History of Alcoholism and the Justice System

2-3 pages in APA format about the history of your selected population. Include citations and references including NASW’s Code of Ethics. Use the select questions below, to guide you:

Population: History of Alcoholism and the Justice System

How has this group been treated historically in our culture? What is the history (e.g., laws, experiences, etc.) related to this type of treatment or discrimination? What assumptions, beliefs, or attributions appear to drive this treatment or discrimination of this group?
What are examples of specific oppressive or discriminatory practices that this group has encountered as they interact with various institutions? You may include social, economic, educational, faith, and health care institutions in your discussion, as well as any other institutions of relevance.
What examples of strength and resilience are, or have been, evident within members of this group?
Discuss how NASW’s Code of Ethics applies to working with this population.

Social Work & Christianity, Vol. 46, No. 3 (2019), 51–

DOI: 10.34043/swc.v46i2.76

Journal of the North American Association of Christians in Social Work

The Christian Social
Worker in Recovery:
A Personal Reflection on
Professional Stigma, Bias
and Discrimination

Denise L. Jaillet Keane

As a professional social worker in long-term substance use recovery, I have
come face-to-face with stigma, bias and discrimination regarding those who
struggle with the disease of addiction. I have made choices regarding when
and where and if to disclose that I am a person in recovery. I have listened
to colleagues engaging in “us” and “them” conversations, forgetting that
I am both them and us, not realizing how offensive and judgmental their
language was. Funders overlooked my identity as a person in recovery, as they
requested agencies to hire more “peer mentors,” but did not count recovering
clinicians or senior management. The result of a qualitative self-interview
on the experiences of being a Christian social worker who just happens to
be that 1-in-7 (Hafner, 2016) who has faced a substance use disorder, this
paper presents a person-centered perspective regarding working as, or with,
a social worker in recovery.

B AVID MILLER, LISW, ACSW, DCSW AND PAST NATIONAL CHAIR OF

Social Workers Helping Social Workers, believes that the field of
social work is in denial about substance use among its 165,000

NASW members (Miller & Fewell, 2002). Although the organization has
been in existence since 1980, Miller reported in 2002 that this organization
that directly assists professional social workers with substance use issues
had only counted 800 members in total over those 22 years. Christine
Fewell, CSW, BCD, CASAC and chair and founding member of the
Peer Consultation Committee of the NASW, New York City Chapter’s
Addictions Committee, concurs (Miller & Fewell, 2002). Fewell believes

it is a “complicated phenomenon” that involves shame, secrets and…
all the feelings that our professional aspirations and training have added
about ethical responsibility, control of our feelings and behavior, (and
the) defensive need to be the caretaker and helper rather than the one
who receives help” (p.102-103). Media stories often feature high-profile
personalities who have substance use disorders looking their worst, being
arrested, going into treatment. The words used to describe them are often
unkind and stigmatizing. The advances in genetics and neurology that
have brought us scientific evidence that makes it clear that substance use
has biological components that cannot just be willed into submission
have not eliminated moral, class, and ethnic stigmas associated with the
disease. If Miller and Fewell are accurate in their conclusion that social
work is in denial about social workers with substance use disorders, it is
not a denial based upon readily available scientific data. The denial is a
demonstration of a socially acceptable, ingrained bias that “good” persons
do not get addicted to substances, fed by negative media portrayals and
perhaps an unconscious desire of social workers to feel they are a step up
from their clients.

Kubek (2007) chronicled the story of a social work student in recovery
who “feels a bit annoyed whenever she hears fellow classmates . . . make
judgmental comments about people who abuse alcohol and other drugs.”
The student in recovery felt they should be learning how to convey ac-
ceptance, and knew from personal experience that judgmental language is
not safe. Without direct confrontation, the negativity towards those with a
substance use disorder will continue into the field. It will impact the future
social workers’ relationships with clients and co-workers. Social work as
a field must not be in denial of this problem – a problem experienced not
just by the story noted above, but one I have also experienced.

Lived Experience

Like all persons, my life is a kaleidoscope of relationships and roles.
Some know me as the person they see in the gym, as someone they have
seen shop in the local food co-op, or as the professor from a course last
semester. Others may recognize my name from a political yard sign, from a
list of board members of a non-profit organization, or as the clinical director
of a behavioral health agency sometimes featured on the local radio show.
To others, I am mom, grandma, aunt, sister, partner, and friend. Those
closest to me recognize that I am a Christian in recovery.

If asked the common interview question of “How would your co-
workers/supervisor describe you?”, I would without hesitation reply, “As
an intelligent, creative, dedicated, hard-working and reliable employee.” I
identify as a Christian, a social worker, a philosopher, and a person in recovery.

CHRISTIAN SOCIAL WORKER IN RECOVERY

I have written this paper from an experiential perspective as a multi-
faceted, multi-talented, and probably over-extended person, who has
embraced life on life’s terms.1 I cannot write from an objective, observer
perspective. My lived experience2 has merit in and of itself. I therefore
write of my experiences through a lens developed by relationships, roles
and experiences too numerous to mention.

The Lens

The overriding paradigm in which I function is that of Christian ide-
als. Faith, hope and love guide my life and my clinical and macro practice.
These ideals are the foundation from which I respect the dignity of each
individual person and fight against oppression at large. My Christianity
informs my interactions, both micro and macro, as a social worker. I attempt
to model Christian ideals. I discuss religion with my clients and co-workers.
I attempt to be a light in the darkness for others, and when feeling unsure
or overwhelmed, I stop and pray for guidance and the knowledge of God’s
will. I am thankful to have faith, hope and love as my guideposts for social
work, as I do not understand how social workers can maintain effective,
quality clinical care without them.

The second paradigm for my life is the Alcoholics Anonymous (AA)
12-step philosophy. Although clearly not designed to be a religion, 12-step
philosophy perfectly complements my religious practices and the way I
live as a Christian. While some persons in AA have embraced G.O.D. as
an acronym for “Group of Drunks” or “Good Orderly Direction,” most of
my AA friends are also Christians who recognize God as the father of Jesus
and the essence of the Holy Spirit. The basic principles of AA – a spiritual
awakening, humility, patience, faith, confession, forgiveness, demonstrated
love for others, making amends for wrongs done, seeking God’s will through
prayer and meditation, and sharing the good news – align perfectly with
basic Christian principles. While both paradigms are therefore complemen-
tarily directing my lens, the 12-step philosophy is more directly guiding
the practice-driven experiences expounded in this paper.

Theoretical Perspective

There are aspects to the participatory and constructivist perspectives
that resonate with me and are relevant to this paper. For example, a
participatory view is that “validity is found in the ability of the knowledge
to become transformative according to the findings of the experiences of the
subjects” (Lincoln, et al., 2011, p. 114). Knowledge which has the ability
to transform must have such a high level of authenticity that it creates an
inherent power; that level of authenticity equates to validity. What can

SOCIAL WORK & CHRISTIANITY54

be more authentic than a person’s lived experience, documented in their
own words? The collaborative nature of the constructivist paradigm in
which people are participants in documenting and telling the truth of their
experiences, rather than subjects, (Guillemin and Gillam, 2004), minimizes
superficial power differentials. It resonates for me as someone who does
not want to choose a label – am I the researcher or the researched? Am
I the professor or the student? Am I the social worker or the client? The
constructivist paradigm amalgamates the interpretations of my multifaceted
perspectives regarding my lived experiences so that they have the power
to inform; that perspective is “ontologically relative, epistemologically
transactional/subjectivist, methodologically hermeneutical and dialectical”
(Lincoln, Lyndham, and Guba, 2011, p. 99). “Denise the Christian” joined
with “Denise the Social Worker” and “Denise the Person in Recovery” in an
integration of the ethics, values, knowledge and experiences of these roles
to form the whole of who I am and how I interact with and experience life.

First Professional Interview

One of the first experiences I recall as a social worker in recovery
was actually quite positive. After obtaining an undergraduate degree in
philosophy and religion, and a Master of Arts degree in education, I felt
the distinct call to social work. I started a Master of Social Work program
and after the first year, I applied to be the program director of a female
gender-specific Department of Correction work release/treatment program
in a larger umbrella behavioral health agency that I will call “XYZ.” During
my interview for this position, I was asked how I felt about incarcerated
women. Knowing that most of the program participants were incarcerated
due to substance-related offenses, I honestly answered that if not for some
good luck and God’s will, I could just as easily be sitting in their seats in-
stead of where I was. This was the first time I publicly acknowledged that
I was a person in recovery from a substance addiction. Due to the nature
of the work for which I was applying, and knowing that the agency was
committed to hiring people in recovery, I felt confident that my disclosure
would not be used against me. Not only did I get the job, but I discovered
that the two social workers on my clinical team were also Christians and
in recovery. What a wonderful experience for us as co-workers and for the
vast majority of women who benefitted from that program who were also
Christians in recovery.

Classroom Experience

However, at the same time as I was experiencing this freedom of
truthfulness, I was not feeling free to have the same level of openness
in other areas of my life: my church family, my volunteerism with local

non-profit organizations, or my MSW program. During my last semester
as a MSW student, one of my professors was upset that I used the term
“alcoholic” in self-reference during a class discussion. She felt that the
terms “alcoholic” and “addict” were too negative; she directed us to use
the terminology “a person with an addiction concern.” Instead, I wanted
to debate this with her, let her know that it took me a year of attending
12-step meetings and actually receiving my one year coin3 before I had
acquired the reality and humility necessary to say, “I’m Denise and I’m an
alcoholic.” I wanted to explain to her and the other almost-social workers
that those of us with the disease of alcoholism want everyone to be as
comfortable with naming the disease for what it is as they are with saying
“he has diabetes” or someone announcing “I’m a vegetarian.” By being oh-
so-careful with the words, social workers can actually endorse the stigma
associated with the name of the disease instead of working to eliminate
it. Additionally, social workers can give the message to those who want
the freedom to tell their truth that they should not, that saying the name
of their disease is like saying the name of “He-who-shall-not-be-named.”
The repetitive declaration in AA rooms that I am an alcoholic has been
vital in keeping me from the fantasy that I am cured. I wanted to say all of
this, but did not. I was not yet confident enough in 2003, after nine years
of sobriety, to enter into this needed discussion on the difference between
labelling others and self-labelling.

This view of the use of the word “alcoholic” is in stark contrast to my
usual testiness at the limitations placed upon persons through labels, catego-
ries, and generalizations. I cringe when others attempt to put me into a box
that fits within their own narrow window of experience, and I fight for the
rights of others to whom this happens. However, I also know the freedom of
finding out the name of the disease or disorder with which one is living and
facing the realities of that disorder head-on. “Alcoholic,” used as a term for
someone with a brain disorder, is liberating to me. It allows me to understand
why my body will crave something that is so harmful to me, something that
I do not intellectually want, and something that affects my ability to live ac-
cording to my Christian ideals. It permits me to give and accept forgiveness
for things I have done while under the influence, and to have empathy and
understanding for those who are just learning they have a chronic addiction.
However, if someone does not understand that alcoholism is an incurable
disease and is using the term “alcoholic” to belittle, deride or dismiss, then
it becomes a pejorative label that should not be tolerated.

Continuing Education Experience

A few years later, after becoming a licensed clinical social worker, I
attended a continuing education course on Cognitive Behavioral Therapy
offered by our state’s Department of Mental Health and Addiction Services.

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SOCIAL WORK & CHRISTIANITY56

This was an intensive four-day course taught by a professor whom I knew,
as I was a field instructor for some of his interns. Over the course of the
four days, he referred to the differences between how “we” (social workers)
think and how “they” (substance users) think so many times that I lost
count. The message I received was that substance users all have cognitively
distorted thinking patterns and social workers do not. At first, I wrote this
off as a necessary convenience of speech, but after a while it became more
and more irritating. Was he implying that there was no social worker in
the class who ever had to reframe their thinking? Was he ever going to
mention that persons in recovery work on being aware of, and reframing
their thinking daily, so that they actually become quite expert at it? Did he
mean to give the message that no one in this class could possibly be both a
social worker and in recovery themselves, despite the one in seven statistic
(Hafner, 2016) of persons in the U.S. facing a substance use disorder?

Alcoholism, as any disease, knows no boundaries based upon class,
ethnicity, gender, religion, or other demographics. The Substance Abuse
and Mental Health Services Administration (SAMHSA) website notes that
6.4% of the population of the United States, or approximately 17 million
persons, met the criteria for an alcohol disorder in 2013 (SAMHSA, 2018).
Bush and Lipari (2015) analyzed statistics from SAMHSA’s 2008-2012 stud-
ies and found 8.7% of persons in full-time employment between the ages
of 18 and 64 used alcohol “heavily” during the last month. Heavy alcohol
use is defined as “drinking five or more drinks on the same occasion (i.e.,
at the same time or within a couple of hours of each other) on five or more
days in the past 30 days.” Given these statistics, the probability that I was
not the only person in recovery in that classroom was high. However,
again, neither I nor anyone else spoke up. I addressed the issue by going
to a 12-step meeting after the last course session where I felt I could safely
talk about how offended I felt.

Reflecting upon this experience, I realize I had two primary reasons
for not confronting the offensive language head on. First, the instructor
was a colleague of mine at my part-time adjunct faculty position in a state
university. I had been a field advisor for that university’s BSW program
and been invited by this very instructor to join the adjunct team. I was
concerned how my being a person in recovery would impact my status
in the department that someday I hoped to join full-time. Secondly, I
was hoping for someone else to speak up on behalf of social workers in
recovery. I still had enough shame and embarrassment associated with
being in recovery at that time to be looking for a rescue from being the
spokesperson on this issue. I understand now that it is unrealistic to
expect those who are not in the same situation to always recognize when
apparently common language is offensive to someone else. Each of us is
the one and only expert on our own experiences. It is incumbent upon

those of us who have experienced discrimination or bias to inform others
as to the impact of their words or actions.

Professional Behavioral Healthcare Experiences

Over the course of my professional social work career in the behavioral
health field, I have become more open about my recovery status–not in all
my roles, but with the staff and clients of XYZ, and with people and orga-
nizations in which I have a trusted status. Many times my self-disclosure
receives a response along the lines of “No way” or “Well, no one would
know it.” They are often surprised to hear that I continue to attend as
many AA meetings a week as I can (usually 3-4). This is despite the fact
that they tell clients with substance use disorders that they will have their
disease forever and might want to try to attend AA or Narcotics Anonymous
(NA) as an aftercare measure when they are no longer in formal treatment.
For some reason, when it comes to a colleague in a senior management
position, these same expert clinicians do not stop and think about the fact
that I will also always have a need to manage my disorder so that it does
not resurface. I know that these colleagues have the academic knowledge
that alcoholism is a chronic, progressive disease, sometimes manageable
into remission, and cannot be “cured.” I wonder if they understand the
“us” and “them” perspective they are portraying by their incredulity that
I vigilantly work my recovery every day. These social workers appear to
have a need for staff to be different (maybe better?) than the individuals
they are assisting. I am thankful that I continue to know that, but for the
grace of God, any one of us could be sitting on the other side of the desk.

Perceptions of Relapse

A message I have often heard from colleagues is that “relapse is a
part of recovery”. This has been said directly to me; I have overheard from
those around me, and it has been reported by clinicians, mentioned by state
experts, and believed by clients themselves who heard it from XYZ staff. It
has become a common phrase used not just after a relapse, but used by those
in the field prior to a person’s recurrence of substance use. However, persons
in recovery who remain compliant with the management of their disease
through a personalized regimen that can include 12 step meetings, working
the program of AA or NA with a sponsor, therapy, medication, or other
treatments, may never experience a recurrence or relapse. Relapse has not
been part of my recovery, nor has it been part of many of my friends’ recovery
stories. I am hopeful that it never will be. Clearly meant to empathetically
support those who slip while walking their recovery paths, the “relapse is
part of recovery” message is not a positive message for those who do not

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SOCIAL WORK & CHRISTIANITY58

experience such a recurrence of use. I feel the hackles of defensiveness
rise in me every time I hear it. An accurate and caring alternative is the
message that “relapse is sometimes a part of recovery.” This message can
prepare a client if a relapse occurs, but also promotes the hope that relapse
need not happen. The reality is that relapse may be a part of recovery, or
it may not. The path of recovery is not a straight path, nor is it without
its potholes and obstacles. Sometimes a person in recovery will traverse it
without slipping despite the trials; sometimes a person may fall down and
reach for an old coping strategy. The innate judgmental quality of “relapse
is a part of recovery” when presented to or about every person challenging
a substance use disorder diagnosis is a message of inevitable failure that
should not be sent to a client or a co-worker. It is not an unconditional
message of faith, hope or love.

Staff Relapse

During the years that I have worked for the company XYZ, it has ex-
perienced the relapse of some of its staff in recovery. These relapses have
ranged from a short-term, easily managed with an outpatient treatment
program bender, to the death of a staff member from an irrecoverable alcohol
overdose. Each time a relapse has occurred, I have witnessed a perceptible
change in management’s willingness to hire another person in recovery.
Rather than look at the feelings generated by the relapse and what we, as
an agency, could do to better support staff with this disease, management
diverted the issue in other directions. The ideas that someone who attends
12-step meetings is unable to maintain professional boundaries and that
persons in recovery just “don’t get it when it comes to professional ethics”
began to be freely floated around in meetings in which I was in attendance.
All participants in those discussions knew I was in recovery. My request to
stop anecdotally generalizing, to recognize that our staff in recovery were
especially effective with our clients, that “you do remember that I am in
recovery too,” were all either ignored or dismissed. When I first joined
XYZ, 40-50% of staff were in recovery from a substance use disorder, with
staff in recovery openly recruited as beneficial to the agency.

At the end of 2017, when after over 10 years of advocacy I finally ob-
tained permission to start an in-house 12-step meeting to support staff in
recovery, I could only find five other staff in recovery out of a workforce of
110. During those years, the percentage of applicants in recovery remained
the same, but the perspective of the hiring managers progressively favored
persons not in recovery. Agency oversight of staff in recovery became more
intense, with a prevailing attitude that persons in recovery were suitable
only as peer mentors for clients, volunteers for leading in-house client sup-
port groups, or to manage the recovery houses. The concept of recruiting

clinical or administrative and management staff in recovery to enhance the
diversity of our teams no longer existed. Unfortunately, reinforcement of
this attitude is an unintended consequence of state-sponsored initiatives to
have agencies hire Recovery Support Specialists and Recovery Coaches —
persons in recovery who operate as paraprofessionals.

Staff in Treatment

One staff member who had not yet made his disease public expe-
rienced direct discrimination. While one of his co-workers went out on
medical leave for several months to deal with a “medical” illness, harsh
questioning ensued as to why he needed more than 30 days to attend an
inpatient program to treat alcoholism. Senior management questioned the
seriousness of his need for treatment because “we’ve never seen any signs
that he had a drinking problem at work.” Having overcome the hesitation
early in my career to speak too much about my disease, I reminded my co-
workers that no one ever knew I was an alcoholic at my previous places of
employment, prior to my being in recovery. I let them know that given my
past pattern of drinking and still functioning at my job, they would never
be able to tell if I relapsed unless I chose to tell them. I reminded them
that, as an agency, we know that once someone has reached the point of
needing residential treatment, 30 days will only clear the head enough to
start in-depth work – that is why XYZ’s residential treatment program has
a four-to-eight month stay, dependent on individual needs. Why would we
hassle a staff member for wanting to take 60 days to learn to manage his
chronic disease? Both the HR manager and the CEO of XYZ were social
workers and they continued to be unsupportive of more than 30 days for
his treatment, despite a letter from his treatment provider stating the need
for a longer stay.

Employment Challenges

Social workers in recovery who choose to work in behavioral
healthcare have many employment-based challenges. For example, some
agencies have restrictions that include not attending or not sharing at 12-
step meetings where agency clients are present, not having enough time
during the day to catch a noon-time 12 step meeting and/or missing dinner
to participate in a distant evening meeting, not sponsoring clients, and not
self-disclosing your recovery status. Staff in recovery couple these stressors
associated with maintaining treatment compliance for their chronic disease
of addiction with the everyday stressors of interacting with clients who often
exhibit the physical signs, smells and behaviors of active addiction and the
vicarious traumatization often experienced by behavioral healthcare staff.

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SOCIAL WORK & CHRISTIANITY60

As another example, it is common to find that persons in recovery
are only considered for entry level “peer” positions in an agency, rather
than professional positions. When state funders requested the presence of
persons on XYZ’s boards and committees with lived experience, they did
not accept “professional” staff in recovery as fulfilling that request. I cannot
speak for others, but this staff-in-recovery is unable to dissociate myself
from a person- in-recovery perspective. I live and breathe my recovery,
as I do my Christianity. It informs and defines who I am. I recall a state
auditor asking how many peer support staff worked within one of XYZ’s
programs. When I included myself in the count, the auditor responded,
“Well, you don’t count – we are only counting peer support.” This was a
perfect example of what Kaplan (2005) called the concept of associating
staff in recovery only with para-professionals. I responded that I was a
peer support and role model, as the residents of the program knew I was
in recovery. The auditor dismissed me with, “but you’re, well, you’re dif-
ferent.” Her perspective appeared to be that only persons in recovery who
are not also behavioral health professionals count as “peers;” alternatively,
it could have been that once you have achieved a professional status, you
no longer count as a person in recovery. Either perspective is offensive and
discriminatory to those of us who are living a life that merges those roles.

An employment-based role challenge for me is the opposite ways
in which some co-workers regard my “Christian” tag as compared to my
“person-in-recovery” tag. When religious clients came to our agency, in-
take screeners often assigned them to receive services from the few openly
“religious” staff (myself included). Other social workers support this move
by saying that they are not familiar enough with religion to give quality
care – they have not read the religious texts, they do not attend religious
services, and they feel inadequate to the task. However, when clients come
to our agency to receive help with their substance use disorders, the same
social workers do not feel inadequate even if they have not read Alcoholics
Anonymous, have not attended 12-step meetings, and do not have a sub-
stance use issue themselves. I believe that any social worker can effectively
work with any client if they are open to a transactional relationship in
which each is learning from the other. What is curious to me is why I am
regarded as a go-to social worker for religious clients, but I am no different
than any other social worker for clients with substance use issues. I would
love to be a resource for co-workers as a person in long-term recovery who
can dispel the myths that surround AA, who can teach the AA lingo5 of
the rooms,6 who may be able to shed some light on why a client does not
appear to be progressing even after “last chance” behavior contracts and
ultimatums. Agency staff whom I have supervised continue to utilize the
knowledge regarding alcoholism and alcoholics I have acquired through
my personal experiences and choose to share with them. However, XYZ

is uncomfortable in acknowledging that I, or other staff in recovery, may
have useful knowledge not found in textbooks.

Despite the prevailing attitude, there have been those staff who have
actively sought my help with challenging recovery situations. A former
MSW intern of mine hired by XYZ often requested my person-in-recovery
perspective, as well as my clinical social worker perspective, in consulta-
tion for challenging clinical situations. In addition, one Program Director
within XYZ often called upon my insight as a person in recovery to assist
with both individual and group situations. I recall a time when a particular
12-step meeting in the wider community requested that the residents of
the treatment program she directed not return to the 12-step meeting. She
realized that neither she, nor any of her staff, understood the etiquette
involved in 12-step rooms and therefore was unsure how to resolve this
dilemma. One of the privileges I afforded myself as a senior management
team member was the opportunity to take off the “big boss” hat and replace
it with the “AA Old-timer”7 hat, as appropriate to the situations. This I did
when speaking with the residents as to the unwritten expectations of at-
tendees at the 12-step rooms in our community. For instance, they needed
to “take the cotton out of their ears and put it in their mouths”8 (just the
opposite of treatment). I also reminded them that there were very few 12-
step group members whom I did not personally know and who did not
hesitate to contact me when there were issues within the meeting rooms
caused by agency clients. This was a dual relationship. However, there are
times when dual relationships are not only permissible, but helpful. I was
able to contact a few of that particular 12-step meeting group’s members
and facilitate the invitation to our program residents to return the following
week. This was not a dual relationship that was exploitative, or resulted in
harm to the clients, or personal gain for the social worker. Dual relationships
between social workers in recovery and their clients are often inevitable,
particularly in small communities. The measure of appropriate ethical be-
havior in those circumstances lies in ensuring that the needs of the client
are paramount (in this case, learning to appropriately participate in AA
and be accepted at any and all AA meetings, so that this life-long treatment
tool can be successfully utilized) and that the social worker is receiving no
personal benefit or gain. I am always thankful when a co-worker recognizes
the unique contribution a social worker in recovery can bring to those to
whom we offer substance use treatment services.

Recommendations for Practice

Working within a behavioral health agency afforded me the opportunity
to be a role model for thousands of clients trying to obtain and maintain their
sobriety and/or clean time. However, careful to maintain my professional

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SOCIAL WORK & CHRISTIANITY62

boundaries, I did not sponsor women in the 12-step meetings I attended.
Living in a small community, there was a chance that if someone was not
already a client of my agency, she would be at some point in the future. I also
did not share my personal story in 12-step meetings where agency clients
are present.4 I do share treatment and other recovery resource information
with anyone in a 12-step meeting who asks for it. Thankfully for my own
recovery, I have a strong recovery network of persons who are also in long-
term recovery that I can call and share with outside of a meeting room.

The field of social work should embrace the experiences of social
workers in recovery as easily and positively as those many clients from XYZ
did. I continue to use discretion about when, where and why to discuss
my disease with others. Due to a lack of education and understanding
about addiction, there are personal contacts and parts of my life in which
disclosure of my disease is not possible. However, I did expect that social
workers would understand and not show any signs of stigmatizing, bias
or discrimination towards fellow social workers who happened to be in
recovery. The basis for that expectation was partly due to social workers’
educational knowledge and partly due to the application of our field’s
ethical principles and statements found in the NASW Code of Ethics (2017):

• Social workers respect the inherent dignity and worth of the person.

• Social workers treat each person in a caring and respectful fashion,
mindful of individual differences and cultural and ethnic diversity

• Social workers strive to ensure… equality of opportunity and
meaningful participation in decision-making for all people.

My personal experiences and reflections can provide a foundation for
positive change by the elimination of professional social work stigma, bias
and discrimination towards peers in recovery. They can also remind social
workers that embracing diversity includes those with addiction. Therefore,
I recommend these steps for social workers to integrate into their practice:

1. Avoid “us” and ‘them” dichotomies between social workers and
persons in recovery.
2. Acknowledge, respect and leverage the lived experience of colleagues
in substance use recovery.
3. Reframe “relapse is a part of recovery” to “relapse may be a part
of recovery.”
4. Recognize that self-labelling as an alcoholic or an addict can be
liberating and empowering for the person living with that chronic
disease – let the person in recovery be your guide in this matter.
5. Maintain unconditional positive regard for anyone in recovery;
examine your reactions toward hearing that someone has an addition
and be sure it is not tinged with moral overtones.

6. Educate yourself on the biological components of the disease.
7. Speak of addiction as the “brain disorder” that it is.
8. Speak up when you hear language, remarks or assumptions that a
person in recovery may find offensive.

I recommend the following action items to behavioral healthcare agencies and
programs in order to better embrace and benefit by the wealth of knowledge
and lived experience brought into an agency through staff in recovery:

1. Acknowledge the usefulness of appropriate staff self-disclosure of
their recovery status.
2. Allow staff in recovery to appropriately share on the tenets of the

program with clients in agency-based 12-step groups.
3. Provide parity in support and care for staff with substance use health

needs to staff with physical health needs.
4. Allow flexibility in work schedules to permit staff in recovery to

attend recovery meetings outside of the agency.
5. Provide opportunities for social workers or other staff in recovery

to hold support meetings within the agency.
6. Allow staff to continue to sponsor someone with whom they had a

recovery relationship prior to becoming employed by the agency.
7. Avoid pigeonholing persons in recovery to para-professional

status – hire persons in recovery for every level of clinical,
supervisory and management positions.

8. Utilize staff in recovery as a resource for staff training and
case consultations.

In Summary

Looking between the lines of my experiences, I find the themes of
shame and embarrassment, stigma and secrets that have slowly transformed
after a quarter of a century of recovery into rebuttal, pride, and willingness
to point out the hidden repression of social workers in recovery. Social work
prides itself on being non-judgmental and empowering. Yet, I believe that
any social worker in recovery would be able to recount experiences similar
to those I have chosen to share. The lived experience of social workers in
recovery is a valuable resource to acknowledge and embrace. Awareness that
one in seven people have a substance use disorder (Hafner, 2016) should
have an impact upon the language social workers utilize in their interactions
with colleagues. Agencies should use that same data to review the sometimes
restrictive parameters within which their social workers in recovery serve
their clients and care for themselves. Substance use is a common, treatable
disorder. Social workers should be taking a leading role in demonstrating,
through words and actions, that we know that the acquisition of a substance

CHRISTIAN SOCIAL WORKER IN RECOVERY

SOCIAL WORK & CHRISTIANITY64

use disorder is not a choice or moral failing. While each person makes the
initial decision to drink a beverage with alcohol in it, to smoke a cigarette,
to take an opiate pain medication, or to try an illegal substance, they do not
choose to incur an addiction to that substance. Social workers, Christian
or not, are as apt to have a substance use disorder as anyone else. It is time
that as a profession we step out of denial and inclusively embrace the lived
experiences of social workers in recovery. ❖

References

Alcoholics Anonymous. (2001). Alcoholics Anonymous, 4th Edition. New York: A.A.
World Services.

Bush, D., & Lipari, R. (2015). Substance Use and Substance Use Disorder by Indus-
try. The CBHSQ Report. Retrieved from https://www.samhsa.gov/data/report/
substance-use-and- substance-use-disorder-industry.

Guillemin, M., & Gillam, L. (2004). Ethics, reflexivity, and ‘’ethically im-
portant moments’’ in research. Qualitative Inquiry, 10: 261-279. DOI:
10.1177/1077800403262360

Hafner, J. (2016). Surgeon general: 1 in 7 in USA will face substance addiction.
Retrieved from https://www.usatoday.com/story/news/nation-now/2016/11/17/
surgeon-general-1-7-us-face-substance-addiction/93993474/

Kaplan, L. (2005). Dual relationships: The challenges for social workers in recovery.
Journal of Social Work Practice in the Addictions, 5:3, p. 73-90.

Kubek, P. (2007). Acceptance, assertive outreach, and social support inspire Jane’s
recovery. Journal of Social Work Practice in the Addictions, 7:1/2, p. 171-176.

Lincoln, Y. S., Lyndham, S. A., Guba, E. G. (2011). Paradigmatic controversies,
contradictions, and emerging confluences, revisited. In N. K. Denzin, Y. S.
Lincoln (Eds.), The SAGE handbook of qualitative research, (4th edition, p. 97-
128). Thousand Oaks, CA: Sage Publications.

Manen, M. (2004). Lived experience. In M. S. Lewis-Beck, A. Bryman & T.
F. Liao (Eds.), The SAGE encyclopedia of social science research methods
(Vol. 1, pp. 580-580). Thousand Oaks, CA: SAGE Publications, Inc. doi:
10.4135/9781412950589.n504

Miller, D. &, Fewell, C. (2002). Social workers helping social workers: Self-help
and peer consultation – A dialogue. Journal of Social Work Practice in the Ad-
dictions, 2:1, p. 93-104.

NASW (2017). Code of Ethics of the National Association of Social Workers.
Retrieved from https://www.socialworkers.org/LinkClick.aspx?fileticket=ms_
ArtLqzeI%3d&portalid=0

Substance Abuse and Mental Health Services Administration (2018). Alcohol [Data
File]. Retrieved from https://www.samhsa.gov/atod/alcohol

Endnotes

1 “Life on life’s terms” is a demonstration of acceptance related to step one
in Alcoholics Anonymous. “Nothing, absolutely nothing, happens in God’s world
by mistake. Until I could accept my alcoholism, I could not stay sober; unless I
accept life completely on life’s terms, I cannot be happy” (Alcoholics Anonymous,
2001, p. 417).

2 ”Lived experience” is a sociological research notion that “aims to provide
concrete insights into the qualitative meanings of phenomena in people’s lives”
(Manen, 2004, p. 580).

3 Coins, or chips, are used in many AA groups to recognize monthly or yearly
periods of recovery.

4 Sharing of one’s personal story at AA meetings is considered part of working
the program of AA, but would violate professional ethics if the person sharing was
behavioral healthcare staff and clients were in attendance.

5 “AA lingo” refers to the phrases, idioms and verbiage that is common to
those who participate in AA meetings, but not normally used by those who do
not attend AA.

6 “The rooms” is a phrase used by AA members to denote AA meetings.
7 “Old-timer” is a term of respect given to AA members with decades of

continuous sobriety.
8 “Take the cotton out of your ears and put it in your mouth” is a phrase

often used by AA sponsors to encourage the person new to AA to listen and learn
in the meetings.

Denise L. Jaillet Keane, LCSW, is Social Work Adjunct Faculty at Eastern
Connecticut State University, Willimantic, CT and doctoral candidate in So-
cial Work at the University of Connecticut, Hartford, CT. Email: dljkeane@
gmail.com

Key Words: Christian, Social worker, recovery, substance use, stigma,
discrimination, AA, alcoholism

CHRISTIAN SOCIAL WORKER IN RECOVERY

Copyright of Social Work & Christianity is the property of North American Association of
Christians in Social Work and its content may not be copied or emailed to multiple sites or
posted to a listserv without the copyright holder’s express written permission. However, users
may print, download, or email articles for individual use.

Imaging resilience and recovery in alcohol dependence

Katrin Charlet1,2, Annika Rosenthal2, Falk W. Lohoff1, Andreas Heinz2 & Anne Beck2

Section on Clinical Genomics and Experimental Therapeutics, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA1 and
Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité—Universitätsmedizin, Berlin, Germany2

ABSTRACT

Background and aims Resilience and recovery are of increasing importance in the field of alcohol dependence (AD).
This paper describes how imaging studies in man can be used to assess the neurobiological correlates of resilience and

if longitudinal, of disease trajectories, progression rates and markers for recovery to inform treatment and prevention op-
tions. Methods Original papers on recovery and resilience in alcohol addiction and its neurobiological correlates were
identified from PubMed and have been analyzed and condensed within a systematic literature review. Results Findings
deriving from functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) studies have iden-
tified links between increased resilience and less task-elicited neural activation within the basal ganglia, and benefits of
heightened neural pre-frontal cortex (PFC) engagement regarding resilience in a broader sense; namely, resilience against
relapse in early abstinence of AD. Furthermore, findings consistently propose at least partial recovery of brain glucose me-
tabolism and executive and general cognitive functioning, as well as structural plasticity effects throughout the brain of
alcohol-dependent patients during the course of short-, medium- and long-term abstinence, even when patients onl

lowered their alcohol consumption to a moderate level. Additionally, specific factors were found that appear to influence
these observed brain recovery processes in AD, e.g. genotype-dependent neuronal (re)growth, gender-specific neural re-
covery effects, critical interfering effects of psychiatric comorbidities, additional smoking or marijuana influences or ado-
lescent alcohol abuse. Conclusions Neuroimaging research has uncovered neurobiological markers that appear to be
linked to resilience and improved recovery capacities that are furthermore influenced by various factors such as gender
or genetics. Consequently, future system-oriented approaches may help to establish a broad neuroscience-based research
framework for alcohol dependence.

Keywords Alcohol dependence, functional, neuroimaging, recovery, resilience, structural.

Correspondence to: Katrin Charlet, Section on Clinical Genomics and Experimental Therapeutics (CGET), National Institute on Alcohol Abuse and Alcoholism
(NIAAA), National Institutes of Health (NIH), 10 Center Drive (10CRC/2-2340), Bethesda, MD 20892-1540, USA. E-mail: katrin.charlet@nih.gov
Submitted 10 September 2015; initial review completed 26 January 2016; final version accepted 25 April 2018

INTRODUCTION

Imaging recovery and resilience

The toxic effects of alcohol are seen particularly in the
brain, as demonstrated by several post-mortem and in-vivo
neuroimaging studies in individuals with alcohol depen-
dence (AD; e.g. [1–3]). Structural changes are observed
clearly in the brain, including atrophy of gray and white
matter with sulcal widening and ventricular enlargement.
In addition, chronic alcohol consumption is accompanied
by neural adaptations within different neurotransmitter
systems, such as the dopamine system (cf. reviews [4–9]).
These neural andmolecular changes have been shown fur-
ther to be associated with dysfunctional brain functio

underlying psychological and behavioral processes in AD
[10–19].

Once harmful alcohol use stops or is reduced, beneficia

recovery processes can be observed regarding physical and
mental health (see [20]) and in the brain, using various
neuroimaging techniques [21–24]. One of the main ques-
tions for neuroimaging research in the field of addictive dis-
orders is to characterize predictors of recovery and
treatment outcome [25]. It is notable, however, that a clear
standard definition of the term ‘recovery’ is not yet gener-
ally established. In this review, we will focus on structural
and functional changes within the brain associated with
reduction of alcohol intake or abstinence in AD investi-
gated by studies using neuroimaging techniques as identi-
fied by our literature search.

Another consideration is to what extent abnormalities
in brain structure and function are caused by the toxic ef-
fects of alcohol, or whether some of these differences might

HORIZONS REVIEW doi:10.1111/add.14259

http://orcid.org/0000-0001-5405-9065

have been pre-existing and putatively predispose some indi-
viduals to develop alcohol dependencewhile others seem to
have a protective effect, i.e. confer resilience [26]. Resil-
ience is defined traditionally as the ability to adapt to
adverse/traumatic environments, thus resulting in healthy
long-term psychological functioning and better develop-
mental outcomes [27–29]. Resilience research also con-
centrates on high-risk groups, which do not develop the
disorder of interest despite carrying risk genes and/or
experiencing adverse environmental conditions. Studying
those individuals already affected, however, adds a new
perspective to the understanding of disease development,
disease progression and future potential treatment strate-
gies by focusing on neurobiological factors that promote a
good treatment outcome despite adversities. Thus, studies
using neuroimaging techniques may help to identify such
resilience mechanisms regarding the structural and func-
tional markers of neural patterns associated with attenuat-
ing further disease progression and/or relapse in AD
[10,11,30,31]. Such factors are not defined by the absence
of vulnerability markers, but rather by compensatory
changes in biological markers that distinguish individuals
with good treatment outcome from those who relapse
and healthy controls.

We therefore reviewed the available literature to an-
swer the following questions: (1) why are some people less
vulnerable in developing addictive disorders in comparison
with others; (2) to what extent can recovery processes be
observed; and (3) why do some individuals with alcohol de-
pendence achieve and maintain abstinence better, i.e. are
more resilient than those who relapse?

METHODS

Search strategy

We reviewed systematically the existing literature up to
November 2017 using the PUBMED electronic database
for the identification of neuroimaging studies investigating
recovery and/or resilience in alcohol dependence or alco-
hol dependence in humans, respectively. We therefore used
the following search terms: imaging, neuroimaging, addic-
tion, dependence, alcohol*, substance use*, substance use
disorder, recovery, resilience. Bibliographies of relevant pa-
pers were additionally screened for further relevant
information.

Study selection

We included peer-reviewed original studies irrespective of
when the study was conducted and excluded single case
studies, reviews and meta-analyses. For the sake of parsi-
mony, we further excluded neuroimaging studies using im-
aging techniques other than functional magnetic
resonance imaging (fMRI), structural MRI, diffusion tensor

imaging (DTI) or positron emission tomography (PET). Ad-
ditional exclusion criteria were: not in English, substances
other than alcohol, neuropsychological studies without
neuroimaging.

Extraction and quality assessment

One reviewer (K.C.) screened abstracts of papers identified
for potential relevance. Then, two reviewers (A.B. and
K.C.) extracted study data independently and screened
further the bibliographies of relevant papers. In the
event of uncertainty or disagreement regarding criteria
for eligibility between A.B. and K.C., selected papers
and manuscript drafts were discussed further with the
third and fourth reviewers (F.W.L. and A.H.). Decisions
on study selection were documented by A.R.

RESULTS

Search results

The initial term search identified a total of 1066 papers,
175 of which were considered potentially relevant. Addi-
tionally, seven were identified through screening the refer-
ence lists of selected papers. Of those, 145 papers were
excluded further, as described in Fig. 1, according to the
Preferred Reporting Items for Systematic Reviews and
Meta-Analyses (PRISMA) group [32]. Finally, 35 studies
were included in our review (for details, please see
Table 1 and Supporting information, Appendix S1).

Resilience and recovery markers detected by fMRI

We found nine relevant fMRI studies [10–12,33–38] inves-
tigating the role of cognitive functions seen commonly in
AD, such as executive, motivational aspects of behavior
and emotion processing (for reviews, see [4,7,39]).

Weiland et al. characterized resiliency as the ability for
flexible adaptation of psychological control functions ap-
propriate to the respective environmental context [36].
As low resiliency is known to be associated with later
alcohol/drug problems and poor working memory perfor-
mance [36], they investigated young healthy adolescents
with and without a positive family history for alcohol de-
pendence using a 2-back working memory task and ob-
servers’ ratings based on the California Child Q-Sort as a
measurement for resiliency. Resiliency correlated nega-
tively with number of alcohol problems and illicit drugs
used but did not differ regarding family history. This might
point to the importance of environmental factors apart
from genetic influences.

