Paul J Gruenewald1, Meme Wang-Schweig1, Christina Mair2. 1. Prevention Research Center, Oakland, California. 2. Department of Behavioral and Community Health Sciences, University of Pittsburgh Graduate School of Public Health, Pittsburgh, Pennsylvania.
Abstract
OBJECTIVE: Many different measures of alcohol use are applied in survey-based epidemiological studies of alcohol-related risks. Differences in the selection of drinking measures and alternative specifications of quantitative relationships of these measures to problem outcomes limit researchers' abilities to compare and assess alcohol effects across studies. We used a quantitative definition of drinking patterns to identify relationships among drinking measures and uncover sources of bias in assessments of drinking risks. METHOD: A census of drinking measures from studies published in four leading journals in the first half of 2013 were mapped onto a "drinking patterns table," quantitatively relating each measure to every other. Relationships among these measures and in relation to two problem outcomes, physiological problems and sexual risks, were examined using data from 41,352 undergraduate college student drinkers in California. RESULTS: Twenty-nine sets of drinking measures appeared across 74 published studies; no common statistical procedure was used to assess drinking risks. Empirically observed distributions of heavy drinking (R(2) = .887, p < .001) and variances in drinking quantities (R(2) = .645, p < .001) were predicted from the drinking patterns table. Heteroscedasticity in drinking measures also biased estimates of physiological risks related to drinking quantities (z = -5.159, p < .001), volume (z = 4.592, p < .001), and heavy drinking (z = -5.431, p < .001). CONCLUSIONS: Relationships between drinking measures can be formally identified and related to one another using drinking patterns tables. Biases related to selections of different drinking measures and unobserved heteroscedasticity can be identified and controlled through formal quantitative assessments of relationships between drinking measures and observed outcomes.
OBJECTIVE: Many different measures of alcohol use are applied in survey-based epidemiological studies of alcohol-related risks. Differences in the selection of drinking measures and alternative specifications of quantitative relationships of these measures to problem outcomes limit researchers' abilities to compare and assess alcohol effects across studies. We used a quantitative definition of drinking patterns to identify relationships among drinking measures and uncover sources of bias in assessments of drinking risks. METHOD: A census of drinking measures from studies published in four leading journals in the first half of 2013 were mapped onto a "drinking patterns table," quantitatively relating each measure to every other. Relationships among these measures and in relation to two problem outcomes, physiological problems and sexual risks, were examined using data from 41,352 undergraduate college student drinkers in California. RESULTS: Twenty-nine sets of drinking measures appeared across 74 published studies; no common statistical procedure was used to assess drinking risks. Empirically observed distributions of heavy drinking (R(2) = .887, p < .001) and variances in drinking quantities (R(2) = .645, p < .001) were predicted from the drinking patterns table. Heteroscedasticity in drinking measures also biased estimates of physiological risks related to drinking quantities (z = -5.159, p < .001), volume (z = 4.592, p < .001), and heavy drinking (z = -5.431, p < .001). CONCLUSIONS: Relationships between drinking measures can be formally identified and related to one another using drinking patterns tables. Biases related to selections of different drinking measures and unobserved heteroscedasticity can be identified and controlled through formal quantitative assessments of relationships between drinking measures and observed outcomes.
Authors: Charles J Holahan; Penny L Brennan; Kathleen K Schutte; Carole K Holahan; J Gregory Hixon; Rudolf H Moos Journal: J Stud Alcohol Drugs Date: 2017-05 Impact factor: 2.582
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