, C T (Dr. Whitehead); Uniuersiiy of Pittsburgh (Or. Ryan): and North Colorado Medical Center, Greelty (Dr. Burklty). Dr. Daviss is current4 at Dartmouth Hitchcock Medical Center, Lebanon, NH. The authors thank Dr. David Okubo and his ofice staf, who helped collect data and distribute supplies; Dr. Robert Lindsay and Dr. Marv Rallison, who allowed us to recruit subjects through their clinics; Daphne Won5 M.D., Landon Coleman, MSII, SLzde Spencer, MSII, and Cindy Argo, R.N., who also helped in data collection; and Miles Laboratories, which donated the laboratory equipment. Reprint requests to Dr. Daviss, Dartmouth Hitchcock Medical Center, I Medical Center Drive, Lebanon, N H 03756-0001. 0890-8567/95/3412-1629$03.00/001995 by the American Academy of Child and Adolescent Psychiatry.

et al., 1988; Leslie and Sperling, 1986; McConce et al., 1989; Tchobroutsky, 1978). Psychological factors may have an impact on long-term control, either directly through neurohormonal mechanisms, or indirectly through effects on patients’ motivation and ability to comply with treatment (Helz and Templeton, 1990; Tarnow and Silverman, 1981-1982). Investigators over the last two decades have explored the association between psychiatric factors and diabetic control with conflicting results. Some have found a significant association between poor diabetic outcomes and psychiatric illnesses (Kovacs et al., 1992; Lustman et al., 1986), interpersonal conflicts (Simonds, 19761977), and other psychosocial problems (Orr et al., 1983; White et al., 1984). Many of these studies have relied on one-time interviews by mental health workers. Other studies using more objective, standardized tools such as structured diagnostic interviews or the Child Behavior Checklist (CBCL) (Achenbach, 1991a) have not found a relationship between psychopathological symptoms and diabetic control (Brown et al., 1991; Rovet et al., 1987; Weist et al., 1993; Wertlieb et al., 1986).

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Investigators have searched for more subtle psychological factors contributing to diabetic outcomes, again with mixed results. For instance, some have shown a significant correlation between patients’ self-esteem and diabetic adherence (Jacobson et al., 1987), while others have failed to show a relationship between self-esteem and control (Grossman et al., 1987; Rovet et al., 1987; Simonds et al., 1981). Studies on anxiety levels have been equally contradictory. Some have shown a significant correlation between levels of anxiety and diabetic management or control (Mazze et al., 1984; Niemcryk et al., 1990; Turkat, 1982) and others have not (Brown et al., 1991; Delamater et al., 1987; Simonds et al., 1981). Others have examined the association between competence, as measured by the CBCL, and diabetic management. Hanson et al. (1987a) found that competence correlated significantly with adherence behaviors but not with metabolic control. Subsequently, Hanson et al. (1987b) showed that diabetic youths with high competence had less likelihood to deteriorate in their control when subjected to life stressors, thus implying competence had a buffering effect. In contrast, Kovacs et al. (1992) and Weist et al. (1993) found no correlation between competence and diabetic adherence and control, respectively. N o study to date has shown a direct correlation between CBCL’s Total Competence and diabetic control. Two longitudinal studies have also been inconsistent. Jacobson et al. (1990) showed that patients’ and parents’ initial measures of general adjustment (using competence, self-esteem, psychopathology, and attitudes about the diabetes) predicted subsequent adherence to the diabetic regimen, but they did not use an objective measure of diabetic control. Kovacs et al. (1990), in a study using glycosylated hemoglobin concentration as the measure of diabetic control, found no significant relationship between measures of selfesteem, anxiety, or depression and subsequent diabetic control. They noted that since the overall sample was well-adjusted, such psychological measures may be of limited utility in an otherwise healthy, nonclinical population. In short, there has been a great deal of ambiguity about the relationship between psychological factors and diabetic outcomes in research up to this point. With so many psychological measures seemingly inconsistent or unreliable in their predictive value, how can

