The difficulties of predicting future violence

1. The difficulties of predicting future violence Edward P. Mulvey, Ph.D. Western Psychiatric Institute and Clinic University of Pittsburgh School of Medicine mulveyep@upmc.edu Conference on Campus Violence Columbia Law School April 4, 2008

3. Goals • Provide some background about general methods for making predictions of violence • Identify the inherent challenges to predicting campus violence • Make some general recommendations about strategies for addressing this problem

4. Approaches to Predicting Incidents of Violence

5. Actuarial Approach • Same information considered for every person • Consistent method for combining information • Factors considered not necessarily “causal” for that individual

6. Clinical Approach • Individualized judgment • Range of relevant factors considered is very broad; ideally, an integrative view • Operates from a theory of how violence might occur or unfold in the individual’s life

7. Problems with the Actuarial Approach • All the information has to be available • Generally assumes that all the factors apply the same to everyone • The risk estimate is devoid of theory

9. Problems with the Clinical Approach • Depends on the person doing it • Variability in: • Information gathered • Ways information is combined • Human biases occur • Recency • Vividness • Affected by organizational demands • Optimization is assumed • “Satisficing” is more common

10. Actuarial vs. Clinical Prediction • Old debate • Suicide • Job/academic success • General findings • Actuarial Approach generally more accurate • Clinical Approach more versatile • Issue of reliability and validity

11. Reliable, but not valid

12. Valid, but not reliable

13. Actuarial Risk Assessment Tools • General violence/recidivism (mainly in mentally ill individuals) • Historical-Clinical-Risk Management-20 (HCR-20) • Violence Risk Appraisal Guide (VRAG) • Classification of Violence Risk (COVR) • Violent Offender Risk Assessment Scale (VORAS) • Special purpose instruments • Domestic violence • Spousal Assault Risk Assessment (SARA) • Risk of sex offense • Static-99 • Risk of violence among juveniles • Early Assessment Risk List for Boys (EARL-20B) • Manual for the Structured Assessment of Violence Risk in Youth (SAVRY)

14. Issues regarding actuarial instruments • Increasingly popular because of technology • Optimization on chance • Shrinkage inevitable • Application on particular sample might not be appropriate • Effect of context • Information availability • Outcome of decision • Not a replacement for clinical judgment. Integration of actuarial and clinical information is the goal

15. Inherent Challenges to Predicting Campus Violence

16. Major General John Sedgwick

17. Problem #1: Low Base Rates Actually violent Yes No Predicted Yes 80 180 260 Violent No 20 720 740 100 900 1,000 true positives false positives true negatives false negatives Assume that one in ten individuals is actually violent over a given time period Assume that the instrument correctly identifies 80% of the violent individuals 80% of the nonviolent individuals

18. Implications • No technology will predict rare events • Secret Service study of school shootings: “There is no accurate or useful profile of the school shooter”. • “profiles” may be valuable, but not because they are predictive • The utility of screening and assessment is not to predict for an individual, but • to identify groups with higher base rates • to focus prevention resources

19. Problem #1: Low Base Rates Problem #2: Context matters and situations change

20. Problem #2: Context matters and situations change • Violence is usually • dependent on proximal situational factors • transactional • Opportunities for violence • may differ substantially across individuals • can be altered by lifestyle changes • presence of alcohol • living arrangements

21. Implications • Risk status may be important, but so are fluctuations in risk state • Move toward management of high risk individuals and situations • Conditional prediction model • “if….then” formulation of risk • Monitoring and management of “dynamic predictors”

22. A research example: Substance use and violence

23. Study Design • Select group of individuals who were highly likely to have frequent involvement in violence • Weekly interviews providing daily reports • Violent incidents • Substance use reports at daily level • Alcohol (number of drinks) • Marijuana use • Other drugs (mostly cocaine) • Analyses of the concurrent and lagged relationships of substance use and violence

25. Odds ratios for substance use and violence one day apart for serious violence

26. Examples Case 8 --------|||||||-|----------------------------------------------------|---|----- Case 2080 -|------|------------|------|---------------|-|------|--------------|---------|--|--------|----|--

27. Testing the relation of violence and alcohol use over time

28. Findings • Evidence for a lagged effect for alcohol use (greater than three drinks) on violence, but not the other way around • No significant lagged relationships either way for marijuana use or other drugs • Even controlling for different types of substance use, violence on one day predicts for the next day • Use of multiple substances on prior day also increases likelihood of violence

29. Problem #1: Low Base Rates Problem #2: Context matters and situations change Problem #3: Late adolescence is all about change

30. Problem #3: Late adolescence is all about change • Late adolescence brings: • Independent social roles • “trying on” lifestyles • ongoing brain development • Different patterns of substance use • Onset period for many mental disorders • Substance abuse: 20 years old • Mood disorders: 30 years old • Schizophrenia: 20 years old (males); 30 years old (females) • Involvement in violence drops off, even in serious offenders

31. Self Reported Offending Serious Adolescent Offenders - males only – average age 16 at first interview Group 5 (8.5%) Group 4 (15.1%) Group 3 (18.3%) Group 2 (33.8%) Group 1 (24.2%)

32. Implications • Lack of solid history for use in making judgments about future violence • Mix of developmental features and valid symptoms • Impulsiveness, moodiness, feelings of being picked on, feelings of rejection, tendency to blame others • Diagnostic labels are less valid • Likely to confuse risk markers with risk factors • Assessments have a limited shelf life

33. Guidelines for structuring risk assessments

34. 1. Get the right information on the right individuals • Screen using structured measures, and assess for risk when warranted • Use all available information consistently • Use relevant assessment tools • Group characteristics • Target behavior of interest • Use actuarial instrument as an “anchor”

35. Best Bets for individual assessment • history of violence • impulsivity (process from ideation to action) • active ideation (mainly hostility and anger) • drug and alcohol use • psychopathy • perceived threat • plan/access to means • opportunities for violent encounters • coping strategies

36. 2. Consider history in detail • When there is past violence, assess: • what happened • what factors explain the incident • what factors protect against violence • which risk and protective factors are currently in effect or likely to be in effect • When there is no past violence, assess: • “close calls” • recent changes in life that may exceed coping capacity • risk factors for violence, based on appropriate assessment protocols

37. 3. For prevention efforts, distinguish between risk markers and risk factors • Key is to focus on causal, dynamic risk factors • Questions • Is this a risk factor for a particular type of violence? • Are there conditions that influence the relationship between the risk factor and violence? • Does the risk factor play a causal role in violence? • If so, is the risk factor capable of being modified?

38. 4. Take context and risk state seriously • A single risk assessment is useless without a management plan • Assess individual factors and conditions periodically in high risk cases • Create an environment where students trust authorities enough to share information