The Incidence of Driving under the Influence of Drugs 1985: An Update of the State of Knowledge
archive: archived pipeline: cataloged verified
Get this paper ↗ (full text — opens at the source; we link to it, we don't host it)
Summary
This 1985 NHTSA technical report, authored by Richard P. Compton and Theodore E. Anderson, updates the state of knowledge regarding the incidence of driving under the influence of drugs (DUID). The study was motivated by growing concern that drugs other than alcohol contribute significantly to highway accidents, despite a lack of empirical evidence defining the nature and magnitude of this problem. The authors review studies published since a 1980 report, categorizing them into three groups: fatally injured drivers, injured drivers, and non-accident-involved drivers detained by police. The primary objective is to determine if available data allows for definitive conclusions about which drugs pose significant safety hazards. The review analyzes field studies that tested blood samples from drivers involved in accidents or detained by police. The authors note that laboratory studies, while showing impairment from drugs like marijuana, tranquilizers, and cocaine, cannot directly estimate accident risk or real-world frequency. Consequently, the report relies on incidence data from accident investigations. The most frequently detected drugs in these studies were marijuana, diazepam (Valium), cocaine, barbiturates, methaqualone, and PCP. Incidence rates for drugs other than alcohol ranged from 10% to 15% in fatally injured drivers, 22% in injured drivers, and 14% to 50% in drivers arrested for impaired driving. However, the authors emphasize that these figures are not statistically valid estimates of the general driving population due to small, non-representative samples and limited drug screening protocols. A critical finding across the reviewed studies is the high prevalence of combined use. The majority of drivers testing positive for drugs also had high blood alcohol concentrations (BACs), often exceeding 0.10%. For example, 54% to 80% of fatally injured drivers with drugs in their system had also consumed alcohol. This co-occurrence complicates the determination of whether drugs independently increase accident risk or merely enhance alcohol’s impairing effects. Furthermore, the report highlights a significant methodological gap: most studies failed to collect exposure data from non-accident-involved drivers. Without a control group to establish baseline usage rates, it is impossible to determine if drug use is overrepresented in accidents, which is necessary to prove causation. The authors conclude that current data is insufficient to precisely estimate the extent of drug use by drivers or to identify specific drugs that increase accident risk. While certain drugs like marijuana and tranquilizers are detected with some frequency, the lack of representative sampling and control data prevents meaningful interpretation of incidence rates. The report asserts that alcohol remains the only substance for which there is sufficient evidence to define its role as a major highway safety problem. To address the DUID issue, the authors call for rigorous field research that includes unbiased sampling of both accident-involved and non-accident-involved drivers to establish causal links between specific drugs and crash risk.
Key finding
Drugs are detected in 10% to 22% of accident-involved drivers, with alcohol co-occurring in 53% to 77% of these cases, but insufficient non-accident control data prevents determining which specific drugs increase accident risk.
Methodology
review
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | rosap | — | — | 2 | 2026-05-23 |
| archive | success | — | — | — | 1 | 2026-05-23 |
| extract | success | cached | — | — | 2 | 2026-06-10 |
| clean | success | — | — | — | 1 | 2026-06-01 |
| chunk | success | — | — | — | 1 | 2026-06-01 |
| embed | success | — | — | — | 1 | 2026-06-02 |
| enrich | success | — | — | — | 1 | 2026-05-23 |
| promote | success | — | — | — | 1 | 2026-05-23 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 3 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 19 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
Information type
What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).
- Empirical Findings: observational prevalence