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clinicians anticipate which patients will be at particular risk of developing poor diabetic control? The purpose of this study was to reexamine different psychological factors associated with diabetic control. We chose measures we believed would be predictive of control in a representative sample of children with diabetes. These measures could be easily used by the clinician to identify high-risk patients. O u r hypothesis was that children with better competence, self-esteem, overall diabetic adjustment and adherence behaviors, and less anxiety and psychiatric symptoms, would show better diabetic control. METHOD Subjects An initial group of subjects was recruited through Camp Utada, a camp for children with diabetes near Ogden, Utah, sponsored by the Utah and American Diabetes Associations. The camp was a week-long experience during the summer of 1993 for 100 children, aged 10 to 14 years, living throughout the Intermountain West. A week before camp, letters were sent to parents of the 70 children who lived within a 100-mile radius of the Salt Lake City area, recruiting campers for a study to investigate the relationship between psychological factors and diabetic control. Campers and parents interested in participating arrived 30 minutes before registration on the opening day of camp to complete the questionnaires and laboratory work. In return for their participation, they were offered $20 worth of diabetic supplies. Of the 70 patients sent letters, 41 volunteered for the study. One camper who volunteered was excluded because he was mentally retarded and unable to understand or complete the questionnaires. Because the available information about nonparticipating campers was limited, we were unable to verify that the 40 volunteers truly represented the 70 campers solicited except in gender, age, and duration of illness. An additional 39 patients were recruited through the diabetic clinic at Salt Lake City’s Primary Children’s Medical Center. This is a clinic of 850 children also from throughout the Intermountain West. Clinic participants were recruited by W.B.D. or P.W. through a telephone call to the parent, in which the same explanation and supplies as compensation were offered. Of 43 clinic families contacted, 39 agreed to participate. The clinic participants were also limited to those who lived within a 100-mile radius of Salt Lake City. Using the clinic records, each camper was matched with a clinic patient based on sex, age, duration of illness, and relative diabetic control, as a part of a separately reported prospective study investigating the effects of going to camp.

Procedure All children with diabetes and parent participants first signed a consent form, approved by the Institutional Review Board of Primary Children’s Hospital, then completed a packet of questionnaires. W.B.D. was present to answer any questions. In the clinic group, an investigator or assistant traveled to each subject’s home to collect the laboratory samples and distribute the questionnaires. Subjects in the clinic group received a stamped envelope to return

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the completed questionnaires within 48 hours; they were given W.B.D.'s telephone number and were instructed to call with any specific questions about the questionnaires.

Measures Diabetic Control. Glycosylated hemoglobin values are recognized as the most accurate measure of general glycemic control, with a higher glycosylated hemoglobin reflecting poorer diabetic control over the previous 2 to 3 months (Dunn et al., 1979; Gonen et al., 1979). In this study, we used the Miles DCA 2000 device, a new immunoassay system with a monoclonal antibody that binds specifically to glycosylated hemoglobin A,, (HbA,,). This device requires only 1 pL of capillary blood. The nondiabetic range for this HbA,, measure is 4.1 to 5.3, with values from 9.1 to 14.0 reflecting increasingly poor control. This measure of HbA,, has been shown to have high validity and reliability (Marrero et al., 1992; Pope et al., 1993). The blood was obtained through a fingerstick, then kept chilled in a capillary tube containing EDTA until the test was conducted, always within 48 hours. Competence and Psychopathology. The parent completed the 4-page CBCL. Responses to the first 20 items give a Total Competence score along with subscores including Activities, Social Competence, and School Competence. The last 118 items measure psychopathology, generating scores for Internalizing Problems (including Withdrawn, Somatic Complaints, and Anxious/Depressed subscales), Externalizing Problems (including Delinquent and Aggressive subscales), and Total Problems (based on the combination of Externalizing, Internalizing, and other subscales that include Social Problems, Thought Problems, and Attention Problems). We scored the CBCL data in our study using the 1991 Achenbach Data Entry and Scorer software (Achenbach, 1991b). This generates Tscores for each scale. In the problems portion of the CBCL, a higher T score suggests greater psychopathology, while in the competence portion, a higher score suggests greater competence. The mean Tscore for a normative population of children of the same gender and age range is 50. Each 10-point deviation from the mean represents one standard deviation. Tscores greater than 70 in the problems portion or less than 30 in the competence portion are considered clinically significant. The CBCL has been shown to have high reliability and high validity (Achenbach, 1991a). DiabetzcAdaptation. T o assess children's psychological adaptation to their diabetes, we used the diabetic adaptation scale (DAS) introduced by Challen et al. (1988). The DAS measures emotional difficulty with and attitude toward diabetes, using a 20-item scale. A higher total score implies less healthy adaptation toward diabetes. Scores can range from 16 to 80. Despite the small size of the sample with which it was piloted, the DAS's reliability is supported by its high level of internal consistency and test-retest correlation, and its validity is supported by correlations with the children's reports of self-esteem, anxiety, and depression, and parents' assessments of their children's adaptation to diabetes and adherence behaviors (Challen et al., 1988). Self-Esteem. The children's self-esteem was measured using the Coopersmith Self-Esteem Inventory (CSEI) (Coopersmith, 1990), a 58-item questionnaire yielding scores between 0 and 100 for total self-esteem, and subscores of general, home, and school selfesteem along with an 8-item lie scale. A higher score suggests better self-esteem. The CSEI has been shown to have high validity and reliability (Kokenes, 1974; Spatz and Johnson, 1973). Anxiety Levels. The Revised Children's Manifest Anxiety Scale (RCMAS) (Reynolds and Richmond, 1978) was used to identify the level of anxiety in the participants. The RCMAS is a 37-item

