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Neuroscience of Addiction: Relevance to Prevention and Treatment

Information & authors, metrics & citations, view options, neurobiology of addiction risk, neurobiology of the addicted brain, accelerating development of new prevention interventions.

Accelerating Development Of New Treatments

Promising Pharmacological Targets

Promising Nonpharmacological Therapies

Biological therapeutics., brain and peripheral stimulation therapeutics., behavioral therapies., the promise of basic research, conclusions, information, published in.

• Substance Use Disorder
• Neurochemistry
• Other Areas Of Neuroscience
• Clinical Drug Studies
• Other Psychosocial Techniques/Treatments

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Psychology of Addictive Behaviors

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Journal scope statement

Psychology of Addictive Behaviors ® publishes peer-reviewed original articles related to the psychological aspects of addictive behaviors. The journal includes articles on the following topics:

• alcohol use and alcohol use disorders
• drug use and drug use disorders
• smoking and nicotine use and disorders
• eating disorders, and
• other addictive behaviors

Randomized trials, laboratory and prospective studies, and meta-analyses are given the highest priorities. Cross-sectional studies, especially those involving convenience samples, will need to make unique contributions to be competitive in this journal.

Disclaimer: APA and the editors of Psychology of Addictive Behaviors assume no responsibility for statements and opinions advanced by the authors of its articles.

Equity, diversity, and inclusion

Psychology of Addictive Behaviors supports equity, diversity, and inclusion (EDI) in its practices. More information on these initiatives is available under EDI Efforts .

Open science

The APA Journals Program is committed to publishing transparent, rigorous research; improving reproducibility in science; and aiding research discovery. Open science practices vary per editor discretion. View the initiatives implemented by this journal .

Editor’s Choice

This journal’s content is highlighted in the APA Editor’s Choice newsletter, a free, bi-weekly compilation of editor-recommended APA Journals articles. More information is available under the submission guidelines .

Author and editor spotlights

Explore journal highlights : free article summaries, editor interviews and editorials, journal awards, mentorship opportunities, and more.

To submit to the Editorial Office of Katie Witkiewitz, PhD, please submit manuscripts electronically through the Manuscript Submission portal Microsoft Word (.docx) or LaTex (.tex) as a zip file with an accompanied Portable Document Format (.pdf) of the manuscript file

All new manuscripts submitted should be prepared according to the 7 th edition of the Publication Manual of the American Psychological Association . APA Style and Grammar Guidelines for the 7 th edition are available.

Submit Manuscript

General correspondence may be directed to

Katie Witkiewitz, PhD Department of Psychology University of New Mexico MSC 03-2220, Logan Hall 1 University of New Mexico Albuquerque, NM 87131 Email: Editor's office

Every attempt will be made to review manuscripts rapidly and to keep publication lag at a minimum. The opinions and statements published are the responsibility of the authors, and such opinions and statements do not necessarily reflect the policies of the publisher or of Division 50 or the views of the editor.

In addition to addresses and phone numbers, please supply electronic mail addresses and fax numbers, if available, for potential use by the editorial office and later by the production office.

Psychology of Addictive Behaviors is using a software system to screen submitted content for similarity with other published content. The system compares the initial version of each submitted manuscript against a database of 40+ million scholarly documents, as well as content appearing on the open web. This allows APA to check submissions for potential overlap with material previously published in scholarly journals (e.g., lifted or republished material).

Each issue of Psychology of Addictive Behavior will highlight one manuscript with the designation as an “ Editor’s Choice ” paper. Selection is based on the recommendations of the editor, who considers the paper’s potential impact to the field, the distinction of expanding the contributors to, or the focus of, our science, or its discussion of an important future direction for science.

Manuscripts

All titles and degrees should be omitted from authors' names. All manuscripts should include the following footnote typed on a separate sheet in APA format: Correspondence concerning this article should be addressed to [give the author's full name and mailing address].

Psychology of Addictive Behaviors has firm page limitations on manuscripts. A full-length manuscript should contain no more than 40 pages inclusive of title page, abstract, text, references, tables, figures, and appendices. A brief report is 10 pages, excluding title page, abstract, author note, references, figures, and tables. Margins of 1 inch and font size of 12 point must be employed, per APA style.

Commentaries are also accepted in response to a single article recently published in Psychology of Addictive Behaviors . The primary purpose would be to provide a meaningful insight, concern, alternative interpretation, clarification, or critical analysis.

Commentaries should not exceed 5 pages, excluding title page, abstract, author note, references, figures, and tables. Margins of 1 inch and font size of 12 point must be employed, per APA style. The title of a Brief Comment should include a subtitle reflecting the actual title and year of publication of the article that engendered the comment. Commentaries should be submitted no later than 6 months after publication of the original article.

Registered reports and replications

In addition to full-length research papers and brief reports reporting novel findings, the journal publishes registered reports, negative findings, replications, commentaries, and reviews. Replication submissions should include “A Replication of XX Study” in the subtitle of the manuscript as well as in the abstract. Preregistration of replication studies is strongly recommended, but not required.

Registered reports require a two-step review process.

The first step is the submission of the registration manuscript. This is a partial manuscript that includes hypotheses, rationale for the study, experimental design, and methods. The partial manuscript will be reviewed for significance and methodological approach.

If the partial manuscript is accepted this amounts to provisional acceptance of the full report regardless of the outcome of the study. The full manuscript will receive rapid editorial review, for adherence to the preregistered design, and expedited production for publication in the journal.

All articles can be published as full-length articles or as brief reports. A registered report should contain no more than 40 pages inclusive of title page, abstract, text, references, tables, figures, and appendices. A brief report is 10 pages, excluding title page, abstract, author note, references, figures, and tables. Margins of 1 inch and font size of 12 point must be employed, per APA style.

The journal has partnered with the Peer Community In Registered Reports (PCI-RR) as an “interested” journal to encourage the publication of registered reports.

Psychology of Addictive Behaviors may offer to review or publish any Stage 1 or Stage 2 Registered Reports within the journal’s disciplinary scope that receives in-principle PCI RR acceptance or recommendation. Eligible registered reports will be subject to Psychology of Addictive Behaviors ’s additional criteria. Further details are available on PCI RR’s website .

Open science badges

Articles are eligible for open science badges recognizing publicly available data, materials, and/or preregistration plans and analyses. These badges are awarded on a self-disclosure basis.

At submission, authors must confirm that criteria have been fulfilled in a signed badge disclosure form (PDF, 33KB) that must be submitted as supplemental material. If all criteria are met as confirmed by the editor, the form will then be published with the article as supplemental material.

Authors should also note their eligibility for the badge(s) in the cover letter.

For all badges, items must be made available on an open-access repository with a persistent identifier in a format that is time-stamped, immutable, and permanent. For the preregistered badge, this is an institutional registration system.

Data and materials must be made available under an open license allowing others to copy, share, and use the data, with attribution and copyright as applicable.

Available badges are:

Note that it may not be possible to preregister a study or to share data and materials. Applying for open science badges is optional.

Registration of clinical trials

As of March 1, 2020 registration will be required for all clinical trials (studies designed to examine the efficacy or effectiveness of a treatment or preventive intervention) reporting primary outcome findings. Prospective registration (i.e., pre-registration) is required if recruitment began on or after March 1, 2020. Retrospective registration will be accepted only if recruitment began before this date.

Clinical trials must be registered at ClinicalTrials.gov or at another recognized registry. A complete list of acceptable trial registries can be found via the WHO International Clinical Trials Registry Platform. Differences between registered and reported methods or outcomes must be explained clearly and transparently in the manuscript.

Trial protocols, including statistical analysis plans, must be made available to readers. Both published and unpublished protocols are acceptable. Published protocols should be cited in the manuscript. Unpublished protocols may be provided in online-only supplements or made available by request.

Use of the Standard Protocol Items: Recommendations for Intervention Trials (SPIRIT) checklist is recommended.

For secondary analyses of existing data sets, where primary analyses have already been published (or are in press), registration is not required. For such analyses, registration status must be made transparent in the manuscript, and authors must follow guidelines about data transparency provided below. The article(s) reporting the primary outcomes, and the findings, must be cited in the manuscript.

Manuscripts reporting long-term outcomes of studies for which the primary outcomes have already been published also will not require registration, but authors must follow the guidelines above for secondary analyses.

For studies that are not clinical trials, registration is encouraged, but not required.

Authors must note registration status in their cover letter, in the manuscript, and in the submission portal.

Required use of JARS and MARS guidelines and the 21-Word Statement

In order to maintain consistency and fairness in the review process and in the reporting of scientific findings,  Psychology of Addictive Behaviors requires that ALL manuscripts conform to Journal Article Reporting Standards (JARS) and Meta-Analysis Reporting Standards (MARS) as described in Applebaum et al. (2018):

Applebaum, Cooper, Kline, Mayo-Wilson, Nezu, & Rao (2018).  Journal Article Reporting Standards for Quantitative Research in Psychology: The APA Publications and Communications Board Task Force Report . American Psychologist, 73, 3-25.

The editorial team will use consistency with the JARS/MARS guidelines as a review criterion, and manuscripts may be rejected if guidelines are not followed.

When deviating from JARS/MARS guidelines, authors must provide the rationale in their cover letter and describe the limitations of doing so in their manuscript. We also recommend checking reporting guidelines from the Equator Network for your particular study design.

Manuscripts must also report (1) how the sample size was determined, (2) all data exclusions, (3) all manipulations, and (4) all study measures. See Simmons, Nelson, & Simonsohn (2012) for details; include the following statement in the Method section:

• We report how we determined our sample size, all data exclusions (if any), all manipulations, and all measures in the study.

Title of manuscript

The title of a manuscript should be accurate, fully explanatory, and preferably no longer than 12 words.

If the paper reports a randomized clinical trial (RCT), this should be indicated in the title. Note that JARS criteria must be used for reporting purposes.

Abstract and keywords

All manuscripts must include an abstract containing a maximum of 250 words typed on a separate page. After the abstract, please supply up to five keywords or brief phrases.

Manuscripts published in the Psychology of Addictive Behaviors will include a structured abstract of up to 250 words.

For studies that report randomized clinical trials or meta-analyses, the abstract also must be consistent with the guidelines set forth by JARS or MARS guidelines, respectively. Thus, in preparing a manuscript, please ensure that it is consistent with the guidelines stated below.

Please include an abstract of up to 250 words, presented in paragraph form.

The abstract should be typed on a separate page (page 2 of the manuscript), and must include each of the following sections:

• Objective: A brief statement of the purpose of the study.
• Method: A detailed summary of the participants (N, age, gender, ethnicity) as well as descriptions of the study design, measures (including names of measures), and procedures.
• Results: A detailed summary of the primary findings that clearly articulate comparison groups (if relevant), and that indicate significance or confidence intervals for the main findings.
• Conclusions: A description of the research and clinical implications of the findings.

Author contributions statements using CRediT

The APA Publication Manual (7th ed.) stipulates that “authorship encompasses…not only persons who do the writing but also those who have made substantial scientific contributions to a study.” In the spirit of transparency and openness, Psychology of Addictive Behaviors  has adopted the Contributor Roles Taxonomy (CRediT) to describe each author's individual contributions to the work. CRediT offers authors the opportunity to share an accurate and detailed description of their diverse contributions to a manuscript.

Submitting authors will be asked to identify the contributions of all authors at initial submission according to this taxonomy. If the manuscript is accepted for publication, the CRediT designations will be published as an Author Contributions Statement in the author note of the final article. All authors should have reviewed and agreed to their individual contribution(s) before submission.

CRediT includes 14 contributor roles, as described below:

• Conceptualization: Ideas; formulation or evolution of overarching research goals and aims.
• Data curation: Management activities to annotate (produce metadata), scrub data and maintain research data (including software code, where it is necessary for interpreting the data itself) for initial use and later reuse.
• Formal analysis: Application of statistical, mathematical, computational, or other formal techniques to analyze or synthesize study data.
• Funding acquisition: Acquisition of the financial support for the project leading to this publication.
• Investigation: Conducting a research and investigation process, specifically performing the experiments, or data/evidence collection.
• Methodology: Development or design of methodology; creation of models.
• Project administration: Management and coordination responsibility for the research activity planning and execution.
• Resources: Provision of study materials, reagents, materials, patients, laboratory samples, animals, instrumentation, computing resources, or other analysis tools.
• Software: Programming, software development; designing computer programs; implementation of the computer code and supporting algorithms; testing of existing code components.
• Supervision: Oversight and leadership responsibility for the research activity planning and execution, including mentorship external to the core team.
• Validation: Verification, whether as a part of the activity or separate, of the overall replication/reproducibility of results/experiments and other research outputs.
• Visualization: Preparation, creation and/or presentation of the published work, specifically visualization/data presentation.
• Writing—original draft: Preparation, creation and/or presentation of the published work, specifically writing the initial draft (including substantive translation).
• Writing—review and editing: Preparation, creation and/or presentation of the published work by those from the original research group, specifically critical review, commentary or revision—including pre- or post-publication stages.

Authors can claim credit for more than one contributor role, and the same role can be attributed to more than one author.

Public health significance statements

Authors submitting manuscripts to Psychology of Addictive Behaviors are required to provide 2–3 brief sentences regarding the public health significance of their paper. This description should be included within the manuscript on the abstract/keywords page. It should be written in language that is easily understood by both professionals and members of the lay public.

When an accepted paper is published, these sentences will be used in dissemination by the journal, including e-mail alerts, the Society of Addiction Psychology website, and on social media (Twitter and Facebook). This new policy is in keeping with efforts to increase dissemination and usage by larger and diverse audiences.

Examples include the following:

• "This study indicates that vaping cannabis is increasing among adolescents and adolescent with lower perceptions of risk were more likely to use cannabis."
• "This review found that mindfulness-based interventions are increasingly being studied as a primary treatment for alcohol and other substance use disorders. The review also found that mindfulness-based interventions are as effective as other treatments."
• "This study highlights the importance of including measures of other substances, including alcohol, tobacco, cannabis, and other prescription and illicit drugs, in studies examining opioid use disorder and chronic pain."

To be maximally useful, these statements of public health significance should not simply be sentences lifted directly out of the manuscript.

Prior to final acceptance and publication, all public health significance statements will be carefully reviewed to make sure they meet these standards. Authors will be expected to revise statements as necessary.

Transparency and openness

APA endorses the Transparency and Openness Promotion (TOP) Guidelines by a community working group in conjunction with the Center for Open Science ( Nosek et al. 2015 ). Empirical research, including meta-analyses, submitted to Psychology of Addictive Behaviors must meet the “disclosure” level for all eight aspects of research planning and reporting and the “requirement” level for data citation and design and analysis transparency. Authors should include a subsection in the Method section titled “Transparency and Openness.” This subsection should detail the efforts the authors have made to comply with the TOP guidelines. For example:

• We report how we determined our sample size, all data exclusions (if any), all manipulations, and all measures in the study, and we follow JARS (Kazak, 2018). All data, analysis code, and research materials are available at [stable link to repository]. Data were analyzed using R, version 4.0.0 (R Core Team, 2020) and the package ggplot , version 3.2.1 (Wickham, 2016). This study’s design and its analysis were not pre-registered.

In order to reduce the likelihood of duplicate or piecemeal publication, authors are required to provide, in their cover letter, a list of published, in press, and under review studies that come from the same dataset as the one in the submitted manuscript, as well as a narrative description of how the submitted manuscript differs from the others.

This narrative description should include how the manuscript differs (or does not) in terms of research question and variables studied.

If requesting masked review (see below), then authors also are required to submit a masked version of the narrative description that can be provided to reviewers. Please add this as an appendix table on the last page of the submitted manuscript. Please base your description on the following examples, edited according to your specific data circumstances.

Narrative example: Multiple uses of data collected from the same sample

• The data reported in this manuscript have been previously published and/or were collected as part of a larger data collection (at one or more points in time). Findings from the data collection have been reported in separate manuscripts. MS 1 (published) focuses on variables 1, 2, and 3; while MS 2 (in press) focuses on variables 4, 5, and 6. MS 3 (the current manuscript) focuses on variables 8, 9, and 15. MS 4 (soon to be submitted) will focus on variables 10, 12, and 14.

Narrative example: Publicly available dataset

• The data reported in this manuscript were obtained from publicly available data, [name of project, along with website link to project description]. A bibliography of journal articles, working papers, conference presentations, and dissertations using the [name of project] is available at [website link to bibliography list]. The variables and relationships examined in the present article have not been examined in any previous or current articles, or to the best of our knowledge in any papers that will be under review soon. [Alternatively, clarify any overlap of variables, as done in the narrative example above].

Upon submission of the manuscript, authors will be required to attest to the provision of the required information described above.

Finally, upon acceptance of a manuscript, authors will be required to provide, as part of the author note, a list of related published papers that come from the same dataset, unless such papers are clearly described and referenced in the manuscript (specifically noting that findings come from the same dataset).

Data, materials, and code

Authors must state whether data and study materials are posted to a trusted repository and, if so, how to access them. Recommended repositories include APA’s repository on the Open Science Framework (OSF), or authors can access a full list of other recommended repositories . Trusted repositories adhere to policies that make data discoverable, accessible, usable, and preserved for the long term. Trusted repositories also assign unique and persistent identifiers.

In a subsection titled “Transparency and Openness” at the end of the Method section, specify whether and where the data and material will be available or include a statement noting that they are not available. For submissions with quantitative or simulation analytic methods, state whether the study analysis code is posted to a trusted repository, and, if so, how to access it.

For example: 

• All data have been made publicly available at the [trusted repository name] and can be accessed at [persistent URL or DOI].
• Materials and analysis code for this study are available by emailing the corresponding author. 
• Materials and analysis code for this study are not available. 
• The code behind this analysis/simulation has been made publicly available at the [trusted repository name] and can be accessed at [persistent URL or DOI].

Preregistration of studies and analysis plans

Preregistration of studies and specific hypotheses can be a useful tool for making strong theoretical claims. Likewise, preregistration of analysis plans can be useful for distinguishing confirmatory and exploratory analyses. Investigators are encouraged to preregister their studies and analysis plans prior to conducting their research via a publicly accessible registry system (e.g., OSF , ClinicalTrials.gov, or other trial registries in the WHO Registry Network). There are many available templates; for example, APA, the British Psychological Society, and the German Psychological Society partnered with the Leibniz Institute for Psychology and Center for Open Science to create Preregistration Standards for Quantitative Research in Psychology (Bosnjak et al., 2022).

Articles must state whether or not any work was preregistered and, if so, where to access the preregistration. If any aspect of the study is preregistered, include the registry link in the method section note.

• This study’s design was preregistered prospectively, before data were collected; see [STABLE LINK OR DOI]. 
• This study’s design and hypotheses were preregistered after data had been collected but before analyses were undertaken; see [STABLE LINK OR DOI]. 
• This study’s analysis plan was preregistered; see [STABLE LINK OR DOI]. 
• This study was not preregistered.

Optional masked review

If authors would like to have masked review of their manuscripts, then the authors must also submit a title page that shows the title of the manuscript, the authors' byline names and institutional affiliations in order of authorship, and the date the manuscript is submitted.

The title page must also include an author note that identifies each author's departmental affiliation at the time the reported research was conducted, any funding or other acknowledgments, details of any prior dissemination of the ideas and data appearing in the manuscript, and one current address that will provide a point of contact for the interested reader.

The first page of the manuscript should omit the authors' names and affiliations but should include the title of the manuscript and the date it is submitted.

It is the authors' responsibility to see that the manuscript itself contains no clues to the authors' identity, including grant numbers, names of institutions providing IRB approval, self-citations, and links to online repositories for data, materials, code, or preregistrations (e.g., Create a View-only Link for a Project ).

Please ensure that the final version for production includes a byline and full author note for typesetting.

Manuscript preparation

Prepare manuscripts according to the Publication Manual of the American Psychological Association using the 7 th edition. Manuscripts may be copyedited for bias-free language (see Chapter 5 of the Publication Manual ). APA Style and Grammar Guidelines for the 7 th edition are available.

In particular, Psychology of Addictive Behaviors recommends against the use of terminology that can stigmatize people who use alcohol, drugs, other addictive substances or who have an addictive behavior. Psychology of Addictive Behaviors is in agreement with the consensus statement on Addiction Terminology developed by the International Society of Addiction Journal Editors .

All empirical manuscripts are required to report on sex and gender, and race and ethnicity of the included samples. Studies that are limited by only including predominantly non-Hispanic and white participants need to acknowledge this limitation and note that findings may not generalize to non-White participants. Explicitly describing the study as relevant to primarily white participants could also be captured by the title of the manuscript and/or reflected in the abstract. The examination of race and ethnicity should not be reified as a biological factor and authors should incorporate and explicitly discuss how race and ethnicity may be proxy measures for systemic racism, as well as cultural, social, environmental, economic, and structural factors. For more information please see these standards for publishing on racial health inequalities (Boyd, Lindo, Weeks, & McLemore, 2020).

Review APA's Journal Manuscript Preparation Guidelines before submitting your article.

Double-space all copy. Other formatting instructions, as well as instructions on preparing tables, figures, references, metrics, and abstracts, appear in the Manual . Additional guidance on APA Style is available on the APA Style website .

Below are additional instructions regarding the preparation of display equations, computer code, and tables.

Display equations

We strongly encourage you to use MathType (third-party software) or Equation Editor 3.0 (built into pre-2007 versions of Word) to construct your equations, rather than the equation support that is built into Word 2007 and Word 2010. Equations composed with the built-in Word 2007/Word 2010 equation support are converted to low-resolution graphics when they enter the production process and must be rekeyed by the typesetter, which may introduce errors.

To construct your equations with MathType or Equation Editor 3.0:

• Go to the Text section of the Insert tab and select Object.
• Select MathType or Equation Editor 3.0 in the drop-down menu.

If you have an equation that has already been produced using Microsoft Word 2007 or 2010 and you have access to the full version of MathType 6.5 or later, you can convert this equation to MathType by clicking on MathType Insert Equation. Copy the equation from Microsoft Word and paste it into the MathType box. Verify that your equation is correct, click File, and then click Update. Your equation has now been inserted into your Word file as a MathType Equation.

Use Equation Editor 3.0 or MathType only for equations or for formulas that cannot be produced as Word text using the Times or Symbol font.

Computer code

Because altering computer code in any way (e.g., indents, line spacing, line breaks, page breaks) during the typesetting process could alter its meaning, we treat computer code differently from the rest of your article in our production process. To that end, we request separate files for computer code.

In online supplemental material

We request that runnable source code be included as supplemental material to the article. For more information, visit Supplementing Your Article With Online Material .

In the text of the article

If you would like to include code in the text of your published manuscript, please submit a separate file with your code exactly as you want it to appear, using Courier New font with a type size of 8 points. We will make an image of each segment of code in your article that exceeds 40 characters in length. (Shorter snippets of code that appear in text will be typeset in Courier New and run in with the rest of the text.) If an appendix contains a mix of code and explanatory text, please submit a file that contains the entire appendix, with the code keyed in 8-point Courier New.

Use Word's insert table function when you create tables. Using spaces or tabs in your table will create problems when the table is typeset and may result in errors.

Academic writing and English language editing services

Authors who feel that their manuscript may benefit from additional academic writing or language editing support prior to submission are encouraged to seek out such services at their host institutions, engage with colleagues and subject matter experts, and/or consider several vendors that offer discounts to APA authors .

Please note that APA does not endorse or take responsibility for the service providers listed. It is strictly a referral service.

Use of such service is not mandatory for publication in an APA journal. Use of one or more of these services does not guarantee selection for peer review, manuscript acceptance, or preference for publication in any APA journal.

Submitting supplemental materials

APA can place supplemental materials online, available via the published article in the PsycArticles ® database. Please see Supplementing Your Article With Online Material for more details.

List references in alphabetical order. Each listed reference should be cited in text, and each text citation should be listed in the references section.

Examples of basic reference formats:

Journal article

McCauley, S. M., & Christiansen, M. H. (2019). Language learning as language use: A cross-linguistic model of child language development. Psychological Review , 126 (1), 1–51. https://doi.org/10.1037/rev0000126

Authored book

Brown, L. S. (2018). Feminist therapy (2nd ed.). American Psychological Association. https://doi.org/10.1037/0000092-000

Chapter in an edited book

Balsam, K. F., Martell, C. R., Jones. K. P., & Safren, S. A. (2019). Affirmative cognitive behavior therapy with sexual and gender minority people. In G. Y. Iwamasa & P. A. Hays (Eds.), Culturally responsive cognitive behavior therapy: Practice and supervision (2nd ed., pp. 287–314). American Psychological Association. https://doi.org/10.1037/0000119-012

All data, program code and other methods must be cited in the text and listed in the References section.

Data set citation

Alegria, M., Jackson, J. S., Kessler, R. C., & Takeuchi, D. (2016). Collaborative Psychiatric Epidemiology Surveys (CPES), 2001–2003 [Data set]. Inter-university Consortium for Political and Social Research. https://doi.org/10.3886/ICPSR20240.v8

Software/Code citation

Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package.  Journal of Statistical Software , 36(3), 1–48. https://www.jstatsoft.org/v36/i03/

Wickham, H. et al., (2019). Welcome to the tidyverse. Journal of Open Source Software, 4 (43), 1686, https://doi.org/10.21105/joss.01686

All data, program code and other methods must be appropriately cited in the text and listed in the references section.

Preferred formats for graphics files are TIFF and JPG, and preferred format for vector-based files is EPS. Graphics downloaded or saved from web pages are not acceptable for publication. Multipanel figures (i.e., figures with parts labeled a, b, c, d, etc.) should be assembled into one file. When possible, please place symbol legends below the figure instead of to the side.

• All color line art and halftones: 300 DPI
• Black and white line tone and gray halftone images: 600 DPI

Line weights

• Color (RGB, CMYK) images: 2 pixels
• Grayscale images: 4 pixels
• Stroke weight: 0.5 points

APA offers authors the option to publish their figures online in color without the costs associated with print publication of color figures.

The same caption will appear on both the online (color) and print (black and white) versions. To ensure that the figure can be understood in both formats, authors should add alternative wording (e.g., “the red (dark gray) bars represent”) as needed.

