Historique
Article de Périodique
Self-report survey measures of alcohol-impaired driving: A systematic review (2023)
Auteur(s) :
BUSHOVER, B. R. ;
MEHRANBOD, C. A. ;
GOBAUD, A. N. ;
BRANAS, C. C. ;
CHEN, Q. ;
GIOVENCO, D. P. ;
HUMPHREYS, D. K. ;
MORRISON, C. N.
Année
2023
Page(s) :
781-790
Sous-type de document :
Revue de la littérature / Literature review
Langue(s) :
Anglais
Domaine :
Alcool / Alcohol
Discipline :
EPI (Epidémiologie / Epidemiology)
Thésaurus mots-clés
ALCOOL
;
CONDUITE DE VEHICULE
;
AUTOEVALUATION
;
METHODE
;
FIABILITE
;
VALIDITE
;
COMPARAISON
;
INDICATEUR
Résumé :
OBJECTIVE: Alcohol-impaired driving is a major contributor to motor vehicle crash deaths and injury. Many survey studies include self-report measures of alcohol-impaired driving, but no guidance is available to help researchers select from among available measures. The aims of this systematic review were to compile a list of measures that researchers have used previously, to compare performance between measures, and to identify the measures with highest validity and reliability.
METHOD: Literature searches of PubMed, Scopus, and Web of Science identified studies that assessed alcohol-impaired driving behavior through self-report. The measures from each study and, if available, indices of reliability or validity were extracted. Using the measures' text, we developed 10 codes to group similar measures and compare them. For example, the "alcohol effects" code refers to driving while feeling dizzy or lightheaded after drinking, and the "drink count" code pertains to the number of drinks someone consumed before driving. For measures with multiple items, each item was categorized separately.
RESULTS: After screening according to the eligibility criteria, 41 articles were included in the review. Thirteen articles reported on reliability. No articles reported on validity. The self-report measures with the highest reliability coefficients contained items from multiple codes, namely alcohol effects and drink count.
CONCLUSIONS: Self-report alcohol-impaired driving measures with multiple items evaluating distinct aspects of alcohol-impaired driving show better reliability than measures using a single item. Future work investigating the validity of these measures is needed to determine the best approach for conducting self-report research in this area.
METHOD: Literature searches of PubMed, Scopus, and Web of Science identified studies that assessed alcohol-impaired driving behavior through self-report. The measures from each study and, if available, indices of reliability or validity were extracted. Using the measures' text, we developed 10 codes to group similar measures and compare them. For example, the "alcohol effects" code refers to driving while feeling dizzy or lightheaded after drinking, and the "drink count" code pertains to the number of drinks someone consumed before driving. For measures with multiple items, each item was categorized separately.
RESULTS: After screening according to the eligibility criteria, 41 articles were included in the review. Thirteen articles reported on reliability. No articles reported on validity. The self-report measures with the highest reliability coefficients contained items from multiple codes, namely alcohol effects and drink count.
CONCLUSIONS: Self-report alcohol-impaired driving measures with multiple items evaluating distinct aspects of alcohol-impaired driving show better reliability than measures using a single item. Future work investigating the validity of these measures is needed to determine the best approach for conducting self-report research in this area.
Affiliation :
Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA
Department of Sociomedical Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA
Department of Social Policy and Intervention, University of Oxford, Oxford, UK
Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Australia
Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA
Department of Sociomedical Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA
Department of Social Policy and Intervention, University of Oxford, Oxford, UK
Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Australia
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