research study on addiction treatment

Addiction Treatment and Telehealth: Review of Efficacy and Provider Insights During the COVID-19 Pandemic

Affiliation.

• 1 RTI International, Rockville, Maryland (Mark, Treiman), and Research Triangle Park, North Carolina (Henretty, Tzeng); Integrated Substance Abuse Programs, University of California, Los Angeles, Los Angeles (Padwa, Gilbert).
• PMID: 34644125
• DOI: 10.1176/appi.ps.202100088

Objective: Addiction treatment via telehealth expanded to unprecedented levels during the COVID-19 pandemic. This study aimed to clarify whether the research evidence on the efficacy of telehealth-delivered substance use disorder treatment and the experience of providers using telehealth during the pandemic support continued use of telehealth after the pandemic and, if so, under what circumstances.

Methods: Data sources included a literature review on the efficacy of telehealth for substance use disorder treatment, responses to a 2020 online survey from 100 California addiction treatment providers, and interviews with 30 California treatment providers and other stakeholders.

Results: Eight published studies were identified that compared addiction treatment via telehealth with in-person treatment. Seven found telehealth treatment as effective but not more effective than in-person treatment in terms of retention, therapeutic alliance, and substance use. One Canadian study found that telehealth facilitated methadone prescribing and improved retention. In the survey results reported here, California addiction treatment providers said that more than 50% of their patients were being treated via telehealth for intensive outpatient treatment, individual counseling, group counseling, and intake assessment. They were most confident that individual counseling via telehealth was as effective as in-person individual counseling and less sure about the relative effectiveness of telehealth-delivered medication management, group counseling, and intake assessments.

Conclusions: Telehealth may help engage patients in addiction treatment by improving access and convenience. Additional research is needed to confirm that benefit and to determine how best to tailor telehealth to each patient's circumstances and with what mix of in-person and telehealth services.

Keywords: Alcohol and drug abuse; Coronavirus/COVID-19; Telecommunications.

Publication types

• Research Support, Non-U.S. Gov't
• Ambulatory Care
• Telemedicine* / methods

• Signs of Addiction

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• Open access
• Published: 06 May 2024

Support after return to alcohol use: a mixed-methods study on how abstinence motivation and app use change after return to alcohol use in an app-based aftercare intervention for individuals with alcohol use disorder

• Catharina Lang   ORCID: orcid.org/0000-0001-7424-4217 1 ,
• Kiona K. Weisel 1 ,
• Sebastian Saur 1 ,
• Lukas M. Fuhrmann 1 ,
• Antonie Schoenleber 1 ,
• Daniela Reichl 2 ,
• Niklas Enewoldsen 2 ,
• Sabine Steins-Loeber 2 &
• Matthias Berking 1  

Addiction Science & Clinical Practice volume  19 , Article number:  35 ( 2024 ) Cite this article

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Metrics details

As the return to alcohol use in individuals with alcohol use disorder (AUD) is common during treatment and recovery, it is important that abstinence motivation is maintained after such critical incidences. Our study aims to explore how individuals with AUD participating in an app-based intervention with telephone coaching after inpatient treatment perceived their abstinence motivation after the return to alcohol use, whether their app use behavior was affected and to identify helpful factors to maintain abstinence motivation.

Using a mixed-methods approach, ten participants from the intervention group of the randomized controlled trial SmartAssistEntz who returned to alcohol use and recorded this in the app Appstinence , a smartphone application with telephone coaching designed for individuals with AUD, were interviewed about their experiences. The interviews were recorded, transcribed and coded using qualitative content analysis. App use behavior was additionally examined by using log data.

Of the ten interviewees, seven reported their abstinence motivation increased after the return to alcohol use. Reasons included the reminder of negative consequences of drinking, the desire to regain control of their situation as well as the perceived support provided by the app. App data showed that app use remained stable after the return to alcohol use with an average of 58.70 days of active app use ( SD  = 25.96, Mdn  = 58.50, range = 24–96, IQR  = 44.25) after the return to alcohol use which was also indicated by the participants’ reported use behavior.

Conclusions

The findings of the study tentatively suggest that the app can provide support to individuals after the return to alcohol use to maintain and increase motivation after the incidence. Future research should (1) focus on specifically enhancing identification of high risk situations and reach during such critical incidences, (2) actively integrate the experience of the return to alcohol use into app-based interventions to better support individuals in achieving their personal AUD behavior change goals, and (3) investigate what type of support individuals might need who drop out of the study and intervention and discontinue app use altogether.

Trial registration

The primary evaluation study is registered in the German Clinical Trials Register (DRKS, registration number DRKS00017700) and received approval of the ethical committee of the Friedrich-Alexander University Erlangen-Nuremberg (193_19 B).

Alcohol use disorder (AUD) as defined by the Diagnostic and Statistical Manual of Mental Disorders-5 (DSM-5) ranges from mild to severe forms based on the amount of fulfilled criteria [ 1 ], is prevalent, often chronic and linked to negative physical and mental health [ 1 , 2 , 3 ]. Evidence-based treatments including pharmacotherapy, withdrawal management, cognitive behavioral therapy, motivational interviewing, and prevention of the return to substance use exist [ 4 , 5 , 6 , 7 ] and recommendation on treatment indication are compiled in national treatment guidelines [ 8 ]. Despite the existence of various treatment forms there is room for improvement in the current treatment landscape reflected by high lifetime rates of the return to substance use for substance use disorders [ 9 , 10 ], low treatment retention, and general treatment barriers [ 11 , 12 , 13 ].

One emerging field in mental health is digital self-help [ 14 ]. Digital self-help has great potential: access is low threshold; after the development, costs are linked to the upkeep, amount of guidance and support provided which makes them scalable; participation is private and flexible [ 15 , 16 ]. Considering smartphone apps as a delivery format for mental health interventions, one of their main advantages is that they could provide support to individuals in times of need [ 17 ].

To date, research on internet interventions for reducing alcohol consumption shows promising results [ 18 , 19 ]. A meta-analysis [ 20 ] examined the efficacy of technology-delivered, cognitive-behavioral interventions for alcohol use (“CBT Tech”; CBT: Cognitive Behavioral Therapy) including 15 randomized controlled trials (RCT) with heavy and at-risk drinking. The effect of stand-alone CBT Tech was not significant in contrast to treatment as usual (TAU), but there was a small, significant effect when compared to a minimal treatment control ( g  = 0.20, 95% CI: 0.22–0.38, k es = 5). When CBT Tech was used as addition to TAU and compared to TAU only, the effect was even larger ( g  = 0.30, 95% CI: 0.10–0.50, k es = 7) and remained stable over a period of 12 months. However, there have only been few RCT studies of app-based AUD interventions to date. In a pilot study by Gonzales and Dulin [ 21 ], 60 participants meeting the criteria for AUD were recruited from the general community and randomized to either the 6-week app-based intervention “Location-Based Monitoring and Intervention for Alcohol Use Disorders” (LBMI-A) or an internet-based motivational intervention for the reduction of alcohol use. Both interventions led to significant decreases in drinks per week and percent of heavy drinking days, but only the LBMI-A resulted in a significant increase in percent of days abstinent. Concerning app use, 71% of the LBMI-A users accessed all app modules although their app usage declined over the course of the intervention period. App-based interventions could also be used to complement in-person treatment. Participants of an RCT [ 22 ] who met the criteria for AUD received residential substance use treatment plus “Addiction-Comprehensive Health Enhancement Support System” (A-CHESS), a smartphone application designed to improve continuing care by offering emotional and instrumental support at any time. 80% of the A-CHESS participants were still using the app at the end of month four. The A-CHESS group ( n  = 170) reported a lower mean number of risky drinking days (1.39 vs. 2.75; p  = .003) and they were more likely to be consistently abstinent (51.9% vs. 39.6%; p  = .030) than participants who received only treatment as usual ( n  = 179). Overall, these findings support the idea of app-based treatments as a potential approach to help people reduce their alcohol use or achieve abstinence. Yet, little is known about how participants’ abstinence motivation – the motivation to become or remain abstinent – and their app use change after the return to alcohol use during the period of app use and what aspects help to maintain abstinence motivation.

Considering factors which might contribute to treatment success, several studies have demonstrated the importance of motivational aspects in the course of therapy [ 23 , 24 ] as well as in the prevention of the return to substance use [ 25 , 26 , 27 ]. Motivational aspects are a determinant of long-term success of as well as engagement and retention in the treatment of substance use disorder [ 23 , 27 , 28 , 29 , 30 , 31 , 32 ]. Motivation for change in particular was found to be a predictor of reduction in substance use [ 33 , 34 ]. Stanick, Laudet and Sands [ 35 ] reported that abstinence commitment at the beginning of substance use disorder treatment increased the probability of treatment completion which in turn significantly increased the likelihood of maintaining abstinence for one year after treatment. They also found that, after controlling for substance use status, the amount of abstinence commitment at the end of the treatment was a predictor for the maintenance of abstinence in the year after substance use disorder treatment (OR = 2.27, 95% CI: 0.79–6.54). DiClemente, Doyle and Donovan [ 36 ] examined predictors of readiness to change, a construct related to substance use [ 37 , 38 ], using baseline data from a study on combined interventions for alcohol dependence [ 39 ]. Results showed that abstinence self-efficacy, positive treatment outcome expectations, lower perceived level of stress, higher quality of life, female gender, higher drinking severity, older age, higher psychiatric comorbidity and greater percentage of days abstinent significantly predicted greater readiness to change drinking behavior. Concerning abstinence motivation at the end of substance use disorder treatment, the findings of another study [ 40 ] revealed perceived damage due to future substance use, abstinence self-efficacy, satisfaction with quality of life and number of network members in a recovery program as predictors of commitment to abstinence. To sum up, abstinence motivation seems to be crucial in substance use disorder treatment and there are several predictors for abstince motivation at the beginning and at the end of treatment, some of which may be targeted by interventions as distinct approach and avoidance goals. However, none of this evidence refers to the particular situation of the return to alcohol use, though this is a very common event during and after substance use disorder treatment.

Besides its associations with the above-mentioned aspects of treatment, abstinence motivation is also understood as an important determinant before and during the process of the return to substance use [ 41 , 42 ]. Yet, there is a lack of research on what happens to abstinence motivation after the return to substance use. The return to substance use might be one critical tipping point at which the motivation following the occurrence might influence how individuals continue with abstinence and their treatment. Therefore, this study aims to examine abstinence motivation and app use, as a matter of behavior related to abstinence motivation, in the phase after the return to alcohol use in order to start filling this research gap. Understanding and assessing motivation is challenging as it requires insight into a person’s attitudes, intentions, confidence and commitment, and decision-making ability [ 43 ]. One manner to explore this is through qualitative research by conducting interviews with participants about their personal experiences in order to better understand processes in substance use disorder treatment [ 44 , 45 ]. Qualitative research promotes a better understanding of the research question and gives context which would not be possible by using only quantitative measures [ 46 ]. Yet, the interpretation of qualitative data is always to a degree subjective, so it makes sense to supplement the knowledge gained from this with quantitative, i.e. objective, data. A mixed-methods approach creates a more powerful research outcome than either method could do on its own [ 46 , 47 ]. For our study, this meant that we combined qualitative data on abstinence motivation and app use with quantitative app use data.

