Behavioral self-regulation: Correlates and 2 year follow-ups for boys at risk for substance abuse

DEPENDENCE ELSEVIER

Drug

and

Alcohol

Dependence

45 (1997)

I65 - 176

Behavioral self-regulation: Correlates and 2 year follow-ups for boys at risk for substance abuse Michael A. Dawes *, Ralph E. Tarter, Levent Kirisci Center

,fbr Education

and Drug

Abuse,

Department

qf’ Psychiutry. PA

Received

12 June

Unicersity 15213,

oJ’ Pittsburgh USA

1996: accepted

2 February

School

qf Medicine,

381 I O’Horcr

Street,

Pittsburgh,

1997

Abstract This investigation demonstrated the heuristic construct of behavioral self-regulation (BSR) as a salient component of the liability to substance abuse. Three dimensions of childhood behaviour were employed to create a dimensional model of BSR: inattention. impulsivity/hyperactivity and aggressivity. Multiple measures and multiple informants were employed to develop indices of the three traits in a sample of lo-12 year old sons of substance abusing fathers (high risk (HR); IZ = 180) and normal controls (low average risk (LAR); n = 200). Informants included mothers, boys and their teachers. The results confirmed the presence of a first-order latent trait of BSR. HR boys had significantly higher scores on BSR than LAR boys. Concurrent validity of the BSR trait scores was supported by significant associations with measures of family dysfunction, deviant peer affiliations and poor school performance. These latter problems are commonly prodromal to substance abuse. Predictive validity of the BSR trait baseline scores (age lo-12 years) was supported at 2 year follow-up by significant associations of BSR scores with magnitude of deviant peer affiliations; trends toward significance were found for family dysfunction and poor school performance. Taken together, these results confirm and extend previous findings which indicate that poor BSR is prodromal to substance abuse. 0 1997 Elsevier Science Ireland Ltd. Kq~ortls:

Drug abuse; Etiology;

Behavioral

self-regulation;

Liability;

1. Introduction Numerous

behavioral

characteristics

have

been

author

0376-8716,97’$17.00 C 1997 Elsevier PI/ S0376-X716(97)01359-8

Science

Ireland

Ltd.

All rights

development

gests that

conduct

disorder

mediates

the

relationship

between disorders of inattention, impulsivity, and hyperactivity and later substance abuse (Crowley and Riggs, 1995). Although these specific behavioral characteristics have been shown to be associated with substance use risk, there is poor understanding of their aggregate constellation and component structure. Empirical evidence (Alterman et al., 1984; Reich et al., 1993; Pelham and Lang, 1993; Windle and Windle, 1993, Wills et al., 1995; Caspi et al., 1996) and theoretical reviews (Gorrenstein and Newman, 1980; Tarter et al., 1989; Newman and Wallace, 1993) suggest that the range of behavioral disturbances comprise a core disorder of behavioral self-regulation (BSR). BSR is defined herein as the degree to which an individual can control their activity and reactivity to environmental stimuli. Disorders of BSR can manifest as inattention, impulsiv-

linked to increased risk for substance use and abuse. Disorders of inattention, impulsivity, and hyperactivity have been posited to be risk factors for drug abuse (Barkley et al., 1990; Biederman et al., 1996). Childhood aggressivity (Brook et al., 1992a,b; Brook and Newcomb, 1995; Brook et al., 1995; 1996; Kellam et al., 1980a; Kellam and Ensminger, 1980b; Kellam and Brown, 1982) conduct disorder (Crowley and Riggs, 1995; Moss et al., 1995a) and other antisocial behaviors (Kandel et al., 1986; Robins and McEvoy, 1990) often precede substance abuse. A substantial literature sug-

* Corresponding

Child

reserved

ity/hyperactivity, and aggressivity. Whether poor BSR increases the risk for family dysfunction, deviant peer affiliation, and other risk factors for drug and alcohol use has only recently been investigated. Recent work by Martin et al. (1994); Mezzich et al. (submitted); Mezzich et al.. 1997: Dawes et al. (submitted) and Caspi et al. (1996) are especially noteworthy. Employing a multitrait multimethod strategy. Martin et al. (1994) identified four behavioral components of dysregulation: inattention, impulsivity. hyperactivity, and aggressivity. Results were consistent with the hypothesis that these traits comprise four indicators of a second-order factor of behavioral dysregulation. Significantly. Martin et al. (1994) found that sons of substance abusing fathers (high risk, HR) were differentiated from sons of normal fathers (low average risk, LAR) on all of the first-order traits except hyperactivity and on the second-order trait of behavioral dysregulation. Mezzich et al. ( 1997, submitted), employing definitions and procedures approximating those used by Martin et al. ( 1994) developed a latent construct of behavioral dysregulation that included measures of hyperactivity, impulsivity and inattention. Mezzich et al. (1997) showed that behavioral dysregulation, negative affectivity, and family impairment were significantly related to violence in substance abusing female adolescents. In a second study, Mezzich et al. (1997) showed that behavioral dysregulation, negative affectivity and childhood victimization were related to substance use and risky sexual behaviour in substance abusing female adolescents. Dawes et al. (submitted), using a measure of behavioral self-regulation (BSR) similar to that described by Martin et al. (1994) also found that HR boys had significantly higher scores on BSR than LAR boys. Findings revealed that parental perceptions of son and self, family dysfunction, deviant peer affiliation, and parental psychopathology were significantly associated in one hierarchical linear regression with BSR in HR sons. Caspi et al. (1996) found that undercontrolled 3 year olds were more likely at age 21 to meet DSM-III-R criteria for antisocial personality disorder, to be recidivistic offenders and to be convicted of a violent offense. In addition, undercontrolled 3 year old boys, but not girls. were more likely to develop alcohol dependence by age 21. Taken together, findings from these five studies suggest that poor BSR during childhood influences the developmental trajectories that lead to substance use in ;idolescence and young adulthood. Moreover, it is posited that poor BSR, along with measures of other individual characteristics, family dysfunction and peer affiliation. bias the developmental trajectory towards adverse outcomes. However. little empirical evidence has documented the concurrent or predictive validity of

