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Actuarial Instruments in Risk Assessment
Yale University Law & Psychiatry DivisionHoward Zonana MD
Madelon Baranoski PhDMichael Norko MD
Alec Buchanan PhD MD
Governor’s Sentencing and Parole Review task Force
December 3, 2007
Antisocial Personality Disorder and Psychopathy
Howard Zonana MD
Connecticut Mental Health Center
Yale University School of Medicine
12/3/2007
Antisocial Personality DisorderDSM IV-TR
• Pervasive pattern of disregard for, and violation of, rights of others that begins in childhood or early adolescence and continues into adulthood.
• The person must be at least age 18 and must have a history of Conduct Disorder before age 15
Conduct Disorder
• Aggression towards people and animals
• Destruction of property,
• Deceitfulness or theft
• Serious violation of rules
Diagnostic Criteria for ASPDThree or more of the following:
• Failure to conform to social norms with respect to lawful behaviors as indicated by repeatedly performing acts that are grounds for arrest
• Deceitfulness, as indicated by repeated lying, use of aliases, or conning others for personal profit or pleasure
• Impulsivity or failure to plan ahead• Irritability and aggressiveness, as indicated by
repeated physical fights or assaults
Diagnostic Criteria for ASPD
• Reckless disregard for safety of self or others
• Consistent irresponsibility as indicated by repeated failure to sustain consistent work or honor financial obligations
• Lack of remorse, as indicated by being indifferent to or rationalizing having hurt, mistreated, or stolen from another
Epidemiology
• Prevalence rates of 2-3% for men and 1% for women in the general population
• Up to 60% in male prisoners• After age 30 the most flagrant antisocial
behaviors tend to decrease• Genetic and environmental factors contribute to
the risk• Both adopted and biological children of parents
with antisocial personality disorder are at increased risk for the disorder
Epidemiology
• The odds of developing antisocial personality disorder for those leaving formal education at 11 years was almost five times that of those remaining in education until 15 years,
Actuarial Measures and Risk
Madelon Baranoski, PhD
Associate Professor
Yale School of Medicine
Outline
• Actuarial measures and how they are developed
• Assessing criminality and antisocial personality
• Measures pertinent to re-offense– PCL-R (Psychopathy Checklist-Revised)– VRAG (Violence Risk Appraisal Guide) – LSI (Level of Service Indicator)
Actuarial Measures
• Actuarial refer to prediction by statistics• Analysis first used by insurance companies to
calculate financial risk • Measures developed through analysis of
outcomes that are associated with “predictor variables”– Variables weighted according to their ability to
differentiate between groups – Weighted variables combined to form a scale– Scale cross-validated on different populations to
derive estimates of probability that specific outcome will occur in a particular time
– Production of “life tables”
Development of life table life expectancies
at age 65 for American males
Paternal Death < 65
Smoking >10 Years
Obesity- BMI>30
Diabetes
DeadLiving
Actuarial Risk Assessment
• Identification of individuals at higher risk because of selected traits that correlate with criminal recidivism or violence
• Established through empirical association of traits with violence
Development of Actuarial Criminal Risk Measures
Personality Studies Criminality Studies
Predictor Variables of Criminal Behavior
• Offenders of Interest– Repeat offender– Violent offender– Sex offender
• Characteristics of offender– Personality – Attitude– Behavior– Substance use and addiction
• Situational characteristics– Poverty– Gang affiliation– Family business
Psychopathy Check List-Revised(PCL-R)
• Developed as research tool to study antisocial personality disorder
• Interview/collateral information provides data for assessing 20 areas of personality/behavior
• Results identify two domains– Behavioral domain– Personality domain
(Robert D. Hare, 1990)
PCL-R
• Personality Domain– Glibness/superficial
charm– Grandiose sense of
self– Pathological lying– Conning/manipulative– Lack of remorse/guilt– Shallow affect– Callous/lack of