questionnaire generating a total score from 0 to 28 and contains subscales that include psychological anxiety, worry/oversensitivity, and social concerns/concentration, along with a 9-item lie scale. The higher total anxiety score reflects increasing anxiety levels. The RCMAS has been shown to have high reliability and validity (Reynolds, 1980; Reynolds and Paget, 1983). Demographic, Medical, and Diabetic Adherence Information. Each child's parent completed additional questions about family and demographic factors, the child's diabetic history and adherence, and the parent's attitudes about the child and his or her diabetes. We used parents' reports of demographic data, their child's number of blood glucose checks per day, and dietary adherence in the present study.

Analyses All statistical analyses were performed using the SAS program package (Statistical Analysis Institute, 1988). An initial regression analysis used the five variables of age, gender, income, number of dependents in the family, and duration of illness to predict HbA,, scores. We assumed these variables may have some confounding effect on the HbA,, levels, so we used residual HbA,, scores from the regression in subsequent analyses to partial out any confounding effects of these variables. We next undertook a principal-components analysis to reduce the number of substantive predictor variables. Table 1 summarizes this analysis. Three eigenvalues exceeded one; therefore, three components were extracted. The three components derived from this analysis accounted for 70.8% of the total variance in the predictor variables, and they were intercorrelated, using the Promax oblique rotation method (Statistical Analysis Institute, 1988). The

TABLE 1 Results of Principal-Components Analysis of Substantive Predictor Variables

Variable Total Problems" Internalizing" Externalizing"

Factor 3: Factor 1: Factor 2: Competence/ Psychopathology Adjustment Adherence 0.96 0.97 0.84

CSEI RCMAS DAS

-0.21 -0.03 -0.15

Total Competence" Dietary adherence Blood glucose checking

-0.25 -0.08

Eigenvalue Yo Variance explained

0.11 3.70 41.2

0.06 -0.20 0.16

0.77 -0.94

-0.76 0.15 0.07 -0.09 1.44 16.0

-0.01 - 0.09

0.08 -0.06 0.10 -0.25

0.52 0.71 0.73 1.23 13.7

Note: Boldface numbers signify variables grouped by the analysis. CSEI = Coopersmith Self-Esteem Inventory (Coopersmith, 1990); RCMAS = Revised Children's Manifest Anxiety Scale (Reynolds and Richmond, 1978); DAS = diabetic adaptation scale (Challen et al., 1988). " From Child Behavior Checklist (Achenbach, 1991a).