the BSR construct, using these putative risk factors during early adolescence in boys at risk for substance abuse. The present study aimed at addressing this lacuna. Expanding on the sample available to Martin et al. (1994), this study investigated the extent to which behavioral self-regulation (BSR) distinguishes high risk from low average risk prepubertal youth. Second, the concurrent and predictive validity of the BSR construct were examined in relation to three domains that commonly presage the onset of adolescent substance abuse: (a) family dysfunction (Rutter and Giller, 1983; Lewis, 1991; Hawkins et al., 1992; Hauser et al., 1984; Brook et al., 1990; Dishion et al., 1995; Zucker et al., 1995) (b) deviant peer affiliation (Elliott et al., 1989; Patterson et al., 1989; Brook et al., 1990; 1992~; Newcomb and Felix-Ortiz, 1992; Dishion et al., 1995), and (c) poor school performance (Kellam and Ensminger, 1980b; Ensminger, 1990; Labouvie et al., 1991; McGee and Newcomb, 1992; Newcomb and Felix-Ortiz, 1992; Hawkins et al., 1992; Moss et al., 1995b; Dishion et al., 1995).

2. Methods 2. I. Subjects High risk (HR) subjects (n = 180) consisted of lo- 12 year old sons of fathers who met criteria for a lifetime DSM-III-R psychoactive substance use disorder (PSUD). Low average risk subjects (LAR) consisted of lo- 12 year old sons (n = 200) of fathers who did not have PSUD or other adult lifetime Axis I or Axis II psychiatric disorders. At 2 year follow-up, the sample consisted of 117 HR boys and 124 LAR boys. The 2 year follow-up rate was 89.7%. Trained interviewers administered an expanded version of the structured clinical interview for DSM-III-R (SCID; Spitzer et al., 1987). The diagnoses in the proband father were made using the ‘best estimate’ procedure (Leckman et al., 1982). Chronic neurological illness, acute neurological injury, organic brain syndrome requiring treatment, uncorrected sensory incapacity (vision and hearing), intelligence quotient less than 75 (Wechsler intelligence scale for children, WISCIII; Wechsler, 1991), and schizophrenia or other psychotic illness were exclusionary criteria. Current or lifetime comorbid psychiatric disorder in the father, other than psychosis, did not disqualify the son from the study. The two groups of boys were derived from intact, single parent, as well as reconstituted families so as to obtain a representative sample of the range of family structures associated with proband fathers with PSUD. Recruitment sources for fathers included local outpatient and inpatient substance abuse treatment

,_,/

M.A. Table

el ul.

Drug

und AIuhl

Depmdencc

45 (1997)

165-

167

176

I

Demographic

characteristics

Demogrdphlc

variables

and

lifetime

Age SES Full scale DSM-II

Duwz~

IQ

I -R diagnoses

Mood disorders Anxiety disorder\ Attention deficit hyperactivity Oppositional defiant disorder Conduct disorder

disorder

DSM-III-R

diagnoses

in high

risk

(HR)

and

low average

HR (:V = 180) Mean (SD.)

LAR Mean

I I .28 (0.99) 36.54 (12.28) 103.95 (15.24)

(LAR)

boys I

I’

I I .40 (0.92) 45.17 (13.32) 113.76 (15.24)

~ 1.25 -6.53 -6.32

NS
‘5’ (‘X3)

N (‘%a)

L2

I’

IO 33 39 29 13

3 23 28 14 3

(5.6) (18.3) (21.7) (16.1) (7.2)

programs, advertisements in radio, television, local newspapers and other printed media and a market research group. Participating fathers and family members received cash incentives for completion of the protocol. The study was approved by the Institutional Review Board of the University of Pittsburgh Medical Centre. All participating parents granted informed consent and sons their assent prior to entry in the study. Table 1 summarizes the persona1 and psychiatric characteristics of the two groups. As can be seen, the high and low average risk boys did not differ on age; however. significant differences were present on the two-factor index of socioeconomic status (Hollingshead, 1990) and WISC-III full-scale intelligence quotient (IQ). The observation of lower socioeconomic status (SES) in high risk families is commensurate with downward social mobility in substance abusing men (Dohrenwend et al., 1992). IQ has been frequently reported to be lower in high risk youth compared to low risk youth (Gabrielli and Mednick, 1983; Tarter et al., 1984; 1995; Moss et al., 1995b). These factors were controlled statistically in the analyses. HR boys had significantly higher rates of DSM-III-R lifetime psychiatric disorders compared to LAR boys. As can be seen in Table 1, mood disorders, attention deficit hyperactivity disorder, oppositional defiant disorder and conduct disorder were significantly more frequent in the HR group. Anxiety disorders showed a trend towards a significant difference between the two groups. DSM-III-R lifetime parental psychiatric disorders are presented in Tables 2 and 3. Table 2 presents the distribution of comorbid DSM-III-R lifetime psychiatric disorders in PSUD (SA + ) fathers and normal control (SA - ) fathers. SA + fathers had higher rates of major depression, antisocial personality disorder, attention deficit hyperactivity disorder, oppositional defiant disorder, and conduct disorder compared to SA - fathers. Note that control (SA - ) fathers reported low rates of childhood disruptive behaviour disorders, as well as alcohol abuse which occurred prior

