Development and validation of a model to predict blood alcohol concentrations: Updating the NHTSA equation

Yiqi Zhang, Changxu Wu, Jingyan Wan

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Objects To date, multiple models have been developed to estimate blood or breath alcohol concentration (BAC/BrAC). Several factors have been identified that affect the discrepancy between BACs/BrACs and retrospective estimation (eBAC) with existing equations. To the best of our knowledge, a model to quantify the effects of factors on the discrepancy between BAC/BrAC and eBAC is still nonexistent. The goal of this work was to develop a model to provide a more accurate retrospective estimation of breath alcohol concentration (eBAC). Method A laboratory study with alcohol consumption and a driving task was conducted with 30 participants (17 male and 13 female) to explore the factors that may contribute to the discrepancy between BrAC and eBAC obtained with existing models. A new eBAC model was developed to improve the estimation of BrAC by modeling effects of gender, weight, and the delay of BrAC measurement on the discrepancy. The validity of the model was tested and established with the data from the experiment conducted in this study and two published research studies, and compared with existing eBAC models. Results Results of the model validity examination indicated that the developed model had higher R squares and lower root-mean-squared errors (RMSE) in estimating BrAC in three experiments compared with the existing eBAC models, including the NHTSA equation, the Matthew equation, the Lewis equation, the Watson equation, and the Forrest equation. Conclusion The developed eBAC model had a better performance of BrAC estimation compared with existing eBAC models. The validation of the model with the data from three empirical studies indicated a high level of generalizability in estimating BrAC.

Original languageEnglish (US)
Pages (from-to)46-53
Number of pages8
JournalAddictive Behaviors
Volume71
DOIs
StatePublished - Aug 1 2017

All Science Journal Classification (ASJC) codes

  • Medicine (miscellaneous)
  • Clinical Psychology
  • Toxicology
  • Psychiatry and Mental health

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