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first component, labeled Psychopathology, consisted of strong loadings on Externalizing, Internalizing, and Total Problems scales of the CBCL. The second component, labeled Adjustment, had a high positive loading on the CSEI and strong negative loadings on the RCMAS and DAS. The last component, labeled Competence/ Adherence, had strong loadings o n parents’ measures of dietary adherence, frequency of blood glucose checks, and Total Competence scores from the CBCL. We derived a structural equation model from the substantive variables in the study to test the effects of these three component groups. The model, depicted in Figure 1, allowed the three components to be intercorrelated (double-headed arrows) and further assumed each component would have a direct effect on HbA,, (single-headed arrows). The model also allowed for a residual effect of Other Factors on HbA,, independent of the three component factors. A multiple regression analysis estimated the standardized regression coefficients. These coefficients showed the direct effect of each component on HbA,,. The residual effect on HbA,, was estimated as d n , where R2 is the variance explained by the model. For further information on structural modeling and path analysis, see Li (1975).

RESULTS Descriptive Statistics

Table 2 contains the descriptive statistics of our sample. Most children were Caucasian, from two-

< ADu)JuSl?lEXT

COMPETENCE/ADHERENCE

Total Problems

Total ccmpetence+

E x t e r n a l i z i n g Problems

Dietary Compliance ~ l u c o s em n i t o r i n g

I n t e r n a l i z i n g Problem

I

AgbAlC

Fig. 1 Model to investigate the effects of three correlated psychological constructs on diabetic control as measured by glycosylated hemoglobin A]<.Double-beaded arrows indicate correlations, while single-beaded arrows indicate standardized regression coefficients. The numbers are parameter estimates of correlations (double-beaded arrows) between the predictive constructs, and direct effects (single-beaded arrows) of the constructs on diabetic control. Significance of parameter estimates is shown by * ( p < .01) and ** ( p < ,001). + = from Child Behavior Checklist (Achenbach, 1991a); CSEI = from Coopersmith Self-Esteem Inventory (Coopersmith, 1990); RCMAS = from Revised Children’s Manifest Anxiety Scale (Reynolds and Richmond, 1978); DAS = from diabetic adaptation scale (Challen et al., 1988); HgbA,, = glycosylated hemoglobin A,,, which inversely reflects diabetic control.

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TABLE 2 Demographic, Diabetic, and Psychological Data in this Study’s Sample (N= 79) Mean or YO

SD

Range

0.210

-0.380‘*

PSYCHOPATHOLCGY+

parent homes, with relatively large family sizes. The range of family incomes was broad. The mean income was in the middle-class range, but income per dependent was decreased because of the larger family sizes. The mean HbA,, value of 9.6 was outside the range of good control for this measure. There was no significant difference with any measure between our study group and normative populations, or within our group between males and females or campers and clinic patients. O n the CBCL, there was a slight trend toward increased psychopathology on all subscales relative to a normative population, the highest T scores occurring for Internalizing Problems (mean = 53.8, SD = 10.7; normative mean = 50.0). The study population’s mean self-esteem score on the CSEI was 73, higher than the mean score of 68 found in a large normative population study (Kimball, 1973). The mean anxiety level on the RCMAS was 9.1, lower than the means of 11.50 to 14.79 seen in a normative population of this age range (Reynolds and Paget,

Age (yr) Male (Yo) Nonwhite (%) From two-parent homes (Yo) Dependents/ family Incorneldependent ($) Years with illness HbA,,“ Dietary adherence’ Glucose checkdday Total Problems‘ Internalizing‘ Externalizing‘ Total Competence‘ Total self-esteemd Total anxiety‘ Diabetic adaptationf Glycosylated hemoglobin

12.8 53.2 1.3 87.3 5.6 5,250 4.5 9.6 3.2 2.4 51.8 53.8 50.6 50.6 73.4 9.1 38.9

1.3

1.7 2,700 3.4 2.0 1.o 0.9 11.0 10.7 9.8 12.7 19.8 7.1 10.2

10.0-16.0

2.0-1 1.o 1,788-20,875 0.3-12.7 5.4-14.0 1.0-5.0 1.0-5.0 23-84 31-80 30-83 26-99 14-100 0-26 16-60

Ale.

’Based on parents’ estimations, ranging from 1

= “almost never” to 5 = “almost always.” From Child Behavior Checklist (Achenbach, 1991a). From Coopersmith Self-Esteem Inventory (Coopersmith, 1990). ‘From Revised Children’s Manifest Anxiety Scale (Reynolds and hchmond, 1978). fFrom diabetic adaptation scale (Challen et al., 1988).