empathy– Failure to accept
responsibility for actions
• Behavioral Domain– Boredom/need for
stimulation– Parasitic lifestyle– Poor behavioral
controls– Early behavioral
problems– Lack of long-term goals– Impulsivity– Irresponsibility– Juvenile delinquency– Revocation conditional
release – Criminal variety
PCL-R Considerations
• Strong correlation with criminal recidivism, violence, and sexual violence
• Inter-rater reliability• Scores indicate need for
monitoring vs. treatment
• Abbreviated version
• Ineffective for assessment of mental health risk
• Predicts life long risk, not imminent risk
• Insensitive to treatment effect or changes in situational factors
• Accuracy depends on extensive collateral data
• Requires extensive training
Strengths Limitations
Level of Service Inventory-Revised• Blend of actuarial and dynamic factors• Measures 54 risk/need factors over 10
domains– Criminal history, employment/education,
family/marital, accommodation, leisure/recreation, friends/assoc, emotional/mental health, attitudes/orientations (Andrews & Bonta, 1995)
• Structured Interview with collateral data • Total risk/need score correlated with re-
offense• Identified target areas for intervention
Comparison
• PCL-R– Extensive use in Canadian
system– Requires specialized training,
collaterals – Best prediction at high and
low scores – Strong reliability across
studies– Specifically excludes AXIS I
mental health disorders– Most data on men
• LSI– Extensive use in American
correctional systems (Ohio studies)
– Requires training in structured interview
– Collateral data recommended, not required
– Variable outcomes across study sites
– Includes persons with mental illness
– Best prediction at high and low scores
– Most data on men
Distribution of Risk Category
0
10
20
30
40
50
60
Low Low-Moderate
Moderate Moderate-High
High
%N=2006
Lowencamp & Latessa, 2006
Re-Incarceration Based on Risk Classification
0
10
20
30
40
50
60
Low Low-Moderate
Moderate Moderate-High
High
%
Risk Category Levels
• Low – 0-13
• Low/Moderate – 14-23
• Moderate – 24-33
• Moderate-High – 34-40
• High – 41-54
Violent and Sexual Offenses by PCL-R Scores
0
10
20
30
40
50
60
70
80
<11 11 to 20 21 to 30 Over 30
Re-ArrestViolent OffenseSexual Offense
%N=3478
10% 13% 42% 35%
Actuarial-Standard Measures on Inmates
• Advantages– Identifies groups
most likely to re-offend
– Assesses criminality as style
– Provides standard data base for program and time evaluation
– Provides bases for cost and program allocation
• Limitations– Requires training
and fidelity checks– Limited accuracy for
any individual assessment
– Cannot predict the unusual
– Accuracy related to time of follow-up
– Requires different tools for different types of criminal acts
What is the Goal?What is the Goal?• What are the outcomes of interest?
– Type of Crime: General criminal recidivism vs. violence – Over what period: Within probation/parole vs. lifetime– Under what circumstance: In prison, in community with
supervision, in community• Who are being assessed?
– Persons with diagnosed mental illness– Persons screened for absence of mental illness– All persons– Men and women
• What level of risk is acceptable?– Zero tolerance vs. violence reduction– Reduction of overall crime vs. specific crime (juvenile,
domestic, sex offenses)
• How certain is adequate certainty?– Would you rather incarcerate many more to avoid one bad
outcomes or risk one bad outcome to avoid over incarceration • What cost is tolerable and for how long?
• What are the outcomes of interest?– Type of Crime: General criminal recidivism vs. violence – Over what period: Within probation/parole vs. lifetime– Under what circumstance: In prison, in community with
supervision, in community• Who are being assessed?
– Persons with diagnosed mental illness– Persons screened for absence of mental illness– All persons– Men and women
• What level of risk is acceptable?– Zero tolerance vs. violence reduction– Reduction of overall crime vs. specific crime (juvenile,
domestic, sex offenses)
• How certain is adequate certainty?– Would you rather incarcerate many more to avoid one bad
outcomes or risk one bad outcome to avoid over incarceration • What cost is tolerable and for how long?