Another study reported that in those with AD who be-
came abstinent, higher functional engagement of brain
areas within and outside of the ‘classical’working memory
network (e.g. rostral/ventrolateral pre-frontal cortex) was

1934 Katrin Charlet et al.

associated with executive behavioral control [11]. This
may constitute a resilience factor in terms of flexible re-
cruitment of neural resources inside the classical working
memory network and further compensatory processes as-
sociated with longer duration of abstinence. This is consis-
tent with another fMRI study that also showed functional
recruitment of neural working memory network in alcohol
dependence [33], and suggests that such higher activity is
productive rather than an impairment.

Drug-associated cue–reactivity has been associated
with drug craving (e.g. [16,40]) and risk of relapse after de-
toxification (e.g. [10,14]). Two recent prospective studies
reported altered cingulate cortex connectivity during indi-
vidualized imaginary scripts provoking either alcohol-,
stress-associated or neutral states in AD [38]. Those pa-
tients who showed greater posterior cingulate connectiv

ity

during alcohol imagery, or less anterior, mid-cingulate con-
nectivity during neutral trials, showed longer abstinence
during the following 90 days and resembled healthy con-
trols. These results emphasize the benefit of functional con-
nectivity analyses in the investigation of neurobiological
substrates and relapse risk in AD [38].

In their prospective study, Beck et al. [10] observed in-
creased neural reactivity during presentation of alcohol-
associated cues within mid-brain/subthalamic nucleus as
well as ventral striatum in those AD who achieved absti-
nence compared to relapsers (< 3 months’ follow-up

)

[10]. Further, patients who remained abstinent demon-
strated increased functional connectivity between mid-

brain and amygdala as well as orbitofrontal cortex (OFC)
during this alcohol-associated ‘cue–reactivity’ task com-
pared to those patients who relapsed within 3 months.
The authors argued that the increased connectivity be-
tween dopaminergic brain areas such as the mid-brain
and the amygdala/OFC might help to discriminate and sig-
nal aversive aspects of drinking alcohol, and thus may sup-
port abstinence.

In the context of reward deficiency, Yau et al. observed
reduced ventral striatal response during the anticipation
of monetary reward and loss using a monetary incentive
delay task (MID) in a group of healthy children of
alcohol-dependent (COA) individuals (aged 18–22 years)
compared with controls [37]. In addition, in COAs only, ac-
tivation of ventral striatum was correlated positively with
externalizing behavior as well as current and life-time alco-
hol consumption.

Another important but rarely studied domain in addic-
tion research regarding recovery or resilience is the neural
basis of emotion processing. Heitzeg et al. [35] conducted a
longitudinal cohort study to investigate externalizing be-
havioral problems and neural activation patterns during
an fMRI task presenting emotional words in adolescents
(aged 16–20 years) with a family history of AD who were
considered vulnerable (risky drinking behavior) or resilient
(no risky drinking behavior). These groups were compared
to adolescents without any parental history of AD or risky
drinking behavior [35]. In response to emotional stimuli,
increased activation in OFC, insula and putamen was

Figure 1 Flow diagram of the selection process of studies for the systematic review on imaging resilience and recovery in alcohol dependence, ac-
cording to Moher et al. (2009) [32] sMRI = structural magnetic resonance imaging; fMRI = functional magnetic resonance imaging; DTI = diffusion
tensor imaging; PET = positron emission tomograph

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st
ud

M
R
I/
D
BM

N
on

e
LD

H
8 m
on

th
s

(A
D

)

A
D
:m

ea
n
49

SD
(1
4)

Co
nt
ro
ls
:m

ea
n

45
SD

(8
)

n
m
al
e
=
60

,
n
fe
m
al
e
=
5

(A
D
↔

LD
;A

lo
ng

itu
di
na

A
tr
op
hy

in
fr
on

ta
la
nd

te
m
po
ra
ll
ob
e
in

A
D

gr
ou

p.
A
bs
ta
in
er
s
sh
ow

fa
st
er

re
co
ve
ry

in
pa
ri
et
al
an

d
fr
on

ta
lt
is
su
e
th
an

LD
.

Te
m
po
ra
ll
ob
es
,t
ha

la
m
us
,b
ra
in
st
em

(C
on

ti
nu

es
)

1936 Katrin Charlet et al.

Ta
bl
e
1.

(C
on

tin
ue
d)

M
et
ho
d

St
ud
y

St
ud
y
de
si
gn

N
eu
ro
im
ag
in
g

Pa
ra
di
gm

O
th
er
te
st
s

Fo
llo
w
-u
p

pe
ri
od

A
ge

St
ud
y
sa
m
pl
e

M
ai
n
fi
nd
in
gs

m
on

th
s

(L
D
)

↔
LD

)
ce
re
be
llu

m
,c
or
pu

s
ca
llo
su
m
,a
nt
er
io
r

ci
ng

ul
at
e,

in
su
la
an

d
su
bc
or
tic
al
w

hi
te

m
at
te
r

w
as

in
cr
ea
se
d
in

ab
st
ai
ne
rs

co
m
pa
re
d
to

re
la
ps
er
s.

R
ec

ov
er

y
pr
ed
ic
te
d
by

ba
se
lin

gr
ay

m
at
te
r

vo
lu
m
es

Ca
rd
en
as

et
al
.2

01
1

Pr
os
pe
ct
iv
e

co
ho

rt
st
ud

y
M
R
I/
D
BM

N
on

e
LD

H
7.
8
m
on

th
s

A
D
:m

ea
n
50

SD
(1
0)

Co
nt
ro
ls
:

ea
n
47

SD
(8
)

n
m
al
e
=
10

4,
n
fe
m
al
e
=
11

(A
D
<

he
al
th
y

co
nt
ro
l;
re
la
ps
er
s

↔
ab
st
ai
ne
rs
)

A
bs
ta
in
er
s
ve
rs
us

co
nt
ro
ls
ha

d
sm

al
le
r
vo
lu
m
e

in
le
ft

hi
pp
oc
am

pu
s,
en
to
rh
in
al
co
rt
ex
,a
m
yg
da
la

an
d
ri
gh

th
al
am

us
bu

t
la
rg
er

vo
lu
m
e
in

le
ft

or
bi
to
fr
on

ta
lr
eg
io
n.

R
el
ap
se
rs
ve
rs
us

ab
st
ai
ne
rs

sm
al
le
r
vo
lu
m
e
in

la
te
ra
lO

FC
,l
ef
t
po
st
er
io
r

m
id
dl
e/
te
m
po
ra
lg
yr
y
an

d
su
pr
am

ar
gi
na

lg
yr
us
.

R
el
ap
se
rs
ha

d
di
ffe
re
nt

pa
tt
er
n

of
vo
lu
m
e
lo
ss
th
an

ab
st
ai
ne
rs

Ch
an

ra
ud

et
al
.2

01
3

Cr
os
s-
se
ct
io
na

l
fM

R
I/
PP

I/
re
s

t
in

st
at
e
fu
nc
tio

na
l

co
nn

ec
tiv

ity

W
or
ki
ng

–

m
em

or
y

ta
sk

N
on

e
N
on

e
A
D
:m

ea
n
40

.1
SD

(1
0.
9)

Co
nt
ro
ls
:m

ea
n
47

.7
SD

(1
2.
29

)

n
m
al
e
=
30

(d
et
ox
ifi
ed

A
D
↔

he
al
th

y
co
nt
ro
ls
)

R
ec
ov
er
y

re
la
te
d

re
cr
ui
tm

en
t
of
do
rs
ol
at
er
al

pr
e-
fr
on

ta
lc
or
te
x

(

D
LP

FC
)-
ce
re
be
lla
r
V

II
I

sy
st
em

ri
ng

re
st
an

d
D
LP

FC
-c
er
eb
el
la
r
V
I
sy
st
em

du
ri
ng

w
or
ki
ng

m
em

or
y
ta
sk

Ch
ar
le
t
et
al
.

20
14

Pr
os
pe
ct
iv
e

co
ho

rt
st
ud

y
fM

R
I/
bi
ol
og
ic
al

pa
ra
m
et
ri
c

m
ap
pi
ng

N
-b
ac
k
ta
sk

A
lc
oh

ol
tim

e-
lin

e
fo
llo
w
-b
ac
k

7 m
on

th
s

A
D
:m

ea
n
44

.9
SD

(1
1.
4)

Co
nt
ro
ls
:m

ea
n
44

.1
SD

(1
2)

n
m
al
e
=
60

,
n
fe
m
al
e
=
20

(d
et
ox
ifi
ed

A
D
↔

H
ea
lth

y
co
nt
ro
ls
)

H
ig
h
re
si
lie
nc
e
(lo

re
la
ps
e

ri
sk

in
al
co
ho

l-d
ep
en
de
nc
e)

as
so
ci
at
ed

w
ith

ne
ur
al

ac
tiv

at
io
n
in

la
te
ra
l/
m
ed
ia
lp
re
-m

ot
or

co
rt
ex
,

ro
st
ra
l/
ve
nt
ro
la
te
ra
lp
re
-fr
on

ta
lc
or
te
x
du

ri
ng

N
-b
ac
k
w
or
ki
ng

m
em

or
y
ta
sk

Ch
ar
le
t
et
al
.

20
14

Pr
os
pe
ct
iv
e

co
ho

rt
st
ud

y
fM

R
I/
bi
ol
og
ic
al

pa
ra
m
et
ri
c

m
ap
pi
ng

H
ar
ir
if
ac
es

ta
sk

(m
od
ifi
ed
)

LD
H

N
on

e
A
D
:m

ea
n
44

.8
SD

(9
.8
)

Co
nt
ro
ls
:m

ea
n
46

.1
SD

(9
.8
)

n
m
al
e
=
50

,
n
fe
m
al
e
=
16

(d
et
ox
ifi
ed

A
D
↔

he
al
th
y
co
nt
ro
ls

)

In
cr
ea
se
d
A
CC

re
sp
on

se
to

af
fe
ct
iv
e
fa
ce
s

rr
el
at
ed

to
ab
st
in
en
ce

an
d
le
ss
re
tr
os
pe
ct
iv
e

al
co
ho

li
nt
ak
e
in

al
co
ho

l-d
ep
en
de
nt

pa
tie
nt
s

Ch
un

g
et
al
.

20
11

Cr
os
s-
se
ct
io
na

l
Ev
en
t-
re
la
te
d
fM

R
I

A
nt
i-s
ac
ca
de

re
w
ar
d
ta
sk

N
on

e
N
on

e
SU

D
:m

ea
n
17

.0
SD

(0
.9
)

Co
nt
ro
l:
m
ea
n
16

.9
SD

(0
.9
)

n
m
al
e/
fe
m
al
e
=
12

[S
U
D
(m

ar
iju

an
a,

al
co
ho

l,
ot
he
r)

↔
he
al
th
y
co
nt
ro
ls
]

D
ur
in
g
re
sp
on

se
pr
ep
ar
at
io
n
SU

D
sh
ow

ed
in
cr
ea
se
d
ac
tiv

at
io
n
in

oc
ul
om

ot
or

co
nt
ro
l

re
gi
on

s
(F
EF
,S
EF
),

D
LP

FC
,r
eg
io
ns

in
th
e

pa
ri
et
al
lo
be

an
d
ar
ea
s
in

th
e
fr
on

ta
lg
yr
us (C
on

ti
nu

es
)

1937

Ta
bl
e
1.

(C
on

tin
ue
d)

M
et
ho
d

St
ud
y

St
ud
y
de
si
gn

N
eu
ro
im
ag
in
g

Pa
ra
di
gm

O
th
er
te
st
s

Fo
llo
w
-u
p

pe
ri
od

A
ge

St
ud
y
sa
m
pl
e

M
ai
n
fi
nd
in
gs

D
es
hm

uk
h

et
al
.2

00
5

Cr
os
s-
se
ct
io
na

M
R
I/
vo
lu
m
et
ri
c

da
ta

N
on

e
N
on

e
A
D
:m

ea
n
49

.4
SD

(1
0.
9)

Co
nt
ro
ls
:m

ea
n

.2
SD

(1
3.
9)

Sc
hi
zo
ph

re
ni
a:
m
ea
n

44
.7

SD
(8
.6
)

Co
m
or
bi
d:

ea
n
41

.0
SD

(7
.5
)

n
m
al
e
=
12

(A
D

de
to
xi
fi
ed

↔

sc
hi
zo
ph

re
ni
a

↔
A
D
/s
ch
iz
op
hr
en
ia

↔
he
al
th
y
co
nt
ro
ls
)

Pu
ta

m
en

an
d
nu

cl
eu
s
ac
cu
m
be
ns

de
cr
ea
se

gr
ea
te
r
in

sc
hi
zo
ph

re
ni
a
th
an

A
D
,c
om

or
bi
d

gr
ou

p
fe
ll
be
tw

ee
n
th
es
e
gr
ou

ps
Sc
hi
zo
ph

re
ni
c

pa
tie
nt
s
tr
ea
te
d
w
ith

at
yp
ic
al
m
ed
ic
at
io
n

sh
ow

ed
gr
ea
te
r
vo
lu
m
e
de
cr
ea
se
s
in

pu
ta
m
en

th
an

th
os
e
tr
ea
te
d
w
ith

ty
pi
ca
lm

ed
ic
at
io
n.

R
ec
en
tly

so
be
r
(<

3
w
ee
ks
)
al
co
ho

lic
s
ha

d
gr
ea
te
r
de
fi
ci
ts
in

nu
cl
eu
s
ac
cu
m
be
ns

th
an

A
D
w
ith

lo
ng

-t
er
m

so
br
ie
ty

D
ur
az
zo

et
al
.2

01
5

Pr
os
pe
ct
iv
e

co
ho

rt
st
ud

y
M
R
I/
vo
lu
m
et
ri
c

da
ta

N
on

e
LD

H
,N

eu
ro
co
gn

iti
ve

ba
tt
er
y

7.
5
m
on

th
s

A
D
sm

ok
in
g:

m
ea
n

(9
)

A
D
no

–
sm

ok
in
g:

m
ea
n
52

(1
1)

Co
nt
ro
ls
:

M
ea
n
47

SD
(9
)

n
m
al
e
=
10

3,
n
fe
m
al
e
=
11

A
D
:v
ol
um

in
cr
ea
se
s

al
lG

M
an

W
M

re
gi
on

s
at

FU
;n

o
si
gn

ifi
ca
nt

pr
ed
ic
to
rs
of

re
gi
on

al
vo
lu
m
e
ch
an

ge
.R

at
es

of
G
M

ga
in

gr
ea
te
st
in

fi
rs
t

m
on

th
.s

A
D
sh
ow

ed
le
ss

vo
lu
m
e
ga
in

ns
A
D
in

fr
on

ta
la
nd

to
ta
l

co
rt
ic
al
G
M
.I
m
pr
ov
em

en
t
pr
oc
es
si
ng

sp
ee
d

as
so
ci
at
ed

w
ith

in
cr
ea
se
d
vo
lu
m
es

in
ns
A
D
,

bu
t

t
in

sA
D
.A

fte
r
7.
5
m
on

th
s

of
ab
st
in
en
ce

ns
A
D
an

d
sA

D
eq
ua

lt
o
co
nt
ro
ls
on

fr
on

ta
lG

M
vo
lu
m
e

G
az
dz
in
sk
i

et
al
.2

00
5

Cr
os
s-
se
ct
io
na

l/
pr
os
pe
ct
iv
e

co
ho

rt
st
ud

M
R
I/
bo
un

da
ry

sh
ift

in
te
gr
al

N
on

e
LD

U
p
to

2
m
on

th
s

A
D
:m

ea
n
50

.6
SD

(9
.3
)

Co
nt
ro
ls
:m

ea
n
45

.0
SD

(6
.8
)

n
m
al
e
=
37

,
n
fe
m
al
e
=
3

(A
D
de
to
xi
fi
ed
/

lo
ng

itu
di
na

l↔
he
al
th
y
co
nt
ro
ls
)

M
os
t
tis
su
e
ga
in

du
ri
ng

th
e
fi
rs
t

ab
st
in
en
t

m
on

th
.F
as
te
st
vo
lu
m
e
re
co
ve
ry

pa
tie
nt
s
w
ith

gr
ea
te
st
ba
se
lin

e
br
ai
n
sh
ri
nk

ag
e
an

dr
in
ki
ng

se
ve
ri
ty
.R

ev
er
sa
lo
fv
ol
um

e
in
cr
ea
se
s
in

no
n-

ab
st
in
en
t

in
di
vi
du

al
s

od
ul
at
ed

by
du

ra
tio

n
of
ab
st
in
en
ce

an
d
no

n-
ab
st
in
en
ce

pe
ri
od
s,
as

w
el
la
s
re
ce
nc
y

of
no

n-
ab
st
in
en
ce
)

G
az
dz
in
sk
i

et
al
.2

00
8

Cr
os
s-
se
ct
io
na

l/
pr
os
pe
ct
iv
e

co
ho

rt
st
ud

M
R
I/
sh
or
t
ec
ho

pr
ot
on

sp
ec
tr
os
co
py

N
on

e
BV

M
T

1
m
on

th
Sm

ok
in
g

al
co
ho

l-
de
pe
nd

en
t:

m
ea
n

50
.7

SD
(9
.0
)

n
m
al
e
=
38

(s
m

ok
in
g
A
D

↔

n-
sm

ok
in
g

N
-a
ce
ty
l-a

sp
ar
ta
te

no
rm

al
iz
ed

in
th
e

M
TL

of
no

n-
sm

ok
in
g
A
D
gr
ou

p,
re
m
ai
ne
d
lo
w

in
th
e

M
TL

of
sm

ok
in
g
A
D
gr
ou

p.
Ch

an
ge
s
in

bo
th

(C
on

ti
nu

es
)

1938 Katrin Charlet et al.

Ta
bl
e
1.

(C
on

tin
ue
d)

M
et
ho
d

St
ud
y

St
ud
y
de
si
gn

N
eu
ro
im
ag
in
g

Pa
ra
di
gm

O
th
er
te
st
s

Fo
llo
w
-u
p

pe
ri
od

A
ge

St
ud
y
sa
m
pl
e

M
ai
n
fi
nd
in
gs

N
on

-s
m
ok
in
g

al
co
ho

l-d
ep
en
de
nt
:

M
ea
n
50

.2
SD

(9
.1
)

N
on

–
sm

ok
in
g
co
nt
ro
ls
:

m
ea
n
47

.3
SD

(8
.2
)

A
D
↔

no
n-
sm

ok
in
g
LD

)
gr
ou

ps
as
so
ci
at
ed

w
ith

im
pr
ov
em

en
ts
in

vi
su
os
pa
tia

lm
em

or
y.

H
ip
po
ca
m

pa
lv
ol
um

es
in
cr
ea
se
d
in

bo
th

gr
ou

ps
du

ri
ng

ab
st
in
en
ce
,b
ut

in
cr
ea
si
ng

vo
lu
m
es

co
rr
el
at
ed

w
ith

vi
su
os
pa
tia

lm
em

or
y

im
pr
ov
em

en
ts
on

ly
in

no
n-
sm

ok
in
g
A
D

G
az
dz
in
sk
i

et
al
.2

01
0

Pr
os
pe
ct
iv
e

co
ho

rt
st
ud

y
M
R
I/
D
TI
/

sp
ec
tr
os
co
py

N
on

e
N
on

e
1
ye
ar

Sm
ok
in

g
al
co
ho

l-
de
pe
nd

en
t:
m
ea
n

47
.7

(9
.5
)

N
on

-s
m
ok
in
g

al
co
ho

l-d
ep
en
de
nt
:

m
ea
n
51

.5
SD

(1
0.
3)

N
on

-s
m
ok
in
g

co
nt
ro
ls
:m

ea
n
48

.3
SD

(8
.4
)

n
m
al
e
=
53

,
n
fe
m
al
e
=
5

(s
m
ok
in
g
A
D
↔

no
n-

sm
ok
in
g
A
D

↔
no

n-
sm

ok
in
g
LD

)

H
ig
he
r

m
ea
n
di
ffu

si
vi
ty

in
A
D

(s
m
ok
in
g:
fr
on

ta
l;

no
n-
sm

ok
in
g:
pa
ri
et
al
,f
ro
nt
al
,t
em

po
ra
l).

Lo
w
er

co
nc
en
tr
at
io
ns

of
N
-a
ce
ty
l-a

sp
ar
ta
te

in
A
D

(s
m
ok
in
g:
fr
on

ta
l;
no

n-
sm

ok
in
g:
pa
ri
et
al
).
In

no
n-

ok
in
g
al
co
ho

l-d
ep
en
de
nt

in
di
vi
du

al
s

in
cr
ea
se

in
FA

an
d
de
cr
ea
se
s
in

m
ea
n
di
ffu

si
vi
ty

ov
er

1
m
on

th
of
ab
st
in
en
ce

hi
te

m
at
te
r

vo
lu
m
e
in
cr
ea
se

in
fr
on

ta
la
nd

te
m
po
ra
ll
ob
es

in
sm

ok
in
g
A
D
gr
ou

p
H
ei
nz

et
al
.

20
04

Cr
os
s-
se
ct
io
na

l
fM

R
I/
PE

T
A
lc
oh

ol
cu
es

A
lc
oh

ol
cr
av
in
g

qu
es
tio

nn
ai
re

N
on

e
A
D
:m

ea
n
44

.5
SD

(6
.5
)

Co
nt
ro
ls
:

m
ea
n
43

.2
SD

(9
.5
)

n
m
al
e
=
24

(d
et
ox
ifi
ed

A
D
↔

he
al
th
y

co
nt
ro
ls
)

In
al
co
ho

l-d
ep
en
de
nt

su
bj
ec
ts
hi
gh

er
ac
tiv

at
io
n

of
th
e
m

ed
ia
lp
re
-fr
on

ta
lc
or
te
x
an

st
ri
at
um

re
la
te
d
to

(1
)
le
ss
av
ai
la
bi

lit
y

of
D
2-
lik
e

re
ce
pt
or

in
V
S,
(2
)
hi
gh

er
cr
av
in
g
se
ve
ri
ty

H
ei
tz
eg

et
al
.

20
08

Cr
os
s-
se
ct
io
na

l
fM

R
I

Le
xi
ca
l

ot
io
na

l
st
im

ul
i

Y
SR

D
ri
nk

in
g
an

d
dr
ug

hi
st
or
y
fo
rm

fo
r

ch
ild
re
n

N
on

e
CO

A
s

re
si
lie
nt
:m

ea
n
18

.4
SD

(1
)
CO

A
s

vu
ln
er
ab
le

:m
ea
n
17

.5
SD

(1
.3
)

Co
nt
ro
ls
:m

ea
n
17

.2
SD

(1
.6
)

n
m
al
e
=
15

,
n
fe
m
al
e
=
13

(C
O
A
s
re
si
lie
nt

↔
CO

A
S
vu

ln
er
ab
le

↔
co
nt
ro
ls
)

In
re
sp
on

se
to

em
ot
io
na

ls
tim

ul
i:
ac
tiv

at
io
n
of

or
bi
ta
lf
ro
nt
al
gy
ru
s
an

d
le
ft
in
su
la
/p
ut
am

en
gr
ea
te
r
in

re
si
lie
nt

th
an

co
nt
ro
la
nd

vu
ln
er
ab
le

gr
ou

ps
.V

ul
ne
ra
bl
e
gr
ou

p
ha

d
m
or
e
ac
tiv

at
io
n
of

do
rs
om

ed
ia
lP

FC
an

d
le
ss
ac
tiv

at
io
n

V
S
an

d
ex
te
nd

d
am

yg
da
la
.I
nc
re
as
ed

do
rs
om

ed
ia
lp
re
-fr
on

ta
la
ct
iv
at
io

n
an

d
de
cr
ea
se
d

V
S
an

d
am

yg
da
la
ac
tiv

at
io
n
co
rr
el
at
ed

po
si
tiv
el
y

w
ith

ex
te
rn
al
iz
in
g
be
ha

vi
or
s

(C
on

ti
nu

es
)

1939

Ta
bl
e
1.

(C
on

tin
ue
d)

M
et
ho
d

St
ud
y

St
ud
y
de
si
gn

N
eu
ro
im
ag
in
g

Pa
ra
di
gm

O
th
er
te
st
s

Fo
llo
w
-u
p

pe
ri
od

A
ge

St
ud
y
sa
m
pl
e

M
ai
n
fi
nd
in
gs

H
oe
fe
r
et
al
.

20
14

Cr
os
s-
se
ct
io
na

l/
pr
o
sp
ec
tiv
e

co
ho

rt
st
ud

M
R
I/
vo
lu
m
et
ri
c

da
ta

N
on

e
LD

H
;

Ta
qm

an
ge
no

ty
pi
ng

as
sa
y;
W
A
IS

II
I;

BV
M
T;

A
M
N
A
RT

7 m
on

th
s

Sm
ok
in
g
al
co
ho

l-
de
pe
nd

en
t:

m
ea
n
49

.6
SD

(9
)

N
on

-s
m
ok
in
g

al
co
ho

l-d
ep
en
de
nt
:

ea
n
53

.6
(1
0.
5)

N
on

–
sm

ok
in
g
co
nt
ro
ls
:

m
ea
n
45

.6
SD

(9
.9
)

n
m
al
e
=
14

n
fe
m
al
e
=
12

(s
m
ok
in
g
al
co
ho

l-
de
pe
nd

en
t

↔
no

n-
sm

ok
in
g

al
co
ho

l-d
ep
en
de
nt

↔
no

n-
sm

ok
in
g

co
nt
ro
ls
)

A
D
ha

d
sm

al
le
r
hi
pp
oc
am

pi
th
an

he
al
th
y

co
nt
ro
ls
at

al
lt
im

e-
po
in
ts
.H

ip
po
ca
m
pa
l

vo
lu
m
e
at

1
m
on

th
of
ab
st
in
en
ce

co
rr
el
at
ed

w
ith

lo
w
er

vi
su
os
pa
tia

lf
un

ct
io
n

Sm
ok
in
g
st
at
us

di
d
no

t
in
fl
ue
nc

e
vo
lu
m
e

re
co
ve
ry
.B

D
N
F
Va

lh
om

oz
yg
ot
es

ha
d

hi
pp
oc
am

pa
lv
ol
um

e
in
cr
ea
se
s
ov
er

7
m
on

th
s

of
ab
st
in
en
ce
,a
nd

Va
lh

om
oz
yg
ot
es

ha
d

si
gn

ifi
ca
nt
ly
la
rg
er

hi
pp
oc
am

pi
th
an

M
et

ca
rr
ie
rs
at

7
m
on

th
s
of
ab
st
in
en
ce

Jo
hn

so
n-

G
re
en
e
et
al
.

19
97

Pi
lo
t
st
ud

y
PE

T
N
on

e
N
eu
ro
ps
yc
h
ol
og
ic
al

ba
tt
er
y

U
p
to

32
m
on

th
s

A
D
m
ea
n:

48
.6

SD
(1
0.
2)

n
m
al
e
=
6
(A
D

lo
ng

itu
di
na

A
bs
tin

en
t
gr
ou

p
sh
ow

ed
pa
rt
ia
lr
ec
ov
er
y
of

IC
M
R
gl
c
in

tw
o
of
th
re
e
di
vi

si
on

s
of
th
e
fr
on

ta
l

lo
be
s
an

d
im

pr
ov
em

en
t
on

ne
ur
op
sy
ch
ol
og
ic
al

te
st
s
of
ge
ne
ra
lc
og
ni
tiv
e
an

d
ex
ec
ut
iv
e

fu
nc
tio

ni
ng

w
he
re
as

th
e
pa
tie
nt
s
w
ho

re
la
ps
ed

ha
d
fu
rt
he
r
de
cl
in
es

in
th
es
e
ar
ea
s

K
üh

n
et
al
.

20
14

Cr
os
s-
se
ct
io
na

l/
pr
os
pe
ct
iv
e

co
ho

rt
st
ud

M
R
I/
vo
lu
m
et
ri
c

da
ta

N
on

e
LD

2
w
ee
ks

A
D
:m

ea
n
42

.1
SD

(1
1.
6)

Co
nt
ro
ls
:m

ea
n
40

.8
SD

(3
.4
)

n
m
al
e
=
53

,
n
fe
m
al
e
=
21

(A
D
de
to
xi
fi
ed

↔
he
al
th
y
co
nt
ro
ls
)

A
D
gr
ou

p
ha

d
lo
w
er

2
+
3

ba
se
lin

e
vo
lu
m
e

d
si
gn

ifi
ca
nt

no
rm

al
iz
at
io
n
of
gr
ay

m
at
te
r

vo
lu
m

e
2
w
ee
ks

la
te
r.
N
eg
at
iv
e
co
rr
el
at
io
n

be
tw

ee
n
ba
se
lin

e
CA

2
+
3
vo
lu
m
e
an

d
al
co
ho

l
co
ns
um

pt
io
n
an

d
al
co
ho

l-w
ith

dr
aw

al
sy
m
pt
om

s.
A
D
pa
tie
nt
s
w
ith

st
ro
ng

er
w
ith

dr
aw

al
sy
m
pt
om

s
di
sp
la
ye
d
th
e
la
rg
es
t
vo
lu
m
e
in
cr
ea
se

of
CA

2
+
3

M
on

et
al
.

20
11

Pr
os
pe
ct
iv
e

co
ho

rt
st
ud

y
M
R
I/
vo
lu
m
et
ri
c

da
ta
/

m
at
he
m
at
ic
al

pr
ed
ic
tio

N
on

e
N
on

e
22

da
ys

A
D
:m

ea
n
50

.7
SD

(1
1.
9)

n
m
al
e
=
13

,
n
fe
m
al
e
=
3

Th
e
da
ta

pr
ed
ic
te
d
fr
om

th
e
fo
rm

ul
a
w
er
e

ve
ry

si
m
ila
r
to

th
e
ex
pe
ri
m

en
ta

lly

m
ea
su
re
d

da
ta

fo
r
al
ll
ob
es

an
d
fo
r
bo
th

gr
ay

an
d

w
hi
te

m
at
te
r
(in

tr
ac
la
ss
co
rr
el
at
io
n
co
ef
fi
ci
en
ts
↔

0.
95

)
M
on

et
al
.

20
13

M
R
I/
vo
lu
m
et
ri
c

da
ta

N
on

e
LD

H
;

5
w
ee
ks

A
D
:m

ea
n
50

.8
SD

(1
0.
6)

n
m
al
e
=
70

,
n
fe
m
al
e
=
9

VA
L
ho

m
oz
yg
ot
e
in

A
D
gr
ou

p
re
la
te
d
to

gr
ay

m
at
te
r
in
cr
ea
se
.V
A
L/
M
ET

he
te
ro
zy
go
te

(C
on

ti
nu

es
)

1940 Katrin Charlet et al.

Ta
bl
e
1.

(C
on

tin
ue
d)

M
et
ho
d

St
ud
y

St
ud
y
de
si
gn

N
eu
ro
im
ag
in
g

Pa
ra
di
gm

O
th
er
te
st
s

Fo
llo
w
-u
p

pe
ri
od

A
ge

St
ud
y
sa
m
pl
e

M
ai
n
fi
nd
in
gs

Cr
os
s-
se
ct
io
na

l/
pr
o
sp
ec
tiv
e

co
ho

rt
st
ud

Ta
qm

an
ge
no

ty
pi
ng

as
sa
y;
W
A
IS

II
I

Co
nt
ro
ls
:m

ea
n
47

.9
SD

(7
)

(A
D
de
to
xi
fi
ed
/

lo
ng

itu
di
na

l↔
LD

)
as
so
ci
at
ed

w
ith

w
hi
te

m
at
te
r
in
cr
ea
se
s.
G
ra
y

m
at
te
r
vo
lu
m
e
in
cr
ea
se

s
co
rr
el
at
ed

po
si
tiv
el
y

to
ne
ur
oc
og
ni
tiv
e
m
ea
su
re

in
cr
ea
se
s

Pf
ef
fe
rb
au

m
et
al
.1

99
5

Pr
os
pe
ct
iv
e

co
ho

rt
st
ud

y
M
R
I/
vo
lu
m
et
ri
c

da
ta

N
on

e
N
on

e
U
p
to

12
m
on

th
s

A
D
:m

ea
n
45

.0
SD

(1
0.
9)

Co
nt
ro
ls
:m

ea
n
45

.3
SD

(1
4.
2)

n
m
al
e
=
11

6
(A
D
de
to
xi
fi
ed
/

lo
ng

itu
di
na

l
↔

co
nt
ro
ls
)

Fr
om

(1
)
to

(2
)
sc
an

,A
D
gr
ou

p
sh
ow

ed
de
cl
in
es

in
CS

F
vo
lu
m
es

of
la
te
ra
lv
en
tr
ic
le
s

an
d
po
st
er
io
r
co
rt
ic
al
su
lc
i,
an

d
an

in
cr
ea
se

in
an

te
ri
or

co
rt
ic
al
gr
ay

m
at
te
r

vo
lu
m
e.
Fr
om

(2
)
to

(3
)

sc
an

th
ir
d
ve
nt
ri
cu
la
r

vo
lu
m
es

de
cr
ea
se
d
in

th
e
ab
st
ai
ne
rs
re
la
tiv
e
to

th
e
re
la
ps
er
s
an

d
co
nt
ro
ls
;c
or
tic
al
w
hi
te

m
at
te
r

vo
lu
m
e
de
cr
ea
se
d
in

th
e
re
la
ps
er
s.
In

th
e

re
la
ps
er
s
al
co
ho

lc
on

su
m
pt
io
n
pr
ed
ic
te
d
la
te
r

vu
ln
er
ab
ili
ty

to
w
hi
te

m
at
te
r
vo
lu
m
e
de
cl
in
e

an
d
th
ir
d
ve
nt
ri
cu
la
r
en
la
rg
em

en
t
w
ith

re
la
ps
e

Pf
ef
fe
rb
au

m
et
al
.2

00
1

Cr
os
s-
se
ct
io
na

l
M
R
I/
vo
lu
m
et
ri
c

da
ta

N
on

e
LD

H
N
on

e
A
D
m
al
e:
m
ea
n
43

.4
SD

(8
.4
)
A
D

fe
m
al
e:
m
ea
n
41

.7
SD

(9
.5
)

Co
nt
ro
ls
m
al
e:
m
ea
n

44
.6

SD
(1
1.
4)

Co
nt
ro
ls
fe
m
al
e:

m
ea
n4

2.
9

SD
(1
3.
4)

n
m
al
e
=
92

,
n
fe
m
al
e
=
79

(A
D
de
to
xi
fi
ed

m
al
e/
fe
m
al
e
↔

he
al
th
y
co
nt
ro
ls

m
al
e/
fe
m
al
e)

Le
ss
br
ai
n
sh
ri
nk

ag
e
w
as

fo
un

d
am

on
g
al
co
ho

lic

w
om

en
th
an

am
on

g
al
co
ho

lic
m
en

Pf
ef
fe
rb
au

m
et
al
.2

01
4

Pr
os
pe
ct
iv
e

co
ho

rt
st
ud

y
M
R
I/
D
TI
/
TB

SS
N
on

e
Se
lf-
re
po
rt
ed

dr
in
ki
ng

hi
st
or
ie
s

U
p
to

8
ye
ar
s

A
D
:m

ea
n
44

.3
SD

(9
.2
)

Co
nt
ro
ls
:m

ea
n

43
.0

(1
0.
1)

n
m
al
e
=
52

,
n
fe
m
al
e
=
51

FA
of
A
D
lo
w
er

th
an

th
at

of
he
al
th
y
co
nt
ro
ls
.

R
el
ap
si
ng

A
D
sh
ow

ed
co
nt
in
ue
d
w
or
se
ni
ng

,
w
he
re
as

ab
st
ai
ni
ng

A
D
sh
ow

ed
im

pr
ov
em

en
t

in
fi
be
r
in
te
gr
ity
.F
A
tr
aj
ec
to
ri
es

of
re
la
ps
er
s

ex
hi
bi
te
d
fa
st
er

ag
in
g
re
la
tiv
e
to

co
nt
ro
ls
,

w
he
re
as

th
e
tr
aj
ec
to
ri
es

of
ab
st
ai
ne
rs
sh
ow

ed
im

pr
ov
em

en
t
to
w
ar
ds

no
rm

al
ity

(C
on

ti
nu

es
)

1941

Ta
bl
e
1.

(C
on

tin
ue
d)

M
et
ho
d

St
ud
y

St
ud
y
de
si
gn

N
eu
ro
im
ag
in
g

Pa
ra
di
gm

O
th
er
te
st
s

Fo
llo
w
-u
p

pe
ri
od

A
ge

St
ud
y
sa
m
pl
e

M
ai
n
fi
nd
in
gs

R
ui
z
et
al
.

20
13

Cr
os
s-
se
ct
io
na

l
M
R
I/
vo
lu
m
et
ri
c

da
ta

N
on

e
N
on

e
A
D
:m

ea
n
53

.9
SD

(1
1)

Co
nt
ro
ls
:m

ea
n
53

.9
SD

(1
2.
4)

n
=
44

(A
D

de
to
xi
fi
ed

m
al
e/
fe
m
al
e
↔

he
al
th
y
co
nt
ro
ls

m
al
e/
fe
m
al
e)

Fe
m
al
e
A
D
sh
ow

ed
st
ro
ng

er
po
si
tiv
e
as
so
ci
at
io
ns

be
tw

ee
n
so
br
ie
ty

du
ra
tio

n
an

d
w
hi
te

m
at
te
r

vo
lu
m
e
th
an

m
en

in
fi
rs
t
ye
ar

of
ab
st
in
en
ce
.

M
en

sh
ow

ed
hi
s
as
so
ci
at
io
n
m
or
e
so

th
an

w
om

af
te
r

1
ye
ar

of
ab
st
in
en
ce

Sa
m
et
ie
t
al
.

20
11

Cr
os
s-
se
ct
io
na

l
M
R
I/
vo
lu
m
et
ri
c

da
ta

N
on

e
C-
D
IS

N
on

e
Lo
ng

-t
er
m

ab
st
in
en
t

A
D
:m

ea
n
46

.6
SD

(6
.7
)

Co
nt
ro
ls
:m

ea
n
45

.6
SD

(6
.5
)

n
m
al
e
=
53

,
n
fe
m
al
e
=
47

(lo
ng

-t
er
m

ab
st
in
en
t

A
D
↔

he
al
th
y

co
nt
ro
ls
)

M
in
im

al
di
ffe
re
nc
es

in
su
bc
or
tic
al
st
ru
ct
ur
e

vo
lu
m
es

be
tw

ee
n
lo
ng

–
te
rm

ab
st
in
en
t
A
D
an

d
co
nt
ro
ls
.I
n
A
D
gr
ou

p
di
ffe
re
nc
es

in
vo
lu
m
e

as
so
ci
at
ed

w
ith

cu
rr
en
t
or

lif
e-
tim

e
ps
yc
hi
at
ri
c
di
ag
no

si
s

Se
go
bi
n
et
al
.

20
14

Cr
os
s-
se
ct
io
na

l/
pr
os
pe
ct
iv
e

co
ho

rt
st
ud

M
R
I/

te
ns
or

-b
as
ed

m
or
ph

om
et
ry

N
on

e
N
on

e
6 m
on

th
s

A
D

pa
tie
nt
s:
m
ea
n
44

.4
SD

(6
.0
7)

Co
nt
ro
ls
:m

ea
n
46

.7
SD

(4
.2
5)

n
m
al
e
=
37

n
fe
m
al
e
=
2

(A
D
↔

he
al
th
y

co
nt
ro
ls
;

A
D
/l
on

gi
tu
di
na

R
ed
uc
ed

th
al
am

us
vo
lu
m
e
as
so
ci
at
ed

w
ith

re
la
ps
e.
R
ec
ov
er
y
of
ce
re
be
llu

m
,s
tr
ia
tu
m

an
d

ci
ng

ul
at
e

gy
ru
s

ev
en

in
A
D
pa
tie
nt
s
w
ith

m
od
er
at
e
al
co
ho

l
in
ta
ke

bu
t

ne
g.
Co

rr
el
at
ed

to
am

ou
nt

of
al
co
ho

lc
on

su
m
ed

ov
er

6
m
on

th
s
in

A
D
gr
ou

p
va
n
Ei
jk
et
al
.