(N = 200) (S.D.)

risk

(1.5) (I 1.5) (14.0) (7.0) (1.5)

4.80 3.68 4.02 8.04 1.82

0.03 0.055 0.04 0.005 0.005

to age 22. Table 3 presents the results for SA + and SA mothers’ lifetime DSM-III-R diagnoses of alcohol abuse and dependence and a broad range of licit and illicit substance use disorders. SA + mothers had significantly higher lifetime rates than SA - mothers for all of the psychoactive substance use disorders and for major depression, panic disorder, phobic disorder, social phobia and conduct disorder. The influence of parental psychiatric comorbidity on the results is addressed in the Section 4 of this report. Sibling order effects were controlled by selection for study of the oldest boy in each family between ages lo-12 years. Comparison of differences in substance use patterns between HR and LAR boys revealed that 2.3% of HR boys versus 2.0% of LAR boys had used alcoholic beverages outside the home (x’ = 0.20, df = 1, NS), 12.1% of HR boys versus 7.8% of LAR boys had smoked tobacco (x2 = 1.95, df = 1, NS) and 5.6% of HR boys versus 5.0% of LAR boys had used drugs other than tobacco or alcohol (x2 = 0.06, df = I, NS). Because the proportion of subjects using substances was low, this factor most likely did not affect results. 2.2. Measures 2.2.1. Behavioral self-regulation The following subscales were z-scored and summed to develop aggregate observed measures of inattention, impulsivity/hyperactivity and aggressivity. This strategy maximized the distributional properties of individual scores while taking advantage of three valuable sources of information (mother, child, and teacher) on the child’s behaviour. 2.2.1.1. Inattention 2.2.1.1.1. Mother :s report. A subscale was created from the expanded K-SADS-E interview (Orvaschel et al., 1982), using the following lifetime symptoms: ‘easily distracted’, ‘difficulty following instructions’, ‘difficulty sustaining attention’, ‘shifting from one activity to an-

I (ei Table 3 Lifetime DSM-III-R DSM-III-R

diagnoses

M.A.

Dawes

of proband

fathers

diagnoses

Comorbid psychiatric disorders Bipolar disorder Major depression Dysthymia Generalized anxiety disorder Panic disorder Phobic disorder Simple phobia Social phobia Antisocial personality disorder Attention deficit hyperactivity Oppositional defiant disorder Conduct disorder Exact

Test.

Drug

in the PSUD

and Alcohol

@A+)

SAf (N= N (‘Xl)

Psychoactive substance use disorders Alcohol abuse Alcohol dependence Cannabis abuse Cannabis dependence Cocaine abuse Cocaine dependence Hallucinogen abuse Hallucinogen dependence Inhalant abuse Opioid abuse Opioid dependence Nicotine dependence Phencyclidine dependence Sedative abuse Sedative dependence

’ Fisher’s

et al.

disorder

Dependence

and control 180)

45 (1997)

(SA-)

165-l

76

groups SA(N= N (Yi)

198)

P

91 114 49 47 18 53 4 5 4 6 31 28 5 7 12

(50.6) (63.3) (27.2) (26.1) (10.0) (29.4) (2.2) (2.8) (2.2) (3.3) (17.2) (15.6) (2.8) (3.9) (6.7)

6 0 0 0 0 0 0 0 0 0 0 6 0 0 0

(3.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (3.0) (0.0) (0.0) (0.0)


4 46 I 2 3 15 7 8 39 14 16 69

(2.2) (25.6) (0.6) (1.1) (1.7) (7.5) (3.9) (4.0) (21.7) (7.8) (8.9) (38.3)

0 0 0 0 0 0 0 0 0 3 2 7

(0) (0) (0) (0) (0) (0) (0) (0) (0) (1.5) (1.0) (3.5)

0.05” 10.001” NS NS NS
two-tail.

other’, ‘often does not seem to listen’ and ‘loses things at home or at school’. 2.2.1.1.2. Child:7 report. A subscale was created using the same lifetime symptoms from the K-SADS-E. 2.2.1.1.3. Teacher’s report. A subscale was created from the symptom list of the disruptive behaviour disorder rating scale (DBD; Pelham and Murphy, 1987): ‘shifts from one uncompleted task to another’, ‘difficulty following through on instructions’, ‘often loses things necessary for tasks or activities’, ‘difficulty sustaining attention in tasks or play’, ‘easily distracted by extraneous stimuli’ and ‘does not seem to listen to what is being said’. The total inattention subscale score from the child behaviour checklist, (CBCL, teacher version; Achenbach and Edelbrock, 1983) was also included. Cronbach’s s( for the four subscales was 0.79.

