title: Prediction of alcohol-related casualties among emergency room admissions.
abstract: Logistic regression models are used to determine the predictive value on injury status of drinking-in-the-event variables, quantity and frequency of alcohol consumption, alcohol-related problems, and demographic characteristics among 1,896 emergency room patients interviewed and breathalyzed at San Francisco General Hospital. Being young, White compared to Black, a positive admission breathalyzer reading, drinking prior to the event, and binge drinking were predictive of admission to the emergency room for an injury compared to a non-injury problem. A positive breathalyzer reading, more frequent usual drinking, blackouts, and a previous accident while drinking were predictive of drinking prior to the presenting injury event.
authors.affiliation: Medical Research Institute of San Francisco, Institute of Epidemiology and Behavioral Medicine, Berkeley, California 94709.
authors.forename: C J
authors.initials: CJ
authors.lastname: Cherpitel
mesh_terms: D015998:Abbreviated Injury Scale; D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D000328:Adult; D000437:Alcoholism / Q000150:complications / Q000453:epidemiology* / Q000534:rehabilitation; D001944:Breath Tests; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D000431:Ethanol / Q000493:pharmacokinetics; D006801:Humans; D012307:Risk Factors; D012495:San Francisco / Q000453:epidemiology; D014947:Wounds and Injuries / Q000453:epidemiology*
chemical_list: D000431:Ethanol