20
13

Cr
os
s-
se
ct
io
na

l/
pr
os
pe
ct
iv
e

co
ho

rt
st
ud

M
R
I/
V
BM

N
on

e
N
on

e
2
w
ee
ks

A
D
:m

ea
n
47

.0
SD

(1
0.
1)

Co
nt
ro
ls
:m

ea
n
45

.3
SD

(1
1.
9)

n
m
al
e
=
82

,
n
fe
m
al
e
=
22

(A
D
de
to
xi
fi
ed
/

lo
ng

itu
di
na

l
↔

he
al
th
y

co
nt
ro
ls
)

G
ra
y
m
at
te
r
vo
lu
m
e
(c
in
gu

la
te

gy
ru
s,
m
id
dl
e

an
d
pr
e-
ce
nt
ra
lp
re
-fr
on

ta
lg
yr
i,
ce
re
be
llu

m
,

in
su
la
)
sm

al
le
r
in

A
D
co
m
pa
re
d
w
ith

co
nt
ro
l

gr
ou

p
at

ba
se
lin

e.
Si
gn

ifi
ca
nt

re
co
ve
ry

af
te
r

2
w
ee
ks

of
ab
st
in
en
ce

Vo
lk
ow

et
al
.

19
94

Pr
os
pe
ct
iv
e

co
ho

rt
st
ud

y
PE

T
N
on

e
N
on

e
U
p
to

2
m
on

th
s

A
D
:m

ea
n
41

.0
SD

(8
)

Co
nt
ro
ls
:m

ea
n
38

.4
SD

(3
)

n
m
al
e
=
20

(A
D
de
to
xi
fi
ed
/

lo
ng

itu
di
na

l
↔

he
al
th
y

co
nt
ro
ls
)

et
ab
ol
is
m

in
cr
ea
se
d
pr
ed
om

in
an

tly
in

fi
rs
t

30
da
ys

of
ab
st
in
en
ce
.I
nc
re
as
es

m
ai
nl
y
in

pr
e-
fr
on

ta
lr
eg
io
ns
.M

et
ab
ol
is
m

co
rr
el
at
ed

ne
ga
tiv
el
y
to

al
co
ho

lu
se

(C
on

ti
nu

es
)

1942 Katrin Charlet et al.

Ta
bl
e
1.

(C
on

tin
ue
d)

M
et
ho
d

St
ud
y

St
ud
y
de
si
gn

N
eu
ro
im
ag
in
g

Pa
ra
di
gm

O
th
er
te
st
s

Fo
llo
w
-u
p

pe
ri
od

A
ge

St
ud
y
sa
m
pl
e

M
ai
n
fi
nd
in
gs

Vo
lk
ow

et
al
.

20
06

Cr
os
s-
se
ct
io
na

l
PE

T
N
on

e
M
ul
ti-
di
m
en
si
on

al
pe
rs
on

al
ity

qu
es
tio

nn
ai
re

N
on

e
FH

P:
m
ea
n

24 SD
(3
)

FH
N
:m

ea
n

26 SD
(4
)

n
m
al
e
=
28

n
fe
m
al
e
=
2

FH
P
gr
ou

p
ha

d
si
gn

ifi
ca
nt
ly
hi
gh

er
m
ea
su
re
s

of
D
2
re
ce
pt
or

av
ai
la
bi
lit
y
in

ca
ud

at
e
an

d
V
S.

FH
P
su
bj
ec
ts
ha

d
lo
w
er

m
et
ab
ol
is
m

in
hi
pp
oc
am

pa
lg
yr
us
,t
em

po
ra
lp
ol
e
an

d
ce
re
be
llu

m
.M

et
ab
ol
is
m

in
pr
e-
fr
on

ta
lc
or
te
x

in
cr
ea
se
d
in

FH
P.
Po

si
tiv
e
co
rr
el
at
io
n
be
tw

ee
n

st
ri
at
al
D
2
re
ce
pt
or

av
ai
la
bi
lit
y
an

d
m
et
ab
ol
is
m

in
O
FC

,v
en
tr
al
ci
ng

ul
at
e
gy
ru
s
an

d
PF

C.
D
2

re
ce
pt
or

an
d
m
et
ab
ol
is
m

in
le
ft
O
FC

w
as

co
rr
el
at
ed

po
si
tiv
el
y
to

po
si
tiv
e
em

ot
io
na

lit
y

W
an

g
et
al
.

20
16

Pr
os
pe
ct
iv
e

co
ho

rt
st
ud

y
M
R
I/
vo
lu
m
et
ri
c

da
ta

(C
T,
SA

)

N
on

e
N
on

e
14

da
ys

A
D
:m

ea
n

47
.0
2

SD
(1
0)

Co
nt
ro
ls
:m

ea
n

46
.6
5

SD
(1
2.
37

)

n
m
al
e
=
47

n
fe
m
al
e
=
12

(A
D
↔

co
nt
ro
ls
)

Lo
w
er

su
bc
or
tic
al
vo
lu
m
es

in
A
D
in

pu
ta
m
en
,

N
A
,a
m
yg
da
la
an

d
hi
pp
oc
am

pu
s.
N
o
su
bc
or
tic
al

vo
lu
m
e
re
ga
in

at
FU

.C
or
tic
al
vo
lu
m
e
re
co
ve
ry

dr
iv
en

by
an

in
cr
ea
se

in
CT

.M
or
e
CT

re
du

ct
io
n

an
d
re
co
ve
ry

in
su
lc
ic
om

pa
re
d
to

gy
ri

W
ei
la
nd

et
al
.2

01
2

Cr
os
s-
se
ct
io
na

l
fM

R
I/
PP

I
N
-b
ac
k
ta
sk

Ca
lif
or
ni
a
Ch

ild
Q
-S
or
t

N
on

m
ea
n
20

.2
SD

(1
.2
)

n
m
al
e
=
43

,
n
fe
m
al
e
=
24

(p
ar
en
ta
l

al
co
ho

lis
m

↔
no

pa
re
nt
al
al
co
ho

lis
m
)

H
ig
h
re
si
lie
nc
e:
co
rr
el
at
ed

ne
ga
tiv
el
y
to

ST
N
,

pa
lli
du

m
ac
tiv

at
io
n;

co
rr
el
at
ed

po
si
tiv
el
y
to

lo
w
er

le
ve
ls
of
su
bs
ta
nc
e
us
e,
fe
w
er

al
co
ho

l
pr
ob
le
m
s
an

d
be
tt
er

w
or
ki
ng

m
em

or
y

pe
rf
or
m
an

ce
Ya

u
et
al
.

20
12

Cr
os
s-
se
ct
io
na

l
fM

R
I

M
ID

ta
sk

D
ri
nk

in
g
an

d
dr
ug

hi
st
or
y

N
on

e
CO

A
s:

m
ea
n
20

.1
2

SD
(1
.2
)

Co
nt
ro
l:
m
ea
n
20

.1
SD

(1
.3
)

n
m
al
e
=
24

,
n
fe
m
al
e
=
16

(C
O
A
s
↔

co
nt
ro
ls
)

R
es
ili
en
ce

re
la
te
d
to

re
du

ce
d
ve
nt
ra
ls
tr
ia
tu
m

ac
tiv

at
io
n
in

CO
A
s

Za
ki
ni
ae
iz

et
al
.2

01
6

Pr
os
pe
ct
iv
e

co
ho

rt
st
ud

y
fM

R
I/
IC
D

In
di
vi
du

al
iz
ed

im
ag
er
y

pa
ra
di
gm

N
on

e
90

da
ys

St
ud

y
1

A
D
:m

ea
n
37

.7
3

SD
(1
.1
6)

St
ud

y
1

n
m
al
e
=
35

,
n
fe
m
al
e
=
10

A
D
sh
ow

ed
de
cr
ea
se
d
ci
ng

ul
at
e
co
nn

ec
tiv

ity
in

re
sp
on

se
s
to

al
co
ho

la
nd

st
re
ss
cu
es

co
m
pa
re
d

ne
ut
ra
lc
ue

s.
W
ea
ke
r
co
nn

ec
tiv

ity
in

A
CC

(C
on

ti
nu

es
)

1943

Ta
bl
e
1.

(C
on

tin
ue
d)

M
et
ho
d

St
ud
y

St
ud
y
de
si
gn

N
eu
ro
im
ag
in
g

Pa
ra
di
gm

O
th
er
te
st
s

Fo
llo
w
-u
p

pe
ri
od

A
ge

St
ud
y
sa
m
pl
e

M
ai
n
fi
nd
in
gs

St
ud

y
2

A
D
:m

ea
n
35

.9
7

SE
(0
.0
8)

Co
nt
ro
ls
:m

ea
n

34
.4
7

SE
(1
.5
5)

St
ud

y
2

n
m
al
e
=
43

n
fe
m
al
e
=
17

an
d
M
CC

du
ri
ng

ne
ut
ra
lc
ue

ex
po
su
re

re
la
te
d

to
lo
ng

er
ab
st
in
en
ce

CC
co
nn

ec
tiv

ity
du

ri
ng

al
co
ho

lc
ue
s
co
m
pa
re
d
to

st
re
ss
cu
e
co
nd

iti
on

s
co
rr
el
at
ed

po
si
tiv
el
y
to

lo
ng

er
tim

e
to

re
la
ps
e

Ci
ng

ul
at
e
co
nn

ec
tiv

ity
si
gn

ifi
ca
nt
ly
di
ffe
re
nt

be
tw

ee
n
gr
ou

ps
.A

D
sh
ow

ed
re
du

ce
d
ci
ng

ul
at
e

co
nn

ec
tiv

ity
du

ri
ng

al
co
ho

la
nd

st
re
ss
cu
es

an
d
in
cr
ea
se
d
ci
ng

ul
at
e
co
nn

ec
tiv

ity
du

ri
ng

ne
ut
ra
lc
ue
s.
A
D
m
or
e
si
m
ila
r
to

co
nt
ro
ls
in

ci
ng

ul
at
e
co
nn

ec
tiv

ity
ha

d
lo
ng

er
ab
st
in
en
ce

an
d
be
tt
er

re
co
ve
ry

M
R
I=

ag
ne
tic

re
so
na

nc
e
im

ag
in
g;
D
TI

=
di
ffu

si
on

te
ns
or

im
ag
in
g;
A
D
=
al
co
ho

l-d
ep
en
de
nt
;S
D
=
st
an

da
rd

de
vi
at
io
n;
↔

=
ve
rs
us
;F
A
=
fr
ac
tio

na
la
ni
so
tr
op
y;
R
D
=
ra
di
al
di
ffu

si
vi
ty
;P
ET

=
po
si
tr
on

em
is
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1944 Katrin Charlet et al.

observed in the resilient group. The vulnerable group
showed more activation of dorsomedial pre-frontal cortex
(PFC) and less activation of ventral striatum and extended
amygdala. Increased dorsomedial PFC activation and de-
creased subcortical activation were linked to greater exter-
nalizing behavior [35].

Another study, by Charlet et al. [11], assessed brain re-
sponses during a face-matching task to investigate implicit
emotion processing among detoxified AD and healthy con-
trols. Greater activation of anterior cingulate cortex (ACC)
during the processing of aversive faces correlated with lon-
ger subsequent abstinence and less subsequent binge
drinking during the subsequent 6 months. This ACC re-
sponse may indicate a possible resilience/recovery factor,
presumably reflecting successful emotion regulation and
error monitoring [12].

Taken together, findings derived from the fMRI studies
indicate potentially important roles of basal ganglia and
pre-frontal brain. While two of three studies in COAs point
to an increased resilience associated with less task-elicited
neural activation within the basal ganglia [36,37], those
in AD patients showed that greater PFC engagement may
underpin resilience against relapse in patients during early
abstinence (cf. [10–12,33,38,41]).

Resilience and recovery markers detected by studies using
PET

Despite the wealth of pre-clinical and clinical evidence
about dopaminergic function in addiction [41–43], studies
focusing on resilience and recovery in alcohol dependence
are sparse [16,44–47].

Two 11C-raclopride PET studies measured D2/D3 do-
pamine receptor availability in healthy young adults with
either a positive (FHP) or a negative (FHN) family history
of AD pre and post an amphetamine challenge. In both,
unaffected FHP displayed a higher level of striatal D2 [46]
and D2/D3 [44] dopamine receptor availability in striatal
regions compared with FHN. Interestingly, while amphet-
amine resulted in the expected increase in dopamine and
positive subjective effects in FHN individuals, this was not
found in FHP individuals [44]. Such results support the hy-
pothesis that high D2 receptor availability may serve as a
protective biomarker compensating for the higher
inherited vulnerability ([46], p. 1004). Further, striatal
D2 receptor availability in FHPwas also linked significantly
to pre-frontal glucosemetabolismwhich, in turn, was asso-
ciated positively with emotional positivity [46]. This sug-
gests that dopaminergic modulation of cognitive control
over emotional responses protects against developing alco-
hol addiction.

In AD, PET studies have demonstrated lower levels of
DA receptor availability and DA release compared with
healthy controls (e.g. [16,43].

Two early studies used PET to assess recovery of brain
glucose metabolism during abstinence in AD. One reported
a significant increase in brain glucose metabolism predom-
inantly within 16–30 days, especially in frontal brain re-
gions, whereas low metabolism persisted in the basal
ganglia [47]. Another study showed that the four patients
who remained abstinent compared with two who relapsed
showed partial recovery in brain metabolismwithin frontal
cortex areas as well as significant improvement in general
cognitive and executive functioning [45].

In sum, PET studies concentrating on recovery and re-
silience in alcohol dependence are sparse, but suggest that
differences in dopaminergic function may result in vulner-
ability or resilience depending on the genetic background
of an individual. While high D2/D3 receptor availability
may serve as protective non-alcoholic FHP, low D2 receptor
availability may render individuals more vulnerable to al-
cohol abuse. Further, similarly to fMRI studies, normaliza-
tion in metabolism is associated with abstinence.

Resilience and recovery markers detected by sMRI

We found 21 relevant studies investigating changes in
brain structure during abstinence ([21,48–67], cf.
Table 1).

Smaller gray matter (GM) and white matter (WM) vol-
umes have been found throughout the brain and were as-
sociated with relapse within 6 months after detoxification
[65]. Interestingly, increases in brain volumes were seen
even in those patients with moderate alcohol consumption
(< 10 g of pure alcohol per day) after detoxification. This indicates beneficial effects of reduced alcohol consumption in AD who are not ready or able to become abstinent [65]. Some brain areas appeared to recover faster, such as the cingulate gyrus in comparison to the fusiform gyrus, which led the authors to propose that recovery in one area trig- gers recovery in other connected areas.

Along with ventricular volume recovery, significant
volume increases in subcortical GM were observed mainly
within the first month of abstinence in AD compared with
the following 7.5 months of abstinence [53,62]. Indeed,
frontal GM normalized to control level, although total cor-
tical and regional GM volumes (e.g. parietal, temporal, tha-
lamic) remained lower after 7.5 months of abstinence [53].
Similarly, Gazdzinski et al. [54] showed that recovery of
brain tissue was six times faster during the first 3 weeks
of abstinence than during the subsequent 12months of ab-
stinence [54]. Brain volume gain was more prominent in
heavier drinkers with less tissue at baseline [54]. Partial re-
covery of cortical thickness was also found after only
2 weeks of sobriety with full normalization seen in medial
OFC and rostral ACC. Regeneration of sulci was more pro-
nounced here in all affected brain areas than in gyri [67].
Another study showed significant normalization of

1945

hippocampal GM volume within the first 2 weeks of absti-
nence in AD, especially in those with greater withdrawal
severity at baseline [21].

Other studies have also found smaller tissue volumes
associated with greater previous alcohol intake [21,51],
e.g. in frontal and temporal cortices [51].

Mon et al. [58] modelled longitudinal brain structure
changes mathematically in AD patients, and found that
in those with greater GM/WM atrophy at baseline (usually
directly after detoxification), greater dynamic neuroplastic
changes occurred within the first month of cessation of
alcohol intake [58]. Using deformation-based morphome-
try, two studies by Cardenas et al. reported that 1week after
detoxification patients had smaller frontal and temporal
GM and WM volume, but those who remained abstinent
regained WM and GM tissue in cortical and subcortical
regions after 6–9 months [51]. Apart from structural GM
reductions in AD patients relative to controls, subsequent
abstainers and relapsers showed different patterns of GM
volume loss [50]. In particular, future relapsers showed
reduced GM in bilateral OFC in relation to abstainers,
which might indicate conservation of GM in this region
to benefit recovery in AD patients [50]. In terms of
subcortical regions, Deshmukh et al. [52] also discovered
regional volume atrophy in caudate, putamen and nucleus
accumbens in AD men abstinent for approximately
204 days compared to healthy controls, with greater
volume deficits in the nucleus accumbens seen in themore
recently abstinent patients [52].

Interestingly, some studies did not find significant WM
differences between AD and controls [53,67], although
WM volume gain has been detected with abstinence. DTI
is probably more sensitive to WM change than structural
MRI, as detailed architecture of white matter tissue can
be analyzed by visualizing molecule diffusion patterns
[48,55]. For example, a longitudinal study utilizing DTI
reported improvement of white matter fiber tract
coherence and myelin integrity in the corpus callosum of
recently detoxified AD during 1 year of abstinence [48].
Notably, these WM indices in AD no longer differed from
controls [48]. However, there was no relationship between
theseWMchanges with normalization of workingmemory
function in the AD [48]. Similarly, normalization of whole
brain fiber tract integrity was observed in abstainers with
multiple scans during the course of 8 years, while relapsers
showed accelerated microstructural damage of the white
matter, i.e. faster aging [61].

Potential modulators

One potential mechanism underlying recovery could be
related to genotype, such as has been shown for brain-
derived neurotropic factor (BDNF Val66Met (rs6265)
polymorphism), a promyelination neurotropin which

serves as a neurobiological marker of neuronal growth
and maintenance [59,68]. AD who are homozygous for
Val demonstrated frontal, parietal and thalamic GM
increase during the first 5 weeks of abstinence and greater
hippocampal volume recovery during 7 months of
sobriety [57]. This was not seen in Val/Met heterozygotes,
although both Val/Val and Val/Met carriers showed tissue
gains in temporal GM [59]. Interestingly, Mon et al. [59]
observed significant increases in frontal WM volumes only
in Val/Met heterozygotes but not in Val homozygotes, as
well as subcortical volume decreases in caudate GM in
Val but not Met carriers. Furthermore, Hoefer et al. found
hippocampal volume changes to be associated with
improvements in visuospatial memory performance only
in BDNF Val homozygotes (but not in Met carriers) [57].

Structural atrophy and recoverymay also vary between
genders. Here, a recent study observed that the duration
and quantity of heavy drinking was related significantly
to WM reductions that differed regionally between male
and female AD [63]. Furthermore, stronger positive associ-
ations between duration of abstinence and WM volume
were seen in women, while men showed this association
more so than women after 1 year of sobriety [63],
confirming gender-specific recovery processes [60,69]. An-
other gender-driven GM difference indicating heightened
vulnerability to brain atrophy in women was observed by
Sameti and coworkers [64]: long-term abstinent alcohol-
dependent women (mean = 6.3 years) displayed smaller
nucleus accumbens volumes compared to healthy women
and male controls. However, no significant gender effects
have also been detected, such as in GM increases and cere-
brospinal fluid (CSF) decreases in some brain areas ob-
served within the first 2 weeks of alcohol abstinence [66].

Comorbid nicotine dependence is also important to con-
sider, because up to 80% of AD smoke [70,71] and is itself
neurotoxic [55,56]. Evidence is, however, inconsistent.
While non-smoking AD revealed faster microstructural
recovery (i.e. in frontal, temporal, parietal and occipital
lobes) compared with smoking alcohol-dependent patients,
faster macrostructural increases in frontal and temporal
WM volume were seen in smokers only, with no changes
of metabolic concentrations in both groups [55]. Contrary
to those WM volume findings, smoking AD were found to
show less recoverywith increasing age, especially in frontal
(and total cortical) GM volume. Moreover, beneficial effects
regarding processing speed were associated with the found
morphological GM increases, but again in non-smoking
AD only [53]. Another study could not support anyof these
smoking-dependent recovery findings [57].

Studying neurobiological underpinning of resilience
and its predication of problematic alcohol use, a recent
European adolescent study by Burt et al., including 1870
teens (average age = 14.56 years), identified elevated GM
volumes in pre-frontal areas (BA 11, 10, 6) in resilient

1946 Katrin Charlet et al.

adolescents (high competence in academic, social and
emotional domains despite experiencing adverse life-time
events in the past) compared with other peers, which also
correlated negatively with problematic drinking, thus
potentially preventing those teens from future AD
development by the PFC regulating behavior with
protective executive control [49].

In summary, structural neuroimaging studies demon-
strate beneficial plasticity effects throughout the brain of
AD during short-, medium- and long-term abstinence,
even when patients lower their alcohol consumption to
only amoderate level. However, recovery of neuronal tissue
(GM versus WM or sulci versus gyri) appears to recover
variably across regions (frontal areas first in early
abstinence) and at different time rates.

DISCUSSION AND FUTURE AVENUES FOR
RESEARCH

Neuroimaging research has been key in shedding light
upon possible dysfunctional domains and affected brain
regions in AD and their potential of recovery after alcohol
cessation (or reduction). In summary, lower dopamine
receptor availability as shown in PET studies related to
craving in AD patients [16] which, in turn, has been
associated with relapse [10,15]. Moreover, fMRI studies
have linked deficient reward and emotion processing to
negative treatment outcomes, while structural MRI studies
have shown that conserved PFC morphology in particular
is linked to resilience and abstinence in AD patients.
Altogether, investigations of morphology identified specific
factors that influenced these observed brain recovery
processes and should be considered in future studies on
brain recovery in AD, e.g. genotype-dependent neuronal
(re)growth [57,59], gender-specific neural recovery effects
[52,60,63,64,66,69], additional smoking influences
[56,57,72] or adolescent alcohol abuse [49].

Overall, the reviewed research suggests that volumetric
brain tissue recovery processes follow non-linear trajecto-
ries, suggesting that faster reconstitution of regionally spe-
cific brain areas during early abstinence might trigger the
recovery of associated regions consecutively. Consistent
with these results, additional life-time and current psychi-
atric diagnoses (such as anxiety disorders, including post-
traumatic stress disorder or externalizing disorder) have
been identified as critical factors that interfere with mor-
phometric brain recovery in alcohol dependence [64].

However, in reviewing these studies, onemust be aware
of some methodological diversity when trying to compare
or summarize the existing study findings. Here, in addition
to replication studies, meta-analyses that weigh findings by
their effect sizes could be employed to preserve false positive
findings or small effect-sized results from overestimation.
Also, the usage of different self-report instruments (without

verification by collateral information) to assess measures of
alcohol consumption (e.g. life-time drinking amount, onset
and pattern of drinking) should be regarded in light of a
potential bias towards socially desirable answers, which
might cause underestimation of reported drinking due to
embarrassment (e.g. [10–12]).

Future studies that aim at systematic investigation of
factors that mediate recovery and resilience are the focus
of some system-oriented approaches (cf. [73]). On a func-
tional level, different domains play a crucial role for the
development and maintenance of addictive disorders and
thus are important factors for recovery, on one hand, and
resilience on the other hand: executive functions, including
inhibitory control andworkingmemory, reward processing
as well as processing of emotional stimuli, are potential tar-
gets for diagnosis, prognosis and therapy [10–12].

However, until now most imaging studies in this field of
research have been cross-sectional, and there is a clear
necessity for longitudinal studies into the characterization
of disease trajectories, progression rates andmarkers for re-
covery and resilience to inform treatment options. Indeed,
cohort studies as carried out by the IMAGEN consortium
(e.g. [74]) can shed light on potential future research direc-
tions; here, researchers from multiple European countries
aim to identify neuronal predictors for developing addictive
disorders as well as potential targets for AD prevention ap-
proaches. Additional application of machine learning algo-
rithms may further help to generate models of current and
future alcohol misuse by incorporating the assessed brain
processes and structures, personality as well as cognitive
factors, environmental conditions and finally genetic
markers [74]. Regarding the identification of intermediate
phenotypes of resilience, more studies are clearly needed,
as this field of neurobiological research is somewhat unex-
plored. Here, investigations of individuals with andwithout
heightened genetic or environmental risk forADareneeded
to help disentangling resilience markers from vulnerability
risk factors. Recent studies also introduced epigenetic
mechanisms in AD, adding valuable information about
modulating processes to the genotype–phenotype interac-
tion [75]. Those investigations shoulduse appropriate study
designs, such as comparisons of (i) adolescent/young adult
COAswith versus without AD on their own or (ii) adult AD
patients versus adult individuals without AD, but with a
positive family history of AD (e.g. first-degree relatives of
AD patients) versus healthy individuals without familiar
or own AD (as in the recent ongoing prospective cohort
study, e:Med SysMedAlcoholism [73]), respectively. Clearly,
findings testing neurobiological traits of vulnerability to AD
(cf. [76–78]) may give rise to new hypotheses and research
questions, but caution is warranted that vulnerability
markers are not simply the opposite of resilience. Rather,
vulnerability demonstrates conditions and aberrations
which exist before AD and may facilitate developing AD

1947

but are not only caused by, for example, neurotoxic alcohol
effects. Conversely, resilience refers to factors that promote
good treatment outcome despite negative effects of long-
term alcohol intake on neural structure and function.

Further, future research should not only continue to
strengthen knowledge concerning recovery processes and
resilience markers (in high-risk groups without alcohol
dependence as well as in already affected AD) but should
also address whether they can be translated to various
drugs of abuse in terms of general markers or can be char-
acterized specifically for different substance classes.

Declaration of interests

None.

Acknowledgements

This work has been supported by the German Ministry of
Education and Research (BMBF; 01ZX1311E and
01ZX1311D/e:Med-program alcohol addiction, Spanagel
et al. 2013; and in part by 01EE1406A) and the German
Research Foundation (DFG; CH 1936/1–1; FOR 16/17;
HE2597 13–1/2, 14–1/2, 15–1/2, Excellence Cluster
Exc 257).

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Supporting Information

Additional supporting information may be found online in
the Supporting Information section at the end of the
article.

Appendix S1 Overview of the systematic literature re-
search: imaging resilience and recovery in alcohol depen-
dence.

1950 Katrin Charlet et al.

This document is a scanned copy of a printed document. No warranty is given about the
accuracy of the copy. Users should refer to the original published version of the material.

British Journal of Addiction (1990) 85,

883

-890

RESEARCH REPORT

Discrimination on the grounds of diagnosis

MICHAEL FARRELL, MB, MRCP, MRCPsych,’ & GLYN LEWIS, MA,
MSc, MRCPsych,2

^Addiction Research Unit & ^General Practice Research Unit, Institute of Psychiatry,
De Crespigny Park, London SE5 8AF, United Kingdom

Summary
The study used the methodology of randomly allocating case vignettes to a sample of British consultant
psychiatrists to assess the influence of a past diagnosis of alcohol dependence on present treatment attitudes. The
case vignettes either did or did not include the previous diagnosis of alcohol dependence and the sex of the ‘case’
was also randomized. Psychiatrists receiving the vignette with the diagnosis of alcohol dependence were more
likely to rate the patient as difficult, annoying, less in need of admission, uncomplaint, having a poor prognosis
and more likely to be discharged from follow-up. There was minimal sex difference. Psychiatrist with a special
interest in addictions regarded people with a past diagnosis of alcohol dependence as less difficult to manage
than their non-specialist colleagues. The implications for education and treatment are discussed.

Introduction
The high rates of alcohol-related problems among
the medical profession might suggest that they
would adopt a sympathetic approach to other
individuals likewise afflicted. However, most com-
mentators have remarked that physicians usually
regard the alcoholic patient as an undesirable client.

Much of the older research now appears weak-
ened by its preoccupation with the ‘disease model’
of alcoholism, lack of attempts to elicit attitudes to
control subjects and the use of vague semantic
differentials. For instance. Fisher et al. (1975) asked
subjects to “rate alcoholic and average persons” on
16 semantic differentials including items such as
‘passive-active’ and ‘delicate-rugged’. Their results
indicated that doctors regarded alcoholics as more
‘hopeless’ than average persons, but this is not a
surprising result. MacDonald & Patel (1975) did
compare psychiatrists attitudes to alcoholics with

Correspondence to: Michael Farrell, Addiction Research Unit,
101 Denmark Hill, London SE5 8AF, United Kingdom.

Other diagnoses but used a single ‘favourable-
unfavourable’ continuum, so illustrating that psy-
chiatrists had more ‘favourable’ views about neu-
roses than alcoholism. It is still possible that the
views expressed in these studies could have been
confounded, for instance, by the sex or age of the
stereotypical patient which was not specified in their
design.

One theme of the research on the topic has been
the influence of proper training and education on
attitudes towards treatment of the alcoholic (Mogar
et al., 1969; Geller et al., 1989). For instance,
Cartwright (1980) found that experience of working
with alcoholics and clinical supervision were associ-
ated with positive therapeutic attitudes. This find-
ing is slightly at odds with the observation that
newly qualified doctors have less hopeful attitudes
than medical students (Fisher et al., 1975; Geller
et al., 1989) though this may be due to the
rather theoretical and idealistic attitudes of medical
students to dealing with all patients, let alone
alcoholics.

General psychiatrists receive the bulk of general

883

884 Michael Farrell & Glynn Lewis

practitioner referrals for alcohol problems and the
aims of this study were to examine the effect of the
past diagnosis of alcohol dependence on the atti-
tudes to and proposed management of a depressed
and potentially suicidal person. Psychiatrists are
also used to dealing with more problematic patients
than their medical colleagues and have received
more education about alcohol problems. Strong
(1989) for instance has argued that alcoholics break
the ‘rules’ which usually govern medical consulta-
tions, these rules are also broken by many psychia-
tric patients. Do psychiatrists show rejecting atti-
tudes to alcoholics when they are compared with
other psychiatric patients?

Though one would expect psychiatrists to know
more about alcohol problems than other physicians,
recent work shows that most registrars in psychiatric
hospitals (Farrell & David, 1988; Mitchell, 1989) do
not take a drinking history though 10% of psychia-
tric hospital admissions may have an ‘alcoholism’
diagnosis (Glass & Jackson, 1988). In a survey of
senior psychiatric registrar training in the UK
(Brook, 1974) only half reported having received
adequate training in substance misuse.

The study used the methodology of randomly
allocating case vignettes to a sample of British
consultant psychiatrists. The case vignettes either
did or did not include the previous diagnosis of
alcohol dependence and the sex of the ‘case’ was also
randomized. For convenience the case vignettes,
with the diagnosis of alcohol dependence will be
referred to as alcoholics. The vignette and methodo-
logy was similar to that employed by Lewis &
Appleby (1988) who investigated the previous
diagnosis of personality disorder.

interested in how experience mfluenced the practice
of psychiatrists and were asked to provide details
about previous qualifications and experience in
psychiatry and in other specialties. The real purpose
of the study was not explained to the subjects.

Case histories
The four case histories differed from each other in
one particular. The case contained the information
which a GP’s letter might provide about a depressed
patient. The amount of information was deliberately
restricted to encourage subjects to draw inferences
based on pre-existing attitudes. The first case
history was as follows:

A 40-year-old man with two children aged 3 and 8
IS seen in outpatients. He complains of feeling
depressed and says he has been crying on his own at
home. He is worried about whether he is having a
nervous breakdown and is requesting admission. He
has thought of killing himself by taking an overdose
of some tablets he has at home. He has taken one
previous overdose, 2 years ago, and at that time he
saw a psychiatrist who gave him a diagnosis of
alcohol dependence. He has recently gone into debt
and is concerned about how he will repay the
money. He is finding it difficult to sleep.

This case was modified slightly in the following
ways:

(1) Case 2: no previous diagnosis was

mentioned.

(2) Case 3: patient changed to female.
(3) Case 4: patient female and no diagnosis was

mentioned.

Method
Sample
Two hundred names of psychiatrists who lived in
England, Wales or Scotland were randomly selected
from the 1985 membership list of the Royal College
of Psychiatrists (approximately 10% of total, DHSS,
1987). Those who were described as registrars,
senior registrars who were retired or were listed as
being child psychiatrists were excluded from the
sample (but several child psychiatrists were in-
cluded in the sample because they were not listed as
such). Subjects were randomly allocated one of the
four brief case histories, which they were asked to
read before completing and returning an accom-
panying questionnaire. They were told that we were

Questionnaire
The subjects were asked their sex, years experi-
ence in psychiatry and to indicate any areas of
special interest from the following list: neuro-
psychiatry, social psychiatry, psychotherapy,
child/adolescent, forensic, liaison, old age and
addiction. This was used to divide the sample
between those who had an interest in addictions
and those who did not.

The questionnaire also included 20 items, ar-
ranged in a similar manner to semantic differentials,
but opposing statements rather than single words
were used. Each 6-point scale was designed to elicit
one aspect of the assessment or management of the
case. Some of the items placed more emphasis on

Discrimination on the grounds of diagnosis 885

Table 1. Comparing the responses to ‘cases’ with a previous diagnosis of alcohol dependence with those given no previous
diagnosis

Admission not indicated
Not a suicide risk
Antidepressants indicated
Trying to manipulate admission
Needs sickness certificate
Discharge from OP follow-up
Unlikely to arouse sympathy
Overdose would be attention-seeking
Would not like to have in one’s clinic
Difficult management problem
Likely to annoy you
Unlikely to improve
Cause of debts probably controllable by patient
Does not merit NHS time
Unlikely to complete course of treatment
Does not have mental illness
Children should be on ‘At Risk’ register
At risk of becoming dependent on you
Unlikely to comply with advice/treatment
Patient’s condition not severe

Higher scores indicate agreement with statement on left.

Mean
No

diagnosis
(iV=77)

3.49
3.03
3.07
2.73
2.10
1.57
2.33
3.07
2.05
2.88
1.96
2.00
3.67
2.40
2.77
2.92
2.00
3.49
2.64
3.15

Scores
Alcohol

dependence
(N=67)

3.21
3.02
2.33
2.91
2.24
1.94
2.86
3.05
2.60
3.52
2.31
2.30
4.05
2.59
3.

3.21
2.50
3.91
3.20
3.08

95% CI mean
difference

-0.20, 0.76
-0.42, 0.44

0.23, 1.25
-0 .20, 0.56
-0 .35 , 0.55
-0.04, 0.78

0.16, 0.92
-0.40, 0.36

0.14, 0.96
0.21, 1.07
0.00, 0.70

-0 .03 , 0.63
0.02, 0.74

-0 .18 , 0.56
0.08, 0.84

-0.17, 0.75
0.07, 0.93

-0 .03 , 0.87
0.23, 0.89

-0.39, 0.25

r test
P

0.25
0.96
0.005
0.37
0.54
0.08
0.007
0.92
0.01
0.004
0.05
0.08
0.04
0.32
0.02
0.22
0.03
0.07
0.001
0.68

practical management issues (e.g. antidepressant
prescription, admission) but most asked directly
about attitudes to the patient (e.g. likely to annoy,
attention-seeking etc.). A full list is given in Table
1. The items were balanced so that the more
rejecting ends of the items were unsystematically
arranged between right and left. The items were
scored so that a higher score represented responses
that were regarded as more rejecting or that
indicated lack of active treatment. For instance, a
response at the end of the scale ‘overdose would be
an attention seeking act’ scored 6 and a response at
the end ‘overdose would be genuine suicidal act’ was
scored 1.

Each subject was asked to complete the question-
naire and then choose a diagnosis from a list of
depression, anxiety, adjustment reaction, alcohol
dependence, personality disorder and neurasthenia.

Analysis
Repeated significance tests have been used and this
will increase the likelihood of a type 1 error. The
Bonferroni Criterion for this study with 20 compari-
sons would be p=0.0025.

It was difficult to present the results for all the 20

items studied here. To aid presentation, on occa-
sions, those items which proved to be statistically
significantly associated with the previous diagnosis
of alcohol dependence at the 5% level were com-
bined in to a composite score, the sum of all those
items. Items were not weighted. This composite
score did not include the item enquiring about
tricyclic antidepressant treatment, because this
could be regarded as an appropriate management
approach to the alcohol-dependent patient. Con-
structing the composite score was done purely to
illustrate results and did not form the basis of any of
the conclusions that were drawn.

Results
Sample characteristics
One hundred and forty-four of the questionnaires
were returned completed (71%) and a farther 17
(8.5%) returned uncompleted because the subjects
refused to complete the questionnaire or had retired
or moved. The randomization to the four groups
was checked using the following variables: years
psychiatric experience, sex of respondent, interest in
addictions and membership of the Royal College of
Physicians. There were no statistically significant
differences between the groups at the 5% level.

886 Michael Farrell & Glynn Lewis

The respondents had worked in psychiatry for an
average of 20.6 years (SD 7.2), 19% were women,
and 16% expressed some interest in the addictions.

Previous diagnosis of alcohol dependence
The two groups who were given the vignette that
contained the previous diagnosis of alcohol depen-̂
dence were combined and compared with the two
groups not given that diagnosis. This analysis
therefore ignored the effect of the case’s sex. The
results of the t tests are shown in Table 1. Some of
the distributions of the scores on the items were
skewed from a normal distribution. Those variables
were also analysed after being transformed by
logarithms, but there were no substantial differences
so the untransformed results are presented.

In 16 of the 20 items, those in the alcohol
dependence group were given higher scores than
those with no diagnosis. In particular, ‘cases’ once
given the diagnosis of alcohol dependence were
judged unlikely to complete the course of treatment
or to comply with advice, would not be liked in the
clinic, would not arouse sympathy and would annoy
the doctor. Alcoholics debts were under their
control, their children were more likely to be
considered for the ‘at risk’ register and management
was considered difficult.

Influence of patient’s sex
A similar analysis was performed by combining the
two groups with the ‘patient’ of the same sex. Two
of the results indicated a statistically significant
difference at the 5% level, women were less likely to
be admitted (male mean=3.10, female mean = 3.62;
95% Cl of difference 0.05, 0.99) and men were
regarded as being more in control of their debts
(male mean = 4.04, female mean = 3.64; 95% Cl of
difference 0.04, 0.76). These results are difficult to
interpret in view of the multiple comparisons that
were made. It is clear however that the previous
diagnosis of alcohol dependence had a more pro-
nounced effect on the attitudes measured here than
the sex of the patient.

Female alcoholics compared with male alcoholics
Were female alcoholics regarded in a less or more
favourable light than those of the male sex? This
was examined by use of interaction terms in a two-
way analysis of variance. Of the 20 items, only 2
showed a statistically significant interaction term

when each item was modelled in turn. These items
were ‘Does not merit NHS time’ (F(l,115) = 4.21,
/> = 0.04) and the item ‘Not a suicide risk’
(F(l,115) = 11.22,/) = 0.001). Examination of the
means (Table 2) indicates that male alocholics ‘were
regarded as meriting less NHS time but this was not
so for women alcoholics. Surprisingly, male alcohol-
ics appeared to be judged less of a suicide risk while
women alcoholics were thought to be at increased
risk of suicide (Table 3).

Table 2. Interaction between sex of ‘case’
and previous diagnosis of alcohol depen-

dence: meriting NHS time

Alcohol No
N dependence diagnosis

Male
Female

71
72

2.69

2.50
2.21
2.61

Higher scores indicate increased agreement
with the statement ‘This case does not merit
NHS time’.

The use of multiple significance tests in this
manner will increase the likelihood of a type 1 error
(false positive) and these results are presented to
guide future research. A multivariate analysis of
variance in which all 20 items were studied did not
indicate a statistically significant interaction term
between sex of vignette and the label of alcohol
dependence.

Table 3. Interaction between sex of ‘case’
and previous diagnosis of alcohol depen-

dence: suicide risk

Alcohol No
N dependence diagnosis

Male
Female

71
72

3.22

2.83

3.44

Higher scores indicate increased agreement
with the statement ‘Not a suicide risk’.

Special interest in the addictions
The hypothesis that psychiatrists who specialize in
the addictions would have less critical attitudes was
examined in a similar way. There was only one
interaction term that was significant ‘Difficult
management problem’ (F( 1,115) = 4.96, /) = 0.03)
(Table 4). Alcoholics were regarded as less difficult
to manage amongst those indicating an interest in
the addictions, while those without a specialist

Discrimination on the grounds of diagnosis 887

interest regarded alcoholics as being more difficult sis. However, examining the individual means

to manage.

Table 4. Interaction between previous diagnosis of
alcohol dependence and interest in the addictions:

difficulty of patient

Alcohol No
N dependence diagnosis

Interest
in addictions

No interest
in addictions

117

2.57

3.77

3.11

2.87

Higher scores indicate icreased agreement with the
statement ‘Difficult management problem’

The possibility of an interaction term between
interest in the addictions and previous diagnosis of
alcohol dependence can be illustrated by computing
a composite score, the sum of all the variables in
Table 1 which showed a statistically significant (5%
level) difference between the alcohol dependence
group and the remainder. These results are shown in
Fig. 1. Overall there is a suggestion that attitudes to
alcoholics are less rejecting in those expressing an
interest in the addictions. As mentioned above it is
difficult to draw too many conclusions from this
analysis.

Diagnosis
At the end of the questionnaire the psychiatrists
were asked to indicate their own preferred diagnosis
(only a single choice was allowed) and the fre-
quency of these are given in Table 5. It can be seen
that the diagnosis of alcohol dependence was only
made in the group given the previous diagnosis of
alcohol dependence. In addition, the diagnosis of
personality disorder was more likely to be made in
the alcohol group. Lewis & Appleby (1988) have
already argued that personality disorder is a pejora-
tive label. Depression was the preferred diagnosis in
the majority of cases.

