This dataset supports the conclusions of the report "Drug and Alcohol Crash Risk: A Case-Control Study." The study used a “case-control” design to estimate the risk of crashes involving drivers using drugs, alcohol or both. Data was collected in Virginia Beach, Virginia, for 20 months. The study obtained biological measures on more than 3,000 crash drivers at the scenes of the crashes, and 6,000 control (comparison) drivers. Control drivers were recruited one week after the crashes at the same time, day of week, location, and direction of travel as the crash-involved drivers. Data included 10,221 breath samples, 9,285 oral fluid samples, and 1,764 blood samples. Oral fluid and blood samples were screened and confirmed for the presence of alcohol and drugs. The crash risk associated with alcohol and other drugs was estimated using odds ratios that indicate the probability of a crash occurring over the probability that such an event does not occur. If a variable (alcohol and/or drugs) is not associated with a crash, the odds ratio for that variable will be 1.00. A higher or lower number indicates a stronger relationship between the probability of a crash occurring and the presence of that variable (alcohol and/or drugs in the driver). Confidence intervals (CIs) of an odds ratio indicate the range in which the true value lies—with 95 percent confidence. Alcohol: Alcohol was the largest contributor to crash risk. The unadjusted crash risk estimates for alcohol indicated drivers with a breath alcohol concentrations (BrACs) of .05 grams per 210 liters (g/210L) are 2.05 times more likely to crash than drivers with no alcohol. For drivers with BrACs of .08 g/210L, the unadjusted relative risk of crashing is 3.98 times that of drivers with no alcohol. When adjusted for age and gender, drivers with BrACs of .05 g/210L are 2.07 times more likely to crash than drivers with no alcohol. The adjusted crash risk for drivers at .08 g/210L is 3.93 times that of drivers with no alcohol. Drugs: Unadjusted drug odds ratio estimates indicated a significant increase in crash risk. For the active ingredient in marijuana, delta-9-tetrahydrocannabinol (THC), this yielded an unadjusted odds ratio of 1.25. However, after adjusting for gender, age, race/ethnicity, and alcohol, there was no indication that any drug significantly contributed to crash risk. The adjusted odds ratios for THC were 1.00, 95 percent CI [.83, 1.22], indicating no increased or decreased crash risk. Odds ratios for antidepressants were .86, 95 percent CI [.56, 1.33]; narcotic analgesics were 1.17, 95% percent drugs as an overall category were .99, 95 percent CI [.84, 1.18], and prescription and over-the-counter medications were 1.02, 95 percent CI [.83, 1.26]. Alcohol and Drugs: Analyses found no statistically significant interaction effects when drivers were positive for both alcohol and drugs. Although initial analyses suggested that the combination of alcohol and other drugs were contributors to increased crash risk, additional analyses adjusting for other risk factors indicated no significant effect. When both alcohol and other drugs were consumed, alcohol alone was associated with crash risk.

The data supports the outputs Drug and Alcohol Crash Risk: A Case-Control Study https://doi.org/10.21949/1525791; and, Drug and Alcohol Crash Risk [Traffic Safety Facts]: Research Note https://doi.org/10.21949/1525811.

NTL staff has reviewed the data. While researchers gathered names, ages, and other demographic information from participants, those sensitive data have either been excluded from the final dataset, anonymized, or pose only a small chance of re-identification. Further, while data collection took place over 20 months, the "Year" that any crash occurred is not revealed, further reducing re-identification risk. NTL staff feel the risk of re-identification of study participants from this dataset is low, but not zero.

The .ZIP folder of datasets and supporting documentation is 37MB in size. The ZIP contains files in the following formats: .CSV files which can be opened with any text editor; .TXT files which can be opened with any text editor; .PDF files that can be opened with any PDF reader; .DOCX files that can be opened in Microsoft Word and some web-based programs; .SAV files which can be opened with IBM SPSS statistical software; .SAS and .sas7bdat files which can be opened with SAS statistical software; .XLSX files which can be opened with Microsoft Excel and other spreadsheet programs; and, .JSON files which can be opened with text editors or metadata editing programs.