Skip to main content Accessibility help
×
Hostname: page-component-cd9895bd7-gbm5v Total loading time: 0 Render date: 2024-12-23T07:44:56.108Z Has data issue: false hasContentIssue false

Foreword

What are the Boundaries of Addictions?

Published online by Cambridge University Press:  13 July 2020

Steve Sussman
Affiliation:
University of Southern California
Get access

Summary

Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

American Psychiatric Association [APA] (2013). Diagnostic and Statistical Manual of Mental Disorders (5th edition) (DSM-5). Washington, DC: American Psychiatric Publishing.Google Scholar
Blanco, C., Compton, W. M. & Grant, B. F. (2016). Toward precision epidemiology. JAMA Psychiatry, 73(10), 10081009.Google Scholar
Blanco, C., Compton, W. M. & Lopez, M. F. (2018). What is an addictive disorder? JAMA Psychiatry, 75(3), 229230.Google Scholar
Blanco, C., García-Anaya, M., Wall, M., et al. (2015). Should pathological gambling and obesity be considered addictive disorders? A factor analytic study in a nationally representative sample. Drug and Alcohol Dependence, 150, 129134.CrossRefGoogle Scholar
Blanco, C., Ogburn, E., Pérez de Los Cobos, J., et al. (2008). DSM-IV criteria-based clinical subtypes of cannabis use disorders: results from the National Epidemiological Survey on Alcohol and Related Conditions (NESARC). Drug and Alcohol Dependence, 96(1–2), 136144.CrossRefGoogle ScholarPubMed
Blanco, C., Rafful, C., Wall, M. M., et al. (2014). Towards a comprehensive developmental model of cannabis use disorders. Addiction, 109(2), 284294.CrossRefGoogle ScholarPubMed
Casey, B. J., Cannonier, T., Conley, M. I., et al. (2018). The Adolescent Brain Cognitive Development (ABCD) study: imaging acquisition across 21 sites. Developmental Cognitive Neuroscience, 32, 4354.CrossRefGoogle ScholarPubMed
Clayton, J. A. & Collins, F. C. (2014). NIH to balance sex in cell and animal studies. Nature, 509, 282283.Google Scholar
Collins, F. S. & Varmus, H. (2015). A new initiative on precision medicine. New England Journal of Medicine, 372(9), 793795.CrossRefGoogle ScholarPubMed
Compton, W. M., Saha, T. D., Conway, K. P. & Grant, B. F. (2009). The role of cannabis use within a dimensional approach to cannabis use disorders. Drug and Alcohol Dependence, 100(3), 221227.CrossRefGoogle ScholarPubMed
Dawson, D. A., Compton, W. M. & Grant, B. F. (2010). Frequency of 5+/4+ drinks as a screener for drug use and drug use disorders. Journal of Studies on Alcohol and Drugs, 71(5), 751760.Google Scholar
Desmond-Hellmann, S. (2016). Progress lies in precision. Science, 353(6301), 731.Google Scholar
Figee, M., Pattij, T., Willuhn, I., et al. (2016). Compulsivity in obsessive-compulsive disorder and addictions. European Neuropsychopharmacology, 26(5), 856868.CrossRefGoogle ScholarPubMed
Franco, S., Olfson, M., Wall, M. M., et al. (2019). Shared and specific associations of substance use disorders on adverse outcomes: a national prospective study. Drug and Alcohol Dependence, 201, 212219.CrossRefGoogle ScholarPubMed
Hasin, D. S., O’Brien, C. P., Auriacombe, M., et al. (2013). DSM-5 criteria for substance use disorders: recommendations and rationale. American Journal of Psychiatry, 170(8), 834851.CrossRefGoogle ScholarPubMed
Kendler, K. S., Gardner, C. O. & Prescott, C. A. (2006). Toward a comprehensive developmental model for major depression in men. American Journal of Psychiatry, 163(1), 115124.Google Scholar
Kendler, K. S., Jacobson, K. C., Prescott, C. A. & Neale, M. C. (2003). Specificity of genetic and environmental risk factors for use and abuse/dependence of cannabis, cocaine, hallucinogens, sedatives, stimulants, and opiates in male twins. American Journal of Psychiatry, 160(4), 687695.Google Scholar
Kendler, K. S., Myers, J. & Prescott, C. A. (2007). Specificity of genetic and environmental risk factors for symptoms of cannabis, cocaine, alcohol, caffeine, and nicotine dependence. Archives of General Psychiatry, 64(11), 13131320.Google Scholar
Koob, G. F. (2015). The dark side of emotion: the addiction perspective. European Journal of Pharmacology, 753, 7387.Google Scholar
Krueger, R. F., Markon, K. E., Patrick, C. J. & Iacono, W. G. (2005). Externalizing psychopathology in adulthood: a dimensional-spectrum conceptualization and its implications for DSM-V. Journal of Abnormal Psychology, 114(4), 537550.CrossRefGoogle ScholarPubMed
Lisdahl, K. M., Sher, K. J., Conway, K. P., et al. (2018). Adolescent brain cognitive development (ABCD) study: overview of substance use assessment methods. Developmental Cognitive Neuroscience, 32, 8096.Google Scholar
Pickering, R. P., Grant, B. F., Chou, S. P. & Compton, W. M. (2007). Are overweight, obesity, and extreme obesity associated with psychopathology? Results from the national epidemiologic survey on alcohol and related conditions. Journal of Clinical Psychiatry, 68(7), 9981009.Google Scholar
Saha, T. D., Compton, W. M., Pulay, A. J., et al. (2010). Dimensionality of DSM-IV nicotine dependence in a national sample: an item response theory application. Drug and Alcohol Dependence, 108, 2128.Google Scholar
Sussman, S. (2017). Substance and Behavioral Addictions: Concepts, Causes, and Cures. Cambridge, UK: Cambridge University Press.Google Scholar
Tsuang, M. T., Lyons, M. J., Meyer, J. M., et al. (1998). Co-occurrence of abuse of different drugs in men: The role of drug-specific and shared vulnerabilities. Archives of General Psychiatry, 55(11), 967972.CrossRefGoogle ScholarPubMed
Volkow, N. D., Wang, G. J., Tomasi, D. & Baler, R. D. (2013). Obesity and addiction: neurobiological overlaps. Obesity Reviews, 14(1), 218.Google Scholar
Witkiewitz, K., King, K., McMahon, R. J., et al. (2013). Evidence for a multi-dimensional latent structural model of externalizing disorders. Journal of Abnormal Child Psychology, 41(2), 223237.Google Scholar
World Health Organization [WHO] (2018). ICD-11 for Mortality and Morbidity Statistics (ICD-11 MMS). Geneva, Switzerland: WHO (https://icd.who.int/)Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×