_7.2.1.2. I. Mother :\. report. A subscale was created from the lifetime K-SADS-E interview impulsivity symptoms: ‘blurts out’, ‘often interrupts excessively’, ‘often engages in physically dangerous activities’. Also

included were lifetime K-SADS-E hyperactivity symptoms: ‘fidgety’, ‘difficulty remaining seated’ and ‘often talks excessively’. The general activity level and sleep activity subscales from the dimensions of temperament survey (DOTS-R) (Windle and Lerner, 1986; Windle, 1992) were also used. 2.2.1.2.2. Child’s report. A subscale was created employing the same lifetime hyperactivity and impulsivity symptoms from the K-SADS-E. 2.2.1.2.3. Teacher’s report. A subscale was created from impulsivity and hyperactivity symptoms from the disruptive behaviour disorder rating scale (DBD): ‘engages in physically dangerous activities without considering possible consequences’, ‘difficulty awaiting turn’. ‘blurts out answers’ and ‘often interrupts or intrudes on others’ (Pelham and Murphy, 1987). Hyperactivity symptoms from the DBD included ‘difficulty remaining seated’, ‘difficulty playing quietly’, ‘often talks excessively’ and ‘often fidgets with hands or feet or squirms in seat’ (Pelham and Murphy, 1987). Also included was the total hyperactivity subscale from the CBCL, teacher version (1983). Cronbach’s cy for the six subscales was 0.66.

M.A. Table

Dunes

et ul.

Drug

in the PSUD

(SA+)

and Alcolzol

Dependence

45 (1997)

165

176

3

Lifetime DSM-III-R

DSM-III-R

diagnoses

of mothers

diagnoses

and control

SA+ (N= N (!!I)

174)

(SA-)

groups SA(N= N (‘Xs)

192)

P

Psychoactive substance use disorders Alcohol abuse Alcohol dependence Cannabis abuse Cannabis dependence Cocaine abuse Cocaine dependence Hallucinogen abuse Hallucinogen dependence Inhalant abuse Opioid abuse Opioid dependence Nicotine dependence Phencyclidine dependence Sedative abuse Sedative dependence

26 35 I6 I2 4 I2 2 2 2 4 I4 25 2 I 5

(14.9) (20.1) (9.2) (6.9) (2.3) (6.9) (1.1) (1.1) (1.1) (2.3) (8.0) (14.4) (1.1) (0.6) (2.9)

8 6 2 4 0 0 0 0 0 0 2 8 0 2 0

(4.2) (3.1) (1.0) (2.1) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (1.00) (4.20) (0.00) (1.00) (0.00)


Comorbid psychiatric disorders Bipolar disorder Major depression Dysthymia Generalized anxiety disorder Panic disorder Phobic disorder Simple phobia Social phobia Antisocial personality disorder Attention deficit hyperactivity Oppositional defiant disorder Conduct disorder

1 60 6 IO 16 24 IO I4 5 4 5 I5

(0.6) (34.5) (3.4) (5.7) (9.2) (13.8) (5.7) (8.0) (3.1) (3) (2.9) (8.6)

3 43 4 4 II I2 3 IO 0 0 I 0

(1.6) (22.4) (2.9) (2.1) (2.1) (6.3) (1.6) (5.7) (0) (0) (0.5) (0)

NT 0.007” NS” O.Oh” NS;’ 0.01.’ 0.03;’ NY 0.02” 0.05” 0.05,’ NS“

.’ Fisher‘s

Exact

Test,

disorder

two-tail.

2.2.1.3. Aggressiuity 2.2.1.3.1. Mother’s

report. A subscale was created

from the list of lifetime symptoms of oppositional defiant disorder (ODD) from the expanded K-SADS-E: ‘often loses temper’, ‘often argues with adults’, ‘defies or refuses adult requests’, ‘annoys other people’, ‘blames others for own mistakes’, ‘touchy or easily annoyed’. ‘angry and resentful’, ‘spiteful or vindictive’ and ‘swears or uses obscene language’. Also included in this subscale were the list of lifetime symptoms of conduct disorder (CD) from the expanded K-SADS-E: ‘runaway at least twice’, ‘frequently truant’, ‘cheating’, ‘vandalism’, ‘sets fires’, ‘goes into buildings in order to steal’, ‘steals from a store’, ‘buying, selling, or holding stolen goods’, ‘joyriding’, ‘stealing a motor vehicle’, ‘purse snatching’, ‘initiates fights’, ‘use of a weapon trying to hurt someone’, ‘physically cruel to people’, ‘physically cruel to animals’ and ‘rape’. Mothers also reported on the total aggression subscale from the CBCL. 2.2.1.3.2. Child’s report. A subscale was created from the same lifetime ODD and CD symptoms from the K-SADS-E interview.

2.2.1.3.3. Teacher’s report. The total count for ODD symptoms and for CD symptoms from the DBD (Pelham and Murphy, 1987) was used to create two subscales. The total aggression subscale from the CBCL (teacher version, 1983) was also included. Cronbach’s x for the six subscales was 0.72.

2.2.2. Outcome measures The boys completed several questionnaires containing measures of family functioning (the family assessment measure, seven domains: Task accomplishment, role performance, communication, affective expression, affective involvement, control, and values and norms), peer affiliation (peer delinquency scale; perception of problem behaviours; and conventional activities of friends), and school performance (the Peabody individual achievement test). The peer delinquency scale (Loeber, 1989a) documented delinquent activity in peers, including their drug involvement. The perception of problem behaviours questionnaire (Loeber, 1989b) rated on the boy’s values and attitudes towards delinquent behaviour. The conventional activities of friends scale (Loeber, 1989~) rated conventional activities of

I70

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DUNYS rr 01.