For authors who prefer their figures to be published in color both in print and online, original color figures can be printed in color at the editor's and publisher's discretion provided the author agrees to pay:

• $900 for one figure
• An additional $600 for the second figure
• An additional $450 for each subsequent figure

Permissions

Authors of accepted papers must obtain and provide to the editor on final acceptance all necessary permissions to reproduce in print and electronic form any copyrighted work, including test materials (or portions thereof), photographs, and other graphic images (including those used as stimuli in experiments).

On advice of counsel, APA may decline to publish any image whose copyright status is unknown.

• Download Permissions Alert Form (PDF, 13KB)

Publication policies

For full details on publication policies, including use of Artificial Intelligence tools, please see APA Publishing Policies .

APA policy prohibits an author from submitting the same manuscript for concurrent consideration by two or more publications.

See also APA Journals ® Internet Posting Guidelines .

APA requires authors to reveal any possible conflict of interest in the conduct and reporting of research (e.g., financial interests in a test or procedure, funding by pharmaceutical companies for drug research).

• Download Full Disclosure of Interests Form (PDF, 41KB)

In light of changing patterns of scientific knowledge dissemination, APA requires authors to provide information on prior dissemination of the data and narrative interpretations of the data/research appearing in the manuscript (e.g., if some or all were presented at a conference or meeting, posted on a listserv, shared on a website, including academic social networks like ResearchGate, etc.). This information (2–4 sentences) must be provided as part of the Author Note.

Ethical Principles

It is a violation of APA Ethical Principles to publish "as original data, data that have been previously published" (Standard 8.13).

In addition, APA Ethical Principles specify that "after research results are published, psychologists do not withhold the data on which their conclusions are based from other competent professionals who seek to verify the substantive claims through reanalysis and who intend to use such data only for that purpose, provided that the confidentiality of the participants can be protected and unless legal rights concerning proprietary data preclude their release" (Standard 8.14).

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Katie Witkiewitz, PhD University of New Mexico, United States

Associate editors

Celestina Barbosa-Leiker, PhD Washington State University, United States

William R. Corbin, PhD Arizona State University, United States

Elizabeth J. D’Amico, PhD RAND Corporation, United States

Christian S. Hendershot, PhD University of Southern California, Los Angeles, United States

Andrea Hussong, PhD University of North Carolina, Chapel Hill, United States

Kevin M. King, PhD University of Washington, United States

Danielle E. McCarthy, PhD University of Wisconsin-Madison, United States

R. Kathryn McHugh, PhD Harvard Medical School, McLean Hospital, United States

James G. Murphy, PhD University of Memphis, United States

Carla J. Rash, PhD University of Connecticut Health Center, United States

Monica Webb Hooper, PhD Bethesda, Maryland, U nited States

Consulting editors

Ana Abrantes, PhD Brown University

Sheila M. Alessi, PhD University of Connecticut School of Medicine, United States

Rebecca L. Ashare, PhD University of Pennsylvania, United States

Devin Banks, PhD University of Missouri at St. Louis, United States

Bruce D. Bartholow, PhD University of Missouri, United States

Warren K. Bickel, PhD Virginia Polytechnic and State University Carilion Research Institute, United States

Gallus Bischof, PhD University of Lübeck, Germany

Daniel M. Blonigen, PhD VA Palo Alto Health Care System, United States

Brian Borsari, PhD San Francisco VA Health Care System and University of California – San Francisco, United States

Kaitlin (Katie) Bountress, PhD Virginia Commonwealth University, United States

Daniel E. Bradford, PhD University of Miami, United States

Adrian J. Bravo, PhD William & Mary, United States

Jennifer F. Buckman, PhD, MBA Rutgers, The State University of New Jersey, United States

Julia Buckner, PhD Louisiana State University, United States

Ekaterina Burduli, PhD Washington State University College of Nursing, United States

Jason J. Burrow-Sanchez, PhD University of Utah, United States

Jessica L. Burris, PhD University of Kentucky, United States

Beatriz Carlini, PhD, MPH University of Washington, United States

Ryan Carpenter, PhD University of Missouri at St. Louis, United States

Yessenia Castro, PhD University of Texas at Austin, United States

Jesus Chavarria, PhD University of Western Ontario, Canada

Kelvin Choi, PhD Bethesda, Maryland, United States

Tammy Chung, PhD Rutgers, The State University of New Jersey, United States

Luke Clark, PhD University of British Columbia, Vancouver, Canada

Michael J. Cleveland, PhD Washington State University, United States

J. Douglas Coatsworth, PhD University of Tennessee, United States

Craig R. Colder, PhD University at Buffalo, The State University of New York, United States

Susan E. Collins, PhD Washington States University, United States

Veronica Cole, PhD Wake Forest University, United States

Fiona N. Conway, PhD The University of Texas at Austin, United States

Jessica W. Cook, PhD University of Wisconsin School of Medicine and Public Health, United States

Melissa Cox, PhD East Carolina University, United States

Kasey G. Creswell, PhD Carnegie Mellon University, United States

Kelly Cue Davis, PhD ASU Edson College of Nursing and Health Innovation, United States

Jordan P. Davis, PhD University of Southern California, United States

Sarah S. Dermody PhD Ryerson University, Toronto Ontario,  Canada

Joseph W. Ditre, PhD Syracuse University, United States

Jonas Dora, PhD University of Washington, United States

Cristiane S. Duarte, PhD New York State Psychiatric Institute, Columbia University, United States

Michael S. Dunbar, PhD RAND Corporation, United States

Kelly E. Dunn, PhD, MBA  Johns Hopkins School of Medicine, United States

Robert D. Dvorak, PhD, ABPP University of Central Florida, United States

David H. Epstein, PhD National Institute on Drug Abuse, United States

Anne Fairlie, PhD University of Washington, United States

Anne Fernandez, PhD University of Michigan, United States

Craig Field, PhD University of Texas at El Paso, United States

Matt Field, PhD University of Sheffield, United Kingdom

Mark T. Fillmore, PhD University of Kentucky, United States

Sally M. Gainsbury, PhD University of Sydney, Australia

Maria A. Gartstein, PhD Washington State University, United States

Nisha C. Gottfredson, PhD University of North Carolina, Chapel Hill, United States

Joshua B. Grubbs, PhD University of New Mexico, United States

Angela Haeny, PhD Yale University, United States

Kevin A. Hallgren, PhD University of Washington Medical Center, United States

Margo Hurlocker, PhD University of New Mexico, United States

Mary Hatch-Maillette, PhD University of Washington, United States

Peter S. Hendricks, PhD University of Alabama at Birmingham, United States

David C. Hodgins, PhD University of Calgary, Alberta, Canada

Rebecca J. Houston, PhD Rochester Institute of Technology, United States

Andrea L. Howard, PhD Carleton University, Canada

Kristina M. Jackson, PhD Rutgers, The State University of New Jersey, United States

Tim Janssen, PhD Brown University, United States

Keanan Joyner, PhD University of California at Berkeley, United States

David C. R. Kerr, PhD Oregon State University, United States

Brian D. Kiluk, PhD Yale School of Medicine, United States

Hyoun Kim, PhD University of Calgary, Canada

Mikhail Koffarnus, PhD University of Kentucky, United States

Benjamin O. Ladd, PhD. Washington State University, United States

Shawn J. Latendresse, M.S., PhD. Baylor University, United States

David M. Ledgerwood, PhD Wayne State University, United States

Robert F. Leeman, PhD. Northeastern University, United States

Teresa M. Leyro, PhD Rutgers, The State University of New Jersey, United States

Ashley N. Linden-Carmichael, PhD Pennsylvania State University, United States

Kristen P. Lindgren, PhD., ABPP University of Washington, United States

Andrew Littlefield, PhD Texas Tech University, United States

Susan Luczak, PhD University of Southern California, United States

Priscilla Lui, PhD University of Washington, United States

Leslie H. Lundahl, PhD Wayne State University School of Medicine, United States

Gregory J. Madden, PhD Utah State University, United States

Jennifer L. Maggs, PhD The Pennsylvania State University, United States

Molly Magill, PhD Brown University School of Public Health, United States

Stephen A. Maisto, PhD Syracuse University, United States

Laura Reid Marks, PhD Florida State University

Nadine R Mastroleo, PhD Binghamton University, United States

Denis M. McCarthy, PhD University of Missouri, United States

Michael G. McDonell, PhD Washing State University, Elson S. Floyd College of Medicine, United States

Sherry A. McKee, PhD Yale University School of Medicine, United States

Madeline H. Meier, PhD. Arizona State University, United States

Jennifer E. Merrill, PhD Brown University, United States

Jane Metrik, PhD Brown University, United States

Cynthia D. Mohr, PhD Portland State University, United States

Brooke S. G. Molina, PhD. University of Pittsburgh, United States

Kevin S. Montes, PhD California State University, Dominquez Hills, United States

Eun-Young Mun, PhD. University of North Texas Health Science Center, United States

Sarah E. Nelson, PhD Harvard Medical School, United States

Roisin M. O'Connor, PhD Concordia University, Montreal, Quebec, Canada

Tian Po S. Oei, PhD The University of Queensland, Australia

Oladunni Oluwoye, PhD Washington State University, Elson S. Floyd College of Medicine, United States

Don Operario, PhD Emory University, United States

Brian D. Ostafin, PhD University of Groningen, The Netherlands

Aesoon Park, PhD Syracuse University, United States

Kathleen A. Parks, PhD State University of New York at Buffalo, United States

Sarah L. Pedersen, PhD University of Pittsburgh, United States

Eric R. Pedersen, PhD University of Southern California, United States

Rory Pfund, PhD University of Memphis, United States

Thomas M. Piasecki, PhD University of Wisconsin  - Madison , United States

Lena C. Quilty, PhD Centre for Addiction and Mental Health and University of Toronto, Ontario, Canada

Patrick D. Quinn, PhD Indiana University Bloomington, United States

Allecia Reid, PhD University of Massachusetts, Amherst, United States

Elizabeth K. Reynolds, PhD Johns Hopkins School of Medicine, United States

Damaris J. Rohsenow, PhD Center for Alcohol and Addiction Studies, Brown University School of Public Health, United States

John M. Roll, PhD Washington State University, United States

Corey R. Roos, PhD Yale University, United States

Hans-Jürgen Rumpf, PhD University of Lübeck, Germany

Jessica Salvatore, PhD Rutgers, The State University of New Jersey, United States

Ty S. Schepis, PhD Texas State University, United States

Frank J. Schwebel, PhD University of New Mexico, United States

William G. Shadel, PhD RAND Corporation, United States

Ryan C. Shorey, PhD University of Wisconsin-Milwaukee Department of Psychology, United States

Patricia Simon, PhD Yale School of Medicine, United States

Monica C. Skewes, PhD Montana State University, United States

Wendy S. Slutske, PhD University of  Wisconsin  - Madison , United States

Claire Spears, PhD Georgia State University, United States

Marc L. Steinberg, PhD. Rutgers Robert Wood Johnson Medical School, United States

Sherry H. Stewart, PhD Dalhousie University, Halifax, Nova Scotia, Canada

Justin Strickland, PhD Johns Hopkins School of Medicine, United States

Elisa M. Trucco, PhD. Florida International University, United States

Jalie A. Tucker, PhD, MPH University of Florida, United States

Joan S. Tucker, PhD RAND Corporation, United States

Matthew T. Tull, PhD. University of Toledo, United States

Tomoko Udo, PhD University at Albany, The State University of New York, United States

Chrystal Vergara-Lopez, PhD Brown University, United States

Anka A. Vujanovic, PhD Texas A&M University, United States

Eric F. Wagner, PhD Florida International University, United States

Jeffrey D. Wardell, PhD York University, Canada

Andrew J. Waters, PhD Uniformed Services University of the Health Sciences, United States

Andrea H. Weinberger, PhD Ferkauf Graduate School of Psychology, Yeshiva University, United States

Thomas A. Wills, PhD University of Hawaii Cancer Center

Stephen J. Wilson, PhD The Pennsylvania State University, United States

Ken C. Winters, PhD Oregon Research Institute, United States

Ali M. Yurasek, PhD University of Florida, United States

Kristyn Zajac, PhD University of Connecticut School of Medicine, United States

William H. Zywiak, PhD Brown University, United States

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Special issue of APA's Psychology of Addictive Behaviors, Vol. 37, No. 1, February 2023. This special issue includes articles that focus on the scientific and applied fruits of molar behaviorism and behavioral economics as they pertain to understanding and changing addictive behavior.

Special issue of the APA journal Psychology of Addictive Behaviors, Vol. 36, No. 6, September 2022. The articles in this special issue address innovative methods and approaches that can be used to reduce AUD among young adults.

Special issue of APA’s journal Psychology of Addictive Behaviors, Vol. 35, No. 6, September 2021. The special issue was assembled to advance our understanding of the characteristics and consequences of combined use of alcohol and cannabis.

Special issue of the APA journal Psychology of Addictive Behaviors, Vol. 34, No. 1, February 2020. The articles span the multiple areas of addiction research to which Dr. Nancy Petry made key contributions, including behavior analysis and behavior pharmacology; contingency management; demographic predictors of outcomes across multiple clinical trials; reinforcer pathology and decision making; and gambling.

Special issue of the APA journal Psychology of Addictive Behaviors, Vol. 30, No. 7, November 2016. The articles profile emerging theory-driven science on PTSD and substance use disorders, specifically with regard to the biological, psychological, and social processes implicated in etiology and maintenance, as well as promising innovations in treatment approach.

Special issue of the APA journal Psychology of Addictive Behaviors, Vol. 27, No. 2, June 2013. Articles include integrative conceptual reviews and innovative empirical research on brain-based mechanisms that may underlie risk for addictive behaviors and response to psychotherapy from adolescence through adulthood.

Transparency and Openness Promotion

APA endorses the Transparency and Openness Promotion (TOP) Guidelines by a community working group in conjunction with the Center for Open Science ( Nosek et al. 2015 ). The TOP Guidelines cover eight fundamental aspects of research planning and reporting that can be followed by journals and authors at three levels of compliance.

For example:

• Level 1: Disclosure—The article must disclose whether or not the materials are posted to a trusted repository.
• Level 2: Requirement—The article must share materials via a trusted repository when legally and ethically permitted (or disclose the legal and/or ethical restriction when not permitted).
• Level 3: Verification—A third party must verify that the standard is met.

At a minimum, empirical research, including meta-analyses, submitted to Psychology of Addictive Behaviors must meet Level 1 (Disclosure) for all eight aspects of research planning and reporting as well as Level 2 (Requirement) for data citation and design and analysis transparency. Authors should include a subsection in their methods description titled “Transparency and Openness.” This subsection should detail the efforts the authors have made to comply with the Transparency and Openness Promotion (TOP) guidelines.

The list below summarizes the minimal TOP requirements of the journal. Please refer to the Center for Open Science TOP guidelines for details, and  contact the editor  (Katie Witkiewitz, PhD) with any further questions. APA recommends sharing data, materials, and code via  trusted repositories (e.g.,  APA’s repository on the Open Science Framework (OSF)). Trusted repositories adhere to policies that make data discoverable, accessible, usable, and preserved for the long term. Trusted repositories also assign unique and persistent identifiers.

We encourage investigators to preregister their studies and to share protocols and analysis plans prior to conducting their research. Clinical trials are studies that prospectively evaluate the effects of interventions on health outcomes, including psychological health. Clinical trials must be registered before enrolling participants on ClinicalTrials.gov or another primary register of the WHO International Clinical Trials Registry Platform (ICTRP) . There are many available preregistration forms (e.g., the APA Preregistration for Quantitative Research in Psychology template, ClinicalTrials.gov , or other preregistration templates available via OSF ). Completed preregistration forms should be posted on a publicly accessible registry system (e.g., OSF , ClinicalTrials.gov, or other trial registries in the WHO Registry Network).

The following table presents the eight fundamental aspects of research planning and reporting, the TOP level required by Psychology of Addictive Behaviors , and a brief description of the journal’s policy.

• Citation: Level 2, Requirement—All data, program code, and other methods developed by others must be cited in the text and listed in the references section.
• Data Transparency: Level 1, Disclosure—Article states whether the raw and/or processed data on which study conclusions are based are posted to a trusted repository and, if so, how to access them.
• Analytic Methods (Code) Transparency: Level 1, Disclosure—Article states whether computer code or syntax needed to reproduce analyses in an article is posted to a trusted repository and, if so, how to access it.
• Research Materials Transparency: Level 1, Disclosure—Article states whether materials described in the method section are posted to a trusted repository and, if so, how to access them.
• Design and Analysis Transparency (Reporting Standards): Level 2, Requirement—Article must comply with APA Style Journal Article Reporting Standards (JARS-Quant, JARS-Qual, and MARS), including information about: 1) how the sample size was determined, 2) all data exclusions, 3) all manipulations, and 4) all study measures. See Simmons, Nelson, & Simonsohn (2012) for details.
• Study Preregistration: Level 1, Disclosure—Article states whether the study design and (if applicable) hypotheses of any of the work reported was preregistered and, if so, how to access it. Authors may submit a masked copy via stable link or supplemental material or may provide a link after acceptance.
• Analysis Plan Preregistration: Level 1, Disclosure—Article states whether any of the work reported preregistered an analysis plan and, if so, how to access it. Authors may submit a masked copy via stable link or supplemental material or may provide a link after acceptance.
• Replication: Level 1, Disclosure—The journal publishes replications.

Other open science initiatives

• Open Science badges: Offered
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• Replications: Published

Explore open science at APA .

Journal equity, diversity, and inclusion statement

Psychology of Addictive Behaviors acknowledges the institutional and structural racism that is inherent in the United States drug policy and the disproportionate harm inflicted onto communities and individuals because of race, ethnicity, nationality, religiosity, socioeconomic status, ability status, gender identification, or sexual orientation. We acknowledge that we have been complicit in systemic oppression, and we are committed to using Psychology of Addictive Behaviors as a platform to promote justice and equity in research examining substance use, substance use disorder, and addictive behaviors.

For authors who are submitting to Psychology of Addictive Behaviors , we require that you report on sex and gender, and race and ethnicity of the included samples. The examination of race and ethnicity should not be reified as a biological factor and authors should incorporate and explicitly discuss how race and ethnicity are often proxy measures for systemic racism, as well as cultural, social, environmental, economic, and structural factors. We encourage authors to use systems-centered language that acknowledges that systemic factors are often the root causes of disproportionate findings across diverse groups. Psychology of Addictive Behaviors recommends person-first language and the use of terminology that does not stigmatize people who use alcohol, drugs, other addictive substances, or who engage in an addictive behavior. Psychology of Addictive Behaviors is in agreement with the consensus statement on Addiction Terminology developed by the International Society of Addiction Journal Editors.

Psychology of Addictive Behaviors encourages addiction psychologists and authors to not only inform themselves but to also inform others about differences and similarities between and within individuals of all backgrounds, recognizing that diversity extends well beyond race and ethnicity, including but not limited to variables such as gender, ability status, sexual orientation, socioeconomic status, religion, language, and acculturation levels, and acknowledging the impact that the intersectionality of each of these aspects has on addictive behaviors. It is imperative that addiction psychologists be committed to cultural sensitivity and cultural humility, increasing our awareness and confronting structural oppression and the biases within our profession and ourselves, and developing the skills necessary to work with individuals of all backgrounds and identities.

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Definitions and further details on inclusive study designs are available on the Journals EDI homepage .

Inclusive reporting standards

• Bias-free language and community-driven language guidelines (required)
• Author contribution roles using CRediT (required)
• Reflexivity (recommended)
• Data sharing and data availability statements (required)
• Impact statements (required)
• Year(s) of data collection (recommended)
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• Sample justifications (required)
• Constraints on Generality (COG) statements (recommended)
• Inclusive reference lists (recommended)

More information on this journal’s reporting standards is listed under the submission guidelines tab .

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Editorial fellowships.

Editorial fellowships help early-career psychologists gain firsthand experience in scholarly publishing and editorial leadership roles. This journal offers an editorial fellowship program for early-career psychologists from historically excluded communities.

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Masked peer review

This journal offers masked peer review (where both the authors’ and reviewers’ identities are not known to the other). Research has shown that masked peer review can help reduce implicit bias against traditionally female names or early-career scientists with smaller publication records (Budden et al., 2008; Darling, 2015).

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From Monitor on Psychology

• Taking a broad view of addiction  (November 2013)

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New compound could supercharge naloxone in fight against opioid overdoses

In a Stanford Medicine-led study, researchers combed through billions of compounds to find one that could enhance naloxone’s ability to fend off more potent opioids, with promising results in mice.

July 3, 2024 - By Nina Bai

Naloxone (orange) treats opioid overdose by kicking out opioids (pink) from the opioid receptor (teal). The newly discovered compound 368 (purple) strengthens the binding of naloxone to the opioid receptor, making it a more effective life-saving medicine. Emily Moskal

Every great superhero needs a sidekick. Now, scientists may have found a drug-busting partner for naloxone.

Naloxone is an opioid antidote that has saved tens of thousands of lives by rapidly reversing opioid overdoses in more than 90% of cases in which it is used. But its powers are temporary, lasting only 30 to 90 minutes. The rise of potent, long-acting opioids such as fentanyl means that someone brought back from the brink can still overdose after the naloxone wears off.

In a new study, Stanford Medicine scientists and collaborators have discovered a novel compound that can work alongside naloxone, supercharging its life-saving effects.

When tested in mice, adding the compound to a miniscule dose of naloxone made it as powerful as the conventional dosage, with the added benefit of milder withdrawal symptoms.

Naloxone, which is given as a nasal spray or injection, works by seizing opioid receptors, kicking out opioids and taking their place. (Naloxone has no addictive properties of its own.) The researchers found that the new compound — known for now as compound 368 — binds next to naloxone on opioid receptors and helpfully holds naloxone in place.

The findings were published July 3 in Nature .

“Naloxone binding to an opioid receptor turns it mostly off, but not all the way,” said Evan O’Brien , PhD, a postdoctoral scholar in molecular and cellular physiology and the lead author of the new study. “Our data shows that compound 368 is able to increase the binding of naloxone and turn the receptor off more completely.”

A new type of drug

The new compound belongs to an unusual class of drugs that don’t directly target the active site on receptors. Instead, they bind elsewhere on the receptor but trigger a structural change that alters the active site. Known as allosteric modulators (allos meaning “other” in Greek), they create new possibilities in drug development, but are trickier to identify, O’Brien said. 

Evan O'Brien

“Allosteric modulators are not common yet, and they’re a lot more difficult to discover and to work with,” he said.

Compound 368 is the first known allosteric modulator that can help turn off opioid receptors.

The researchers picked out compound 368 from a library of 4.5 billion compounds. Using advanced high-throughput techniques, they were able to screen the entire molecular library in just two days. To identify potential allosteric modulators that could cooperate with naloxone, they selected for compounds that bind only to receptors already saturated with naloxone.

Compound 368 — an otherwise rather unremarkable compound, O’Brien said — stood out for its ability to tightly bind to opioid receptors only in the presence of naloxone. Like a loyal sidekick, it doesn’t work with other drugs, and it doesn’t work alone.

Powers combined

When researchers exposed cells with opioid receptors to compound 368, they found that the compound alone made little difference. But when cells were given the compound with naloxone, the combination was a powerful deterrent against opioid binding.

The more compound 368 they added, the better naloxone was able to block opioids, including morphine and fentanyl.

“The compound itself doesn’t bind well without naloxone,” O’Brien said. “We think naloxone has to bind first, and then compound 368 is able to come in and cap it in place.”

Indeed, using cryoEM imaging to visualize frozen molecular structures, the researchers found that compound 368 docks right next to naloxone on the opioid receptor, forming bonds that secure the drug in place and slow its natural degradation by the body.

Boosting naloxone

Next, collaborators in McLaughlin’s lab tested the new compound in mice that had been given morphine. Because opioids reduce pain sensation, the researchers observed how quickly a mouse removed its tail from hot water. The stronger the opioid antidote, the faster a mouse would take its tail out of the water.

When mice on morphine were treated with compound 368 alone, nothing changed.

“The compound in mice, at least from the assays we’ve run, does nothing on its own,” O’Brien said. “We don’t observe any off-target effects. We don’t see anything happen to the mice even when we inject a massive amount of compound 368.”

This was exactly what the researchers had predicted from their molecular work and a good sign of the compound’s safety, he added.

The more tools at our disposal, the better we’ll be able to fight this epidemic of fentanyl overdoses.

When they also gave the mice a small dose of naloxone — an amount that typically would have no effect — the pairing with compound 368 dramatically improved naloxone’s effects.

“When we start to give them more and more of compound 368 with that low dose of naloxone, they take their tail out of the water pretty quickly,” O’Brien said.

Other effects of opioids, such as respiratory depression (the usual cause of death in opioid overdoses), were also reversed by a small dose of naloxone enhanced with the new compound.

Remarkably, the combination of compound 368 with a half dose of naloxone was strong enough to counter fentanyl, which is about 100 times more potent than morphine and the main culprit of overdoses in the United States.

By requiring less naloxone, the new compound could also ease the withdrawal symptoms that opioid users experience after overdose treatment. These symptoms — including body aches, shivering, nausea and diarrhea — are immediate and can be extremely uncomfortable, O’Brien said.

The researchers found that a low dose of naloxone plus compound 368 could reverse the effects of opioids with much milder withdrawal symptoms — in mice, this meant less teeth chattering, jumping and diarrhea.

Saving lives

The team, with the Majumdar lab’s expertise in medicinal chemistry, is now tweaking compound 368 so it can help naloxone counter strong opioids for longer durations.

“We’re still working on optimizing the compound’s properties for those longer-lasting effects,” O’Brien said. “But first showing that it works cooperatively with these low doses of naloxone suggests that we’re on the right track.”

O’Brien is optimistic that this track will lead to trials in humans. Overdoses from synthetic opioids, primarily fentanyl, continue to surge, killing nearly 74,000 Americans in 2022. “The more tools at our disposal, the better we’ll be able to fight this epidemic of fentanyl overdoses,” he said.

