The correlation of marijuana, alcohol, and other drug use with risky driving behavior. Gloriam Vanine Guenzburger, CA DMV

Transcription

1 The correlation of marijuana, alcohol, and other drug use with risky driving behavior Gloriam Vanine Guenzburger, CA DMV

2 Background Marijuana use affects perception, concentration, decision making, attention, reaction time, and coordination (Huestis, 2002). All involved in driving. Recent marijuana use (in the last 4 hours before driving) associated with 2-6 times higher risk of crashing (Huestis, 2002) Attitudes about marijuana use are rapidly changing. Public support for legalizing marijuana is currently at an all-time high (Pew Research Center, 2013). Marijuana use among drivers has increased (Guenzburger & Masten, 2013).

3 Approach: Build on findings from Bédard, Dubois, & Weaver (2007). The Impact of Cannabis on Driving. Canadian Journal of Public Health, 98,1, Refinements: Larger sample. All age groups. Other drugs. Additional predictors.

4 Research Questions 1. Are California fatal-crash-involved and THC-bloodpositive drivers more likely to have engaged in risky behavior immediately before crashing? 2. Does the combination of THC and alcohol show increased odds of drivers engaging in risky driving behavior in a dose-response manner?

5 Sample 37,786 CA drivers recorded in the Fatality Analysis Reporting System (FARS) from 1993 to CA consistently tests annually ~ 80% of deceased drivers and ~40% of all drivers stable sample. Included only drug blood tested drivers better measure of impairment than urine testing. Included all demographic groups. Included both single vehicle and multiple vehicles crashes.

6 Annual State Driver Drug Testing Proportion Years CALIFORNIAdead drivers CALIFORNIAall drivers

7 Sample Characteristics Drivers: 83.0% died; 77.3% were male; 67.1% were younger than 45 years of age; 76.6% tested negative for drugs; 62.8% tested negative for alcohol. Crashes: 49.4% involved a single vehicle; 81.5% involved a passenger car; 93.2% occurred during clear weather conditions; 57.1% occurred on urban roadways; All were nearly evenly distributed across all day hours and across all weekdays.

8 Logistic Regression Outcome Variable : Risky Driving Behavior Yes/No FARS Variable Related Factors-Driver Level 30 used (out of 99) actions recorded by investigating officer such as: Failure to Keep in Proper Lane or Running off the Road 37.7% of drivers. Making Improper Turn 28.5% of drivers. Driving Too Fast for Conditions or in Excess of Posted Speed Limit 27.7% of drivers.

9 Predictors of Interest: Logistic Regression (cont.) Drug Presence : - THC_Only - THC+OtherDrugs - OtherDrugs BAC Level : 8 categories Other Predictors: Age. Gender. Previous Crashes. Driver s Previous DUI. Time of Day. Urban/Rural Road. Intersection. Motor Vehicle Type. Number of Crash Vehicles. Driver Restraint System. Average CA Annual Gasoline Usage.

10 Logistic Regression (cont.) Research Question 1 Model Drug Presence effect and other predictors. Research Question 2 Model Drug Presence + BAC Level main and interaction effects and other predictors.

11 Descriptive Results Annual Fatal Crash Frequencies by Drug Presence Drug Negative THC-Only THC+Other Drugs Other Drugs Frequency Years

12 Are THC-positive drivers more likely to have engaged in risky behavior immediately before crashing? Drug Presence Odds Ratio CI Negative (reference) 1.00 NA THC_Only Other_Drugs THC+OtherDrugs χ 2 (29, 37,074) = 5,070.21, p < 0.001

13 How the combination of THC and alcohol relate to risky driving behavior odds? BAC Levels Drug Presence Odds Ratio CI Odds Ratio CI Odds Ratio CI Odds Ratio CI Negative (reference) 1.00 NA 1.00 NA 1.00 NA 1.00 NA THC-Only Other Drugs THC + Other Drugs χ2 (57, 37,074) = , p < 0.001

14 Does the combination of THC and alcohol increase risky driving behavior odds? (cont.) Drug Presence Negative (reference) BAC Levels & above Odds Ratio CI Odds Ratio CI Odds Ratio 1.00 NA 1.00 NA 1.00 NA CI THC-Only Other Drugs THC + Other Drugs χ2 (57, 37,074) = , p < 0.001

15 Conclusions THC-Only drivers were significantly more likely to have engaged in risky behavior than drug-free drivers 35% higher odds in the model without accounting for BAC Levels effect. THC-Only drivers were significantly more likely to have engaged in risky behavior than drug negative drivers in the BAC range of 0.001%-0.049% 67% higher odds. THC+OtherDrugs drivers showed the highest odds of risky behavior in all BAC Levels categories in comparison to all other drug conditions.

16 Recommendations Present FARS crash data collection and storage systems should be improved. Enhanced consequences of DUIs when there is indication of drug + alcohol mixing. A follow-up study to assess the relationship between specific risky behaviors and specific drugs.

17 Questions & Discussion Gloriam Vanine Guenzburger Tel: (916) Research & Development Branch