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1983). The mean measure of diabetic adaptation using DAS was 38.9, worse than the median scores of 32 and 34 reported in Challen and colleagues’ two samples (1988). Effects of Background Variables

Table 3 summarizes the result of the regression predicting HbAI, from the set of five background variables. These background variables explained 2 1.2% of the total variance in HbA,,. Note that the number of dependents in the family had a significant effect (T= -2.46, p = .02) such that larger family size predicted lower HbA,, levels. In addition, duration of illness had a significant effect (T= 3.14, p = .003), with longer durations predicting higher HbA,, levels. There were no effects of gender, age, or income. In subsequent analyses, we used residual HbAI, levels from this regression to eliminate the effects of these background variables. Substantive Effects

Figure 1 shows the effects of the three components derived from the questionnaire variables. The model explained an additional 12.6% of the variance in the residual HbAI, measures. The overall fit of the model to the data was good ( F = 3.60, p = .02). Tests of each parameter suggested that only the direct effect of Competence/Adherence was significant ( T = -2.77, p = .007). The effect of this component accounted for most of the variance in HbA,, explained by the model (9.4% of the total 12.6% observed). The correlation between Psychopathology and Adjustment was significant ( p = .0006). All other correlations between the components were small. Consequently, there were no significant direct or indirect effects on HbAI, due to these other components. TABLE 3 Effects of Background Variables on Glycosylated Hemoglobin A,, Variable Gender Age Income No. of dependents Duration of diabetes

Regression Coefficient

T

Probability

-.015 ,005 ,014

-0.14 0.05 0.14

- .264

-2.46 3.14

.89 .96 .89 .02 .003

.338

Note: Boldface numbers designate background variables with a significant effect on glycosylated hemoglobin A,‘.

Competence/Adherence contained the variables Total Competence (as measured by the parents’ CBCL) and parents’ estimates of dietary adherence and blood glucose checks per day. Controlling for all other background and substantive variables, we used each of the three Competence/Adherence measures separately to predict HbAI,. This analysis showed that Total Competence had the most influence on HbA,,. The standardized regression coefficient for this variable was -.227 ( T = -1.93, p = .057), suggesting that increased competence predicted lower HbAI, levels. Coefficients for dietary adherence and blood glucose checks showed lower predictive power ( p = .12 and p = .52, respectively).

DISCUSSION

The purpose of this study was to examine how different measures of psychological adjustment, psychopathology, and behavior predicted diabetic control in a sample of 79 school-age children. We used the HbA,, as an objective measure of children’s diabetic control and made this the dependent variable of our conceptual model. W e also selected widely accepted measures such as the CBCL, RCMAS, and CSEI, along with the new DAS, and parents’ reports of adherence to diet and glucose monitoring to serve as predictive variables. One finding of our study was the significant effect of two background variables. Curiously, larger family size predicted better control in our sample ( Y = - .264, t = -2.46, p = .02). Investigators have shown that other family factors including family organization and cohesiveness have a significant association with diabetic control in youths (Hanson et al., 1989; Hauser et al., 1990; Newbrough et al., 1985). No study, however, has looked at family size as a factor. Mormonism is a significant cultural influence in this region, and it espouses large, organized, and cohesive families. If family size covaries with other Mormon ideals of family cohesion and organization, this could explain the surprising correlation between family size and diabetic control in our sample. We did not examine the religious orientation of the participant families or other family factors, but our results suggest that these may warrant further study. The confounding effects of family size on diabetic control will need to be replicated in an independent sample.