Drug

und Alcohol

friends, such as participation in school clubs, athletics, religious activities, and family activities. The dysfunctional family index (DFI) was the summary index on the family assessment measure (Skinner et al., 1983). The drug use chart (unpublished) documented the number of drugs which the boy had ever tried. The Peabody individual achievement test (Markwardt, 1989) total test score was used, summing the subtests for general information. reading recognition, reading comprehension, mathematics and spelling). Mothers completed the family environment scale (Moos and Moos. 1981) to characterize the home atmosphere. Teachers completed the child behaviour checklist (CBCL, teacher version; Achenbach and Edelbrock, 1983). including subscales for learning, work and academic performance.

Substance use disorder diagnoses were made according to DSM-III-R criteria. using the CEDAR substance use disorder interview. This structured interview combined the information contained in the substance use disorders section of the structured clinical interview for DSM-111-R diagnosis (SCID: Spitzer et al., 1987) and the lifetime alcohol use interview (Skinner, 1982). Information was also gathered regarding 40 classes of psychoactive substances using the drug and alcohol checklist (unpublished. CEDAR). This checklist also ranked the four most frequently consumed substances. For each drug consumed. lifetime information was organized according to time period (i.e. phases), typically when drug use pattern was most consistent and thus. reflective of their level of involvement. Psychiatric diagnoses in the father and mother were made according to DSM-III-R criteria, based on information obtained from several instruments. Conduct disorder (CD) and antisocial personality disorder (ASPD) were assessed by administering the personality disorders questionnaire from the SCID (Spitzer et al., 1987). The psychiatric disorders were assessed by the Axis I section of the SCID (Spitzer et al., 1987). With respect to diagnoses in the offspring, the child and one parent, usually the mother, completed a modified version of the schedule for affective disorders and schizophrenia for children interview (Orvaschel et al., 1982). Mothers and teachers also completed the child behaviour checklist (Achenbach and Edelbrock, 1983) and other questionnaires. Following informed consent. the protocols were administered in fixed order. All of the questionnaires and rating scales were administered and scored in standard manner. The interviews were administered by trained research associates. The results were discussed in a consensus conference consisting of two clinical psychol-

Drpmdcwc~r

45 (1997)

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76

ogists or psychiatrists, interviewers of the family and an evaluation coordinator. The diagnostic team also reviewed all other information such as psychiatric treatment records and school records where available. Psychiatric diagnoses were then determined using the best estimate procedure (Leckman et al., 1982). To rule out neurodevelopmental delay, parents were additionally interviewed about the boys’ cognitive development. General intelligence was determined using the Wechsler intelligence scale for children, revised (WISC-III; Wechsler, 1991). 2.4. Data analyses Subscale scores for the observed measures were first standardized, because the subscales were scored using different metrics. Next, the scores were summed to obtain summary indices of inattention, impulsivity/hyperactivity, and aggressivity. Scores that were skewed were logarithmic transformed to approximate normality. Confirmatory factor analysis (CFA) was employed to develop a first-order factor using the aggregated observed measures for each of the three traits. LISREL 7 (Joreskog and Sorbom, 1989) was then used to test the fit of the model relating the indicators to the first-order latent construct. Significance was tested by x2 for the overall model, the goodness of fit index and the adjusted goodness of fit index. A weighted leastsquares (WLS) method was employed to minimize the effects of nonnormality, because of ordinal scales (Hayduk, 1987; Joreskog and Sot-born, 1989). Three general factorial ANOVA models were used to examine differences in BSR between the high and low average risk boys. The first analysis was conducted with full scale IQ and SES as covariates; the second with full scale IQ, SES, and the proband fathers’ number of comorbid lifetime DSM-III-R psychiatric disorders as covariates; and the third with full scale IQ, SES and the mothers’ number of comorbid lifetime DSM-III-R psychiatric disorders as covariates. Two-group linear discriminant function analysis was used to test whether the observed measures comprising the BSR construct discriminated the HR from LAR boys. A series of linear regression analyses were used to test concurrent and predictive validity of BSR scores. To test concurrent validity, separate linear regression analyses were used to determine the associations between the BSR scores and multiple measures of peer affiliation, family dysfunction and school performance at baseline and two-year follow-up, controlling for SES. To test predictive validity, separate linear regressions were employed, to determine two year follow-ups of the same measures of peer affiliation, family dysfunction and school performance, controlling for time one dependent variables. Because of the large number of concurrent and pre-

M.A.

Dnnrs

et al.

Drug

and Aluhl

lMP~lvlTY/ BYPEtRAcllvlTY I 1 AGGlWSMTY

I

1

I

L

A

BEHAVLORAL SELF-REGULATION

Fig. 1. Completely standardized sions of self-regulation.

solution

for

the behavioral

dimen-

dictive analyses, Bonferroni corrections were employed. With 19 linear regressions, the threshold for significance was c1< 0.003.