The effect of previous diagnosis of alcohol
dependence could be mediated by the diagnoses
made by the psychiatrist. Two-way analyses of
variance were performed on each of the 20 variables
to examine whether the effect of previous diagnosis
of alcohol dependence was independent of the effect
of the psychiatrists’ own diagnosis. Overall, there
did appear to be a reduction in the size of effect
after adjustment for the psychiatrists’ own diagno-

illustrated that even when the respondents made a
diagnosis of depression there was still an effect of

3.0

2.8

2.6 –

2.4 –

No Diagnosis Alcohol
Dependence

Figure 1. Illustrating the interaction between past diagnosis
of alcohol dependence and a special interest in addictions.
The right-hand axis is the mean score on the composite
variable. It is the sum of all items in Table 1 that were
significant at the 5% level. Higher scores indicate more
rejecting attitudes. (0) Special interest in addictions; (C)

other interests.

previous diagnosis of alcohol dependence. In addi-
tion, the alcohol dependence and personality dis-
order diagnoses were associated with more critical
attitudes. These results are illustrated in Table 5
using a composite variable calculated by summing
all those variables in Table 1 which were statistically
significant at the 5% level.

Discussion
This study has demonstrated that psychiatrists
viewed people with a previous diagnosis of alcohol
dependence as uncompliant, not accepting advice
and having a poor prognosis. The responses to the
questionnaire indicated alcoholics were judged in
less need of admission to hospital, less likely to be
prescribed antidepressants and were more likely to
be discharged from follow-up. They were more
annoying and less likely to arouse sympathy. There
was no evidence that the sex of the case vignette
substantially altered these rejecting attitudes.

Surprisingly, the male problem drinkers were
regarded as having a lower suicide risk, while female
‘cases’ given a previous diagnosis of alcohol depen-

888 Michael Farrell & Glynn Lewis

Table 5. Diagnoses made by the responding psychiatrists in the experimental groups and
illustration of the influence of psychiatrists’ diagnosis on the composite

variable

Depression
Alcohol dependence
Personality disorder
Adjustment reactions
Anxiety
Neurasthenia

Total

Frequency
of diagnoses

Alcohol
dependence

35 (56%)
11 (18%)
8 (13%)
5 (8%)
2 (3%)
1 (2%)

No
diagnosis

55 (74%)
0

3 (4%)
10 (13%)
6 (8%)

0
74

Mean score on
composite

Alcohol
dependence

2.65
2.96
3.24
3.00
2.65

variable

No
diagnosis

2.27

3.20
2.52
2.50

dence were rated as higher suicide risks. Since
alcoholics have a higher suicide rate, the lack of
overall difference between the two groups (Table 1)
may indicate a lack of concern for the safety of
alcoholics. This is unlikely to be due to lack of
knowledge.

Methodology
A previous similar study demonstrated how a past
diagnosis of personality disorder led to pejorative
attitudes and the methodological issues of using this
method have been discussed there (Lewis & Ap-
pleby, 1988).

The experimental nature of this method is an
important advantage. Critical variables in the ‘cases’
can be manipulated independently of all potential
confounders and subjects can be randomly allocated
to the different conditions. Though some questions,
for instance the prescribing of antidepressants, may
indicate appropriate management for an alcoholic
patient many of the other questions tap attitudes
and beliefs that most would regard as unfavourable
to the alcoholic.

This study was designed to elicit the attitudes to a
short clinical vignette. The actual mangement of
patients cannot be inferred from responses to this
questionnaire. However, attitudes to patients are an
important area of study, particularly when consider-
ing the management of emotional issues.

Reasons for negative attitudes
There is general agreement that the management of
problems drinkers can be a difficult business, but
this property is shared with many other psychiatric
disorders. Four possible explanations will be dis-
cussed here.

Personal and social prejudice. It is not surprising to
find that many psychiatrists share a common social
prejudice about substance abusers. It has been
demonstrated that an ‘alcoholic’ carrying a bottle is
less likely to be helped than the same subject
carrying a stick and presumed disabled. Weiner
(1980) has argued that this is because people are
more likely to attribute responsibility to the actions
of alcoholics. Such theories of causal attribution
were supported in this study by the observation that
alcoholics were regarded as being more in control of
their debts than others. Doctors cannot divorce
themselves from the culture in which they live but
one hopes that such popularly held beliefs will not
adversely affect the treatment of problem drinkers.

Doctors are pressured by competing clinical
demands and such views may lead him/her to think
that other patients are more deserving of precious
clincal time. A study of medical students asked them
to write a referral letter for a patient with a peptic
ulcer with two earlier diagnoses of alcoholism.
Four-fifths of them omitted to mention the history
of alcohol problems in their letter and only one in
seven recommended follow-up care for the alcohol
problem (Flaherty & Flaherty, 1983).

Diagnostic labelling and stigmatization. This study
has provided some evidence that alcohol depen-
dence is a stigmatizing diagnosis. This is not a good
argument, on its own, to abandon this diagnosis
considering its importance and usefulness in the
management of substance abuse. Lewis & Appleby
(1988) suggested that the critical attitudes towards
personality disorder argued towards abandoning
that concept. However, the important issue is
whether the adverse effects of labelling are
outweighed by the helpful and useful results of the

Discrimination on the grounds of diagnosis 889

label. The advantages of the dependence syndrome
in guiding managment and research are consider-
able. It is difficult to provide equivalent support for
the diagtiosis of personality disorder. One weakness
of many of the explanations of pejorative attitudes
towards problem drinkers is the assumption that
they only approach doctors with alcohol problems.
Drinkers are at great risk of presenting with alcohol
related physical or psychiatric problems and in this
study’s case vignette, someone with a past diagnosis
of alcohol dependence was presenting with depres-
sive symptoms. It would appear to be particularly
unhelpful if the past diagnosis of alcohol depen-
dence were to adversely affect medical treatment for
different though related conditons.

Training. In the present study there were weak
suggestions that the group of doctors with a special
interest in addictions were somewhat less rejecting
in their ratings. This could be because training in the
addictions leads to a less judgemental approach, or
that doctors with a less judgemental approach are
attracted to work in the area of addictions. This
association is consistent with other literature that
indicates positive attitudes are associated with more
knowledge of and experience with alcoholics
(Cartwright, 1980; Geller et al., 1989).

In this study the psychiatrists regarded the
alcoholic patient as more difficult. ‘Difficulty’ can
be interpreted as reflecting professional uncertainty
where there is an absence of clear guidelines and
little confidence that one’s therapeutic approach is
appropriate. The results of this study are consistent
with the view that psychiatric education does not
provide sufficient training in the addictions and this
results in psychiatrists who lack confidence in
treating alcoholics.

Therapeutic pessimism. Many doctors expect poor
compliance and high drop-out rates from problem
drinkers but no research has been done to compare
this with other persisting problems like anxiety or
depression. Thus therapeutic pessimism may result
in negative attitudes to patients which may discour-
age patients on the brink of positive change
(Edwards, 1987).

Among professional helpers liking for clients is
consistently correlated with the belief that the client
has improved or will improve (Doherty 1971;
Willis, 1978).

Implications
There is a high prevalence of psychiatric disorders
among problem drinkers (Ross et al., 1988) with
20% of men and 27% of women suffering severe
depression, and an elevated parasuicide rate in both
sexes. This study has suggested that the previous
diagnosis of alcohol dependence has an adverse
effect on psychiatrists’ attitudes to a depressed
patient.

A possible way to address this problem is through
the training provided at an undergraduate and
postgraduate level in substance misuse. Surveys
have shown that British medical schools allocate
4-8 h to formal teaching in substance misuse (Glass,
1988). There are not figures available for postgra-
duate psychiatric training but it is suggested that
substance misuse ranks as a similar low status
subject at this level.

One of the basic responsibilities of a doctor is to
provide a non-judgemental, humane and sympa-
thetic ear to the patient and to make management
decisions unbiased by prejudice. Is the diagnosis of
alcohol dependence grounds for discrimination in
medical care?

Acknowledgements
We wish to thank all those who completed and
returned questionnaires. We would like to thank
Professor Edwards, Graham Dunn and Troy Cooper
for help and advice and Karen Lovell for secretarial
assistance. This project was funded by the Addiction
Research Unit. Michael Farrell is supported by
Action on Addiction. Glyn Lewis is currently
supported by the Department of Health.

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Aggression and Violent Behavior 65 (2022) 101761

Available online 30 June 202

1359-1789/© 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-
nc-nd/4.0/).

Developmental predictors of offending and persistence in crime: A
systematic review of meta-analyses

Miguel Basto-Pereira a,*, David P. Farrington b

a William James Center for Research, ISPA-Instituto Universitário, R. Jardim do Tabaco 34, 1100-304 Lisboa, Portugal
b Institute of Criminology, Cambridge University, Sidgwick Avenue, Cambridge CB3 9DA, United Kingdom

A R T I C L E I N F O

Keywords:
Developmental predictors
Offending
Juvenile delinquency
Meta-analyses
Longitudinal studies

A B S T R A C T

Meta-analyses have provided major findings about developmental predictors of offending. However, there has
been little focus on their relative ability to predict offending behaviour. Therefore, we conducted a systematic
review of meta-analyses with two aims: 1) to summarize all well-established knowledge about developmental
(explanatory) predictors of offending, and 2) to sort those predictors according to their effect size. The strongest
predictors of general offending were related to family/parental dimensions. Delinquent peers, school/employ-
ment problems, family problems, certain types of mental health problems, and alcohol/substance abuse were the
most important predictors of persistence in crime. Our findings suggest the crucial role of family-related
developmental predictors in preventing offending. The predictors of persistence in crime highlight the multi-
systemic nature of persistent antisocial behaviour.

1. Introduction

A deep understanding of developmental factors that longitudinally
predict offending and persistence in crime is particularly relevant in
explaining offending and in addressing the causes of criminal behaviou

effectively. Because our interest is in explanation rather than pure pre-
diction, we focus on explanatory predictors, defined as those that are
measuring an underlying construct that is different from antisocial
behaviour. Thus, we exclude behavioural predictors such as previous
offending, aggression or conduct disorder

Over the last 50 years, multiple longitudinal studies have been
initiated to advance knowledge about the factors predating or causing
criminal behaviour (Farrington, 2013; Jolliffe et al., 2017). Different
longitudinal studies have addressed distinct sets of different predictors,
and these studies have found a multitude of important risk factors for
delinquency and conduct disorder, such as poor parental supervision,
impulsiveness, low IQ, family disruption, social inequality, school
problems, and antisocial models (Farrington et al., 2017; Murray &
Farrington, 2010).

The various longitudinal studies (e.g., Cambridge Study in Delin-
quent Development; Pittsburgh Youth Study; Dunedin Longitudinal
Study) conducted over the years have resulted in a new era of theories
on developmental criminology (Farrington, 2006; Loeber, 2019; Moffitt,

2018; Wikstrom et al., 2012), with several practical implications for
justice policies (e.g., Zane, 2021), assessment tools (e.g., Wormith,
2011), and more effective interventions (e.g., Tremblay et al., 2003).

These advancements have also led to some scientific consensus
across studies and contemporary theoretical approaches. For example,
nowadays it is known that juvenile delinquency is an important risk
factor for adult criminal behaviour, although it is also known that most
youth offenders will cease their criminal behaviour when entering
adulthood (e.g., Farrington, 2003; Laub & Sampson, 2001; Moffitt,
1993, 2018). In addition, the most serious and chronic criminal careers
are influenced by both environmental (e.g., antisocial peers) and indi-
vidual/temperamental (e.g., impulsiveness) risk factors during child-
hood (Cicchetti, 2016; Farrington, 2003; Laub & Sampson, 2001;
Moffitt, 1993).

In contrast, there are still many controversial issues about criminal
career development. Whereas we know that some of the risk factors that
explain or predate delinquency are similar across longitudinal studies (e.
g., antisocial models), the relative importance of each of these causes,
the interaction of each with age or gender, or the processes explaining
how each potential causal mechanism influences the decision to initiate,
persist, or desist from a criminal career are still far from achieving sci-
entific consensus (e.g., for a review, see Basto-Pereira & Maia, 2017 and
Siegel, 2015). For example, Laub and Sampson (2001) argued that

* Corresponding author.
E-mail address: miguelbastopereira@hotmail.com (M. Basto-Pereira).

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Received 1 October 2021; Received in revised form 27 May 2022; Accepted 25 June 2022

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Aggression and Violent Behavior 65 (2022) 101761

youths with delinquent patterns share similar childhood risk factors
regardless of the seriousness or chronicity of criminal behaviour, and it
is the strengthening of bonds with society later in life (e.g., entering the
labor market, marriage) that is the main reason for the cessation of
criminal careers. In opposition, for Moffitt (1993, 2018), the factors
explaining persistence/desistance from crime during adulthood are
mostly dependent on causal mechanisms already present during child-
hood (e.g., neuropsychological problems, uncontrolled behaviour,
inadequate parenting).

To overcome many of these controversial issues, meta-analyses of
longitudinal studies appeared as a solution to provide a reliable and
replicable strategy of summarizing results and identifying common
patterns across studies. Consequently, in recent years, there have been
an increasing number of meta-analyses in this field, as a result of the
need to summarize the studies from multiple cohorts and provide solid
evidence-based knowledge about the mechanisms underlying crime.
Different dimensions of general offending have been tested across a set
of meta-analyses of longitudinal studies, including child maltreatment
(Braga et al., 2017), parental supervision (Flanagan et al., 2019), and
individual/temperamental characteristics (e.g., intelligence; Ttofi et al.,
2016). In addition, some meta-analyses have also reviewed the pre-
dictors of persistence in crime among justice-involved youths.

The main aim of meta-analyses that analyse long-term longitudinal
studies is to understand whether different types of social, psychological,
or biological factors during development temporally predict offending
or persistence in crime. Therefore, the intrinsic question in this type of
study is often related to the level of significance: Do scientific studies
indicate that factors during development increase the risk of later
offending? A significance value below 0.05 is typically interpreted as a
“yes”. However, p-values do not measure effect size (e.g., Wasserstein &
Lazar, 2016). In the case of very small effect sizes, conclusions based on
p-values might be misleading or deceptive for various reasons. First,
predictors with very small effect sizes might be so close to zero that in
practice their effect is irrelevant for interventions or public policies
(Sullivan & Feinn, 2012; Szucs & Ioannidis, 2017). Second, criminal
behaviour, like any other human behaviour, is an extremely complex
phenomenon reflecting the interaction of a large and intricate network
of societal, familial, and biological factors (Szucs & Ioannidis, 2017;
Woods, 1988). In this article, we focus on effect sizes.

1.1. The current study

To advance knowledge about the mechanisms underlying criminal
behaviour, there is a need to map the multitude of relationships pro-
vided by meta-analytic studies and to refocus on what effect sizes across
meta-analyses of longitudinal studies tell us about the causes of criminal
behaviour. In other words, a deeper understanding of the most impor-
tant mechanisms underlying offending reported across meta-analyses
has several advantages. It enables us 1) to test the empirical validity
of current theories of crime, 2) to know what needs to be tested in future
meta-analyses, and maybe most crucially, 3) to identify the most
important explanatory predictors of crime; these will contribute to
developing more accurate risk assessment tools and more effective in-
terventions to prevent offending and future recidivism.

In addition, previous research (Basto-Pereira et al., 2015) has noted
significant differences between developmental predictors of youth
offending in the community population when compared with predictors
of recidivism among justice-involved youths. From a theoretical and
legal point of view, youths previously exposed to the justice system are
typically older and affected by a larger number of risk factors (Loeber &
Farrington, 2012). In this regard, the first contact with the justice system
often causes or aggravates the risks of labeling effects and deviant peer
contagion (e.g., Bernburg et al., 2006; Dishion & Tipsord, 2011). Thus,
predictors of general offending in community populations, particularly
when measured during childhood, are normally more representative of
the very early stages of a criminal career, while developmental

predictors of persistence in crime may involve the presence of multiple
risk factors developed as a consequence of criminal career progression
during adolescence in interplay with the consequences of early justice
contact. For this reason, it is particularly relevant to study develop-
mental predictors of offending in both cases, in the general community,
and among justice-involved youths.

Therefore, focusing on addressing these concerns, we conducted a
systematic review of meta-analyses to address these two main aims: 1) to
summarize all the well-established knowledge about developmental
predictors of offending and 2) to sort those predictors by their impor-
tance (effect size) for predicting offending in the general population or
persistence in crime among justice-involved youths.

2. Methods

2.1. Search process and eligibility criteria

Following the Preferred Reporting Items for Systematic Reviews and
Meta-Analyses (PRISMA) guidelines (Moher et al., 2009), systematic
literature searches were conducted in six major databases—Web of Sci-
ence, Scopus, PsychINFO, PsychARTICLES, Scielo, and PubMed—to iden-
tify meta-analyses analysing characteristics that longitudinally predict
antisocial behaviour during adolescence and adulthood. In addition, a
search was conducted manually in key journals that publish meta-
analyses. Meta-analyses were searched from the very beginning of the
databases until January 25, 2020. The following search terms and
Boolean operators were used: meta-analysis AND delinquen* OR offend

OR violen* OR recid* OR crim* OR antisocial OR conduct problems OR
disruptive OR rearrest OR reoffend* AND (None) OR predict* OR factors
OR desist* OR persist*; this resulted in 50 search combinations.

For a study to be considered eligible, it must a) be a meta-analysis, b

)

analyse psychological, social, or biological characteristics during
childhood or adolescence predicting antisocial outcomes (e.g., rearrests,
convictions) later in life, c) analyse explanatory predictors of criminal
behaviour, d) have diversified samples of offenders or community
samples, and e) be published in English, Spanish, or Portuguese in peer-
reviewed journals up to January 25, 2020. The following exclusion
criteria were adopted: a) the outcome evaluated only a particular type of
crime, b) the meta-analysis was conducted in a community or offending
sample with specific characteristics (e.g., offenders with mental illness),
c) longitudinal effect sizes were not reported, or could not be directly
calculated using the data provided, d) the meta-analysis did not provide
well defined and rigorous definitions of measures, outcomes, and in-
clusion/exclusion criteria, e) there was a lack of explanatory predictors
of crime tested, f) the meta-analysis did not examine predictors during
childhood/adolescence, and g) the predictors of interest for this study
were based on fewer than two studies.

2.2. Study selection and data collection

The study selection process was conducted in the following order: 1)
removal of duplicates, 2) screening abstracts for exclusion of papers not
fulfilling the eligibility criteria, and 3) all the papers that were not
excluded after abstract-screening were read through carefully to ensure
the exclusion of all studies that were not in compliance with the pre-
established criteria.

Information was obtained from the selected meta-analyses on the
following topics: a) the source (bibliographic reference), b) the partici-
pants’ genders, c) the types of predictors, d) the participants’ ages when
predictors were measured (childhood versus adolescence versus mixed),
e) the number of studies analysed by predictors in each meta-analysis, f)
the standardized mean effect sizes, g) the p-values, h) the types of
outcome, and i) the participants’ ages when outcomes were measured.

M. Basto-Pereira and D.P. Farrington

Aggression and Violent Behavior 65 (2022) 101761

2.3. Synthesis of results and analytic strategy

Detailed information about each predictor, from each meta-analysis
included, was collected and described in detail. Mean effect sizes were
converted to r metrics. Each predictor was placed in one of two tables:
predictors of crime or predictors of persistence in crime during adult-
hood. The outcome of persistence in crime includes meta-analyses
assessing predictors of recidivism during adulthood among justice-
involved youths, and meta-analyses assessing predictors of life-course
persistent (versus adolescence-limited) trajectories of criminal behav-
iour. The effect size of each longitudinal predictor of crime is presented
separately for males and females if an included meta-analysis reports
that effect size separately by gender (e.g., predictor: low-attain-
ment–Females, predictor: low-attainment–Males).

Static predictors (e.g., gender; ethnicity), and behavioural predictors
of crime, measuring a similar underlying construct to offending (e.g.,
previous offending, aggression or conduct disorder), were excluded from
our analyses, which focused on explanatory predictors. Subsequently,
the developmental predictors were separated into two different cate-
gories: predictors of general offending and predictors of recidivism
among youths with a history of offending. For reasons of simplicity and
clarity, all predictive factors were coded in the risk direction. Informa-
tion about reversed factors are provided in each table (e.g., Prosocial
peer relations reversed to Low prosocial peer relations).

In a subsequent analysis, those predictors were ordered by their ef-
fect size, from the larger to the smaller effects. Detailed information
about each predictor was provided (e.g., bibliographic reference, num-
ber of effect sizes included, etc.). Non-significant predictors, or pre-
dictors with an effect size r smaller than 0.10, were excluded from these
analyses because we aimed to identify the strongest explanatory pre-
dictors of crime. Also, to avoid bias caused by a very small number of
independent samples (including an overestimation of the real effect
size), in this meta-synthesis of findings, all the predictors tested in less
than five samples were excluded. According to Jackson and Turner
(2017, p. 290): “5 or more studies are needed to reasonably consistently
achieve powers from random-effects meta-analyses that are greater than
the studies that contribute to them”. Lastly, predictors were analysed as
major dimensions to add comprehensibility and interpretability to our
analysis.

We have excluded predictors with small values of r from Tables 3 and
4 in order to highlight the strongest predictors. However, small values of
r (e.g., r = 0.10) do not necessarily indicate weak relationships. For
example, consider a 2 × 2 table relating a dichotomous risk factor to a
dichotomous outcome such as delinquency. Assume that there are 100
people in the risk category out of a total of 400, and that 100 of the total
number of people become delinquent. Now, if 33 out of 100 (33 %) in
the risk category become delinquent, compared with 67 out of 300 (22.3
%) in the non-risk category, the product-moment correlation r (also
called the phi correlation in a 2 × 2 table) would be 0.107. In general, an
r value of 0.10 would correspond to an absolute difference of about 10 %
between risk and non-risk categories in a 2 × 2 table (for all the relevant
formulae, see Farrington & Loeber, 1989). However, the relative dif-
ference is substantial; almost 50 % more of those in the risk category
became delinquent, compared with those in the non-risk category (33 %
compared with 22.3 %). This effect therefore cannot be considered
insignificant.

3. Results

3.1. Selected meta-analyses

A total of 4095 articles were found. Of the 4095 articles, 3149 were
duplicates. Titles and abstracts were screened for the remaining 946,
and 869 were excluded from these initial screening, mainly because the
articles found were not meta-analyses. Seventy-seven meta-analyses
passed the initial screening and were retained for full-text reading.

Sixty-three meta-analyses were excluded for the following reasons: a) 21
meta-analyses did not examine predictors during childhood/adoles-
cence, b) 14 meta-analyses did not test longitudinal predictors of general
offending, c) 13 meta-analyses did not test causal, explanatory, dynamic
predictors of crime, d) 10 meta-analyses evaluated only a particular type
of crime or category of crime, e) two meta-analyses were conducted in a
sample with specific characteristics (e.g., only individuals with psychi-
atric diagnoses), f) in two studies, predictors of general offending were
evaluated in samples mixing minors and adults, g) in one meta-analysis,
the predictors of interest were tested with fewer than two studies.
Fourteen meta-analyses of longitudinal studies assessing developmental
predictors of general offending and/or persistence in crime during
adulthood were included (see Fig. 1).

3.2. Study characteristics

This systematic review included 14 meta-analyses of longitudinal
studies. Eleven meta-analyses tested developmental predictors of gen-
eral offending (Braga et al., 2017; Braga et al., 2018; Derzon, 2010;
Flanagan et al., 2019; Hoeve et al., 2012; Leschied et al., 2008; Portnoy
& Farrington, 2015; Reaves et al., 2018; Spruit et al., 2016; Ttofi et al.,
2016; Wilson et al., 2009), while three meta-analyses tested develop-
mental predictors of recidivism (Assink et al., 2015; Cottle et al., 2001;
Scott & Brown, 2018). The meta-analyses were published between 2001
(Cottle et al., 2001) and 2019 (Flanagan et al., 2019) in peer-reviewed
journals. Thirteen meta-analyses examined our predictors of interest
using gender-mixed samples, while one of the studies (Scott & Brown,
2018) conducted analyses separately for males and females. Twelve
meta-analyses reported mean effect sizes using r or Cohen’s d metrics,
while two studies used different metrics, namely, the Odds-Ratio or OR
(Ttofi et al., 2016) and Fisher’s Z, which is similar to r (Cottle et al.,
2001). For all the effect sizes provided, a conversion to r metrics was
performed.

Eleven meta-analyses included only longitudinal designs, while three
meta-analyses included and analysed separately studies with cross-
sectional and longitudinal designs (Portnoy & Farrington, 2015; Spruit
et al., 2016; Wilson et al., 2009). All the studies testing predictors of
recidivism included only studies with longitudinal designs. The number
of samples included in those meta-analyses ranged between 13 (Reaves
et al., 2018) and 119 (Derzon, 2010); on average each meta-analysis
included approximately 44 independent samples. Three longitudinal
studies (Braga et al., 2018; Flanagan et al., 2019; Portnoy & Farrington,
2015) evaluated criminal and non-criminal forms of antisocial behav-
iour together; we recalculated the mean effect size only for criminal
behaviour.

Eight meta-analyses did not differentiate childhood from adolescent
predictors of offending or recidivism, while six meta-analyses (Braga
et al., 2017; Cottle et al., 2001; Leschied et al., 2008; Scott & Brown,
2018; Spruit et al., 2016; Wilson et al., 2009) analysed the impact of
predictors on specific phases of life using a longitudinal design (child-
hood or adolescence). All the meta-analyses testing predictors of
offending used outcomes measured during adolescence or adulthood,
while outcomes of recidivism among young offenders were always
assessed during adulthood. For a detailed description of all the tested
developmental predictors of offending and persistence in crime, see
Tables 1 and 2.

Generally, the significance tests use two-tailed p-values, but one-
tailed tests would be justified in the light of clear directional pre-
dictors (based on risk factors). Therefore, the number of significant re-
sults is underestimated.

3.3. Summary of the meta-findings

Tables 3 and 4 summarize all the well-established longitudinal pre-
dictors of offending and persistence in crime and sort those predictors by
their importance according to effect size. Since the objective was to

M. Basto-Pereira and D.P. Farrington

Aggression and Violent Behavior 65 (2022) 101761

summarize the most important predictors of antisocial outcomes, non-
significant predictors, predictors with relatively small effect sizes (r < 0.10) and those tested in fewer than five independent samples were excluded from these tables.

Of the 14 meta-analyses included in this study, 112 longitudinal
predictors were identified: 1) 53 predictors of general offending across
11 meta-analyses, and 2) 59 predictors of persistence in crime across
three meta-analyses. The largest effect sizes for general offending during
childhood and adolescence were the family structure (e.g., child
involved in the child welfare system; marital status of the parents)
during adolescence (r = 0.32; Leschied et al., 2008), lack of child-rearing
skills (r = 0.26; Derzon, 2010), home discord (r = 0.26; Derzon, 2010),
family structure during childhood/adolescence (r = 0.23; Leschied et al.,
2008), and low level of parental knowledge (r = 0.22; Flanagan et al.,
2019). Lack of parental management was the best predictor of general
offending during childhood (r = 0.20; Leschied et al., 2008), and family
structure was the best predictor of general offending during adolescence
(r = 0.32; Leschied et al., 2008).

The most important longitudinal predictor of persistence among

juvenile justice youths was non-severe pathology, such as stress or
anxiety (r = 0.30; Cottle et al., 2001), female education/employment (r
= 0.25; Scott & Brown, 2018), male-lack of prosocial peer relations (r =
0.23; Scott & Brown, 2018), family problems (r = 0.22; Cottle et al.,
2001), and alcohol/drug abuse (r = 0.21; Assink et al., 2015).

Education/employment problems (r = 0.25 for females; Scott &
Brown, 2018; to r = 0.15, Assink et al., 2015), family problems (r = 0.22,
Cottle et al., 2001; to r = 0.10 for males, Scott & Brown, 2018), and
(delinquent) Peers (r = 0.20, Cottle et al., 2001; to r = 0.13 for females,
Scott & Brown, 2018), were consistent predictors of persistence in crime
across all the meta-analyses, always showing effect sizes r > 0.10 across
all three meta-analyses.

In addition, dimensions of alcohol or substance abuse and specific
dimensions related to mental health were statistically significant pre-
dictors of persistence in crime. Specific dimensions of mental health,
such as non-severe psychopathology (Cottle et al., 2001) and emotional
and behavioural problems (Assink et al., 2015) were statistically sig-
nificant predictors of crime with r > 0.15. The meta-analyses conducted
by Scott and Brown (2018) addressing mental health as the presence of a

Fig. 1. Flow-diagram.

M. Basto-Pereira and D.P. Farrington

Aggression and Violent Behavior 65 (2022) 101761

Table 1
Childhood and adolescent predictors of general offending.

Reference Predictors k r Age period – Predictor Age period – Outcome

Braga et al. (2017) Maltreatment 7 0.11* Childhood Adolescence

Braga et al. (2018)** Maltreatment 8 0.14* Childhood/adolescence Adulthood

Derzon (2010)

Parent’s education and expectations 3 0.30 Childhood/adolescence Adolescence/adulthood
(Lack of) child rearing skills 13 0.26* Childhood/adolescence Adolescence/adulthood
Home discord and instability 11 0.26* Childhood/adolescence Adolescence/adulthood
Family stress 10 0.21* Childhood/adolescence Adolescence/adulthood
Maltreated as child 8 0.21* Childhood/adolescence Adolescence/adulthood
Other family deviance 9 0.19* Childhood/adolescence Adolescence/adulthood
(Lack of) warmth and relationship 22 0.18* Childhood/adolescence Adolescence/adulthood
(Inappropriate) discipline 9 0.17* Childhood/adolescence Adolescence/adulthood
Parent antisocial behaviour 11 0.15* Childhood/adolescence Adolescence/adulthood
Foster care 5 0.14* Childhood/adolescence Adolescence/adulthood
Urban housing 9 0.13* Childhood/adolescence Adolescence/adulthood
Family size 9 0.11* Childhood/adolescence Adolescence/adulthood
Broken home 25 0.10* Childhood/adolescence Adolescence/adulthood
Unwanted pregnancy 5 0.10 Childhood/adolescence Adolescence/adulthood
Residential mobility 3 0.08* Childhood/adolescence Adolescence/adulthood
Separated from parents 2 0.08* Childhood/adolescence Adolescence/adulthood
Parent use and tolerate ATOD (alcohol, tobacco,
and drug use of adolescents)

1 0.08 Childhood/adolescence Adolescence/adulthood

Young parent(s) 4 0.08 Childhood/adolescence Adolescence/adulthood
(Lack of) supervision and involvement 10 0.06 Childhood/adolescence Adolescence/adulthood
Parental psychopathology 4 0.02* Childhood/adolescence Adolescence/adulthood

Flanagan et al. (2019)**

Low level of parental knowledge a 8 0.22* Childhood/adolescence Adolescence/adulthood
Low supervision a 18 0.18* Childhood/adolescence Adolescence/adulthood
Child closure a 6 0.16* Childhood/adolescence Adolescence/adulthood
Lack of parental rule setting a 4 0.12* Childhood/adolescence Adolescence/adulthood

Hoeve et al. (2012) Low attachment a 17 0.17* Childhood/adolescence Adolescence/adulthood

Leschied et al. (2008)

Family structure adolescence 19 0.32* Adolescence Adulthood
Family structure total 36 0.23* Childhood/adolescence Adulthood
Parent management middle childhood 8 0.20* Childhood Adulthood
Adverse family environment adolescence 15 0.19* Adolescence Adulthood
Internalizing concerns adolescence 24 0.14 Adolescence Adulthood
Family structure middle childhood 5 0.13 Childhood Adulthood
Parent management total 17 0.12* Childhood/adolescence Adulthood
Internalizing concerns – Total 42 0.11* Childhood/adolescence Adulthood
Adverse family environment total 35 0.11* Childhood/adolescence Adulthood
Family structure early childhood 12 0.08* Childhood Adulthood
Adverse family environment early childhood 9 0.08* Childhood Adulthood
Adverse family environment mid childhood 11 0.08* Childhood Adulthood
Parent mental health-Total 36 0.07* Childhood/adolescence Adulthood
Social and interpersonal concerns middle childhood 7 0.07 Childhood Adulthood
Parent mental health early childhood 21 0.07 Childhood Adulthood
Parent mental health adolescence 15 0.07 Adolescence Adulthood
Parent management adolescence 4 0.06 Adolescence Adulthood
Internalizing concerns middle childhood 14 0.05 Childhood Adulthood
Social and interpersonal concerns total 18 0.04 Childhood/adolescence Adulthood
Social and interpersonal concerns early childhood 7 0.01 Childhood Adulthood

Portnoy and Farrington (2015)** Low resting heart rate a 6 0.07* Childhood/adolescence Adolescence/adulthood

Reaves et al. (2018) Negative school climate – Interpersonal relationships a 3 0.21* Childhood/adolescence Adolescence
Negative school climate- Institutional environment a 16 0.14* Childhood/adolescence Adolescence

Spruit et al. (2016) Lack of sports participation a 8 0.07* Adolescence Adolescence

Ttofi et al. (2016) Low intelligence a 4 0.08 Childhood/adolescence Adolescence/adulthood

Wilson et al. (2009) Childhood violence exposure 3 0.15* Childhood Adolescence

Note. ** Using the data provided in the meta-analyses or direct contact with the author, overall effect sizes were recalculated including only longitudinal studies
assessing childhood/adolescent predictors of general offending; k = Number of studies. *, p < .05. a = Reversed Protective Factor.

M. Basto-Pereira and D.P. Farrington

Aggression and Violent Behavior 65 (2022) 101761

Table 2
Childhood and adolescent predictors of persistence in crime.

Reference Predictor k r Age period – Predictor Age period – Outcome

Assink et al. (2015)

Alcohol/drug abuse 57 0.21* Childhood/adolescence Adulthood
Sexual behaviour 7 0.20* Childhood/adolescence Adulthood
Relationship 51 0.19* Childhood/adolescence Adulthood
Emotional and behavioural problems 150 0.18* Childhood/adolescence Adulthood
School/employment 63 0.15* Childhood/adolescence Adulthood
Other 27 0.13* Childhood/adolescence Adulthood
Family (problems) 273 0.12* Childhood/adolescence Adulthood
Attitude 19 0.10* Childhood/adolescence Adulthood
Physical health 14 0.04 Childhood/adolescence Adulthood
Neighborhood 16 − 0.04 Childhood/adolescence Adulthood

Cottle et al. (2001)

Nonsevere pathology 7 0.30* Adolescence Adulthood
Ineffective use of leisure timea 2 0.23* Adolescence Adulthood
Family problems 5 0.22* Adolescence Adulthood
Delinquent peers 7 0.20* Adolescence Adulthood
Number of out-of-home placements 2 0.18* Adolescence Adulthood
Low standardized achievement scorea 3 0.15* Adolescence Adulthood
Substance abuse 6 0.15* Adolescence Adulthood
Low full scale IQa 5 0.14* Adolescence Adulthood
History of special education 2 0.13* Adolescence Adulthood
Victim of abuse 5 0.11* Adolescence Adulthood
Low verbal IQ scorea 4 0.11* Adolescence Adulthood
Single parent 5 0.07* Adolescence Adulthood
Low performance IQ scorea 2 0.31 Adolescence Adulthood
Severe pathology 2 0.07 Adolescence Adulthood
Low school attendancea 2 0.05 Adolescence Adulthood
Parent pathology 3 0.04 Adolescence Adulthood
Low school report of achievementa 6 0.03 Adolescence Adulthood
History of treatment 2 0.02 Adolescence Adulthood
Substance use 2 0.01 Adolescence Adulthood

Scott and Brown (2018)

Female Education/employment 8 0.25* Adolescence Adulthood
Female-Lack of prosocial peer relations (outliers removed)a 4 0.15* Adolescence Adulthood
Female problematic family circumstances and parenting 12 0.14* Adolescence Adulthood
Female antisocial peer relations 12 0.13* Adolescence Adulthood
Female education/school concerns (outlier removed) 5 0.10* Adolescence Adulthood
Female substance abuse 16 0.05* Adolescence Adulthood
Female mental health 5 0.04* Adolescence Adulthood
Female-Low level of prosocial values and attitudesa 3 0.52 Adolescence Adulthood
Female-Low of family relationships and supporta 4 0.38 Adolescence Adulthood
Female-Personality – Low self-efficacy, positive problem solvinga 3 0.26 Adolescence Adulthood
Female-Low of extracurricular activities and community supporta 6 0.16 Adolescence Adulthood
Female Child abuse 4 0.1 Adolescence Adulthood
Female-Low of education and employment opportunitiesa 3 0.06 Adolescence Adulthood
Female poor use leisure/recreation (outlier removed) 9 0.05 Adolescence Adulthood

Male- Low education and employment opportunitiesa 3 0.32* Adolescence Adulthood
Male-Low family relationships and supporta 4 0.27* Adolescence Adulthood
Male- Lack of rejection or non-absence of substance usea 3 0.27* Adolescence Adulthood
Male- Lack of prosocial peer relationsa 5 0.23* Adolescence Adulthood
Male education/employment problems (outlier removed) 7 0.21* Adolescence Adulthood
Male antisocial peer relations (outlier removed) 10 0.20* Adolescence Adulthood
Male poor use leisure/recreation 10 0.16* Adolescence Adulthood
Female – Lack of rejection or non-absence of substance usea 3 0.15* Adolescence Adulthood
Male education/school concerns (outlier removed) 5 0.13* Adolescence Adulthood
Male problematic family circumstances and parenting 12 0.10* Adolescence Adulthood
Male substance abuse 16 0.08* Adolescence Adulthood
Male- low extracurricular activities and community supporta 6 0.2 Adolescence Adulthood
Male-low level of prosocial values and attitudesa 3 0.14 Adolescence Adulthood
Male mental health 5 0.02 Adolescence Adulthood
Male child abuse 4 0 Adolescence Adulthood
Male-personality – Low self-efficacy, positive problem solvinga 3 − 0.01 Adolescence Adulthood

Note. ** Using the data provided in the meta-analyses or direct contact with the author, overall effect sizes were recalculated including only longitudinal studies
assessing childhood/adolescent predictors of persistence. k = Number of studies. *, p < .05. a = Reversed Protective Factor.

M. Basto-Pereira and D.P. Farrington

Aggression and Violent Behavior 65 (2022) 101761

mental health problem or diagnosis (Yes/No) showed substantially
lower effect sizes. Another important disparity between effect sizes was
found for substance abuse. The dimension of alcohol/drug abuse (Assink
et al., 2015) was an important longitudinal predictor of persistence in
crime (r = 0.21), but predictors exclusively addressing substance abuse
in the other two meta-analyses showed statistically significant but sub-
stantially smaller effect sizes (r = 0.15, Cottle et al., 2001; r = 0.08 for
males, r = 0.05 for females, Scott & Brown, 2018).

Finally, Scott and Brown’s (2018) meta-analyses provided separate

analyses by gender, and these found education/employment problems
as the most important predictors of recidivism among males (r = 0.21)
and females (r = 0.25), followed by masculine antisocial peers (r = 0.20)
and female problematic family circumstances/parenting (r = 0.14).
Table 5 summarizes the concepts and effect sizes in each meta-analysis
of persistence associated with each one of the five key categories
identified.

Table 3
Most important predictors of general offending ordered by overall effect size.