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Another significant background variable was the children’s duration of illness. The deterioration in control could be explained physiologically by the socalled “honeymoon effect” in newly diagnosed diabetics, who have better control due to some residual endogenous insulin production (Olefsky, 1985). Increasing duration of illness has also been associated with worsening compliance Uacobson et al., 1987, 1990; Johnson et al., 1986; Kovacs et al., 1992), which could also lead to poor control. Our subjects’ ages seemed to have no significant effect (independent of duration of illness) on control, agreeing with the findings of Kovacs et al. (1990). Once we had statistically eliminated the effects of these and other background variables, we used principal-components analysis to simplify our model. The analysis identified three meaningful, correlated components that accounted for more than 70% of the variance in the predictor variables: Psychopathology, Adjustment, and Competence/Adherence. The fact that Total Competence grouped closely with both dietary adherence and blood glucose checks replicated previous associations between competence and adherence behaviors shown by Hanson et al. (1987a) and Jacobson et al. (1 990). Our model showed a significant direct effect of Competence/Adherence, such that increased competence and greater dietary adherence and blood glucose monitoring predicted better diabetic control. When Competence/Adherence was broken down further to study the effects of the individual variables, we found this effect was primarily due to Total Competence. This contrasts with previous studies that have shown no significant association between CBCL Total Competence and diabetic control (Hanson et al., 1987a; Weist et al., 1993). Our results support Hanson and coworkers’ subsequent finding that competence relates to diabetic control (1987b). We observed a direct effect, however, of competence on control rather than a buffring effect as they described (1987b). Aside from adherence behaviors, perhaps greater attention to social, school, and other activities, as measured by the CBCL‘s Total Competence, would enable clinicians to better identify those at risk of poor control. The weaker correlation between measures of children’s diabetic adherence and metabolic control is worth noting. This highlights the discrepancy between parents’ subjective reports of adherence to diet or

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glucose monitoring and objective measures of diabetic control such as HbAI,. Previous studies of the effects of psychological factors have used diabetic adherence as the outcome measure (Jacobson et al., 1987, 1990; Kovacs et al., 1992). The frequent assumption has been that diabetic adherence varies with diabetic control. Some investigators have found this to be true (Hanson et al., 1987a), while others have not (Niemcryk et al., 1990; Weist et al., 1993). In a review article, Bradley (1985, p. 378) argues that clinicians tend “to assume the patient is responsible for any discrepancies,” when there could be a number of alternative explanations. Some parents may indeed assess their children’s adherence inaccurately, but a higher degree of adherence may also be a consequence of poor control. Children who monitor their blood glucose level frequently, for instance, might do so out of necessity because they have marked fluctuations in blood glucose concentration (brittle diabetes). Since dietary compliance in our sample was more predictive of HbA,,, it may have a more direct, linear relationship with diabetic control. Perhaps clinicians should use parents’ reports of adherence with caution, as they should any studies that rely solely on reports of adherence instead of on objective measures such as glycosylated hemoglobin to gauge diabetic outcomes. The grouping of DAS, CSEI, and RCMAS under the Adjustment component gives further validity to Challen and colleagues’ diabetic adaptation scale (1988). Of the three substantive components, however, the Adjustment component had the least predictive effect on HbAI,. Adjustment measures such as anxiety could have a bidirectional effect on control. Children with higher anxiety may be less denying of their illness and more motivated to control it. Yet they may have more control problems due to the neurohormonal linkages between anxiety/stress and diabetic control (Tarnow and Silverman, 198 1-1982). Similarly, higher self-esteem may lead a youth with diabetes to be assume more autonomous responsibility for the illness, but autonomous management by youths has been associated negatively with diabetic outcomes (Anderson et al., 1981; Fonagy et al., 1987; Newbrough et al., 1985; White et al., 1984). The Adjustment component consisted of measures all based on the diabetic youth’s self-reports. Perhaps children’s effort to portray themselves as psychologically healthy and well adjusted may also limit any predictive significance of these measures.

