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Use of Driver Record and Datalogger Data to Predict Recidivism

Use of Driver Record and Datalogger Data to Predict Recidivism. Dr. William J. Rauch Center for Studies on Alcohol Substance Abuse Research Group Westat 8 th Ignition Interlock Symposium August 26, 2007 Seattle, Washington. 1st Maryland RCT. Program Effectiveness Evaluation

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Use of Driver Record and Datalogger Data to Predict Recidivism

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  1. Use of Driver Record and Datalogger Data to Predict Recidivism Dr. William J. Rauch Center for Studies on Alcohol Substance Abuse Research Group Westat 8th Ignition Interlock Symposium August 26, 2007 Seattle, Washington

  2. 1st Maryland RCT • Program Effectiveness Evaluation • 64% Reductions in AR Recidivism • AR Crashes; Moving Violations; Administrative Actions • Program Success: • Interlock Device • Interlock License Restriction • Close Monitoring of Offenders • High Program Acceptance by Offenders (86%)

  3. Post-Intervention • Recidivism Returns to Pre-Intervention Levels • Low Probability of Arrest • 1/3 Multiple Offenders Recidivate • Cycle Begins Anew

  4. Questions of Interest? • Can Driver Record Data & Datalogger Data Be Used to Predict Recidivism? • During the Intervention • Post-Intervention • Is there an added benefit to including datalogger data in addition to driver record data in predictive models?

  5. Why Predict Recidivism? • Limited State Resources • Focus on High-Risk Offenders • Criterion Based Removal • IRS Model

  6. Datalogger Data • 15,000 Drivers • 3 Vendors • 6 Models • Data Quality

  7. Data Requirements • Need Long Exposure • Explore Time in Program • 6, 12, 24 • Interlock Installed 12 Consecutive Months • Linked to Driver Record

  8. Data • Demographic Variables Known at Installation • Age, Gender, Race • Violation History at Installation • Alcohol-Related and Non-Alcohol Related Violations at Program Entry • 2, 5, All Years • Datalogger Summary Over 12 Months

  9. Summarizing Datalogger • Gap in Days Between Events • Initial Tests Passed Per Month • Initial BAC Test Failures: • BAC > .025% / 1,000 Engine Starts • BAC > .040% / 1,000 Engine Starts • BAC > .080% / 1,000 Engine Starts • Other Initial BAC Failures Within 1 Hour • Weighted Average of Initial Test Failures / 1,000 Engine Starts • Retest Failures / 1,000 Engine Starts • Retest Refusals / 1,000 Engine Starts • Disconnects / 1,000 Engine Starts • Weighted Sum of Non-Compliance Measures / 1,000 Engine Starts

  10. Variables • Base (B) Variables • Demographic & Violation History • Prior to Interlock Installation • Compliance (C) Variables • Interlock Datalogger Summary Variables • Examine Recidivism During & Post-Interlock Intervention

  11. Methods • Proportional Hazard (PH) Models Fitted for 3 Time Periods: • During Intervention • Post-Intervention • During + Post-Intervention

  12. Methods (Continued) • Defined and Fitted Stepwise Models for Variable Groups Representing Various Risk Factors: • Base Variables • Base & Compliance Variables • Base & Compliance Variables • No Disconnect During Period of Interest • Base & Compliance Variables • > 1 Disconnects During Period of Interest

  13. Methods (Continued) • PH Models Summed Combined Effects of the Statistically Significant Risk Factors • Xβ • Drivers - Larger Xβ More Likely Have an AR than Drivers With Smaller Xβ • Drivers - Similar Xβs Roughly the Same AR Risk

  14. Methods (continued) • Grouped Drivers on Combined Risk Estimates into Deciles • Combined Deciles into 4 Groups of Varying Size: • 1 – 4 • 5 – 7 • 8 – 9 • 10

  15. Results • 3,334 Drivers* with 12 Consecutive Months of Datalogger Events • 1 Vendor • 960 Never Disconnected the Interlock • 2,374 Disconnected > 1 Time • Model Performance Assessed: • Observed AR-Recidivism with Estimates for High Risk of AR-Recidivism

  16. Model Performance • Drivers Placed in Highest Risk Decile by Combined Risk Factor were Considered to have “High AR Recidivism Risk” • Calculated Sensitivity and Specificity

  17. Sensitivity and Specificity • Sensitivity Defined as the Ratio of Top-Decile Drivers with an AR (True Positive) to All Drivers with AR • Specificity Defined as the Ratio of Lower-Decile Drivers with No AR (True Negative) Among Drivers in the Bottom Nine Risk Deciles

  18. Sensitivity and Specificity (Continued) • Sensitivities Ranged from 0.21 – 0.37 • Specificities Ranged from 0.90 - 0.92 • Hard to Predict Recidivists – Rare Event • Relatively Easy to Predict Non-Recidivists • Both Measures were Better for Models that Included both Base & Compliance Variables than Models with Only Base Variables

  19. Survival Analyses • Generated Life-Table Survival Probability Estimates & Plotted by Strata with 4-Way Risk Stratification for Drivers with No Disconnect or 1 or more Disconnects for Intervention Period (B, BC, BCND, BCD) • Risk Group Effected Survival Probability • Riskiest Drivers had Substantially Lower Probabilities than Drivers in Other Risk Groups

  20. Probability of Survival by 4-Level Risk Groups from Model with Base Risk Factors

  21. Probability of Survival by 4-Level Risk Groups from Model with Base and Interlock History Risk Factors

  22. Results • During the Intervention, the Rate per 1000 Engine Starts of Initial Test Failures at BACs > .04% were Associated with Reduced Survival Time (p<.0001) • Regardless of Stratification even after Controlling for Combined Risk

  23. Recidivism Risk • Recidivism Risk of the 10% High Risk Drivers Relative to Remaining 90% of Drivers • Ranged from 2.26 to 4.29 • Models with Compliance Variables Predicted Higher Recidivism Risk than Models with Only Base Variables

  24. Conclusion • Adding Compliance Measures to Driver Characteristics and Pre-Enrollment AR History Marginally Improves the Identification of Drivers Most at Risk of Recidivating

  25. Conclusion (Continued) • Even when All Base and 12 Month Compliance Data were Included in the Model for Identifying AR Recidivists, the Most at Risk 10% of All Drivers Accounted for No More than about 24- 37% of all Drivers who had a Post-Enrollment AR

  26. Conclusion (Continued) • Appears to be No Statistical Method that Promises to Increase Sensitivity Except for Relaxing the Criterion for determining Risky Drivers (Base + Compliance Measures) • Including More than 10% of all Drivers in the at Risk Group would Increase Sensitivity • Need to Examine Trade-Off between Sensitivity and Specificity

  27. Possibilities • Focus on Last 45 Days in Intervention • Add Instrument Measures • CAGE • AUDIT • RIASI • Personal Interview • Biomarkers

  28. Contact Information Dr. William J. Rauch Center for Studies on Alcohol Substance Abuse Research Group Westat 1650 Research Boulevard Rockville, MD 20850 USA Voice: (301) 517-4011 E-Mail: rauchb1@westat.com

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