3. Results

3.1. Confirmatory jbctor analysis Fig. 1 reveals the completely standardized solution of the first-order trait of behavioral self-regulation (BSR). The model provided reasonable fit to the data (x2 = 0.70, df = 1, N = 380, p = 0.404). Error terms were first set to be free. In order to obtain a better fit, the first and third error terms were set to be equal. The x2/df ratio of 0.70 was within the range regarded as acceptable fit (Hayduk, 1987; 1996; Loehlin, 1992). The goodness of fit (0.999) and adjusted goodness of fit (0.997) were both acceptable. All of the factors loadings were significant at g < 0.0001. 3.2. Dijferences in BSR scores between high risk and low average risk boys Table 4 summarizes the significant differences between HR and LAR boys. As can be seen, BSR scores were significantly different between HR and LAR boys, controlling for IQ, SES and mother’s lifetime psychiTable 4 Differences BSR,

in BSR

scores

between

high

risk and

low average

control

BSR controlling for SES and full scale IQ BSR controlling for SES, full scale IQ and number of lifetime psychiatric and substance use disorders in the mother BSR controlling for SES, full scale IQ and number of lifetime psychiatric disorders in the father

risk

boys,

Dependence

45 (1997)

171

176

atric and substance use disorders. BSR scores were not significantly different between HR and LAR boys, controlling for IQ, SES and father’s lifetime psychiatric disorders. The overall difference in number of lifetime psychiatric disorders in the SA + and SA - fathers was statistically significant (x2 = 122.55, N = 378, p < 0.0001). Among SA + fathers, 58.9’% had at least one lifetime DSM-III-R psychiatric disorder other than substance use. Only 6.1% of SA - fathers had a lifetime psychiatric disorder. The correlation between the number of lifetime psychiatric disorders in fathers and the BSR score in their sons was 0.88. The significance of this very high correlation is presented in the discussion. Discriminant function analyses were also employed to determine the percentage of correct classification and identify the measures which best discriminated between HR and LAR boys. A two-group linear discriminant function analysis revealed that the three observed measures of inattention, impulsivity/hyperactivity, aggressivity, along with household socioeconomic status (SES) and full scale IQ correctly classified 72.11% of subjects, with a sensitivity of 74.4% and a specificity of 70.0%. Results from the structure matrix revealed that aggressivity best discriminated the groups, followed in order by inattention and impulsivity/hyperactivity. Two-group linear discriminant function analysis for the measures of BSR, SES, and full scale IQ correctly classified 69.21% of subjects, with a sensitivity of 69.4% and a specificity of 69.0%. 3.3. Concurrent and predictive va1idit.v analyses Table 5 summarizes the linear regression analyses results pertaining to the associations between the BSR scores and peer affiliation, family dysfunction and school performance scores, controlling for SES. As can be seen, R2 (e.g. the proportion of explained variance) for each of the linear regression analyses had P values less than or equal to 0.0005, which was less that the threshold for significance set at 0.003. BSR, measured at age lo- 12, accounted for a significant proportion of variance on each of the above measures. For the do-

controlling

for

High risk (N = 180) Mean (SD.)

Low average (N = 200) Mean (SD.)

2.41 (0.29) 2.41 (0.29)

2.41

(0.29)

16%

IQ, SES, and lifetime risk

psychiatric

comorbidity

F

dl

P

Effect

2.29 (0.29) 2.29 (0.29)

10.86 5.68

I372 1356

0.00 I 0.018

0.03 0.02

2.29 (0.29)

I .90

1368

0.005

NS

in parents size (4’)

171 Table Linear

M.A.

rt al. ,’ Drug

and Alcohol

Dependence

45 (1997)

165-l

76

5 regression

Dependent

analyses

for associations

variables

with

behavioral

self-regulation

Total o/I, of variance R’ (P)

Peer qffiliutiorr Peer delinquency Perception of problem Conventional activities

behaviors of friends

Funlily ~(vsfuncfiorr Dysfunctional family Family environment Cohesion Organization .%~lOO~

Dawes

index

(BSR)

at age 10-12,

explained

controlling

by the model

for

SES

SES P @I

B (PI

BSR

0.10 (<0.0001) 0.12 (<0.0001) 0.04 (0.0005)

-0.04 (NS) 0.13 (0.009) 0.09 (0.07)

0.31 (<0.0001) 0.33 (<0.ooo1) -0.18 (0.0009)

0.10 (<0.0001)

-0.08

(0.10)

0.29 (
0.07 (<0.0001) 0.05 (0.0002)

0.09 (0.07) 0.07 (0.17)

-0.24 -0.20

(
0.15 (
0.26 (
-0.27 -0.31 -0.24

(
0.16 (
0.38 (
-0.11

(0.04)

pWfbW?~lFfW

Child behavior checklist Learning Work Academic performance Peabody (Total

individual test score)

(teacher

achievement

version)

test

main of peer affiliation, BSR was significantly and positively associated with the peer delinquency scale (PDS), the perception of problem behaviour (PPB) and significantly negatively associated with the conventional activities of friends (CAF). Under the domain of family dysfunction, BSR was significantly and positively associated with the dysfunctional family index and significantly and negatively associated with the cohesion and organization subscales from the family environment scale. For the domain of school performance, BSR was significantly and negatively associated with CBCL (teacher version) subscales for learning, work, and academic performance, and with the Peabody individual achievement test. total test. Table 6 summarizes the results of the linear regression analyses pertaining to BSR scores predicting peer affiliation, family dysfunction, and school performance at 2 year follow-up, controlling for SES and time one dependent variables. As can be seen, the R’s (e.g. the proportions of explained variances) for the linear regressions under the domain of peer affiliation were significant at less than 0.0001. BSR at baseline was significantly and positively associated with the PDS and significantly and negatively associated with the CAF, while controlling for SES and time one dependent variable (PDS and CAF scores at baseline, respectively). BSR at baseline showed a trend towards a significant and positive association for PPB, controlling for SES and PPB at baseline. BSR, therefore, significantly predicted at 2 year follow-up two different measures of peer affiliation. For the domain of family dysfunction. the P value for the model predicting the family organization was significant. while the cohesion subscale was above the

