Moving from ethnography to epidemiology: Lessons learned in Appalachia

• Authors: Ryan A. Brown, Northwestern University; Jennifer Kuzara, Emory University; William E. Copeland, Duke University; E. Jane Costello, Duke University; Adrian Angold, Duke University; Carol Worthman, Emory University
• Journal Title: Annals of Human Biology
• Volume: Volume 36, Number 3
• Publisher: Taylor & Francis | 2009-01, Pages 248-260
• Type of Work: Article | Post-print: After Peer Review
• Publisher DOI: http://doi.org/10.1080/03014460902832942
• Subjects: Anthropology, Medical and Forensic; Health Sciences, Epidemiology; Health Sciences, Mental Health
• Keywords: Appalachia; depression; population health; life course; American Indian

Abstract

Background: Anthropologists are beginning to translate insights from ethnography into tools for population studies that assess the role of culture in human behavior, biology, and health. Aim: We describe several lessons learned in the creation and administration of an ethnographically-based instrument to assess the life course perspectives of Appalachian youth, the Life Trajectory Interview for Youth (LTI-Y). Then, we explore the utility of the LTI-Y in predicting depressive affect, controlling for prior depressed mood and severe negative life events throughout the life course. Subjects and methods: In a sample of 319 youth (190 White, 129 Cherokee), we tested the association between depressive affect and two domains of the LTI-Y - life course barriers and milestones. Longitudinal data on previous depressed mood and negative life events were included in the model. Results: The ethnographically-based scales of life course barriers and milestones were associated with unique variance in depressed mood, together accounting for 11% of the variance in this outcome. Conclusion: When creating ethnographically-based instruments, it is important to strike a balance between detailed, participant-driven procedures and the analytic needs of hypothesis testing. Ethnographically-based instruments have utility for predicting health outcomes in longitudinal studies.