Kenneth G. Busch

Adolescents who kill

From a sample of 1,956 adolescent delinquents referred to us by the court for physical, psychological, psychiatric, educational, and social examinations, 71 delinquents convicted of homicide were matched with 71 nonviolent delinquents by age, race, sex, and socioeconomic status (SES). These two groups were compared on the basis of these evaluations by stepwise discriminant analysis, matched pairs, two-tailed t-tests, and nonparametric tests. Adolescents who kill have a tetrad of symptoms: (1) criminally violent family members; (2) gang membership; (3) severe educational difficulties; and (4) alcohol abuse.

Homicidal adolescents: a replication.

30 homicidal delinquents were matched with 30 nonviolent delinquents on age, race, sex, and socioeconomic status. Both groups received physical, psychological, educational, psychiatric and social examinations. Data were evaluated using stepwise discriminant analysis. Previous research was replicated. Homicidal adolescents shared four symptoms: criminally violent families, gang participation, alcohol abuse, severe learning difficulties. In addition, when the sample and the original group of murderers were combined the contrast between groups became clearer. Juvenile murderers lived in criminally violent families learning aggression during early childhood. Cognitive and perceptual deficits associated with epilepsy and central nervous system (CNS) conditions led to difficulties in the school years. During adolescence, gang participation and alcohol abuse added to earlier risk factors.

Developmental and Disruptive Behavior Disorders among Delinquents

Medical histories, psychological tests, psychiatric examinations, and social investigations were reviewed to determine the frequency of independent diagnoses of retardation, attention deficit disorder with hyperactivity (ADD-H), and attention deficit disorder without hyperactivity (ADD). In a sample of 1,956 (384 females and 1,572 males) adjudicated delinquents with a mean age of 13.9 +/- 1.9 years, 9% had ADD-H, 15% were retarded, and 46% had ADD. The reliabilities of the diagnoses were 0.95 for retardation, 0.91 for ADD-H, and 0.94 ADD. Retarded delinquents had the greatest scholastic delays followed by the ADD-H and ADD-adolescent offenders.

Summary of Studies of Abused Infants and Children Later Homicidal, and Homicidal, Assaulting later Homicidal, and Sexual Homicidal Youth and Adults

To study the risks of abuse, violence, and homicide, 5 studies of groups at risk for violence are summarized. 192 Abused Infants, 181 Abused Children, 127 Homicidal Youth, 425 Assaulters, 223 Rapists, and 223 Molesters were randomly selected and tracked in court, probation, medical, and school records, then compared with carefully matched groups of Controls and (in older groups) Nonviolent Delinquents. In adolescence or adulthood, these groups were classified into Later Homicidal (N = 234), Later Violent or Nonviolent Delinquent, and Later Nondelinquent subgroups for more detailed comparisons. Shaos bootstrapped logistic regressions were applied to identify risks for commission of homicide. Significant predictors for all homicidal cases in these samples were number of court contacts, poorer executive function, lower social maturity, alcohol abuse, and weapon possession. Predictors for the 373 Abused cases (Infants and Children) were court contacts, injury, burn, poisoning, fetal substance exposure, and parental alcohol abuse. Predictors for the 871 Violent Delinquent cases (Assaulters, Rapists, Molesters) were court contacts, poorer executive function, and lower social maturity. Accuracies of prediction from the regressions ranged from 81% for homicidal sex offenders to 87 to 99% for other homicidal groups.

Looking Forward and Backward in Records for Risks among Homicidal Youth

To identify risks for commission of homicide, 26 convicted Homicidal Youth (M age =14.9 yr., SD =1.4; n = 26; 1 girl, 25 boys) were matched with 26 Nonviolent Delinquents and 26 clinic-referred Controls. Youth were tracked backward 8 years (M = 7.7 yr., SD =1.5) and forward 3 years (M = 3.1 yr., SD =1.2) in records. Data analysis was Shaos bootstrapped logistic regression yielding area under the curve (AUC) and odds ratios (OR). Predictors of homicide were poorer executive function (OR = 7.04e+40), violent family (OR = 4.01e−16), and alcohol abuse (OR = 7.33e−17; AUC=.97, 95%CI = .77−.99). From earlier studies, 101 Homicidal Youth and their Controls were reanalyzed similarly. Predictors were poorer executive function (OR = 6.51), lower social maturity (OR = 0.28), weapon possession (OR = 26.10), and gang membership (OR = 4.14; AUC= .98, 95%CI = .96−.99). Groups were combined, i.e., 26 and 101 Homicidal; 127 Homicidal Youth (7 girls, 120 boys) and their matched Controis were tracked in records. The predictor was poorer executive function (OR= 3.34e−21; AUC = .98, 95%CI = .96−.97). When 127 Homicidal Youth were compared with 127 matched Nonviolent Delinquents, predictors were poorer executive function (OR = 2.83e–02), weapon possession (OR = 1.63e−10), lower social maturity (OR= 1.15), and use of special education services (OR = .94; AUC= .94, 95%CI= .37−.99).