Researchers from Kurume University, SLAC National Acceleration Laboratory, Princeton University and University of Copenhagen also contributed to the work.

The study received funding from an American Diabetes Association Postdoctoral Fellowship, an American Heart Association Postdoctoral Fellowship, the National Institute of Health (grant RO1DA057790) and the Chan Zuckerberg Biohub.

About Stanford Medicine

Stanford Medicine is an integrated academic health system comprising the Stanford School of Medicine and adult and pediatric health care delivery systems. Together, they harness the full potential of biomedicine through collaborative research, education and clinical care for patients. For more information, please visit med.stanford.edu .

Hope amid crisis

Psychiatry’s new frontiers

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While addiction is deadlier than ever, research shows most Americans heal

The U.S. is facing the deadliest drug overdose epidemic in its history, but there is hope. Research shows most people with addiction do survive and recover, especially when they get quality treatment.

Copyright © 2022 NPR. All rights reserved. Visit our website terms of use and permissions pages at www.npr.org for further information.

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Telehealth builds autonomy and trust in treating addiction, study finds

by Erik Robinson, Oregon Health & Science University

Even as the nation's opioid epidemic continues to ravage families and communities nationwide—with more than 100,000 Americans dying of drug overdoses each year—stigma remains a barrier for many people accessing treatment for addiction.

A new study from Oregon Health & Science University suggests that telehealth may be an important antidote to overcoming stigma and reducing barriers for people seeking out the treatment they need.

The study, published recently in the Harm Reduction Journal , compiled in-depth interviews with 30 people treated for substance use disorder at OHSU from March of 2020 to December of 2021. Due to the COVID-19 pandemic, federal regulations eased the ability of people to enter treatment through virtual visits during that time, as opposed to having to visit a clinic in person.

"You feel like you're being watched or judged by everyone, and telehealth can reduce that sense whether it's real or perceived," said senior author Ximena Levander, M.D., assistant professor of medicine ( general internal medicine and geriatrics) in the OHSU School of Medicine. "Telehealth can lower that barrier."

Patients reported that they appreciated the implicit sense of autonomy and trust involved in being able to connect with clinicians through video or telephone visits. Patients received prescriptions for buprenorphine, a partial opioid receptor agonist that inhibits opioid withdrawal symptoms.

Co-authors identified four themes among patients interviewed in the study:

• Autonomy: Telehealth offers improved control over the treatment setting.
• Patient-centered: Concern over stigma and privacy can cut both ways. In some cases, patients preferred in-person visits, especially if they live in congregant settings where others might see or hear their virtual visit.
• Social distancing: The social distance of telehealth presents an opportunity to reduce or worsen perceptions of stigma by clinicians—especially if patients perceive the clinician isn't fully paying attention or maintaining eye contact.
• Flexibility: Patients reported the flexibility of telehealth translated into perceptions of increased trust and respect from clinicians.

"Our results support a more individualized approach to care, whereby patients may choose whether they receive care in person or via telehealth," the authors write. "Given that aspects of both telehealth and in-person treatment left some participants feeling judged by their clinicians, our findings also highlight the need to further explore how clinicians perpetuate stigma through telehealth -based programs, and how training and clinical guidelines could mediate this."

In addition to Levander, OHSU co-authors included Jessica V. Couch, Mackenzie Whitcomb, M.D., Bradley M. Buchheit, M.D., David A. Dorr, M.D., Darren J. Malinoski, M.D., Todd Korthuis, M.D., and Sarah S. Ono, Ph.D.

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• Published: 22 February 2021

Addiction as a brain disease revised: why it still matters, and the need for consilience

• Markus Heilig 1 ,
• James MacKillop   ORCID: orcid.org/0000-0003-4118-9500 2 , 3 ,
• Diana Martinez 4 ,
• Jürgen Rehm   ORCID: orcid.org/0000-0001-5665-0385 5 , 6 , 7 , 8 ,
• Lorenzo Leggio   ORCID: orcid.org/0000-0001-7284-8754 9 &
• Louk J. M. J. Vanderschuren   ORCID: orcid.org/0000-0002-5379-0363 10  

Neuropsychopharmacology volume  46 ,  pages 1715–1723 ( 2021 ) Cite this article

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A Correspondence to this article was published on 03 May 2021

The view that substance addiction is a brain disease, although widely accepted in the neuroscience community, has become subject to acerbic criticism in recent years. These criticisms state that the brain disease view is deterministic, fails to account for heterogeneity in remission and recovery, places too much emphasis on a compulsive dimension of addiction, and that a specific neural signature of addiction has not been identified. We acknowledge that some of these criticisms have merit, but assert that the foundational premise that addiction has a neurobiological basis is fundamentally sound. We also emphasize that denying that addiction is a brain disease is a harmful standpoint since it contributes to reducing access to healthcare and treatment, the consequences of which are catastrophic. Here, we therefore address these criticisms, and in doing so provide a contemporary update of the brain disease view of addiction. We provide arguments to support this view, discuss why apparently spontaneous remission does not negate it, and how seemingly compulsive behaviors can co-exist with the sensitivity to alternative reinforcement in addiction. Most importantly, we argue that the brain is the biological substrate from which both addiction and the capacity for behavior change arise, arguing for an intensified neuroscientific study of recovery. More broadly, we propose that these disagreements reveal the need for multidisciplinary research that integrates neuroscientific, behavioral, clinical, and sociocultural perspectives.

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Introduction.

Close to a quarter of a century ago, then director of the US National Institute on Drug Abuse Alan Leshner famously asserted that “addiction is a brain disease”, articulated a set of implications of this position, and outlined an agenda for realizing its promise [ 1 ]. The paper, now cited almost 2000 times, put forward a position that has been highly influential in guiding the efforts of researchers, and resource allocation by funding agencies. A subsequent 2000 paper by McLellan et al. [ 2 ] examined whether data justify distinguishing addiction from other conditions for which a disease label is rarely questioned, such as diabetes, hypertension or asthma. It concluded that neither genetic risk, the role of personal choices, nor the influence of environmental factors differentiated addiction in a manner that would warrant viewing it differently; neither did relapse rates, nor compliance with treatment. The authors outlined an agenda closely related to that put forward by Leshner, but with a more clinical focus. Their conclusion was that addiction should be insured, treated, and evaluated like other diseases. This paper, too, has been exceptionally influential by academic standards, as witnessed by its ~3000 citations to date. What may be less appreciated among scientists is that its impact in the real world of addiction treatment has remained more limited, with large numbers of patients still not receiving evidence-based treatments.

In recent years, the conceptualization of addiction as a brain disease has come under increasing criticism. When first put forward, the brain disease view was mainly an attempt to articulate an effective response to prevailing nonscientific, moralizing, and stigmatizing attitudes to addiction. According to these attitudes, addiction was simply the result of a person’s moral failing or weakness of character, rather than a “real” disease [ 3 ]. These attitudes created barriers for people with substance use problems to access evidence-based treatments, both those available at the time, such as opioid agonist maintenance, cognitive behavioral therapy-based relapse prevention, community reinforcement or contingency management, and those that could result from research. To promote patient access to treatments, scientists needed to argue that there is a biological basis beneath the challenging behaviors of individuals suffering from addiction. This argument was particularly targeted to the public, policymakers and health care professionals, many of whom held that since addiction was a misery people brought upon themselves, it fell beyond the scope of medicine, and was neither amenable to treatment, nor warranted the use of taxpayer money.

Present-day criticism directed at the conceptualization of addiction as a brain disease is of a very different nature. It originates from within the scientific community itself, and asserts that this conceptualization is neither supported by data, nor helpful for people with substance use problems [ 4 , 5 , 6 , 7 , 8 ]. Addressing these critiques requires a very different perspective, and is the objective of our paper. We readily acknowledge that in some cases, recent critiques of the notion of addiction as a brain disease as postulated originally have merit, and that those critiques require the postulates to be re-assessed and refined. In other cases, we believe the arguments have less validity, but still provide an opportunity to update the position of addiction as a brain disease. Our overarching concern is that questionable arguments against the notion of addiction as a brain disease may harm patients, by impeding access to care, and slowing development of novel treatments.

A premise of our argument is that any useful conceptualization of addiction requires an understanding both of the brains involved, and of environmental factors that interact with those brains [ 9 ]. These environmental factors critically include availability of drugs, but also of healthy alternative rewards and opportunities. As we will show, stating that brain mechanisms are critical for understanding and treating addiction in no way negates the role of psychological, social and socioeconomic processes as both causes and consequences of substance use. To reflect this complex nature of addiction, we have assembled a team with expertise that spans from molecular neuroscience, through animal models of addiction, human brain imaging, clinical addiction medicine, to epidemiology. What brings us together is a passionate commitment to improving the lives of people with substance use problems through science and science-based treatments, with empirical evidence as the guiding principle.

To achieve this goal, we first discuss the nature of the disease concept itself, and why we believe it is important for the science and treatment of addiction. This is followed by a discussion of the main points raised when the notion of addiction as a brain disease has come under criticism. Key among those are claims that spontaneous remission rates are high; that a specific brain pathology is lacking; and that people suffering from addiction, rather than behaving “compulsively”, in fact show a preserved ability to make informed and advantageous choices. In the process of discussing these issues, we also address the common criticism that viewing addiction as a brain disease is a fully deterministic theory of addiction. For our argument, we use the term “addiction” as originally used by Leshner [ 1 ]; in Box  1 , we map out and discuss how this construct may relate to the current diagnostic categories, such as Substance Use Disorder (SUD) and its different levels of severity (Fig.  1) .

Risky (hazardous) substance use refers to quantity/frequency indicators of consumption; SUD refers to individuals who meet criteria for a DSM-5 diagnosis (mild, moderate, or severe); and addiction refers to individuals who exhibit persistent difficulties with self-regulation of drug consumption. Among high-risk individuals, a subgroup will meet criteria for SUD and, among those who have an SUD, a further subgroup would be considered to be addicted to the drug. However, the boundary for addiction is intentionally blurred to reflect that the dividing line for defining addiction within the category of SUD remains an open empirical question.

Box 1 What’s in a name? Differentiating hazardous use, substance use disorder, and addiction

Although our principal focus is on the brain disease model of addiction, the definition of addiction itself is a source of ambiguity. Here, we provide a perspective on the major forms of terminology in the field.

Hazardous Substance Use

Hazardous (risky) substance use refers to quantitative levels of consumption that increase an individual’s risk for adverse health consequences. In practice, this pertains to alcohol use [ 110 , 111 ]. Clinically, alcohol consumption that exceeds guidelines for moderate drinking has been used to prompt brief interventions or referral for specialist care [ 112 ]. More recently, a reduction in these quantitative levels has been validated as treatment endpoints [ 113 ].

Substance Use Disorder

SUD refers to the DSM-5 diagnosis category that encompasses significant impairment or distress resulting from specific categories of psychoactive drug use. The diagnosis of SUD is operationalized as 2 or more of 11 symptoms over the past year. As a result, the diagnosis is heterogenous, with more than 1100 symptom permutations possible. The diagnosis in DSM-5 is the result of combining two diagnoses from the DSM-IV, abuse and dependence, which proved to be less valid than a single dimensional approach [ 114 ]. Critically, SUD includes three levels of severity: mild (2–3 symptoms), moderate (4–5 symptoms), and severe (6+ symptoms). The International Classification of Diseases (ICD) system retains two diagnoses, harmful use (lower severity) and substance dependence (higher severity).

Addiction is a natural language concept, etymologically meaning enslavement, with the contemporary meaning traceable to the Middle and Late Roman Republic periods [ 115 ]. As a scientific construct, drug addiction can be defined as a state in which an individual exhibits an inability to self-regulate consumption of a substance, although it does not have an operational definition. Regarding clinical diagnosis, as it is typically used in scientific and clinical parlance, addiction is not synonymous with the simple presence of SUD. Nowhere in DSM-5 is it articulated that the diagnostic threshold (or any specific number/type of symptoms) should be interpreted as reflecting addiction, which inherently connotes a high degree of severity. Indeed, concerns were raised about setting the diagnostic standard too low because of the issue of potentially conflating a low-severity SUD with addiction [ 116 ]. In scientific and clinical usage, addiction typically refers to individuals at a moderate or high severity of SUD. This is consistent with the fact that moderate-to-severe SUD has the closest correspondence with the more severe diagnosis in ICD [ 117 , 118 , 119 ]. Nonetheless, akin to the undefined overlap between hazardous use and SUD, the field has not identified the exact thresholds of SUD symptoms above which addiction would be definitively present.

Integration

The ambiguous relationships among these terms contribute to misunderstandings and disagreements. Figure 1 provides a simple working model of how these terms overlap. Fundamentally, we consider that these terms represent successive dimensions of severity, clinical “nesting dolls”. Not all individuals consuming substances at hazardous levels have an SUD, but a subgroup do. Not all individuals with a SUD are addicted to the drug in question, but a subgroup are. At the severe end of the spectrum, these domains converge (heavy consumption, numerous symptoms, the unambiguous presence of addiction), but at low severity, the overlap is more modest. The exact mapping of addiction onto SUD is an open empirical question, warranting systematic study among scientists, clinicians, and patients with lived experience. No less important will be future research situating our definition of SUD using more objective indicators (e.g., [ 55 , 120 ]), brain-based and otherwise, and more precisely in relation to clinical needs [ 121 ]. Finally, such work should ultimately be codified in both the DSM and ICD systems to demarcate clearly where the attribution of addiction belongs within the clinical nosology, and to foster greater clarity and specificity in scientific discourse.

What is a disease?

In his classic 1960 book “The Disease Concept of Alcoholism”, Jellinek noted that in the alcohol field, the debate over the disease concept was plagued by too many definitions of “alcoholism” and too few definitions of “disease” [ 10 ]. He suggested that the addiction field needed to follow the rest of medicine in moving away from viewing disease as an “entity”, i.e., something that has “its own independent existence, apart from other things” [ 11 ]. To modern medicine, he pointed out, a disease is simply a label that is agreed upon to describe a cluster of substantial, deteriorating changes in the structure or function of the human body, and the accompanying deterioration in biopsychosocial functioning. Thus, he concluded that alcoholism can simply be defined as changes in structure or function of the body due to drinking that cause disability or death. A disease label is useful to identify groups of people with commonly co-occurring constellations of problems—syndromes—that significantly impair function, and that lead to clinically significant distress, harm, or both. This convention allows a systematic study of the condition, and of whether group members benefit from a specific intervention.

It is not trivial to delineate the exact category of harmful substance use for which a label such as addiction is warranted (See Box  1 ). Challenges to diagnostic categorization are not unique to addiction, however. Throughout clinical medicine, diagnostic cut-offs are set by consensus, commonly based on an evolving understanding of thresholds above which people tend to benefit from available interventions. Because assessing benefits in large patient groups over time is difficult, diagnostic thresholds are always subject to debate and adjustments. It can be debated whether diagnostic thresholds “merely” capture the extreme of a single underlying population, or actually identify a subpopulation that is at some level distinct. Resolving this issue remains challenging in addiction, but once again, this is not different from other areas of medicine [see e.g., [ 12 ] for type 2 diabetes]. Longitudinal studies that track patient trajectories over time may have a better ability to identify subpopulations than cross-sectional assessments [ 13 ].

By this pragmatic, clinical understanding of the disease concept, it is difficult to argue that “addiction” is unjustified as a disease label. Among people who use drugs or alcohol, some progress to using with a quantity and frequency that results in impaired function and often death, making substance use a major cause of global disease burden [ 14 ]. In these people, use occurs with a pattern that in milder forms may be challenging to capture by current diagnostic criteria (See Box  1 ), but is readily recognized by patients, their families and treatment providers when it reaches a severity that is clinically significant [see [ 15 ] for a classical discussion]. In some cases, such as opioid addiction, those who receive the diagnosis stand to obtain some of the greatest benefits from medical treatments in all of clinical medicine [ 16 , 17 ]. Although effect sizes of available treatments are more modest in nicotine [ 18 ] and alcohol addiction [ 19 ], the evidence supporting their efficacy is also indisputable. A view of addiction as a disease is justified, because it is beneficial: a failure to diagnose addiction drastically increases the risk of a failure to treat it [ 20 ].

Of course, establishing a diagnosis is not a requirement for interventions to be meaningful. People with hazardous or harmful substance use who have not (yet) developed addiction should also be identified, and interventions should be initiated to address their substance-related risks. This is particularly relevant for alcohol, where even in the absence of addiction, use is frequently associated with risks or harm to self, e.g., through cardiovascular disease, liver disease or cancer, and to others, e.g., through accidents or violence [ 21 ]. Interventions to reduce hazardous or harmful substance use in people who have not developed addiction are in fact particularly appealing. In these individuals, limited interventions are able to achieve robust and meaningful benefits [ 22 ], presumably because patterns of misuse have not yet become entrenched.

Thus, as originally pointed out by McLellan and colleagues, most of the criticisms of addiction as a disease could equally be applied to other medical conditions [ 2 ]. This type of criticism could also be applied to other psychiatric disorders, and that has indeed been the case historically [ 23 , 24 ]. Today, there is broad consensus that those criticisms were misguided. Few, if any healthcare professionals continue to maintain that schizophrenia, rather than being a disease, is a normal response to societal conditions. Why, then, do people continue to question if addiction is a disease, but not whether schizophrenia, major depressive disorder or post-traumatic stress disorder are diseases? This is particularly troubling given the decades of data showing high co-morbidity of addiction with these conditions [ 25 , 26 ]. We argue that it comes down to stigma. Dysregulated substance use continues to be perceived as a self-inflicted condition characterized by a lack of willpower, thus falling outside the scope of medicine and into that of morality [ 3 ].

Chronic and relapsing, developmentally-limited, or spontaneously remitting?

Much of the critique targeted at the conceptualization of addiction as a brain disease focuses on its original assertion that addiction is a chronic and relapsing condition. Epidemiological data are cited in support of the notion that large proportions of individuals achieve remission [ 27 ], frequently without any formal treatment [ 28 , 29 ] and in some cases resuming low risk substance use [ 30 ]. For instance, based on data from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) study [ 27 ], it has been pointed out that a significant proportion of people with an addictive disorder quit each year, and that most afflicted individuals ultimately remit. These spontaneous remission rates are argued to invalidate the concept of a chronic, relapsing disease [ 4 ].

Interpreting these and similar data is complicated by several methodological and conceptual issues. First, people may appear to remit spontaneously because they actually do, but also because of limited test–retest reliability of the diagnosis [ 31 ]. For instance, using a validated diagnostic interview and trained interviewers, the Collaborative Studies on Genetics of Alcoholism examined the likelihood that an individual diagnosed with a lifetime history of substance dependence would retain this classification after 5 years. This is obviously a diagnosis that, once met, by definition cannot truly remit. Lifetime alcohol dependence was indeed stable in individuals recruited from addiction treatment units, ~90% for women, and 95% for men. In contrast, in a community-based sample similar to that used in the NESARC [ 27 ], stability was only ~30% and 65% for women and men, respectively. The most important characteristic that determined diagnostic stability was severity. Diagnosis was stable in severe, treatment-seeking cases, but not in general population cases of alcohol dependence.

These data suggest that commonly used diagnostic criteria alone are simply over-inclusive for a reliable, clinically meaningful diagnosis of addiction. They do identify a core group of treatment seeking individuals with a reliable diagnosis, but, if applied to nonclinical populations, also flag as “cases” a considerable halo of individuals for whom the diagnostic categorization is unreliable. Any meaningful discussion of remission rates needs to take this into account, and specify which of these two populations that is being discussed. Unfortunately, the DSM-5 has not made this task easier. With only 2 out of 11 symptoms being sufficient for a diagnosis of SUD, it captures under a single diagnostic label individuals in a “mild” category, whose diagnosis is likely to have very low test–retest reliability, and who are unlikely to exhibit a chronic relapsing course, together with people at the severe end of the spectrum, whose diagnosis is reliable, many of whom do show a chronic relapsing course.

The NESARC data nevertheless show that close to 10% of people in the general population who are diagnosed with alcohol addiction (here equated with DSM-IV “dependence” used in the NESARC study) never remitted throughout their participation in the survey. The base life-time prevalence of alcohol dependence in NESARC was 12.5% [ 32 ]. Thus, the data cited against the concept of addiction as a chronic relapsing disease in fact indicate that over 1% of the US population develops an alcohol-related condition that is associated with high morbidity and mortality, and whose chronic and/or relapsing nature cannot be disputed, since it does not remit.

Secondly, the analysis of NESARC data [ 4 , 27 ] omits opioid addiction, which, together with alcohol and tobacco, is the largest addiction-related public health problem in the US [ 33 ]. This is probably the addictive condition where an analysis of cumulative evidence most strikingly supports the notion of a chronic disorder with frequent relapses in a large proportion of people affected [ 34 ]. Of course, a large number of people with opioid addiction are unable to express the chronic, relapsing course of their disease, because over the long term, their mortality rate is about 15 times greater than that of the general population [ 35 ]. However, even among those who remain alive, the prevalence of stable abstinence from opioid use after 10–30 years of observation is <30%. Remission may not always require abstinence, for instance in the case of alcohol addiction, but is a reasonable proxy for remission with opioids, where return to controlled use is rare. Embedded in these data is a message of literally vital importance: when opioid addiction is diagnosed and treated as a chronic relapsing disease, outcomes are markedly improved, and retention in treatment is associated with a greater likelihood of abstinence.

The fact that significant numbers of individuals exhibit a chronic relapsing course does not negate that even larger numbers of individuals with SUD according to current diagnostic criteria do not. For instance, in many countries, the highest prevalence of substance use problems is found among young adults, aged 18–25 [ 36 ], and a majority of these ‘age out’ of excessive substance use [ 37 ]. It is also well documented that many individuals with SUD achieve longstanding remission, in many cases without any formal treatment (see e.g., [ 27 , 30 , 38 ]).

Collectively, the data show that the course of SUD, as defined by current diagnostic criteria, is highly heterogeneous. Accordingly, we do not maintain that a chronic relapsing course is a defining feature of SUD. When present in a patient, however, such as course is of clinical significance, because it identifies a need for long-term disease management [ 2 ], rather than expectations of a recovery that may not be within the individual’s reach [ 39 ]. From a conceptual standpoint, however, a chronic relapsing course is neither necessary nor implied in a view that addiction is a brain disease. This view also does not mean that it is irreversible and hopeless. Human neuroscience documents restoration of functioning after abstinence [ 40 , 41 ] and reveals predictors of clinical success [ 42 ]. If anything, this evidence suggests a need to increase efforts devoted to neuroscientific research on addiction recovery [ 40 , 43 ].

Lessons from genetics

For alcohol addiction, meta-analysis of twin and adoption studies has estimated heritability at ~50%, while estimates for opioid addiction are even higher [ 44 , 45 ]. Genetic risk factors are to a large extent shared across substances [ 46 ]. It has been argued that a genetic contribution cannot support a disease view of a behavior, because most behavioral traits, including religious and political inclinations, have a genetic contribution [ 4 ]. This statement, while correct in pointing out broad heritability of behavioral traits, misses a fundamental point. Genetic architecture is much like organ structure. The fact that normal anatomy shapes healthy organ function does not negate that an altered structure can contribute to pathophysiology of disease. The structure of the genetic landscape is no different. Critics further state that a “genetic predisposition is not a recipe for compulsion”, but no neuroscientist or geneticist would claim that genetic risk is “a recipe for compulsion”. Genetic risk is probabilistic, not deterministic. However, as we will see below, in the case of addiction, it contributes to large, consistent probability shifts towards maladaptive behavior.

In dismissing the relevance of genetic risk for addiction, Hall writes that “a large number of alleles are involved in the genetic susceptibility to addiction and individually these alleles might very weakly predict a risk of addiction”. He goes on to conclude that “generally, genetic prediction of the risk of disease (even with whole-genome sequencing data) is unlikely to be informative for most people who have a so-called average risk of developing an addiction disorder” [ 7 ]. This reflects a fundamental misunderstanding of polygenic risk. It is true that a large number of risk alleles are involved, and that the explanatory power of currently available polygenic risk scores for addictive disorders lags behind those for e.g., schizophrenia or major depression [ 47 , 48 ]. The only implication of this, however, is that low average effect sizes of risk alleles in addiction necessitate larger study samples to construct polygenic scores that account for a large proportion of the known heritability.

However, a heritability of addiction of ~50% indicates that DNA sequence variation accounts for 50% of the risk for this condition. Once whole genome sequencing is readily available, it is likely that it will be possible to identify most of that DNA variation. For clinical purposes, those polygenic scores will of course not replace an understanding of the intricate web of biological and social factors that promote or prevent expression of addiction in an individual case; rather, they will add to it [ 49 ]. Meanwhile, however, genome-wide association studies in addiction have already provided important information. For instance, they have established that the genetic underpinnings of alcohol addiction only partially overlap with those for alcohol consumption, underscoring the genetic distinction between pathological and nonpathological drinking behaviors [ 50 ].

It thus seems that, rather than negating a rationale for a disease view of addiction, the important implication of the polygenic nature of addiction risk is a very different one. Genome-wide association studies of complex traits have largely confirmed the century old “infinitisemal model” in which Fisher reconciled Mendelian and polygenic traits [ 51 ]. A key implication of this model is that genetic susceptibility for a complex, polygenic trait is continuously distributed in the population. This may seem antithetical to a view of addiction as a distinct disease category, but the contradiction is only apparent, and one that has long been familiar to quantitative genetics. Viewing addiction susceptibility as a polygenic quantitative trait, and addiction as a disease category is entirely in line with Falconer’s theorem, according to which, in a given set of environmental conditions, a certain level of genetic susceptibility will determine a threshold above which disease will arise.

A brain disease? Then show me the brain lesion!