Reference Predictor k r Type Age period -Predictor Age period – Outcome

Leschied et al. (2008) Family structure – Adolescence 19 0.32* General offending Adolescence Adulthood
Derzon (2010) (Lack of) child rearing skills 13 0.26* General offending Childhood/adolescence Adolescence/adulthood
Derzon (2010) Home discord and stability 11 0.26* General offending Childhood/adolescence Adolescence/adulthood
Leschied et al. (2008) Family structure – Total 36 0.23* General offending Childhood/adolescence Adulthood
Flanagan et al. (2019) Low level of parental knowledgea 8 0.22* General offending Childhood/adolescence Adolescence/adulthood
Derzon (2010) Family stress 10 0.21* General offending Childhood/adolescence Adolescence/adulthood
Derzon (2010) Maltreated as child 8 0.21* General offending Childhood/adolescence Adolescence/adulthood

Leschied et al. (2008) Lack of parent management middle childhood
(supervision/discipline)

8 0.20* General offending Childhood Adulthood

Leschied et al. (2008) Adverse family environment adolescence 15 0.19* General offending Adolescence Adulthood
Derzon (2010) Other family deviance 9 0.19* General offending Childhood/adolescence Adolescence/adulthood
Derzon (2010) (Lack of) warmth and relationship 22 0.18* General offending Childhood/adolescence Adolescence/adulthood
Flanagan et al. (2019) Poor supervisiona 18 0.18* General offending Childhood/adolescence Adolescence/adulthood
Hoeve et al. (2012) Low attachment 17 0.17* General offending Childhood/adolescence Adolescence/adulthood
Derzon (2010) (Inappropriate) discipline 9 0.17* General offending Childhood/adolescence Adolescence/adulthood
Flanagan et al. (2019) Child closurea 6 0.16* General offending Childhood/adolescence Adolescence/adulthood
Derzon (2010) Parent antisocial behaviour 11 0.15* General offending Childhood/adolescence Adolescence/adulthood
Reaves et al. (2018) Negative school climate – Institutional environment 16 0.14* General offending Childhood/adolescence Adolescence
Braga et al. (2018) Maltreatment 8 0.14* General offending Childhood/adolescence Adulthood
Derzon (2010) Foster care 5 0.14* General offending Childhood/adolescence Adolescence/adulthood
Derzon (2010) Urban housing 9 0.13* General offending Childhood/adolescence Adolescence/adulthood
Leschied et al. (2008) Parent management total 17 0.12* General offending Childhood/adolescence Adulthood
Leschied et al. (2008) Internalizing concerns – Total 42 0.11* General offending Childhood/adolescence Adulthood
Leschied et al. (2008) Adverse family environment total 35 0.11* General offending Childhood/adolescence Adulthood
Derzon (2010) Family size 9 0.11* General offending Childhood/adolescence Adolescence/adulthood
Braga et al. (2017) Maltreatment 7 0.11* General offending Childhood Adolescence
Derzon (2010) Broken home 25 0.10* General offending Childhood/adolescence Adolescence/adulthood

Notes. Including only dynamic predictors with k ≥ 5, r ≥ 0.10 and p < .05; k = Number of studies. *, p < .05. a = Reversed Protective Factor.

Table 4
Predictors of persistence in crime during adulthood.

Reference Predictor k r Outcome type Age period -Predictor Age period – Outcome

Cottle et al. (2001) Nonsevere pathology 7 0.30* Recidivism Adolescence Adulthood
Scott and Brown (2018) Female – Education/employment 8 0.25* Recidivism Mostly adolescence Adulthood
Scott and Brown (2018) Male-Lack of prosocial peer relations a 5 0.23* Recidivism Mostly adolescence Adulthood
Cottle et al., 2001 Family problems 5 0.22* Recidivism Adolescence Adulthood
Assink et al. (2015) Alcohol/drug abuse 57 0.21* Persistent Del Behav Childhood/adolescence Adulthood
Scott and Brown (2018) Male-Education/employment problems 7 0.21* Recidivism Mostly adolescence Adulthood
Scott and Brown (2018) Male-Antisocial peer relations 10 0.20* Recidivism Mostly adolescence Adulthood
Assink et al. (2015) Sexual behaviour problem 7 0.20* Persistent Del Behav Childhood/adolescence Adulthood
Cottle et al. (2001) Delinquent peers 7 0.20* Recidivism Adolescence Adulthood
Assink et al. (2015) Relationship 51 0.19* Persistent Del Behav Childhood/adolescence Adulthood
Assink et al. (2015) Emotional and Behavioural problems 150 0.18* Persistent Del Behav Childhood/adolescence Adulthood
Scott and Brown (2018) Male-Poor use leisure/recreation 10 0.16* Recidivism Mostly adolescence Adulthood
Assink et al. (2015) School/employment 63 0.15* Persistent Del Behav Childhood/adolescence Adulthood
Cottle et al. (2001) Substance abuse 6 0.15* Recidivism Adolescence Adulthood
Scott and Brown (2018) Female-Problematic family circumstances/parenting 12 0.14* Recidivism Adolescence Adulthood
Cottle et al. (2001) Low full scale IQ 5 0.14* Recidivism Adolescence Adulthood
Assink et al. (2015) Other 27 0.13* Persistent Del Behav Childhood/adolescence Adulthood
Scott and Brown (2018) Female-Antisocial peer relations 12 0.13* Recidivism Adolescence Adulthood
Scott and Brown (2018) Male-Education/school concerns 5 0.13* Recidivism Adolescence Adulthood
Assink et al. (2015) Family (problems) 273 0.12* Persistent Del Behav Childhood/adolescence Adulthood
Cottle et al. (2001) Victim of abuse 5 0.11* Recidivism Adolescence Adulthood
Assink et al. (2015) Attitude 19 0.10* Persistent Del Behav Childhood/adolescence Adulthood
Scott and Brown (2018) Male-Problematic family circumstances/parenting 12 0.10* Recidivism Adolescence Adulthood
Scott and Brown (2018) Female-Education/school concerns 5 0.10* Recidivism Adolescence Adulthood

Notes. Including only explanatory predictors with k ≥ 5, r ≥ 0.10 and p < .05. Persistent Del Behav = Persistence in crime was assessed through persistent (versus adolescence limited) trajectories of criminal behaviour during adulthood. Persistent Del Behav = Persistent Delinquent Behaviour. a = Reversed Protective Factor.

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Aggression and Violent Behavior 65 (2022) 101761

4. Discussion

This study addresses one of the major aims of developmental crim-
inology, which is to evaluate the childhood and adolescent factors that
precede or explain offending behaviour (Farrington et al., 2017; Loeber
& Le Blanc, 1990). To our knowledge, this is the first systematic review
of meta-analyses that maps all the well-established knowledge about the
developmental predictors of offending and to sort those risk/protective
factors according to their relative importance in predicting offending
and persistence in crime. In addition, this study is particularly useful
because the in-depth knowledge of these factors is crucial in developing
better theories and more effective assessment tools, interventions, and
justice policies.

We identified 11 meta-analyses addressing longitudinal predictors of
general offending, most of them showing statistically significant pre-
dictors; however, three meta-analyses did not present longitudinal
predictors of offending with effect sizes equal or larger than r = 0.10. In
addition, for most of the predictors that were assessed across meta-
analyses addressing persistence in crime, many effect sizes were also
small. These initial findings support the notion that complex events
influenced by a variety of factors, such as criminal behaviour, result in a
large network of statistically significant factors; however, some of those
factors may have low theoretical and practical relevance (Orben &
Przybylski, 2019). Therefore, this work is an opportunity to sort each
one of the meta-analysed predictors by their effect size and identify
major dimensions in criminal behaviour during child/adolescent
development.

4.1. Developmental predictors of general offending and persistence in
crime

The results of our systematic review of meta-analyses show that early
family-related factors had some of the larger effect sizes in predicting
general offending. Those family-related variables include family struc-
ture, home discord, (lack of) child-rearing skills, family stress (Derzon,
2010), level of parental knowledge (Flanagan et al., 2019), parental
management during middle childhood (supervision/discipline), and an
adverse family environment during adolescence (Leschied et al., 2008).

Three meta-analyses also highlighted the effect of child (Braga et al.,
2017; Derzon, 2010) and adolescent maltreatment (Braga et al., 2018)
on later general offending. These findings clearly support previous
psychobiological (e.g., Lee & Hoaken, 2007) and psychosocial (e.g.,
Kerig & Becker, 2015) approaches stressing the detrimental impact of
child abuse and neglect on later delinquency. In addition, child
maltreatment is exclusively (e.g., neglect) or often (sexual or physical
abuse) perpetrated by family members (Langevin et al., 2019; Papalia
et al., 2020). Moreover, children from dysfunctional families are
particularly at risk of being victims of maltreatment (e.g., Stith et al.,
2009).

Contrary to our expectations, dimensions like resting heart rate
(Portnoy & Farrington, 2015) or child internalizing concerns (Leschied
et al., 2008) showed small and/or non-significant effect sizes in pre-
dicting general offending. Whereas family predictors among children
and youths appear to be the most important predictors of general
offending (Flanagan et al., 2019; Leschied et al., 2008), among adoles-
cents with justice involvement, family problems are only one of the key
predictors of persistence during adolescence and adulthood (Assink
et al., 2015; Cottle et al., 2001; Scott & Brown, 2018). We identified five
key developmental predictors: occupation (education/employment)
problems (Assink et al., 2015; Scott & Brown, 2018), delinquent/anti-
social peers (Assink et al., 2015; Cottle et al., 2001; Scott & Brown,
2018), specific dimensions related to mental health problems (Cottle
et al., 2001), alcohol/drug abuse (Assink et al., 2015; Cottle et al.,
2001), and family problems (Assink et al., 2015; Cottle et al., 2001; Scott
& Brown, 2018). More primary research is needed comparing predictors
of offending versus recidivism (see e.g., Farrington, 2020). Ta

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M. Basto-Pereira and D.P. Farrington

Aggression and Violent Behavior 65 (2022) 101761

Families have a primary role in socialization and social learning, and
most developmental theories of offending have recognized the critical
role of families, particularly parents (e.g., child rearing skills, parental
supervision, caring families) in preventing versus promoting offending
(Farrington, 2006). It is possible that family problems not only predict
offending, but also play an important role as a potential cause of later
persistence in crime. In this regard, a series of systematics reviews have
shown important links between early family risk factors and school
dropout (Gubbels et al., 2019), unemployment (Bunting et al., 2018),
mental health problems, including addiction (Rasic et al., 2014), and
inadequate interactions with peers (Groh et al., 2014).

As the multiple systems of which a youth is part are contaminated by
psychosocial problems (e.g., delinquent peers, lack of parental super-
vision, mental health problems), the risk of recidivism appears to in-
crease. Therefore, while parental training and family support are
suggested as key components of interventions that prevent offending in
the first place, multisystemic approaches may be a more adequate
approach for youths with histories of criminal behaviour.

The predictive ability of mental health dimensions for persistence in
crime substantially changes across meta-analyses, which indicates that
inside the broader concept of mental health problems, some diagnoses
and psychopathological symptoms might be more or less important in
predicting persistence in crime. Interestingly, non-severe pathology,
which is focused on symptoms of anxiety, stress, and other general
psychopathological symptoms, is the most important predictor of
persistence in crime found across all meta-analyses (Cottle et al., 2001).
In addition, the Assink et al. (2015) meta-analysis found emotional and
behavioural problems as one of most important predictors. However, in
the opposite direction, the dimension of mental health assessed by Scott
and Brown (2018) had small effects for females and did not even reach
statistical significance in predicting persistence in crime for males.

Most of the current studies and developmental and life-course the-
ories (DLC) of offending have neglected the role of mental health vul-
nerabilities as important explanations for criminal career development
(for a review of DLC theories, see Farrington, 2006). It would be
important to understand, for example, if specific psychopathologies
linked to high vulnerability to stress or anxiety are important predictors
of relapse among justice-involved youths, and why. For example, is this
mediated by emotion regulation deficits? More research is needed. Also,
the long-term neurological and psychosocial impact of alcohol and
substance abuse on the development of youthful criminal careers is
underexplored across developmental theories of offending.

In contrast, most of the DLC theories take into account family dy-
namics, school/employment problems, and antisocial models as central
causes of youth antisocial behaviour (e.g., Farrington, 2006; Laub &
Sampson, 2001; Moffitt, 2018; Thornberry & Krohn, 2005). Nonethe-
less, the way each one of those theories operationalizes each one of these
constructs may vary (e.g., informal social control, attachment, social
learning). Thus, a deeper understanding of how each one of these risk
factors leads to the development of criminal behaviour is an important
line for future research.

Only one meta-analysis addressed the gender-specific roles of each of
the tested predictors across longitudinal studies addressed our research
questions. The way gender (and ethnicity) shapes predictors from crime
during development is one of the most underexplored topics across
meta-analyses. The important findings from Scott and Brown’s (2018)
meta-analysis suggest that there are similar effect sizes for males and
females in the most important predictors of recidivism, supporting the
hypothesis of gender neutrality for global risk factors. More primary
research is needed comparing risk factors for males and females in
relation to offending and recidivism.

There is also a lack of meta-analyses studying the longitudinal impact
of childhood biological and temperamental characteristics on later
offending or recidivism. From the few meta-analyses addressing indi-
vidual characteristics versus offending behaviour, the meta-analysis
conducted by Portnoy and Farrington (2015) shows a low resting

heart rate (r = 0.07) as a statistically significant predictor of later
offending. Also, the meta-analysis conducted by Cottle et al. (2001)
showed the role of low verbal IQ and low full-scale IQ as predictors of
recidivism across a limited number of longitudinal studies. Some meta-
analyses, not specifically addressing longitudinal predictors of crime
during the developmental period (and for that reason not included in
this systematic review), suggest an important role of other individual
characteristics in general offending, such as low self-control (Vazsonyi
et al., 2017) or low cognitive and affective empathy (Van Langen et al.,
2014). The role of many of these individual characteristics in predicting
childhood or adolescence in later offending or persistence in crime is still
underexplored.

4.2. Limitations

This systematic review is not free of limitations. First, it includes only
meta-analyses addressing explanatory predictors of crime evaluated in
the first 18 years of life. This decision allows us to focus our discussion
on early predictors of offending, but at the same time adult factors
promoting changes in criminal patterns later in life are neglected.
Because behavioural predictors such as conduct disorder were excluded
from consideration, our focus is on explanation rather than pure pre-
diction. Second, this systematic review includes only meta-analyses
addressing longitudinal predictors of crime, excluding overall effect
sizes from cross-sectional studies, and our discussion is focused on
predictors tested across five or more independent samples. This decision
allows us to guarantee that predictors precede offending outcomes and
to focus on predictors that are well tested across multiple studies,
reducing the risk of bias in our conclusions. Nonetheless, this decision is
not free from consequences, since it also substantially reduced the
number of studies included.

Third, measures of association depend partly on the true association
and partly on the methods of measuring the predictor and outcome
variables. For example, the product moment correlation r is based on the
assumptions that the variables are measured on equal-interval scales
(like height and weight), that they are normally distributed, and that
they are linearly related. Most variables in the social sciences violate
these assumptions. Therefore, differences in r values may reflect dif-
ferences in measurement methods rather than differences in the true
underlying association (unless all variables are measured in the same
way to make them comparable). The same problem applies when OR
and d values are converted into r values; the conversion formulae are
based on assumptions that may be violated by the nature of the vari-
ables. Nevertheless, large differences in r values probably reflect real
differences in predictive efficiency.

Lastly, this work includes and compares overall effect sizes from
meta-analyses that include studies from different years and use different
inclusion/exclusion criteria and analytic strategies; this may have
introduced some bias in our conclusions. Also, it would be desirable in
future meta-analyses to investigate which variables predicted outcomes
after controlling for (independently of) other variables. Nevertheless,
the most important findings of this work are replicated across different
meta-analyses, despite the bias introduced by methodological discrep-
ancies across studies.

4.3. Final conclusions

Family factors (parental supervision/parental warmth, family
structure) are the most important childhood and/or adolescent pre-
dictors of general offending, followed by child maltreatment. Among
adolescents already involved in the justice system, there are five major
predictors of persistence in crime across meta-analyses, namely, edu-
cation/employment problems, delinquent peers, family problems,
alcohol/drug abuse, and specific forms of mental health problems.

Our findings support the crucial role of programs working with
families, particularly parents, with the aim to prevent offending in the

M. Basto-Pereira and D.P. Farrington

Aggression and Violent Behavior 65 (2022) 101761

first place (see e.g. Farrington, 2021). Among juvenile justice youths,
there is a constellation of long-term problematic factors explaining
persistence in crime. Programs to prevent recidivism should evaluate
and intervene in each of the above-identified factors (e.g., school failure,
psychopathology, families, relationships with peers, addiction prob-
lems) that could cyclically create the perfect conditions to recidivate.
Since many of those predictors might be avoided or attenuated by a
healthy family environment, programs promoting desirable parenting
and strengthening families should be the top policy priority.

Declaration of competing interest

None.

Acknowledgements

We are grateful for the reviewers’ valuable comments. Thank you for
your help in improving our manuscript.

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Developmental predictors of offending and persistence in crime: A systematic review of meta-analyses

1 Introduction

1.1 The current study

2 Methods

2.1 Search process and eligibility criteria

2.2 Study selection and data collection

2.3 Synthesis of results and analytic strategy

3 Results

3.1 Selected meta-analyses

3.2 Study characteristics

3.3 Summary of the meta-findings

4 Discussion

4.1 Developmental predictors of general offending and persistence in crime

4.2 Limitations

4.3 Final conclusions

Declaration of competing interest

Acknowledgements

References

Associative stigma and other harms in a sample of families of heavy drinkers in
Lithuania
Ilona Tamutienea and Anne-Marie Laslettb,c,d

aDepartment of Public Administration, Vytautas Magnus University, Kaunas, Lithuania; bNational Drug Research Institute, Curtin University,
Melbourne, Australia; cCentre for Alcohol Policy Research, School of Psychology and Public Health, La Trobe University, Victoria, Australia; dSchool
of Population and Global Health, University of Melbourne, Melbourne, Australia

ABSTRACT

Introduction

: The main aim of this study is to identify and contextualize the harms Lithuanian families
experience when they include a heavy drinker. Methods: Twenty-four qualitative interviews with coha-
biting spouses, and ex-partners of heavy drinkers were conducted in 2013–2014 and analysed for
emergent themes.

Results

: Interviewees experienced an array of harms. These were categorised as:
direct harms caused by the drinker; drinker-centred coping strategies which did not take children’s
(and other adults’) needs into account and affected family members indirectly; abdication of or redirec-
tion of the drinker’s responsibilities to other family members; associative (reflected) stigma and isolation.

Discussion

: The direct harm caused by the drinker is only one fragment of alcohol’s harm to others. The
drinker’s family members are stigmatised and commonly take on the usual roles and responsibilities of
the drinker, with this particularly the case for women and their children. Internalisation of responsibility
and drinker-centred coping styles also result in neglect of other family members’ needs. Conclusions:
There are multiplicative effects from one family member’s heavy drinking on others, affecting adult
members and children as they develop.

KEYWORDS
Alcohol’s harm to family;
redirected responsibility;
stigma; isolation; synergistic
harm effect

ARTICLE HISTORY
Received 18 April 2016
Revised 6 August 2016
Accepted 17 August 2016
Published online 16
November 2016

Introduction

An introduction to the Lithuanian context and harms to
families from heavy drinking

In 2010, Lithuania had nearly the world’s highest rates of
alcohol use and related deaths (World Health Organization,
2014). Lithuania, like other Baltic states and Eastern Europe,
has a history of heavy drinking and a culture which tolerates
drinking to intoxication (Popova et al., 2007). On average 14.9
liters of pure alcohol were consumed by every resident aged
15 or older in 2014 in Lithuania (Statistikos, 2014). Almost
one third (30.9%) of deaths in Lithuania were alcohol-
attributable. The alcohol drinking problem was identified in
48.3% of men and 16.2% of women (Kalasauskas et al., 2012),
and harmful use of alcohol (drinking till drunkenness) was
self-identified by 13.5% of women and 25.7% of men
(Tamutienė, 2014). Such alcohol use is also harmful to the
people drinkers know and interact with. For instance, this is
confirmed by official data on domestic violence. No less than
75% of perpetrators were intoxicated when they committed
domestic violence in Lithuania in 2014 (Informatikos, 2015).

Quantitative research conducted in other countries has
similarly shown that the heavy drinker’s family members
commonly experience harm. In Northern European countries,
alcohol-related problems because of a range of people, includ-
ing family members, were reported by one in four to one in
two respondents (Ramstedt et al., 2015). Seventeen per cent of
Australians reported being affected by the drinking of a family

member in the past year (2010). In New Zealand, respondents
with heavy drinkers in their lives experienced reduced perso-
nal wellbeing and poorer health status because of these drin-
kers (Casswell et al., 2011). In Finland, there is a substantial
body of work on the effects of heavy drinking family members
on spouses and children, detailing additional harms, including
hospitalizations, poorer mental health, and removal of chil-
dren (Holmila, 1987, 1994, 1997; Holmila et al., 2011;
Raitasalo, 2011; Raitasalo et al., 2015). Harm from heavy
drinking family members, including intimate partner vio-
lence, is near universal, also being common in the Americas
and around the world (Cahalan, 1970; Graham et al., 2008).

Qualitative analyses by Orford et al. (2005) describe how
people in different cultures (commonly from studies of people
in contact with the treatment system) show similarities in
their core experiences, revealing stress, strain, coping, and
support among members of problematic drinkers’ families.
Cultural differences are also apparent. For example, threats
to individual autonomy were revealed more in England, gen-
der inequalities and poverty were highlighted in Mexico City,
and the public nature of excessive drinking and associated
violence were noted in an Aboriginal Australian community
(Orford et al., 2005). More recently, in Australia, Manton
et al. (2014) reported that the nature of harm experienced
by children and families in the general population was similar
to that identified previously by Orford and others in treat-
ment systems (2005), including physical abuse, verbal abuse,
emotional abuse (including emotional neglect), threat of

CONTACT Ilona Tamutiene i.tamutiene@pmdf.vdu.lt Department of Public Administration, Vytautas Magnus University, V.Putvinskio str. 23-514, Kaunas,
44243 Lithuania.

JOURNAL OF SUBSTANCE USE
2017, VOL. 22, NO. 4, 425–433
http://dx.doi.org/10.1080/14659891.2016.1232760

physical abuse, fear of physical harm, sleep disruption, wit-
nessing of conflict (fights, physical abuse, verbal abuse), wit-
nessing of drinking and inappropriate behavior, as well as fear
of health risks their parents were taking. They noted that
although a number of children were doing well, others were,
for example, scared and needed to sleep with their mother, or
experienced longer-term behavioral effects, including beha-
vioral problems, shame and embarrassment and schooling
instability (Laslett et al., 2015). Intimate partners of men
who drank heavily reported physical, verbal, and emotional
abuse, financial insecurity, stalking, and damage to their
property, as well as the drinker controlling whom they contact
and monitoring and derision of what they do (Laslett et al.,
2015). The impacts on the children’s families of the harm
from others’ drinking resulted in police interventions to pro-
tect partners and children, as well as loss of custody, break-
down of parents’ relationships, issues of access to children
after separation, and financial insecurity. The quality of the
relationship with children was affected, and there was diffi-
culty with separation if the drinker was an adult son rather
than a partner (Laslett et al., 2015).

Additionally, “associative” stigma isolated family members
and prevented them from bringing home friends or attending
social occasions because of shame and embarrassment (Park
& Park, 2014). Goffman defined stigma as “undesirable, “dee-
ply discrediting” attributes that “disqualify one from full social
acceptance” and motivate efforts by the stigmatized individual
to hide their mark whenever possible” (Goffman, 1963). The
process by which a person is stigmatized by virtue of associa-
tion with another stigmatized individual has been referred to
as “courtesy” (Goffman, 1963) or “associative” stigma (Mehta
& Farina, 1988)”. Associative stigma is directed by others
(including the drinker, others, society, and the affected family
member themselves) away from the drinker and toward the
family member. This may also occur when governments per-
ceive family drinking problems as “personal matters”
(Valstybinio, 2015).

Despite increasing literature on harm to others, there is
limited research on alcohol’s harm to others in the Baltic
States and a need for greater in depth analysis of what alco-
hol’s harm to others means in different settings, including for
those outside the treatment system. Members of Al-Anon
potentially comprise an accessible sample – beyond family
members in formal treatment yet involved in peer support –
and have been studied in the 1960s and 1970s (Ablon, 1979),
as well as more recently (Zajdow, 2002). Al-Anon is a mutual-
help organization for family members and friends of alco-
holics which uses a program of recovery adapted from
Alcoholics Anonymous (AA), though it is a separate organi-
zation from AA (Humphreys, 2003). While these groups may
provide perspectives of families in severe or crisis situations,
there is still much to be learned from these families about the
types of problems they face and how they cope.

Neither quantitative nor qualitative studies of alcohol’s
harm to others have been undertaken in Lithuania, and this
area of research has been largely under-researched in low and
middle income countries. Drawing on the range and magni-
tude of alcohol’s harm to others studies (Laslett et al., 2010;
Room et al., 2010) and the qualitative analyses of Manton

et al. (2014), this study examines alcohol’s harm to families in
a Lithuanian sample of heavy drinkers. The main aim of this
study is to use qualitative interviews in order to contextualize
and identify the harms families experience when they include
a heavy drinker. Heavy drinkers were defined by the inter-
viewees as “alcohol abusers” or “alcoholics”. In this paper, the
authors have used the term heavy drinker throughout,
although the terminology of the interviewees has been
retained in quotes.

Research methodology

This qualitative study employed a grounded theory method
(Glaser and Strauss, 2009), which necessitates developing,
refining, revising, and synthetizing new understandings of
previous studies and the interviewees’ experiences (Charmaz,
1990). Accordingly, this study was informed by the analytical
framework of Room and colleagues (2010) on alcohol’s harm
to others which identified the relationships and roles likely to
be affected by a heavy drinker, including the spouses and
partners of the heavy drinker, colleagues, friends, and society.

Data collection procedures and tools (sampling,
collecting, and analyzing data)

Qualitative semi-structured individual interviews were used to
understand the experiences of interviewees, including their
perceptions of harm, feelings and communication with the
drinker. The qualitative data was collected by the first author
in May and June of 2013, and in July of 2014. Al-Anon group
members (all women) and AA members were the primary
informants and were the links by which other informants
were reached via snowballing. Participants were purposively
sought via Al-Anon and secondary contacts in order to
include respondents who were: still living with, divorced,
and separated from the problem drinker; living with and
without children; and who were reported to be actively and
passively coping. Keeping in mind that self-stories can be
changed and re-conceptualized within the context of the Al-
Anon group (Zajdow, 2002), respondents were asked whether
they had experience of self-help group practices and more
than half of the informants were selected to ensure they did
not have any experience of self-help groups. One man was
reached through AA, because his wife was attending AA
meetings. The three other men were secondary contacts iden-
tified through the snowballing process. Six men were
approached who were divorced and/or living in new families
but were not willing to talk about their past experience. An
additional five men were not retained in this sample, because
during the interviews they were found to also be heavy drin-
kers. Only informants who identified themselves as current or
ex intimate partners of heavy drinkers were retained in this
sample.

The interview tool focused on the harm experienced by
interviewees related to their partner’s or ex-partner’s drinking.
The primary research questions were about direct harms from
the heavy drinker experienced in the family setting, and the
ways in which the interviewees coped. The interview began
with demographic and general questions and then asked:

426 I. TAMUTIENE AND A.-M. LASLETT

What does it mean to be a spouse or partner of a heavy
drinker? What core feelings arise being the heavy drinker’s
spouse or partner? What direct harms have you experienced
from this drinker? How do you cope with these problems?
How has being the spouse or partner of a heavy drinker and
experiencing direct harm affected your family life and your
relationships with other relatives, friends etc.?

Textual analysis, coding and continued sampling were
iterative and developed as part of the research process by
the first author. After each interview audio data were tran-
scribed into Word, reflected upon, and coded into main
categories and subcategories. During the first research stage
in 2013, 14 interviews were collected and core categories were
extracted. The research questions were reflexive and flowed
from the interviewees’ experiences of alcohol’s harm in their
families and related to the physical, social and environmental
harms caused by the drinker. The themes that emerged e.g.,
that spouses and partners took or refused responsibility for
their drinking partners, as has been described as “courtesy”
(Goffman, 1963) or “associative” stigma (Mehta & Farina,
1988), were probed more deeply in the second round of
interviews. Thus, information was gathered in a further 10
interviews in 2014 via theoretical purposive sampling and
reflective interviewing (Charmaz, 1990; Flick, 2009).
Emergent themes were integrated into emerging theories.
Sampling was finished when theoretical saturation of emer-
gent themes had been reached and pragmatically due to
financial and time constraints.

Settings which allowed private conversation were used to
conduct interview sessions and included a room at the uni-
versity, interviewees’ private homes, and Al-Anon meeting
places in a small regional city of Lithuania. The interviewing
and coding was done in Lithuanian, and only the quotes were
translated into English. The average interview time was 1 hour
and 35 minutes.

Interviewees
In total, 24 individual semi-structured qualitative interviews
with heavy drinkers’ partners and ex-partners born and living
in Lithuania were conducted. Twenty interviews were con-
ducted with females who had or currently lived with male
heavy drinkers and four with men who had or currently lived
with female heavy drinkers. The youngest interviewee was 23
years old, the oldest was 62. They had completed either basic
or university education. Five women had 15–25 years of
experience living with a male heavy drinker; others had one
to five years of experience. Twelve females still lived with a
heavy drinker, six females were divorced, and four of them
had formed new families, two still lived without partners. Ex-
partners of the heavy drinkers were divorced or separated one
up to eight years. Five females had adult children, the rest of
the interviewees had from one to five children aged under 18
years living at home. Two females were unemployed, the rest
of the interviewees were employed. Four women were actively
engaged in Alcoholics Anonymous, and five in Al-Anon
groups. While the initial contacts were from Al Anon, the
majority of the interviewees were not in formal counseling or
peer support organizations.

Ethics
All participants were informed about the research aims and
their rights, and their consent was given voluntarily.
Participants could withdraw from the interview at any
time – one woman did so as the interview broached a trau-
matic incident. The ethical principles of autonomy, benefi-
cence, and justice were ensured. The personal information of
all respondents has been removed in order to ensure anon-
ymity and confidentiality. The study protocol was approved
by the Lithuanian Council of Science and the European Social
Fund (VP1-3.1-SMM-07-K).

Results

Direct harm from the drinker

Speaking with the spouses and partners of the heavy drinkers,
harms to others affected not only the interviewees themselves
but also children, mothers, sisters, brothers, uncles, aunts,
grandparents, and others. Members of the heavy drinker’s
family suffered various kinds of violence and abuse. Most
commonly this was emotional abuse, but in a two-thirds of
interviewees the violence was physical and financial or a
combination of several types. The experiences of the female
informants demonstrated that inebriety was a significant risk
factor for experiencing violence:

“I was hurt and beaten by men. They would just get drunk and
that’s it, they didn’t attack me sober” (Female, age 32).

All the participants of the qualitative research, without
exception, experienced disrespect, humiliation, contempt and
other instances of emotional abuse. All of them suffered
physical violence, which ranged from a shove to serious inju-
ries; third of the participants revealed that they experienced
sexual violence. All of them without exception confirmed that
there were episodes when the heavy drinker was intoxicated
and the respondent had to take care of all family matters, and
often of the drinker himself (after particularly heavy
drinking):

From the informants’ perspective, the greatest harm in the
situation of the drinker’s harm to the family was experienced
by the children. The mothers who participated in the research
observed how the drinker had injured the children. For exam-
ple, one child was beaten because he poured out alcohol.
Another child was hurt in response to crying:

“the little one was injured because of his crying” (Female, age 43).

In other situations, children were indirectly affected when
they witnessed harm inflicted upon others. Some of the
mothers described how the drinker harmed them and their
unborn children even before their babies were born:

“When I was 8 months pregnant, he punched me in the stomach”
(Female, age 48).

Alcohol’s harm in the family may influence every impor-
tant aspect of family functioning. Direct harm is but one
aspect of the totality of alcohol’s harm to family members.

JOURNAL OF SUBSTANCE USE 427

Associative stigma and isolation

Our findings show that associative stigma and isolation are
common to all of these women – because of the family
member’s drinking. On the one hand, women suffer shame
because their family members drink. On the other hand, they
feel society’s pressure to change the situation, and, when they
fail, they feel disappointment. In this context, social isolation
is the woman’s (and the family’s) coping strategy; chosen in
order to minimize emotional pain and shame. The situation is
illustrated by this typical experience:

“I was ashamed about the divorce, because what will people say?
Now, when I’m looking back from a distance, overall I had a
project, when I married, to save my husband and I failed…. And
10 years passed, I failed, but I couldn’t admit on the outside that I
failed, that I caved in. I didn’t even get divorced because of this….
It’s better that nobody visits me, nobody sees and knows, and I’ll
live isolated like this” (Female, age 51).

The family’s responsibility for the drinker’s behavior is
followed by isolation, because neither the relatives nor the
employers want to be involved, especially when the drinker,
who is unable to take care of himself, has to be handled, or
when they are confronted with the harm the drinker causes.
This is illustrated by the following experience:

“His colleague calls me and asks me to come and do something,
because he is lying down drunk in the workshop. I was already
going to the support group. So I said, if he is uncomfortable, and
they drank together, then he should take care of him. You can’t
even imagine the pressure I had to go through from his colleagues
and relatives, even my closest brothers, who seemed to understand,
blamed me in this situation’(Female, age 55).

These experiences of women living with heavy drinking
husbands revealed that social isolation was exacerbated
though continuous and repeated incidents:

“This isolation is also because you comfort yourself once, twice, six
times, 106 times, and you realize, how much longer can you cry
about the same thing?” (Female, age 55).

There are indications that men living with heavy drinking
women experience the same feelings of shame, pain, frustra-
tion and worry, and have to take on all of the family respon-
sibilities when their spouse or partner is drinking:

“Being at work I was worried about the safety of our child. Many
times I found her drunk with my son in the park, and I had to take
care of them both. I felt shame, pain and pitied myself being
married to an alcoholic woman” (Male, age 41).

Our research underlines that associative stigma can be
reinforced by assumed or redirected responsibility for the
drinker’s behavior.

Assumed or redirected responsibility, associative stigma,
and release

Assumed or re-directed responsibility and associative stigma
emerged as themes in the interviews in a gender specific
context. It is true that men living with heavy drinking
women took on the responsibilities of the family duties
when their wives and partners were drinking. But the societal
attitudes to this situation seem different to when the partners

of heavy drinkers are women. It seems men more often
experienced pressure from relatives, friends and general
society to divorce their heavy drinker wives and find other
women who would take on the traditional wife and mother
roles:

“People have said openly that I am stupid enough to live with an
alcoholic. They said that I’m a good man and I can find another
woman who will love me and take care of my children” (Male, age
32).

Women whose husbands drank heavily expressed opinions
consistent with publicly espoused attitudes that in Lithuania
the woman is responsible for the man, including his drinking
habits. Interviewees reported that even before marriage they
had seen their men drinking too much and too often, but they
believed in romantic love, for example saying that:

“if he loves me, he won’t drink” or “my love for him is too great, he
will understand and won’t drink”, “he’ll stop drinking when the
children are born” etc.

This assumed responsibility (assigned by society and cul-
tural pressure) for being able to moderate their partner’s
drinking increased the perceived guilt, tension, shame and
pain. This is confirmed by a typical experience of an
informant:

“It was very painful to admit that the person I love drinks. From the
mother-in-law’s side, I’d heard ‘she doesn’t handle the husband’.
Well she did give me a good one, but others handle theirs, whereas
you can’t control yours… This attitude creates [the attitude] inside
you that you are responsible and must control him, take care of
him, follow, check, and make a person out of him. And then, when
you fail, you end up being a victim. You try to save him, follow
him, work as a detective, and when it all finally collapses you
become helpless, a complete victim with inner pain and a wound”
(Female, age 55).

Every interviewee who had indicated that they had a heavy
drinking family member also pointed out that when the
family member drank, they had to take care of all family
matters. This involved not only simple care of the children,
housework and other practical things, but additional specific
tasks such as managing the place of the drinking, cleaning up
after broken windows and furniture, etc. This harm involved
substantial time and cost, along with the worry about their
own and their children’s safety, and, quite often, the drinker’s:

“when he came back drunk and threatened, I had to run away from
home together with the children” (Female, age 43).

“I had to chase him and his friends away from home numerous
times so that he would stop drinking” (Female, age 32).

“so many times he’d returned beaten and bloody, I even had to call
the paramedics” (Female, age 51).

The drinker’s family members were burdened not only by
the burden of practical work and the care of the drinker, but
also by the financial weight of the drinker’s financial abuse
and the poverty:

“we sank into debt, we ran up debt for the apartment, no car, no
anything” (Female, age 43).

According to the interviewees, especially those older than
30, the woman assumes the main responsibility for managing

428 I. TAMUTIENE AND A.-M. LASLETT

the household budget, as he “brings money home and gives it to
the woman”, who controls it (this norm was generally common
in Lithuania). The typical experience of this woman shows that
she tried to control the family’s income by buying a large
amount of food, because the rest of the money “was simply
taken nicely or by force by the man, when he needed to drink”.
Unemployment of the heavy drinker, which was quite frequent,
also contributed to the financial difficulties.

Some of the women who participated in the qualitative
research revealed that they had sought help from a range of
public institutions. All of these interviewees reported positive
features, although over half mentioned negative experiences,
especially regarding the police. The range of supports and
service responses available and their effectiveness should be
researched further. For instance, in Lithuania, the police can
act to separate violently abusive drinkers from the family for
two days. However, informants reported that “nothing changes
because of this arrest, he comes back and it gets even worse”.
Another woman reported that when the police arrived, they
only moralized and asked her whether she was going to
submit a statement about the domestic violence. A third
female informant claimed that the police officers who came
accused her of not being able to handle the drunk husband:

“why are you picking on him, if the husband is drunk, everyone
drinks, then don’t provoke him and you won’t have to call the
police” (Female, age 28).

In this gendered Lithuanian traditional culture the man’s
drinking is normalized, even to the point where the man
cannot fulfil the responsibilities of the typical masculine role
because of it. The woman, meanwhile, has responsibility for
“the family image”. This social norm implies that she “has to
handle the man”, “make a person out of him” and makes plain
the societal condemnation of the heavy drinker and their
wives, who are “unable” to change their husbands.

The Lithuanian government is reluctant to acknowledge
and address the harm caused by drinkers within the family,
viewing this as a personal matter . This reinforces the notion
that the responsibility is that of the family and compounds the
associative stigma. The direct harm from the drinker and the
associative stigma from redirected responsibility for the drin-
ker’s behavior promote drinker-centered coping strategies.
Having a person who understands the problem, and guides
the family member to relevant services (helpline, court etc.) is
a crucial point in releasing the family member from the
redirected responsibilities:

“My colleague at work helped me to realize that I have my own
needs. I live with him now. He is such a good man” (Female, age
28).

“My social worker helped me to exit an unhealthy relationship, I
learnd to meet my children’s and my own needs” (Female, age 41).

“I met very understanding women” (Male, age 35).

Drinker-centered coping strategies vs. recognizing and
realizing one’s own needs

The majority of interviewees reported that they tried to cater
to the whims of the drinker for the sake of peace. Adult family

members tried various strategies to evade harm: from passive
avoidance (trying not to provoke), motivating the drinker to
treat their addiction, to physical violence against the drinker:

“I tried nicely, I also beat him, just to make him stop drinking, but
nothing helped” (Female, age 23).

Others became involved in the drinking themselves:

“I used to do all kinds of things, I also drank, just so he would have
less, and I vomited. I got lost in the drinking for a while, to avoid
pain and to relieve it…” (Female, age 51).

The heavy drinker’s family members, especially the spouses/
partners, became so involved in solving the drinker’s problems
and in avoiding potential harm that they overlooked their own
needs. This situation is illustrated by the following quote:

“I was pregnant with my second child. I spent all day making every
effort, gathering the alcoholics, buying vodka, just to ensure that he
wouldn’t go and hang himself. What would I tell his mother? How
would I be? I couldn’t live. All day I put in a lot of effort. I don’t
remember where my daughter was, definitely not with me….
After that incident, the [foetal] movement disappeared, after a
celebration, a couple of days later I went to the doctor and told
him passingly that there was less movement, he quickly checked me
with ultrasound, eyes all scared, I have to go to the local city
hospital promptly. But I don’t want to go. I can’t leave him, not
the children, I can’t leave my husband” (Female, age 51).

As can be seen in this quote, the mother’s care for her
husband and the fear that he would commit suicide and about
what she would tell her mother-in-law is so deep, that she is
unable to think about her own needs, about the unborn child,
and she completely neglects her daughter, as did her partner.
The most common of these strategies is the suppression of
noise from the children, which shifts the responsibility to
them for the father’s reactions, because his anger may be
provoked:

“kids, father has returned, everyone go to sleep, everyone be quiet,
because [otherwise] discipline will begin… ” (Female, age 51).

These coping strategies are drinker-centered, characterized
by neglect of the children’s and other family members’ needs,
and their constant tension and worry. This context by itself is
harmful to the child, and the damage is increased by explo-
sions of uncontrolled negative emotions of the spouse:

“It’s common to swallow an insult, neglecting yourself, a humilia-
tion, just to ignore it, not to argue or say that I dislike this
behaviour, or that I’m offended. …And it [my temper] then
explodes at the smallest child, because you can’t really shout in
public. I exploded at the children and to this day it’s hard for me to
deal with it” (Female, age 48).

This honest reflection by an informant reveals not only the
cumulative aspect of the harm, but also the synergic effect of
the harm on the child from the father and the mother in the
family (because the child, like the mother, suffers direct harm
from the father, and also suffers harm inflicted by the mother
due to the stress she is under). Two other examples describe
further ripple effects on family members’ decisions:

“the older one dropped out of school and got a job to contribute to
the family’s budget” (Female, age 51).

JOURNAL OF SUBSTANCE USE 429

“the older one started dealing drugs, because he saw the critical
situation of the family, he felt responsible for the younger brother
and sister” (Female, age 55).

