Greenfield T K, Rogers J D
Alcohol Research Group Public Health Institute, Berkeley, California, USA.
Addiction. 1999 Nov;94(11):1735-43. doi: 10.1046/j.1360-0443.1999.9411173510.x.
The present study examined effects of measurement on risk curve analysis in an application involving prediction of frequency and indicator measures of drunk driving with beverage-specific alcohol consumption and risk perception measures.
From a 1995 in-person survey of the US adult household population (response rate = 77%) the responses of 1260 adult drivers who reported any drinking in the prior year were selected for analysis. Regression and graphical techniques were used to investigate relationships between drinking pattern, beverage choice, perception of risks of drinking before driving, and frequency of drunk driving.
Self-reported drunk driving (occurrence) was measured by a question assessing driving after drinking enough to be in trouble if stopped by the police within the prior 12 months; those affirming this (n = 191) were asked how many times they did so (frequency). Alcohol consumption was assessed by beverage and in combination. Risk perception was assessed as a factor score from three correlated measures. Demographic variables included age, ethnicity, education and income.
Controlling for demographics, heavy beer consumption (p < 0.01) more than heavy wine (NS) or liquor/spirits (p < 0.05) intake was strongly predictive of risk perception. A regression analysis showed a significant interaction between heavy beer consumption and perceived risk (p < 0.001) in predicting reported frequency of drunk driving, after controlling for heavy beer consumption (p < 0.05), total alcohol consumption and risk perception (both ps < 0.001). No interactions were important in equivalent models predicting dichotomous occurrence. Graphic analysis showed the shape of the risk curve is altered when frequency of drunk driving is taken into account rather than simple occurrence.
Individuals' underestimation of beer's intoxicating effects, compared to other alcoholic beverage types, helps explain beer's over-representation in drinking driving violation reports. There is a need for creative public health campaigns designed to inform young men of beer's alcohol content and associated risks.
本研究在一项应用中考察了测量对风险曲线分析的影响，该应用涉及通过特定饮料的酒精消费量以及风险认知测量来预测酒后驾车的频率和指标测量。
从1995年对美国成年家庭人口的当面调查（回复率 = 77%）中，选取了1260名报告上一年有饮酒行为的成年司机的回复进行分析。采用回归和图形技术来研究饮酒模式、饮料选择、对酒后驾车风险的认知以及酒后驾车频率之间的关系。
自我报告的酒后驾车（发生情况）通过一个问题来测量，该问题评估在过去12个月内饮酒后如果被警察拦下是否会有麻烦；确认有这种情况的人（n = 191）被问及这样做的次数（频率）。酒精消费量通过饮料类型以及综合情况进行评估。风险认知通过三个相关测量的因子得分来评估。人口统计学变量包括年龄、种族、教育程度和收入。
在控制人口统计学因素后，大量饮用啤酒（p < 0.01）比大量饮用葡萄酒（无显著差异）或烈酒（p < 0.05）更能强烈预测风险认知。回归分析表明，在控制大量饮用啤酒（p < 0.05）、总酒精消费量和风险认知（均为p < 0.001）后，大量饮用啤酒与感知风险之间在预测报告的酒后驾车频率方面存在显著交互作用（p < 0.001）。在预测二分法发生情况的等效模型中，没有交互作用是重要的。图形分析表明，当考虑酒后驾车频率而非简单的发生情况时，风险曲线的形状会发生改变。
与其他酒精饮料类型相比，个体对啤酒致醉效果的低估有助于解释啤酒在酒后驾车违规报告中占比过高的现象。需要开展有创意的公共卫生运动，以告知年轻男性啤酒的酒精含量及相关风险。