Research questions

In this study qualitative interviews were conducted with individuals with alcohol use disorder from the intervention group (access to the Appstinence app and telephone coaching) of the SmartAssistEntz  project who reported in the app to having returned to alcohol use. The aim of this mixed-methods study was to explore whether abstinence motivation and app use changed after the return to alcohol use and what aspects of the intervention and in general were perceived as supportive concerning abstinence and treatment motivation. Furthermore, we wanted to understand underlying motivational aspects and how these might affect behavior change. For this, we categorized reported factors of abstinence motivation into individual approach and avoidance goals.

Study design

Additional qualitative interviews were conducted with participants from the SmartAssistEntz  project after they had completed the primary study’s observation period of six months. All interviews were conducted from April to June 2021 while the primary study was still ongoing for other participants - with the goal of gathering information for subsequent agile intervention development. They took place after the individual end of the observation period ( Mdn  = 8 weeks, range = 3–13) in order to have app usage data for the full six months observed. The primary study aims to evaluate the newly developed app-based intervention Appstinence for individuals with AUD after inpatient treatment in a randomized controlled trial compared to only access to treatment as usual. More information on the primary study, which was registered in the German Clinical Trials Register (DRKS, registration number DRKS00017700) and received approval of the ethical committee of the Friedrich-Alexander University Erlangen-Nuremberg (193_19 B), can be found in the published study protocol [ 48 ]. The qualitative aspect of this mixed-methods study complies with the recommendations on data collection, extraction and interpretation of [ 49 ] and follows the consolidated criteria for reporting qualitative research (COREQ) [ 50 ].

Participants

Participants were included in the study who were in the intervention group with access to the Appstinence  app, had surpassed the study observation time frame of six months of the primary study, reported at least once having used alcohol in the app and continued to use the app at least once after reporting of the return to alcohol use. General primary study participation criteria and detailed information on the recruitment procedure are described in the published study protocol [ 48 ]. The potential sample ( N  = 38; 25 men, 13 women) was approached by email with the request to participate in the telephone interviews about their experiences with the app. Interview participation was compensated with 15 Euros. Of the 38 approached, 27 were not interested in participating or didn’t respond to the request and 11 individuals (8 men, 3 women) between the age of 21 and 57 completed the interview. One interview was later excluded as the individual was intoxicated during the conversation.

• Intervention

The intervention consists of the Appstinence app with a use period of six months and weekly 30 min telecoaching - phone calls with psychotherapists - for the first six weeks after discharge of inpatient treatment. The goal of the coaching was to support individuals in finding appropriate aftercare, strengthen motivation, and help create an emergency plan. Participants were able to use a chat in the app to communicate with the coaches about app content. The app had four basic modules to complete and ten elective modules to choose from. Modules were for example “boosting motivation”, “management of risky situations”, “prevention of the return to alcohol use”, “coping with the return to alcohol use”, “relaxation” and “emotion regulation”. Content was based on a cognitive-behavioral approach including psychoeducation, exercises to support behavior change and motivation, and practical information on finding an aftercare program. The app consists of texts, videos, audio files and some tasks based on an Approach-Avoidance Bias Modification paradigm [ 51 ] in which dysfunctional attitudes about alcohol intake are to be pushed away and functional attitudes about abstinence are to be pulled towards the users via screen swiping. Gamification aspects were integrated, for example feedback and praise, progress indicators for abstinence and task completion and customization of an avatar. A special motivation area was included in which participants were asked to add their own individual motivators for abstinence in form of images, texts and audio files. There were app use reminders sent via push notification. Participants were also encouraged to use a daily abstinence and craving tracker in which they were asked to self-report whether they had been abstinent the day before and how intense their craving was on a 5-point Likert scale ranging from very weak to very strong. Additionally, there was an emergency area to deliver support during a crisis.

Data collection and analysis

Qualitative interview, data collection.

Following an a priori formulated, semi-structured interview guide, the participants were interviewed by telephone and recorded via Internet telephone, known as Voice over IP using the applications Sipgate and PhonerLite. To design the interview guideline, first, a pool of potential questions was collected on the topic of the effect of a return to alcohol use on motivation and utilization of the app which led to the following sections: (1) current treatment situation , (2) app use behavior , (3) motivation for app use and behavior change , (4) situation of the return to alcohol use , (5) effect of the return to alcohol use on motivation and app use , (6) general app evaluation . Current treatment situation assessed whether the individual was currently receiving any type of support and whether the 7-day point abstinence was fulfilled. App use behavior assessed how the app was used including frequency, duration, and type of situation of use as well as the assessment of helpfulness of app reminders and the abstinence tracker. Motivation for app use and behavior change looked at the primary reason for wanting to achieve abstinence or to change drinking behavior and expectations before participating. Situation of the return to alcohol use confirmed the return to alcohol use reported in the app had happened, and it was inquired whether the app was used during or after the return to alcohol use and what other type of support was available in that time. Effect of the return to alcohol use on motivation and app use contained questions on how these aspects were influenced. General app evaluation aimed to explore whether the expectations had been met. The translated interview questions are displayed in Table  1 .

Data analysis

The recorded interviews were transcribed using the software MAXQDA 2020 (VERBI GmbH). Transcription rules were determined prior to transcription and followed general guidelines by [ 52 ] and the analysis was based on [ 53 ]. The coding process is illustrated in Fig.  1 .

Flowchart of qualitative data analysis

The first draft of categories was deduced from the interview themes themselves, the subcodes then emerged from the data taking an inductive approach. To identify the categories, 50% of the interviews were chosen by a random generator and used to identify and create new subcodes, and adapt the already existing code system. All steps were performed by two researchers (KKW, AS). Disagreement was discussed subsequently until satisfactory agreement was reached. To validate the categorical system, the last 50% of the interview material was coded by both researchers while adapting and finalizing the code system. After this step, all material was recoded based on the finalized code system and the intercoder correlation for reliability testing was assessed. The code segment intersection rate was set to 10% as a threshold, meaning that a minimum of 10% on segment level should match with each other to count as agreement. The intercoder agreement on the final coding was 90%. The process of qualitative content analysis led to the emergence of the following categories: 7-day point abstinence, current treatment options, evaluation of app, evaluation of coaching, evaluation of inpatient detoxification, abstinence motivation, app use behavior, return to alcohol use, motivation after return to alcohol use, app use after return to alcohol use, expectations of the app, reasons to participate.

To enhance reliability and validity six guidelines formulated by Mayring [ 54 ] were followed: (1) Documentation: Every step in the research process must be reported and justified in a comprehensible manner, (2) Validation of Interpretations: Every interpretation made must be proven and justified utilizing the available material, (3) Rule-guidedness: Data analysis must follow certain predetermined guidelines, (4) Object Proximity: Research should connect as best as possible to everyday life of the participants, (5) Communicative Validation: The codes, categories, and interpretations must be validated through an open discourse between the researchers, (6) Triangulation: Different data sources, theoretical approaches and methods should be included, i.e. by comparing qualitative and quantitative analyses.

Quantitative information

App use data was collected and extracted from the study administration tool. All other quantitative data relevant to this study was collected in the primary study via self-report diagnostic telephone interviews and web-based assessments. For this study only descriptive outcomes for socio-demographic data and abstinence self-efficacy at baseline were included.

Socio-demographic data included age, gender, level of education, employment status, current occupation, location of residency, and previous diagnosis of other psychological disorders.

Abstinence motivation and abstinence self-efficacy. Abstinence motivation for the coming six months was assessed as well as confidence levels of achieving this abstinence on a rating scale of 1 ( not confident at all ) to 5 (very confident ).

App use behavior was explored by log data and assessed by utilizing the number of days of active app use, the number of resolved tasks, the number of enters of the emergency area and the number of enters of the motivation area – in total and before and after the return to alcohol use. Alcohol use is defined here as any consumption of alcohol the previous day. Furthermore reported abstinence data was explored by using reported days of abstinence and alcohol use in the abstinence tracker.

Data is presented per person and additionally by presenting mean ( M ), standard deviation ( SD ), median ( Mdn ), range and interquartile range ( IQR ) per assessment time point.

Study population

The total study sample was ten participants, of which seven were male, and the average age was 40.60 years ( SD  = 12.40, Mdn  = 43.50, range = 21–57, IQR  = 20.75). Six participants were single and four had a university degree or a higher educational level, five were unemployed at the time of the beginning of the study. Eight participants reported having at least one other diagnosed psychological disorder and five reported living in a small town or village (≤ 20.000 inhabitants). All sociodemographic characteristics are presented in Table  2 . All ten participants reported to wanting to stay abstinent in the coming six months, of which five were confident and four somewhat confident to be able to reach this goal, while one person reported not being confident at all.

7-day point abstinence

Eight participants reported a 7-day point abstinence at the time of the interview, while two (P01, P09) reported to having consumed alcoholic beverages in the past seven days. The participants reported on average 117.20 days of abstinence in the abstinence tracker ( SD  = 56.05, Mdn  = 107.50, range = 32–205, IQR  = 78.25). These data included an initial input for days of abstinence between beginning of inpatient treatment and first app use.

Abstinence motivation and return to alcohol use

Return to alcohol use.

The participants reported on average 6.10 days of alcohol use in the abstinence tracker ( SD  = 4.61, Mdn  = 6, range = 1–15, IQR  = 6.75) over a period of six months. All interviewees were asked whether they had returned to alcohol use during the study period and whether they had reported this in the app. Although participants had been selected for this study based on recorded alcohol use in the app, two participants (P02, P10) stated to have been abstinent during the complete study period. Being asked about it, they said that they would have reported any alcohol use in the app. One participant (P07) stated to have discontinued use after the coaching period. All others reported to having used alcohol at least once and having reported this in the app. Some individuals explained their situations of the return to alcohol use, one participant (P01) stated she had returned to alcohol use due to a feeling of having been hurt by others, another (P06) stated the death of her brother had been a trigger: “It was just a bad farewell the last four weeks and yes, bad. And then, yes, it was actually logical for me that something like that would happen”. Others mentioned stressful situations and anger (P07), loss of contact with friends, a break-up, problems at work, and the loss of someone to talk to after the end of the withdrawal program (P09).

Five participants (P02, P04, P05, P06, P08) reported they had received support from family and friends after the return to alcohol use, three participants had received professional help from a psychologist (P06), additional counselors (P05), and a self-help group (P03). Three participants stated not having received any help, one participant felt this was due to the COVID-19-pandemic: “[…] That was the time when everything really shut down and I had an appointment, I had already called an addiction counseling center and they said they would try me somehow, but this Corona broke so much”. One person (P01) said she had wanted to be left alone after returning to alcohol use.

• Abstinence motivation

Based on the answers provided, abstinence motivation was categorized in approach and avoidance goals. One major theme in the avoidance goals was fear of negative consequences. One person (P01) disclosed she had difficulty believing that she could be struggling with AUD, indicating difficulty integrating her current situation into her constructed self-image and explaining abstinence motivation. Another person (P03) expressed that she wanted to avoid social judgment by others in her self-help group as well as the study team behind the app: “I didn’t want to have to admit to my addiction group that I had been drinking, and in there I also thought, uh, otherwise I would have to say yes, and I didn’t want to do that, so sometimes I rather didn’t drink anything because I didn’t want to admit that […] So I never knew whether someone would look at what I typed in there or not or something. And I always imagined that I would type in crap like that, maybe I didn’t disappoint them now because they don’t know me personally, but it was very uncomfortable for me to type in no”. She was also afraid of losing her job and driver’s license. One person (P06) reported she did not want to suffer the consequences, in the short term a hangover and in the long term, weight gain.