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Severe levels of psychopathology have previously been associated with poor diabetic outcomes (Lustman et al., 1986; Orr et al., 1983; Simonds, 1976-1977; White et al., 1984). Psychopathology in our model was based exclusively on Total, Internalizing, and Externalizing Problems scales from the CBCL. Psychopathology and Adjustment correlated significantly with each other ( u = - .38, p = .0006), but not with Competence/ Adherence, so these had no significant direct or indirect effect on the HbAI,. As with previously discussed measures such as anxiety, self-esteem, and diabetic adjustment, our sample appeared in the normal range based on mean CBCL problem Tscores. Our results suggest that most children with diabetes may be psychologically healthy enough that such measures lack any discriminative or predictive power, in agreement with Brown et al. (199 I), Kovacs et al. (1990), and Wertlieb et al. (1986). This underscores the significance and potential usefulness of Total Competence as the only substantive measure in our sample that significantly predicted diabetic control. As in other studies that are cross-sectional in design, neither can the associations we have found be accepted as causal, nor can the temporal relationship of diabetic control and psychosocial status be inferred. Moreover, with regard to the effects of family size, distinct psychosocial attributes of our sample such as the higherthan-average percentage of Caucasian, two-parent families of larger size, and other possible influences of the Mormon culture, may have affected our results. Although our results will need to be replicated in an independent and less homogeneous sample, we would argue that the relatively high level of psychological adjustment seen in our sample is representative of most children with diabetes because it replicates the findings of several others (Brown et al., 1991; Jacobson et al., 1990; Kovacs et al., 1990; Wertlieb et al., 1986). Our study suggests hypotheses about how psychosocia1 variables relate to diabetic control and proposes a model to be tested by more powerful case-control or cohort studies. Based on our results, an ideal followup study would have several features. Subjects would be recruited from a well-defined and heterogeneous sample of children at the onset of diabetes. Broad measures of individual and peer group function such as the CBCL’s Total Competence would be assessed periodically, along with measures of family function and dietary compliance. Subgroups distinguished by

these measures could be compared over time using an objective measure of metabolic control such as HbAI, as the dependent variable. Even with the limitations of a cross-sectional study design, our model and findings show initial promise in predicting the diabetic control with these measures. We have used psychological measures that could be easily used by clinicians, along with a more objective outcome measure in HbAI,. Our background and substantive variables explained approximately one third of the variance in HbA,, in our sample. Since most of the variables studied here relate to individuals’ and not family or other environmental factors, further research will be needed to identify additional predictive factors of diabetic control. Our sample appeared fairly healthy psychologically, but on average showed less than optimal diabetic adherence and control. This suggests that many diabetic youths in an otherwise nonclinical sample may warrant more intensive medical or psychosocial interventions. Clinicians may thus need to look more for subtle signs of maladjustment, such as low scores on the CBCL’s Total Competence rather than overt signs of psychopathology, to effectively target children a t risk of poor control. REFERENCES Achenbach T M (1991a), Manualfor ChildBehavior Cherklist/4-18and 1991 Profle. Burlington: University of Vermont Department of Psychiatry Achenbach T M (1991b), Program Manualfor the 1991 CBCL/4-18 Profle IBM PC Version. Burlington: University Associates in Psychiatry Anderson BJ, Miller JP, Auslander W , Santiago JV (1981), Family characteristics of diabetic adolescents: relationship to glycemic control. Diabetes Care 4:586-591 Bradley C (1985), Psychological aspects of diabetes. Diabetes Annu 1:378 Brown RT, Kaslow NJ, Sansbuly L, Meacham L, Culler FL (1991), Internalizing and externalizing symptoms and attributional style in youths with diabetes. / A m Arad Child Adolesc Psychiaty 30:921-925 Challen AH, Davies AG, Williams RJW, Haslum MN, Baum J D (1988). Measuring psychosocial adaptation to diabetes in adolescence. Diabet Med 5:739-746 Coopersmith S (1990), Self-Esteem Inventories. Palo Alto, CA: Consulting Psychologists Press Delamater AM, Kurtz SM, Bubb J , White NH, Santiago JV (1987), Stress and coping in relation to metabalic control of adolescents with rype I diabetes. / Dev Behav Pediatr 8:136-140 Dunn PJ, Cole RA, Soeldner JS (1979), Temporal relationship of glycosylated hemoglobin concentration to glucose control in diabetics. Diabetologia 17:2 13-220 Fonagy P, Moran GS, Lindsay MKM, Kurtz AB, Brown R (1987), Psychological adjustment and diabetic control. Arch Dis Child 62:1009-1013 Gonen 8 , Rochman H, Rubenstein AH (1979), Metabolic control in diabetic patients: assessment by hemoglobin A1 values. Metabolism 28:448-452 Grossman HY, Brink S, Hauser ST (1987), Self-efficacy in adolescent girls and boys with insulin-dependent diabetes mellitus. Diabetes Care 10:324-329

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