These excerpts show early assumption of familial responsi-
bility and hint that the children not only suffer direct harm and
are drawn into the parents’ coping and harm evasion strategies,
but also are themselves reacting to the harm and looking for
their own ways to deal with these situations. Interviewees
engaged in Al-Anon groups reported self-help as one of the
major coping strategies which helped them to reconstruct their
lives, and to recognize and realize one’s own needs:

“I feel very strongly supported by the others members of our group,
especially when I feel down during the periods of my husband’s
heavy drinking” (Female, age 47).

Our findings reveal that not all interviewees who were ex-
partners of the drinker had assumed either responsibility for,
or taken on the responsibilities of, the drinker (redirected
responsibility) and instead developed a withdrawal strategy,
divorcing the partner, and/or recognizing their own needs
and the limits of their responsibility and made changes to
their relationship. A woman with two children shared her
experience on how she decided to divorce:

“I tried really hard to take care of the family, tried to influence him
to stop drinking. The crucial turning point in my decision to divorce
him was the realization that I don‘t want be a wife of an alcoholic,
that I’m not like myself with daily negative feelings, that my
children need me positive and optimistic, and healthy. I was
already convinced that I can‘t change my husband, that it is his
responsibility. I had divorced him and therefore do not regret”
(Female, age 47).

Of course, removing oneself from the situation means that
the family composition changes and there are additional
redirected responsibilities, but the partner in this situation re-
exerts control over his or her own situation. However, chil-
dren have no such choice, and other evidence suggests that
women leaving relationships are particularly vulnerable to
intimate partner violence and may need additional protection
and support at this time. For the interviewees very important
was informal support:

“When he injured me, I realized that this will continue. I was
afraid. My self-esteem and my body was hurt. I didn’t want
children to see such a situation. My mother and my sister helped
a lot. I came back to my mother’s home with my children. Security
was more important to me than what others said. I was surprised
that there were more people who helped than condemned me”
(Female, age 46).

One ex-partner man and two ex-partner women reflected
the role of informal supporters who helped them through a
difficult period of life, encouraging them to divorce, and
giving temporary shelter. For some of them the former life
seems like a bad nightmare:

‘My friend supported me, gave me shelter. Her help was so valuable,
because I was so sad and angry, and even had the thought to kill
her (drinker) or myself. I remarried. The life with my ex-wife I
remember like a bad nightmare now’ (Male, age 36).

Recognizing and taking responsibility for realizing one’s
own needs and the needs of children, as well as resistance to
the assignation of redirected responsibility for the drinker’s

behavior and “family image”, along with the informal support
from others, were the major factors which allowed informants
not to be entrapped in drinker-centered coping strategies. The
experiences of the complex harms due to the heavy drinking
marked all interviewees’ biographies. The harm done by an
intimate partner’s heavy drinking are long lasting.

Discussion

While the group varied in age, educational level, employment
status and family composition, the majority were employed,
middle aged women born and living in Lithuania who were
not engaged in formal treatment (although nine were involved
in Al-anon and AA). The way individuals experience and
respond to heavy drinking situations within families may
vary by gender and in different cultural groups and by a
range of other factors including the level and types of support
available. Yet, despite differences in cultural backgrounds the
numerous years of living with a heavy drinker meant that the
majority were affected, often substantially.

A number of themes emerged in the qualitative interviews
with the spouses and ex-partners of heavy drinkers. Heavy
drinkers directly harmed others in the family unit (more
deeply when they were still living in the family, but also if
they were no longer together). Drinker-centered coping stra-
tegies adopted by the interviewees often unintentionally
meant children’s (and other adults’ needs) were disregarded
or de-prioritized. The interviewees had no choice but to take
on the responsibilities the heavy drinker was incapable of
when intoxicated. Additionally, a number of spouses felt
pressured to assume responsibility for their spouse’s drinking
and/or their behaviors while drinking. It seems that tradi-
tional gender roles pressure women to take on the responsi-
bility of their spouse’s or partner’s behavior. Holmila
described a similar phenomenon of assumption of “over-
responsibility” for problem drinkers by intimate partners in
Finland (Holmila, 1994). Interviewees in Lithuania felt not
only shame, but also experienced a sense of responsibility for
the situation they found themselves in and could not control.
This resulted in greater isolation and associative stigma.
However, other spouses rejected this “responsibility” and
employed strategies to regain autonomy and control.
Withdrawal from drinker-centered strategies were employed
and resulted in resolution for a few, albeit with exacerbation
of concerns (particularly when they were leaving the relation-
ship). These categories of harm are interrelated. Family mem-
bers suffer direct harm (ranging from passive harm when the
drinker does not perform their duties to severe violence) in
private, and experience associative stigma and isolation in
public.

Our modest findings about the experiences of men living
with heavy drinking women, show that they experience simi-
lar feelings and take on the majority of the family responsi-
bilities for when their female partners are drinking. Orford
et al. (2005) noted that husbands of drinkers’ also experience
signs of strain, threats to home and family, worry about their
relatives, and a range of stresses. However, Orford et al.
(2005) also described previous studies which found “husbands
of women with drinking problems, when they had been

430 I. TAMUTIENE AND A.-M. LASLETT

noticed at all, had been described in very unsympathetic
terms, being stereotyped as men who left their wives at the
earliest opportunity (p. 187)”. Our findings indicate that dee-
per study should be undertaken in this area, because it seems
that both traditional and changing gender roles may be in
play in Lithuania, with men taking on caring roles yet still
more likely to be pressured and encouraged (than women) to
“to find another woman who will love and take care of them”.

It seems family members of heavy drinkers in Lithuania
experience similar harms to family members in other coun-
tries (Arcidiacono et al., 2009; Orford et al., 2005). Stark
(2007) underlines that the violence paradigm provides an
incomplete description, and that coercive control where
women are entrapped in relationships has a more profound
negative impact on women than the violence itself. Our qua-
litative findings support Pescosolido et al.’s (2010) theory that
stigma is embedded in the social and cultural norms, includ-
ing prejudicial attitudes, that discredit individuals, marking
them as tainted and devalued. On a societal level, stigma
becomes attached to these families. Corrigan et al.’s (2005)
research with adolescents about the stigma of mental illness
and alcohol abuse support the notion that stigma may be
passed on from stigmatized people to members of their social
network. Much research has shown that the drinker experi-
ences stigma (Room, 2005; Schomerus et al., 2011a, b; Luoma
et al., 2007; Schomerus et al., 2014). But, associative stigma
research, meanwhile, is just beginning. Research conducted in
South Korea revealed that the overall level of stigma perceived
by family members was significantly higher than that of their
ill relatives (Song et al., 2015). The qualitative research pre-
sented in this article provides evidence of this associative
stigma and isolation of family members and should be
researched further.

Our findings fit into the conceptual model of family stigma
presented by Park & Park (2014), and provides the cultural
context in the case of the family of a heavy alcohol user in
Lithuania. Societal norms in Lithuania redirect the responsi-
bility for family functioning, including a spouse’s behavior
within the family, to women. Assumed responsibility for
drinker behavior reinforces drinker-centered coping strategies
and disregards the woman’s needs, as well as her children’s.
Rejection of this assumed or redirected responsibility for the
drinker’s behavior, often by separating or divorcing the man,
was a strategy that enabled the woman to attend better to her
own and the children’s needs. To have a confidant who does
not judge but understands and supports is crucial for the
release of redirected responsibility and realization of the
woman’s and her children’s needs Brief intervention coping
strategies such as those developed by Velleman et al. (2006)
should be evaluated for use in Lithuania and may be useful for
people affected in these situations We are in agreement with
Holmila et al. (2011), who conclude that children’s ways of
coping can differ from those of adults and need further
exploration and support.

Family members of people who drink heavily in Lithuania
suffer from fundamentally similar harms to those in other
countries. As Barnard (2007) in the UK found, this study’s
qualitative findings revealed that direct harm from heavy
drinkers to family members is only one (severe) piece of the

totality of alcohol’s harm to family members. Contextualizing
the harm experienced in these families, it is argued here that
alcohol’s harm to others extends beyond the direct harms
reported within the family unit.

New understandings about alcohol’s harm to others arise
from these qualitative findings. The flexibility of the qualita-
tive methodology, including the grounded theory approach,
allowed the researcher to go beyond the direct harm from the
drinker to explore an array of additional associated harms in
public and private settings, including new and unanticipated
themes e.g. assumed responsibility, covering for the drinker
and associative stigma. A picture of complex harm to the
drinker’s family development emerged. The diverse experi-
ences of direct violence, family reactions to violence, assumed
and redirected responsibility for another family member’s
drinking, associative stigma and isolation, in the context of
cumulative harm, create a synergistic effect – a larger extent of
harm – which diminishes satisfaction of other family mem-
bers’ needs, their personal development, quality of life, and
capability building.

Grounded theory based qualitative findings add to the
alcohol’s harm to others field by contextualizing the experi-
ence of alcohol’s harm to others and by making clear the
multiplicative (or synergetic) harm to the drinker’s family.
Direct violence, family reactions to this violence, responsibil-
ity for making up for another family member’s drinking,
stigma and isolation are cumulative. This expanded concept
of harm to others subjugates other family members’ needs,
their personal development, quality of life, and their capacity.
There are substantial follow-on effects for families and sup-
port services.

Limitations

The sample was limited only to the spouses and intimate
partners of heavy drinkers. Nine interviewees from twenty
four were connected to Al-Anon or AA, and their views
could be influenced by the material on AA and Al-Anon
ideology. Finding men who identified themselves as ex or
current intimate partners of heavy drinkers was difficult. In
Lithuania Al-Anon is not viewed traditionally (as a treatment
option) for men, and many men approached were reluctant to
talk about their past experiences. The qualitative data repre-
sents more a feminist perspective and the views of wives,
female partners or mothers, because of the small number of
interviewed males. Voices of more men and other relatives
should be added in future studies, perhaps by approaching
family support agencies or other formal organizations such as
child protection agencies or social services departments
(although there may be confidentiality issues that would
need to be managed in doing so). Alternatively men’s support
groups may be useful contacts.

Acknowledgments

The authors are grateful for the valuable comments and suggestions by
Sarah MacLean and Robin Room of the Centre for Alcohol Policy
Research and by anonymous reviewers. The interviewees gave their

JOURNAL OF SUBSTANCE USE 431

time and shared their often difficult stories and we acknowledge their
substantial contribution.

Funding

The study was supported by the Lithuanian Council of Science and the
European Social Fund (VP1-3.1-SMM-07-K). Laslett’s salary was sup-
ported by a National Health and Medical Research Council of Australia
Early Career Fellowship (1090904).

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JOURNAL OF SUBSTANCE USE 433

http://osp.stat.gov.lt/informaciniai-pranesimai?eventId=62471

https://www.vkontrole.lt/audito_ataskaitos.aspx?tipas=2

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Abstract

Introduction

An introduction to the Lithuanian context and harms to families from heavy drinking

Research methodology

Data collection procedures and tools (sampling, collecting, and analyzing data)

Interviewees

Ethics

Results

Direct harm from the drinker

Associative stigma and isolation

Assumed or redirected responsibility, associative stigma, and release

Drinker-centered coping strategies vs. recognizing and realizing one’s own needs

Discussion

Limitations

Acknowledgments

Funding

References

JOURNAL OF ETHNICITY IN SUBSTANCE ABUSE

https://doi.org/10.1080/15332640.2017.1371657

Children exposed to alcohol-related problems and
DSM-5 alcohol use disorder in San Juan, Puerto Rico
Raul Caetanoa, Patrice A. C. Vaetha, and Glorisa Caninob

aPacific Institute for Research and Evaluation, Oakland, California; bUniversity of Puerto Rico,
San Juan, Puerto Rico

ABSTRACT
This article estimates the proportion of children (17 and
younger) exposed to an adult with an alcohol problem or
alcohol use disorder (AUD) in San Juan, Puerto Rico. Data are
from a household random sample of 1,510 individuals 18–64
years of age. A total of 20.9% of children in sample households
were exposed to an adult with an alcohol problem, and 5.7%
were exposed to an adult with DSM-5 AUD. These considerable
proportions suggest that alcohol treatment and family support
programs should include help for adults in the family, and
special support for exposed children in the household.

KEYWORDS
Alcohol problems; AUD;
children; Puerto Rico

Introduction

There is an extensive body of literature on the consequences during childhood
and later adulthood of being raised in a household in which one or both
parents have an alcohol use disorder (AUD) (Harter, 2000; Kearns-Bodking
& Leonard, 2008; Lieberman, 2000; Schuckit et al., 2003). Some of these
consequences include a greater risk of developing an AUD, a higher rate of
externalizing behavior and internalizing symptoms, decreased response to
alcohol’s intoxicating effects, and difficulties in adult romantic relationships
(Finn & Justus, 1997; Harter, 2000; Kearns-Bodking & Leonard, 2008;
Lieberman, 2000).

Fewer efforts have been directed at estimating the number of children
exposed to alcohol problems in U.S. households. Unfortunately, however,
the existing estimates vary considerably. Russell, Henderson, and Blume
(1985) estimated that there were approximately 6.6 million children of
alcoholics under the age of 18, and that one out of every eight Americans
(12.5%) was the child of problem drinkers. Eigen and Rowden (1995)
provided a larger estimate: 17.5 million children of alcoholics under the age
of 18 lived in the United States. In a more recent study, Grant (2000) esti-
mated that 15% of all U.S. children under the age of 18 (9.7 million children)
were exposed to alcohol abuse and/or dependence in the family. Drawing on

none defined

CONTACT Raul Caetano, MD, PhD raul.caetano@utsouthwestern.edu Prevention Research Center, 80 Grand
Avenue, Suite 1200, Oakland, CA 94612.
© 2017 Taylor & Francis

2019, VOL. 18, NO. 3, 374–386

https://doi.org/10.1080/15332640.2017.1371657

https://crossmark.crossref.org/dialog/?doi=10.1080/15332640.2017.1371657&domain=pdf&date_stamp=2017-10-24

mailto:raul.caetano@utsouthwestern.edu

the 1995 National Alcohol Survey, Ramisetty-Mikler and Caetano (2004)
estimated that the numbers of children in U.S. households exposed to an adult
with an alcohol problem or to someone who was alcohol dependent were 11.5
million and 2 million, respectively. The Center for Behavioral Health Statistics
and Quality (2012) estimated that 7.5 million children, 10.5% of all children in
the United States, lived with a parent with an alcohol problem. Finally,
Kaplan, Nayak, Greenfield, and Karriker-Jaffe (2017) reported that 7.4%
of individuals with parental responsibilities over children in a 2015 U.S.
household sample reported that alcohol harmed a child they cared for in
the past year.

No previous articles have focused on estimating the proportion of children
that could be affected by an adult with an alcohol problem or an AUD in San
Juan, Puerto Rico. Yet this is an important part of assessing the harm done to
others by those who drink excessively. An overall conceptual framework for
assessment of alcohol’s harm to others besides the drinker, which includes
the family and the household, has been described by Room et al. (2010). This
assessment is particularly important for children because they are a highly
vulnerable population with no or little means to avoid such harm. In San
Juan, the proportion of children affected could be considerably high given
that 16% of the men and 9% of the women reported a drinking-related social
or health problem in the past 12 months (Caetano, Vaeth, & Canino, 2016b),
and 38% of men and 16% of women were affected by AUD on a lifetime basis
(Caetano, Gruenewald, Vaeth, & Canino, 2017). This article therefore
estimates the number of children 17 years of age and younger living in a
household in San Juan, Puerto Rico, with at least one adult with an alcohol
problem or a DSM-5 defined AUD (American Psychiatric Association,
2013). The U.S. Census Bureau provides detailed information by age for the
population in Puerto Rico. It is possible therefore to apply prevalence rates
from representative household surveys to the population and estimate the
number of individuals affected by any kind of health problem, including AUD.

This article also examines levels of family cohesion/pride and the
sociodemographic characteristics of families with children in which an adult
has alcohol problems or a DSM-5 AUD. Family cohesion/pride is an
important focus because in Latin cultures families provide considerable
emotional support to relatives during both normal and stressful times (Ayón,
Marsiglia, & Bermudez-Parsai, 2010; Coohey, 2001; Gallo, Penedo, Monteros,
& Arguelles, 2009; Sabogal, Marín, Otero-Sabogal, Marín, & Perez-Stable,
1987). The decrease or loss of a close family life and accompanying loss of
emotional support has been considered a risk factor for use of alcohol and
illicit drugs among U.S.-born Hispanics (Marsiglia, Kulis, Parsai, & Garcia,
2009; Rivera et al., 2008). In Puerto Rico, high family cohesion/pride has been
identified as a factor that shields individuals against drinking problems
(Canino, Anthony, Freeman, Shrout, & Rubio-Stipec, 1993; Canino, Burnam,

JOURNAL OF ETHNICITY IN SUBSTANCE ABUSE 375

& Caetano, 1992; Warner, Canino, & Colon, 2001) and disruptive behavior
associated with illicit drug use (Warner et al., 2001) and is protective against
DSM-5 AUD.(Caetano, Vaeth, & Canino, 2016a).

Judging from previous findings, we expect that households with children
and with an adult with an alcohol problem or a DSM-5 AUD will have a lower
level of family cohesion/pride (Caetano et al., 2016a). Given that the identifi-
cation of an alcohol problem is less stringent than that of DSM-5 AUD, we
also expect that the rate of children exposed to a parent with alcohol problems
will be higher than that for children exposed to an adult with an AUD.

Methods

Sample and data collection

Interviews were conducted with 1,510 residents of the metropolitan area of
San Juan between May 2013 and October 2014. Respondent selection followed
a multistage cluster sampling procedure, with 220 primary sampling units
represented by census block groups. Each selected block was divided into
segments of ten households, with a segment then randomly selected in each
block. Interviews were then carried out with a household member randomly
selected using a Kish table (Kish, 1949). Information about AUD in the
household was self-reported by the interviewee for her/himself only. Eligibility
was based on age (18–64 years), ability to speak Spanish, no incapacitating
cognitive impairment, and self-identification as Puerto Rican. The response
rate for the survey was 83%. Trained interviewers conducted computer-
assisted personal interviews at the respondents’ home; interviews lasted about
1 hour. Respondents received a $25 incentive for participation and provided
written informed consent. The survey was approved by the Committee for the
Protection of Human Subjects of the University of Texas Houston Health
Science Center and the University of Puerto Rico.

Measurements

Alcohol problems
Respondents were asked about 12 alcohol-related problems that they might
have experienced in the 12 months prior to the interview. This is a classical
list of problems that has been used in alcohol epidemiology research for the
past 30 years (see, e.g., Clark & Hilton, 1991). The list is independent from
the DSM-5 AUD criteria described below and consists of a series of
statements to which the respondent agrees or disagree. The list of problems
included salience of drinking, craving, impairment of control over drinking,
withdrawal syndrome, belligerence, problems with police (drinking and
driving, drinking-related arrest), health problems, problems with spouse,

R. CAETANO ET AL.376

problems with other people, loss of a job because of drinking, prolonged
intoxication, and drinking-related accidents. Households in which a
respondent reported one or more problems were identified as having an
adult with an alcohol problem. The problems scale reliability as measured
by Cronbach’s alpha coefficient was 0.77.

Alcohol use disorder
The identification of alcohol use disorder was based on DSM-5 criteria for AUD
(American Psychiatric Association, 2013) and implemented with the Spanish
version of the World Health Organization’s Composite Diagnostic Interview
(CIDI). The instrument was translated from English and adapted for use in
Spanish-speaking populations following a cultural adaptation model described
by Alegria et al. (2004) The Spanish version of the instrument has adequate
concordance in clinical reappraisal studies with the Structured Clinical Interview
for Axis 1 Disorders (SCID) (kappa = .51; specificity = .82 for lifetime substance
use disorders) (Alegria et al., 2009). According to DSM-5 criteria, respondents
reporting the presence of two or more indicators during the 12 months prior
to the interview were identified as positive for DSM-5 AUD.

The measures of alcohol problems and DSM-5 AUD are not totally
independent. Given that some of the alcohol problems under consideration
(e.g., withdrawal) are related to alcohol dependence and as such are also part
of the DSM-5 criteria, most of the households identified as positive for DSM-5
AUD (14 out of 20) were also positive for an alcohol problem. However,
unexpectedly, six households with a respondent positive for AUD were not
positive for alcohol problems.

Family cohesion/pride
This concept was measured with a 10-item scale: seven from Olson’s (1986)
Family Environment Scale and three from Olson’s (1986) Family Cohesion
Scale (see also (Canino, Vega, Sribney, Warner, & Alegria, 2008; Rivera
et al., 2008). Cronbach’s alpha for the scale is .93. Scores vary from 10 to
40, with higher scores indicating higher cohesion. The mean score for the
sample was 36.1 (95% CI [35.8, 36.4]). For ease of interpretation, this variable
was divided into three categories: high, medium, and low cohesion. A total of
41% of the sample had the highest possible score in the scale and were
categorized as high cohesion. Scores for the rest of the sample were then
evenly split and represented those with low and medium cohesion.

Sociodemographic variables
Employment status. Respondents were categorized into four employment
categories: (a) employed part-time; (b) employed full-time (35 or more hours
of work per week; reference); (c) unemployed (but looking for work); (d) not
in the workforce (retired, homemaker, never worked, unemployed and not

JOURNAL OF ETHNICITY IN SUBSTANCE ABUSE 377

looking for work, students). Very few respondents were underemployed
(employed part-time but wants to work more) to form a separate category
and were therefore classified as part-time.

Level of education. Respondents were categorized into four categories: (a)
less than high school; (b) completed high school or GED; (c) some college,
technical, or vocational school; (d) completed four-year college or higher
(reference group).

Religion. This variable had four categories: Protestant, no religious
preference, Catholic (reference), other religion.

Income. This is a continuous variable. Respondents were asked to report their
monthly family household income, which was then multiplied by 12 to
provide the household annual family income. For the logistic analysis,
respondents’ incomes were grouped into < $4,000, $4,001 to $18,000, $18,001 to $36,000, ≥ $36,001.

Supplementary income. This dichotomous variable was coded “1” if anyone
in the selected household received assistance from the following programs:
Supplemental Nutrition Assistance Program; Special Supplemental Assistance
Program for Women, Infants and Children; Temporary Assistance for Needy
Families; Low Income Home Energy Assistance Program.

Marital status. Respondents were categorized as (a) married;)b) separated or
divorced, (c) single, or (d) widowed.

Number of children in the household. Respondents were asked about the
number of children 17 years of age and younger who lived “at home
permanently.”

Statistical analyses
All analyses were conducted using Stata 14.2 “svy” prefix (Stata, 2015).
Analyses were conducted on data weighted to correct for unequal probabil-
ities of selection into the sample. In addition, a poststratification weight
was applied, which corrects for nonresponse and adjusts the sample to known
population distributions on certain demographic variables (age and gender).
Bivariate analyses (Tables 1 and 2) included chi-square tests to detect statisti-
cally significant associations between dependent and independent variables.
Multivariate logistic analysis (Table 3) was used to assess associations between
selected sociodemographic factors and family characteristics (family cohesion/
pride, employment status, family income, education level, marital status,
religion, and receipt of supplemental income) and a dichotomous outcome
variable coded as follows: “0” for households with children in which the

R. CAETANO ET AL.378

survey respondent did not have an alcohol problem or AUD; “1” for
households with children in which the survey respondent had an alcohol
problem or AUD. In this analysis, all households with an adult with an
alcohol problem or AUD were therefore combined in a single category (coded
as 1) because the number of households with AUD only was too small for a
separate analysis. As the coding implies, households without any children
were not part of this analysis.

The following formulas were used in the construction of Table 2: (a)
Proportion of sample households (HHs) with alcohol problems or AUD = HHs
with alcohol problems or AUD/total number of HHs × 100. (b) Proportion of

Table 1. Sociodemographic characteristics of sample households.

All
households

no
children

All
households

with
children

Household
with children

no alcohol
problem or
DSM-5 AUD

Household
with children
and alcohol

problem

Household
with

children and
DSM-5 AUD

(1,143) (367) (271) (53) (20)
% College degree 43 41 39 35 55
% Catholic 51 50 49 49 45
% Separated, divorced 21 19 57 51 59
% Supplemental

income
21 43** 43 41 42

% High family cohesion 42 45 50 42 16*
Mean annual income 21,295 27,686** 27,243 28,733 33,773
Mean number of

children
– 1.50 1.56 1.50 1.48

AUD = alcohol use disorder; DSM-5 = Diagnostic and Statistical Manual of Mental Disorders, fifth edition
(American Psychiatric Association, 2013).

*p < .05 for distribution across households with children; **p < .01, households with versus households without children.

Table 2. Puerto Rican children exposed to 12-month alcohol problems and DSM-5 alcohol use
disorder.

One or more
alcohol problems

DSM-5 Alcohol
use disorder

All sample households with alcohol problems
or alcohol use disorder1

24.3 (366/1,501) 10.2 (145/1,416)

Sample households with alcohol problems
or alcohol use disorder that have children2

21.5 (79/366) 13.7 (20/145)

Proportion of sample children exposed3 20.9 (119/569) 5.7 (30/523)
Population estimate of children exposed 16,624a 4,534b

DSM-5 = Diagnostic and Statistical Manual of Mental Disorders, fifth edition (American Psychiatric Association,
2013).

1Proportion of sample households (HHs) with alcohol problems (AP) or alcohol use disorder (AUD) = HHs with
AP or AUD/total no. of HHs x 100.

2Proportion of sample HHs with AP or AUD that have children = HHs with AP or AUD that have children/no. of
HHs with AP or AUD x 100.

3Proportion of children exposed = no. of children in the HHs with AP or AUD/total no. of children in the
sample x 100.

a79,541 (Children <18 years in 2009–2013 American Community Survey 5-year estimate) x .217 = 16, 624. b79,541 (Children <18 years in 2009–2013 American Community Survey 5-year estimate) x .058 = 4,534.

JOURNAL OF ETHNICITY IN SUBSTANCE ABUSE 379

sample HHs with alcohol problems or AUD that have children = HHs with
alcohol problems or AUD that have children/number of HHs with alcohol
problems or AUD × 100. (c) Proportion of sample children exposed = number
of children in HHs with alcohol problems or AUD/total number of children in
the sample × 100. The total number of children under 18 years of age (79,541)
estimated from the 2009–2013 American Community Survey for the urban
population of San Juan (United States Census Bureau, 2017) was used to calcu-
late the estimates of children exposed to alcohol problems and AUD. The for-
mula applied to calculate the population estimate of children exposed to alcohol
problems or AUD was as follows: proportion of children exposed to alcohol
problems or AUD in the sample × total number of children under 18 years of
age from the 2009–2013 five-year estimate from the American Community Sur-
vey (United States Census Bureau, 2017).

Results

Household sociodemographic characteristics

The sample had 1,510 households of which 24.3% had at least one child 17
years of age or younger. Because some households had more than one child,

Table 3. Multiple logistic regression of households with children and with alcohol problems or
DSM-5 AUD on sociodemographic characteristics.

OR 95% CI

Family cohesion/support (ref: high)
Low 1.94 [.92, 4.1]
Medium** 2.66 [1.20, 5.89]

Religion (ref: Catholic)
Protestant .75 [.36, 1.59]
Other religious preference .55 [.10, 2.89]
No religious preference 1.32 [.52, 3.30]

Employment status (ref: employed full-time)
Unemployed 1.22 [.36, 4.11]
Employed part-time .86 [.30, 2.44]
Not in workforce .62 [.19, 1.95]

Education (Ref: Less than high school)
High school diploma .89 [.25, 3.12]
Some college/technical .74 [.21, 2.52]
College degree .75 [.21, 2.60]

Marital status (ref: married)
Separated/divorced 1.13 [.46, 2.81]
Widowed 1.22 [.09, 16.31]
Never married 1.01 [.43, 2.32]

Income (ref: less than $4,001)
$4,001–$18,000 2.79 [.91, 8.49]
$18,001–$36,000* 4.38 [1.25, 15.29]
$36,001+* 3.98 [1.01, 15.77]
Received income supplement (ref: no supplement) 1.18 [.61, 2.30]

DSM-5 = Diagnostic and Statistical Manual of Mental Disorders, fifth edition (American Psychiatric Association,
2013); AUD = alcohol use disorder; CI = confidence interval.

*p < .05; **p < .01.

R. CAETANO ET AL.380

the total number of children in all sample households was 570. Most of the
households with children had one (58%) or two children (32%). The largest
number of children per household was five, but these households represented
only 0.1% of all households with children in the sample. Not surprisingly, given
that some alcohol problems are indicators of DSM-5 AUD, there was an over-
lap between households with problems and households with AUD. Among
households with at least one child, 4% had both AUD and one or more alcohol
problems. Among households with no children, the percentage was 9%.

The percentage of households with at least one child receiving supplemental
income was two times that of households with no children (p < .001) (Table 1). This is partially because some sources of supplemental income require the presence of children in the household. Households with at least one child also had a higher mean annual family income than households without children (p < .05). High family cohesion was less frequent in households with one or more child in which an adult had DSM-5 AUD compared to households with one or more child in which an adult had one or more alcohol problems and households with neither alcohol problems nor DSM-5 AUD (p < .05).

Proportion and number of children exposed to alcohol problems or
DSM-5 AUD

The proportions of households in the sample with alcohol problems and
DSM-5 AUD were about a quarter and a tenth, respectively (Table 2). About
a fifth of the households with an adult with an alcohol problem had at least
one child. A little over a tenth of the households with an adult with AUD
had at least one child. About 20% of the children in sample households were
exposed to an adult with alcohol problems. About 6% of the children in
sample households were exposed to an adult with AUD. The numbers of
children represented by these percentages in the urban population of San Juan
in 2013 were 16,624 and 4,534, respectively.

Family cohesion/pride and sociodemographic correlates of children’s
exposure to alcohol problems or DSM-5 AUD

There are two variables in Table 3 with statistically significant associations
with households with an adult with alcohol problems or DSM-5 AUD: House-
holds in which the respondent reported a medium level of family cohesion/
pride were almost three times as likely to have a child exposed to an adult with
alcohol problems or AUD compared to households with families with high
cohesion/pride. Households in which the respondent reported an annual
household income between $18,001 and $36,000 or above $36,000 were about
four times as likely to have a child exposed to alcohol problems or AUD
compared to households with an annual income below $4,000.

JOURNAL OF ETHNICITY IN SUBSTANCE ABUSE 381

Discussion

First, and as expected, the results show a higher estimate for the proportion of
households affected by an alcohol problem compared to DSM-5 AUD. As sta-
ted previously, this is probably because the cutoff for positive identification of
an AUD in DSM-5, which requires the presence of at least two DSM-5 criteria
in the past 12 months, is more stringent than that for positive identification of
a problem. There are almost four times as many children exposed to an adult
with an alcohol problem as to an adult with AUD. However, even though the
presence of an adult with AUD may indicate a more severe level of problem-
atic involvement with alcohol in the household, the presence of a single alco-
hol problem can be a source of conflict and stress, and a potentially damaging
factor for a child. Recognizing the importance of this issue, the American
Academy of Pediatrics Committee on Substance Abuse (1993) has issued
repeated reports with guidance for pediatricians on how to identify and
respond to substance abuse in families with children (Fraser & McAbee,
2004; Kulig & Committee on Substance Abuse, 2005; Smith & Wilson, 2016).

Second, two types of comparisons can be made between the results in this
article and those in the more recent literature. First, there is a comparison of
the proportion of households with an adult with an alcohol problem or AUD
among all households, irrespective of whether they have children. These
proportions for an alcohol problem (24.3%) or DSM-5 AUD (10.2%) are
higher in San Juan than on the U.S. mainland (15.6% and 3.2%), as reported
by Ramisetty-Mikler and Caetano (2004). However, when considering only
households with children, the situation reverses: San Juan has a lower
proportion of households with children exposed to an adult with alcohol
problems (21.5%) or DSM-5 AUD (13.7%) than the U.S. mainland (problems:
41.4%; DSM-IV alcohol dependence: 33.9%) (Ramisetty-Mikler & Caetano,
2004). This difference may exist in part because the proportion of households
with children was lower in San Juan (32% versus 40% on the mainland), as
was the mean number of children per household (1.5 versus 1.9).

The second comparison between the results reported here and those of
others is on the proportion of children, not households, exposed to an alcohol
problem or AUD. This is because some households have more than one child,
and thus the proportion of children affected is not equal to the proportion of
households with an adult with alcohol problems or AUD. In this case, the
situation is the inverse of that described in the paragraph preceding. For
example, comparing results in this article with those of Ramisetty-Mikler
and Caetano (2004) shows that the proportion of children exposed to alcohol
problems or DSM-5 AUD in San Juan is slightly higher than that on the U.S.
mainland (alcohol problems, 20.9% versus 16%, and AUD, 5.7% versus 2.9%).

Grant (2000) reported a higher estimate of 15% for the proportion of
children on the U.S. mainland affected by AUD. As in the comparison

R. CAETANO ET AL.382

between DSM-5 AUD and alcohol problems, differences in the definitions
employed to determine exposure explain the discrepancies in percentages.
Thus, the slightly higher estimate for the proportion of children exposed to
DSM-5 AUD in San Juan compared to those mentioned from Ramisetty-
Mikler and Caetano for alcohol dependence on the U.S. mainland may be
because DSM-5 has a less stringent requirement for identifying AUD
compared to DSM-IV, which was used by those authors. DSM-5 requires the
presence of two criteria compared to three for alcohol dependence in DSM-
IV. DSM-5 also considers indicators of what was alcohol abuse in DSM-IV
for a total of 11 versus 7 indicators for alcohol dependence in DSM-IV.

Because Grant estimated the proportion of children exposed by considering
exposure to both alcohol abuse and dependence as defined by DSM-IV, her
measure approximates that in DSM-5 more so than the measure utilized by
Ramisetty-Mikler and Caetano. However, Grant’s estimate of 15% is 2.6 times
as high as that reported here and three times as high as that reported by
Ramisetty-Mikler and Caetano. The difference between Grant’s estimate and
that in this article for San Juan is most probably associated with methodological
differences in the measurement of alcohol dependence, different household
sampling procedures, and cultural differences between the U.S. mainland and
San Juan. It is important to note that the 12-month rate of DSM-5 AUD is only
slightly higher on the U.S. mainland than in San Juan (mainland and San Juan
men, 17.6% and 14%, respectively; mainland and San Juan women, 10.4% and
7%, respectively) (Caetano, Vaeth, Mills, & Canino, 2016; Grant et al., 2015).

There were not many sociodemographic differences between households
with and without children, although those with one or more children had a
higher mean annual family income and a higher proportion reporting receipt
of supplemental income. There also were not many sociodemographic differ-
ences across households with children and problems or AUD and households
with children but without alcohol problems or AUD. The hypothesis that
households with children and with an adult with alcohol problems or AUD
would have lower cohesion/pride was confirmed. This result is present in
the cross-tabulation in Table 1 and is confirmed in the logistic analysis in
Table 3. It is in accordance with several articles in the literature with Hispanics
on the U.S. mainland and in Puerto Rico (Ayón et al., 2010; Coohey, 2001;
Warner et al., 2001). High family cohesion/pride may be a protective factor
against alcohol problems and AUD because it may be associated with the
availability of resources (e.g., housing, financial help, emotional support)
that if absent could lead to heavier drinking and increased chances of AUD
affecting an adult in the family (Caetano et al., 2016a).

The multivariate analysis also showed that a higher mean annual family
income was associated with households where children were exposed to alcohol
problems or AUD. This is an interesting result because mean family income
was not associated with current drinking, binge drinking, drinking and driving,

JOURNAL OF ETHNICITY IN SUBSTANCE ABUSE 383

or DSM-5 AUD in previous analyses of these data (Caetano et al., 2016b;
Caetano, Vaeth, Mills, et al., 2016). A mean family income of $30,001 to
$40,000 was protective against drinking-related social/health problems (Caetano
et al., 2016b). This may be because in this latter analysis the grouping of respon-
dents in income categories was different; the reference group in the analysis was
also different ($0–$4,000 herein, 0–$10,000 in the previous analysis).

Strengths and limitations

The study has many strengths. It is based on analyses of a random sample of
the adult population of San Juan that was interviewed face to face in a survey
with a particularly high response rate of 83%. Data collection covered several
drinking outcomes in detail and used state-of-the-art interviewing techniques
and questions.

The study also has limitations. Data collection was based on self-reports,
which may lead to underreporting of alcohol problems and DSM-5 AUD
indicators. The proportion of households with a child exposed to an adult
with alcohol problems or AUD may underrepresent the true proportion of
these households in the community. This is because the identification was
based on the interview with one adult selected at random from all adults in
the household. If the adult interviewed did not report problems or AUD
but another adult in the household had such problems, the household would
have been misidentified as free of problems and AUD. It is also possible
that the proportion of children exposed to an alcohol problem or AUD is
underestimated if children who were living at a dormitory in school or spent
time with a divorced parent were not reported by the respondent.

Funding

This work was supported by the National Institute on Alcohol Abuse and Alcoholism [Grant
Number RO1-AA020542].

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Methods

Sample and data collection

Measurements

Alcohol problems

Alcohol use disorder

Family cohesion/pride

Sociodemographic variables

Statistical analyses

Results

Household sociodemographic characteristics

Proportion and number of children exposed to alcohol problems or DSM-5 AUD

Family cohesion/pride and sociodemographic correlates of children’s exposure to alcohol problems or DSM-5 AUD

Discussion

Strengths and limitations

Funding

References

ORIGINAL ARTICLE

Changes in depression mediate the effects of AA attendance on alcohol use
outcomes
Claire E. Wilcox, MDa and J. Scott Tonigan, PhD b

aDepartment of Psychiatry, University of New Mexico, Albuquerque, NM, USA; bCenter on Alcoholism, Substance Abuse, and Addiction,
University of New Mexico, Albuquerque, NM, USA

ABSTRACT
Background: Depression may contribute to increased drinking in individuals with alcohol use disorder.
Although Alcoholics Anonymous (AA) attendance predicts drinking reductions, there is conflicting
information regarding the intermediary role played by reductions in depression. Objectives: We
explored whether AA attendance reduces depressive symptoms, the degree to which improvement
in depression results in reductions in drinking, and in which subgroups these effects occur. Methods:
253 early AA affiliates (63%male) were recruited and assessed at baseline 3, 6, 9, 12, 18, and 24months.
Depression was measured using the Beck Depression Inventory (BDI) and was administered at baseline
3, 6, 12, 18, and 24months. AA attendance and alcohol use outcomes were obtained with the Form 90.
Mediation analyses were performed at early (3, 6, and 9 months) and late (12, 18, and 24 months)
follow-up to investigate the degree to which reductions in depression mediated the effect of AA
attendance on drinking, controlling for concurrent drinking. In addition, a series of moderated media-
tion analyses were performed using baseline depression severity as a moderator. Results: At early
follow-up, reductions in depression (6 months) mediated the effects of AA attendance (3 months) on
later drinking (drinks per drinking day) (9 months) (b = −0.02, boot CI [−0.055, −0.0004]), controlling for
drinking at 6months. Baseline depression severity did notmoderate the degree towhich BDImediated
the effects of AA attendance on alcohol use (ps > .05). Conclusion: These findings provide further
evidence that depression reduction is a mechanism by which AA attendance leads to reductions in
alcohol use. Improving depression may help reduce alcohol use in individuals with AUD, and AA
attendance may be an effective way to achieve that goal.

ARTICLE HISTORY
Received 18 July 2016
Revised 7 October 2016
Accepted 13 October 2016

KEYWORDS
12-step; alcoholics
anonymous; depression;
alcohol use disorders;
negative affect

Background

Relatively consistent evidence has accumulated, indicating
that community-basedAlcoholics Anonymous (AA) atten-
dance is predictive of decreased alcohol use for many, but
not all, problem drinkers (1–5). Importantly, self-selection
bias does not appear to account for AA-related benefit
(6–8), and the salutary effects of AA have been observed
in diverse populations including urban Native Americans
(9), dually diagnosed adults (10,11), and ethnic minorities
(12,13). Factors that do appear to account for AA-related
benefit during early AA affiliation include acquiring an AA
sponsor (14–16) social support for abstinence (17,18), gains
in spiritual practices (19,20), and increased abstinence self-
efficacy (21–24).

Central in the core AA literature (25) is the proposi-
tion that negative affect is a leading precipitant to
relapse and much of the prescribed step work in
12-step programs is therefore directed at reducing the
unbridled expression of anger, depression, and selfish-
ness. The degree to which AA attendance influences

AA member depression and, in turn, how changes in
depression among AA members actually explains the
beneficial effect of AA on drinking, remains unclear,
however. Specifically, three rigorous longitudinal stu-
dies have investigated changes in depression among AA
members (26–28) (hereafter referred to as Wilcox et al.
(26), Worley et al. (27) and Kelly et al. (28), respec-
tively). Found in each of these studies, AA exposed
adults reported significant reductions in depressive
symptoms over time, with such pre-post reductions
observed at 6-month follow-up among veterans with
substance use and major depressive disorder [Worley
et al. (27); Hedges g (g, Cohen’s d adjusted for small
sample bias) = 0.55], at 9 months in both outpatient
and aftercare treatment seeking Project MATCH sam-
ples (Kelly et al. (28); g = 0.29), and over the course of 2
years in a community-recruited sample (Wilcox et al.
(26); g = 0.18). Lagged analyses in each aforementioned
study showed that frequency of AA attendance was
associated with later reductions in depression.