Considering approach goals, two individuals (P10, P09) stated they wished to regain quality of life and be present in the moment: “Yes, the recovery of my quality of life, but I really only understood that through withdrawal, then so after 3 weeks, when I then sat on a park bench, and again perceived birds chirping again, and it just got all better somehow” (P09). The person also stated to enjoy receiving positive feedback on his appearance after a period of abstinence which additionally motivated him. There were also social aspects for abstinence motivation. One person (P07) stated he wanted to take part in social life again and three others (P09, P03, P02) mentioned their biggest motivation was their family, friends and partners. Two individuals had responsibilities towards others (P01, P06). Four individuals (P04, P05, P07, P10) reported they wanted to feel proud of themselves and their achievements and have a “ clear head ”. One person (P08) was motivated by wanting to maintain physical health and fitness and two others (P01, P02) felt they needed to stop drinking due to existing comorbid diseases.

Motivation after return to alcohol use

Considering change in motivation after the return to alcohol use, the following categories were determined: (1) decrease of motivation, (2) increase of motivation, (3) no change in motivation. Seven of the 10 participants reported an increase in motivation. The participants had the following explanations for their increase in motivation, two individuals (P03, P09) reported the way the app “reacted” had an impact: “[…] and also the app, it didn’t wag its finger nastily, but you’ll manage it again tomorrow and so on. That was a motivating reaction.“; “I also thought it was good that when negative pressure was applied, that was about 5 times in the half year, that it didn’t say, oh my God, get help, but it was motivating somehow, keep going, keep at it”. One participant (P03) said the non-judgmental support of her support group after opening up about the return to alcohol use had a positive effect. For four participants (P01, P02, P05, P07) realizing the negative effects of their drinking behavior increased their desire to regain control over their life and decisions. Two individuals (P07, P08) began to seek additional professional treatment. Three out of ten participants stated that they had an initial decrease of motivation after the return to alcohol use. They explained that they became depressed after the return to alcohol use and reported that, at first, abstinence didn’t matter anymore.

Quantitative app use

App use defined as days of active app use varied strongly between individuals, with a range of 31 to 181 ( M  = 109.70, SD  = 52.42, Mdn  = 103.00, IQR  = 76.75). While there was no large difference in the mean number of days of active app use before ( M  = 51.00, SD  = 36.76, Mdn  = 41.50, range = 2–97, IQR  = 60.00) and after ( M  = 58.70, SD  = 25.96, Mdn  = 58.50, range = 24–96, IQR  = 44.25) the return to alcohol use, variance was high and a closer look at the individual app use data reveals that participants with less use days before the return to alcohol use seemed to have less use days in total. Nevertheless, both participants with an early return to alcohol use as well as participants with a later return to alcohol use used the app for several weeks after the return to alcohol use. Participants completed 86.80 tasks on average but the high range from 16 to 222 ( SD  = 59.00, Mdn  = 63.00, IQR  = 65.00) shows that participants differed greatly in their use behavior. The mean number of resolved tasks decreased from 55.30 before the return to alcohol use ( SD  = 36.03, Mdn  = 49.50, range = 13–125, IQR  = 39.75) to 31.50 after ( SD  = 53.06, Mdn  = 13.00, range = 0-175, IQR  = 35.00). Especially those participants with a higher number of use days before the return to alcohol use seemed to severely reduce their completion of tasks, with three of them resolving zero tasks after the return to alcohol use (P03, P04 and P09) although they still had a high number of active use days following their return to alcohol use. The number of times the emergency area was accessed ranged from 2 to 25 in total, with a mean of 10.10 ( SD  = 9.12, Mdn  = 5.00, IQR  = 13.25). There was no clear pattern of change in the number of access of the emergency area before and after the return to alcohol use. Participants accessed the motivation area 24 times on average, but again, the high range from 6 to 82 times of access reveals a large difference in the use behavior ( SD  = 22.34, Mdn  = 16.50, IQR  = 11.25). The app use data indicate a decrease in the number of access of the motivation area after the return to alcohol use, with a mean of 20.50 before ( SD  = 22.07, Mdn  = 14.50, range = 2–79, IQR  = 6.00) and a mean of 3.50 after ( SD  = 3.37, Mdn  = 2.50, range = 0–11, IQR  = 4.50) the return to alcohol use. Table  3 displays detailed information on app use of all ten participants.

Subjective app use behavior in general

Eight participants reported using the app on a daily basis, mainly to complete the abstinence tracker. The other two (P05, P07) reported having used the app once or twice a week. Some (P01, P02, P09) stated that their use frequency decreased over the study period of six months. Seven participants stated their average use was about ten minutes a session while three (P04, P05, P09) reported to having used the app for about one hour a session. When asked in what situations and when the interviewees used the app, five (P01, P02, P04, P05, P06) reported to having used the app when they felt they had spare time and one individual (P03) was using it every evening. One person (P08) used the app when he was feeling especially good while two others (P01, P08) used it when they felt they were struggling. Two participants (P01, P04) stated they used the app when feeling craving and one participant (P10) described use when passing through high risk situations: “[…] that was when I was out and about, for example when I passed a beer garden, or a restaurant or so ”. Two (P08, P01) stated they used the app in situations of alcohol use. Prompted on further situations individuals used the app, one (P09) stated in situations of boredom, two (P03, P08) wanted to keep engagement in the abstinence tracker high and one person (P07) said that he used it before preparing for the coaching which was part of the intervention. He also stated to have discontinued app use after the coaching was finished.

Four individuals clearly stated that the end of the coaching sessions did not affect their use behavior. In response to whether the reminder push notifications increased use, six participants answered affirmatively, two (P09, P06) stated not having received the reminders as their app use was already on a daily basis and one (P02) did not receive reminders due to technical issues. One person (P07) stated the reminders did not motivate app use.

Subjective app use behavior after return to alcohol use

Seven participants (P01, P02, P03, P04, P05, P06, P08) stated their app use behavior had not changed after the return to alcohol use. Two (P02, P09) had reduced their use frequency, one person (P02) because he attended a rehabilitation program. Upon further exploration, five participants used specific content in the app after the return to alcohol use, two (P02, P03) reported frequenting the motivation section, one (P03) chose to do the “Swipe” exercises: “I even did one of those exercises with pushing away and stuff, I thought who knows even if it doesn’t seem to make sense to my mind. But for the psyche it might be good anyway, and I just did that, just, again without much expectation, but I did it and thought, who knows, might be helpful”. Two participants (P08, P09) mentioned having used the emergency plan and one person (P05) engaged in daily practice to be mindful of his current situation and heighten awareness for risky situations.

Helpful and hindering factors of the intervention regarding abstinence motivation

Helpful and hindering factors of the app regarding abstinence motivation.

Statements about the app could be divided into (1) positive and (2) negative evaluations, and (3) general suggestions for adaptions. All participants mentioned at least one positive aspect. One participant (P09) felt supported by the daily notion for reflection and confrontation with their problems. Another participant stated something similar and added that the app could not be a total treatment substitute “[…] using the app alone is like medication, it can only be supportive, but can’t replace everything” (P08). Two individuals (P04, P05) found the diversity of content appealing and one person (P05) liked the interactive approach. “The interactive approach has definitely brought me a lot, so that when you read something, you tend to digress or forget that you’ve read it, but when you record it as an audio file or write it down and photograph it or whatever, then the learning material simply sticks better and the content simply sticks better”. Three individuals (P01, P09, P10) stated that they found the section for personal motivation particularly helpful, in which they were able to upload a personal photo. One participant (P09) exclaimed that he liked that there were daily exercises he could do and highlighted the emergency plan.

The daily abstinence tracker was rated positively by all interviewees. For some it was motivating to see how many days of abstinence they had already achieved, “I find that very helpful, I now also have that in an app that counts that, […] because that keeps the success in front of your eyes” (P02), and the daily reminder helped: “It helped me to then be reminded every day and I had to answer the couple of questions and yeah, I was looking forward to it part of the time that I had to do it or be reminded of it” (P10).

Negative evaluations of the app included that one participant (P01) was displeased that abstinence days could not be reported retrospectively but had to be reported daily. One participant (P05) criticized the general structure of the app: “Because there is no logical sequence of questions and topics, but because you have to find your own way around and then the app suggests, for example, some other module and you say, no, I don’t really want that now and I would have preferred a tighter guidance in quotation marks or a more structured guidance […]”. Another comment was dissatisfaction with the Swipe exercise about feeling it was too basic: “I kind of felt like I was in kindergarten there, I mean when I see pictures there of someone drinking alcohol and I’m supposed to press no there, I feel like I’m taking an idiot test 20 years ago, so that wasn’t that thrilling.” Further comments were that one person (P07) would have needed more pressure to engage in the program and another (P06) stated she believed her craving would still be strong even if she used the app in personal risk situations. She also felt the app was overloaded with content thus not being able to complete all modules.

Some participants also made suggestions for improvements of the app, for example to have all days of abstinence displayed per month for an overview (P01), one participant (P09) felt he would like more support after alcohol use was reported and suggested the implementation of a peer chat group to communicate with others: “Especially in these times when you’re not allowed to meet, when you’re there, this exchange is very important, because I’m still in contact with former drinkers who have all made it and so on, the solidarity is insane.”

Helpful and hindering factors of the coaching regarding abstinence motivation

Four individuals (P03, P06, P08, P10) commented positively on the coaching while two of them (P06, P08) returned to alcohol use during the first weeks, when telecoaching was still ongoing. One person (P06) recalled the support of the coaching after her return to alcohol use: “She also talked to me on the phone in the meantime, where I wrote that it had happened that I had been drinking. And I wrote that to her as an email and then she called me, and I thought that was totally super.” One person (P01) who also returned to alcohol use during coaching period felt the coaching was not helpful: “Yes, […] I got along well with the lady, but for me it’s just hard when they do not know me. To me so many things are simple, and then I have to tell her something, and then tell her something else, so that she understands the context, why and back and forth”.

Principal findings

In this mixed-methods study, we examined abstinence motivation and app use behavior after the return to alcohol use and aimed to identify the perceived supporting aspects regarding abstinence motivation. Overall, the results show an increase in abstinence motivation after the return to alcohol use and a stable app use.

Most of the participants stated that they were more motivated after the return to alcohol use. Williams and colleagues [ 55 ] found that patients with greater alcohol use show more readiness to change. Since the return to alcohol use can be seen as greater alcohol use or rather higher AUD symptom severity than before the return to alcohol use, the increase in motivation after the return to alcohol use would be an extension of the study by Williams and colleagues. Becoming aware of the negative consequences of the drinking behavior and the desire to regain control over one’s own life were the most common reported reasons for the increased motivation. The content in the app directly addresses these issues, which suggests that the app can serve as a mean to deliver motivational content. This raises the question if the app content might also be crucial in ending an ongoing return to alcohol use. Additionally, the manner in which the app was programmed to respond upon recording a return to alcohol use was described as an appreciative and encouraging reaction. Another factor was the non-judgmental support of a self-help group as aftercare. This is not surprising as respectful and supporting feedback and interactions are also part of Motivational Interviewing which has already been shown to be effective in the treatment of substance use disorder [ 25 ]. Altogether it might be that a return to alcohol use could only have a motivational impact if the return to alcohol use was adequately addressed and if affected individuals were supported in an appreciative manner. Yet, this has to be examined systematically, for example by comparing intervention and control group regarding their motivation after the return to alcohol use.

