Association Between Childhood Residential Mobility and Non-medical Use of Prescription Drugs Among American Youth
Prescription drug abuse is a public health epidemic, resulting in 15,000 deaths annually. Disruption of childhood residence has been shown to increase drug-seeking behavior among adolescents; however, little research has explored its association specifically with non-medical use of prescription drugs (NMUPD). The objective of the study was to measure the association between residential mobility and NMUPD.
The 2010 National Survey on Drug Use and Health data were analyzed for 15,745 participants aged 12–17 years. NMUPD was defined as self-report of any non-medical use (i.e., taking a prescription drug that was not prescribed to them or consumption for recreational purposes) of tranquilizers, pain relievers, sedatives, or stimulants. Logistic regression for survey data was used to estimate the association between residential mobility and NMUPD, adjusting for potential confounders.
After controlling for demographic, intrapersonal, interpersonal, and community factors, adolescents with low mobility (1–2 moves in the past 5 years) and residential instability (≥3 moves) were 16 % (OR 1.16, 95 % CI 1.01, 1.33) and 25 % (OR 1.25, 95 % CI 1.00, 1.56) more likely to report NMUPD compared to non-mobile adolescents (0 moves). Low-mobile adolescents were 18 % (OR 1.18, 95 % CI 1.01, 1.38) more likely to abuse pain relievers, specifically. No relationship was found between moving and tranquilizer, stimulant, or sedative use.
Increasing childhood residential mobility is associated with NMUPD; therefore, efforts to prevent NMUPD should target mobile adolescents. Further examination of the psychological effects of moving and its association with pain reliever abuse is indicated.
KeywordsResidential mobility Adolescents Health behavior Prescription drug misuse Opioids
Kelly Gurka was partially supported by the Centers for Disease Control and Prevention, National Center for Injury Prevention and Control, Grant R49CE002109. The content is solely the responsibility of the authors and does not necessarily represent the official views of the CDC.
Conflict of interest
The authors declare no conflict of interest.
- 1.Brown, D., Benzeval, M., Gayle, V., Macintyre, S., O’Reilly, D., & Leyland, A. H. (2012). Childhood residential mobility and health in late adolescence and adulthood: Findings from the West of Scotland Twenty-07 Study. Journal of Epidemiology and Community Health, 66(10), 942–950.CrossRefPubMedPubMedCentralGoogle Scholar
- 3.Buu, A., diPaazza, C., Jing, W., Puttler, L. I., Fitzgerald, H. E., & Zucker, R. A. (2009). Parent, family, and neighborhood effects on the development of child substance use and other psychopathology from preschool to the start of adulthood. Journal of Studies on Alcohol and Drugs, 70(4), 489–498.CrossRefPubMedPubMedCentralGoogle Scholar
- 4.Centers for Disease Control and Prevention. (2012). Policy impact: Prescription painkiller overdoses. Retrived from http://www.cdc.gov/homeandrecreationalsafety/rxbrief/
- 8.Dong, M., Anda, R. F., Felitti, V. J., Williamson, D. F., Dube, S. R., Brown, D. W., & Giles, W. H. (2005). Childhood residential mobility and multiple health risks during adolescence and adulthood: The hidden role of adverse childhood experiences. Archives of Pediatrics and Adolescent Medicine, 159(12), 1104–1110. doi: 10.1001/archpedi.159.12.1104.CrossRefPubMedGoogle Scholar
- 10.Ersing, R., Sutphen, R., & Loeffler, D. (2009). Exploring the impact and implications of residential mobility: From the neighborhood to the school. Advances in Social Work, 10(1), 1–18.Google Scholar
- 13.Forum on Child and Family Statistics. (2013). Pop1 child population: Number of children (in millions) ages 0–17 in the United States by age, 1950–2013 and projected 2014–2050. ChildStats. Retrived from http://www.childstats.gov/americaschildren/tables/pop1.asp
- 19.Lin, K.-C., Twisk, J. W. R., & Huang, H.-C. (2012). Longitudinal impact of frequent geographic relocation from adolescence to adulthood on psychosocial stress and vital exhaustion at ages 32 and 42 years: The Amsterdam Growth and Health Longitudinal Study. Journal of Epidemiology, 22(5), 469–476.CrossRefPubMedPubMedCentralGoogle Scholar
- 20.Lin, K. C., Twisk, J. W., & Rong, J. R. (2011). Longitudinal interrelationships between frequent geographic relocation and personality development: Results from the Amsterdam Growth and Health Longitudinal Study. American Journal of Orthopsychiatry, 81(2), 285–292. doi: 10.1111/j.1939-0025.2011.01097.x.CrossRefPubMedGoogle Scholar
- 22.Morton, K. B., Aldworth, J., Chromy, J. R., Foster, M. S., Hirsch, E. L., & Kott, P. (2009). 2010 National Survey on Drug Use and Health: Sample design plan. Prepared for the Substance Abuse and Mental Health Services Administration, Office of Applied Studies, under Contract No. 283-08-0210, Phase I, Deliverable No. 7, RTI/0211838.103. Research Triangle Park, NC: RTI International.Google Scholar
- 24.Office of National Drug Control Policy. (2013). Prescription drug abuse. Retrived from http://www.whitehouse.gov/ondcp/prescription-drug-abuse
- 30.Substance Abuse and Mental Health Services Administration. (2011). Results from the 2010 National Survey on Drug Use and Health: Summary of national findings. HHS Publication No. (SMA) 11-4658, NSDUH Series H-41. Rockville, MD: Substance Abuse and Mental Health Services Administration.Google Scholar
- 32.US Census Bureau. (2012). Census Bureau reports national mover rate increases after a record low in 2011. Retrieved from http://www.census.gov/newsroom/releases/archives/mobility_of_the_population/cb12240.html
- 33.US Department of Health and Human Services, Substance Abuse and Mental Health Services Administration, Center for Behavioral Health Statistics and Quality. (2013). National Survey on Drug Use and Health, 2010. Retrieved from doi: 10.3886/ICPSR32722.v4
- 34.Viana, A. G., Trent, L., Tull, M. T., Heiden, L., Damon, J. D., Hight, T. L., & Young, J. (2012). Non-medical use of prescription drugs among Mississippi youth: Constitutional, psychological, and family factors. Addictive Behaviors, 37(12), 1382–1388. doi: 10.1016/j.addbeh.2012.06.017.CrossRefPubMedGoogle Scholar