Looking forward in Records of Young Adults Convicted of Sexual Homicide, Rape, or Molestation as Youth: Risks for Reoffending

To assess the risks predicting reoffense, 223 Rapists (M age = 14.2 yr., SD = 1.5; 25 girls, 198 boys) were matched with 223 Nonviolent Delinquents; risks were analyzed using logistic regression. The one predictor was prior court contacts (OR = 1.55e+12; AUC = .99, 95%CI = .98−.99). 223 Molesters were similarly matched with 223 Nonviolent Delinquents; this comparison yielded three predictors: previous court contacts (OR = 4.55e+23), poorer executive function (OR = 2.01), and lower social maturity (OR = .97; AUC = .98, 95%CI = .97−.99). Records for all cases (now M age = 24.2 yr., SD = 1.4) were reviewed forward 10 years and youth were classified into groups: Sexual Homicidal (1%, n = 7), Delinquent Rapists Later Adult Rapists (11%, n = 73), Delinquent Rapists (21%, n = 144), Delinquent Molesters Later Adult Molesters (10%, n = 69), Delinquent Molesters (23%, n = 153), Nonviolent Delinquent Later Nonviolent Adult Criminals (7%, n = 45), and Nonviolent Delinquents (27%, n = 178). Comparison of Sexual Homicidal cases (n = 7) with their matched Controls (n = 7) yielded one predictor, poorer executive function (AUC = .89, 95 %CI = .71−.93). When Sexual Homicidal cases were matched with Nonviolent Delinquents, predictors were low social maturity and prior court contacts (AUC = .81, 95%CI= .64−.93).

Empirical risk factors for delinquency and best treatments: where do we go from here?

Youth development and prevention of violence are two sides of the same public policy issue. A great deal of theoretical and empirical effort has focused on identification of risk factors for delinquency and development of interventions for general risks. Recent calls for changes in public policy are evaluated here—and challenged—in light of new comprehensive, longitudinal empirical data on urban violent delinquency. Treatments such as prenatal care, home visitation, prevention of bullying, prevention of alcohol and/or drug abuse, promotion of alternative thinking, mentoring, life skills training, rewards for graduation and employment, functional family therapy, and multidimensional foster care are effective because they prevent or ameliorate risks for delinquency occurring during development. At present, the best treatments yield 10 to 40% reductions in delinquent recidivism. Better controlled application of developmentally appropriate treatments in higher doses, with narrow targeting of the highest-risk youth based on actuarial testing—rather than less accurate clinical judgment—should result in higher effectiveness. Such a focused approach in a geographical area with high homicide rates should be cost-effective. A prediction of cost-benefit outcomes for a carefully constructed example of a large-scale program is presented.

Looking Forward in Records of Youth Abused as Children: Risks for Homicidal, Violent, and Delinquent Offenses

To study risks of abuse, violence, and homicide, 181 Abused Children (M age= 12.85 yr., SD = 2.14; 58 girls, 123 boys) were matched with 181 clinic-referred Controls. Data analysis was Shaos bootstrapped logistic regression with area under curve (AUC) and odds ratios (OR). Predictors of abused status were court contacts (OR = 2.04e+22) and poorer executive function (OR = .81; AUC=.99; 95%CI = .91−.99). Groups were tracked forward in records for 9 years (M=8.78 yr., SD =1.41). Looking forward, youth (M age = 21.63 yr., SD = 2.07) were classified into Abused Children Later Homicidal (5%, n = 10), Abused Children Later Violent (23%, n = 41), Abused Children Later Delinquent (28%, n = 50), Abused Children Later Nondeliquent (44%, n = 80), and Controls (n = 181). Data were analyzed with two more logistic regressions. Predictors of Abused Children Later Homicidal compared with Controls were number of court contacts (OR =50,398.78) and poorer executive function (OR = 79.72; AUC =.91; 95%CI= .80−.95). The predictor of Abused Children Later Homicidal contrasted with Abused Children Later Nondelinquent was court contacts (OR = 2,077,089,352; AUC=.87; 95%CI =.65−.95). The common predictor for Abused Children and Abused Children Later Homicidal groups was court contacts.

Medical, family, and scholastic conditions in urban delinquents

Effects of medical, family, and scholastic conditions were evaluated for the number and type of offenses and test score performances in 1,962 urban delinquents. Conditions were evaluated using multivariate followed by univariate analysis of variance with post-hoc tests. There were subgroup differences in the number and type of offenses and test score performances. Also, interactions of medical, family, and scholastic conditions changed the number and type of offenses and lowered test scores. Findings supported a developmental biopsychosocial model of delinquency that emphasized the predominance of medical/scholastic conditions. Orphaned or one-parent delinquents with nervous system or neonatal conditions, retardation, or hyperactivity committed assault.

Looking forward in records of youth abused as infants

To study risks for abuse and later homicidal behavior, 192 abused infants (M age = 3.12 yr., SD = 1.48; 82 girls, 110 boys) and 192 controls were matched on demographics and examined; data discriminating abused and later homicidal cases were analyzed with Shaos bootstrapped logistic regression. Predictors of Abused status were injury, burn, poisoning, fetal substance exposure (OR = 2.47), later parental or youth court contacts (OR = 1.86e+12), and parental alcohol abuse (OR = .54; AUC = .99; 95%CI= .96−.99). Youth tracked through records 12 years (to M age = 12.17 yr., SD =1.89) were classified into Abused Later Homicidal (11%, n = 21), Abused Later Violent (14%, n = 27), Abused Later Delinquent (31%, n = 60), Abused Later Nondeliquent (n = 44), and Control groups (n = 192). Data were analyzed similarly. When the Abused Later Homicidal was contrasted with the Control group, predictors of homicide were three or more home/school moves (OR = .78), illnesses (OR = .90), and later court contacts (OR= 1.75e+07; AUC=.99; 95%CI=.90−.98). When the Abused Later Homicidal was compared with the Abused Later Nondelinquent group, predictors of homicide were poorer executive function (OR = 2.29) and later court contacts (OR = 7.78e+06;AUC=.94; 95%CI= .90−.98).