0.003 threshold. BSR at baseline showed trends toward significant associations for organization and cohesion at 2 year follow-up, controlling for SES and time one dependent variables (organization and cohesion, respectively). Hence, BSR showed trends toward significant prediction of two measures of family dysfunction at 2 year follow-up. For the domain of school performance, the models predicting CBCL (teacher report) learning and work were significant, while academic performance was above the 0.003 threshold. The model predicting the Peabody individual achievement test was significant. BSR at baseline showed trends toward significant positive associations for learning and academic performance and a negative association for work, controlling for SES and time one dependent variables (learning, academic performance and work, respectively). Therefore, BSR showed trends toward significant prediction of three measures of school performance at 2 year follow-up.

4. Discussion This investigation demonstrates the presence of a latent trait of behavioral self-regulation (BSR), consisting of inattention, impulsivity/hyperactivity and aggressivity. The results also reveal that the derived BSR latent trait differentiates boys who have a father with a lifetime substance use disorder from boys of fathers with no substance use disorder, controlling for full scale IQ, SES, and number of lifetime DSM-III-R psychiatric disorders in the mother. Concurrent validity is supported by the significant associations of BSR scores

M.A. Table 6 Linear regression analyses predicting one dependent variable (DV) Dependent

variable

Danw

rt (11. Drug

peer affiliation,

at 2 year follow-up

family

and Alcohol

functioning,

Total ‘%I of variance the model R’ @I

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and school

explained

by

45 (1997)

performance

173

165--176

at 2 year follow-up.

Independent

variables

controlling

for SES and time

at baseline

B @)

Time one DV at baseline /I (JJ)

SES B (PI

BSR

-0.03 (NS) -0.01 (NS) 0.16 (0.0087)

0.21 (0.0008) 0.13 (0.0245) -0.20 (0.0011)

0.36 (
.-.___ Peer @lioriorr Peer delinquency Perception of problem Conventional activities

0.21 (<0.0001) 0.28 (
behaviors

Family dy~sfunction Family environment Cohesion Organization School prrjormunc~e Child behavior checklist Learning Work Academic performance Peabody individual (Total test score) * Time

one dependent

(teacher

achievement

variable

0.3 1 (0.0084) 0.49 (tO.OOO1)

0.15 0.13

(0.0084) (0.0093)

-0. I1 (0.0622) - 0. I3 (0.0074)

0.47 ( < 0.0001) 0.63 (
0.13 (0.0003) 0. I3 (0.0002) 0.09 (0.0037)

0.15 (0.0633) 0.12 (NS) 0.07 (0.0289)

0.16 (0.0677) - 0.22 (0.0133) 0.19 (0.0289)

0.20 (0.0191) 0.15 (0.0828) 0.16 (0.0639)

0.75

0.10 (0.0300)

-0.05

0.82 (
version)

test

is the measure

(<0.0001)

of the dependent

variable

with measures of family functioning, peer affiliation, and school performance. Predictive validity of BSR scores is supported by significant associations at 2 year follow-up with measures of peer affiliation, and trends toward significant associations at 2 year follow-up for measures of family dysfunction and poor school performance. It is not surprising that BSR scores did not differentiate high risk from low average risk boys when psychiatric disorder in the father is considered. Vanyukov et al. (1993) documented a significant positive correlations between a history of childhood conduct disorder (CD) in parents and number of CD symptoms in their sons, and number of antisocial personality disorder symptoms in parents and number of CD symptoms in their sons. In addition, Moss et al. (1995a) found that stress reactivity, alienation, and aggression in fathers correlated with magnitude of aggression in boys. Paternal personality characteristics were highly significantly associated with BSR in their male offspring; that is, the factors comprising the BSR measure in the boys were to a large extent present in the fathers. Therefore, controlling for paternal antisocial personality disorder and other psychiatric disorders eliminated the group differences between measures of behavioral self-regulation in HR and LAR boys. Recognizing that the characteristics contained in the BSR trait covary between father and son, it would appear important to investigate the etiological determinants with respect to the genetic and environmental factors underlying this component of PSUD liability for both fathers and sons.

at baseline

(age

lo-

(NS)

12 years).

The results of this study confirm and elaborate upon the findings by Martin et al. (1994) and Dawes et al. (submitted). In Martin et al., a comparable measure, termed behavioral dysregulation, was identified from three behavioral dimensions. The results reported herein confirm Martin et al. (1994) by demonstrating the presence of a similar latent trait of BSR. These results extend their findings by revealing that BSR scores covary with multiple measures of peer affiliation, family dysfunction, and school performance at baseline and two-year follow-up. The results reported herein also extend Dawes et al. (submitted). Dawes et al. reported in one hierarchical linear model that when HR boys were age lo- 12, parental perceptions of son and self, family dysfunction, peer affiliation, and parental psychopathology were significantly associated with BSR. Taken together, these findings suggest that BSR influences multiple domains of functioning in HR boys during adolescence. Whether BSR scores in late childhood predict BSR scores in middle and late adolescence remains to be investigated. Moreover, the extent to which BSR influences substance use, abuse, or dependence on subsequent follow-ups remains to be elucidated. Several caveats are noteworthy. First, estimates of method variance could not be obtained, because a multitrait multimethod model using the conceptual framework of Campbell and Fiske (1959) was not employed. This shortcoming aside, it is noteworthy that the model provided good fit to the data. Moreover, multiple measures were used, thereby increasing the

