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Substance Use Among American Indian Youth in an Eastern City

NCJ Number
218551
Journal
Journal of Ethnicity in Substance Abuse Volume: 6 Issue: 1 Dated: 2007 Pages: 15-29
Author(s)
Kenneth J. Steinman Ph.D.; Yini Hu MPH
Date Published
2007
Length
15 pages
Annotation
This study examined substance use among American Indian (AI) youth in Columbus, Ohio.
Abstract
Results indicated that AI youth were more likely than their White peers to regularly use most substances included in the survey. This finding held true when gender, grade, and family structure were controlled. Survey results revealed that approximately 8 percent of AI participants reported lifetime substance use. The findings are interesting because given their distance from reservations in the Western United States, the AI population in Ohio may be considered more acculturated. Yet, rates of AI substance use among adolescents closely resembled rates found among urban AI in Western States. The authors suggest that substance use among AI youth may have less to do with culture and more to do with patterns of poverty, dislocation, and familial patterns of substance abuse that have persisted for generations. Strategies for substance use intervention with AI youth should be culturally appropriate and should also take into account the high prevalence of smokeless tobacco use noted among AI girls. Participants were 596 self-identified AI youth in grades 6 through 12 who completed surveys regarding their use of alcohol, cigarettes, smokeless tobacco, marijuana, and inhalants. The survey, known as the Primary Prevention Awareness Attitude and Use Survey, was developed to assess adolescent risk behaviors in an effort to inform public health policy. The survey was administered by trained teachers and school staff in 16 public school districts and 35 nonpublic schools in Columbus, Ohio during the fall of 2003. Data were examined using statistical measures including a series of multinomial logistic regression models. Future studies should attempt to replicate these findings using large, local datasets. Tables, note, references

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