What Actuarial/Standard Measures Can Not Do
• Predict rare occurrence (“crime of the century”)• Address violence from mental health disorders• Predict first offenses• Prove prevention• Hold statistical accuracy for individual
assessments • Replace educated assessors
Requirements for All Actuarial Measurements
• Availability of data
• Standard use of measure
• Use on standardized population
• Adequate follow-up
• Customized to cultural, setting, and group
Meaning of Actuarial Test Outcome
Michael Norko MD
Associate Professor of Psychiatry
Yale University School of Medicine
Meaning of Actuarial Test Outcome
• Risk level
• Positive predictive power
ACME Risk Screening Tool
(ARST)
ARST Validation Data
• Separates into low risk and high risk
– Statistically significant separations
– Quite good AUC of 75%
• High risk has average risk of 37%
• Low risk has average risk of 9%
• Overall risk in population is 18.5%
What does 37% risk mean?
What does 37% risk mean?
What does 37% risk mean?
So what does it mean?
Using the ARST
The Results
What’s the Outcome?
The “Low Risk” Group
The “High Risk” Group
Meaning of Actuarial Test Outcomes
• % Risk level
–X% of people just like the subject will commit act w/in y period of time
• Positive predictive power–The % of the people predicted to
commit the act who actually do
Positive Predictive Power
• PPP almost never > .50
• In other words, the majority of nearly all identifiable high risk populations never commit the predicted act– For example, False Positive rates for PCL-R
in literature are between 50-75%• Freedman: J Am Acad Psych Law 2001
Accuracy of Predictions of Offending
Alec Buchanan PhD MD
Associate Professor of Psychiatry
Yale University School of Medicine
Indices of effectiveness of validated prediction studies 1970 – 2000 (from Buchanan and Leese, 2001)
0.5
1
1970 1980 1990 2000year
IoE Fitted values
Number needed to detain
• NND
• the number of individuals who would need to be detained in order to prevent one violent act
• the inverse of positive predictive value
Buchanan and Leese Lancet (2001) 358, 1955-59
Relationship between Number Needed to Detain (NND) and prevalence (p) when sensitivity = 0.73 and specificity = 0.63
0
5
10
15
20
25
30
0 0.1 0.2 0.3 0.4 0.5 0.6
p
NN
D
NND and base rates
20 % 10% 5%
Receiver operating characteristics of predictions of conviction at 10.5 years (all offences)
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
1-specificity
Sensitivity
Chance
Receiver operating characteristics of predictions of conviction at 10.5 years (all offences)
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
1-specificity
Sensitivity
ASChance
Receiver operating characteristics of predictions of conviction at 10.5 years (all offences)
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
1-specificity
Sensitivity
ASChanceAS + C
Receiver operating characteristics of predictions of conviction at 10.5 years (all offences)
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
1-specificity
Sensitivity
ASChanceAS + CAS + C + D
Receiver operating characteristics of predictions of conviction at 10.5 years (serious offences)
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
1-specificity
Sens
itivi
ty
Chance
Receiver operating characteristics of predictions of conviction at 10.5 years (serious offences)
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
1-specificity
Sens
itivi
ty
AS
Chance
Receiver operating characteristics of predictions of conviction at 10.5 years (serious offences)
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
1-specificity
Sens
itivi
ty
AS
Chance
AS + C
Receiver operating characteristics of predictions of conviction at 10.5 years (serious offences)
0
0.2
0.4
0.6
0.8
1
0 0.2 0.4 0.6 0.8 1
1-specificity
Sens
itivi
ty
AS
Chance
AS + C
AS + C + D
How accurate are predictions of offending ?
• … not sufficiently better than chance to allow “prevention by detention” of unusual offences without detaining many people who would not have offended
• this may not improve much
• at 10% prevalence present psychiatric technology would detain 6 who would not offend for every 1 who would
• … at best
How accurate are predictions of offending (1)?
• Better than chance
• How much better?
Index of effectiveness
3/{log[Sn/(1-Sn)] +log[(Sp/(1-Sp)]}
Which information helps us predict?
Will accuracy improve?
What does this mean?