Data Necessary to Develop a Sentinel Surveillance System for Drug Use by Drivers in Crashes: A Review of the Existing Landscape
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Summary
This research brief addresses the critical lack of reliable data regarding non-alcohol drug impairment in motor vehicle crashes. While alcohol-impaired driving is well-documented, the role of other drugs remains poorly understood due to significant barriers in data collection, including the high cost and complexity of toxicology testing and inconsistent reporting protocols. The study aims to identify existing data sources, establish mechanisms for their use, and assess the feasibility of developing a sentinel surveillance system to monitor trends in drug-involved driving. Such a system would leverage existing networks to provide prompt, high-quality reporting with limited resources, similar to public health surveillance models used for HIV/AIDS. The researchers employed a systematic three-stage approach. Stage one involved a literature review and interviews with state officials and database administrators to map available data sources. Stage two evaluated these sources against eleven optimal standards, such as consistent drug-testing protocols, linkable data, minimal missing data, and cost-effectiveness. Stage three determined the most accessible approach for implementation. The study assessed two primary categories of data sources: transportation-related databases (e.g., FARS, CODES, DRE databases) and trauma-related data sources (e.g., trauma centers, NTDB, NEMSIS). The findings revealed that no single existing source met all optimal standards. Transportation-related databases, while widely used, suffered from severe limitations in toxicology data quality and inconsistent testing protocols. For instance, FARS data is cautioned against for drugged-driving research due to these limitations. In contrast, trauma-related data sources, particularly trauma centers, emerged as the most viable option for a sentinel surveillance system. Trauma centers offer comprehensive patient records, inherent research missions, and the ability to treat patients regardless of injury severity, reducing selection bias. Although challenges exist—such as drug metabolism during transport and HIPAA privacy restrictions—the study concluded that trauma center data requires fewer procedural alterations to access than other sources. Existing surveillance programs like MNDOSA demonstrate that HIPAA waivers for public health research are feasible. The significance of this work lies in its recommendation to establish a new sentinel surveillance system based on trauma centers to fill the current public safety gap. The authors argue that accurate, representative data are essential for understanding the impact of the opioid epidemic and shifting cannabis laws, and for developing effective countermeasures. An effective system would require a representative sample of drivers, comprehensive drug panels with confirmation testing, near real-time prevalence estimates, and consistent toxicology protocols. The brief concludes that a pilot program involving a national agreement with trauma centers is a feasible, sustainable, and cost-effective next step to generate the reliable data needed to address drugged driving.
Key finding
No existing transportation or trauma database fully satisfies optimal standards for nationwide sentinel surveillance of drug use by crash-involved drivers; trauma centers offer the most feasible pilot platform but require HIPAA waivers and standardized toxicology protocols across participating hospitals.
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_aaa_foundation on 2026-05-23 (5 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | aaa_foundation | — | — | 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 | 2 | 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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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: crash risk outcomes, observational prevalence
- Methodological Resource: dataset resource