CONTACT Claire E. Wilcox, MD cewilcox@salud.unm.edu Department of Psychiatry, University of New Mexico, MSC 09-5030, 1 University of New
Mexico, Albuquerque, NM 87131, USA.

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VOL. 44, NO. 1, 103–112
http://dx.doi.org/10.1080/00952990.2016.1249283

http://orcid.org/0000-0002-6668-2038

Findings diverge, however, when changes in depres-
sive symptoms were examined after statistically con-
trolling for concurrent drinking. In the Kelly et al.
(28) study, for example, the inclusion of current drink-
ing in the lagged models eliminated the direct and
independent effect of AA attendance on levels of
depression symptoms, so reductions in depressive
symptoms were attributed to the direct effect of AA
attendance on reducing drinking. Furthermore, Kelly
et al. (28) determined that changes in depressive symp-
toms did not mediate that association between fre-
quency of AA attendance and later drinking in either
the aftercare or outpatient MATCH samples. A differ-
ent relationship between AA attendance and changes in
depression was presented in Worley et al. (27) and
Wilcox et al. (26). Specifically, these studies found
that the independent effect of AA on later levels of
depression symptoms remained even after controlling
for concurrent alcohol and substance use, suggesting
that reductions in levels of depression symptoms may
be attributable to the influence of AA beyond that
explained by reductions in alcohol or substance use.
Also noteworthily, Worley et al. (27) reported that
reductions in depression significantly mediated 15% of
the direct effect of AA on drinking. Wilcox et al. (26)
did not examine whether reductions in depression
mediated the effect of AA on drinking.

There are some possible explanations for the con-
flicting results between these three studies. Foremost, at
baseline, the absolute severity of depressive symptoms
was significantly different across the three studies, with
the MATCH aftercare and outpatient samples for Kelly
et al. (28) reporting significantly lower levels of depres-
sion. Combining the MATCH aftercare and outpatient
samples, for example, yielded a mean Beck Depression
Inventory-II (BDI) (29) score at intake of 10.16
(SD 8.25). The mean score for the community-based
AA sample for Wilcox et al. (26) was 19.79 (SD 11.35),
almost twice as depressed as the Kelly et al. (28) sample,
and approached the BDI cutoff score of 20 for moder-
ate depression. The most depressed sample studied was
that of Worley et al. (27) which had a mean Hamilton
Depression Scale score of 28.55 (SD 10.82), well above
the cutoff of 17 for moderate depression (30), and all
subjects had a diagnosis of major depressive disorder.
We therefore suspected that adults with major depres-
sive disorders were disproportionately represented in
Worley et al. (27) and Wilcox et al. (26), and that, to
some extent, the significantly lower levels of depression
symptoms found in the MATCH sample from Kelly
et al. (28) could have resulted in lower effects of AA

on depression. Other related work, using multiple med-
iator analyses, has also found a stronger relationship
between AA, depression, and later drinking (media-
tion) in individuals with more severe depression (19).

Objectives

The objective of this study was to shed light on the
reasons for conflicting findings in studies of the rela-
tionship between AA exposure, changes in depression,
and drinking outcomes by performing additional ana-
lyses on the dataset used in the Wilcox et al. (26) study,
with a particular focus on whether or not changes in
depression mediated the reductions in drinking
observed AA attendance. To do so, we investigated
whether changes in levels of depression could explain
the effects of AA exposure on later drinking in our
sample, using similar approaches to those utilized by
Kelly et al. (28) and Worley et al. (27). Furthermore, we
investigated whether or not the presence or absence of
a clinically relevant depression score at study entry
moderated the strength of these associations. We
hypothesized that since our sample was more depressed
than that of Kelly et al. (28), and therefore more similar
to that of Worley et al. (27), changes in depression
would mediate the effects of AA on drinking (Aim 1),
similar to the results in Worley et al. (27), but unlike
those in Kelly et al. (28). Furthermore, if differences in
baseline depression levels between the Worley et al.
(27) and Kelly et al. (28) studies were driving the
differences in findings, we hypothesized that baseline
depression would moderate the meditational effect, and
that mediation would be more pronounced (larger
indirect effect) in those with greater baseline depression
(Aim 2).

Methods

Participants and procedure

This is a follow-up report to a previously published
paper (26), and details about the community-based
AA sample can be obtained from the original paper.
In brief, participants were early AA affiliates with little
previous exposure to 12-step programs (participants
were excluded for more than 16 weeks of lifetime AA
exposure and if they reported ever having had a period
of alcohol abstinence of at least 12 months at any time
in their life after their alcohol use had become a pro-
blem). Two hundred and fifty-three adults with alcohol
use problems were recruited from AA groups; from

104 C. E. WILCOX AND J. S. TONIGAN

outpatient substance abuse treatment facilities; and
from community sources including homeless shelters,
advertisement in local newspapers, and flyers (68 were
recruited from community-based AA, 87 were recruited
from outpatient treatment abuse treatment centers, and
98 at shelters or through advertisements and flyers).
Participants were also required to have attended one
or more AA meetings in the prior 3 months, to have
consumed alcohol in the prior 90 days, and to meet
Diagnostic and Statistical Manual of Mental Disorders
(31) criteria for alcohol dependence or abuse (32). All
procedures were approved by the institutional review
board at the University of New Mexico (UNM Protocol
No. 24028).

Breathalyzers were used to ensure that participants’
blood alcohol concentration did not exceed 0.05 prior
to the consent process, or at any subsequent interview.
Once consented, participants were administered a base-
line interview that included 15 self-report question-
naires, 3 semi-structured interviews, and a urine
toxicology screen. Follow-up interviews were con-
ducted in 3-month increments for one year and then
at 18 and 24 months. More than 85% of the original
sample provided follow-up data at 24 months. No
intervention was offered in this assessment-only study,
although clinical referrals were made upon participant
request, or when deemed warranted by clinical staff.

Measures

Alcohol use
Alcohol use data were obtained using the Form 90 (33),
which is a calendar-based semi-structured interview.
Two alcohol use measures from the Form 90 were
computed. Proportion of days abstinent from alcohol
(PDA) was defined as the number of alcohol-abstinent
days in an assessment period divided by the total num-
ber of days in the period. Drinks per drinking day
(DPDD) was defined as number of drinks consumed
per drinking (i.e., non-abstinent) days in the assess-
ment period.

12-step meeting attendance
A single item from the Form 90 interview documented
frequency of 12-step meeting attendance during an
interview period. The proportion days of 12-step atten-
dance for each participant was calculated as a ratio of
days 12-step meeting attendance divided by the number
of days in an interview period.

Depression
The Beck Depression Inventory (BDI) (29), commonly
used in both clinical and research settings to screen for

and establish severity of depression, was used as a
measure of depressive symptomatology. The BDI is
composed of 21 questions each scored on a scale of 0
to 3 asking about symptoms over the past 2 weeks such
as hopelessness, irritability, guilt, feelings of being pun-
ished, fatigue, weight loss, and lack of interest in sex.
Total scores on the BDI of 0–13 indicate minimal
depression, 14–19 mild depression, 20–28 moderate
depression, and 29–63 severe depression. For mediation
analyses, we used the BDI as a continuous variable to
mark the degree of depressive symptomatology. When
used as a moderator, we used a dichotomous modera-
tor variable by grouping individuals with minimal or
mild depression into one group, and those with mod-
erate or severe depression into the other group as a
marker of the presence or absence of clinically relevant
depression.

Statistical analyses

To examine whether the effect of AA attendance on drink-
ing (DPDD, PDA) could be partially or fully accounted for
by changes in BDI scores, we conducted mediation tests
using the bias-corrected bootstrap (34) as implemented in
the PROCESS macro for SPSS (35), which uses ordinary
least squares regression. In all models, we bootstrapped
1000 samples and significant effects were determined by
95%bias-corrected confidence intervals that do not contain
zero. We conducted two single-mediation models (Aim 1:
Analysis 1 for PDA, Analysis 2 for DPDD) in which we
examined the total, direct, and indirect effects of AA atten-
dance on alcohol use via BDI scores at early follow-up
(3-month AA; 6-month BDI; 9-month alcohol use;
n = 194). For these two models, drinking and BDI at
study entry (baseline) were entered as covariates, and con-
current drinking (6 months) was entered as a mediator
operating in parallel [Figure 1; Model 4 in PROCESS
Macro (36)]. We then conducted two tests of moderated
mediation (Aim 2: Analysis 3 for PDA, Analysis 4 for
DPDD) by entering baseline categorical depression as a
moderator into the two single-mediation models defined
above [Figure 2; Model 58 in PROCESS Macro (36)].
Although we did not formally test for heteroskedasticity,
we used the heteroskedasticity-consistent standard errors
option in the process macro (37). For our post hoc late
follow-up analyses, we used exactly the same methods
described above, except that we used 12 month AA, 18
month BDI and 24 month alcohol use (n = 183) instead of
the 3-, 6-, and 9-month time points described above
(Figures 1 and 2; Analyses 5, 6, 7, 8). The outcome variables
were transformed prior to the analyses using a transforma-
tion that eliminated extreme outliers (DPDD was square
root transformed; PDA was arc sin of square root

THE AMERICAN JOURNAL OF DRUG AND ALCOHOL ABUSE 105

transformed), although this may not have been necessary,
as bootstrapping (as opposed to the Sobel test for indirect
effects) significantly minimizes the need for normally dis-
tributed outcome variables (38). We note for the reader
that p values are appropriate for all of the reported para-
meters except the indirect effects. In this case, we are
interested in whether the CI contains 0 or not.

Results

Participant information

Of the 253 participants, 194 participants had complete data
for the planned analyses at early follow-up. Of these 194
participants, 63% of the sample was male (n=123) and the
mean age of participants was 39.14 years (SD 9.84). A

majority of the reduced sample had a high school degree
(22.2%nodegree, 12.4%GED, 38.1%high school diploma),
were unemployed (63.9%), and were single or divorced
(48.1%, 34.5%); 16.0% of the participants reported being
homeless. Of the 194 participants, 42.9% were Hispanic,
32.1% were non-Hispanic White, 18.7% were Native
American, and the remaining participants were of
African, Asian, or unspecified ancestry. Finally, at study
entry, participants reported that they were attending AA
meetings 16.0%of the days (SD18.5%) andhad ameanBDI
score of 20.1 (SD11.3), amean proportion of days abstinent
of alcohol of 52.7 (SD 30.5) and amean drinks per drinking
day of 17.60 (12.81).

Simple correlations between primary measures [alcohol
use (baseline), BDI (baseline), AA (3 month), BDI
(6 month), alcohol use (9 month)] were in the expected

Figure 1. Pictorial representation of statistical mediation model used for Aim 1/Analysis 1 and 2 (early follow-up for PDA and DPDD) and
Analysis 5 and 6 (late follow-up for PDA and DPDD). Definitions: AA = AA attendance; PDA = percent days abstinent; DPDD = drinks per
drinking day; BDI = Beck Depression Inventory; Early = early follow-up; Late = late follow-up; A = path A, B = path B, C = path C
representing total effect beforemediator is entered intomodel, C’ = path C’ representing direct effect after mediator is entered intomodel.
Coefficients and significance values for Analysis 1 and 2 are in Table 2, and for Analysis 5 and 6 are in Table 3.

Figure 2. Pictorial representation of statistical moderated mediation model used for Aim 2/Analysis 3 and 4 (early follow-up for PDA and
DPDD) andAnalysis 7 and 8 (late follow-up for PDA andDPDD). Definitions: AA=AA attendance; PDA=percent days abstinent; DPDD=drinks
per drinking day; BDI = Beck Depression Inventory; Early = early follow-up; Late = late follow-up; A = path A, B = path B, C = path C
representing total effect before mediator is entered into model, C’ = path C’ representing direct effect after mediator is entered into model.
Coefficients and significance values for Analysis 7 and 8 are in Table 4.

106 C. E. WILCOX AND J. S. TONIGAN

directions such that greater AA attendance was associated
with lower BDI scores and lower alcohol consumption, and
lower BDI scores were associated with lower alcohol con-
sumption (Table 1). Of note, the full sample (n = 253) and
the complete data sample for the early follow-up analyses
(n = 194) had very similar mean values for the key variables
in our analyses (Supplementary Table 1).

Mediation analyses—Aim 1/early follow-up
(Analysis 1, 2)

There was a significant direct effect of AA (3 months) on
drinking (6 months) for both PDA (p = 0.000, b = 0.441,
t= 5.88) andDPDD (p= 0.009, b= −1.482, t=−3.388). The
direct effect of AA (3 months) on drinking (9 months) was
not significant, although the total effect was highly signifi-
cant for both PDA (p=0.006, b = 0.102, t = 2.757) and
DPDD (p = 0.008, b = −1.177, t = −2.694). Furthermore,
in support of previous findings (26), there were significant
effects of AA (3 months) on BDI (6 months) for PDA
(p = 0.040, b = −5.043, t = −2.073) and DPDD (p = 0.050,
b = −4.723, t = −1.973) (path A; Figure 1). For DPDD, but
not PDA, BDI (6 month) was associated with DPDD
(9 months) (p = 0.041, b = 0.023, t = 2.060) (path B;
Figure 1), and the indirect effect of AA (3 months) on
alcohol use at 9 months via BDI (6 months) was also
significant. Not surprisingly, alcohol use at 6 months
(PDA and DPDD) was a significant mediator of the effects
of AA (3 months) on alcohol use (9 months) (Table 2).

Moderated mediation—Aim 2/early follow-up
(Analysis 3, 4)

We then entered a BDI categorical moderator variable
(as a marker of the likely presence or absence of a
depressive disorder diagnosis) into the mediation mod-
els (Figure 2). When we did so, none of the indirect
effects via BDI for either category of depression and
neither of the indices of moderated mediation were
statistically significant for the two models tested, indi-
cating that baseline depression severity did not moder-
ate the degree to which BDI (6 months) mediated the

relationship between AA attendance (3 months) and
alcohol use (9 months). The interaction terms were
not significant for either model for either path A or
path B, indicating that the baseline BDI categorical
variable did not moderate either the relationship
between AA (3 months) and alcohol use 6 months) or

Table 1. Correlations between AA attendance, depression, and alcohol use (early).
PDA BL DPDD BL BDI BL AA 3 months PDA 6 months DPDD 6 months BDI 6 months PDA 9 months

DPDD BL −0.046, 253
BDI BL −0.247**, 227 −0.014, 227
AA 3 months 0.196**, 239 0.238**, 239 −0.201**, 215
PDA 6 months 0.269**, 239 0.077, 239 −0.227**, 214 0.438**, 236
DPDD 6 months −0.113, 239 0.020, 239 0.202*, 214 −0.282**, 236 −0.817**, 239
BDI 6 months −0.097, 215 −0.133, 215 0.455**, 199 −0.235**, 215 −0.381**, 214 0.327**, 214
PDA 9 months 0.278**, 237 0.072, 237 −0.212**, 213 0.370**, 233 0.705**, 234 −0.532**, 234 −0.362**, 211
DPDD 9 months −0.142*, 237 0.076, 237 0.172*, 213 −0.258**, 233 −0.573**, 234 0.575**, 234 0.305**, 211 −0.837**, 237

*p < 0.05, ** p< 0.01. All values are reported in the format: Spearman’s rho, number of participants; BL = baseline, BDI = Beck Depression Inventory, PDA = percent days abstinent, DPDD = drinks per drinking day (transformed variables for PDA and DPDD).

Table 2. Results from mediation analyses to examine whether
changes in depression mediate the effect of AA attendance on
alcohol use outcomes at early follow-up (3, 6, 9 months; Fig.1;
Aim 1; Analysis 1, 2).
PDA early (n = 194) Coeff SE t p

Path A (to BDI 6 months)
AA 3 months −5.043 2.432 −2.073 0.040
PDA baseline −0.009 2.336 −0.004 0.997

Path A (to PDA 6 months)
AA 3 months 0.441 0.075 5.875 0.000
BDI baseline −0.004 0.002 −1.751 0.082

Path B (to PDA 9 months)
BDI 6 months −0.004 0.003 −1.415 0.159
AA 3 months −0.059 0.088 −0.669 0.505
BDI baseline 0.001 0.003 0.526 0.599

Total Effect (to PDA 9 months)
AA 3 months 0.282 0.102 2.757 0.006
BDI baseline −0.003 0.003 −1.195 0.234

Coeff Boot
SE

Boot
LLCI

Boot
ULCI

Indirect effect
(BDI 6 months is mediator)

0.013 0.012 −0.002 0.051

Indirect Effect
(PDA 6 months is mediator)

0.221 0.043 0.141 0.310

DPDD early (n=194) Coeff SE t p

Path A (to BDI 6 months)
AA 3 months −4.723 2.394 −1.973 0.050
DPDD baseline −0.293 0.507 −0.579 0.563

Path A (to DPDD 6 months)
AA 3 months −1.482 0.437 −3.388 0.001
BDI baseline 0.023 0.010 2.204 0.029

Path B (to DPDD 9 months)
BDI 6 months 0.023 0.011 2.060 0.041
AA 3 months −0.293 0.347 −0.845 0.399
BDI baseline −0.0001 0.010 0.008 0.994

Total Effect (to DPDD 9 months)
AA 3 months −1.177 0.437 −2.694 0.008
BDI baseline 0.022 0.011 2.023 0.044

Coeff Boot
SE

Boot
LLCI

Boot
ULCI

Indirect effect (BDI 6 months is
mediator)

−0.019 0.013 −0.055 −0.0004

Indirect effect (DPDD 6 months is
mediator)

−0.138 0.043 −0.234 −0.065

PDA = percent days abstinent, DPDD = drinks per drinking day, BDI = beck
depression inventory, AA = AA attendance, Coeff = coefficient, SE = standard
error, LLCI = lower level of confidence interval, ULCI = upper level of con-
fidence interval. All indirect effect values are completely standardized.

THE AMERICAN JOURNAL OF DRUG AND ALCOHOL ABUSE 107

BDI (6 months) or the relationship between BDI
(6 months) and alcohol use (9 months), respectively
(Table 4).

Post hoc mediation Analysis—late follow-up
(Analysis 5, 6)

In the aforementioned analyses looking at the 3-, 6-,
and 9-month follow-up time points, we supported our
findings from previous work in Wilcox et al. (26),
showing that AA attendance was associated with reduc-
tions in later depression, controlling for concurrent
drinking (26). Since our previous analysis included all
time points (baseline to 24 months) and did not look at
early and late time points separately, we wanted to

explore whether or not changes in depression mediated
the effect of AA on alcohol use.

As seen at early follow-up, at late follow-up, AA
(12 month) was significantly associated with alcohol use
(18months) for PDA (p= 0.009, b= 0.397, t= 2.627) and at
a trend level for DPDD (p = 0.064, b = −1.478, t = −1.864).
In addition, AA (12 months) was significantly associated
with BDI (18 months) for PDA (p = 0.047, b = −7.947,
t = −2.004) and at a trend level for DPDD (p = 0.053,
b = 7.801, t = −1.945) (path A; Figure 1), supporting pre-
vious findings from Wilcox et al. (26). However, BDI
(18 months) was not significantly associated with alcohol
use (24 months) (path B; Figure 1) for either DPDD or
PDA, and the indirect effects via BDI were not significant,
indicating that at late follow-up, BDI (18 months) did not
mediate the effects of AA (12 month) on alcohol use
(24 months) (Table 3).

Post hoc moderated mediation analysis—late
follow-up (Analysis 7, 8)

When the baseline BDI categorical variable was entered
into the model, none of the indirect effects via BDI for
either category of depression and neither of the indices
of moderated mediation were statistically significant for
the two models tested, indicating that baseline depres-
sion severity as a categorical variable did not moderate
the degree to which BDI (18 months) mediated the
relationship between AA (12 months) and alcohol use
(24 months). For path A for the PDA analysis, both the
BDI moderator variables and the interaction terms in
the moderated mediation analysis were significant for
AA (12 months) to both BDI (18 months) and PDA (18
months) (Table 4, Figure 2). For path A for the DPDD
analysis, the BDI moderator variable and the interac-
tion term for AA (12 months) to BDI (18 months) were
significant (Table 4, Figure 2). Plotting slopes by group
indicated that those with greater depression levels at
baseline were more likely to show a decrease in BDI
scores (18 months) with higher levels of AA attendance
(12 months).

Discussion

This study investigated the relationship between AA
attendance, depression, and alcohol consumption in
individuals new to AA. In particular, our primary aim
was to see whether the beneficial effects of AA atten-
dance on drinking were mediated by effects on depres-
sion. Previous work has shown conflicting results using
similar approaches at similar time points (26–28), and
our findings help clarify things, somewhat. First, and
most interestingly, we found that changes in depression

Table 3. Results from analyses to examine whether changes in
depression mediate the effect of AA attendance on alcohol use
outcomes at late follow-up (12, 18, 24 months; Fig. 1; Analysis 5,
6).
PDA late (n = 183) Coeff SE t p

Path A (to BDI 18 months)
AA 12 months −7.947 3.966 −2.004 0.047
PDA baseline 0.134 2.114 0.064 0.949

Path A (to PDA 18 months)
AA 12 months 0.397 0.151 2.627 0.009
BDI baseline −0.004 0.003 −1.779 0.077

Path B (to PDA 24 months)
BDI 18 months 0.000 0.003 0.080 0.937
AA 12 months 0.067 0.097 0.691 0.490
BDI baseline 0.001 0.003 0.169 0.866

Total effect (to PDA 24 months)
AA 12 months 0.351 0.131 2.680 0.008
BDI baseline −0.003 0.003 −0.839 0.403

Coeff Boot
SE

Boot
LLCI

Boot
ULCI

Indirect Effect (BDI 18 months is
mediator)

−0.006 0.103 −0.293 0.159

Indirect effect (PDA 18 months is
mediator)

0.137 0.050 0.033 0.234

DPDD late (n=183) Coeff SE t p

Path A (to BDI 18 months)
AA 12 months 7.801 4.010 −1.945 0.053
DPDD baseline −0.109 0.486 −0.225 0.823

Path A (to DPDD 18 months)
AA 12 months −1.478 0.793 −1.864 0.064
BDI baseline 0.029 0.012 2.417 0.017

Path B (to DPDD 24 months)
BDI 18 months −0.004 0.012 −0.362 0.718
AA 12 months −0.356 0.807 −0.441 0.660
BDI baseline 0.006 0.011 0.570 0.570

Total effect (to DPDD 24 months)
AA 12 months −1.358 0.730 −1.860 0.065
BDI baseline 0.024 0.012 2.021 0.045

Coeff Boot
SE

Boot
LLCI

Boot
ULCI

Indirect effect (BDI 18 months is
mediator)

−0.004 0.012 −0.015 0.034

Indirect effect (DPDD 18 months is
mediator

−0.122 0.066 −0.257 0.001

PDA = percent days abstinent, DPDD = drinks per drinking day, BDI = beck
depression inventory, AA = AA attendance, Coeff = coefficient, SE = standard
error, LLCI = lower level of confidence interval, ULCI = upper level of
confidence interval. All indirect effect values are completely standardized.

108 C. E. WILCOX AND J. S. TONIGAN

mediated the beneficial effects of AA attendance at 3
months on drinking (DPDD) at 9 months controlling
for concurrent drinking, consistent with Worley et al.
(27), but in contrast to Kelly et al. (28). Our results were
in support of our hypothesis, given that our sample was
more similar to that of Worley et al. (27) than that of
Kelly et al. (28) in terms of baseline depression levels. By
contrast, in our sample, for PDA, the indirect effects
were not significant, although they were still in the
expected directions; changes in depression appeared to
have a greater effect on the quantity consumed, rather
than the decision to drink or not drink on a particular
day. Other related work has also found that drinking
mediated the relationship between AA and later drink-
ing (19), and that the effect was stronger for DPDD.

We also hypothesized that baseline depression severity
would moderate the indirect effect, but this hypothesis
was not supported. Had it been supported, this would

have indicated that differences in depression levels were
driving the differences in results between the three stu-
dies. The absence of an observed effect could have
resulted from the fact that BDI scores are fluid in this
population and that a single time point measurement of
BDI (our dichotomous moderator variable) is not a reli-
able marker of the presence or absence of a depressive
syndrome. An alternative explanation for the differences
lies in the variability in rates of AA attendance. Namely,
our sample and that of Worley et al. (27) had higher AA
attendance frequency than that of Kelly et al. (28); in our
sample, rates were 29%, 19%, and 15% of days attending
AA meetings during the preceding assessment periods at
3, 6, and 9 months, respectively (26), whereas in Kelly
et al. (28) participants had minimal AA attendance.
Moreover, a clear majority of the participants in Wilcox
et al. (26) attended AA throughout the 24 months and, in
the Worley et al. (27) study, participants reported, on

Table 4. Results from moderated mediation analyses to examine whether baseline depression severity moderates
the degree to which changes in depression mediate the effect of AA attendance on alcohol use outcomes at late
follow-up (12, 18, 24 months; Fig. 2; Analysis 7, 8).
PDA late with baseline depression severity moderator variable (n=194) Coeff SE t p

Path A (to BDI 18 months)
BDI mod 6.227 3.108 2.004 0.047
BDI mod × AA 12 months −15.811 7.644 −2.068 0.040

Path A (to PDA 18 months)
BDI mod −0.403 0.124 −3.242 0.001
BDI mod × AA 12 months 0.712 0.284 2.507 0.013

Path B (to PDA 24 months)
BDI mod 0.080 0.227 0.351 0.726
BDI mod ×BDI 18 months 0.001 0.006 0.209 0.835
BDI mod × PDA 18 months −0.055 0.139 −0.396 0.692

Coeff Boot SE Boot LLCI Boot ULCI

Indirect effect (BDI 18 months is mediator); minimal/mild sepression 0.001 0.042 −0.036 0.065
Indirect effect (BDI 18 months is mediator); moderate/severe depression −0.009 0.067 −0.139 0.135
Indirect effect (PDA 18 months is mediator); minimal/mild depression 0.073 0.157 −0.254 0.346
Indirect effect (PDA 18 months is mediator); moderate/severe depression 0.570 0.129 0.326 0.844
Index of moderated mediation (BDI 18 months is mediator) −0.009 0.072 −0.155 0.132
Index of moderated mediation (PDA 18 months is mediator) 0.497 0.204 0.137 0.922

DPDD late with baseline depression severity moderator variable (n=183) Coeff SE t p

Path A (to BDI 18 months)
BDI mod 6.157 3.115 1.977 0.050
BDI mod ×AA 12 months −15.868 7.628 2.080 0.039

Path A (to DPDD 18 months)
BDI mod 0.447 0.475 0.942 0.602
BDI mod × AA 12 months −0.169 1.680 −0.101 0.920

Path B (to DPDD 24 months)
BDI mod 0.667 0.606 1.100 0.273
BDI mod × BDI 18 months 0.014 0.029 0.476 0.635
BDI mod × DPDD 18 months 0.117 0.192 0.610 0.542

Coeff Boot SE Boot LLCI Boot ULCI

Indirect effect (BDI 18 months is mediator); minimal/mild depression −0.0042 0.119 −0.412 0.172
Indirect effect (BDI 18 months is mediator); moderate/severe depression 0.129 0.238 −0.266 0.687
Indirect effect (DPDD 18 months is mediator); minimal/mild depression −1.094 0.758 −2.806 0.156
Indirect effect (DPDD 18 months is mediator); moderate/severe depression −1.028 0.945 −2.954 0.487
Index of moderated mediation (BDI 18 months is mediator) 0.133 0.262 −0.301 0.733
Index of moderated mediation (DPDD 18 months is mediator) 0.066 1.180 −2.282 2.389

BDI mod = Categorical BDI moderator variable, DPDD = drinks per drinking day, BDI = beck depression inventory, AA = AA attendance,
Coeff = coefficient, SE = standard error, LLCI = lower level of confidence interval, ULCI = upper level of confidence interval; none of
the paths for the analysis using percent days abstinent as the outcome were significant so these results are not reported in a table.

There were no significant interaction terms nor were the indices of moderated mediation significant for PDA or DPDD at early follow-up
(Analyses 3,4) and so no table was created.

THE AMERICAN JOURNAL OF DRUG AND ALCOHOL ABUSE 109

average, attending more than 10 AAmeetings prior to the
3-month follow-up. By contrast, in Kelly et al. (28) study,
a majority of the outpatient participants did not attend
AA during any follow-up and, by the 9-month follow-up,
the percent of outpatient MATCH participants reporting
any AA attendance had declined to 29%. The absence of
mediation in the Kelly et al. (28) study may have occurred
because participants had significantly less overall expo-
sure to AA.

Although we used different methods than we had
used in our previous paper (26), we still observed that
AA attendance was associated with reductions in later
depressive symptoms, controlling for concurrent drink-
ing (path A; Figures 1 and 2). Moreover, we obtained
further detail about the time points at which these
effects were stronger (early follow-up) and in which
populations (greater baseline depression levels for late
follow-up). One possible reason that baseline depres-
sion severity did not moderate the relationship between
AA attendance and later depression at early follow-up
is that there was more room for improvement in all
individuals, and due to the powerful effect of novelty,
otherwise known as the “pink cloud” by AA attendees
early in their AA exposure. At late follow-up, there may
have been a stronger effect for those with greater
depression at baseline, perhaps because they were the
individuals for whom depression was not tightly linked
with alcohol consumption, and who took longer to
improve in their depressive symptom levels. Finally,
that those with moderate or severe depression scores
at baseline benefited even more at late follow-up than
those with minimal or mild depression also supports
findings in the literature that individuals with co-occur-
ring psychiatric issues are just as likely to benefit from
AA (11,39), and that drinking reduction improves
chances of recovery from psychiatric illness.

Negative affect has long been considered a trigger for
drinking and is considered a reasonable target for AUD
treatment both within the AA literature (25) and treat-
ment literature at large (40). This is supported by
studies showing that compulsive alcohol use is driven
by negative reinforcement (41), that people with AUD
report drinking to relieve negative affect (42–44), that
depression precedes relapse and is associated with
greater drinking in individuals with AUD seeking treat-
ment (45–49), and that treatments targeting negative
affect and emotion regulation result in improvements
in drinking (42,50). On the other hand, previous work
has also shown that antidepressants in individuals with
AUD but without depression are not effective at redu-
cing drinking (51), and has shown a minimal role of
depression in predicting later drinking (52). Our results
supported the possibility that changes in negative affect

(depression) do indeed mediate the effects of AA on
later drinking, that this effect occurs above and beyond
the effect of AA on drinking (for DPDD at early follow-
up), and that improving negative affect is a reasonable
treatment target.

One limitation of our study has to do with the fact
that we only had a single measurement tool, the BDI,
for negative affect. Although anger has been explored as
a mediator of AA’s effect on drinking (53), to our
knowledge, neither anxiety nor irritability has been.
Furthermore, we had some limitations to our analysis
methods. For one, we only analyzed subjects with com-
plete data, which could have limited our power to
detect effects and may have caused us to have null
effects at late follow-up, for example. Second, although
lagging the time points in our analyses for the indepen-
dent, mediator, and dependent variables was a strength
in our study in terms of being able to make inferences
about causality, latent growth curve modeling would
have allowed us to include all time points and may
have had more sensitivity to detect effects (54).
Finally, this is an observational cohort study rather
than, say, a randomized trial targeting depression or
assigning individuals to degrees of AA attendance fre-
quency, and therefore, causality cannot be definitively
attributed to the independent variable or mediator in
such a design; associations could have been driven by
unmeasured variables.

Conclusions

In conclusion, we observed that changes in depression
mediated the effects of AA attendance on later drinking
(DPDD) at early follow-up consistent with some but not
all studies. However, baseline depression severity did not
moderate the indirect effect and therefore did not provide
support to the hypothesis that baseline depression differ-
ences were driving the differences between studies.
Importantly, however, these findings have clinical signifi-
cance by increasing support for models that ameliorating
negative affect may help to reduce alcohol use in indivi-
duals with AUD and that encouraging AA attendance
may be one way to achieve drinking reduction via this
mechanism.

Disclosure statement

The authors report no relevant financial conflicts.

Funding

This research was supported by National Institute on Alcohol
Abuse and Alcoholism (NIAAA) Grants K02-AA00326

110 C. E. WILCOX AND J. S. TONIGAN

and R01-AA014197. CEW is supported by NIA-AA Grant
K23-AA021156. JST is supported by NIAAA Grant
K24-AA021157. The views expressed are those of the authors
and do not necessarily represent the views of the NIAAA.

ORCID

J. Scott Tonigan http://orcid.org/0000-0002-6668-2038

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Treatment rates for alcohol use disorders: a systematic
review and meta-analysis

Tesfa Mekonen1,2 , Gary C. K. Chan3 , Jason Connor3,4 , Wayne Hall3,5 , Leanne Hides1,3

& Janni Leung1,3,

School of Psychology, The University of Queensland, Brisbane, QLD, Australia,1 Psychiatry Department, Bahir Dar University, Bahir Dar, Ethiopia,2 National Centre for
Youth Substance Use Research, The University of Queensland, Brisbane, QLD, Australia,3 Discipline of Psychiatry, The University of Queensland, Brisbane, QLD,
Australia,4 Queensland Alliance for Environmental Health Sciences, The University of Queensland, Brisbane, QLD, Australia5 and National Drug and Alcohol Research
Centre, University of New South Wales, Sydney, NSW, Australia6

ABSTRACT

Aims To estimate the treatment rate for alcohol use disorders (AUDs) in the general adult population. Treatment rates
were also considered in relation to economic differences. Methods Systematic review and meta-analysis. We searched
PubMed, EMBASE, PsycINFO and CINAHL databases to identify studies that reported treatment rates for alcohol use
disorders in the general population. Independent reviewers screened the articles based on predefined inclusion criteria.
Data were extracted using a standardized data extraction form.We conducted quality assessments of the included studies.
The overall treatment rates were estimated from studies that reported any treatment for AUDs from healthcare or informal
non-healthcare settings (any treatment). We estimated the separate treatment rates for each diagnostic category as
reported in the primary studies: AUD as a single disorder, alcohol abuse and alcohol dependence. Data were pooled using
a random-effect model. Results Thirty-two articles were included to estimate the treatment rates (percentage treated
among the total number of people with AUDs). The pooled estimate of people with AUDs who received any treatment were
14.3% (95% CI: 9.3–20.3%) for alcohol abuse, 16.5% (95% CI: 12–21.5%) for alcohol dependence and 17.3% (95% CI:
12.8–22.3%) for AUD. A subgroup analysis by World Bank economic classification of countries found that the treatment
rate for AUD was 9.3% (95% CI: 4.0–15.7%) in low and lower-middle-income countries. Conclusion Globally, approx-
imately one in six people with AUDs receives treatment. Treatment rates for AUDs are generally low, with even lower rates
in low and lower-middle-income countries.

Keywords Alcohol use disorders, global mental health, health service utilization, help-seeking, mental health
systems, treatment gap.

Correspondence to: Tesfa Mekonen, Level 3 McElwain Building (24A), School of Psychology, The University of Queensland, Brisbane QLD 4072, Australia.
E-mail: t.yimer@uq.net.au

Submitted 13 August 2020; initial review completed 11 October 2020; final version accepted 25 November 2020

INTRODUCTION

Alcohol use disorders (AUDs) are among the most
prevalent mental disorders [1,2]. In the age group of
15–44 years, AUDs were among the top five leading
causes of disability-adjusted life years (DALYs) [3]. The
most recent global data shows that harmful use of
alcohol resulted in 3 million deaths worldwide and this
disease burden was higher in low and middle-income
countries (LMICs) [4].

Effective pharmacological and psychosocial interven-
tions for AUDs [5] are under-used in LMICs [6–8]. This
leads to a large ‘treatment gap’, defined as the difference

between the prevalence of the illness and the proportion
of individuals who received treatment [9]. Increasing
treatment rates is an effective way of reducing this global
alcohol-related disease burden.

The 2001 World Health Report [3] made multiple
recommendations to address the treatment gap for mental
and substance use problems, including AUDs. The World
Health Organization’s Mental Health Gap Action Program
(mhGAP) to reduce the burden of mental and substance
use disorders and promote mental health [10] was
endorsed by all member states [11]—health service
coverage and treatment for mental and substance use dis-
orders were among its actions.

REVIEW doi:10.1111/add.15357

https://orcid.org/0000-0002-3188-0173

https://orcid.org/0000-0002-7569-194

https://orcid.org/0000-0002-7020-1196

https://orcid.org/0000-0003-1984-0096

https://orcid.org/0000-0002-4550-8460

https://orcid.org/0000-0001-5816-295

mailto:t.yimer@uq.net.au

Despite these recommendations, the treatment rate re-
mains low. A review of global psychiatric epidemiology
studies in 2004 indicated that the treatment rate for AUDs
was 22% (78% treatment gap) [9]. In other words, approx-
imately 2 in 10 individuals who could benefit from AUD
treatment were accessing it. Multi-national surveys in 1

countries reported that unmet needs for mental health
and substance use treatment were persistently high espe-
cially in low resource settings [6]. The treatment rate for
AUDs is generally low [9], such rate is low even in
developed countries [12,13]. The limited evidence in
LMICs has reported that the treatment rate for AUDs is be-
low 10% [14,15].

Critical to effective international policy is contemporary
data that takes into account advances in health systems
and treatment approaches and changes in policy and
economic conditions. This systematic review and
meta-analysis aimed to estimate the treatment rates for
AUDs among the general adult population and examine if
these rates varied by economic differences between
high-income countries (HIC) and LMICs.

Review question

The question addressed was ‘What is the treatment rate for
Alcohol Use Disorders?’ A secondary question was ‘Does
this treatment rate differ byWorld Bank economic groups?’

METHODS

This review followed the preferred reporting items
for systematic reviews andmeta-analyses (PRISMA) guide-
line (Supporting information Table S1) and the protocol
followed PRISMA for systematic review protocols
(PRISMA-P) [16,17]. The protocol was registered at the in-
ternational prospective register of systematic reviews
(PROSPERO) with registration ID CRD42020161683 as
part of a larger systematic review on the treatment rates
of depression and AUDs.

Eligibility criteri

The eligibility criteria were developed based on population,
exposure, comparison, outcome and studydesign/type (PE-
COS) framework [18].

Population

Studies reporting the treatment rate for AUDs in the
general adult population (community-based studies)
were included.

We excluded studies that focused on a specific popula-
tion (e.g. elderly only, ethnic minority group, clinical set-
tings, students, prisoners, etc.), and studies that focused
on specific comorbid medical conditions (diabetes,

hypertension, etc.). Studies of healthcare utilization were
excluded unless they reported service utilization specifically
for AUDs.

Exposure

We included studies that assessed AUDs by the Diagnostic
and Statistical Manual of Mental Disorders (DSM) or
International Classification of Diseases (ICD) based mea-
surement tools.

The ICD [19,20] has separate diagnoses for ‘harmful
alcohol use’ and ‘dependence’. This distinct diagnostic
system was also applied as ‘alcohol abuse’ and ‘alcohol
dependence’ from the 3rd edition, DSM-III [21] to the
DSM-IV [22]. The DSM-5 [2] integrates the two DSM-IV
categories into a single disorder, AUD. Studies that have
used ICD-10 or DSM-IV criteria also defined AUD as
the presence of abuse or dependence [15,23]. For the
purpose of this review, we estimated the separate treatment
rates for each diagnostic category as reported in the
primary studies (i.e. studies reported (i) ‘AUD’ as a single
disorder; (ii) ‘alcohol abuse’; and (iii) ‘alcohol dependence’).

Outcomes

The outcome is ‘treatment rate for AUDs’. The treatment
rates for the individual studies were calculated as follows:

Treatment rate ¼ n

N
�100%

Where ‘n’ is the number of treated people with AUDs, and
‘N’ is the total number of people with AUDs.

Study design

We included all community-based observational studies
(cross-sectional studies, longitudinal studies and cohort
studies) published in peer-reviewed journals that reported
treatment rates for AUDs (or information that enabled us
to retrieve the treatment rates).

Reviews, case reports, case series, conference abstracts,
dissertations, book chapters, editorials and commentaries
were excluded, but their reference lists were checked for
relevant studies.

Search strategy

Four database searches (PubMed, EMBASE, PsycINFO and
CINAHL) were conducted on September 2019 to identify
articles published in English since 2004. The current
systematic review follows on from the previous review
published in 2004 that reported on the treatment rate for
AUDs [9], therefore, we included studies published from
2004 onward. The search for relevant citations was done
using subject headings (MeSH, Thesaurus and EMTREE)
and synonym terms. Because this review is part of a larger
systematic review on the treatment rates of depression and

2618 Tesfa Mekonen et al.

AUD, the search terms were combined by the concepts
of AUDs, depression and treatment rate as: ((AUD OR
Depression) AND Treatment rate) (Supporting information
Table S2).

