BACKGROUND: Despite growing concerns about non-medical prescription drug use and prescription drug use disorders, whether vulnerability for these conditions is drug-specific or occurs through a shared liability and common risk factors is unknown. METHODS: Exploratory and confirmatory factor analysis of Wave 1 of the National Epidemiologic Survey on Alcohol and Related Conditions were used to examine the latent structure of non-medical prescription drug use and prescription drug use disorders. Multiple Indicators Multiple Causes (MIMIC) analysis was used to examine whether the effect of sociodemographic and psychiatric covariates occurred through the latent factor, directly on each drug class or both. RESULTS: A one-factor model described well the structure of both non-medical prescription drug use and prescription drug use disorders. Younger age, being White, having more intense pain or one of several psychiatric disorders increased the risk of non-medical prescription drug use through the latent factor. The same covariates, except for anxiety disorders also significantly increased the risk of prescription drug use disorders through the latent factor. Older age directly increased the risk of non-medical use of sedatives, and alcohol use disorders decreased the risk of non-medical tranquilizer use. No covariates had direct effects on the risk of any prescription drug use disorders beyond their effect through the latent factor. CONCLUSION: The risk for non-medical prescription drug use and prescription drug use disorders occurs through a shared liability. Treatment, prevention and policy approaches directed at these drugs as a group maybe more effective than those focused on individual classes of drugs.
BACKGROUND: Despite growing concerns about non-medical prescription drug use and prescription drug use disorders, whether vulnerability for these conditions is drug-specific or occurs through a shared liability and common risk factors is unknown. METHODS: Exploratory and confirmatory factor analysis of Wave 1 of the National Epidemiologic Survey on Alcohol and Related Conditions were used to examine the latent structure of non-medical prescription drug use and prescription drug use disorders. Multiple Indicators Multiple Causes (MIMIC) analysis was used to examine whether the effect of sociodemographic and psychiatric covariates occurred through the latent factor, directly on each drug class or both. RESULTS: A one-factor model described well the structure of both non-medical prescription drug use and prescription drug use disorders. Younger age, being White, having more intense pain or one of several psychiatric disorders increased the risk of non-medical prescription drug use through the latent factor. The same covariates, except for anxiety disorders also significantly increased the risk of prescription drug use disorders through the latent factor. Older age directly increased the risk of non-medical use of sedatives, and alcohol use disorders decreased the risk of non-medical tranquilizer use. No covariates had direct effects on the risk of any prescription drug use disorders beyond their effect through the latent factor. CONCLUSION: The risk for non-medical prescription drug use and prescription drug use disorders occurs through a shared liability. Treatment, prevention and policy approaches directed at these drugs as a group maybe more effective than those focused on individual classes of drugs.
Authors: Carlos Blanco; Donald Alderson; Elizabeth Ogburn; Bridget F Grant; Edward V Nunes; Mark L Hatzenbuehler; Deborah S Hasin Journal: Drug Alcohol Depend Date: 2007-05-21 Impact factor: 4.492
Authors: Katherine M Keyes; John E Schulenberg; Patrick M O'Malley; Lloyd D Johnston; Jerald G Bachman; Guohua Li; Deborah Hasin Journal: Arch Gen Psychiatry Date: 2012-12
Authors: Bridget F Grant; Deborah A Dawson; Frederick S Stinson; Patricia S Chou; Ward Kay; Roger Pickering Journal: Drug Alcohol Depend Date: 2003-07-20 Impact factor: 4.492
Authors: Carlos Blanco; Robert F Krueger; Deborah S Hasin; Shang-Min Liu; Shuai Wang; Bradley T Kerridge; Tulshi Saha; Mark Olfson Journal: JAMA Psychiatry Date: 2013-02 Impact factor: 21.596
Authors: Wilson M Compton; Beth Han; Carlos Blanco; Kimberly Johnson; Christopher M Jones Journal: Am J Psychiatry Date: 2018-04-16 Impact factor: 18.112
Authors: Christine K Morioka; Donna E Howard; Kimberly M Caldeira; Min Qi Wang; Amelia M Arria Journal: Addict Behav Date: 2017-09-01 Impact factor: 3.913
Authors: John W Burns; Stephen Bruehl; Christopher R France; Erik Schuster; Daria Orlowska; Asokumar Buvanendran; Melissa Chont; Rajnish K Gupta Journal: Pain Date: 2017-03 Impact factor: 7.926
Authors: Danil Gamboa; Benedicte Jørgenrud; Evgeny A Bryun; Vigdis Vindenes; Evgenya A Koshkina; Aleksei V Nadezhdin; Saranda Kabashi; Elena J Tetenova; Thomas Berg; Anna Armika Tussilago Nyman; Alexey J Kolgashkin; Aleksei E Petukhov; Sergey N Perekhodov; Elena N Davydova; Anners Lerdal; Gudmund Nordby; Stig Tore Bogstrand Journal: BMJ Open Date: 2020-09-17 Impact factor: 2.692