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Multiple imputation of missing marijuana data in the Fatality Analysis Reporting System using a Bayesian multilevel

Qixuan Chen1, Sharifa Z Williams1, Yutao Liu1

  • 1Department of Biostatistics, Columbia University Mailman School of Public Health, New York, NY 10032, USA.

Accident; Analysis and Prevention
|September 4, 2018
PubMed
Summary
This summary is machine-generated.

A new method estimates marijuana use in drivers involved in fatal crashes. This approach addresses missing data in the Fatality Analysis Reporting System (FARS), improving drugged driving monitoring.

Keywords:
Bayesian multilevel modelsDrugged drivingFatality Analysis Reporting SystemMarijuana useMissing dataMultiple imputation

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Area of Science:

  • Traffic Safety Research
  • Data Science
  • Epidemiology

Background:

  • The Fatality Analysis Reporting System (FARS) is crucial for studying marijuana's role in motor vehicle crashes.
  • Limited marijuana testing data (34% of drivers) in FARS hinders comprehensive analysis.
  • This data gap poses a significant barrier to understanding drugged driving.

Purpose of the Study:

  • To develop and validate a statistical procedure for estimating marijuana positivity among drivers with missing test results in FARS.
  • To address the major barrier of incomplete marijuana testing data in FARS.
  • To enhance the FARS's capacity for monitoring drugged driving.

Main Methods:

  • A multiple imputation (MI) procedure was developed using a Bayesian multilevel model.
  • The model incorporated nonlinear associations with blood alcohol concentrations (BACs).
  • Markov chain Monte Carlo simulations generated 10 imputations for missing data.

Main Results:

  • Older age, female gender, seatbelt use, and valid license were associated with lower marijuana positivity.
  • A reverse U-shaped association was observed between BACs and marijuana positivity.
  • The MI procedure estimated a national marijuana positivity rate of 11.7%, lower than the observed 14.8% in 2013.

Conclusions:

  • The developed MI procedure is a valid method for handling missing marijuana data in FARS.
  • This approach can strengthen FARS for monitoring drugged driving.
  • The findings aid in understanding marijuana's role in fatal motor vehicle crashes.