Study selection and data extraction

All citations were exported to EndNote X9 reference library
[24] and duplicates were removed. Two independent re-
viewers screened the title, abstract and full text based on
the predefined inclusion and exclusion criteria. Discrepan-
cies between the two reviewers were solved by discussion.
The agreement between the reviewers during the first
stage (title and abstract screening) and second stage (full
text) screening were 86% and 95%, respectively.

Articles eligible for full-text reviewwere extracted using
a standardized data extraction form recorded in an excel
spreadsheet. Data were collected on characteristics of the
studies (author’s name, year of data collection, year of pub-
lication, countrywhere the studywas conducted, study de-
sign, response rate, measurement tool and pertinent
findings) and participant characteristics.

Assessment of quality

The methodological quality of the included articles was
critically appraised using the modified form of Joanna
Briggs Institute (JBI) critical appraisal of prevalence studies
tool [25] and the risk of bias tool [26]. The validated tool
comprises 10 items to assess the methodological quality
of studies of any design reporting prevalence data with
modified yes/no scales [27,28] (Supporting information
Table S3).

Data analysis and presentation of the results

Results were presented in a narrative summary, tables and
charts. Meta-analysis was done using the random effect
models, as recommended by JBI’s methodological guidance
[29]. The meta-analysis was done using MetaXL version
5.3 [30]. To address the problem of variance instability
[29,31], the treatment rate was reported as a proportion
by using a double arcsine transformation method that
gives the pooled proportion with a 95% CI.

Heterogeneity was assessed using Cochran’s Q value
and I2 statistics [32]. Publication bias was assessed by
visual examination of funnel plots, Eggers test, and fill-
and-trim method [33]. We conducted subgroup analysis
by World Bank economic classification of countries to
compare the treatment rates between HICs and LMICs.
To further examine the heterogeneity, a random-effects
meta-regression was performed on selected study charac-
teristics, including publication year, gender proportion,
urbanicity and measurement tools for AUDs [34]. The
meta-regression model was fitted with restricted

maximum likelihood and corrected by the Knapp–Har-
tung variance estimator [35] using Stata version 16
(StataCorp, College Station, Texas). Sensitivity analysis
(one by one exclusion of individual studies) was
conducted to examine the effect of excluding individual
studies on the heterogeneity. We examined the changes
in I2 values after excluding the outlier studies to identify
studies responsible for a significant decrease in I2

[36,37].
The treatment rate was estimated by the treatment

types reported in the studies. In this study, treatment types
were summarized as follows (all are separate categories,
one is not the composite of others): (i) any form of treat-
ment; (ii) treatment from healthcare facilities (general
medical andmental health service); (iii) informal help from
non-healthcare settings (Alcoholics Anonymous, religious
places, etc.). To estimate the overall treatment rate,
estimated from studies that reported ‘any form of treat-
ment’ were used.

RESULTS

In the larger systematic reviewon depression and AUD, 16
681 unique records were identified through database
searching and supplementary searches. After screening
titles and abstracts, 185 articles were eligible for full-text
assessment. Out of the 185 full text articles, 43 studies
were for depression only and 142 were for AUDs. After
full-text screeningof AUD studies, 32 articles were included
in the qualitative synthesis and meta-analysis (Fig. 1).

Study characteristics

All the included studies were cross-sectional in design and
had been conducted in the general population of 25 differ-
ent countries. Studies were mostly from HICs (n = 20,
62.5%) [23,38–56] and upper-middle-income countries
(n = 8) [57–64]. A small number of studies were from low
and lower-middle-income countries (n = 4) [15,65–67].
Two studies were multinational, one from four LMICs and
the other from six HICs. The majority of the studies
(n = 22) were from nationally representative surveys.
According to the World Bank region classification of coun-
tries, the highest number of studies (n = 9) were found in
Europe and Central Asia region followed by East Asia and
Pacific (n = 8) (Table 1).

Almost all (n = 31) studies reported the overall
treatment rate (any type of treatment) for AUD, abuse or
dependence irrespective of whether it was a formal
healthcare service or informal help from non-health care
settings. One study reported the prevalence of using reha-
bilitation programs, detoxification wards and psychiatric
treatment separately, but it did not report the overall treat-
ment rate [61]. Regarding the treatment settings, four

Treatment rates for alcohol use disorders 2619

studies reported formal treatment specific to AUDs, treat-
ment from any health care provider (n = 6), mental health
professionals or mental health setting (n = 16) and general
medical setting (n = 16). One study reported estimates for
minimally adequate treatment [56] and another study re-
ported separate treatment rate estimates for pharmaco-
therapy and psychotherapy [38].

Based on the AUDs classification, most studies reported
treatment for AUD (n = 17), followed by alcohol depen-
dence (n = 16) and alcohol abuse (n = 10). Only one study
had a complete report on the treatment rate of all AUDs
classifications [42]. Eleven studies reported a lifetime treat-
ment rate and 26 studies reported a 12-month treatment
rate (Table 2).

Figure 1 PRISMA flow chart

Table 1 Number of included studies by World Bank region and economic classification

WB classificationa Total countries (n) Countries with data (n) Studies found (n)

By region East Asia and Pacific 38 6 8
Europe and Central Asia 58 10 9
Latin America and the Caribbean 42 3 6
Middle East and North Africa 21 N/A No
North America 3 2 5
South Asia 8 2 2
Sub-Saharan Africa 48 2 2

By income Low-income 31 3 2
Lower-middle-income 47 2 3
Upper-middle-income 60 5 8
High-income 80 15 20

a
World Bank 2020 classification

2620 Tesfa Mekonen et al.

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(4
.3
)

A
bu

se
•
A
ny

tr
ea
tm

en
t
(9
.3
)

•
A
U
D
tr
ea
tm

en
t
(2
.9
)

•
M
en
ta
lh

ea
lth

tr
ea
tm

en
t

(5
.4
)

43
09

3
(3
.4
)

D
ep
en
de
nc
e

•
A
ny

tr
ea
tm

en
t
(2
2.
9)

•
A
U
D
tr
ea
tm

en
t
(7
.2
)

•
M
en
ta
lh

ea
lth

tr
ea
tm

en
t

(9
.6
)

[4
2]

U
SA

(2
00

1–
20

02
)

74
25

.6
66

.4
D
SM

-I
V
ba
se
d

to
ol

12
m
on

th
s

55
27

9
(8
.2
)

A
U
D

•
A
ny

tr
ea
tm

en
t
(2
4.
9)

•
A
U
D
tr
ea
tm

en
t
on

ly
(4
.8
)

•
M
en
ta
lh

ea
lth

tr
ea
tm

en
t
(1
6.
6)

55
27

9
(4
.6
)

A
bu

se
•
A
ny

tr
ea
tm

en
t
(2
0.
7)

•
A
U
D
tr
ea
tm

en
t
on

(3
.9
)

•
M
en
ta
lh

ea
lth

tr
ea
tm

en
t
(1
5.
1)

(C
on

ti
nu

es
)

Treatment rates for alcohol use disorders 2621

Ta
bl
e
2.

(C
on

tin
ue
d)

St
ud

y
[R
ef
.]

Co
un

tr
y

(s
ur
ve
y
ye
ar
)

R
es
po
ns
e

ra
te

%
M
ea
n

ag
e

% M
en

A
ss
es
sm

en
t

to
ol
s

12
m
on

th
s
or

lif
et
im

e
pe
ri
od

Sa
m
pl
e
si
ze

N
(%

co
nd

iti
on

)
Co

nd
iti
on

/
ex
po
su
re

Tr
ea
tm

en
t
ra
te

(
%
)

Q
ua

lit
y
sc
or
e

[2
5,
26

]

55
27

9
(3
.6
)

D
ep
en
de
nc
e

•
A
ny

tr
ea
tm

en
t
(3
0.
1)

•
A
U
D
tr
ea
tm

en
t
on

ly
(6
.0
)

•
M
en
ta
lh

ea
lth

tr
ea
tm

en
t

(1
8.
5)

[4
3]

Fr
an

ce
(1
99

9–
03

10
0

47
.7

46
.1

M
IN
I
(I
CD

-1
0)

Li
fe
tim

e
39

61
7(
4.
3)

A
U
D

•
A
ny

tr
ea
tm

en
t
(4
4.
0)

[4
4]

U
SA

(2
01

2–
20

13
)

60
.1

46
48

.1
D
SM

-5
ba
se
d

to
ol

12
m
on

th
s

36
30

9
(1
3.
9)

A
U
D

•
A
ny

tr
ea
tm

en
t
(7
.7
)

•
12

-s
te
p
pr
og
ra
m

(4
.5
)

•
Fa
m
ily
/s
oc
ia
ls
er
vi
ce
s
(1
.4
)

•
D
et
ox
ifi

ca
tio

n
w
ar
d/
cl
in
ic

(1
.3
)

•
O
th
er

in
pa
tie
nt

fa
ci
lit
y
(1
.2
)

•
O
ut
pa
tie
nt

cl
in
ic
(2
.0
)

•
R
eh
ab
ili
ta
tio

n
pr
og
ra
m

(1
.8
)

•
Em

er
ge
nc
y
de
pa
rt
m

en
t
(1
.4
)

•
H
al
fw
ay

ho
us
e/
th
er
ap
eu
tic

co
m
m
un

ity
(0
.4
)

•
Cr
is
is
ce
nt
re

(0
.2
)

•
Em

pl
oy
ee

as
si
st
an

ce
(0
.3
)

•
Cl
er
gy

(0
.9
)

•
H
ea
lth

ca
re

pr
of
es
si
on

al
(3
.6
)

•
O
th
er
s
(0
.4
)

[4
4]

U
SA

(2
01

2–
20

13
)

60
.1

46
48

.1
D
SM

-5
ba
se
d

to
ol

Li
fe
tim

e
36

30
9

(2
9.
1)

A
U
D

•
A
ny

tr
ea
tm

en
t
(1
9.
8)

•
12

-s
te
p
pr
og
ra
m

(1
5.
4)

•
Fa
m
ily
/s
oc
ia
ls
er
vi
ce
s
(4
.1
)

•
D
et
ox
ifi
ca
tio

n
w
ar
d/
cl
in
ic
(6
.2
)

•
O
th
er

in
pa
tie
nt

fa
ci
lit
y
(4
.6
)

•
ou

tp
at
ie
nt

cl
in
ic
(6
.5
)

•
R
eh
ab
ili
ta
tio

n
pr
og
ra
m

(9
.1
)

•
Em

er
ge
nc
y
de
pa
rt
m
en
t
(5
.7
)

•
H
al
fw
ay

ho
us
e/
th
er
ap
eu
tic

co
m
m
un

ity

(1
.9
)

•
Cr
is
is
ce
nt
re

(0
.9
)

•
Em

pl
oy
ee

as
si
st
an

ce
(1
.2
)

•
Cl
er
gy

(3
.0
)

•
H
ea
lth

ca
re

pr
of
es
si
on

al
(8
.7
)

(C
on

ti
nu

es
)

2622 Tesfa Mekonen et al.

Ta
bl
e
2.

(C
on

tin
ue
d)

St
ud

y
[R
ef
.]

Co
un

tr
y

(s
ur
ve
y
ye
ar
)

R
es
po
ns
e

ra
te

%
M
ea
n

ag
e

% M
en

A
ss
es
sm

en
t

to
ol
s

12
m
on

th
s
or

lif
et
im

e
pe
ri
od

Sa
m
pl
e
si
ze

N
(%

co
nd

iti
on

)
Co

nd
iti
on

/
ex
po
su
re

Tr
ea
tm

en
t
ra
te

(
%
)

Q
ua

lit
y
sc
or
e

[2
5,
26

]

•
O
th
er
s
(1
.8
)

[4
6]

Cz
ec
h
R
ep
ub

lic
(2
01

7)
75

48
.8

46
M
IN
I
(I
CD

-1
0)

12
m
on

th
s

33
06

(1
0.
6)

A
U
D

•
A
ny

tr
ea
tm

en
t
(7
.0
)

8
[4
7]

U
SA

(2
00

6–
20

07
)

74
26

59
.8

M
IN
I
(I
CD

-1
0)

12
m
on

th
s

11
0
71

4
(3
.6
)

D
ep
en
de
nc
e

•
A
ny

tr
ea
tm

en
t
(1
1.
0)

[6
0]

Br
az
il
(2
00

5–
20

06
)

66
.4

34
.1

50
.8

CI
D
I
(D
SM

-I
V
)

12
m
on

th
s

30
07

(9
.6
)

A
U
D

•
A
ny

tr
ea
tm

en
t
(1
2.
4)

•
A
lc
oh

ol
ic
an

on
ym

ou
s
(3
.4
)

•
Sp
ec
ia
liz
ed

su
rg
er
y
(3
.0
)

•
G
en
er
al
ho

sp
ita

l(
2.
5)

•
Ps
yc
hi
at
ri
c
ho

sp
ita

l(
2.
1)

•
Pr
iv
at
e
cl
in
ic

(0
.7
)

•
O
th
er

al
co
ho

lp
ro
gr
am

(0
.7
)

[4
9]

K
or
ea

(2
00

6–
20

07
)

81
.7

36
.1

39
.6

CI
D
I
(D
SM

-I
V
)

Li
fe
tim

e
65

10
(7
)

D
ep
en
de
nc
e

•
A
ny

tr
ea
tm

en
t
(1
2.
0)

7
12

m
on

th
s

65
10

(3
.2
)

D
ep
en
de
nc
e

•
A
ny

tr
ea
tm

en
t
(3
.4
)

[5
0]

Fi
nl
an

d
20

00
–2

00
1

75
51

.7
48

.1
CI
D
I
(D
SM

-I
V
)

12
m
on

th
s

60
05

(3
.9
)

D
ep
en
de
nc
e

•
A
ny

tr
ea
tm

en
t
(2
5.
3)

•
A
lc
oh

ol
tr
ea
tm

en
t
(1
5.
6)

•
H
ea
lth

tr
ea
tm

en
t
(1
6.
8)

[1
5]

Et
hi
op
ia
(2
01

4)
98

.5
39

.4
49

.4
A
U
D
IT

(I
CD

–
10

)
Li
fe
tim

e
14

86
(1
3.
9)

A
U
D

•
A
ny

tr
ea
tm

en
t
(1
3.
1)

•
Sp
ec
ia
lis
t

he
al
th

pr
ov
id
er

(0
.0
)

•
G
en
er
al
is
t
he
al
th

pr
ov
id
er

(9
.1
)

•
Co

m
pl
em

en
ta
ry

pr
ov
id
er

(3
.9
)

In
di
a
(2
01

3)
99

.6
40

.2
54

.6
A
U
D
IT

(I
CD

–
10

)
12

m
on

th
s

32
20

(5
.6
)

A
U
D

•
A
ny

tr
ea
tm

en
t
(2
.8
)

•
Sp
ec
ia
lis
t
he
al
th

pr
ov
id
er

(0
.0
)

•
G
en
er
al
is
t
he
al
th

pr
ov
id
er

(1
.1
)

•
Co

m
pl
em

en
ta
ry

pr
ov
id
er

(1
.7
)

N
ep
al
(2
01

3)
98

.9
39

8
40

.2
A
U
D
IT

(I
CD

–
10

)
12

m
on

th
s

20
40

(5
)

A
U
D

•
A
ny

tr
ea
tm

en
t
(5
.1
)

•
Sp
ec
ia
lis
t
he
al
th

pr
ov
id
er

(0
.0
)

•
G
en
er
al
is
t
he
al
th

pr
ov
id
er

(1
.3
)

•
Co

m
pl
em

en
ta
ry

pr
ov
id
er

(4
.5
)

U
ga
nd

a
(2
01

3)
99

.9
36

34
.4

A
U
D
IT

(I
CD

–
10

)
12

m
on

th
s

12
90

(1
.7
)

A
U
D

•
A
ny

tr
ea
tm

en
t

(3
.5
)

•
Sp
ec
ia
lis
t
he
al
th

pr
ov
id
er

(3
.5
)

•
G
en
er
al
is
t
he
al
th

pr
ov
id
er

(0
.0
)

•
Co

m
pl
em

en
ta
ry

pr
ov
id
er

(0
.0
)

[6
1]

M
ex
ic
o
(2
01

6–
20

17
)

73
.6

29
41

.9
CI
D
I
(D
SM

-I
V
)

12
m
on

th
s

56
87

7
(2
.2
)

D
ep
en
de
nc
e

•
R
eh
ab
ili
ta
tio

n
pr
og
ra
m

(3
2.
4)

(C
on

ti
nu

es
)

Treatment rates for alcohol use disorders 2623

Ta
bl
e
2.

(C
on

tin
ue
d)

St
ud

y
[R
ef
.]

Co
un

tr
y

(s
ur
ve
y
ye
ar
)

R
es
po
ns
e

ra
te

%
M
ea
n

ag
e

% M
en

A
ss
es
sm

en
t

to
ol
s

12
m
on

th
s
or

lif
et
im

e
pe
ri
od

Sa
m
pl
e
si
ze

N
(%

co
nd

iti
on

)
Co

nd
iti
on

/
ex
po
su
re

Tr
ea
tm

en
t
ra
te

(
%
)

Q
ua

lit
y
sc
or
e

[2
5,
26

]

•
D
et
ox
ifi
ca
tio

(2
4.
8)

•
Ps
yc
hi
at
ri
c
tr
ea
tm

en
t
(1
3.
2)

[5
1]

Ca
na

da
(2
00

0–
20

01
)

84
.7

38
40

.9
CI
D
I
(D
SM

-I
V
)

12
m
on

th
s

12
5
49

3
(1
.9
)

D
ep
en
de
nc
e

•
A
ny

tr
ea
tm

en
t
(1
7.
2)

•

N
on

-p
hy

si
ci
an

on
ly
(8
.4
)

•
Ph

ys
ic
ia
n
on

ly
(3
.6
)

•
Ps
yc
hi
at
ri
st
on

ly
(1
.1
)

•

M
ul
tip

le
pr
of
es
si
on

al
(3
.9
)

[5
2]

Si
ng

ap
or
e
(2
00

9–
20

10
)

75
.9

42
48

.5
CI
D
I
(D
SM

-I
V
)

Li
fe
tim

e
66

16
(3
.6
)

A
U
D

•
A
ny

he
lp
(1
8.
9)

•
Ps
yc
hi
at
ri
st
(7
.9
)

•
G
P
(5
.0
)

•
Ps
yc
ho

lo
gi
st
(5
.5
)

•
M
en
ta
lh

ea
lth

sp
ec
ia
lis
t
(4
.0
)

•
Co

un
se
llo
r
(7
.6
)

•
O
th
er

he
al
th

pr
of
es
si
on

al
(0
.4
)

•
R
el
ig
io
us

an
d
ot
he
r
he
al
er
s
(4
.5
)

[5
3]

Si
ng

ap
or
e
(2
01

6)
69

.5
45

.2
50

.1
CI
D
I
(D
SM

-I
V
)

12
m
on

th
s

61
26

(0
.2
)

D
ep
en
de
nc
e

•
A
ny

tr
ea
tm

en
t
(3
.0
)

7
61

26
(0
.6
)

A
bu

se
•
A
ny

tr
ea
tm

en
t
(1
9.
4)

[5
4]

A
us
tr
al
ia
(2
00

7)
60

46
.6

49
.7

CI
D
I
(D
SM

-I
V
)

12
m
on

th
s

88
41

(4
.3
)

A
U
D

•
A
ny

tr
ea
tm

en
t
(2
2.
4)

•
G
P
(1
4.
5)

•
Ps
yc
hi
at
ri
st
(4
.3
)

•
Ps
yc
ho

lo
gi
st
(9
.8
)

•
M
en
ta
lh

ea
lth

sp
ec
ia
lis
t
(1
6.
5)

•
O
th
er

he
al
th

pr
of
es
si
on

al
(5
.0
)

•
Ps
yc
hi
at
ri
c
in
pa
tie
nt

(4
.2
)

[5
5]

N
et
he
rl
an

d
(2
00

7–
20

09
)

65
.1

–
–

CI
D
I
(D
SM

-I
V
)

Li
fe
tim

e
66

46
(1
2.
4)

A
bu

se
•
A
ny

tr
ea
tm

en
t
(6
.5
)

7
66

46
(1
.7
)

D
ep
en
de
nc
e

•
A
ny

tr
ea
tm

en
t
(3
6.
9)

[6
2]

Tu
rk
ey

(2
00

7–
08

)
76

.5
37

.4
42

CI
D
I
(D
SM

-I
V
)

12
m
on

th
s

40
11

(3
.2
)

A
bu

se
•
A
ny

tr
ea
tm

en
t
(1
1.
6)

8
40

11
(1
.6
)

D
ep
en
de
nc
e

•
Tr
ea
tm

en
t
(1
6.
7)

[6
4]

Ch
in
a
(2
00

3)
94

.8
46

46
.2

CI
D
I
(I
CD

-1
0)

Li
fe
tim

e
59

26
(4
.3
)

D
ep
en
de
nc
e

•
A
ny

tr
ea
tm

en
t
(2
.4
)

7
12

m
on

th
s

59
26

(1
.7
)

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ep
en
de
nc
e

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ny

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ea
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t
(1
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[6
7]

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hi
op
ia
(2
01

4)
99

39
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45
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A
U
D
IT

(I
CD

–
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fe
tim

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14

85
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ig
io
us

se
tt
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(C
on

ti
nu

es
)

2624 Tesfa Mekonen et al.

Ta
bl
e
2.

(C
on

tin
ue
d)

St
ud

y
[R
ef
.]

Co
un

tr
y

(s
ur
ve
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ar
)

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es
po
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ra
te

%
M
ea
n

ag
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% M
en

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ss
es
sm

en
t

to
ol
s

12
m
on

th
s
or

lif
et
im

e
pe
ri
od

Sa
m
pl
e
si
ze

N
(%

co
nd

iti
on

)
Co

nd
iti
on

/
ex
po
su
re

Tr
ea
tm

en
t
ra
te

(
%
)

Q
ua

lit
y
sc
or
e

[2
5,
26

]

[4
8]

N
ew

Ze
al
an

d
(2
00

3–
20

04
)

73
.3

43
43

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CI
D
I
(D
SM

-I
V
)

12
m
on

th
s

12
99

2
(2
.6
)

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ea
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8)

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9)

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ea
lth

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[5
6]

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SA

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03
)

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44

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(D
SM

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m
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s

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(1
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3.
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[5
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6)

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in
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ly
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te
tr
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t

(3
1.
9)

[6
3]

Br
az
il
(2
00

5–
20

07
)

81
.3

39
43

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CI
D
I
(D
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)

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m
on

th
s

50
37

(2
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)

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bu

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(1
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tt
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ex
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(2
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58

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(3
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[5
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M
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(2
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(2
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5]

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nl
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(2
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75
51

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(D
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m
on

th
s

47
06

(5
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[5
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rg
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77

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th

se
tt
in
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(4
.3
)

(C
on

ti
nu

es
)

Treatment rates for alcohol use disorders 2625

The quality of included studies

The quality of included studies ranged from 60–90% with
an average quality of 74.7%, summarized as moderate
quality. Themost common shortcomingswere failure to re-
port appropriate statistical measures (confidence intervals
or standard errors), lack of complete reports on the treat-
ment rate for each type of care provided and a lack of de-
tailed description of participants and settings (Supporting
information Table S4).

Treatment rate for AUDs

The pooled treatment rate of AUD from any source of treat-
ment was 17.3% (12.8–22.3%) with significant evidence
of between studies heterogeneity (Q = 2649, P < 0.001, I2 = 99%). For alcohol abuse, the pooled treatment rate from any source of treatment was 14.3% (9.2–20.3%) with significant between studies heterogeneity (Q = 292, P < 0.001, I2 = 97%). The pooled treatment rate for alco- hol dependence from any source of treatment was 16.5% (12.0–21.5%) with significant heterogeneity between studies (Q = 636, P < 0.001, I2 = 97%) (Supporting information Figs S1, S2 and S3). The treatment rate for AUDs widely varied between countries, ranging from 3.5% in Uganda to 51.8% in the United Kingdom for AUD, 0% in Laos to 25.8% in New Zealand for alcohol abuse, 0% in Laos to 36.9% in New Zealand for alcohol dependence (Fig. 2).

The subgroup analysis by World Bank economic classi-
fication of countries indicated that the treatment rate was
very low in low and lower-middle-income countries across
all treatment types. There were no studies from low and
lower-middle-income countries reporting separate treat-
ment rate for general medical care, mental health care or
informal sources of help for alcohol abuse and dependence
(Table 3). The univariate meta regression did not show any
significant association between the pooled estimate and
the selected study characteristics (measurement tools for
AUDs, study setting, percentage of male participants and
year of publication) (Supporting information Table S8).

Publication bias and sensitivity analysis

As indicated in Fig. 3, the visual inspection of the funnel
plots was slightly asymmetric for the treatment rate of
AUDs from any source of treatment. However, an Eggers
test indicated no evidence of small study effect (AUD
[t = 0.67, P = 0.51], alcohol dependence [t = 0.05,
P = 0.96] and alcohol abuse [t = �0.08, P = 0.94]). Sen-
sitivity analysis demonstrated no significant difference in
I2 statistics. The I2 statistics for the AUDs treatment rates
remained very high after one-by-one exclusion of studies
(Supporting information Tables S5, S6 and S7) and even

Ta
bl
e
2.

(C
on

tin
ue
d)

St
ud

y
[R
ef
.]

Co
un

tr
y

(s
ur
ve
y
ye
ar
)

R
es
po
ns
e

ra
te

%
M
ea
n

ag
e

% M
en

A
ss
es
sm

en
t

to
ol
s

12
m
on

th
s
or

lif
et
im

e
pe
ri
od

Sa
m
pl
e
si
ze

N
(%

co
nd

iti
on

)
Co

nd
iti
on

/
ex
po
su
re

Tr
ea
tm

en
t
ra
te

(
%
)

Q
ua

lit
y
sc
or
e

[2
5,
26

]

[3
8]

M
ul
tip

le
co
un

tr
ie
s
in

Eu
ro
pe

(2
00

1–
20

03
)

61
.2

47
48

CI
D
I
(D
SM

-I
V
)

12
m
on

th
s

21
42

5
(1
)

A
U
D

•
A
ny

tr
ea
tm

en
t
(8
.3
)

•
D
ru
g
tr
ea
tm

en
t
on

ly
(1
.6
)

•
Ps
yc
ho

lo
gi
ca
lo
nl
y
(2
.8
)

•
D
ru
g
an

d
ps
yc
ho

lo
gi
ca
l(
2.
6)

•
N
o
dr
ug

/p
sy
ch
ol
og
ic
al
(1
.2
)

n/
a
=
no

tr
ep
or
te
d;
A
U
D

=
al
co
ho

lu
se

di
so
rd
er

U
D
IT

=
al
co
ho

lu
se

di
so
rd
er

id
en
tifi

ca
tio

n
te
st
;C

ID
I=

co
m
po
si
te
in
te
rn
at
io
na

ld
ia
gn

os
tic

in
te
rv
ie
w
;G

P
=
ge
ne
ra
lp
ra
ct
iti
on

er
;M

IN
I=

m
in
ii
nt
er
na

tio
na

ln
eu
ro
ps
yc
hi
at
ri
c
in
te
rv
ie
w
.a B

el
gi
um

,
Fr
an

ce
,G

er
m
an

y,
It
al
y,
th
e
N
et
he
rl
an

ds
,S
pa
in
.N

ot
e:
se
e
su
pp
le
m
en
ta
lfi
le
fo
r
qu

al
ity

sc
or
e
as
se
ss
m
en
t
ta
bl
e.

2626 Tesfa Mekonen et al.

after removal of four outlier studies (I2 = 97% for AUD,
I2 = 90% for dependence and I2 = 77% for alcohol abuse).

DISCUSSION

This systematic review and meta-analysis found that the
treatment rate for AUDs is generally low in the global set-
ting. Lowest treatment rates were estimated in low and
lower-middle-income countries. The number of included
studies in low and lower-middle-income countries was
small (n=4, from India, Ethiopia, Nepal, Uganda and Laos)
[15,65–67], which introduces considerable variability and
therefore restricts generalizability. We also did not have

data on some countries with large populations (e.g.
Pakistan and Nigeria) and countries with very high heavy
episodic drinking (e.g. Russia Federation) [4]. Although
there was heterogeneity at the individual studies level,
the pooled estimate and sub-group analysis results were
within narrow range (overlapped confidence intervals
between the subgroups and the pooled estimate) that
indicates a meaningful result from pooled summary
statistics [68].

This study estimates the treatment rate for AUDs from
different dimensions of treatment sources (any source of
treatment, formal healthcare setting and informal
non-healthcare settings) based on the AUDs classification

Figure 2 Treatment rates by country for any treatment type. AUD = alcohol use disorder

Treatment rates for alcohol use disorders 2627

reported in the studies. The treatment rates from any
source of treatment for AUD, alcohol abuse and alcohol de-
pendence were 17.3% (12.8–22.3%), 14.3% (9.2–20.3%)
and 16.5% (12.0–21.5%) respectively. This translates
to treatment gaps (untreated percentage) of 82.7%
(77.7–87.2%) for AUD, 85.7% (79.7–90.8%) for alcohol
abuse and 83.5% (78.5–88.0%) for alcohol dependence.
The treatment rates estimated in our revieware lower than
the previous review reported by Kohn et al., which was an
average treatment rate of 23.8% (76.2% treatment gap)
for AUDs [9]. This suggests that the treatment rate for
AUDs has not increased in the last 15 years despite the
WHO recommendations and actions to address the treat-
ment gap [3,10,11]. The low treatment rate, including in
HICs, may indicate that making mental health services
available/accessible alone cannot increase the treatment

seeking behaviour [69,70] unless the public mental health
literacy is increased [71]. Moreover, AUDs have not been
historically considered as a mental illness, and people with
AUDs have been considered to be moral failures, provoking
rejection and stigma [72]. These factors are likely to pre-
vent treatment seeking. The recent recognition of mental
health in the global health agenda [73] is an important
milestone to bridge the mental health treatment gap but
international funding for mental health remains inade-
quate [74].

Natural recovery of AUDs does occur, [75,76] which
may in part offset the perceived urgency of the health sys-
tem adequately resourcing their treatment [9]. However,
untreated remission is associated with a higher risk of re-
lapse compared to treated remission [77]. Alcohol con-
sumption has a detrimental effect on general health

Table 3 Pooled treatment rate for AUD, abuse and dependence based on the type of treatment

Treatment typea
Country
income

Treatment rate
AUD Abuse Dependence
Treated % (CI) N Treated % (CI) N Treated % (CI) N

Treatment from any sourceb Low
incomec

9.3 (4.0–15.7) 6 0.00 (0.0–12.9) 1 0.00 (0.00–5.0) 1

Upper-middle 20.3 (9.2–33.0) 3 13.6 (10.1–17.7) 3 11.2 (1.8–23.2) 5
High-income 20.4 (14.2–27.3) 13 15.7 (9.0–23.5) 7 20.8 (15.2–27) 12
Overall 17.3 (12.8–22.3) 22 14.3 (9.2–20.3) 11 16.5 (12–21.5) 18
Statistics Q = 2649,

I2 = 99%
Q = 292,
I2 = 97%

Q = 636, I2 = 97%

Treatment from any Healthcare
settings

Low income 1.9 (0.1–4.4) 10 0.00 (0.00–13.0) 1 0.00 (0.00–5.0) 1
Upper-middle 4.2 (0.3–9.5) 5 12.6 (9.4–16.7) 3 14.6 (7.5–22.8) 8
High-income 6.3 (4.8–8.0) 39 17.8 (8.3–29.1) 4 183 (13.3–23.8) 8
Overall 4.8 (3.7–6.0) 54 14.6 (8.5–21.9) 8 15.5 (11.5–20) 17
Statistics Q = 1276,

I2 = 98%
Q = 217,
I2 = 97%

Q = 541, I2 = 97%

General medical setting Low income 3.7 (0.5–8.0) 5 No data – No data –

Upper-middle 2.3 (1.2–3.7) 4 9.4 (4.7–15.2) 4 7.3 (0.0–22.5) 3
High-income 4.4 (3.1–6.0) 21 22.0 (15.0–29.7) 4 22.6 (4.4–45.7) 5
Overall 4.0 (2.9–5.3) 30 15.6 (9.9–22.3) 8 16.2 (5.5–30.6) 8
Statistics Q = 2120,

I2 = 99%
Q = 69, I2 = 90% Q = 284, I2 = 98%

Mental health setting Low income 0.2 (0.00–25.1) 4 No data – No data –

Upper-middle 7.8 (0.0–25.1) 2 8.8 (6.7–11.3) 6 17.7 (13.3–25.2) 7
High-income 5.1 (3.7–6.6) 25 13.9 (8.8–19.7) 6 15.0 (7.5–24.1) 8
Overall 4.5 (3.3–5.9) 31 11.7 (8.6–15.2) 12 16.1 (9.5–24.1) 15
Statistics Q = 1595,

I2 = 98%
Q = 58, I2 = 81% Q = 1123,

I2 = 99%
Informal help Low income 3.3 (1.9–4.9) 5 No data – No data –

Upper-middle 2.1 (0.7–4.0) 3 3.1 (1.6–4.9) 6 3.9 (0.8–8.2) 4
High-income 2.3 (1.2–3.5) 24 6.4 (3.9–9.2) 6 9.4 (5.0–14.7) 6
Overall 2.4 (1.4–3.5) 32 4.9 (3.3–6.8) 12 7.1 (4.1–10.8) 10
Statistics Q = 4732,

I2 = 99%
Q = 36, I2 = 70% Q = 39.2, I2 = 77%

CI= confidence interval; N = number of observations (a studymay havemore than one observation). P< 0.001 for all statistics. a Treatment types are separate

categories (one is not the composite of others) and each treatment type category has its own pooled estimate.
b
From studies that reported an overall estimate

for treatment from any source.
c
Low and lower-middle-income countries.

2628 Tesfa Mekonen et al.

(contributes to 4% of the global mortality) [78], imposes a
significant economic burden [79,80] and is responsible for
99.2 million DALYs, [81] which is higher than the disease
burden attributable to mood disorders (43.1 million
DALYs) [82]. Effective pharmacological treatments for
AUDs are available, such as acamprosate and naltrexone
that have 9–12 number needed to treat (NNT) [83], an ef-
ficacy comparable to tricyclic antidepressants in treating
depression (NNT = 8.5) [84]. Low treatment rates are
therefore a missed opportunity to effectively treat AUDs.

People with AUDsmight seek services, but this does not
imply that they received effective treatment from appropri-
ately trained staff. A study conducted in the United States
reported that within a sample of treatment seeking individ-
uals, only 27.4% received minimally adequate treatment
(based on available evidence-based guidelines) for alcohol

abuse and 31.9% for alcohol dependence [56]. This was
more pronounced for pharmacotherapies where only 3%
of people with alcohol dependence in Australia received a
pharmacotherapy and only 15–25% of these were treated
for the recommended duration [85]. Low treatment
seeking is compounded by low detection rate of AUDs in
primary care [86,87]. This underdiagnosis misses
opportunities for brief interventions that are effective in
reducing problem drinking [83].

HICs (20.4%) and upper-middle-income countries
(20.3%) had higher treatment rates for AUD than low
and lower-middle-income countries (9.3%). There were
not enough studies to reliably compare treatment rates of
alcohol abuse and dependence across regions. Our findings
were consistent with the previous data that showed lower
coverage for mental health services, lower health service

Figure 3 Funnel plots of AUD, alcohol dependence and alcohol abuse for ‘any treatment’. AUD = alcohol use disorder

Treatment rates for alcohol use disorders 2629

utilization and low treatment rate in low-income countries
[6,7,88]. Integration of the services for AUDs to primary
health care might increase the treatment rate. Informal
community-based services are available and can be effec-
tive/cost-effective in the treatment of AUDs [89]. These
cost-effective informal services could benefit resource lim-
ited settings and remote areas in the treatment of AUDs.

The pooled estimates for AUD from studies reporting
treatment rates from healthcare settings (4.8% from any
healthcare, 4% from general medical and 4.5% from men-
tal health settings) were lower than the estimate from any
source of treatment (17.3%). Compared to alcohol abuse
and dependence, the treatment rate was relatively similar
across all treatment sources except for the informal
sources. An individuals’ perceived need for care affects
their use of healthcare services [90] whereas the severity
of the disorder might contribute to seeking help from spe-
cialized care. This was supported in our study that found
a higher treatment rate for alcohol dependence in mental
health services. Lower income countries had the lowest
treatment rate from healthcare settings for AUD, and there
was only one study that reported the treatment rate for al-
cohol abuse and dependence [65]. A research priority is to
conduct more high-quality studies on the treatment rate of
AUDs in low-income countries.

Previous studies have found that women had higher
service use behaviour for general medical purposes and
mental health care [91–93]. Our study however did not
find statistically significant gender differences in the treat-
ment of AUDs. This might be because of the small number
of studies and the difference between countries, culture
and context that warrants the need for further studies on
gender difference in different settings.

Limitations

There are limitations to be considered in the interpretation
of these findings. Insufficient data, especially from
low-income countries and between studies heterogeneity,
makes it difficult to generalize the finding to the global set-
ting. Further, this study had insufficient data to estimate
the treatment rate by the countries geographical classifica-
tion and our estimate was only of countries’ economic clas-
sification. Because we estimated the overall treatment rate
from any source of help regardless of treatment outcomes
and treatment completion, we cannot report if the treat-
ment was effective. Given the chronic and relapsing nature
of AUDs, individuals often seek treatment many times
within the 12 months/lifetime period. Our study did not
capture these data.

It is likely there are fewer AUD services in rural areas.
Our study was not able to explore this by a robust
meta-regression analysis because of the limited number of
studies. Within available data, we found a trend that the

treatment rate was low in the rural areas, which was not
statistically significant (Supporting information Table S8).
For cultural or religious reasons, some population groups
are prohibited from drinking alcohol in many countries
[94] and therefore would not seek treatment—this study
was not able to include these data. It is worth noting that
our findings were limited to the articles published in En-
glish. Diagnostic differences between studies and across
the legacy version of DSM were also a potential limitation.

CONCLUSION

Studies investigating the treatment rate of AUDs are limited
and there was substantial inter-study heterogeneity. The
meta-analysis showed that the overall treatment rate for
AUD from any source of treatment is 17.3%. Lower treat-
ment rates were observed in low and lower-middle-income
countries. Given the limited data identified in this review,
further treatment rate estimation studies are required, par-
ticularly in low-income countries. Treatment planners
should increase services for people with AUDs.

Data statement

The data associated with this study are available from the
corresponding author.

Declaration of interests

None.

Funding

There is no specific funding for this study. T.M. is supported
by the University of Queensland research and training pro-
gram scholarship.

Acknowledgements

We would like to thank Miranda Newell, Librarian, The
University of Queensland (contribution in search strategy),
Calvert Tisdale and Sarah Ford, The University of Queens-
land (contribution in proofreading) and Danielle Dawson,
The University of Queensland (contribution in Title/Ab-
stract screening and proofreading). T.M. is supported by
the University of Queensland research and training pro-
gram scholarship.

Author contributions

Gary C.K. Chan: Conceptualization; data curation; formal
analysis; investigation; methodology; resources; supervi-
sion; validation. Jason Connor: Data curation; investiga-
tion; methodology; resources; supervision; validation.
Wayne Hall: Data curation; investigation; methodology;
resources; supervision; validation. Leanne Hides:

2630 Tesfa Mekonen et al.

Conceptualization; data curation; investigation; methodol-
ogy; resources; supervision; validation. Janni Leung:
Conceptualization; data curation; formal analysis; investi-
gation; methodology; project administration; resources;
software; supervision; validation; visualization. Tesfa
Mekonen: Conceptualization-lead; data curation-lead; for-
mal analysis-lead; investigation-lead; methodology-lead;
project administration-lead; resources-equal; software-
equal; validation-equal; visualization-lead; writing-original
draft-lead; writing-review; editing-lead

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Supporting Information

Additional supporting information may be found online in
the Supporting Information section at the end of the
article.

Treatment rates for alcohol use disorders 2633

Figure S1 AUD any treatment by World Bank economy
(Forest plot).
Figure S2 Alcohol abuse any treatment by WB economic
group.
Figure S3 Alcohol dependence any treatment by WB eco-
nomic group.
Table S1 PRISMA checklist.
Table S2 PubMed search strategy.
Table S3 Quality assessment tool.

Table S4 Quality assessment of included studies.
Table S5 Sensitivity analysis for AUD treatment rate (any
treatment).
Table S6 Sensitivity analysis for alcohol abuse treatment
rate (any treatment).
Table S7 Sensitivity analysis for alcohol dependence treat-
ment rate (any treatment).
Table S8 Univariate meta-regression for AUD (n = 22), al-
cohol abuse (n = 11) and alcohol dependence (n = 18).

2634 Tesfa Mekonen et al.

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