The motivation to use the app seemed to be stable after the return to alcohol use which reaffirms the findings of an increase in abstinence motivation since abstinence motivation should be crucial for an individual’s drive to continue to use the app. Although participants differed greatly in their individual app use behavior, there was no apparent change in the pattern of the app use concerning the number of days of active app use. This finding is in line with the study of Gustafson et al. [ 22 ] in which participants were still using the app A-CHESS four months after start of the intervention. Maybe the increase in motivation after the return to alcohol use has the effect of compensating an otherwise possible decline in the app use over time [ 21 ], which has yet to be confirmed by further research. All in all, one may argue that different interventions vary in their ability to motivate AUD patients, which would explain mixed results regarding intervention adherence. However, the earlier a return to alcohol use occurred, the less days of active app use were observed in total – although participants with an early return to alcohol use as well used the app for several weeks after the return to alcohol use. A plausible reason for this could be that someone who returns to alcohol use earlier might be generally less motivated and might therefore stop using the app earlier – even if there is a possible increase in motivation at first. The findings also raise the question why some people tend to stop participating in the intervention earlier than others and if an early return to alcohol use might predict an earlier discontinuation of an intervention. If an early return to alcohol use was found to be a predictor for an early intervention dropout it could be helpful to lengthen the abstinence period so that participants would use the intervention for a longer time and thus do more exercises. This should improve their self-efficacy and their AUD recovery prognosis. To achieve this, factors that are related to app use need to be identified to optimize tailoring of the intervention. Moreover, other potential predictors for an intervention discontinuation should be examined as well to facilitate the best possible adaptation of the intervention.

Most of the participants reported to having used the app daily – especially because of the abstinence tracker – and that their app use did not change after the return to alcohol use. These subjective reports about app use match up to the above-mentioned objective app data that showed no change of use after the return to alcohol use, demonstrate a stable motivation to use the app and therefore further corroborate the findings that abstinence motivation, as a driving force for stable app use, increased after the return to alcohol use. In addition, participants stated that they had used the app particularly during free time, when they experienced craving, in risk situations or when they had strong positive or negative emotions. This leads to the conclusion that the app can be a motivational support in AUD-related crisis and may possibly even prevent a return to alcohol use in those individuals who use the app in these kind of risky situations. Additionally, the varying use situations show that participants have different needs when using the app which speaks for the requirement of personalization and individualization of app-based AUD interventions.

Altogether, the app may have helped maintain abstinence motivation even after the return to alcohol use which may in turn have been a driving factor for continued stable app use after the return to alcohol use.

Considering underlying motives for abstinence, participants mentioned more approach than avoidance goals. This is consistent with the reported reasons for the increase in motivation after the return to alcohol use since the majority of these reasons (besides becoming aware of the negative consequences of drinking) could be rated as approach goals as well. The reported approach and avoidance goals, especially the fear of negative consequences of drinking and the desire to regain quality of life, also correspond to some of the aforementioned predictors of abstinence commitment at beginning and end of treatment [ 36 , 40 ] which in turn was associated with the maintenance of abstinence in the year after treatment [ 33 , 34 , 35 ]. Prior research provided evidence that the pursuit of a high proportion of avoidance goals relative to approach goals was harmful to one’s psychological functioning and well-being [ 56 ]. Wollburg and Braukhaus [ 57 ] examined the relevance of approach and avoidance goals for treatment outcome using a sample with depressed inpatient individuals. Having just one goal phrased in avoidance terms was linked to less improvement of symptoms, though they did not hinder goal achievement. Another study on approach and avoidance goals in the prevention of a return to alcohol use with sexual offenders [ 58 ] showed that participants in an approach-focused intervention vs. an avoidance-focused intervention were more willing to report a return to alcohol use, had a higher treatment engagement and were rated by therapist to have a higher end-of-treatment motivation to live without offending. Transferring these results into substance use disorder samples, it may be important to encourage approach goals in AUD treatment and focus more on the positive consequences of behavior change and abstinence.

Strengths and limitations

To the best of our knowledge, this is the first mixed-methods study to examine motivation after the return to alcohol use, to explore whether and how a possible change of motivation is reflected in participants’ app use behavior and to identify helpful factors for maintaining motivation. The advantages of qualitative research are manifold [e.g. 59 , 60 , 61 ]. By using a mixed-methods approach, we tried to gain a deeper understanding of the underlying factors of the examined motivational change and aimed to take the complexity of the participants’ experiences into account. We combined the qualitative information with quantitative data in order to get to a more complete picture of the investigated research questions [ 46 , 47 , 62 ].

Despite this mixed-methods design, there are certain limitations to our study. Our sample size was small and there was a possible selection bias since we included particularly those individuals who used the app to report their return to alcohol use and who voluntarily completed the interview. These participants might be more engaged in their treatment generally and also have higher abstinence motivation. Since this type of app use behavior would already assume some kind of motivation, it was less likely in the beginning that participants express decreased motivation after the return to alcohol use. Although there were participants that did not experience an increase in motivation after the return to alcohol use, this still remains a limitation. It could also be seen as a limitation that the study included only three women. Yet, the gender distribution is representative as it corresponds to the higher prevalence in men concerning substance use disorders in general [ 63 ] and AUD in particular [ 64 ]. Furthermore, our assessment of the explored change in motivation was retrospective, which could have influenced the findings.

Future research

Future research should utilize the derived information of this study to improve digital interventions for AUD treatment. This could be achieved in a number of ways. First, interventions should focus more on motivation by incorporating the aspects that were perceived as supporting after the return to alcohol use. Second, these interventions should implement the app functions and factors that were evaluated as helpful by the participants, for example the abstinence tracker. Third, app factors that were evaluated as not helpful or hindering should be eliminated. For example, an algorithm for task sequence based on individual needs could fulfill the need for a tighter guidance. Fourth, the use of approach goals before and after the return to alcohol use might be of advantage, which also needs to be explored regarding AUD treatment. These adaptations may lead to more motivation and adherence and thereby drive conversion into AUD-related behavior change.

Regarding the app “Appstinence” that was used by participants in our study, future research should examine whether this app is actually able to assist AUD patients with ending a return to alcohol use or even with preventing a return to alcohol use in risk situations. These hypotheses need to be tested in an appropriate study design by implementing a quantitative approach and using an adequate sample size.

Next, future studies should include individuals who dropped out of the intervention directly after the return to alcohol use, i.e. participants who returned to alcohol use but do not report this in the app, to prevent potential selection bias. As participants with an earlier return to alcohol use stopped using the app earlier, future research should also reach out to these individuals and to individuals who dropped out of the intervention in general to find out what factors could keep them motivated and more adherent and to identify predictors of intervention discontinuation. Presumably, a better personalization of content to meet individual needs may be crucial to attain this goal.

The findings suggest that abstinence motivation seems to generally increase after the return to alcohol use for participants in an app-based guided intervention for treatment of AUD. Future interventions should focus on motivation to deliver better support before and after a return to alcohol use and thereby potentially improve adherence and treatment outcomes. Furthermore, future studies need to reach out to individuals who drop out of the intervention after the return to alcohol use and to those with an early return to alcohol use.

Data availability

The data generated and/or analysed during the current study are not publicly available due to privacy reasons but are available in pseudonymised form from the corresponding authors on reasonable request.

Abbreviations

• Alcohol Use Disorder

German Registry of Clinical trials

Diagnostic and Statistical Manual of Mental Disorders-5

Cognitive Behavioral Therapy

Randomized controlled trial

Treatment as usual

Location-Based Monitoring and Intervention system for Alcohol Use Disorders

Addiction-Comprehensive Health Enhancement Support System

Consolidated criteria for reporting qualitative research

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Acknowledgements

The authors would like to thank the participants of the interviews without whom the study would not have been possible.

The primary study SmartAssistEntz was supported by the Innovation Fund of The Federal Joint Committee (G-BA) [01NVF18025]. The Innovation Fund had no role in study design, decision to publish or preparation of the manuscript. It is not involved in data collection, analysis and interpretation of data, the decision to publish or preparation of future papers regarding the project.

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CL: Conceptualization, Methodology, Investigation, Formal Analysis, Data Curation, Visualization, Writing- Original draft preparation, Writing – Draft revision. KKW: Conceptualization, Methodology, Investigation, Formal Analysis, Data Curation, Visualization, Writing- Original draft preparation. SS: Writing- Reviewing and Editing. LMF: Writing- Reviewing and Editing. AS: Investigation, Formal Analysis. DR: Writing- Reviewing and Editing. NE: Writing- Reviewing and Editing. SSL: Writing- Reviewing and Editing. MB: Supervision, Writing- Reviewing and Editing.

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Supplementary Material 1. An additional table shows the checklist for the consolidated criteria for reporting qualitative studies in detail [see Supplementary Table 1, Consolidated criteria for reporting qualitative studies (COREQ): 32-item checklist; “Support_after_return_to_alcohol_use_COREQ.docx”].

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Lang, C., Weisel, K.K., Saur, S. et al. Support after return to alcohol use: a mixed-methods study on how abstinence motivation and app use change after return to alcohol use in an app-based aftercare intervention for individuals with alcohol use disorder. Addict Sci Clin Pract 19 , 35 (2024). https://doi.org/10.1186/s13722-024-00457-7

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Cannabis has no clear effect on treatment of opioid addiction, study finds

by Taylor & Francis

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Cannabis is not an effective treatment for opioid addiction, a new study of thousands of people being treated for opioid use disorder suggests.

Experts, publishing their results today in  The American Journal of Drug and Alcohol Abuse , have found that cannabis is having no significant effect on peoples' use of opioids, taken outside of medical guidance.

The findings have substantial implications for U.S treatment programs, some of which still require patients to abstain from cannabis before they qualify for potentially life-saving treatment. This is based on the belief they are more likely to use opioids non-medically if they are using cannabis.

The opposing, and increasingly popular, viewpoint, that cannabis can help wean people with opioid use disorder off opioids, is also called into question in this new study.

Opioids are effective painkillers, but they can also be addictive, and the U.S. remains in the grip of an opioid use disorder crisis.

Around 120 people die a day from drug overdoses involving opioids (prescription, such as oxycodone, and non-prescription, such as heroin) and opioid use disorder and related deaths cost the US economy more than $1 trillion a year.

As cannabis gains popularity among individuals with opioid use disorder in the U.S., its medicinal use is now legally recognized in thirty-seven states and Washington D.C. While pain remains the most common reason for medical cannabis authorization (i.e., "medical cannabis registration card"), an increasing number of states are adding "alternatives to opioids" or "opioid-treatable disorders" to their lists of approved conditions. In certain states, this includes treatment for opioid use disorder.

The study's authors say this partly because the legalization of the recreational use of cannabis in many states means the drug is being perceived as being less harmful than in the past. Some cannabis dispensaries have promoted medicinal cannabis as a treatment for opioid use disorder.

It isn't clear, however, whether cannabis helps or hinders the treatment of opioid use disorder. Some studies have found it helps alleviate pain and opioid withdrawal, but others suggest it makes a return to opioids more likely.

