Dustin T Duncan1,2,3,4,5, Michael Rienti6,7, Martin Kulldorff8, Jared Aldstadt6, Marcia C Castro9,10, Rochelle Frounfelker11, James H Williams1, Glorian Sorensen12,13,14, Renee M Johnson14, David Hemenway15, David R Williams11,13,16. 1. a Department of Population Health , New York University School of Medicine , New York , NY , USA. 2. b College of Global Public Health , New York University , New York , NY , USA. 3. c Center for Drug Use and HIV Research , New York University College of Nursing , New York , NY , USA. 4. d Population Center , New York University College of Arts and Science , New York , NY , USA. 5. e Center for Data Science , New York University , New York , NY , USA. 6. f Department of Geography , University at Buffalo, State University of New York , Buffalo , NY , USA. 7. g Center for Health and Social Research , SUNY Buffalo State, Buffalo , NY , USA. 8. h Department of Medicine , Brigham and Women's Hospital and Harvard Medical School , Boston , MA , USA. 9. i Department of Global Health and Population , Harvard T.H. Chan School of Public Health , Boston , MA , USA. 10. j Harvard Center for Population and Development Studies , Harvard University , Cambridge , MA , USA. 11. k Department of Social and Behavioral Sciences , Harvard T.H. Chan School of Public Health , Boston , MA , USA. 12. l Center for Community-based Research , Dana-Farber Cancer Institute , Boston , MA , USA. 13. m Lung Cancer Disparities Center , Harvard T.H. Chan School of Public Health , Boston , MA USA. 14. n Department of Mental Health , Johns Hopkins Bloomberg School of Public Health , Baltimore , MD , USA. 15. o Department of Health Policy and Management , Harvard T.H. Chan School of Public Health , Boston , MA , USA. 16. p Departments of African and African American Studies, and Sociology , Harvard University , Cambridge , MA , USA.
Abstract
BACKGROUND: Understanding geographic variation in youth drug use is important for both identifying etiologic factors and planning prevention interventions. However, little research has examined spatial clustering of drug use among youths by using rigorous statistical methods. OBJECTIVES: The purpose of this study was to examine spatial clustering of youth use of tobacco, alcohol, and marijuana. METHODS: Responses on tobacco, alcohol, and marijuana use from 1,292 high school students ages 13-19 who provided complete residential addresses were drawn from the 2008 Boston Youth Survey Geospatial Dataset. Response options on past month use included "none," "1-2," "3-9," and "10 or more." The response rate for each substance was approximately 94%. Spatial clustering of youth drug use was assessed using the spatial Bernoulli model in the SatScan™ software package. RESULTS: Approximately 12%, 36%, and 18% of youth reported any past-month use of tobacco, alcohol, and/or marijuana, respectively. Two clusters of elevated past tobacco use among Boston youths were generated, one of which was statistically significant. This cluster, located in the South Boston neighborhood, had a relative risk of 5.37 with a p-value of 0.00014. There was no significant localized spatial clustering in youth past alcohol or marijuana use in either the unadjusted or adjusted models. CONCLUSION: Significant spatial clustering in youth tobacco use was found. Finding a significant cluster in the South Boston neighborhood provides reason for further investigation into neighborhood characteristics that may shape adolescents' substance use behaviors. This type of research can be used to evaluate the underlying reasons behind spatial clustering of youth substance and to target local drug abuse prevention interventions and use.
BACKGROUND: Understanding geographic variation in youth drug use is important for both identifying etiologic factors and planning prevention interventions. However, little research has examined spatial clustering of drug use among youths by using rigorous statistical methods. OBJECTIVES: The purpose of this study was to examine spatial clustering of youth use of tobacco, alcohol, and marijuana. METHODS: Responses on tobacco, alcohol, and marijuana use from 1,292 high school students ages 13-19 who provided complete residential addresses were drawn from the 2008 Boston Youth Survey Geospatial Dataset. Response options on past month use included "none," "1-2," "3-9," and "10 or more." The response rate for each substance was approximately 94%. Spatial clustering of youth drug use was assessed using the spatial Bernoulli model in the SatScan™ software package. RESULTS: Approximately 12%, 36%, and 18% of youth reported any past-month use of tobacco, alcohol, and/or marijuana, respectively. Two clusters of elevated past tobacco use among Boston youths were generated, one of which was statistically significant. This cluster, located in the South Boston neighborhood, had a relative risk of 5.37 with a p-value of 0.00014. There was no significant localized spatial clustering in youth past alcohol or marijuana use in either the unadjusted or adjusted models. CONCLUSION: Significant spatial clustering in youth tobacco use was found. Finding a significant cluster in the South Boston neighborhood provides reason for further investigation into neighborhood characteristics that may shape adolescents' substance use behaviors. This type of research can be used to evaluate the underlying reasons behind spatial clustering of youth substance and to target local drug abuse prevention interventions and use.
Authors: Kosuke Tamura; Dustin T Duncan; Jessica Athens; Marc Scott; Michael Rienti; Jared Aldstadt; Laurie M Brotman; Brian Elbel Journal: GeoJournal Date: 2017-07-27