174

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et crl. / Drug

and Alcohol

validity of the findings. Second. the three traits employed in the BSR measure did not include indicators of cognition or emotion. It is possible that a more multifaceted characterization of BSR besides its behavioral features would have improved both its discriminative and predictive properties. Unfortunately, the dataset did not afford the opportunity for a broader examination of dysregulation. Third, although the effect sizes for BSR in sons at age lo-- 12 were small (Cohen, 1988) several points are salient regarding predicted developmental changes in effect size and stability of BSR during adolescence. First. in a Monte Carlo study, Ahadi and Diener (1989) reported that for multifactorial models, the effect of adding multiple independent variables to predict specific behaviours is to further decrease the upper bound of correlations that can be expected among behaviours and between determinants of specific behaviours. Second. Caspi et al. (1996) have recently shown that measures of behavioral undercontrol at age three have persisting significant, albeit weak, associations with antisocial personality disorder, involvement with crime, and alcohol dependence at age 21. It is posited that our measure of BSR in childhood may have significant associations with repeated measures of BSR, as well as future psychopathology, in later adolescence and young adulthood. Finally, post hoc general factorial ANOVAs were conducted to ascertain whether effect sizes for behavioral measures increased with age in our sample. The outcomes were teacher ratings for boys’ total aggression, total inattention, and total externalizing score at ages lo-- 12, 12-14 and 16, with the factor being group status, controlling for SES. Findings revealed that the effect sizes consistently increased over this four year period. (The effect sizes increased as follows: for total aggression, r2 was 0.01, 0.04 and 0.09; for total inattention, ye’ was 0.01, 0.03 and 0.05; and for total externalizing score, v* was 0.009, 0.06 and 0.08). Taken together, these three points suggest that the effect size of BSR, albeit small, will increase between ages lo-12 and 16 and will influence behavioral outcomes, when considered within a multifactorial developmental framework. Fourth, HR boys had significantly lower full scale IQ scores than LAR boys. These findings are consistent with prior studies (Gabrielli and Mednick, 1983; Tarter et al., 1984; 1995; Moss et al., 1995b). Furthermore, the HR boys lived in households having lower SES than LAR boys. These data are consistent with the social causation (adversity and stress) hypothesis reported in substance abusers (Dohrenwend et al., 1992). Nonetheless, the results demonstrate that, controlling for full scale IQ and SES, BSR discriminates HR from LAR boys. Fifth. our control group fathers and mothers had considerably less psychopathology than was found in

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the national comorbidity study (NCS). The NCS was designed to study the comorbidity of substance use disorders and nonsubstance psychiatric disorders in the United States general population between the ages of 15-54 (Kessler et al., 1994). By design, the control group fathers in our study had essentially no psychiatric disorders (e.g. less than 10% with a childhood disruptive behaviour disorder and six who met criteria for alcohol abuse prior to age 22). This compares to a lifetime prevalence of 48.7% for any of 14 DSM-III-R psychiatric disorders for males reported in the NCS. The control group mothers, however, had lifetime rates approaching the NCS. For control group mothers, 35.6% had at least one lifetime psychiatric disorder, compared to 47.3% of females in the NCS. Taken together, these findings suggest that the low the rates of psychiatric disorders among control fathers reported herein should not be generalized to the US general population. Moreover, the low rates of diagnoses in control fathers may contribute to the observed significant differences in the rates of psychiatric disorder among male offspring. Caution must also be exercised in interpreting the generalizability of the results for the proband fathers, who were all volunteers. Hence, the results could be biased by the characteristics of the individuals who, for whatever reason, decided to participate in this study. The method of subject recruitment is, however, a potential bias in most studies employing the high risk paradigm (Lilienfeld and Stolley, 1994). In sum, whereas the findings reported herein may not be generalizable to the general population, they implicate nonetheless the importance of the BSR construct. In conclusion, the traits of inattention, impulsivity/ hyperactivity, and aggressivity form a single latent trait of behavioral self-regulation (BSR). The BSR scores distinguish boys, according to the presence or absence of a lifetime substance use disorder in their fathers. In addition, the BSR scores measured at age lo- 12 significantly predict a prodromal indicator of substance abuse measured at ages 12- 14, namely, deviant peer affiliation; whereas, family dysfunction and poor school performance show a trend towards significant prediction. Continued follow-up of the sample is needed, however, to demonstrate the predictive validity of the BSR trait on PSUD outcomes. From such research, the mechanisms through which BSR contributes to the liability of substance use disorder can be delineated.

Acknowledgements

We would like to thank the boys and their parents who participated in this research. Special thanks are in order to Howard Moss and Michael Vanyukov for their comments on earlier drafts of this manuscript.

M.A.

Dawes

et al. /Drug

and Alcohol

This project was supported in part by training grants T 32 MH18957-04 and T 32 AA07453-15 and by a centre grant from the National Institute on Drug Abuse (DA05605) to the Center for Education and Drug Abuse Research. CEDAR is a consortium between St. Francis Medical Center and the University of Pittsburgh.

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