"Clarifying how cannabis and opioids interact is crucial if we are to equip health care professionals to provide evidence-based addiction treatment, prevent overdose deaths and save lives," says researcher Gabriel Costa, of University of Ribeirão Preto in Brazil.

Costa, under the mentorship of Dr. Joao P. De Aquino, of Yale University, and colleagues, carried out a systematic review and meta-analysis of existing research on the influence of cannabis on non-medical opioid use.

The meta-analysis combined the results of ten longitudinal studies involving 8,367 individuals who were receiving medication (buprenorphine, methadone or naltrexone) to treat their opioid use disorder.

As part of this, over the course of an average of 10 months, individuals were monitored for their non-medical opioid use—including the use of opioids not prescribed to them, taking more opioids than prescribed, or using opioids without a prescription.

The study compared the frequency of this use between individuals who used cannabis, typically obtained from non-regulated sources, and those who did not use cannabis.

Results showed there to be no link between cannabis use and rates of non-medical opioid use.

"Overall, we found no significant association between cannabis and non-medical opioid use among patients receiving pharmacotherapies for opioid use disorder," states Costa.

"These findings neither confirm concerns about cannabis increasing non-medical opioid use in individuals being treated for opioid use disorder, nor do they endorse its efficacy in reducing non-medical opioid use."

The implications for opioid use disorder treatment programs are significant, adds Dr. De Aquino, who is a specialist in the treatment of persons with substance use disorders and co-occurring medical and psychiatric disorders.

He explains, "Our finding questions the ineffective practice of enforcing cannabis abstinence as a condition to offer life-saving medications for opioid use disorder.

"Our data suggests health care systems should instead adopt individualized treatment approaches which take into account each patient's circumstances.

"This would include assessing cannabis use disorder, a problematic pattern cannabis use that affects a person's well-being and ability to function, addressing pain management needs and treating co-occurring psychiatric conditions, such as depression and anxiety."

Dr. De Aquino adds that there have been very few experimental studies into cannabis and its constituent cannabinoids' ability to alleviate symptoms of opioid use disorder, and randomized placebo-controlled trials are needed to thoroughly assess its safety and effectiveness.

He says, "As high-potency synthetic opioids such fentanyl become increasingly available, it is of utmost importance that individuals with opioid use disorder have access to FDA-approved treatments.

"Methadone, buprenorphine, and extended-release intramuscular naltrexone—are known to be life-saving and are the cornerstone of opioid use disorder management."

Limitations include a lack of consistency in how the studies in systematic review and meta-analysis were conducted. This includes differences in how cannabis and opioid use were measured and variations in baseline opioid use status.

In addition, although the results are applicable to general cannabis use, they may not apply to individuals with cannabis use disorder.

More information:  The Impact of Cannabis on Non-Medical Opioid Use Among Individuals Receiving Pharmacotherapies for Opioid Use Disorder: A Systematic Review and Meta-Analysis of Longitudinal Studies,  The American Journal of Drug and Alcohol Abuse  (2024). DOI: 10.1080/00952990.2023.2287406. www.tandfonline.com/doi/full/1 … 0952990.2023.2287406

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FDA panel to consider MDMA for PTSD treatment

The Food and Drug Administration's panel of independent advisers will on June 4 deliberate whether they should recommend approval for the first MDMA-assisted therapy for post-traumatic stress disorder, Lykos Therapeutics said on Monday.

This would be the first FDA panel of outside experts to review a potential new PTSD treatment in 25 years.

PTSD is a disorder caused by very stressful events and can significantly disrupt patients’ lives.

Decades of studies has shown that psychoactive ingredients, whether derived from cannabis, LSD or magic mushrooms, have long captivated mental health researchers in their quest for treatments.

In support of its application, Lykos Therapeutics, formerly known as Multidisciplinary Association for Psychedelic Studies (MAPS), studied the party drug MDMA, more commonly called ecstacy or molly, in two late-stage studies.

More news on psychedelic research

• More high school students report using Delta-8 THC, also known as "weed lite."
• Effects of psychedelic drugs can last for days or weeks.
• High-potency marijuana highlights the risk of cannabis-induced psychiatric disorders.

The drug is intended to be used in combination with psychological intervention, which includes psychotherapy, or talk therapy, and other supportive services provided by a qualified healthcare provider.

No psychedelic-based therapy has been approved yet in the U.S., but MAPS and companies such as Compass Pathways are testing such drugs to find cures for a range of mental health disorders.

Reduced drug use is a meaningful treatment outcome for people with stimulant use disorders

NIH-supported findings suggest the need to expand definitions of addiction treatment success beyond abstinence

Reducing stimulant use was associated with significant improvement in measures of health and recovery among people with stimulant use disorder, even if they did not achieve total abstinence. This finding is according to an analysis of data from 13 randomized clinical trials of treatments for stimulant use disorders involving methamphetamine and cocaine. Historically, total abstinence has been the standard goal of treatment for substance use disorders, however, these findings support the growing recognition that a more nuanced perspective on measuring treatment success may be beneficial.

The study, published in Addiction , was led by scientists at the Johns Hopkins Bloomberg School of Public Health, Baltimore, in collaboration with researchers at the National Institute on Drug Abuse (NIDA), part of the National Institutes of Health.

Researchers found that transitioning from high use (five or more days a month) to lower use (one to four days a month) was associated with lower levels of drug craving, depression, and other drug-related challenges compared to no change in use. These results suggest that reduction in use of methamphetamine or cocaine, in addition to abstinence, is a meaningful surrogate or intermediate clinical outcome in medication development for stimulant addiction. Unlike other substance use disorders, such as opioid use disorder or alcohol use disorder, there are currently no U.S. Food and Drug Administration-approved pharmacological treatments for stimulant use disorders.

“These findings align with an evolving understanding in the field of addiction, affirming that abstinence should be neither the sole aim nor only valid outcome of treatment,” said NIDA Director Nora Volkow, M.D. “Embracing measures of success in addiction treatment beyond abstinence supports more individualized approaches to recovery, and may lead to the approval of a wider range of medications that can improve the lives of people with substance use disorders.”

Temporary returns to use after periods of abstinence are part of many recovery journeys, and relying exclusively on abstinence as an outcome in previous clinical trials may have masked beneficial effects of treatment. To help address this research gap, investigators analyzed data from previous clinical trials to study the effects of transitioning to reduced drug use or abstinence on a broad range of health measures. Researchers analyzed data from 13 randomized clinical trials evaluating the impact of potential pharmacological medications for stimulant use disorders, which included more than 2,000 individuals seeking treatment for cocaine or methamphetamine use disorders at facilities across the United States. The trials were of varying duration and were undertaken from 2001 to 2017.

Researchers compared “no reduced use,” “reduced use,” and “abstinence” in association with multiple health outcomes, such as severity of drug-related symptoms, craving, and depression. The study found that more participants reduced the frequency of primary drug use (18%) than achieved abstinence (14%). While abstinence was associated with the greatest clinical improvement, reduced use was significantly associated with multiple measures of improvements in psychosocial functioning at the end of the trials, such as a 60% decrease in craving for the primary drug, 41% decrease in drug-seeking behaviors, and a 40% decrease in depression severity, compared to the beginning of the trial.

These findings suggest that improvements in health and functioning can occur with reduced use and should be considered in the development and approval of treatments for substance use disorders. Research on alcohol use disorder has shown similar results, with studies finding that transitioning from high-risk to low-risk drinking is associated with functional improvement and fewer mental and general health consequences caused by alcohol. As a result, a reduced number of heavy drinking days is already recognized as a meaningful clinical outcome in medication development for alcohol use disorder.

“With addiction, the field has historically acknowledged only the benefits of abstinence, missing opportunities to celebrate and measure the positive impacts of reduced substance use,” said Mehdi Farokhina, M.D., M.P.H., a staff scientist in the NIDA Intramural Research Program, and author on the paper. “This study provides evidence that reducing the overall use of drugs is important and clinically meaningful. This shift may open opportunities for medication development that can help individuals achieve these improved outcomes, even if complete abstinence is not immediately achievable or wanted.”

The authors note that the study did not include behavioral treatment trials, which were too varied to harmonize their data. In addition, the study featured only people who enrolled in clinical trials, which could limit generalizability. Additional research is needed to understand the potential clinical benefits of reduced drug use, along with other harm reduction-based indicators of clinical improvement in real-world populations. The authors highlight that the findings of this study should encourage researchers to re-evaluate treatment outcome measures in their studies and consider non-abstinence treatment outcomes in the development of new medications for the treatment of stimulant use disorders. The authors also write that these new findings need to be replicated in other contexts with additional substance use disorders such as opioid use disorder.

“By promoting an understanding of addiction as a treatable disorder with multifaceted causes, society can work towards providing better support, resources, and care for individuals on their way to recovery,” said Masoumeh Aminesmaeili, M.D., lead author of the paper. “This approach is not only compassionate, but also clinically valid in addressing the complex nature of addiction.”

For more information on substance and mental health treatment programs in your area, call the free and confidential National Helpline 1-800-662-HELP (4357) or visit www.FindTreatment.gov .

• M Aminesmaeili, et al. Reduced drug use as an alternative valid outcome in individuals with stimulant use disorders: Findings from 13 multisite randomized clinical trials . Addiction . DOI: 10.1111/add.16409 (2024).

About the National Institute on Drug Abuse (NIDA): NIDA is a component of the National Institutes of Health, U.S. Department of Health and Human Services. NIDA supports most of the world’s research on the health aspects of drug use and addiction. The Institute carries out a large variety of programs to inform policy, improve practice, and advance addiction science. For more information about NIDA and its programs, visit www.nida.nih.gov .

About the National Institutes of Health (NIH): NIH, the nation’s medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit www.nih.gov .

About substance use disorders: Substance use disorders are chronic, treatable conditions from which people can recover. In 2022, nearly 49 million people in the United States had at least one substance use disorder. Substance use disorders are defined in part by continued use of substances despite negative consequences. They are also relapsing conditions, in which periods of abstinence (not using substances) can be followed by a return to use. Stigma can make individuals with substance use disorders less likely to seek treatment. Using preferred language can help accurately report on substance use and addiction. View NIDA’s online guide .

