# Robust estimates of the prevalence of drinking-and-driving using secondary data

> **NIH NIH R21** · OHIO STATE UNIVERSITY · 2022 · $193,059

## Abstract

Project Summary
Researchers and policymakers require reliable estimates of both the prevalence and the relative risk of
drinking-and-driving. The optimal allocation of public health and traffic safety resources, the efficient level of
enforcement, and the appropriate penalty to criminal offenses depend critically on whether: a) relatively few
drivers expose other road-users to extreme risk, or b) relatively many drivers expose other road-users to
moderate risk. Existing surveys and statistical methods each impose questionable behavioral assumptions and
produce an uninformatively wide range of values. To overcome these challenges, we propose a bias-corrected
version of the LPDT method developed by Levitt and Porter (2001) and Dunn and Tefft (2020) (hereafter, bc-
LPDT) that relaxes the assumption that the probability of one driver-type interacting with another driver-type
depends only on the share of each driver-type on the road. This project builds vital research infrastructure by:
1) augmenting the maximum likelihood function to account for the spatiotemporal sorting of drivers; 2)
estimating driver interaction probabilities; and 3) generating unbiased estimates of the prevalence and crash
risk associated with drinking-and-driving using existing administrative data. We will apply this method to two
data sources: the Fatality Analysis Reporting System (FARS) (Aim 1) and Crash Report Sampling System
(CRSS) (Aim 2). FARS is a census of fatal crashes in the US and will allow us to recover the relative risk of
alcohol-involved (BAC>0) and alcohol-impaired (BAC≥0.08) drivers causing a fatal crash. CRSS is a nationally
representative sample of all motor vehicle crashes, allowing us to recover the relative risk of causing any
crash. The accompanying estimates of prevalence will make an immediate contribution by resolving the
outstanding question of whether prevalence has fallen steadily over the past four decades (consistent with
NRS estimates) or stagnated since the late 1990s (consistent with previous LPDT results and crash statistics).
The former would imply the existing policy portfolio continues to show important returns; the latter that existing
policy has exhausted its returns and innovation is necessary to reduce the public health cost of drinking and
driving. The benefits of demonstrating the feasibility and robustness of this approach, including reconciling
estimates across method and dataset (Aim 3) are manifest. First, the bc-LPDT method can be applied to crash
data that are published annually, greatly increasing the frequency and timeliness of prevalence and crash risk
estimates than is possible with survey instruments. Second, the marginal cost of generating new estimates of
prevalence and relative risk from these data is nearly zero. Third, the bc-LPDT method is exceptionally flexible:
the approach can be applied to smaller geographies (state-level) and subpopulations (age, gender, race,
ethnicity) or characteristics of drivers, vehicles,...

## Key facts

- **NIH application ID:** 10830694
- **Project number:** 7R21AA030105-02
- **Recipient organization:** OHIO STATE UNIVERSITY
- **Principal Investigator:** Lauren Jones
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $193,059
- **Award type:** 7
- **Project period:** 2022-09-13 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10830694

## Citation

> US National Institutes of Health, RePORTER application 10830694, Robust estimates of the prevalence of drinking-and-driving using secondary data (7R21AA030105-02). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10830694. Licensed CC0.

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