Citation

Rogeberg O. Accid. Anal. Prev. 2019; 123: 69-78.

Affiliation

Frisch Centre, Norway. Electronic address: ole.rogeberg@frisch.uio.no.

Copyright

(Copyright © 2019, Elsevier Publishing)

DOI

10.1016/j.aap.2018.11.011

PMID

30468948

Abstract

BACKGROUND: Culpability studies, a common study design in the cannabis crash risk literature, typically report odds-ratios (OR) indicating the raised risks of a culpable accident. This parameter is of unclear policy relevance, and is frequently misinterpreted as an estimate of the increased crash risk, a practice that introduces a substantial "interpretational bias".

METHODS: A Bayesian statistical model for culpability study counts is developed to provide inference for both culpable and total crash risks, with a hierarchical effect specification to allow for meta-analysis across studies with potentially heterogeneous risk parameter values. The model is assessed in a bootstrap study and applied to data from 13 published culpability studies.

RESULTS: The model outperforms the culpability OR in bootstrap analyses. Used on actual study data, the average increase in crash risk is estimated at 1.28 (1.16-1.40). The pooled increased risk of a culpable crash is estimated as 1.42 (95% credibility interval 1.11-1.75), which is similar to pooled estimates using traditional ORs (1.46, 95% CI: 1.24-1.72). The attributable risk fraction of cannabis impaired driving is estimated to lie below 2% for all but two of the included studies.

CONCLUSIONS: Culpability ORs exaggerate risk increases and parameter uncertainty when misinterpreted as total crash ORs. The increased crash risk associated with THC-positive drivers in culpability studies is low.

Copyright © 2018 The Author. Published by Elsevier Ltd.. All rights reserved.

Keywords: Cannabis impaired driving

Language: en

Keywords

Bayesian inference; Cannabis; Crash risks; Culpability study; Meta-analysis