NIH…Turning Discovery Into Health®

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• Introduction
• Conclusions
• Article Information

AIANO, American Indian, Alaska Native, or other; API, Asian or Pacific Islander. Other is a racial and ethnic group listed in the National Cancer Database and represents patients who were classified as other by local cancer registries. The National Cancer Database does not specifically define race and ethnicity classified into Other. Kaplan-Meier curves for patients who received treatment are provided in the eFigure in Supplement 1 .

eTable 1. Percentage of Treatment “Declined/Received” Missingness Across Patient Cohorts Over Time

eTable 2. Distributions of Patient Characteristics by Chemotherapy Decision Comparing “Declined/Received” Not Missing vs Missing

eTable 3. Distributions of Patient Characteristics by Hormone Therapy Decision Comparing “Declined/Received” Not Missing vs Missing

eTable 4. Distributions of Patient Characteristics by Radiotherapy Decision Comparing “Declined/Received” Not Missing vs. Missing

eTable 5. Distributions of Patient Characteristics by Surgery Decision Comparing “Declined/Received” Not Missing vs Missing

eTable 6. Overall Characteristics of Patients With Breast Cancer in the National Cancer Database

eTable 7. Sociodemographic and Clinicopathologic Factors Associated With Treatment Declination: Multivariable Logistic Regression after Inverse Probability Weighting Adjusting for Missingness of Treatment Decision

eTable 8. Associated Characteristics With Decision on Chemotherapy in Patients With Stage I-IV Breast Cancer

eTable 9. Distributions of Chemotherapy “Declined/Received” Among Patients With Early-Stage, HR+/ ERBB2 - Breast Cancer Post-Surgery, by Multigene Assay Testing Result

eTable 10. Associated Characteristics With Decision on Hormone Therapy in Patients With Stage I-IV, Hormone Receptor–Positive Breast Cancer

eTable 11. Associated Characteristics With Decision on Radiation Therapy in Patients With Stage I-III Breast Cancer

eTable 12. Associated Characteristics With Decision on Surgery in Patients With Stage I-III Breast Cancer

eTable 13. Kaplan-Meier Estimates of Median Overall Survival Time in Breast Cancer Patients Stratified By Treatment Decision and Race and Ethnicity

eTable 14. Kaplan-Meier Estimated 5-Year and 10-Year Overall Survival of Breast Cancer Patients Stratified by Treatment Decision

eTable 15. Associated Factors With Overall Survival in Patients With Stage I-IV Breast Cancer by Treatment Decision on Chemotherapy

eTable 16. Associated Factors With Overall Survival in Patients With Stage I-IV, HR-Positive Breast Cancer by Treatment Decision on Hormone Therapy

eTable 17. Associated Factors With Overall Survival in Patients With Stage I-III Breast Cancer by Treatment Decision on Radiotherapy

eTable 18. Associated Factors With Overall Survival in Patients With Stage I-III Breast Cancer by Treatment Decision on Surgery

eFigure. Kaplan-Meier Curves for Overall Survival Stratified by Race and Ethnicity in Patients Who Received Treatment

Data Sharing Statement

• Treatment Declination, Racial and Ethnic Inequities, and Breast Cancer Survival JAMA Network Open Invited Commentary May 9, 2024 Gregory S. Calip, PharmD, MPH, PhD; Kent F. Hoskins, MD; Jenny S. Guadamuz, PhD, MSPH

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Freeman JQ , Li JL , Fisher SG , Yao KA , David SP , Huo D. Declination of Treatment, Racial and Ethnic Disparity, and Overall Survival in US Patients With Breast Cancer. JAMA Netw Open. 2024;7(5):e249449. doi:10.1001/jamanetworkopen.2024.9449

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Declination of Treatment, Racial and Ethnic Disparity, and Overall Survival in US Patients With Breast Cancer

• 1 Department of Public Health Sciences, University of Chicago, Chicago, Illinois
• 2 Center for Health and the Social Sciences, University of Chicago, Chicago, Illinois
• 3 Pritzker School of Medicine, University of Chicago, Chicago, Illinois
• 4 NorthShore Research Institute, NorthShore University HealthSystem, Evanston, Illinois
• 5 Department of Surgery, NorthShore University HealthSystem, Evanston, Illinois
• 6 Center for Clinical Cancer Genetics and Global Health, University of Chicago, Chicago, Illinois
• Invited Commentary Treatment Declination, Racial and Ethnic Inequities, and Breast Cancer Survival Gregory S. Calip, PharmD, MPH, PhD; Kent F. Hoskins, MD; Jenny S. Guadamuz, PhD, MSPH JAMA Network Open

Question   What are the treatment declination trends and are there racial and ethnic disparities in treatment declination and overall survival among patients with breast cancer?

Findings   In this cross-sectional study of 2 837 446 patients with breast cancer, the treatment declination rate was highest for chemotherapy and lowest for surgery. American Indian, Alaska Native, or other; Asian or Pacific Islander; and Black patients were more likely to decline chemotherapy, radiotherapy, or surgery than White patients; Asian or Pacific Islander, Black, and Hispanic patients were less likely to decline hormone therapy than White patients, with racial and ethnic disparities in overall survival differing by treatment decision.

Meaning   These findings highlight racial and ethnic disparities in treatment declination and overall survival of patients with breast cancer, suggesting that equity-focused interventions are needed to address the disparities to improve patients’ survival.

Importance   Declining treatment negatively affects health outcomes among patients with cancer. Limited research has investigated national trends of and factors associated with treatment declination or its association with overall survival (OS) among patients with breast cancer.

Objectives   To examine trends and racial and ethnic disparities in treatment declination and racial and ethnic OS differences stratified by treatment decision in US patients with breast cancer.

Design, Setting, and Participants   This retrospective cross-sectional study used data for patients with breast cancer from the 2004 to 2020 National Cancer Database. Four treatment modalities were assessed: chemotherapy, hormone therapy (HT), radiotherapy, and surgery. The chemotherapy cohort included patients with stage I to IV disease. The HT cohort included patients with stage I to IV hormone receptor–positive disease. The radiotherapy and surgery cohorts included patients with stage I to III disease. Data were analyzed from March to November 2023.

Exposure   Race and ethnicity and other sociodemographic and clinicopathologic characteristics.

Main Outcomes and Measures   Treatment decision, categorized as received or declined, was modeled using logistic regression. OS was modeled using Cox regression. Models were controlled for year of initial diagnosis, age, sex, health insurance, median household income, facility type, Charlson-Deyo comorbidity score, histology, American Joint Committee on Cancer stage, molecular subtype, and tumor grade.

Results   The study included 2 837 446 patients (mean [SD] age, 61.6 [13.4] years; 99.1% female), with 1.7% American Indian, Alaska Native, or other patients; 3.5% Asian or Pacific Islander patients; 11.2% Black patients; 5.6% Hispanic patients; and 78.0% White patients. Of 1 296 488 patients who were offered chemotherapy, 124 721 (9.6%) declined; 99 276 of 1 635 916 patients (6.1%) declined radiotherapy; 94 363 of 1 893 339 patients (5.0%) declined HT; and 15 846 of 2 590 963 patients (0.6%) declined surgery. Compared with White patients, American Indian, Alaska Native, or other patients (adjusted odds ratio [AOR], 1.47; 95% CI, 1.26-1.72), Asian or Pacific Islander patients (AOR, 1.29; 95% CI, 1.15-1.44), and Black patients (AOR, 2.01; 95% CI, 1.89-2.14) were more likely to decline surgery; American Indian, Alaska Native, or other patients (AOR, 1.13; 95% CI, 1.05-1.21) and Asian or Pacific Islander patients (AOR, 1.21; 95% CI, 1.16-1.27) were more likely to decline chemotherapy; and Black patients were more likely to decline radiotherapy (AOR, 1.05; 95% CI, 1.02-1.08). Asian or Pacific Islander patients (AOR, 0.81; 95% CI, 0.77-0.85), Black patients (AOR, 0.86; 95% CI, 0.83-0.89), and Hispanic patients (AOR, 0.66; 95% CI, 0.63-0.69) were less likely to decline HT. Furthermore, Black patients who declined chemotherapy had a higher mortality risk than White patients (adjusted hazard ratio [AHR], 1.07; 95% CI, 1.02-1.13), while there were no OS differences between Black and White patients who declined HT (AHR, 1.05; 95% CI, 0.97-1.13) or radiotherapy (AHR, 0.98; 95% CI, 0.92-1.04).

Conclusions and Relevance   This cross-sectional study highlights racial and ethnic disparities in treatment declination and OS, suggesting the need for equity-focused interventions, such as patient education on treatment benefits and improved patient-clinician communication and shared decision-making, to reduce disparities and improve patient survival.

In the US, breast cancer (BC) is the most common malignant neoplasm and the second leading cause of cancer deaths among women, with an estimated 287 850 new diagnoses and 43 250 deaths in 2022. 1 , 2 BC diagnosis and treatment can take a heavy toll on patients’ physical, mental, psychosocial, and financial health. Cancer treatment and care services require interdisciplinary and multidisciplinary collaborations and effective patient-clinician communication and shared decision-making, while respecting patient autonomy. 3 Some patients with cancer choose to decline treatment despite clinician recommendations and treatment benefits. Declining curative treatment can have a detrimental effect on these patients’ short-term and long-term health outcomes and quality of life. 4 - 6 Studies have documented elevated risks of all-cause and disease-specific mortality in patients with cancer who forgo treatment recommended by their clinicians. 7 - 11

Previous research in colorectal, 8 , 9 , 11 ovarian, 12 lung, 13 - 15 or mixed cancer cohorts 16 , 17 has found that older age, racial and ethnic minority background (eg, Hispanic, non-Hispanic Asian or Pacific Islander, or non-Hispanic Black), low socioeconomic status, and late-stage presentations are associated with declination of therapies. For BC, several analyses have reported similar sociodemographic and clinical factors associated with treatment declination in this patient population. 7 , 10 , 18 - 22 However, these studies focused on either chemotherapy or surgery only or the associations of treatment decisions with mortality, and most of them did not evaluate the pattern and long-term trends associated with treatment declination among patients with BC. Although a few studies have assessed racial and ethnic disparities in declination of surgery or chemotherapy, they largely focused on Black and Hispanic patients, and, to a lesser extent, Asian patients, 10 , 18 , 19 , 21 and 2 analyses included only White women. 4 , 7 There remain gaps in the literature regarding national trends in declination of treatment recommendations, racial and ethnic disparities, sociodemographic and clinicopathologic characteristics associated with treatment declination, and the implication of treatment declination for overall survival (OS) among patients with BC.

To fill these gaps, we conducted this study with the primary aim of examining trends and factors associated with declination of 4 treatment modalities (ie, chemotherapy, hormone therapy [HT], radiotherapy, and surgery), using a US nationwide oncology registry. The secondary aim was to assess the OS of patients with BC stratified by race and ethnicity and treatment decision.

This cross-sectional study was granted a waiver for informed consent and a review exemption by the University of Chicago institutional review board because we used deidentified data that do not identify hospitals, health care practitioners, or patients. The study followed the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline.

This retrospective study analyzed data collected from patients with BC in the 2004 to 2020 National Cancer Database (NCDB), a joint project of the Commission on Cancer of the American College of Surgeons and the American Cancer Society. 23 The NCDB is a clinical oncology registry that captures approximately 72% of new cancer diagnoses from more than 1500 Commission on Cancer–accredited programs in the US annually. 24 - 26

We constructed 4 patient cohorts with 4 treatment modalities: chemotherapy, HT, radiotherapy, and surgery. Stage group was based on the American Joint Committee on Cancer cancer staging. The chemotherapy cohort included patients with stage I to IV disease who were recommended for chemotherapy in the neoadjuvant or adjuvant setting. The HT cohort consisted of patients with stage I to IV hormone receptor–positive BC with recommended HT. The radiotherapy or surgery cohort was limited to patients with stage I to III disease, because neither treatment is the standard of care for stage IV BC.

Decision on recommended treatment was classified as received or declined. Chemotherapy, HT, radiotherapy, or surgery administered as the first course of therapy was categorized as received. If the treatment was recommended by a patient’s clinician but declined by the patient, their family members, or guardians, it was categorized as declined. Trends in declination of therapies or surgeries from 2004 to 2020 were assessed. Moreover, to examine the pattern of treatment declination, we tabulated the number of therapies patients eligible were for (only 1, 2, 3, all) and the number of therapies declined by the patients (0, only 1, 2, 3, all).

