American Indian (AI) adolescents experience disproportionate alcohol-related consequences. The present study evaluated the psychometric properties and application of the American Drug and Alcohol Survey (ADAS™) alcohol-related consequence scale for AI adolescents through a secondary analysis of a large population-based sample of adolescents living on or near AI reservations. We found support for the ADAS alcohol-related consequence scale as a one-factor model, invariant discretely across race, sex assigned at birth, and age, and with good internal consistency. Evidence for construct validity was found through significant positive correlations between frequency of past 12 months of drinking, frequency of past 12 months of intoxication, and lifetime alcohol-related consequences. AI adolescents were significantly more likely to report more alcohol-related consequences than their non-Hispanic White peers. Race significantly interacted with frequency of drinking in predicting alcohol-related consequences such that these associations were stronger for AI adolescents. However, race did not significantly interact with frequency of intoxication in predicting alcohol-related consequences. Results from this study demonstrate the utility of the ADAS alcohol-related consequence scale for use across demographic groups with little risk of measurement bias.

美洲印第安人 (AI) 青少年经历了与酒精相关的不成比例的后果。本研究通过对生活在人工智能保护区或附近的大量青少年样本进行二次分析，评估了美国毒品和酒精调查 (ADAS™) 酒精相关后果量表对人工智能青少年的心理测量特性和应用。我们发现 ADAS 酒精相关后果量表作为一种单因素模型得到了支持，该模型在不同种族、出生时的性别和年龄之间离散不变，并且具有良好的内部一致性。通过过去 12 个月的饮酒频率、过去 12 个月的中毒频率以及终生酒精相关后果之间的显着正相关，发现了结构有效性的证据。与非西班牙裔白人同龄人相比，人工智能青少年更有可能报告更多与酒精相关的后果。在预测酒精相关后果方面，种族与饮酒频率存在显着的相互作用，因此这些关联对于人工智能青少年来说更为强烈。然而，在预测酒精相关后果时，种族与醉酒频率并没有显着的相互作用。这项研究的结果证明了 ADAS 酒精相关后果量表适用于跨人口群体，测量偏差风险很小。