The notion of addiction as a brain disease is commonly criticized with the argument that a specific pathognomonic brain lesion has not been identified. Indeed, brain imaging findings in addiction (perhaps with the exception of extensive neurotoxic gray matter loss in advanced alcohol addiction) are nowhere near the level of specificity and sensitivity required of clinical diagnostic tests. However, this criticism neglects the fact that neuroimaging is not used to diagnose many neurologic and psychiatric disorders, including epilepsy, ALS, migraine, Huntington’s disease, bipolar disorder, or schizophrenia. Even among conditions where signs of disease can be detected using brain imaging, such as Alzheimer’s and Parkinson’s disease, a scan is best used in conjunction with clinical acumen when making the diagnosis. Thus, the requirement that addiction be detectable with a brain scan in order to be classified as a disease does not recognize the role of neuroimaging in the clinic.

For the foreseeable future, the main objective of imaging in addiction research is not to diagnose addiction, but rather to improve our understanding of mechanisms that underlie it. The hope is that mechanistic insights will help bring forward new treatments, by identifying candidate targets for them, by pointing to treatment-responsive biomarkers, or both [ 52 ]. Developing innovative treatments is essential to address unmet treatment needs, in particular in stimulant and cannabis addiction, where no approved medications are currently available. Although the task to develop novel treatments is challenging, promising candidates await evaluation [ 53 ]. A particular opportunity for imaging-based research is related to the complex and heterogeneous nature of addictive disorders. Imaging-based biomarkers hold the promise of allowing this complexity to be deconstructed into specific functional domains, as proposed by the RDoC initiative [ 54 ] and its application to addiction [ 55 , 56 ]. This can ultimately guide the development of personalized medicine strategies to addiction treatment.

Countless imaging studies have reported differences in brain structure and function between people with addictive disorders and those without them. Meta-analyses of structural data show that alcohol addiction is associated with gray matter losses in the prefrontal cortex, dorsal striatum, insula, and posterior cingulate cortex [ 57 ], and similar results have been obtained in stimulant-addicted individuals [ 58 ]. Meta-analysis of functional imaging studies has demonstrated common alterations in dorsal striatal, and frontal circuits engaged in reward and salience processing, habit formation, and executive control, across different substances and task-paradigms [ 59 ]. Molecular imaging studies have shown that large and fast increases in dopamine are associated with the reinforcing effects of drugs of abuse, but that after chronic drug use and during withdrawal, brain dopamine function is markedly decreased and that these decreases are associated with dysfunction of prefrontal regions [ 60 ]. Collectively, these findings have given rise to a widely held view of addiction as a disorder of fronto-striatal circuitry that mediates top-down regulation of behavior [ 61 ].

Critics reply that none of the brain imaging findings are sufficiently specific to distinguish between addiction and its absence, and that they are typically obtained in cross-sectional studies that can at best establish correlative rather than causal links. In this, they are largely right, and an updated version of a conceptualization of addiction as a brain disease needs to acknowledge this. Many of the structural brain findings reported are not specific for addiction, but rather shared across psychiatric disorders [ 62 ]. Also, for now, the most sophisticated tools of human brain imaging remain crude in face of complex neural circuit function. Importantly however, a vast literature from animal studies also documents functional changes in fronto-striatal circuits, as well their limbic and midbrain inputs, associated with addictive behaviors [ 63 , 64 , 65 , 66 , 67 , 68 ]. These are circuits akin to those identified by neuroimaging studies in humans, implicated in positive and negative emotions, learning processes and executive functions, altered function of which is thought to underlie addiction. These animal studies, by virtue of their cellular and molecular level resolution, and their ability to establish causality under experimental control, are therefore an important complement to human neuroimaging work.

Nevertheless, factors that seem remote from the activity of brain circuits, such as policies, substance availability and cost, as well as socioeconomic factors, also are critically important determinants of substance use. In this complex landscape, is the brain really a defensible focal point for research and treatment? The answer is “yes”. As powerfully articulated by Francis Crick [ 69 ], “You, your joys and your sorrows, your memories and your ambitions, your sense of personal identity and free will, are in fact no more than the behavior of a vast assembly of nerve cells and their associated molecules”. Social and interpersonal factors are critically important in addiction, but they can only exert their influences by impacting neural processes. They must be encoded as sensory data, represented together with memories of the past and predictions about the future, and combined with representations of interoceptive and other influences to provide inputs to the valuation machinery of the brain. Collectively, these inputs drive action selection and execution of behavior—say, to drink or not to drink, and then, within an episode, to stop drinking or keep drinking. Stating that the pathophysiology of addiction is largely about the brain does not ignore the role of other influences. It is just the opposite: it is attempting to understand how those important influences contribute to drug seeking and taking in the context of the brain, and vice versa.

But if the criticism is one of emphasis rather than of principle—i.e., too much brain, too little social and environmental factors – then neuroscientists need to acknowledge that they are in part guilty as charged. Brain-centric accounts of addiction have for a long time failed to pay enough attention to the inputs that social factors provide to neural processing behind drug seeking and taking [ 9 ]. This landscape is, however, rapidly changing. For instance, using animal models, scientists are finding that lack of social play early in life increases the motivation to take addictive substances in adulthood [ 70 ]. Others find that the opportunity to interact with a fellow rat is protective against addiction-like behaviors [ 71 ]. In humans, a relationship has been found between perceived social support, socioeconomic status, and the availability of dopamine D2 receptors [ 72 , 73 ], a biological marker of addiction vulnerability. Those findings in turn provided translation of data from nonhuman primates, which showed that D2 receptor availability can be altered by changes in social hierarchy, and that these changes are associated with the motivation to obtain cocaine [ 74 ].

Epidemiologically, it is well established that social determinants of health, including major racial and ethnic disparities, play a significant role in the risk for addiction [ 75 , 76 ]. Contemporary neuroscience is illuminating how those factors penetrate the brain [ 77 ] and, in some cases, reveals pathways of resilience [ 78 ] and how evidence-based prevention can interrupt those adverse consequences [ 79 , 80 ]. In other words, from our perspective, viewing addiction as a brain disease in no way negates the importance of social determinants of health or societal inequalities as critical influences. In fact, as shown by the studies correlating dopamine receptors with social experience, imaging is capable of capturing the impact of the social environment on brain function. This provides a platform for understanding how those influences become embedded in the biology of the brain, which provides a biological roadmap for prevention and intervention.

We therefore argue that a contemporary view of addiction as a brain disease does not deny the influence of social, environmental, developmental, or socioeconomic processes, but rather proposes that the brain is the underlying material substrate upon which those factors impinge and from which the responses originate. Because of this, neurobiology is a critical level of analysis for understanding addiction, although certainly not the only one. It is recognized throughout modern medicine that a host of biological and non-biological factors give rise to disease; understanding the biological pathophysiology is critical for understanding etiology and informing treatment.

Is a view of addiction as a brain disease deterministic?

A common criticism of the notion that addiction is a brain disease is that it is reductionist and in the end therefore deterministic [ 81 , 82 ]. This is a fundamental misrepresentation. As indicated above, viewing addiction as a brain disease simply states that neurobiology is an undeniable component of addiction. A reason for deterministic interpretations may be that modern neuroscience emphasizes an understanding of proximal causality within research designs (e.g., whether an observed link between biological processes is mediated by a specific mechanism). That does not in any way reflect a superordinate assumption that neuroscience will achieve global causality. On the contrary, since we realize that addiction involves interactions between biology, environment and society, ultimate (complete) prediction of behavior based on an understanding of neural processes alone is neither expected, nor a goal.

A fairer representation of a contemporary neuroscience view is that it believes insights from neurobiology allow useful probabilistic models to be developed of the inherently stochastic processes involved in behavior [see [ 83 ] for an elegant recent example]. Changes in brain function and structure in addiction exert a powerful probabilistic influence over a person’s behavior, but one that is highly multifactorial, variable, and thus stochastic. Philosophically, this is best understood as being aligned with indeterminism, a perspective that has a deep history in philosophy and psychology [ 84 ]. In modern neuroscience, it refers to the position that the dynamic complexity of the brain, given the probabilistic threshold-gated nature of its biology (e.g., action potential depolarization, ion channel gating), means that behavior cannot be definitively predicted in any individual instance [ 85 , 86 ].

Driven by compulsion, or free to choose?

A major criticism of the brain disease view of addiction, and one that is related to the issue of determinism vs indeterminism, centers around the term “compulsivity” [ 6 , 87 , 88 , 89 , 90 ] and the different meanings it is given. Prominent addiction theories state that addiction is characterized by a transition from controlled to “compulsive” drug seeking and taking [ 91 , 92 , 93 , 94 , 95 ], but allocate somewhat different meanings to “compulsivity”. By some accounts, compulsive substance use is habitual and insensitive to its outcomes [ 92 , 94 , 96 ]. Others refer to compulsive use as a result of increasing incentive value of drug associated cues [ 97 ], while others view it as driven by a recruitment of systems that encode negative affective states [ 95 , 98 ].

The prototype for compulsive behavior is provided by obsessive-compulsive disorder (OCD), where compulsion refers to repeatedly and stereotypically carrying out actions that in themselves may be meaningful, but lose their purpose and become harmful when performed in excess, such as persistent handwashing until skin injuries result. Crucially, this happens despite a conscious desire to do otherwise. Attempts to resist these compulsions result in increasing and ultimately intractable anxiety [ 99 ]. This is in important ways different from the meaning of compulsivity as commonly used in addiction theories. In the addiction field, compulsive drug use typically refers to inflexible, drug-centered behavior in which substance use is insensitive to adverse consequences [ 100 ]. Although this phenomenon is not necessarily present in every patient, it reflects important symptoms of clinical addiction, and is captured by several DSM-5 criteria for SUD [ 101 ]. Examples are needle-sharing despite knowledge of a risk to contract HIV or Hepatitis C, drinking despite a knowledge of having liver cirrhosis, but also the neglect of social and professional activities that previously were more important than substance use. While these behaviors do show similarities with the compulsions of OCD, there are also important differences. For example, “compulsive” substance use is not necessarily accompanied by a conscious desire to withhold the behavior, nor is addictive behavior consistently impervious to change.

Critics question the existence of compulsivity in addiction altogether [ 5 , 6 , 7 , 89 ], typically using a literal interpretation, i.e., that a person who uses alcohol or drugs simply can not do otherwise. Were that the intended meaning in theories of addiction—which it is not—it would clearly be invalidated by observations of preserved sensitivity of behavior to contingencies in addiction. Indeed, substance use is influenced both by the availability of alternative reinforcers, and the state of the organism. The roots of this insight date back to 1940, when Spragg found that chimpanzees would normally choose a banana over morphine. However, when physically dependent and in a state of withdrawal, their choice preference would reverse [ 102 ]. The critical role of alternative reinforcers was elegantly brought into modern neuroscience by Ahmed et al., who showed that rats extensively trained to self-administer cocaine would readily forego the drug if offered a sweet solution as an alternative [ 103 ]. This was later also found to be the case for heroin [ 103 ], methamphetamine [ 104 ] and alcohol [ 105 ]. Early residential laboratory studies on alcohol use disorder indeed revealed orderly operant control over alcohol consumption [ 106 ]. Furthermore, efficacy of treatment approaches such as contingency management, which provides systematic incentives for abstinence [ 107 ], supports the notion that behavioral choices in patients with addictions remain sensitive to reward contingencies.

Evidence that a capacity for choosing advantageously is preserved in addiction provides a valid argument against a narrow concept of “compulsivity” as rigid, immutable behavior that applies to all patients. It does not, however, provide an argument against addiction as a brain disease. If not from the brain, from where do the healthy and unhealthy choices people make originate? The critical question is whether addictive behaviors—for the most part—result from healthy brains responding normally to externally determined contingencies; or rather from a pathology of brain circuits that, through probabilistic shifts, promotes the likelihood of maladaptive choices even when reward contingencies are within a normal range. To resolve this question, it is critical to understand that the ability to choose advantageously is not an all-or-nothing phenomenon, but rather is about probabilities and their shifts, multiple faculties within human cognition, and their interaction. Yes, it is clear that most people whom we would consider to suffer from addiction remain able to choose advantageously much, if not most, of the time. However, it is also clear that the probability of them choosing to their own disadvantage, even when more salutary options are available and sometimes at the expense of losing their life, is systematically and quantifiably increased. There is a freedom of choice, yet there is a shift of prevailing choices that nevertheless can kill.

Synthesized, the notion of addiction as a disease of choice and addiction as a brain disease can be understood as two sides of the same coin. Both of these perspectives are informative, and they are complementary. Viewed this way, addiction is a brain disease in which a person’s choice faculties become profoundly compromised. To articulate it more specifically, embedded in and principally executed by the central nervous system, addiction can be understood as a disorder of choice preferences, preferences that overvalue immediate reinforcement (both positive and negative), preferences for drug-reinforcement in spite of costs, and preferences that are unstable ( “I’ll never drink like that again;” “this will be my last cigarette” ), prone to reversals in the form of lapses and relapse. From a contemporary neuroscience perspective, pre-existing vulnerabilities and persistent drug use lead to a vicious circle of substantive disruptions in the brain that impair and undermine choice capacities for adaptive behavior, but do not annihilate them. Evidence of generally intact decision making does not fundamentally contradict addiction as a brain disease.

Conclusions

The present paper is a response to the increasing number of criticisms of the view that addiction is a chronic relapsing brain disease. In many cases, we show that those criticisms target tenets that are neither needed nor held by a contemporary version of this view. Common themes are that viewing addiction as a brain disease is criticized for being both too narrow (addiction is only a brain disease; no other perspectives or factors are important) or too far reaching (it purports to discover the final causes of addiction). With regard to disease course, we propose that viewing addiction as a chronic relapsing disease is appropriate for some populations, and much less so for others, simply necessitating better ways of delineating the populations being discussed. We argue that when considering addiction as a disease, the lens of neurobiology is valuable to use. It is not the only lens, and it does not have supremacy over other scientific approaches. We agree that critiques of neuroscience are warranted [ 108 ] and that critical thinking is essential to avoid deterministic language and scientific overreach.

Beyond making the case for a view of addiction as a brain disease, perhaps the more important question is when a specific level of analysis is most useful. For understanding the biology of addiction and designing biological interventions, a neurobiological view is almost certainly the most appropriate level of analysis, in particular when informed by an understanding of the behavioral manifestations. In contrast, for understanding the psychology of addiction and designing psychological interventions, behavioral science is the natural realm, but one that can often benefit from an understanding of the underlying neurobiology. For designing policies, such as taxation and regulation of access, economics and public administration provide the most pertinent perspectives, but these also benefit from biological and behavioral science insights.

Finally, we argue that progress would come from integration of these scientific perspectives and traditions. E.O. Wilson has argued more broadly for greater consilience [ 109 ], unity of knowledge, in science. We believe that addiction is among the areas where consilience is most needed. A plurality of disciplines brings important and trenchant insights to bear on this condition; it is the exclusive remit of no single perspective or field. Addiction inherently and necessarily requires multidisciplinary examination. Moreover, those who suffer from addiction will benefit most from the application of the full armamentarium of scientific perspectives.

Funding and disclosures

Supported by the Swedish Research Council grants 2013-07434, 2019-01138 (MH); Netherlands Organisation for Health Research and Development (ZonMw) under project number 912.14.093 (LJMJV); NIDA and NIAAA intramural research programs (LL; the content is solely the responsibility of the author and does not necessarily represent the official views of the National Institutes of Health); the Peter Boris Chair in Addictions Research, Homewood Research Institute, and the National Institute on Alcohol Abuse and Alcoholism grants AA025911, AA024930, AA025849, AA027679 (JM; the content is solely the responsibility of the author and does not necessarily represent the official views of the National Institutes of Health).

MH has received consulting fees, research support or other compensation from Indivior, Camurus, BrainsWay, Aelis Farma, and Janssen Pharmaceuticals. JM is a Principal and Senior Scientist at BEAM Diagnostics, Inc. DM, JR, LL, and LJMJV declare no conflict of interest.

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Heilig, M., MacKillop, J., Martinez, D. et al. Addiction as a brain disease revised: why it still matters, and the need for consilience. Neuropsychopharmacol. 46 , 1715–1723 (2021). https://doi.org/10.1038/s41386-020-00950-y

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News from Brown

Most americans don’t know their doctors can prescribe addiction treatment, study finds.

A federally supported study, led by Brown researcher Brandon del Pozo, reveals a disconnect between primary care physicians' ability to prescribe medications for opioid use disorder and public awareness and demand.

PROVIDENCE, R.I. [Brown University] — Most Americans don’t realize that primary care physicians can prescribe medication for opioid use disorder, according to a recent national survey of more than 1,000 people.

These perceptions are significant considering the efforts that have been made to lower the barriers to treatment of opioid use disorder, said lead study author Brandon del Pozo, an assistant professor (research) at Brown University’s Warren Alpert Medical School and School of Public Health. The analysis of the survey was published in JAMA Network Open.

“We’ve made great strides in making it easier for primary care doctors to prescribe these safe and effective treatments, but our study indicates a critical disconnect between the need for medications for opioid use disorder and people’s knowledge about how to access them,” del Pozo said.

Decades of research have shown the effectiveness of medications such as buprenorphine and methadone for opioid use disorder. Federal policy changes such as the elimination of specialized training requirements and patient caps have made it simpler for primary care physicians to prescribe medication for opioid use disorder. Yet a recent study found that, in the year after the elimination of a waiver requirement to prescribe buprenorphine, the number of prescribers increased while the number of people receiving the medication did not.

Del Pozo and other Brown University researchers hypothesized that public health factors may impede access to these medications. They formulated survey questions related to people’s awareness of and comfort around opioid use disorder treatment in primary care. In collaboration with the National Institute on Drug Abuse, the researchers added these questions to a survey conducted in English and Spanish by the Justice Community Opioid Innovation Network, led by the National Institute on Drug Abuse and supported through the NIH Helping to End Addiction Long-term Initiative. JCOIN researchers administered the survey in June 2023, targeting a nationally representative sample of adults.

Of the 1,234 survey respondents, 61% were unaware that primary care physicians can prescribe medication for opioid use disorder, and 13% incorrectly believed that they could not.

Black survey respondents were most likely to incorrectly believe they could not receive medications for opioid use disorder via primary care, pointing to an important disparity in information that may further impede access to treatment, del Pozo noted.

Yet a majority of the respondents agreed (53%) or strongly agreed (24%) that the office of a primary care physician should be a place where people can receive treatment for an opioid use disorder. Among the respondents who reported ever misusing prescription or illicit opioids, 82% expressed comfort in personally going to their primary care physicians for medications for opioid use disorder. Of those who had not misused opioids, 74% reported they would be comfortable referring their loved ones to primary care for these medications.

“People aren’t asking primary care physicians about treatment for opioid use disorder because they aren’t aware of the ways these medical professionals can help,” del Pozo said. “Raising awareness is critical to increasing effective treatment of opioid use disorders, and to reducing the racial and ethnic disparities in knowledge about access to treatment.”

By increasing public awareness and demand, the study authors said, primary care physicians may be more incentivized to offer medications for opioid use disorder, especially with appropriate clinical and administrative support. With approximately 209,000 primary care physicians in the U.S., channeling addiction treatment through primary care could have a significant public health impact, they said.

The findings suggest there is an opportunity to increase awareness and access through culturally specific strategies to reach different groups, the study authors said.

The authors suggested that future research explore targeted strategies to enhance public awareness and investigate the impact of increased primary care physicians’ involvement in providing medications for opioid use disorder. For example, they noted, messaging campaigns similar to those for HIV testing and cancer screening, which include educational materials in medical settings, as well as proactive screening by primary care physicians, may help address the gap in public knowledge.

“Science, public health, insurance, policy and public perception all must align to improve access to treatment,” del Pozo said.  

This study was funded by NIDA, part of the National Institutes of Health, with additional support from NIH’s National Institute of General Medical Sciences, under award numbers K01DA056654, U2CDA050098, and P20GM125507. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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Institute of Medicine (US) Committee on Community-Based Drug Treatment; Lamb S, Greenlick MR, McCarty D, editors. Bridging the Gap between Practice and Research: Forging Partnerships with Community-Based Drug and Alcohol Treatment. Washington (DC): National Academies Press (US); 1998.

Bridging the Gap between Practice and Research: Forging Partnerships with Community-Based Drug and Alcohol Treatment.

• Hardcopy Version at National Academies Press

D The Treatment of Addiction: What Can Research Offer Practice?

A. Thomas McLellan and James R. McKay

Penn-VA Center for Studies of Addiction and The Treatment Research Institute at the University of Pennsylvania

• INTRODUCTION

Problems of substance dependence produce dramatic costs to society in terms of lost productivity, social disorder and of course health care utilization (NIDA, 1991; Merrill, 1993). Over the past twenty years many of the traditional forms of substance abuse treatment (e.g., methadone maintenance, therapeutic communities, outpatient drug free and others) have been evaluated multiple times and shown to be effective (Ball and Ross, 1991; DATOS, 1992; Hubbard et al., 1986, 1997; IOM, 1989, 1990b; McLellan et al., 1980; Simpson, 1981, 1997; Simpson et al., 1997a,b). Importantly, this research has shown that the benefits obtained from addiction treatments typically extend beyond the reduction of substance use, to areas that are important to society such as reduced crime, reduced risk of infectious diseases, and improved social function (Ball and Ross, 1991; Institute of Medicine, 1989, 1990b; McLellan et al., 1980). Finally, research findings indicate that the costs associated with the provision of substance abuse treatment provide 3- to 7-fold returns to the employer, the health insurer, and to society within approximately three years following treatment (Everingham and Rydell, 1994; Gerstein et al., 1994; Holder et al., 1991; IOM, 1990b; OTA, 1983; State of Oregon, 1996).

How Do These Research Results Translate into Recommendations that Can Be Useful for Treatment Providers?—Although the conclusions from reviews of the recent treatment research literature are important and gratifying, they are not adequate to inform important clinical questions regarding the delivery of substance abuse treatment services. Simply knowing that those who stay in treatment longer have better outcomes does not help when the funding and duration of treatment in ''real world" settings is regularly reduced (McLellan et al., 1996a). Further, research demonstrating that highly specialized and resource-intensive treatments "work" with highly selected samples of patients may not be helpful to "real world" treatment providers who have no prospects of accessing those treatments and whose caseloads contain very few of the patients on whom the specialized treatment was tested. This is particularly true at the level of the "community-based" public sector treatment programs that have been forced to operate under limited budgets with little access to sophisticated services. How can research in the treatment setting inform these providers? How can these providers use information from research studies to upgrade or expand their treatment efforts—within the practical constraints of budget and personnel available?

Parameters of the Literature Review— In response to these questions, we have reviewed the existing treatment outcome literature to summarize the available knowledge regarding the important patient and treatment factors that have been shown to influence the outcomes of addiction rehabilitation treatments. We felt this was an important first step in recognizing and recommending proven, practical, and cost-effective treatment strategies that can be implemented by community behavioral treatment programs. In this regard, we have elected not to review literature on detoxification methods in order to better focus on standard rehabilitation treatments for drug and alcohol dependence—typically following detoxification. Our review does not include the adolescent drug abuse treatment literature since it is still a developing field and there is a paucity of pertinent outcome studies in this area. In addition, we elected not to include a review of the smoking cessation literature as there have been excellent recent reviews of this entire field (see Fiore et al., 1996).

From a methodological perspective, we included only those clinical trials, treatment matching, or health services studies where the patients were alcohol or drug dependent by contemporary criteria (e.g., DSM); where the treatment provided was a conventional form of rehabilitation (any setting or modality); and where there were measures of either treatment processes or patient change during the course of treatment as well as posttreatment measures of outcome as defined later in the chapter. We have elected to include methadone maintenance (as well as its long acting form, levo -alpha-acetylmethadol [LAAM) as part of the general category of outpatient rehabilitation treatments, rather than create a special category.

In the review that follows we first discuss some of the basic assumptions underlying rehabilitation forms of addiction treatment since they set the stage for the clinical methods currently in use and for the types of studies that are in the research literature. Next we discuss some of our considerations regarding definitions of "outcome." With these assumptions and considerations in mind, we then review the most significant patient and treatment process contributors to the outcomes of addiction treatment.

• REHABILITATION TREATMENTS IN ADDICTION: WHO ARE THEY FOR, WHAT SHOULD THEY DO?

What Is Addiction Rehabilitation Designed to Do? —In contrast to "detoxification," which is a relatively brief, usually medical procedure designed to stabilize the acute physical and emotional distress and instability caused by recent termination of heavy alcohol and/or drug use, "rehabilitation" is a much longer process, usually involving multiple types of medical and social services, that is designed to help recently stabilized patients achieve sustained periods of drug-free living and stable personal and social function.

There are clear physical signs and symptoms associated with the cessation of most addictive substances and there are standard medications and withdrawal procedures that are very effective in ameliorating these acute "detoxification" symptoms and restoring physiological and emotional stability. Despite the efficacy of these detoxification methods, there is uniform agreement among professionals that detoxification by itself—regardless of the type or the duration—is rarely associated with sustained periods of abstinence or even improved function. Well after the return of physiological and emotional stability, most patients continue to experience regular periods of intense craving for alcohol and drugs and this can lead to "loss of control" in situations where these drugs of abuse are (or have been) present. There has been substantial research showing that among former addicts who have been abstinent for up to a year, even the sight or sound of stimuli associated with former periods of drug use can produce (through learned association) measurable changes in brain chemistry that mimic the actual use of the drug and the withdrawal symptoms produced by those drugs (see Childress et al., 1985, 1986, 1992; O'Brien et al., 1991).

Rehabilitation Methods— While there is universal agreement that some form of rehabilitation is necessary, there has been a very wide range of professional opinion regarding the nature or amount of rehabilitation necessary to produce sustained benefits. In part this is due to disagreement regarding the etiology and course of the addiction syndrome. These etiological theories include a genetic predisposition, an acquired metabolic abnormality, learned negative behavioral patterns, self medication of underlying psychiatric or physical medical problems, and lack of family and community support for positive function. For this reason, there is an equally wide range of treatment methods that have been applied to address these etiological and predisposing factors and to provide continuing support for the targeted behavioral changes. These have included such diverse elements as psychotropic medications to relieve underlying psychiatric problems, "anti-craving" medications to relieve alcohol and drug craving, acupuncture to correct acquired metabolic imbalances, educational seminars, films and group sessions to correct false impressions about alcohol and drug use, group and individual counseling and therapy sessions to provide insight, guidance and support for behavioral changes, and peer help groups (AA/ NA/CA) to provide continued support for the behavioral changes thought to be important for sustaining improvement.