OS was defined as an event or censored at the time of death from all causes or last known patient contact. The index time for OS was the date of initial diagnosis of BC. Per the NCDB, mortality information was not available for patients diagnosed in 2020 due to limited time of follow-up; therefore, these patients were excluded from survival analysis. Median follow-up time and 5-year and 10-year rates of OS were calculated stratified by race and ethnicity and treatment decision.

Race and ethnicity were self-reported, and racial and ethnic groups were categorized as American Indian, Alaska Native, or other (non-Hispanic), Asian or Pacific Islander (non-Hispanic), Black (non-Hispanic), Hispanic, and White (non-Hispanic). Other is a racial and ethnic group listed in the NCDB and represents patients who were classified as other by local cancer registries. The NCDB does not specifically define race and ethnicity classified into other. Additional patient characteristics included age at diagnosis, sex assigned at birth, type of health insurance (uninsured, private, Medicaid, Medicare, and other government or unknown), median household income quartile (<$40 227, $40 227-$50 353, $50 354-$63 332, and ≥$63 333), rural-urban residence, facility type, Charlson-Deyo comorbidity index (CCI; 0, 1, and ≥2), cancer stage group, histology, molecular subtype, tumor grade, and year of initial diagnosis.

Sociodemographic and clinicopathologic factors were compared between treatment administration and declination using Pearson χ 2 tests for nominal data and t tests for continuous data. To examine time trends in treatment declination, we fit generalized linear models with the log link and binomial distribution. Multivariable logistic regression was used to model the odds of treatment declination as a function of race and ethnicity and other patient characteristics. We fit separate logistic regression models for the 4 cohorts and reported adjusted odds ratios (AORs) with 95% CIs. The Kaplan-Meier method was used to estimate medial survival time, 5-year and 10-year OS rates, and corresponding 95% CIs. Stratified by treatment decision, we assessed differential OS by race and ethnicity using log-rank tests, followed by modeling the risk of all-cause mortality using multivariable Cox proportional hazards regression. Adjusted hazard ratios (AHRs) and 95% CIs were calculated. The level of significance was set at P  < .05 and hypothesis tests were 2-sided.

Per the NCDB, patients who did not receive recommended treatment and did not have a reason noted or with an unknown status of treatment administration in their medical records were categorized as missing. We observed that the rate of missing treatment decision varied over time (eTable 1 in Supplement 1 ) and patient characteristics differed by missing status (eTables 2-5 in Supplement 1 ). Therefore, we conducted a sensitivity analysis using inverse probability weighting (IPW) to examine the robustness of the results. The probability of missing each treatment was estimated using multivariable logistic regression in the 4 patient cohorts. All statistical analyses were performed using the Stata 17 software package (StataCorp) from March to November 2023.

The study included 2 837 446 patients (mean [SD] age, 61.6 [13.4] years; 99.1% female), of whom 1.7% were American Indian, Alaska Native, or other, 3.5% were Asian or Pacific Islander, 11.2% were Black, 5.6% were Hispanic, and 78.0% were White. By insurance status, 49.9% of patients had private insurance or managed care, 39.3% of patients had Medicare insurance, and 6.3% of patients had Medicaid insurance. Most patients (55.6%) had stage I disease, and nearly three-quarters of patients (74.0%) had hormone receptor-positive and ERBB2 -negative disease (eTable 6 in Supplement 1 ).

Overall, 124 721 of 1 296 488 patients (9.6%) who were offered chemotherapy declined; 99 276 of 1 635 916 patients (6.1%) declined radiotherapy; 94 363 of 1 893 339 patients (5.0%) declined HT; and 15 846 of 2 590 963 patients (0.6%) declined surgery. Regarding the pattern of declination, 8516 patients (0.4%) declined all treatments for which they were eligible and 240 223 patients (9.8%) declined 1 to 3 therapies; 2 210 675 (89.9) patients received all recommended treatments ( Table 1 ). From 2004 to 2020, there were significant increasing trends in declination of HT (change per year, 1.97%; 95% CI, 0.50% to 3.45%; P for trend = .008), radiotherapy (change per year, 5.62%; 95% CI, 4.73% to 6.52%; P for trend < .001), and surgery (change per year, 11.12%; 95% CI, 8.43% to 13.88%; P for trend < .001), while the declination of chemotherapy decreased over time (change per year, –0.96%; 95% CI, –1.07% to –0.84%; P for trend < .001) ( Figure 1 ). Because the IPW-adjusted (eTable 7 in Supplement 1 ) and IPW-unadjusted AORs ( Table 2 ) were very similar, we report the AORs and 95% CIs without missingness adjustment.

In the chemotherapy cohort, 10.3% of White patients declined, compared with 8.7% of American Indian, Alaska Native, or other patients; 8.8% of Asian or Pacific Islander patients; 8.1% of Black patients; and 5.7% of Hispanic patients ( P  < .001) (eTable 8 in Supplement 1 ). After covariate adjustment, American Indian, Alaska Native, or other patients (AOR, 1.13; 95% CI, 1.05 to 1.21), Asian or Pacific Islander patients (AOR, 1.21; 95% CI, 1.16-1.27), and Black patients (AOR, 1.03; 95% CI, 1.01 to 1.06) were more likely to decline chemotherapy, while Hispanic patients (AOR, 0.78; 95%, 0.75 to 0.82) were less likely than White patients to decline chemotherapy ( Table 2 ). Older age was associated with greater odds of declination (AOR per 10-year increase, 2.38 ; 95% CI, 2.35 to 2.40). Compared with privately insured patients, uninsured patients (AOR, 1.61; 95% CI, 1.51 to 1.72) and patients with Medicaid (AOR, 1.51; 95% CI, 1.46 to 1.57) had greater odds of declination. Patients with a lower median household income or tumor grade had higher odds of declining chemotherapy, while those with late-stage disease were less likely to decline ( Table 2 ). To explore chemotherapy decisions based on multigene assays, we performed a subgroup analysis of patients with early-stage, hormone receptor–positive and ERBB2 -negative BC after surgery. Consistent in both 21-gene and 70-gene assay groups, patients with high risk scores were less likely to have declined chemotherapy than those with low to intermediate risk scores, while those who were not tested had a declination rate in the middle (eTable 9 in Supplement 1 ).

In the HT cohort, the distribution of treatment declination differed by race and ethnicity (American Indian, Alaska Native, or other, 4.8%; Asian or Pacific Islander, 4.1%; Black, 4.2%; Hispanic, 3%; and White, 5.2%; P  < .001) (eTable 10 in Supplement 1 ). After controlling for potential confounders, American Indian, Alaska Native, or other patients (AOR, 0.66; 95% CI, 0.63 to 0.69), Asian or Pacific Islander patients (AOR, 0.81; 95% CI, 0.77 to 0.85), and Black patients (AOR, 0.86; 95% CI, 0.83 to 0.89) were less likely to decline HT than White patients ( Table 2 ). Older age was associated with higher odds of declination (AOR per 10-year increase, 1.44; 95% CI, 1.42 to 1.45). Uninsured patients (AOR, 1.61; 95% CI, 1.49 to 1.73) and patients with Medicaid (AOR, 1.44; 95% CI, 1.38 to 1.50) had greater odds of declination than privately insured patients. Late-stage disease was associated with lower odds of declining HT ( Table 2 ).

In the radiotherapy cohort, the treatment declination rates were 5.5% for American Indian, Alaska Native, or other patients, 5.2% for Asian or Pacific Islander patients, 6.2% for Black patients, 4.1% for Hispanic patients, and 6.2% for White patients ( P  < .001) (eTable 11 in Supplement 1 ). On multivariable analysis, Black patients (AOR, 1.05; 95% CI, 1.02 to 1.08) were more likely to decline radiotherapy, while Hispanic patients (AOR, 0.74; 95% CI, 0.70 to 0.77) were less likely to decline radiotherapy than White patients ( Table 2 ). Older patients had greater odds of declination (AOR per 10-year increase, 2.08; 95% CI, 2.06 to 2.10). Compared with privately insured patients, uninsured patients (AOR, 1.97; 95% CI, 1.83 to 2.12), patients with Medicaid (AOR, 1.87; 95% CI, 1.79 to 1.94), and patients with Medicare (AOR, 1.09; 95% CI, 1.07 to 1.12) had a higher likelihood of declining treatment. Having a lower median household income, greater CCI scores, stage II to III disease, or grade 1 to 2 disease were associated with greater odds of declination ( Table 2 ).

In the surgery cohort, 0.7% of American Indian, Alaska Native, or other patients, 0.6% of Asian or Pacific Islander patients, 1.1% of Black patients, 0.4% of Hispanic patients, and 0.6% of White patients declined ( P  < .001) (eTable 12 in Supplement 1 ). After adjusting for covariates, American Indian, Alaska Native, or other patients (AOR, 1.47; 95% CI, 1.26 to 1.72), Asian or Pacific Islander patients (AOR, 1.29; 95% CI, 1.15 to 1.44), and Black patients (AOR, 2.01; 95% CI, 1.89 to 2.14) were more likely to decline, while Hispanic patients (AOR, 0.80; 95% CI, 0.71 to 0.89) were less likely to decline surgery than White patients ( Table 2 ). Older patients had greater odds of declination (AOR per 10-year increase, 2.83; 95% CI, 2.77 to 2.90). Patients without insurance (AOR, 4.83; 95% CI, 4.22 to 5.51) and patients with Medicaid (AOR, 3.19; 95% CI, 2.91 to 3.48) had higher odds of declining than privately insured patients. Having a median household income of less than $40 227 (AOR,1.14; 95% CI, 1.07 to 1.22), $40 227 to $50 353 (AOR, 1.13; 95% CI, 1.07 to 1.20), or $50 354 to $63 332 (AOR, 1.06; 95% CI, 1.01 to 1.12) was associated with higher odds of declining surgery. Patients with late-stage disease or lower tumor grade were more likely to decline surgery ( Table 2 ).

Consistent across all treatment cohorts, patients who received treatment had a longer median follow-up time (eTable 13 in Supplement 1 ) and higher 5-year and 10-year OS survival rates (eTable 14 in Supplement 1 ) than patients who declined treatment. When stratified by treatment decision, there were significant differences in OS across racial and ethnic groups ( Figure 2 and eFigure in Supplement 1 ). In the adjusted Cox models ( Table 3 ), Black patients who received chemotherapy (AHR, 1.15; 95% CI, 1.13 to 1.17), HT (AHR, 1.15; 95% CI, 1.13 to 1.17), radiotherapy (AHR, 1.13; 95% CI, 1.11 to 1.16), or surgery (AHR, 1.10; 95% CI, 1.09 to 1.12) had a greater risk of dying than White patients who received the treatment. Among patients who declined chemotherapy, Black patients also had a higher mortality risk than White patients (aHR, 1.07; 95% CI, 1.02 to 1.13) ( Table 3 ). A similar OS rate was observed between Black and White patients who declined HT (AHR, 1.05; 95% CI, 0.97 to 1.13) or radiotherapy (AHR, 0.98; 95% CI, 0.92 to 1.04). Among patients who declined surgery, Black patients had a lower mortality risk than White patients (aHR, 0.82; 95% CI, 0.75 to 0.91). Regardless of treatment decision, American Indian, Alaska Native, or other; Asian or Pacific Islander; and Hispanic patients had a lower risk of dying than White patients ( Table 3 ). Additionally, no insurance or public insurance, lower median household income, higher CCI scores, and late-stage disease were independently associated with a greater mortality risk among patients with BC stratified by treatment decision across all cohorts (eTables 15-18 in Supplement 1 ).

