Actuarial assessment of risk among sex offenders.
The appraisal of risk among sex offenders has seen recent advances through the advent of actuarial assessments. Statistics derived from Relative Operating Characteristics (ROCs) permit the comparison of predictive accuracies achieved by different instruments even among samples that exhibit different base rates of recidivism. Such statistics cannot, however, solve problems introduced when items from actuarial tools are omitted, when reliability is low, or when there is high between-subject variability in the duration of the follow-up. We present empirical evidence suggesting that when comprehensive actuarial tools (VRAG and SORAG) are scored with high reliability, without missing items, and when samples of offenders have fixed and equal opportunity for recidivism, predictive accuracies are maximized near ROC areas of 0.90. Although the term "dynamic" has not been consistently defined, such accuracies leave little room for further improvement in long-term prediction by dynamic risk factors. We address the mistaken idea that long-term, static risk levels have little relevance for clinical intervention with sex offenders. We conclude that highly accurate prediction of violent criminal recidivism can be achieved by means of highly reliable and thorough scoring of comprehensive multi-item actuarial tools using historical items (at least until potent therapies are identified). The role of current moods, attitudes, insights, and physiological states in causing contemporaneous behavior notwithstanding, accurate prediction about which sex offenders will commit at least one subsequent violent offense can be accomplished using complete information about past conduct.
Harris
Grant T
GT
Research Department, Mental Health Centre Penetanguishene, Penetanguishene, Ontario L9M 1G3, Canada. gharris@mhcp.on.ca
Rice
Marnie E
ME
D000206:Actuarial Analysis / Q000379:methods*; D005500:Follow-Up Studies; D006801:Humans; D018570:Risk Assessment; D012742:Sex Offenses / Q000523:psychology* / Q000706:statistics & numerical data*