These rehabilitation methods have been traditionally provided in two types of settings—inpatient and outpatient. At this writing, inpatient rehabilitation programs can be divided into three general categories (Hubbard et al., 1989, 1997):

Inpatient hospital-based treatment (now very rare)—from 7 to 11 days.

Nonhospital "residential rehabilitation"—from 30 to 90 days.

Therapeutic Communities—from 6 months to 2 years.

Outpatient forms of treatment (at least abstinence oriented treatments) range from 30 to 120 days (Hubbard et al., 1989, 1997). Many of the more intensive forms of outpatient treatment (Intensive Outpatient, Day-Hospital) begin with full or half-day sessions, five or more times per week for approximately one month. As the rehabilitation progresses the intensity of the treatment reduces to shorter duration sessions (one to two hours) delivered twice weekly to semi-monthly.

Regardless of whether the rehabilitation process is initiated in an inpatient or outpatient setting, most rehabilitation programs recognize the need for some level of continuing involvement with the rehabilitation process. Thus the final part of outpatient rehabilitation is typically called "Continuing Care" or "Aftercare" and includes weekly to monthly group support meetings continuing (in association with parallel activity in self-help groups) for as long as two years (McKay et al., 1998).

A Special Note on Maintenance Forms of Treatment. The opiate dependence treatment field has had the availability of orally administered, long-acting agonist medications. Three forms of opiate maintenance medications are currently available, Methadone, Levo-alpha-acetyl methadol (LAAM) and Buprenorphine. While each is different in nature and duration of action, they provide 24-72 hours of continuing relief from opiate withdrawal and craving; and serve as the basis for adjunctive social supportive therapy and medical care. This maintenance modality is quite similar in purpose and practice to the combined regimens of pharmacotherapy and supportive therapy now provided for depressed, diabetic, hypertensive, asthmatic, and other chronic illness patients. Like most forms of pharmacotherapy for patients with chronic illnesses, opiate maintenance treatments are designed with an indeterminate length—possibly continuing throughout the life of the patient.

Outcomes Expected from Addiction Rehabilitation Treatments— We have argued in earlier work (McLellan et al., 1996b, 1997a) that outcome expectations for substance abuse treatment should not be confined simply to reduction of alcohol and drug use since the public, the payers of treatment, and even the patients themselves are interested in a broader definition of "rehabilitation." Further, we have argued that for substance abuse treatments to be "worth it" to the multiple stakeholders who are involved in treatment, the positive effects of addiction treatment should be sustained beyond the end of the treatment period and carry on at least six to twelve months. Most researchers in the addiction field have taken a similar, broad view of outcome expectations in the addiction treatment field (See Anglin et al., 1989; Babor et al., 1988; Ball and Ross, 1991; De Leon, 1984; Hubbard et al., 1989, 1997; Simpson, 1981, 1997; Simpson et al., 1997a,b).

Thus in the review that follows we have given greater attention to studies where multiple outcomes were measured six to twelve months following inpatient discharge or at the same points during the course of the outpatient period of care. Further, we have considered three domains that we feel are relevant to the rehabilitative goals of the patient and to the public health and safety goals of those societal stakeholders that support treatment:

Sustained reduction of alcohol and drug use. This is the foremost goal of substance dependence treatments and we consider it as the primary outcome domain. Within the review, we accepted as operational evidence for improvement in this domain both objective data from urinalysis and breathalyzer readings as well as patients' self reports of alcohol and drug use when those reports were recorded by independent interviewers under conditions of privacy and impartiality.

Sustained increases in personal health and social function. Improvements in the medical/psychiatric health and social function of addicted patients are important from a societal perspective in that these improvements reduce the problems and expenses produced by the addiction. In addition, improvements in these areas are important for maintaining reductions in substance use. Within the review, we accepted evidence from measures such as general health status inventories, psychological symptom inventories, family function measures, and simple measures of days worked and dollars earned, collected either directly from the patient via confidential self report or from independent medical/psychiatric evaluations and employment records.

Sustained reductions in public health and public safety threats. The threats to public health and safety from substance abusing individuals come from behaviors that spread infectious diseases and from behaviors associated with personal and property crimes. With regard to infectious disease, the sharing of needles, unprotected sex, and trading sex for drugs are serious behaviors that have clearly been linked to addiction and are significant threats to public health. Within the review, we accepted evidence of improved public health from confidential self reporting techniques or (rarely) through laboratory tests. Public safety threats were measured in the studies reviewed either by confidential interviews and questionnaires or by objective records of arrests and incarcerations.

In our view, the first two domains are quite consistent with the "primary and secondary measures of effectiveness" typically used by the Food and Drug Administration to evaluate new drug or device applications in controlled clinical trials (FDA) and quite consistent with the mainstream of thought regarding the evaluation of other forms of health care (Stewart and Ware, 1989). The final outcome dimension we believe is more specific to the treatment of substance use disorders since it acknowledges the significant public health and public safety concerns associated with addiction.

• RESEARCH ON PATIENT FACTORS RELATED TO TREATMENT OUTCOME

Demographic Factors— While demographic factors are typically important predictors of the development of drug abuse problems (IOM, 1990b; Wilsnack and Wilsnack, 1993) there is little evidence that race, gender, age, or educational level are consistent predictors of treatment outcome—among those who begin a treatment episode. An inspection of a wide range of treatment outcome studies in the substance abuse rehabilitation field suggests that demographic factors such as age, education, race, and even treatment history are relatively poorly related to the three outcome domains defined above in any of the major rehabilitation modalities (see Ball and Ross, 1991; Finney and Moos, 1992; McLellan et al., 1994; Rounsaville et al., 1987). For example, a study of 649 patients entering 22 treatment programs (seven inpatient, eight outpatient, seven methadone maintenance) for treatment of primary alcohol, opiate, or cocaine dependence evaluated the contribution of demographic variables including age, ethnicity, gender, marital status, years of education, and years of problematic substance abuse (McLellan et al., 1994). Results showed that none of the demographic measures was a significant predictor of either posttreatment substance use or posttreatment social adjustment. Similarly, studies by Simpson and Savage (1980) showed no significant effect of demographic and social indicators in predicting multiple outcome domains among heroin addicts treated in methadone maintenance and outpatient drug free treatment.

Though less studied at this time, there may be some important exceptions to this conclusion. For example, pregnant and parenting women are an important subgroup of the larger patient population who require different features to permit access to treatment as well as different constellations of treatment to address their often significant treatment problems (see Gomberg and Nirenberg, 1993; Wilsnack and Wilsnack, 1993). There has been indication that these patients have been reluctant to get into "standard" treatments because of stigma and because of the absence of services for their children. There have been experimental programs created to meet the needs of this important subgroup—and some excellent evaluations have followed these groups posttreatment (see Hagan et al., 1994). There have been very few longer term outcome studies of specialized treatments for pregnant and parenting women and only the most obvious conclusions can be drawn regarding the factors that appear to be important for attraction, retention, and improved outcomes for these patients. These factors would include but not be restricted to:

The availability of care for children—and sometimes a residence that will accommodate the patients and their children. Many of the addicted women who could benefit from treatment are responsible for the care of children and facilities that will provide respite care are likely to be necessary for these women to be able to enter outpatient treatment. Other women will not have the resources to be self supporting and may need temporary accommodations for themselves and their children. Still others may require a facility that will offer protection from aggressive and/or drug involved partners. Problems of safety from physical and sexual abuse and separation from drug involved relationships are common in a large proportion of these women (Hagan et al., 1994; Wilsnack and Wilsnack, 1993; Schmidt and Weisner, 1995; Weisner and Schmidt, 1992). Residential settings are potentially important to address these problems.

The availability of general medical, OB/GYN, and psychiatric services. Disproportionately high numbers of these women have shown significant medical and psychiatric problems (Finnegan, 1991; Hagan et al., 1994; Schmidt and Weisner, 1995; Weisner and Schmidt, 1992; Wilsnack and Wilsnack, 1993). Therefore, it is important for programs that treat women substance abusers to provide adjunctive services in these areas.

Severity of Substance Use— Various measures of higher levels of severity and greater chronicity of patients' substance use patterns have been reliably associated with poorer retention in treatment and more rapid relapse to substance use following treatment. This has been true of both alcohol dependent patients (Babor et al., 1988; Finney and Moos, 1992); opiate dependent patients in therapeutic communities and in methadone maintenance (Ball and Ross, 1991; De Leon et al., 1984, 1994; Simpson, 1981, 1997a); and cocaine dependent patients treated in outpatient and inpatient settings (Alterman et al., 1994; Carroll et al., 1991; McLellan et al., 1994). The uniform nature of these predictive relationships across different types of drug dependence and treatment modalities suggests a pervasive trend toward poorer performance across all forms of treatment among those with longer durations and/or more intensive use patterns. This relationship is strongest between severity of substance use at treatment admission and posttreatment substance use. It is less clear whether the severity of alcohol and drug use at treatment admission is predictive of the other domains of personal health and social function, or public health and safety (McLellan et al., 1981b, 1992b, 1994). Thus, while the severity of substance use prior to treatment admission (measured in terms of amount, duration, and intensity of alcohol and drug use) is negatively related to posttreatment substance use—accounting for perhaps 10%-15% of outcome variance in that measure—it is less related to outcome in the other outcome domains (Babor et al., 1988; McLellan et al., 1994).

Severity of Psychiatric Problems— After the severity of the substance abuse problem, perhaps the most robust general patient variable predicting treatment response and posttreatment outcome has been the chronicity and severity of the psychiatric problems presented by the patient at the start of treatment (Carroll et al., 1993; Kadden et al., 1990; McLellan et al., 1983a,b, 1994; Powell et al., 1982; Project MATCH, 1997; Rounsaville et al., 1987; Woody et al., 1984, 1987). It is important to note that psychiatric problems have been measured using many scales and interviews in these studies, and all have attempted to distinguish more enduring or chronic psychiatric symptoms from the acute and temporary effects of alcohol and drug withdrawal. In the case of methadone maintained, opiate dependent patients, studies by McLellan and colleagues (1983a,b) indicated that the psychiatric severity scale from the Addiction Severity Index was the single best predictor of six month substance use, personal health, and social adjustment. Similar findings have been shown by Ball and Ross (1991) and by Kosten and colleagues (1987) and Rounsaville and colleagues (1983, 1987) with methadone maintained patients.

Measures of psychiatric severity have also been shown to be predictive of outcome in studies of opiate and multiple drug dependent patients entering an inpatient therapeutic community setting. For example, De Leon (1984) showed that opiate and non-opiate dependent patients with MMPI profiles indicative of high levels of psychopathology entering a therapeutic community were more likely to drop out of treatment and showed significantly less improvement on all outcome measures at discharge and at subsequent twelve month follow-up evaluations. In an earlier study of mixed opiate and non-opiate dependent male veterans entering into a therapeutic community McLellan and colleagues (1984) found that patients with the highest scores on the ASI psychiatric severity scale were most likely to drop out prematurely and actually showed 20%-40% less improvement than other patients who entered treatment at the same time. In that study, the "high psychiatric severity" patients who stayed in treatment longest actually showed the worst posttreatment status—suggesting that the therapeutic environment that had been demonstrably effective for patients with lower levels of psychiatric severity, was actually counter therapeutic for the high severity patients.

In the case of cocaine dependent patients, Carroll et al. (1991) also found poorer outcomes for patients with greater psychiatric pathology, as defined by scores on the Addiction Severity Index (ASI) psychiatric problem scale. Her findings were obtained in an outpatient rehabilitation setting. Similar results were found among cocaine dependent patients by Alterman et al. (1994) for patients treated in both a day-hospital and an inpatient rehabilitation setting.

Finally, there has been a great deal of evidence for the predictive power of general psychiatric symptomatology among alcohol dependent patients. Rounsaville and colleagues showed that psychiatric severity as measured by the ASI psychiatric scale was the best predictor of overall adjustment among previously treated alcohol dependent patients at a 2.5 year posttreatment follow-up (Rounsaville et al., 1987). Other authors have found that severity of depression (Powell et al., 1982; Schuckit et al., 1990) and anxiety (Brown et al., 1991; Schuckit et al., 1990) have been predictive of posttreatment drinking and posttreatment social adjustment among various samples of alcohol dependent patients. More recently, findings from the NIAAA sponsored, multisite study of patient treatment matching (Project MATCH, 1997) showed that the ASI psychiatric scale was a significant general predictor of posttreatment drinking and posttreatment social adjustment in a sample of more than 1200 alcohol dependent patients in three types of outpatient treatment.

Note: While there are a number of studies relating severity of psychopathology to posttreatment outcome, it should be noted that Schuckit and his colleagues have argued cogently against "over diagnosing" psychiatric symptoms, especially among alcohol dependent patients (Brown et al., 1991; Schuckit and Monteiro, 1988). These authors have shown that much of the serious psychopathology seen among alcohol dependent patients at treatment admission is reduced following even four weeks of abstinence. There is also evidence for rapid dissipation of psychiatric symptoms following abstinence from cocaine (Satel et al., 1991; Weddington, 1992). This proviso suggests that care should be taken to distinguish acute alcohol and/or drug related psychopathology from more enduring and chronic psychiatric symptoms.

Patient Motivation and Stage of Change— Evidence for patient "motivation for treatment" has traditionally been measured as the extent to which patients have freely entered into treatment. Conversely, patients who have been coerced into treatment based on pressure from legal, family, or employment sources, have been considered "treatment resistant." While this is a face valid measure of motivation—and presumably a good predictor of patient performance during and following treatment—the large literature on coerced treatment indicates the opposite of what would be expected. That is, patients who have been forced to enter a substance abuse treatment have shown during and posttreatment results that are quite similar to those shown by supposedly "internally motivated" patients (Inciardi, 1988; Lawental et al., 1996; Roman, 1988). This rather broad literature has led to the conclusion that when "motivation" is conceptualized and measured in terms of the degree to which the patient has been coerced into treatment, it is not an important predictor of treatment response.

However, there is rapidly growing body of research indicating that when motivation is defined as "readiness for change" and is conceptualized and measured in stages as suggested by Prochaska, DiClemente and their associates (e.g., Prochaska and DiClemente, 1984; Prochaska et al., 1992), "stage of change" motivation can be a very important predictor of treatment response and treatment outcome. According to the stage of change model, the process of behavior change occurs in a progression of five distinct stages, each characterized by a different constellation of attitudes and behaviors. An individual in the "precontemplation stage" has no awareness of a problem and no desire to change. A patient in the "preparation stage" has made the decision to change and is already taking steps to do so. A patient in the "maintenance stage" has shown change and is maintaining the changed behavior (see Prochaska and DiClemente, 1984).

The power of the model comes from two sets of findings. First, a relatively simple measure of stage of change such as the University of Rhode Island Change Assessment (URICA) (Prochaska and DiClemente, 1984; Prochaska et al., 1992) can apparently identify individuals in the precontemplation stage of change for whom traditional forms of rehabilitation treatment (most of which assume desire and ability to change as a precondition of admission) will not be effective. Specifically, there are several studies showing failure of traditional forms of counseling and therapy in patients identified as "precontemplators" on the URICA (DiClemente et al., 1991; Heather et al., 1993; Marlatt, 1988). The second important finding from work with this measure is that the "stage of change" is apparently an important predictor of treatment response and treatment outcome across all types of substance dependent patient samples (especially alcohol and nicotine dependent patients, but it is less studied among cocaine and opiate dependent patients), even those who are not in treatment (DiClemente et al., 1991).

The model provides a way of identifying patients with different levels of motivation and outlines a way of tailoring interventions to match their stage of change. It makes sense that those patients who consciously intend to change are more likely to succeed in treatment than those who do not. In this regard, the majority of the predictive power of the stage of change model has been the identification of precontemplators. Additional research is warranted to determine the extent to which the remaining stages of change can predict response to standard rehabilitation treatments.

Employment— There is ample indication from research with methadone maintained patients that employment, employability, and self support skills are a significant problem for this population; and that unemployed patients are more likely to drop out of treatment prematurely and to relapse to substance use early following treatment (Dennis et al., 1993; Hubbard et al., 1989; Platt, 1995). This was illustrated in a study of male veterans in methadone maintenance treatment by McLellan and colleagues (1981a). These authors found that patients who derived most of their income from employment showed more improvement and better six-month outcomes in several outcome domains including drug use, legal, and psychiatric problems and of course employment, than similar patients who derived the majority of their income from unemployment or welfare.

Hubbard and his colleagues (1989) showed that the development of employable skills and the capacity for self support were among the most important requirements for sustained reductions in drug use among a large cohort of drug dependent patients in treatment. Similar findings were shown by De Leon among opiate dependent patients in a therapeutic community setting (1984). Finally, Hall and her colleagues showed that unemployment was a significant predictor of early relapse to opiate use among detoxified heroin dependent males (1981). Similarly, in a sample of primarily employed, multiple substance abusers entering private inpatient or outpatient, abstinence oriented treatment programs, McLellan and colleagues showed that employment problems (getting along with supervisor, dissatisfaction with present job and salary, etc.) were one of the most significant predictors of both posttreatment substance use as well as posttreatment personal health and social function, measured at six-month follow-up (McLellan et al., 1994).

Family and Social Supports— Social supports have been widely studied in the field of alcohol and drug dependence. Social support has been conceptualized variously as the active participation in peer-supported treatments such as AA/NA; as the availability of relationships that are not conflict producing (McLellan et al., 1980, 1984) and in more detailed models, as the level of support for abstinence from those relationships (Longabough et al., 1993, 1995). Among alcohol dependent patients, there is often indication of significant ''dysfunction" among the families, and in turn, the level of this disruption has been associated with earlier drop out from outpatient treatment (McLellan et al., 1983a, 1994), earlier relapse to drinking following treatment (Moos and Moos, 1984) and generally worse posttreatment function (McKay et al., 1994; McCrady et al., 1986; Moos and Moos, 1984).

Among opiate dependent patients there has been very little work associated with family and social supports as they relate to outcome. One prominent exception has been the work of Stanton and colleagues who showed both significant disruption and social pathology among families of methadone maintained patients; and a significant relationship between level of social pathology in the family of origin (typically also the posttreatment family environment in these patients) and use of heroin during methadone treatment (Stanton 1979; Stanton and Todd, 1982). McLellan et al. (1983a,b) found that the family relationship scale on the ASI was one of three significant predictors of posttreatment drug use and general personal and social function among opiate dependent patients in either inpatient therapeutic community or outpatient methadone maintenance treatment. In a subsequent study, this group also found that the family relationship scale was a significant predictor of posttreatment social function and relapse to cocaine and alcohol use among insured, working patients referred to substance abuse treatment through their employee assistance program (McLellan et al., 1993a, 1997a). An interesting, paradoxical finding in this area was reported by Havassy and her colleagues (1991). Among primarily African-American cocaine dependent patients, these authors found a paradoxically negative relationship between the reported number of available family and friends of the patient and relapse to cocaine use following treatment: the more friends and family available to the patient, the earlier the return to cocaine use. The authors' hypothesize that in this severely affected cohort of patients, the only available sources of social support may have been associates with whom the patients had previously used drugs.

• RESEARCH ON TREATMENT PROCESS FACTORS RELATED TO REHABILITATION OUTCOME

Patient factors have been much more widely studied than treatment setting, modality, process, and service factors as predictors of outcome from addiction rehabilitation treatments. Perhaps the major reason for this is that while there have been many reliable and valid measures of various patient characteristics, there are still very few measures of treatment setting (Moos, 1974; Moos et al., 1990) or treatment services (McLellan et al., 1992a; Widman et al., 1997).

There is good news however, regarding the study of treatment factors in the substance abuse field. Recent developments in the psychotherapy field have led to the creation of manual-based treatments and with them, appropriate measures of treatment fidelity and integrity. Following on this progress, the multisite NIAAA study of patient treatment matching (Project MATCH, 1997) has provided the field with new manuals for the three Project MATCH treatments as well as additional measures of the nature and fidelity of each treatment. These are likely to improve the study of addiction treatment process in the years to come. Below we review several dimensions or characteristics of treatment that have been studied and that have shown some relationship with outcome following treatment.

Setting of Treatment— There have now been many studies investigating potential differences in outcome between various forms of inpatient and outpatient rehabilitation. For example, studies by McCrady et al. (1986) and Alterman et al. (1994) randomly assigned alcohol dependent patients to an equal length (28-30 days) of either inpatient or day-hospital rehabilitation, where the treatment elements were also designed to be similar. Both studies showed very similar findings. Patients in both the inpatient and outpatient arms of both these studies showed substantial and significant reductions in alcohol use, as well as improvements in many other areas of personal health and social function—suggesting that both settings of care were able to produce substantial benefits. At the same time, a wide range of outcome measures collected at six-month follow-up in both studies, showed essentially no statistically significant or clinically important differences between the two settings of care—suggesting that the setting of care might not be an important contributor to outcome. A further analysis of data from the Alterman et al. study (McKay et al., 1995) indicated that 12-month outcomes in the day hospital group were generally at least equal to outcomes following inpatient care, and pertained to both randomized and nonrandomized subjects.

Consistent with the results of these two studies, reviews of the literature on inpatient and outpatient alcohol rehabilitation by Miller and Hester (1986) and Holder et al. (1991) also concluded that across a range of study designs and patient populations there was no significant advantage provided by inpatient care over outpatient care in the rehabilitation of alcohol dependence, despite the substantial difference in costs. In contrast, a widely cited study by Walsh et al. (1991) did find a significant difference in outcome favoring an inpatient program. However, this difference was shown among employed alcohol dependent patients who were assigned to either an inpatient program plus Alcoholics Anonymous (AA) or to AA meetings only (rather than to formal outpatient treatment). One recent review of the alcohol inpatient-outpatient literature did conclude that in studies that found an advantage to inpatient care over outpatient treatment, outpatients did not receive inpatient detoxification and the studies tended to not have social stability inclusion criteria or to require randomization (Finney et al., 1996). This review points to the need to consider "real world" factors when evaluating the effectiveness of different treatment settings.

In the field of cocaine dependence treatment, there have also been several studies examining the role of treatment setting. Again, while there is evidence for high attrition rates (e.g., Kang et al., 1991), there is still evidence indicting that outpatient treatments for cocaine dependence can be effective, even for patients with relatively limited social resources. In a recent study, Alterman and his colleagues followed up a prior comparison study of inpatient and day-hospital treatment of alcohol dependence (1994) with an identical examination comparing the effectiveness of four weeks of intensive, highly structured day hospital treatment (27 hours weekly) with that of inpatient treatment (48 hours weekly) for cocaine dependence. The subjects were primarily inner city, male African Americans treated at a Veterans Administration Medical Center. The inpatient treatment completion rate of 89% was significantly higher than the day-hospital completion rate of 54% . However, at seven months posttreatment entry self reported outcomes indicated considerable improvements for both groups in drug and alcohol use, family/social, legal, employment, and psychiatric problems. The finding of reduced self reported cocaine use was supported by urine screening results. Both self report and urine data indicated 50%-60% abstinence for both groups at the follow-up assessment. The comparability of both treatment settings was also evident in 12-month outcomes in both randomized and self-selecting patients (McKay et al., 1994).

Similar findings have been shown in field studies of private substance abuse treatment programs treating primarily cocaine and cocaine-plus-alcohol-dependent patients (McLellan et al., 1993a; Pettinati et al., 1998). In all of these studies, patients who were assigned to one of several outpatient treatment programs, were less likely to complete treatment than those assigned to the inpatient programs; but those who did complete treatment showed equal levels of improvement and outcome in the inpatient and outpatient settings. It is important to note that virtually all studies of this type have shown greater engagement and retention of patients in inpatient settings.

There have been at least two attempts to formalize clinical decision processes regarding who should, and should not be assigned to inpatient and outpatient settings of care (Cleveland Criteria; American Society of Addiction Medicine [ASAM] Criteria). McKay et al. (1992) failed to show evidence for the predictive validity of the Cleveland placement criteria at least when applied to the assignment of alcohol and drug dependent patients to day hospital or inpatient care. That is, patients who met the Cleveland Criteria for inpatient treatment did not have worse outcomes than those who met criteria for day hospital only when both groups received day hospital treatment. If the Cleveland Criteria had been valid, those who "needed inpatient treatment" but did not receive it should have had poorer outcomes than those who were appropriately "matched" to day hospital. In a similar study evaluating the psychosocial predictors from the ASAM criteria, McKay et al. (1997b) did find at least partial support for the predictive validity of these placement variables. That is, among patients who "needed inpatient treatment" as defined by the psychosocial elements of the ASAM criteria, those who were randomly assigned to outpatient care did show somewhat worse abstinence rates and generally poorer social outcomes than those who were randomly assigned to inpatient rehabilitation. The retrospective nature of this study made it impossible to complete a full evaluation of these criteria.

The most recent versions of the ASAM criteria have attempted to make very fine grained decisions regarding placements to levels of care defined by the amount and quality of medical supervision and monitoring. Research is needed to determine the predictive validity of these finer distinctions and whether placements to settings and modalities with "more medical supervision" actually receive more medical contact or services than placements that are not expected to receive such services.

Length of Treatment/Compliance with Treatment— Perhaps the most robust and pervasive indicator of favorable posttreatment outcome in all forms of substance abuse rehabilitation has been length of stay in treatment. Virtually all studies of rehabilitation have shown that patients who stay in treatment longer and/or attend more treatment sessions, have better posttreatment outcomes (Ball and Ross, 1991; De Leon, 1984, 1994; Hubbard et al., 1997; Simpson 1981, 1997; Simpson et al., 1997a,b). Specifically, several studies have suggested that outpatient treatments of less than 90 days are more likely to result in early return to drug use and generally poorer response than treatments of longer duration (Ball and Ross, 1991; Simpson, 1981, 1997; Simpson et al., 1997a,b).