In this cross-sectional study using data from a large retrospective cohort of patients with BC, we found significant increasing trends in declination of HT, radiotherapy, and surgery from 2004 to 2020 and racial and ethnic and socioeconomic disparities in treatment declination. In particular, the increasing declination of treatment recommendations was more pronounced for radiotherapy and surgery. Older age, having public or no insurance, lower median household income, comorbidities, nonmetastatic disease, and lower tumor grade were associated with treatment declination. Furthermore, racial and ethnic differences in OS varied by treatment decision. Specifically, Black patients who declined chemotherapy had a greater mortality risk than White patients, while there were no OS differences between Black and White patients who declined HT or radiotherapy.

Our study expands on prior research findings by including radiotherapy and HT (in addition to chemotherapy and surgery), American Indian, Alaska Native, or other and Asian or Pacific Islander races and ethnicities as well as pattern and long-term trends of treatment declination. We found that 1 in 10 patients declined at least 1 type of recommended treatment, 1 in 10 patients declined chemotherapy, 5.0% to 6.0% of patients declined HT or radiotherapy, and less than 1.0% of patients declined surgery. These results are aligned with prior study observations in patients with BC using the Surveillance, Epidemiology, and End Results (SEER) and early-year NCDB data. 4 , 7 , 19 - 22 Fwelo et al 21 and Gaitanidis et al 10 further observed increasing trends in 2004 to 2013 and 2010 to 2017 SEER data. However, these studies 10 , 21 assessed surgery only; whereas we found that rates of declination of HT and radiotherapy also significantly increased, while the chemotherapy declination rate decreased from 2004 to 2020. Given that the exact reasons for declining treatment recommendations are not collected by the NCDB, it is unclear what has driven the increases or decrease over time. Meanwhile, it is important to note the decreased trends in declination of chemotherapy between 2018 and 2020, which we hypothesized were probably due to the more accurate chemotherapy decisions based on multigene assays, eg, the 21-gene assay and the 70-gene assay. The findings from our subgroup analysis suggest that multigene assay testing results probably influence the decision or receipt of chemotherapy and may partially explain the decreasing trend in the declination of chemotherapy recommendations from 2018 to 2020, as more patients received multigene assay testing in recent years. Future investigations are needed to decipher the growing trends and patterns of treatment declination in populations of patients with BC.

Compared with White patients, Black patients were more likely to decline chemotherapy or surgery; Hispanic patients had a 20% lower likelihood of declining either treatment. Our results support previous findings, as Rapp et al 17 and Shahi et al 22 have reported that Black patients were twice as likely as White patients to decline surgery in early-stage BC cohorts. Studies also have documented that among patients with stage III to IV BC or hormone receptor–positive and ERBB2 -negative BC and high-risk scores on multigene assays, Black patients had a 9.0% to 20.0% greater likelihood of declining chemotherapy, while Hispanic patients were 18.0% less likely to do so, compared with White patients. 18 , 19 However, these studies did not compare American Indian, Alaska Native, or other patients or Asian or Pacific Islander patients with White patients; whereas, we found that American Indian, Alaska Native, or other patients and Asian or Pacific Islander patients were 13.0% and 21.0% more likely to decline chemotherapy and were 29.0% and 47.0% more likely to decline surgery, respectively. Patients from racial and ethnic minority groups, except American Indian, Alaska Native, or other patients, were 19.0% to 34.0% less likely than White patients to decline HT. Black patients were 5.0% more likely to decline radiotherapy and Hispanic patients had a 26.0% lower likelihood of declining radiotherapy. In addition, older age, lack of insurance or Medicaid, lower median household income, advanced stage group, and higher tumor grade were associated with a significantly greater likelihood of declining systemic therapies or surgery, suggesting that differential rates of treatment declination not only are affected by clinicopathological factors but also may reflect socioeconomic disparities.

Qualitative studies have indicated that older patients with metastatic cancer or advanced chronic conditions forgo clinician recommendations because of diagnosis denial and fear of treatment adverse effects. 27 - 30 Patient-clinician communication, shared decision-making, and trust can affect patients’ treatment decisions. 29 , 31 , 32 Other factors, including lack of health care access and advanced disease, also are associated with treatment declination, consistent with our observations in this study. There are other reasons for forgoing treatment recommendations, and they may differ across racial and ethnic groups. Further research is necessary to explore and quantitatively measure these reasons and the complex interplay with socioeconomic and health care access measures that leads to racial and ethnic disparities in treatment declination among patients with BC. Closing these socioeconomic inequity gaps, patient education on treatment benefits, patient-clinician relationship building, and improved communication and share decision-making are essential to reduce the racial and ethnic disparities.

Our survival analysis results of patients who received treatment align with existing literature on racial and ethnic OS differences in the US BC population. 1 , 2 OS disparities between Black and White patients remained after controlling for patient characteristics. Consistent with previous findings in patients with colorectal, breast, or ovarian cancers, 7 - 12 patients with BC who forwent treatment recommendations experienced worse survival than those who received therapies. Furthermore, we found racial and ethnic disparities in OS stratified by treatment decision. In particular, among patients who declined chemotherapy, Black patients had a 7% greater mortality risk than White patients, but both groups had a similar OS if they declined radiotherapy or HT. Mortality risks were lower among American Indian, Alaska Native, or other patients, Asian or Pacific Islander patients, and Hispanic patients across all treatment cohorts. Interestingly, Black patients who declined surgery had better survival than White patients. Patients lacking access to care, with late-stage presentation or higher tumor grade, or with multiple comorbid conditions also experienced poor OS, irrespective of treatment modality. These findings suggest that treatment decisions, socioeconomic indicators, and clinical factors do not address racial and ethnic survival differences in patients with BC. Lifestyle behaviors, genetic predisposition, the environment, and other risk factors that are not collected by the NCDB could have contributed to these survival disparities, which warrants future research on the intersections of these factors and treatment declination.

To our knowledge, this study is the largest to date evaluating the pattern and long-term trends of and racial and ethnic disparities in treatment declination and mortality risk among patients with BC at the national level, but it has some limitations. First, underreporting is likely, given the nature of the NCDB registry, and patient perceptions toward treatment recommendations are not ascertained. Further research is necessary to explore and accurately capture the reasons why patients with BC decline recommendations. Another limitation pertains to the lack of information on whether patients sought a second opinion from other clinicians or whether patients who declined treatment later decided to receive the treatment. Third, there are unmeasured potential confounders, eg, marital status, social support, cultural backgrounds, and religious beliefs, that may play an important role in treatment decisions, affecting the racial and ethnic disparities observed, as well as patient frailty in the survival analysis. This study was also limited by not assessing declination of various specific systemic therapy regimens, as the rate probably differs; nor did were assess how these might impact other health outcomes, which is worth exploring in future studies. Additionally, the patient cohorts may not be representative of all patients with BC in the US. However, our findings were consistent with SEER population-based study results.

In this nationwide cross-sectional study of patients with BC, the treatment declination rate was highest for chemotherapy and lowest for surgery, with significantly increased trends over time in HT, radiotherapy, and surgery cohorts. Patients from racial and ethnic minority groups were more likely to decline chemotherapy, radiotherapy, or surgery but less likely to decline HT than White patients. Older age, socioeconomic disparities, and advanced disease also were associated with patients’ decision to forgo treatment recommendations. Black patients who declined chemotherapy had a higher risk of mortality than White patients, while no OS difference between Black and White patients who declined HT or radiotherapy. Regardless of treatment modality, American Indian, Alaska Native, or other, Asian or Pacific Islander, and Hispanic patients had better survival. Our findings highlight racial and ethnic disparities in declination of treatment recommendations and OS, suggesting the need for equity-focused interventions, eg, patient education on treatment benefits, patient-clinician relationship building, and improved patient-clinician communication and shared decision-making, to reduce the disparities and improve patients’ survival outcomes.

Accepted for Publication: February 28, 2024.

Published: May 9, 2024. doi:10.1001/jamanetworkopen.2024.9449

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2024 Freeman JQ et al. JAMA Network Open .

Corresponding Author: Dezheng Huo, MD, PhD, Department of Public Health Sciences, University of Chicago, 5841 S Maryland Ave, MC2000, Chicago, IL 60637 ( [email protected] ).

Author Contributions: Drs Freeman and Huo 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: Freeman, Huo.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Freeman, Li, Yao.

Critical review of the manuscript for important intellectual content: Freeman, Li, Fisher, David, Huo.

Statistical analysis: Freeman, Li, Fisher, Huo.

Obtained funding: Freeman, Huo.

Administrative, technical, or material support: Freeman, Freeman, Fisher, Fisher, Huo, Huo.

Supervision: David, Huo.

Conflict of Interest Disclosures: Dr None reported.

Funding/Support: This study received funding support in part from Breast Cancer Research Foundation (grant No. BCRF-23-071), Susan G. Komen Foundation (grant No. TREND21675016), the National Cancer Institute (grant No. P20CA233307), and the National Institute on Aging (grant No. T32AG000243).

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The National Cancer Database (NCDB) is a joint project of the Commission on Cancer of the American College of Surgeons and the American Cancer Society. The data used in the study are derived from a deidentified NCDB file. The American College of Surgeons and the Commission on Cancer have not verified and are not responsible for the analytic or statistical methods used, or the conclusions drawn from these data by the investigators. The contents of this work are solely the responsibility of the authors and do not necessarily represent the official views of the National Cancer Institute or the National Institute on Aging.

Meeting Presentation: This findings of this study were presented at the 46th Annual San Antonio Breast Cancer Symposium; December 8th, 2023; San Antonio, Texas.

Data Sharing Statement: See Supplement 2 .

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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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Acknowledgements

The authors want to acknowledge comments by Drs. David Epstein, Kenneth Kendler and Naomi Wray.

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Center for Social and Affective Neuroscience, Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden

Markus Heilig

Peter Boris Centre for Addictions Research, McMaster University and St. Joseph’s Healthcare Hamilton, Hamilton, ON, Canada

• James MacKillop

Homewood Research Institute, Guelph, ON, Canada

New York State Psychiatric Institute and Columbia University Irving Medical Center, New York, NY, USA

Diana Martinez

Institute for Mental Health Policy Research & Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada

Jürgen Rehm

Dalla Lana School of Public Health and Department of Psychiatry, University of Toronto (UofT), Toronto, ON, Canada

Klinische Psychologie & Psychotherapie, Technische Universität Dresden, Dresden, Germany

Department of International Health Projects, Institute for Leadership and Health Management, I.M. Sechenov First Moscow State Medical University, Moscow, Russia

Clinical Psychoneuroendocrinology and Neuropsychopharmacology Section, Translational Addiction Medicine Branch, National Institute on Drug Abuse Intramural Research Program and National Institute on Alcohol Abuse and Alcoholism Division of Intramural Clinical and Biological Research, National Institutes of Health, Baltimore and Bethesda, MD, USA

Lorenzo Leggio

Department of Population Health Sciences, Unit Animals in Science and Society, Faculty of Veterinary Medicine, Utrecht University, Utrecht, the Netherlands

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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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Received : 10 November 2020

Revised : 11 December 2020

Accepted : 14 December 2020

Published : 22 February 2021

Issue Date : September 2021

DOI : https://doi.org/10.1038/s41386-020-00950-y

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