Finding workers, offenders, or students most at-risk for violence: actuarial tests save lives and resources.

147 adults (107 men, 40 women) and 89 adolescents (61 boys, 28 girls), selected randomly from referrals and volunteers, were given the Ammons Quick Test (QT), the Beck Suicide Scale (BSS), the Minnesota Multiphasic Personality Inventory Second (MMPI-2) or Adolescent Versions (MMPI-A), the Raven's Advanced Progressive Matrices, and the Standard Predictor (SP) of Violence Potential Adult or Adolescent Versions. The goals were to: (a) demonstrate computer and paper-and-pencil tests correlated; (b) validate tests to identify at-risk for violence; (c) show that identifying at-risk saves lives and resources; and (d) find which industries benefited from testing at-risk. Paper-and-pencil vs. computer test correlations (.83-.99), sensitivity (.97-.98), and specificity (.50-.97) were computed. Testing at-risk saves lives and resources. Critical industries for testing at-risk individuals may include airlines, energy generating industries, insurance, military, nonprofit-religious, prisoners, trucking or port workers, and veterans.

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. Shao's 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 in records of youth abused as infants: risks for homicidal, violent, and delinquent offenses.

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 Shao's 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 = 15.17 [corrected] 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 Nondelinquent (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).

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 = 2.74; 58 girls, 123 boys) were matched with 181 clinic-referred Controls. Data analysis was Shao's 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 = .97-.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 Nondelinquent (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.

Looking forward in records of young adults who were convicted of homicide or assault as youth: risks for reoffending.

To assess risks of violent offending in young adults, 425 Delinquent Assaulters (M age = 14.1 yr., SD = 1.7; 77 girls, 348 boys) were matched with 425 Nonviolent Delinquents. Analysis of data from court, school, and medical records used Shao's bootstrapped logistic regressions. Predictors of Assaulter status were poorer executive function (OR = 0.97) and prior court contacts for violent offenses (OR = 3.5e+ 23; AUC = .97; 95% CI = .82-.99). Looking in records backward 4 years (M = 4.1, SD = 2.6) and forward 10 years to mean age 24.5 yr. (SD = 2.1), adults were classified as Homicidal (8%, n = 69); Delinquent Assaulters Later Adult Assaulters (10%, n = 86); Delinquent Assaulters Later Noncriminals (32%, n = 270); Nonviolent Delinquents Later Nonviolent Criminals (10%, n = 87); and Nonviolent Delinquents Later Noncriminals (40%, n = 338). The Homicidal group (n = 69) was compared to matched Control and Nonviolent Delinquent groups (n = 69) using logistic regression. Predictors of Homicidal versus Control were poorer executive function and alcohol or substance abuse (AUC = .97; 95% CI = .93-.99). Predictors of Homicidal versus Nonviolent Delinquents were unemployment, poorer executive function, and prior court contacts (AUC = .98; 95% CI = .95-.99).

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 = 15) and forward 3 years (M = 3.1 yr., SD = 1.2) in records. Data analysis was Shao's 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 Controls 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).

Comparing early and late twentieth-century Boston and Chicago male juvenile offenders: what changed?

To investigate changes in characteristics of delinquents over time, randomly selected contemporary delinquents (Zagar, et al., 1980-1988; n=2,031) were compared with 3 historical Chicago and Boston samples (Healy & Bonner, 1909-1915, n=2,000; Healy & Bonner, 1917-1923, n=2,000; Glueck & Glueck, 1917-1922, n=1,000). All underwent physical, psychiatric, psychological, school, and social examinations. Contemporary delinquents had more assault, burglary, homicide, alcohol and substance abuse, gang membership, head injury, overdose, and single parents. Historical delinquent samples had more thieves and families with both biological parents. Historical delinquent mean IQ was 5 points below standardization average; modern delinquents were 22 points lower. Contemporary offenders were a greater portion of the county public school-aged population. Current more sensitive and specific examinations account for increases in observed overdoses and head injuries in the 1980s sample. Other demographic differences between contemporary and 3 historical delinquent samples were robust. Findings are discussed with respect to a need for early actuarial assessment and empirical treatment of the costliest delinquents: the dropouts, alcoholics, addicts, career delinquents-criminals, and homicide-prone youth.

Can violent (re)offense be predicted? Review of the role of the clinician and use of actuarial tests in light of new data.

This article is an evaluation of established actuarial probation-parole tests in light of new data on violent and homicidal behavior. Probation-parole tests originally were developed by observing risks related with recidivism or return to court after release (i.e., the "danger" of releasing an individual) by following offenders in court records for up to 10 years. Commonly used probation-parole tests together comprise 82 distinct items related to characteristics of the offender: home, school, peers, job, family, individual-medical, community, and court contacts. The risks for violence and homicide found by Zagar, et al. were compared with prior meta-analyses of risks with the criterion of violent delinquency. Bootstrapped logistic regressions in Zagar and colleagues' new data yielded highly accurate predictions of violence, showing that improved methods and sampling can lead to still higher accuracy than had been achieved by established probation-parole tests. A general discussion of the usefulness of actuarial tests and answers to challenges of their validity for decision making are provided.

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.