Drug-Impaired Driving Data Collection: Report to Congress
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Summary
This report, mandated by Section 25025 of the Infrastructure Investments and Jobs Act, addresses the critical need for improved data collection regarding drug-impaired driving (DUID) in the United States. Motivated by rising rates of illicit drug use, prescription medication misuse, and cannabis legalization, the report highlights that current data limitations prevent accurate assessment of the societal costs and prevalence of DUID. While alcohol-impaired driving costs are well-documented, DUID data remains fragmented and insufficient for comprehensive analysis. The report aims to identify barriers states face in submitting toxicology results to the Fatality Analysis Reporting System (FARS) and to outline federal actions to enhance testing and reporting standards. The document synthesizes existing literature, legislative requirements, and operational data from the National Highway Traffic Safety Administration (NHTSA). It examines the FARS data collection process, which relies on police crash reports, coroner records, and state toxicology laboratories. The analysis reviews the evolution of the FARS toxicology reporting framework, noting updates in 2022 and 2023 that expanded fields to capture drug test status, specimen types, testing methods, and drug quantities. The report also evaluates the "Recommendations for Toxicological Investigations of Drug-Impaired Driving and Motor Vehicle Fatalities – 2021 Update" issued by the National Safety Council, which seeks to standardize laboratory testing practices. Key findings reveal significant barriers to consistent DUID data submission across multiple stages of the process. At the traffic event level, state laws and local policies vary widely regarding who is tested and under what circumstances, often leading to inconsistent testing of surviving drivers versus fatally injured individuals. Sample collection faces challenges due to delays that affect the detection of rapidly metabolizing drugs like THC, and the use of urine matrices, which detect non-psychoactive metabolites rather than active impairment. Toxicology testing is hindered by variations in drug panels, cutoff levels, and instrumentation capabilities among laboratories, with many lacking advanced confirmation technologies. Furthermore, data reporting is compromised by inconsistent formats from source agencies and the frequent loss of specificity as results are transcribed through multiple entities before reaching FARS analysts. The report concludes with four primary recommendations to address these barriers: supporting statewide comprehensive data systems, encouraging standardized variables in laboratory information systems, improving national DUID data collection policies, and providing necessary funding for laboratory staffing, training, and technology. NHTSA is implementing several initiatives to assist states, including traffic safety grants, the Model Minimum Uniform Crash Criteria, and a Regional Toxicology Liaison Program. These efforts aim to align state practices with national standards, thereby improving the accuracy and utility of DUID data for traffic safety research and policy development.
Key finding
The report identifies significant barriers to submitting drug and alcohol toxicology results to FARS, including inconsistent testing practices, laboratory limitations, and data reporting challenges, and provides recommendations to improve data quality and collection.
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.
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- Empirical Findings: observational prevalence
- Methodological Resource: dataset resource