Though length of stay is a very robust, positive predictor of treatment outcome, the nature of this relationship is still ambiguous. Clearly, one possibility is that patients who enter treatment gradually acquire new motivation, skills, attitudes, knowledge, and supports over the course of their stay in treatment; that those who stay longer acquire more of these favorable attributes and qualities; and that the gradual acquisition of these qualities or services is the reason for the favorable outcomes. An equally plausible possibility is that "better motivated and better adjusted patients" come into treatment ready and able to change; that the decisions they made to "change their lives" were made in advance of their admission and because of this greater motivation and "treatment readiness" they are likely to stay longer in treatment and to do more of what is recommended. These two interpretations of the same facts have very different implications for treatment practice. If treatment gradually produces positive changes over time, it is obviously clinically sound practice to retain patients longer—perhaps even through coercion—and to provide them with more services during treatment. On the other hand, if well motivated, high functioning, compliant patients enter treatment with the requisite skills and supports necessary to do well, then efforts to provide more services or to coerce patients into longer stays may not add to the effectiveness of more streamlined and less expensive rehabilitation efforts.

Participation in AA/NA —AA is of course recognized as a self-help or mutual support organization and not a formal treatment. For this reason, and because of the anonymous quality of the group, not much research has been done to evaluate this important part of substance abuse rehabilitation until recently (McLatchie and Lomp, 1988; McCrady and Miller, 1993; Nowinsky and Baker, 1992; Project MATCH, 1997). While there has always been consensual validation for the value of AA and other peer support forms of treatment, the past few years have witnessed new evidence showing that patients who have an AA sponsor, or who have participated in the fellowship activities—have much better abstinence records than patients who have received rehabilitation treatments but have not continued in AA. McKay and his colleagues (1997a) found that participation in posttreatment self-help groups predicted better outcome among a group of cocaine or alcohol dependent veterans in a day hospital rehabilitation program. Timko et al., (1994) found that more AA attendance was associated with better 1-year outcomes among previously untreated problem drinkers regardless of whether they received inpatient, outpatient, or no other treatment. Finally, a recent review of the literature on the impact of self-help programs concluded that greater participation was generally associated with better alcohol and psychosocial outcomes, although the magnitude of the effects tended to vary as a function of the quality of the study and whether patients were treated in inpatient or outpatient settings (Tonigan et al., 1996).

There has been less research in the use of self-help organizations among cocaine and/or opiate dependent patients. However, a recent study of cocaine patients participating in outpatient counseling and psychotherapy showed that while only 34% attended a cocaine anonymous (CA) meeting, 55% of those who did became abstinent as compared with only 38% of those who did not attend CA.

In contemporary addiction treatment, AA has become synonymous with the last part of rehabilitation—aftercare. Virtually all alcohol dependence rehabilitation programs and most cocaine dependence rehabilitation programs refer patients to AA programs with instructions to get a sponsor, "share and chair" at meetings, and to attend 90 meetings in 90 days as a continued commitment to sobriety. Thus, while the research studies done to date have generally suggested that the peer support component of rehabilitation is valuable, it is also difficult to sort out the extent to which AA attendance constitutes an active ingredient of successful treatment and/or the extent to which it is simply a marker of general treatment compliance and commitment to abstinence.

In this regard, several investigators have studied the relationship of completing various 12-step processes during the course of rehabilitation, to relapse following treatment. Morgenstern and colleagues reported that patients who adopted more of the attitudinal and behavioral tenets of the 12-step model of rehabilitation such as admission of powerlessness, acceptance of a higher power, commitment to AA, and agreement that alcoholism is a disease, were no more (or less) likely to relapse following treatment than patients who had adopted very few of the 12-step tenets by the end of the rehabilitation treatment (Morgenstern et al., 1997). At the same time, two general tenets found in all rehabilitation models—greater commitment to abstinence and greater intention to avoid high risk situations—did predict a lower likelihood of relapse (Morgenstern et al., 1997). In another analysis from the same study, greater affiliation with AA following treatment predicted better outcomes. AA affiliation was in turn positively associated with self-efficacy, motivation, and coping efforts, which were themselves significant predictors of outcome (Morgenstern et al., 1997). Thus, more research in this area is warranted to determine how participation in AA exerts its positive effects.

The Therapist or Counselor— There is a growing body of research suggesting that having access to regular drug/alcohol counseling can make an important contribution to the engagement and participation of the patient in treatment and to the posttreatment outcome. Perhaps the clearest example of the role of the counselor and at least individual counseling was shown in a study of methadone maintained patients, all within the same treatment program and all receiving the same methadone dose, who were randomly assigned to receive counseling or no counseling in addition to the methadone (McLellan et al., 1993b). Results were unequivocal showing that 68% of patients assigned to the no counseling condition failed to reduce drug use (confirmed by urinalysis) and 34% of these patients required at least one episode of emergency medical care. In contrast, no patient in the counseling groups required emergency medical care, 63% showed sustained elimination of opiate use, and 41% showed sustained elimination of cocaine use over the six months of the trial.

A study by Fiorentine and Anglin (1997) as part of a larger "Target Cities" evaluation also showed the contribution of counseling in drug rehabilitation. Group counseling was the most common modality (averaging 9.5 sessions per month) followed by 12-step meetings (average 7.5 times per month) and individual counseling (average 4.7 times per month). Greater frequency of both group and individual counseling sessions were shown to decrease the likelihood of relapse over the subsequent six months. One important contribution of this study, given the above cautions regarding the role of simple length of stay in determining treatment outcome (see above), is that the relationships shown between more counseling and lower likelihood of relapse to cocaine use were seen even among patients who completed treatment—that is, having approximately the same tenure in the programs. Thus, it may be that beyond the simple effects of attending a program, more involvement with the counseling activities is important for improved outcome.

At least four studies of substance abuse treatment have documented between-therapist differences in patient outcomes. These differences have emerged both among professional psychotherapists with doctoral level training and among paraprofessional counselors. Luborsky et al. (1985) found outcome differences in a variety of areas among nine professional therapists providing ancillary psychotherapy to methadone maintenance patients. McLellan et al. (1988) found that assignment to one of five methadone maintenance counselors resulted in significant differences in treatment progress over the following six months. Specifically, patients transferred to one counselor achieved significant reductions in illicit drug use, unemployment, and arrests while concurrently reducing their average methadone dose. In contrast, patients transferred to another counselor evidenced increased unemployment and illicit drug use while their average methadone dose went up. In a study of two different interventions for problem drinkers, Miller, Taylor, and West (1980) found significant differences between paraprofessional therapists in the percentage of their patients who improved by six-month follow-up. These percentages varied from 25 % for the least effective therapist to 100% for the most effective therapist. Finally, McCaul and Svikis (1991) reported significant differences in posttreatment drinking rates and several other outcomes among alcohol dependent patients assigned to different individual counselors within an alcohol treatment program.

There is much research that needs to be done in this area. Although it is relatively clear that therapists and counselors differ considerably in the extent to which they are able to help their patients achieve positive outcomes, it is less clear what distinguishes more effective from less effective therapists. In an experimental study of two different therapist styles, Miller, Benefield, and Tonigan (1993) found that a client centered approach emphasizing reflective listening was more effective for problem drinkers than a directive, confrontational approach. In a review of the literature on therapist differences in substance abuse treatment, Najavits and Weiss (1994) concluded, "The only consistent finding has been that therapists' in-session interpersonal functioning is positively associated with greater effectiveness" (p. 683). Among indicators of interpersonal functioning were the ability to form a helping alliance (Luborsky et al., 1985), measures of the level of accurate empathy (Miller et al., 1980; Valle, 1981), and a measure of "genuineness," "concreteness," and "respect" (Valle, 1981).

It should be noted that there are a variety of certification programs for counselors (Committee on Addiction Rehabilitation [CARF] and Certified Addictions Counselor [CAC]) as well as other professions treating substance dependent patients (American Society of Addiction Medicine; American Academy of Psychiatrists in Addiction; recent added certification for psychologists through the American Psychological Association). These "added qualification certificates" are offered throughout the country, usually by professional organizations. Although the efforts of these professional organizations to bring needed training and proficiency to the treatment of addicted persons are commendable, we were unable to find any studies validating whether patients treated by "certified" addictions counselors, physicians, or psychologists have better outcomes than patients treated by noncertified individuals. This is an important gap in the existing literature and results from such studies would be quite important for the licensing efforts and health policy decisions of many states and health care organizations.

Medications— At this writing, there is a great deal of research sponsored by both the National Institute on Alcoholism and Alcohol Abuse and the National Institute on Drug Abuse aimed at developing useful medications for the treatment of substance dependent persons. Great progress has been made over the past ten years in the development of new medications and in the application of existing medications for the treatment of particular conditions associated with substance dependence and for particular types of substance dependent patients (see IOM, 1995; O'Brien, 1996; O'Brien and McKay, in press). Here we have only summarized some of the clearest results from the use of agonist and antagonist medications in the treatment of substance dependence and have provided citations for more comprehensive medication reviews for interested readers.

Agonist Medications— Methadone has been an approved agonist medication for the maintenance treatment of opiate dependence for more than 25 years. The long-acting form of methadone (48- to 72-hour duration), LAAM has recently received FDA approval and has been accepted by 16 states for prescription only at methadone maintenance programs. Buprenorphine is a partial opiate agonist that has been widely used in Europe and in the United States. It is thought to have some advantages over methadone in that it produces far fewer (often none) withdrawal symptoms (see Bickel et al., 1997). At this writing, it is not yet approved for use.

Among the most robust findings in the treatment literature is the relationship between dose of methadone and general outcome in methadone treatment (Ball and Ross, 1991; D'Aunno and Vaughn, 1992, 1995; Institute of Medicine, 1995). Higher doses are more effective than lower doses. In a well controlled double blind multisite VA study, Ling et al. (1976) found that 100 mg per day was superior to 50 mg as indicated by staff ratings of global improvement and by a drug use index comprised of weighted results of opiate urine tests. In a more recent randomized, double-blind study, Strain et al. (1993) compared 50 mg and 20 mg with a 0 mg placebo-only group. They found orderly dose-response effects on treatment retention, and they found that 50 mg was more effective than 20 mg or 0 mg at decreasing opiate and cocaine use as measured by urinalysis results. In a randomized double blind comparison of moderate (40-50 mg) and high (80-100 mg) dose methadone, Strain and his colleagues (1996) found a significantly lower rate of opiate positive urine specimens among patients receiving the high dose of methadone (53% vs. 62%). There are many other studies of opiate agonist medications, but space limitations do not permit more detail here (see IOM, 1995 for additional information).

Antagonist and Blocking Agents —Naltrexone has been used for more than 20 years in the treatment of opiate dependence (see Greenstein et al., 1981; O'Brien et al., 1984). It is an orally administered opiate antagonist that blocks actions of externally administered opiates such as heroin by competitive binding to opiate receptors. It has been particularly effective as an adjunct to probation in opiate addicted federal probationers (see Cornish et al., 1997). More recently, naltrexone (marketed under the trade name Revia®) has been found to be effective in the treatment of alcohol dependence (O'Malley et al., 1992; Volpicelli et al., 1992). Naltrexone at 50mg/day has been approved by the FDA for use with alcohol dependent patients since independent studies have shown it to be a safe, effective pharmacological adjunct for reducing heavy alcohol use among alcohol dependent patients. Its mechanism of action appears to be the blocking of at least some of the "high" produced by alcohol consumption, again through competitive binding with the mu opiate receptors (O'Malley et al., 1992; Volpicelli et al., 1992).

With regard to other medications designed to block the effects of an abused drug, disulfiram (Antabuse ®) has been used the longest and most pervasively in the treatment of alcohol dependence (see Fuller et al., 1986). However, disulfiram seems to be most effective under certain conditions, such as when the patient contracts to having a significant other witness him or her take the medication each day. More recently, European researchers have found encouraging results with acamprosate as a treatment for alcoholism (Ladewig et al., 1993; Lhuintre et al., 1990). While acamprosate acts on different receptor systems than naltrexone, the clinical results are remarkably similar (Anton, 1995; Ladewig et al., 1993; Lhuintre et al., 1990). Alcohol dependent patients who take acamprosate have shown 30% greater posttreatment abstinence rates at six-month follow-up than those randomly assigned to placebo. Further, those who have returned to drinking while taking acamprosate report less heavy drinking (greater than five drinks per day) than those who returned to drinking while prescribed placebo (Anton, 1995). While both of these medications can be used for extended periods, in practice they are generally prescribed for about one to three months as part of a more general rehabilitation program that includes behavioral change strategies (see review by Anton, 1995).

There have been many agents tried as blocking agents in the treatment of cocaine dependence and while this literature is quite large, it has been disappointing (see Institute of Medicine, 1995; O'Brien, 1996; O'Brien and McKay, in press). At this writing, there is no convincing evidence that any of the various types of cocaine blocking agents are truly effective for even brief periods of time or for even a significant minority of affected patients. Research continues in this important area and there have been indications of a potentially successful "vaccine" that may be able to immediately metabolize and inactivate active metabolites of cocaine (see Fox, 1997). This promising work is currently being tested in animal models, but there are no treatment relevant medications available for cocaine rehabilitation at this time.

Although the use of opiate and alcohol antagonists or blocking agents is increasing as addiction physicians are more comfortable with the prescription of adjunctive medications and as more substance dependence is treated by primary care physicians in office settings (see Fleming and Barry, 1992), there are still relatively few patients that receive—or practitioners that prescribe—these medications (Institute of Medicine, 1995). Furthermore, the available literature in this area still does not provide an unambiguous conclusion regarding the parameters that are most effective when using antagonist or ''blocking" pharmacotherapy. For example, a recent cautionary article by Moitto and colleagues warned about an unusually high rate of deaths (particularly suicides) among opiate dependent individuals who were transferred to naltrexone (Moitto et al., 1997). The appropriate use of these antagonist or blocking medications in "real world" treatment of substance dependence disorders may be among the most important topics for future research in the treatment field. These medications are often expensive and managed care companies have been slow to permit these medications to reach formularies (see Institute of Medicine, 1995; O'Brien, 1996; O'Brien and McKay, in press). In addition, there is a need for long-term studies of patients who have been prescribed these medications as well as studies examining the most appropriate and efficient mix of psychosocial and pharmacological services to maximize rehabilitation for various types of substance dependent patients.

Provision of Specialized Services— The majority of patients admitted to substance abuse treatment have significant "addiction related" problems in one or more areas such as medical status, employment, family relations, and/or psychiatric function (McLellan and Weisner, 1996). As has been indicated above, the severity of these problems at the time of treatment admission is generally a good negative predictor of posttreatment outcome. Studies have documented that strategies designed to direct and focus specialized services to these "addiction related" problems can be applied in standard clinical settings and can be effective in improving the results of substance abuse treatment. Again, this conclusion follows more than a decade of research showing that the addition of professional marital counseling (Fals-Stewart et al., 1996; McCrady et al., 1986; O'Farrell et al., in press; Stanton and Todd, 1982), psychotherapy (Carroll et al., 1991, 1993, 1994a,b; Woody et al., 1983, 1984, 1987, 1995) and medical care (Fleming and Barry, 1992) produces clinically and significantly better outcomes from substance abuse treatment.

It should be noted that in some cases, these adjunctive forms of therapy and services have been most clearly associated with improved personal health and social function following treatment but not as well related to reduced alcohol and drug use. In addition, and not surprisingly, these treatments have only been shown to be effective with those patients having more severe problems in the target area (matching effect)—that is, if there has been no indication of a relatively severe problem in the target area, there has typically been no evidence that the provision of the target therapy is effective or worthwhile (see Woody et al., 1984). One exception to this appears to be behavioral marital or couples therapy, which has typically demonstrated a "main effect" for all couples in the studies. This might be because most marriages in which one or both partners are actively abusing alcohol or drugs could be characterized by fairly severe marital problems. However, even in the case of marital therapy, some matching effects have been found. One study found that the effectiveness of couples therapy for alcoholics varied as a function of complex interactions involving the patient's degree of investment in relationships, degree of support for abstinence from significant others, and planned number of conjoint sessions (Longabaugh et al., 1995).

Community Reinforcement and Contingency Contracting— Azrin and colleagues initially developed the "Community Reinforcement Approach" (CRA) and tested it against other "standard" treatment interventions (Azrin et al., 1982). CRA includes conjoint therapy, job finding training, counseling focused on alcohol-free social and recreational activities, monitored disulfiram, and an alcohol-free social club. The goal of CRA is to make abstinence more rewarding than continued use (Meyers and Smith, 1995). In a study in which patients were randomly assigned to CRA or to a standard hospital treatment program, those getting CRA drank less, spent fewer days away from home, worked more days, and were institutionalized less over a 24-month follow-up (Azrin et al., 1982).

A more recent set of studies by Higgins et al. (Higgins et al., 1991, 1993, 1994, 1995) has used the CRA approach with cocaine dependent patients. Here, cocaine dependent patients seeking outpatient treatment were randomly assigned to receive either standard drug counseling and referral to AA, or a multicomponent behavioral treatment integrating contingency managed counseling, community-based incentives, and family therapy comparable to the CRA model (Higgins et al., 1991). The CRA model retained more patients in treatment, produced more abstinent patients and longer periods of abstinence, and produced greater improvements in personal function than the standard counseling approach. Following the overall findings, this group of investigators systematically "disassembled" the CRA model and examined the individual "ingredients" of family therapy (Higgins et al., 1993), incentives (Higgins et al., 1994), and the contingency based counseling (Higgins et al., 1995) as compared against groups who received comparable amounts of all components except the target ingredient. In each case, these systematic and controlled examinations indicated that these individual components made a significant contribution to the outcomes observed, thus proving their added value in the rehabilitation effort. Extending this work on the use of positive reinforcement and behavioral contracting, Silverman and colleagues (Silverman et al., 1996) used essentially the same reinforcement contingencies and contracting procedures that had been applied by Azrin and Higgins to improve the performance of methadone maintained patients.

"Matching" Patients and Treatments— The past two decades have witnessed a great number of research studies attempting to "match" patients with specific types, modalities or settings of treatment. The approach to patient-treatment "matching" that has received the greatest attention from substance abuse treatment researchers involves attempting to identify the characteristics of individual patients that predict the best response to different forms of addiction treatments (e.g., cognitive-behavioral vs. 12-Step, or inpatient vs. outpatient) (Mattson et al., 1994; Project MATCH Research Group, 1997). In general, the majority of these "patient-to-treatment" matching studies have not shown robust or generalizable findings (see Gastfriend and McLellan, 1997). Another approach to matching has been to assess patients' problem severity in a range of areas at intake and then ''match" the specific and necessary services to the particular problems presented at the assessment. This has been called "problem-to-service" matching (McLellan et al., 1997b). This approach may have more practical application as it is consonant with the "individually tailored treatment" philosophy that has been espoused by most practitioners.

Substance abusers with comorbid psychiatric problems may be particularly good candidates for the "problem-to-service" matching approach; especially the addition of specialized psychiatric services for those most severely affected by psychiatric problems. For example, recent studies suggest that tricyclic antidepressants and the selective serotonergic medication fluoxetine may reduce both drinking and depression levels in alcoholics with major depression (Cornelius et al., 1997; Mason et al., 1996; McGrath et al., 1996). Similarly, the anxiolytic buspirone may reduce drinking in alcoholics with a comorbid anxiety disorder (Kranzler et al., 1994). Highly structured relapse prevention interventions may also be more effective in decreasing cocaine use, as compared to less structured interventions, in cocaine abusers with comorbid depression (Carroll et al., 1995).

Woody and colleagues have evaluated the value of individual psychotherapy when added to paraprofessional counseling services in the course of methadone maintenance treatment (Woody et al., 1983). In that study patients were randomly assigned to receive standard drug counseling alone (DC group) or drug counseling plus one of two forms of professional therapy: supportive-expressive psychotherapy (SE) or cognitive-behavioral psychotherapy (CB) over a six month period. Results showed that patients receiving psychotherapy showed greater reductions in drug use, more improvements in health and personal function, and greater reductions in crime than those receiving counseling alone. Stratification of patients according to their levels of psychiatric symptoms at intake showed that the main psychotherapy effect was seen in those with greater than average levels of psychiatric symptoms. Specifically, patients with low symptom levels made considerable gains with counseling alone and there were no differences between types of treatment. However, patients with more severe psychiatric problems showed few gains with counseling alone but substantial improvements with the addition of the professional psychotherapy.

Another type of substance abuser that can pose particular problems for outpatient treatment is the cocaine dependent patient who is unable to achieve remission from cocaine dependence early in outpatient treatment. Several randomized studies suggest that highly structured cognitive-behavioral treatment is particularly efficacious with such individuals. In two outpatient studies with cocaine abusers, those with more severe cocaine problems at intake had significantly better cocaine use outcomes if they received structured relapse prevention rather than interpersonal or clinical management treatments (Carroll et al., 1991, 1994b). In a third study, cocaine dependent patients who continued to use cocaine during a four-week intensive outpatient treatment program (IOP) had much better cocaine use outcomes if they subsequently received aftercare that included a combination of group therapy and a structured relapse prevention protocol delivered through individual sessions rather than aftercare that consisted of group therapy alone (McKay et al., 1998).

The impact of adding additional, professionally delivered treatment services to a basic methadone program was investigated by McLellan and colleagues (McLellan et al., 1993b). In this study, patients were randomly assigned to receive (a) methadone only; (b) methadone plus standard counseling; or (c) methadone and counseling plus on-site medical, psychiatric, employment, and family therapy services (the "enhanced" condition). Although these additional services were not "matched" to patients on an individual basis, most of the patients in the study were polydrug abusers with relatively high problem levels in other areas. On most outcome measures, the best results were obtained in the enhanced condition, followed by methadone plus counseling, and methadone alone. Improvements in the enhanced condition were significantly better than those in the methadone plus counseling condition in the areas of employment, alcohol use, criminal activity, and psychiatric status. These results demonstrate the value of providing additional professional treatment services to polyproblem substance abusers, even when these services are not "matched" to specific problems at the level of the individual patient.

McLellan and colleagues recently attempted a different type of "problems to services" matching research in two inpatient and two outpatient private treatment programs (McLellan et al., 1997b). Patients in the study (N = 130) were assessed with the ASI at intake and placed in a program that was acceptable to both the Employee Assistance Program referral source and the patient. At intake, patients were also randomized to either the standard or "matched" services conditions. In the standard condition, the treatment program received information from the intake ASI, and personnel were instructed to treat the patient in the "standard manner, as though there were no evaluation study ongoing." The programs were instructed to not withhold any services from patients in the standard condition. Patients who were randomly assigned to the matched services condition were also placed in one of the four treatment programs and ASI information was forwarded to that program. However, the programs agreed to provide at least three individual sessions in the areas of employment, family/social relations, or psychiatric health delivered by a professionally trained staff person to improve functioning in those areas when a patient evidenced a significant degree of impairment in one or more of these areas at intake. For example, a patient whose intake ASI revealed significant impairments in the areas of social and psychiatric functioning would receive at least six individual sessions, three by a psychiatrist and three by a social worker.

The standard and matched patients were compared on a number of measures, including number of services received while in treatment, treatment completion rates, intake to six-month improvements in the seven problem areas assessed by the ASI, and other key outcomes at six months. Matched patients received significantly more psychiatric and employment services than standard patients, but not more family/social services or alcohol and drug services. Second, matched patients were more likely to complete treatment (93% vs. 81%), and showed more improvement in the areas of employment and psychiatric functioning than the standard patients. Third, while matched and standard patients had sizable and equivalent improvements on most measures of alcohol and drug use, matched patients were less likely to be retreated for substance abuse problems during the six-month follow-up. These findings suggest that matching treatment services to adjunctive problems can improve outcomes in key areas and may also be cost-effective by reducing the need for subsequent treatment due to relapse.

Limitations of the Matching Services to Problems Approach— It is difficult to argue against the face validity of a treatment approach for polyproblem substance abusers that stresses the importance of providing additional services to address co-occurring medical, economic, psychiatric, family, and legal problems. After all, effective substance abuse focused interventions such a Cognitive Behavioral Therapy or Twelve Step Facilitation (Project MATCH, 1997), no matter how well delivered, are not designed to address serious problems in other areas. If left untreated, co-occurring problems can increase risk for poor treatment response and poor posttreatment outcome. And in some cases, it may be impossible to even initiate treatment for a substance abuse problem until treatment for a severe co-occurring problem has been provided. In addition to benefits for the patients, the matching services to problems approach can also reduce stress levels in clinicians who treat polyproblem individuals, provided that a team approach to treatment is taken and regular lines of communication are established between clinicians involved with a case.

The primary limitation of this approach concerns the potential lack of resources in a time of health care cost containment. Funding may not be available to substance abuse treatment providers for adjunctive services in areas such as medical and psychiatric care, unless the level of problem severity is high enough that these co-occurring disorders can be considered as "primary." Recent research has shown that substance abuse programs vary widely in the number and frequency of adjunctive services they provide (D'Aunno and Vaughn, 1995; McLellan et al., 1993a; Widman et al., 1997), which may reflect differences between programs in the funding available for such services. Obviously, it is impossible to match services to problems if the appropriate services are not available. The scarcity of resources underlies the need for accurate assessment and diagnosis of co-occurring problems, so as to ensure that patients who are more in need of such services will stand a better chance of receiving them. Also, not all services may be potent enough to make a significant impact on the target problem area. For example, despite the importance of employment related problems in predicting treatment outcome, and the range of interventions that have been developed to improve employment and self-support among substance dependent patients (see French et al., 1992), there is little evidence that this type of specialized service is effective in improving the employment of the patients or in improving abstinence from drugs (Hall et al., 1981 is an exception).

Another potential problem with the matching services-to-problems approach is that even when adjunctive services are available in the community, they may not be offered at the clinic or agency in which the patient is receiving substance abuse focused treatment. In cases where patients have to go to other agencies to obtain additional services, there is a greater chance of attrition due to logistical problems or flagging motivation. This is a strong argument for combining substance abuse treatment with a broader array of services, which is sometimes referred to as "one-stop shopping," in settings where a more interdisciplinary approach can be taken for the treatment of the polyproblem individual.

• SUMMARY AND DISCUSSION

In the text above we have attempted to review the substance abuse treatment research literature to identify patient and treatment process variables that have been shown to be important in determining outcome from addiction rehabilitation efforts; and in this way to contribute to the discussion of what treatment research may offer to practitioners in the field. While it is true that many of the research studies reviewed employed highly selected patient samples and/or sophisticated, resource-intensive interventions that would not be practical in "real world" community treatment programs, it is also true that this literature offers some important starting points for our larger effort to fill the gaps between what is known and what needs to be known at the level of the treatment program. This in turn is important for identifying clinical and policy issues that should be the focus of future research. Our review of this research has suggested the following three points:

The existing literature on treatment outcomes has been disappointing with regard to informing treatment practice at the level of the community treatment program . Most of the outcome studies in the current literature were conducted by clinical researchers, typically in controlled trials. The purpose of these studies was generally to determine whether the index treatment, when delivered under specified conditions to rather highly selected samples of patients, could effect the expected changes relative to standard or minimal treatment conditions. Many of the clinical trials reviewed here excluded important classes of patients (e.g., polysubstance users) that are most prevalent in community treatment agencies. In addition, many of these studies used very specific, resource-intensive interventions studied under rarefied conditions for fixed periods of time. In most clinical practice settings, when a patient fails to respond to one type of intervention, the sensitive clinician will alter the approach. Thus the interventions that are compared in experiments may not reflect what happens in practice.

Despite these caveats, there are important findings from controlled clinical research that suggest important directions for treatment practice in the "real world" —Given a definition of good outcome from rehabilitation treatment as "lasting improvements in those problems that led to the treatment admission and that were important to the patient and to society," the following patient and treatment process factors have been significantly and repeatedly related to favorable outcomes.

• Patient variables associated with better outcome from rehabilitation included:

low severity of dependence,

few psychiatric symptoms at admission,

motivation beyond the precontemplation stage of change,

being employed or self supporting, and

having family and social supports for sobriety.

• Treatment variables associated with better outcome from rehabilitation included:

staying longer in/ being more compliant with treatment—especially through behavioral contracting for positive reinforcement;

having an individual counselor or therapist;

having specialized services provided for associated medical, psychiatric, and/or family problem;

receiving proper medications—both for psychiatric conditions and anticraving medications; and

participating in AA or NA following treatment.

• In contrast to the above findings, it was surprising that some of the treatment elements that are most widely provided in substance abuse treatment have not been associated with better outcome. For example, our review of the literature has shown little indication that any of the following lead to better or longer lasting outcomes following treatment:

alcohol/drug education sessions;

general group therapy sessions, especially "confrontation" sessions;

acupuncture sessions;

patient relaxation techniques; and

treatment program accreditation or professional practice certification criteria.

• For the sake of brevity, studies of these five interventions were not described above. These findings are generally in accordance with a review of the alcohol rehabilitation field by Miller and Holder (1994), which concluded that there are a number of therapeutic practices and procedures that remain prevalent in the field that have not yet shown indication of success. It is important to note that "the absence of evidence" does not prove a treatment element is ineffective. Some of the treatment practices or conventions cited may actually have benefits for some patients or under some circumstances but we have found little support for these in the existing literature.

A reviewer of this field will get substantially different views about the "outcome" of an addiction treatment depending upon the perspective taken regarding what "outcome" is; and when, how, and by whom it is measured. Consider three common perspectives on the evaluation of an outpatient addiction treatment program. A quality assurance or service delivery evaluation of that treatment might conclude that the program "had very good outcomes" since there was no waiting for treatment entry and at discharge, more than 80% of the patients were "highly satisfied'' with their counselor and physician. A clinical researcher, having interviewed a sample of patients at admission to the program, and again six months following discharge, might conclude that the program "had mixed outcomes" since at the follow-up point, only 50% of the patients were abstinent (the intended goal of the program) but there was a 70% reduction in frequency of drinking and a 50% reduction in medical and psychiatric symptoms. Meanwhile, an economist or health policy analyst might have used Medicaid data tapes to compare the health services utilization rates of a sample of discharged patients, two years prior to their treatment admission and two years following their discharge. The conclusion here might be that "treatment had very poor outcome" since there had been no decrease in health care utilization from the pre- to the posttreatment period, hence no "cost-offset" to the public.

This example illustrates two points. First, that these three common perspectives on outcome have different purposes for their evaluations and different expectations regarding treatment, they measure different elements of the treatment process and the patient population, and at different points in time. Following from the first point, these different measures of outcome are not well related to each other; and it has been the case that clinical research has often focused upon a rather narrow set of outcomes (e.g., abstinence from alcohol or drugs) to evaluate treatments while interventions delivered at community treatment organizations are being evaluated on a different and often broader set of outcomes (e.g., reduction of crime, reincarceration, reduction of family violence, reduction of Medicaid claims, etc.). If research is to be able to inform clinical practice, there should be efforts made to agree upon and adopt common expectations and measures.

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Supported by grants from the National Institute on Drug Abuse, the Center for Substance Abuse Treatment, and the Robert Wood Johnson Foundation. Parts of this paper appear in McKay and McLellan, 1997 and an earlier IOM report on Managing Managed Care, 1997.

• Cite this Page Institute of Medicine (US) Committee on Community-Based Drug Treatment; Lamb S, Greenlick MR, McCarty D, editors. Bridging the Gap between Practice and Research: Forging Partnerships with Community-Based Drug and Alcohol Treatment. Washington (DC): National Academies Press (US); 1998. D, The Treatment of Addiction: What Can Research Offer Practice?
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Addiction to Studying

Is 'study addiction' a pre-cursor to 'workaholism'.

Posted February 3, 2016

In today’s modern society, students face multiple academic pressures. The best colleges and universities require the best grades for entry and parents push and expect their children to succeed educationally. At school, pupils learn early on that success comes through dedication, discipline, and hard work. For some individuals, the act of educational study may become excessive and/or compulsive and lead to what has been termed ‘study addiction ’.

Although there is little research and no generally accepted definition of study addiction to date, such behaviour (as a way of dealing with academic stress and pressure) has been conceptualized within contemporary research into workaholism . Consequently, from a ‘ work addiction ’ (i.e., workaholism) perspective, study addiction was defined by Dr. Cecilie Andreassen and her colleagues in a 2014 issue of the Journal of Managerial Psychology as: “Being overly concerned with studying, to be driven by an uncontrollable studying motivation , and to put so much energy and effort into studying that it impairs private relationships, spare-time activities, and/or health”.

The many similarities between studying and working lead to the notion that study addiction may be a precursor for or an early form of workaholism that might manifest itself in childhood or adolescence . Work appears to share many similarities to that of learning and studying, as both involve sustained effort in order to achieve success, often related to skills and knowledge, and both fulfill important social roles. In previous studies (including some of my own – see ‘Further reading’ below), workaholism has been shown to be a relatively stable entity over time. This suggests that the behavioral tendency to work excessively may be manifesting itself early in the development of an individual in relation to learning and associated academic behaviors. Given the similarities between excessive work and excessive study, there is no theoretical reason to believe that ‘study addiction’ (like work addiction) does not exist.

Given that most scales to assess workaholism have been developed without adequate consideration of all facets of addiction , my colleagues and I developed the Bergen Work Addiction Scale (BWAS). This was published in a 2012 issue of the Scandinavian Journal of Psychology and was developed to overcome the theoretical and conceptual weaknesses of previous instrumentation. This BWAS assesses core elements of addiction (salience, mood modification, tolerance, withdrawal, conflict, relapse , and problems). As no current measure of study addiction exists, we adapted the BWAS by replacing the words ‘work’ and ‘working’ with ‘study’ and ‘studying’ (creating the Bergen Study Addiction Scale) and carried out the first ever study on ‘study addiction’ and some of the results of this study that have just been published in the Journal of Behavioral Addictions are highlighted later in this article.

Unlike most other behavioral addictions (e.g., pathological gambling , video gaming addiction , shopping addiction , etc.), workaholism – like exercise addiction – has often been regarded as a positive and productive kind of addiction . Notably, workaholics typically score higher on personality traits such as conscientiousness and perfectionism compared to other addicts. As with the workaholic , the 'perfect student' is hard working and involved, and it is likely that study addiction is also associated with conscientiousness. Along with the academic pressure derived from many differing sources (such as the fear of failure), it is also conceivable that such individuals – like workaholics – will score higher on neuroticism .

Although the societal notion of workaholism as a positive behaviour has received some support, most current scholars conceive it as a negative condition due to its association with impaired health, low perceived quality of life, diminished sleep quality, work-family conflicts, and lowered job performance. Given these well-established associations, we hypothesized in our study that extreme studying behaviour (i.e., study addiction) would be negatively related to psychological wellbeing, health, and academic performance, and positively related to stress.

On the basis of previous theoretical frameworks and empirical research into work addiction, we hypothesized that study addiction would be (i) positively and significantly associated with conscientiousness and neuroticism, (ii) positively and significantly associated with stress, and lower quality of life, health, and sleep, and (iii) negatively and significantly related to academic performance. Our study comprised two samples of students (n=1,211). The first sample comprised 218 first-year psychology undergraduate students at the University of Bergen in Norway. The second sample comprised 993 participants studying at three Polish universities.

We found there were positive associations between study addiction, neuroticism and conscientiousness, and lack of relationship with agreeableness (in both the Polish and Norwegian samples). In the Polish sample, extraversion was negatively related to study addiction. Our results also showed that study addiction was positively related to perceived stress and negatively associated with general quality of life, general health, and sleep quality above and beyond personality factors. These results parallel current knowledge about negative correlates of work addiction. When controlling for personality traits, study addiction was negatively associated with immediate academic performance (although not statistically significant in the Norwegian sample, probably due to the relatively small sample size in terms of exam results compared to the much bigger Polish sample).

As expected, study addiction was related to several negative consequences and problems. Although our results were interesting and (on the whole supported our hypotheses) the two groups of students comprised convenience samples, were predominantly female, and mainly comprised psychology and education students. Therefore, the results of our study cannot be generalized to other populations. However, our study is first ever study to conceptualize ‘study addiction’ and to test psychometric properties of a corresponding measurement tool (which for all you psychometricians out there had good reliability and validity). We also used several variables comprising possible antecedents and consequences of study addiction, including valid and reliable measures of personality, psychological wellbeing, health, stress, and academic performance. We believe that our study significantly adds to the existing literature on workaholism and behavioural addictions, and our initial findings appear to support the concept of study addiction and provide an empirical base for its further investigation.

If we had an unlimited research budget, we’d like to carry out longitudinal studies in younger samples (e.g., high school) as such data would likely provide useful information in terms of possible developmental risk factors, determinants, and correlates of study addiction. The relationship between study addiction and later work addiction should also be investigated longitudinally in order to investigate if these are aspects are part of the same phenomenon and/or pathological process.

(Please note: This article was written in conjunction with Paweł Atroszko University of Gdańsk, Poland), Cecilie Schou Andreassen (University of Bergen, Norway), and Ståle Pallesen (University of Bergen, Norway).

References and further reading

Andreassen, C. S. (2014). Workaholism: An overview and current status of the research. Journal of Behavioral Addictions, 3, 1-11.

Andreassen, C., Griffiths, M., Gjertsen, S., Krossbakken, E., Kvam, S., & Pallesen, S. (2013). The relationships between behavioral addictions and the five-factor model of personality. Journal of Behavioral Addictions, 2, 90-99.

Andreassen, C. S., Griffiths, M. D., Hetland, J., Kravina, L., Jensen, F., & Pallesen, S. (2014). The prevalence of workaholism: a survey study in a nationally representative sample of norwegian employees. PLoS ONE , 9, e102446. doi: 10.1371/journal.pone.0102446

Andreassen, C. S., Griffiths, M. D., Hetland, J., & Pallesen, S. (2012). Development of a work addiction scale. Scandinavian Journal of Psychology, 53, 265-272.

Andreassen, C. S., Hetland, J., & Pallesen, S. (2014). Psychometric assessment of workaholism measures. Journal of Managerial Psychology, 29, 7-24.

Atroszko, P.A., Andreassen, C.S., Griffiths, M.D. & Pallesen, S. (2015). Study addiction – A new area of psychological study: Conceptualization, assessment, and preliminary empirical findings. Journal of Behavioral Addictions, 4, 75–84.

Burke, R. J., Matthiesen, S. B., & Pallesen, S. (2006). Personality correlates of workaholism. Personality and Individual Differences , 40, 1223-1233.

Griffiths, M.D. (2005). A ‘components’ model of addiction within a biopsychosocial framework. Journal of Substance Use, 10, 191-197.

Griffiths, M.D. (2005). Workaholism is still a useful construct Addiction Research and Theory , 13, 97-100.

Griffiths, M.D. (2011). Workaholism: A 21st century addiction. The Psychologist: Bulletin of the British Psychological Society , 24, 740-744.

Griffiths, M.D. & Karanika-Murray, M. (2012). Contextualising over-engagement in work: Towards a more global understanding of workaholism as an addiction. Journal of Behavioral Addictions, 1(3), 87-95.

Quinones, C. & Mark D. Griffiths (2015). Addiction to work: A critical review of the workaholism construct and recommendations for assessment. Journal of Psychosocial Nursing and Mental Health Services, 10, 48-59.

Spence, J. T., & Robbins, A. S. (1992). Workaholism - definition, measurement, and preliminary results. Journal of Personality Assessment , 58, 160-178.

van Beek, I., Taris, T. W., & Schaufeli, W. B. (2011). Workaholic and work engaged employees: dead ringers or worlds apart? Journal of Occupational Health Psychology, 16, 468-482.

Mark Griffiths, Ph.D., is a chartered psychologist and Director of the International Gaming Research Unit in the Psychology Division at Nottingham Trent University.

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Understanding Drug Use and Addiction DrugFacts

Many people don't understand why or how other people become addicted to drugs. They may mistakenly think that those who use drugs lack moral principles or willpower and that they could stop their drug use simply by choosing to. In reality, drug addiction is a complex disease, and quitting usually takes more than good intentions or a strong will. Drugs change the brain in ways that make quitting hard, even for those who want to. Fortunately, researchers know more than ever about how drugs affect the brain and have found treatments that can help people recover from drug addiction and lead productive lives.

What Is drug addiction?

Addiction is a chronic disease characterized by drug seeking and use that is compulsive, or difficult to control, despite harmful consequences. The initial decision to take drugs is voluntary for most people, but repeated drug use can lead to brain changes that challenge an addicted person’s self-control and interfere with their ability to resist intense urges to take drugs. These brain changes can be persistent, which is why drug addiction is considered a "relapsing" disease—people in recovery from drug use disorders are at increased risk for returning to drug use even after years of not taking the drug.

It's common for a person to relapse, but relapse doesn't mean that treatment doesn’t work. As with other chronic health conditions, treatment should be ongoing and should be adjusted based on how the patient responds. Treatment plans need to be reviewed often and modified to fit the patient’s changing needs.

Video: Why are Drugs So Hard to Quit?

What happens to the brain when a person takes drugs?

Most drugs affect the brain's "reward circuit," causing euphoria as well as flooding it with the chemical messenger dopamine. A properly functioning reward system motivates a person to repeat behaviors needed to thrive, such as eating and spending time with loved ones. Surges of dopamine in the reward circuit cause the reinforcement of pleasurable but unhealthy behaviors like taking drugs, leading people to repeat the behavior again and again.

As a person continues to use drugs, the brain adapts by reducing the ability of cells in the reward circuit to respond to it. This reduces the high that the person feels compared to the high they felt when first taking the drug—an effect known as tolerance. They might take more of the drug to try and achieve the same high. These brain adaptations often lead to the person becoming less and less able to derive pleasure from other things they once enjoyed, like food, sex, or social activities.

Long-term use also causes changes in other brain chemical systems and circuits as well, affecting functions that include:

• decision-making

Despite being aware of these harmful outcomes, many people who use drugs continue to take them, which is the nature of addiction.

Why do some people become addicted to drugs while others don't?

No one factor can predict if a person will become addicted to drugs. A combination of factors influences risk for addiction. The more risk factors a person has, the greater the chance that taking drugs can lead to addiction. For example:

• Biology . The genes that people are born with account for about half of a person's risk for addiction. Gender, ethnicity, and the presence of other mental disorders may also influence risk for drug use and addiction.
• Environment . A person’s environment includes many different influences, from family and friends to economic status and general quality of life. Factors such as peer pressure, physical and sexual abuse, early exposure to drugs, stress, and parental guidance can greatly affect a person’s likelihood of drug use and addiction.
• Development . Genetic and environmental factors interact with critical developmental stages in a person’s life to affect addiction risk. Although taking drugs at any age can lead to addiction, the earlier that drug use begins, the more likely it will progress to addiction. This is particularly problematic for teens. Because areas in their brains that control decision-making, judgment, and self-control are still developing, teens may be especially prone to risky behaviors, including trying drugs.

Can drug addiction be cured or prevented?

As with most other chronic diseases, such as diabetes, asthma, or heart disease, treatment for drug addiction generally isn’t a cure. However, addiction is treatable and can be successfully managed. People who are recovering from an addiction will be at risk for relapse for years and possibly for their whole lives. Research shows that combining addiction treatment medicines with behavioral therapy ensures the best chance of success for most patients. Treatment approaches tailored to each patient’s drug use patterns and any co-occurring medical, mental, and social problems can lead to continued recovery.

More good news is that drug use and addiction are preventable. Results from NIDA-funded research have shown that prevention programs involving families, schools, communities, and the media are effective for preventing or reducing drug use and addiction. Although personal events and cultural factors affect drug use trends, when young people view drug use as harmful, they tend to decrease their drug taking. Therefore, education and outreach are key in helping people understand the possible risks of drug use. Teachers, parents, and health care providers have crucial roles in educating young people and preventing drug use and addiction.

Points to Remember

• Drug addiction is a chronic disease characterized by drug seeking and use that is compulsive, or difficult to control, despite harmful consequences.
• Brain changes that occur over time with drug use challenge an addicted person’s self-control and interfere with their ability to resist intense urges to take drugs. This is why drug addiction is also a relapsing disease.
• Relapse is the return to drug use after an attempt to stop. Relapse indicates the need for more or different treatment.
• Most drugs affect the brain's reward circuit by flooding it with the chemical messenger dopamine. Surges of dopamine in the reward circuit cause the reinforcement of pleasurable but unhealthy activities, leading people to repeat the behavior again and again.
• Over time, the brain adjusts to the excess dopamine, which reduces the high that the person feels compared to the high they felt when first taking the drug—an effect known as tolerance. They might take more of the drug, trying to achieve the same dopamine high.
• No single factor can predict whether a person will become addicted to drugs. A combination of genetic, environmental, and developmental factors influences risk for addiction. The more risk factors a person has, the greater the chance that taking drugs can lead to addiction.
• Drug addiction is treatable and can be successfully managed.
• More good news is that drug use and addiction are preventable. Teachers, parents, and health care providers have crucial roles in educating young people and preventing drug use and addiction.

For information about understanding drug use and addiction, visit:

• www.nida.nih.gov/publications/drugs-brains-behavior-science-addiction/drug-abuse-addiction

For more information about the costs of drug abuse to the United States, visit:

• www.nida.nih.gov/related-topics/trends-statistics#costs

For more information about prevention, visit:

• www.nida.nih.gov/related-topics/prevention

For more information about treatment, visit:

• www.nida.nih.gov/related-topics/treatment

To find a publicly funded treatment center in your state, call 1-800-662-HELP or visit:

• https://findtreatment.samhsa.gov/

This publication is available for your use and may be reproduced in its entirety without permission from NIDA. Citation of the source is appreciated, using the following language: Source: National Institute on Drug Abuse; National Institutes of Health; U.S. Department of Health and Human Services.

• Introduction
• Conclusions
• Article Information

Mean (SE) estimates for screening (84 days prior to screening), weeks 1-4 (28 days prior to first double-blind medication session; covariate in the model), and eight 28-day bins following the first double-blind medication session (shaded area: weeks 5-8, 9-12, 13-16, 17-20, 21-24, 25-28, 29-32, and 33-36). Arrows represent double-blind medication sessions 1 and 2.

Trial protocol

eTable 1. Treatment Effects on Problems Related to Drinking

eTable 2. Adverse Events

eFigure. Treatment Effects on Cardiovascular Outcomes

Data sharing statement

• Psilocybin for Treatment of Alcohol Use Disorder JAMA News From the JAMA Network October 4, 2022 Anita Slomski
• Treating Alcohol Use Disorder With Hallucinogens—Renewed Interest After a 50-Year Hiatus JAMA Psychiatry Editorial October 1, 2022 This Viewpoint discusses the use of hallucinogens to treat alcohol use disorder. Henry R. Kranzler, MD; Emily E. Hartwell, PhD
• Error in Race and Ethnicity Data JAMA Psychiatry Correction November 1, 2022

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Bogenschutz MP , Ross S , Bhatt S, et al. Percentage of Heavy Drinking Days Following Psilocybin-Assisted Psychotherapy vs Placebo in the Treatment of Adult Patients With Alcohol Use Disorder : A Randomized Clinical Trial . JAMA Psychiatry. 2022;79(10):953–962. doi:10.1001/jamapsychiatry.2022.2096

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Percentage of Heavy Drinking Days Following Psilocybin-Assisted Psychotherapy vs Placebo in the Treatment of Adult Patients With Alcohol Use Disorder : A Randomized Clinical Trial

• 1 Department of Psychiatry, New York University Langone Center for Psychedelic Medicine, New York University Grossman School of Medicine, New York
• 2 Department of Psychiatry and Behavioral Sciences, University of New Mexico School of Medicine, Albuquerque
• 3 The Change Companies, Carson City, Nevada
• 4 Department of Population Health, Division of Biostatistics, New York University Grossman School of Medicine, New York
• 5 Department of Psychology, University of Alabama at Birmingham
• 6 University of New Mexico Center on Alcohol, Substance Use and Addictions, Albuquerque
• Editorial Treating Alcohol Use Disorder With Hallucinogens—Renewed Interest After a 50-Year Hiatus Henry R. Kranzler, MD; Emily E. Hartwell, PhD JAMA Psychiatry
• News From the JAMA Network Psilocybin for Treatment of Alcohol Use Disorder Anita Slomski JAMA
• Correction Error in Race and Ethnicity Data JAMA Psychiatry

Question   Does psilocybin-assisted treatment improve drinking outcomes in patients with alcohol use disorder relative to outcomes observed with active placebo medication?

Findings   In this double-blind randomized clinical trial with 93 participants, the percentage of heavy drinking days during 32 weeks of follow-up was significantly lower in the psilocybin group than in the diphenhydramine group.

Meaning   The results in this trial showed that psilocybin administered in combination with psychotherapy produced robust decreases in the percentage of heavy drinking days compared with those produced by active placebo and psychotherapy.

Importance   Although classic psychedelic medications have shown promise in the treatment of alcohol use disorder (AUD), the efficacy of psilocybin remains unknown.

Objective   To evaluate whether 2 administrations of high-dose psilocybin improve the percentage of heavy drinking days in patients with AUD undergoing psychotherapy relative to outcomes observed with active placebo medication and psychotherapy.

Design, Setting, and Participants   In this double-blind randomized clinical trial, participants were offered 12 weeks of manualized psychotherapy and were randomly assigned to receive psilocybin vs diphenhydramine during 2 day-long medication sessions at weeks 4 and 8. Outcomes were assessed over the 32-week double-blind period following the first dose of study medication. The study was conducted at 2 academic centers in the US. Participants were recruited from the community between March 12, 2014, and March 19, 2020. Adults aged 25 to 65 years with a DSM-IV diagnosis of alcohol dependence and at least 4 heavy drinking days during the 30 days prior to screening were included. Exclusion criteria included major psychiatric and drug use disorders, hallucinogen use, medical conditions that contraindicated the study medications, use of exclusionary medications, and current treatment for AUD.

Interventions   Study medications were psilocybin, 25 mg/70 kg, vs diphenhydramine, 50 mg (first session), and psilocybin, 25-40 mg/70 kg, vs diphenhydramine, 50-100 mg (second session). Psychotherapy included motivational enhancement therapy and cognitive behavioral therapy.

Main Outcomes and Measures   The primary outcome was percentage of heavy drinking days, assessed using a timeline followback interview, contrasted between groups over the 32-week period following the first administration of study medication using multivariate repeated-measures analysis of variance.

Results   A total of 95 participants (mean [SD] age, 46 [12] years; 42 [44.2%] female) were randomized (49 to psilocybin and 46 to diphenhydramine). One participant (1.1%) was American Indian/Alaska Native, 3 (3.2%) were Asian, 4 (4.2%) were Black, 14 (14.7%) were Hispanic, and 75 (78.9%) were non-Hispanic White. Of the 95 randomized participants, 93 received at least 1 dose of study medication and were included in the primary outcome analysis. Percentage of heavy drinking days during the 32-week double-blind period was 9.7% for the psilocybin group and 23.6% for the diphenhydramine group, a mean difference of 13.9%; (95% CI, 3.0–24.7; F 1,86  = 6.43; P  = .01). Mean daily alcohol consumption (number of standard drinks per day) was also lower in the psilocybin group. There were no serious adverse events among participants who received psilocybin.

Conclusions and Relevance   Psilocybin administered in combination with psychotherapy produced robust decreases in percentage of heavy drinking days over and above those produced by active placebo and psychotherapy. These results provide support for further study of psilocybin-assisted treatment for AUD.

Trial Registration   ClinicalTrials.gov Identifier: NCT02061293

The past 2 decades have witnessed growing interest in the clinical potential of psilocybin and other classic psychedelics to treat neuropsychiatric conditions, including substance use disorders. 1 - 8 Although the mechanisms of psychedelic-assisted treatments remain unclear, the action of these drugs at the serotonin 2A receptor and downstream effects on neurotransmission, intracellular signaling, epigenetics, and gene expression appear to enhance plasticity at multiple levels, including neuronal structure, neural networks, cognition, affect, and behavior. 9 - 24 However, some clinically relevant effects may be independent of serotonin 2A receptor activation. 24 , 25 Moreover, the direction and magnitude of change observed in a therapeutic context can be influenced by the subjective experience under the influence of the drug 26 - 29 and by contextual factors, including concomitant psychotherapy. 30 - 32

Alcohol use disorder (AUD) is a particularly promising target for treatment with psychedelics. A meta-analysis of results from 6 randomized clinical trials published between 1966 and 1971 33 - 38 revealed that participants with alcohol dependence treated with lysergic acid diethylamide (LSD) demonstrated remission during follow-up nearly twice as often as those in comparator conditions to (odds ratio, 1.96, 95% CI, 1.36-2.84; z , 3.59; P  < .001). 39 Picking up on this line of research after a hiatus of more than 40 years, an open-label study published in 2015 demonstrated that moderately high doses of psilocybin (21 to 28 mg/70 kg) were well tolerated by participants with alcohol dependence, and large reductions in drinking were observed over a 32-week follow-up period. 3

Building on the proof-of-concept study, this multisite randomized clinical trial evaluated the efficacy of psilocybin-assisted psychotherapy for the treatment of AUD. Here we report drinking outcomes for the double-blind phase of the trial.

The study was reviewed and approved by the Heffter Research Institute, the institutional review boards of each site (New York University Grossman School of Medicine and the University of New Mexico Health Sciences Center), the US Food and Drug Administration and Drug Enforcement Administration, the New Mexico Board of Pharmacy, and the New York State Bureau of Narcotics Enforcement. Psilocybin was provided by the Usona Institute, Madison, Wisconsin, Nicholas Cozzi, PhD, at the University of Wisconsin–Madison, and David Nichols, PhD, at Purdue University, West Lafayette, Indiana. The study was overseen by a data and safety monitoring board. One of the authors (M.P.B.) .was the investigational new drug application holder for the trial. This report followed the Consolidated Standards of Reporting Trials ( CONSORT ) reporting guideline for parallel-group randomized trials. All participants provided written informed consent. The trial protocol and statistical analysis plan can be found in Supplement 1 .

Participants were recruited from March 12, 2014, to May 13, 2015, at the University of New Mexico and from July 9, 2015, to March 19, 2020, at New York University, using advertisements in local media. Participants were aged 25 to 65 years, had a diagnosis of alcohol dependence ascertained using the Structured Clinical Interview for DSM-IV , 40 and had at least 4 heavy drinking days during the 30 days prior to screening (defined as 5 or more drinks in a day for a man and 4 or more drinks in a day for a woman). Exclusion criteria included major psychiatric and drug use disorders, any hallucinogen use in the past year or more than 25 lifetime uses, medical conditions that contraindicated either of the study medications, use of exclusionary medications, and current treatment for AUD. Race and ethnicity were determined by participant self-report according to standard National Institutes of Health categories in order to assess the representativeness of the sample. The trial protocol in Supplement 1 describes full inclusion and exclusion criteria.

Qualifying participants were assessed at screening, baseline (week 0), and weeks 4, 5, 8, 9, 12, 24, and 36. They were randomly assigned in a 1:1 ratio to receive either psilocybin or diphenhydramine, administered in two 8-hour sessions at weeks 4 and 8. All participants who completed the double-blind observation period (weeks 5 to 36) and still met safety criteria were offered an open-label psilocybin session at week 38, including 4 additional psychotherapy sessions and assessment for an additional 18 weeks. Participants received up to a total of $560 for completing assessments in the course of the trial but were not reimbursed for attending the therapy and medication sessions.

All participants were offered a total of 12 psychotherapy sessions from a team of 2 therapists, including a licensed psychiatrist: 4 before the first medication session, 4 between the first and second medication sessions, and 4 in the month following the second medication session. The psychotherapy, described in detail in a separate publication, 41 included motivational interviewing and cognitive behavioral therapy for AUD as well as material designed to help the participants to manage and make use of the psychoactive effects of the study medication. Training, supervision, and fidelity monitoring procedures are described in the protocol in Supplement 1 .

Randomization was stratified by site and consisted of balanced blocks of varying size. A study pharmacist at each site generated the randomization sequence and assigned treatment in order of randomization. All other study staff and investigators as well as participants were blinded to treatment assignment.

Study medication was taken orally in a single opaque capsule of unvarying appearance and weight. Psilocybin doses were weight based to control for participant body weight, which ranged from 49.0 to 116.1 kg (mean [SD], 78.3 [15.6] kg). Doses for the first session were psilocybin, 25 mg/70 kg, or diphenhydramine, 50 mg. Participants received an increased dose in the second session if there were no dose-limiting adverse events and they agreed to the increase. The increased dose of psilocybin was 30 mg/70 kg if the participant’s total score on the Pahnke-Richards Mystical Experience Questionnaire (MEQ) 42 was 0.6 or greater in the first session (indicating a robust subjective response to the 25 mg/70 kg dose) or 40 mg/70 kg if the MEQ total score in the first session was less than 0.6. The increased dose of diphenhydramine was 100 mg regardless of subjective response.

Study medication was administered at approximately 9 am , after which participants were required to stay in the session room with the therapists for at least 8 hours (except for bathroom breaks). During the session, participants were encouraged to lie on a couch wearing eyeshades and headphones providing a standardized playlist of music. Medications were available in the session room to treat hypertension, severe anxiety, or psychotic symptoms as specified in the protocol.

Subjective effects of psilocybin vs diphenhydramine were assessed using the States of Consciousness Questionnaire, 42 containing the 43-item MEQ. This questionnaire was completed immediately after each medication session.

The prespecified primary drinking outcome was the percentage of heavy drinking days (PHDD) during weeks 5 to 32, assessed at weeks 8, 12, 24, and 36 using timeline followback, a reliable and valid calendar-based method, which is the criterion standard outcome for AUD clinical trials. 43 - 47 One standard drink was defined as 14 g of ethanol. Secondary outcomes included percentage of drinking days (PDD), mean drinks per day (DPD), and dichotomous outcomes: abstinence, defined following a recent study 48 evaluating the use of WHO risk levels as a treatment outcome; lack of heavy drinking days; and reduction in World Health Organization (WHO) risk level 49 by 1, 2, or 3 levels. Hair or fingernail samples were collected at week 24 and assayed for ethylglucuronide (EtG) concentration to confirm self-reported abstinence. The Short Index of Problems (SIP-2R) 50 was used to assess drinking-related problems at baseline and at weeks 12, 24, and 36.

Blood pressure and heart rate were assessed at 30- to 60-minute intervals during the first 6 hours of each medication session. Adverse events were solicited at each postscreening assessment. After each session, participants and therapists were asked to guess which medication had been administered and rate their degree of certainty on a 100-point visual analog scale (0 = not at all confident; 100 = extremely confident).

The statistical analysis plan was developed in accordance with published guidelines 51 and contains a full description of statistical methods. The statistical analysis plan can be found in Supplement 1 .

The study was originally designed to randomize up to 180 participants. An interim analysis was planned after recruitment of 100 participants to reestimate the necessary sample size to yield power of 0.8 to detect a small to moderate effect ( f 2  = 0.16) with no correction for multiple comparisons. However, following an indefinite mandatory suspension of recruitment beginning on March 19, 2020, due to the outbreak of COVID-19, enrollment for this trial was halted at 95 randomized participants.

MEQ scores for the first and second medication sessions were computed and contrasted by group (psilocybin vs diphenhydramine) using t tests for independent samples. To evaluate the effects of treatment on continuous drinking outcomes (PHDD, PDD, and DPD), 3-dimensional multivariate repeated-measures analysis of variance was used, including fixed categorical effects of treatment, assessment, and site; site-by-treatment and treatment-by-assessment interactions; fixed baseline covariates for each dependent measure (PHDD, PDD, and DPD during weeks 1 to 4); and monthly values of PHDD, PDD, and DPD (weeks 5 to 8, 9 to 12, 13 to 16, 17 to 20, 21 to 24, 25 to 28, 29 to 32, and 32 to 36) as a nested multivariate dependent measure. All missing monthly values of PHDD, PDD, and DPD were imputed simultaneously using Multivariate Imputation by Chained Equations in R (MICE) version 3.14.0 (R Foundation). 52 Significant multivariate treatment effects were decomposed with univariate repeated-measures F tests within each drinking dimension (PHDD, PDD, and DPD). 53

Treatment contrasts for dichotomous outcomes were obtained using χ 2 statistics. Effects of treatment on problems related to drinking were compared using univariate mixed models for repeated measures and generalized linear models. Hedges g was computed as a measure of effect size for between- and within-group differences on continuous outcomes, and odds ratios were computed for dichotomous outcomes. No correction was made for multiple comparisons, so analyses of secondary outcomes should be considered exploratory.

Blood pressure and heart rate treatment contrasts were based on mixed models for repeated measures with fixed categorical effects of treatment and assessment, a treatment-by-assessment interaction, and a fixed covariate (value of each outcome prior to drug administration). All adverse events occurring after informed consent were coded according to the Medical Dictionary for Regulatory Activities and tabulated, and prevalence within treatment groups (proportion of participants affected) was compared using Fisher exact tests. Two-sided P  < .05 was considered statistically significant.

Figure 1 summarizes recruitment of participants, treatment exposure, and retention. A total of 95 participants were randomized: 49 to psilocybin and 46 to diphenhydramine. Table 1 describes baseline characteristics of the randomized sample. The mean (SD) age was 45.8 (11.6) years, and 42 participants (44.2%) were female. One participant (1.1%) was American Indian/Alaska Native, 3 (3.2%) were Asian, 4 (4.2%) were Black, 14 (14.7%) were Hispanic, and 75 (78.9%) were non-Hispanic White (sum is greater than 100% due to multiple categories selected by 2 participants). Participants met a mean (SD) 5.3 (1.2) of the 7 alcohol dependence criteria and had been alcohol dependent for a mean (SD) 14.2 (9.7) years. During the 12 weeks prior to screening, they consumed alcohol a mean (SD) 74.9% (28.1%) of days, including heavy consumption on a mean (SD) 52.7% (30.58) of days, and consuming a mean (SD) 7.1 (4.1) standard drinks per drinking day.

Participation in the nonmedication therapy sessions was high and did not substantially differ between treatment groups. Participants treated with psilocybin and diphenhydramine completed a mean (SD) 11.75 (0.76) and 11.47 (1.20) of the 12 sessions, respectively ( F 1,91  = 1.88; P  = .17). Of 95 participants randomized, 93 received at least 1 dose of medication: 48 received psilocybin (25 mg/70 kg) and 45 received diphenhydramine (50 mg) in the first medication session. Forty-three of participants treated with psilocybin (89.6%) and 35 of those treated with diphenhydramine (77.8%) received a second double-blind medication session ( F 1,91  = 2.40; P  = .13). In the second session, psilocybin doses were 25 mg/70 kg (n = 1), 30 mg/70 kg (n = 27), and 40 mg/70 kg (n = 15), and diphenhydramine doses were 50 mg (n = 11) and 100 mg (n = 24). Mean (SD; range) absolute dosages of psilocybin were 28.3 (5.4; 19.3-40.0) mg for psilocybin session 1 and 37.7 (8.6; 24.1-64.5) mg for psilocybin session 2.

Valid drinking outcome data were obtained for 717 of 744 months (96.4%) in the 8-month follow-up period for the 93 participants receiving treatment (366 of 384 [95.3%] in the psilocybin group and 351 of 360 [97.5%] in the diphenhydramine group). A total of 63 of 337 follow-up TLFB assessments (18.7%) were collected by phone due to inability to complete in-person visits. EtG results were available for 50 of 93 participants (53.8%), with missing data due to telephone visits (n = 24), insufficient hair samples (n = 12), missing visits (n = 5), or other reasons (n = 2). Participants missing EtG data did not differ from other participants on baseline drinking measures, age, race, ethnicity, or sex.

Participants correctly guessed their treatment assignment in 93.6% of the first sessions, reporting a mean (SD) certainty of 88.5% (23.2%). In the second session, 94.7% guessed correctly, and mean (SD) certainty was 90.6% (21.5%). Study therapists correctly guessed treatment 92.4% of the time for first sessions and 97.4% for second sessions, and their mean (SD) certainties were 92.8% (16.3%) and 95.4% (2.9%), respectively.

Among the 50 participants for whom valid EtG results were obtained at week 24, 14 (28%) reported total abstinence on the week 24 TLFB. EtG results were negative (less than 8 pg/ng) for all of these participants, providing some objective support for the veracity of self-report in this sample.

Psilocybin administration was associated with increased systolic and diastolic blood pressure relative to diphenhydramine (eFigure in Supplement 2 ), but no participant reported symptoms or was treated for hypertension. By 360 minutes, blood pressure was no longer significantly elevated. Heart rate was also higher in the psilocybin group until approximately 300 minutes after drug administration.

Mean (SD) MEQ scores for session 1 were 0.59 (0.24) in participants treated with psilocybin vs 0.10 (0.13) in those receiving diphenhydramine ( t 1,74.3  = 12.41; P  < .001). For session 2, mean (SD) scores were 0.64 (0.21) vs 0.11 (0.16), respectively ( t 1,75.5  = 13.01; P  < .001). These scores indicate high average intensity of experiences in the psilocybin group and low average intensity in the diphenhydramine group.

Substantial decreases in PHDD, PDD, and DPD were observed in both treatment groups between screening and week 4, during which time participants received 4 psychotherapy sessions and attempted to stop drinking in preparation for the first medication session ( Table 2 ). Among participants who subsequently received psilocybin, PHDD decreased by a mean of 32.37 (95% CI, 23.68-41.07; Hedges g , 1.08; 95% CI, 0.74-1.47). Similar changes in PHDD were observed among participants who subsequently received diphenhydramine (mean decrease, 27.26; 95% CI, 20.83-33.69; Hedges g , 1.02; 95% CI, 0.75-1.44).

The primary outcome analysis demonstrated a main effect of treatment on the 3-dimensional drinking outcome vector ( F 1,86  = 6.18; P  = .02). During weeks 5 to 36, participants who received psilocybin had lower PHDD than those who received diphenhydramine (mean [SD], 9.71 [26.21] vs 23.57 [26.21]; mean difference, 13.86; 95% CI, 3.00-24.72; Hedges g , 0.52; P  = .01). Results for the secondary continuous drinking outcomes, PDD and DPD, are shown in Table 2 . Figure 2 displays estimated monthly means for each of the 3 continuous outcome variables.

Participants who were treated with psilocybin were more likely than those receiving diphenhydramine to have no heavy drinking days and to have a 2-level reduction in WHO risk level during weeks 5 to 36 ( Table 3 ). During the final month of follow-up (weeks 33 to 36), these differences persisted, and the rates of abstinence as well as 1- and 3-level reductions in WHO risk levels were also higher in the psilocybin group than in the diphenhydramine group. Numbers needed to treat for these outcomes ranged from 4.0 to 8.2, and odds ratios ranged from 2.03 to 4.74. Participants treated with psilocybin also showed moderate to large reductions in several categories of drinking-related problems at week 24 and/or week 36 (eTable 1 in Supplement 2 ). Including all available data at the final double-blind time point (week 36), the mean (SD) total problems score was 6.59 (8.80) in those who received psilocybin vs 13.00 (10.48) in those who received diphenhydramine (mean difference, 6.4; 95% CI, 2.22-10.60; Hedges g , 0.67; P  = .003).

A total of 204 adverse events (119 in the psilocybin group and 85 in the diphenhydramine group) were reported during the 32 weeks following the first administration of study medication (eTable 2a in Supplement 2 ). Three serious adverse events were reported, all in the diphenhydramine group. One participant had 2 psychiatric admissions due to suicidal ideation reported during binge drinking episodes. A second participant was hospitalized for a Mallory-Weiss tear due to severe vomiting during a binge drinking episode.

eTable 2b in Supplement 2 summarizes treatment-emergent adverse events occurring within 48 hours of study drug administration. Headaches were common after psilocybin administration, occurring in 21 of 48 participants who received psilocybin (43.8%) vs 2 of 45 who received diphenhydramine (4.4%). Anxiety and nausea were also reported more frequently during psilocybin administration sessions. Two participants assigned to psilocybin received diazepam, 10 mg, by mouth for anxiety during their second medication session. The anxiety resolved within 45 minutes in one individual and 210 minutes in the other. One participant assigned to psilocybin reported passive suicidal ideation for 15 minutes during a medication session, which resolved without sequelae. There were no persistent disturbances suggestive of psychosis or hallucinogen persisting perception disorder.

In this randomized clinical trial of psilocybin-assisted psychotherapy treatment for AUD, psilocybin treatment was associated with improved drinking outcomes during 32 weeks of double-blind observation. PHDD among participants treated with psilocybin was 41% of that observed in the diphenhydramine-treated group. Exploratory analyses confirmed a between-group effect across a range of secondary drinking measures. Although this was, to our knowledge, the first controlled trial of psilocybin for AUD, these findings are consistent with a meta-analysis 39 of trials conducted in the 1960s evaluating LSD as a treatment for AUD.

Adverse events associated with psilocybin administration were mostly mild and self-limiting, consistent with other recent trials evaluating the effects of psilocybin in various conditions. 1 - 8 However, it must be emphasized that these safety findings cannot be generalized to other contexts. The study implemented measures to ensure safety, including careful medical and psychiatric screening, therapy and monitoring provided by 2 well-trained therapists including a licensed psychiatrist, and the availability of medications to treat acute psychiatric reactions.

This trial had methodological strengths that enhance confidence in these findings. The sample size, although smaller than planned, was the largest of any psilocybin trial yet published to our knowledge. Additional strengths include rigorous assessment and high retention rates over a 32-week period of double-blind follow-up. The psychotherapy used in this trial was manualized and included elements of empirically supported treatments that are commonly used in addiction treatment programs. The effects of psilocybin observed in this trial were over and above the substantial improvement observed in control participants who received the same psychotherapy and reduced their PHDD by more than 50% relative to screening.

Several limitations of the study warrant discussion. First, diphenhydramine was ineffective in maintaining the blind after drug administration, so biased expectancies could have influenced results. Control medications such as methylphenidate, 42 niacin, 2 and low-dose psilocybin 1 likewise did not adequately maintain blinding in past psilocybin trials, so this issue remains a challenge for clinical research on psychedelics. Second, EtG samples, used to validate self-reported drinking outcomes, were available for only 53.8% of treated participants. Third, the study did not have adequate power to evaluate effects in subgroups, such as women, ethnic and racial minority groups, and individuals with psychiatric comorbidity, nor was it designed to identify causal mechanisms, optimal dosing, or predictors of treatment response. Fourth, the study population was lower in drinking intensity at screening than in most AUD medication trials, and results cannot be assumed to generalize to populations with more severe AUD. Fifth, the 2-group design does not permit evaluation of the effects of psychotherapy or the interaction between psychotherapy and medication. Sixth, the study does not provide information on the duration of the effects of psilocybin beyond the 32-week double-blind observation period, which is important given the often chronic, relapsing course of AUD. Further studies will be necessary to address these questions and many others concerning the use of psilocybin in the treatment of AUD.

In this randomized clinical trial in participants with AUD, psilocybin administered in combination with psychotherapy was associated with robust and sustained decreases in drinking, which were greater than those observed following active placebo with psychotherapy. These results provide support for further study of psilocybin-assisted treatment for adults with AUD.

Accepted for Publication: May 31, 2022.

Published Online: August 24, 2022. doi:10.1001/jamapsychiatry.2022.2096

Correction: This article was corrected on September 14, 2022, to fix the numbers of participants presented by race and ethnicity.

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2022 Bogenschutz MP et al. JAMA Psychiatry .

Corresponding Author: Michael P. Bogenschutz, MD, Department of Psychiatry, New York University Langone Center for Psychedelic Medicine, New York University Grossman School of Medicine, One Park Avenue, 8th Floor, New York, NY 10016 ( [email protected] ).

Author Contributions: Dr Bogenschutz had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design : Bogenschutz, Ross, Forcehimes, Rotrosen, Tonigan.

Acquisition, analysis, or interpretation of data : Bogenschutz, Ross, Bhatt, Baron, Laska, Mennenga, O'Donnell, Owens, Podrebarac, Rotrosen, Tonigan, Worth.

Drafting of the manuscript : Bogenschutz, Ross, Mennenga, Owens.

Critical revision of the manuscript for important intellectual content : All authors.

Statistical analysis : Bogenschutz, Laska, Mennenga, Tonigan.

Obtained funding : Bogenschutz, Ross.

Administrative, technical, or material support : Bogenschutz, Ross, Bhatt, Baron, Forcehimes, Mennenga, O'Donnell, Owens, Podrebarac, Rotrosen, Worth.

Supervision : Bogenschutz, Ross, Forcehimes, Podrebarac.

Conflict of Interest Disclosures: Dr Bogenschutz reported grants from the Heffter Research Institute, Carey and Claudia Turnbull, Efrem Nulman, MD, Rodrigo Niño, and Cody Swift during the conduct of the study and the National Institute on Alcohol Abuse and Alcoholism, the National Institute on Drug Abuse, the Heffter Research Institute, Mind Medicine, Inc, Tilray Canada, the Multidisciplinary Association for Psychedelic Studies (MAPS), B. More, Inc, the Turnbull Family Foundation, the Fournier Family Foundation, Dr Bronner’s Family Foundation, and the Riverstyx Foundation as well as personal fees from the Heffter Research Institute, Ajna Labs LLC, Beckley Psytech Limited, Journey Colab, and Bright Minds Biosciences outside the submitted work; Dr Bogenschutz also reports being listed as inventor in a provisional patent application related to this work, filed by his employer (New York University Grossman School of Medicine), for which Dr Bogenschutz has formally waived all rights and has no prospect of financial gain; moreover, Dr Bogenschutz’s employer has licensed the commercial rights to the data from the study described in this manuscript to B.More, Inc, for a nominal sum, and Dr Bogenschutz has no financial stake in this agreement. Dr Ross reported grants from Heffter Research Institute during the conduct of the study and from Usona Institute and Reset Pharmaceutical outside the submitted work; in addition, Dr Ross had a patent for N420838US and for N419987US, both issued by Reset Pharmaceuticals. Dr Forcehimes reported personal fees from New York University Grossman School of Medicine during the conduct of the study. Dr Mennenga reported grants from Ceruvia Lifesciences outside the submitted work. Dr O’Donnell reported personal fees from MAPS-Public Benefit Corporation and Polaris Insight Center outside the submitted work. Dr Rotrosen reported having served as a principal investigator or a coinvestigator on studies for which support in the form of donated or discounted medication, smartphone apps, and/or funds has been or is provided by Alkermes, Inc (vivitrol, extended-release injectable naltrexone), Indivior, Inc (formerly Reckitt-Benckiser; suboxone, buprenorphine/naloxone combination), Braeburn Pharmaceuticals, Inc (extended-release injectable buprenorphine), Pear Therapeutics (smartphone apps ReSET and ReSET-O), CHESS Health (Connections smartphone app), and Data Cubed (smartphone apps SOAR and mSAPPORT), directed to either New York University, or to National Institute on Drug Abuse, or to National Institute on Drug Abuse’s contractor Emmes, Inc; served in a nonpaid capacity as a member of an Alkermes study steering committee, as a nonpaid scientific advisor to Mind-Medicine, Inc, as principal investigator on RD-i15-00461 (National Institute on Drug Abuse Clinical Trials Network: New York Node), which is an umbrella grant that supports numerous studies, as a member on each of 2 doctoral dissertation committees at the University of Oslo, Norway, and as a nonpaid chair of the data and safety monitoring board for the OPTIMA trial conducted by CRISM, the Canadian addiction treatment clinical trials network; and currently serves as Chair of the data and safety monitoring board for the US Department of Veterans Affairs Cooperative Studies Program 2014. No other disclosures were reported.

Funding/Support: Support for this research was provided by the Heffter Research Institute, the New York University-Health and Hospitals Corporation Clinical and Translational Science Institute (grant UL1 TR000038 from the National Center for Advancing Translational Sciences, National Institutes of Health), and individual donations from Carey and Claudia Turnbull, Efrem Nulman, Rodrigo Niño, and Cody Swift. Psilocybin was provided by the Usona Institute, Nicholas Cozzi at the University of Wisconsin-Madison, and David Nichols at Purdue University.

Role of the Funder/Sponsor: The Heffter Research Institute reviewed and approved the study protocol and significant protocol amendments. The funders were provided with progress reports to monitor study progress. The funders played no role in the conduct of the study.

Data Sharing Statement: See Supplement 3 .

Additional Contributions : We thank the study participants for their contributions to the study. For contributions to data collection, we thank Jessica Pommy, PhD, University of New Mexico School of Medicine, and Gabrielle Agin-Liebes, PhD, Jane Dowling, RN, Ursula Rogers, MA, Christina Marini, BA, and Noah Gold, BA, New York University Grossman School of Medicine. For their work as study clinicians, we thank Robert Voloshin, MD, University of New Mexico School of Medicine, and Jessie Duane, MSW, Jeffrey Guss, MD, Elizabeth Nielson, PhD, and Michael Cooper, MD, New York University Grossman School of Medicine. For serving as pharmacists for the study, we thank Richard Gomez, PharmD, University of New Mexico School of Medicine, and Sam Bliss, PharmD, New York University Grossman School of Medicine. For reading and evaluating screening electrocardiograms, we thank Jeffrey Lorin, MD, New York University Grossman School of Medicine. We thank Ziqiang Lin, PhD, New York University Grossman School of Medicine, for performing multiple imputation of drinking outcome data. For serving on the data and safety monitoring board, we thank Ryan McCormack, MD, James Babb, PhD, Helena Hansen, MD, and Babak Tofighi, MD, New York University Grossman School of Medicine. For thoughtful review of the study protocol, we thank George Greer, MD, Heffter Research Institute. None of these individuals received compensation outside of their usual salary. Finally, we thank all of the individual donors who contributed to the funding of this trial, either directly or through donations to the Heffter Research Institute, and Carey Turnbull for his tireless efforts to ensure that sufficient funding was available to complete the study.

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