Psychiatric Disorders in Smokers Seeking Treatment for Tobacco Dependence: Relations With Tobacco Dependence and Cessation

Journal of Consulting and Clinical Psychology
© 2010 American Psychological Association
2010, Vol. 78, No. 1, 13–23
0022-006X/10/$12.00
DOI: 10.1037/a0018065
Psychiatric Disorders in Smokers Seeking Treatment for Tobacco Dependence:
Relations With Tobacco Dependence and Cessation
Megan E. Piper, Stevens S. Smith,
Amy A. Bittrich
Tanya R. Schlam, and Michael F. Fleming
Division of Public Health, Milwaukee, Wisconsin
University of Wisconsin School of Medicine and Public Health
Jennifer L. Brown
Cathlyn J. Leitzke
Medical College of Wisconsin
University of Wisconsin School of Medicine and Public Health
Mark E. Zehner
Michael C. Fiore and Timothy B. Baker
University of Wisconsin College of Engineering
University of Wisconsin School of Medicine and Public Health
Objective: The present research examined the relation of psychiatric disorders to tobacco dependence and
cessation outcomes. Method: Data were collected from 1,504 smokers (58.2% women; 83.9% White;
mean age
44.67 years, SD
11.08) making an aided smoking cessation attempt as part of a clinical
trial. Psychiatric diagnoses were determined with the Composite International Diagnostic Interview
structured clinical interview. Tobacco dependence was assessed with the Fagerstro¨m Test of Nicotine
Dependence (FTND) and the Wisconsin Inventory of Smoking Dependence Motives (WISDM). Results:
Diagnostic groups included those who were never diagnosed, those who had ever been diagnosed (at any
time, including in the past year), and those with past-year diagnoses (with or without prior diagnosis).
Some diagnostic groups had lower follow-up abstinence rates than did the never diagnosed group ( ps
.05). At 8 weeks after quitting, strong associations were found between cessation outcome and both
past-year mood disorder and ever diagnosed anxiety disorder. At 6 months after quitting, those ever
diagnosed with an anxiety disorder (OR
.72, p
.02) and those ever diagnosed with more than one
psychiatric diagnosis (OR
.74, p
.03) had lower abstinence rates. The diagnostic categories did not
differ in smoking heaviness or the FTND, but they did differ in dependence motives assessed with the
WISDM. Conclusion: Information on recent or lifetime psychiatric disorders may help clinicians gauge
relapse risk and may suggest dependence motives that are particularly relevant to affected patients. These
findings also illustrate the importance of using multidimensional tobacco dependence assessments.
Keywords: smoking cessation, psychiatric disorders, nicotine dependence, depression, anxiety
Smokers are more likely to have a psychiatric disorder than are
2004; Gwynn et al., 2008; Kalman, Morissette, & George, 2005;
nonsmokers; conversely, individuals with a psychiatric disorder
Lasser et al., 2000; Wiesbeck, Kuhl, Yaldizli, & Wurst, 2008). As
are almost twice as likely to smoke as individuals without a
a group, smokers with psychiatric disorders consume a dispropor-
psychiatric disorder (Grant, Hasin, Chou, Stinson, & Dawson,
tionate number of cigarettes (Grant et al., 2004), and some data
Megan E. Piper, Stevens S. Smith, Tanya R. Schlam, Cathlyn J. Leitzke,
1KL2RR025012-01). Timothy B. Baker was supported by NCI
Michael C. Fiore, and Timothy B. Baker, Center for Tobacco Research and
1K05CA139871.
Intervention, Department of Medicine, University of Wisconsin School of
Megan E. Piper, Tanya R. Schlam, Michael F. Fleming, Amy A. Bit-
Medicine and Public Health; Michael F. Fleming, Department of Family
trich, Jennifer L. Brown, Cathlyn J. Leitzke, and Mark E. Zehner have no
Medicine, University of Wisconsin School of Medicine and Public Health;
potential conflicts of interest to disclose. Stevens S. Smith has received
Amy A. Bittrich, Division of Public Health, Milwaukee, Wisconsin; Jen-
research support from GlaxoSmithKline and Elan Corporation, plc. Tim-
nifer L. Brown, Department of Pediatric Oncology, Medical College of
othy B. Baker has served as an investigator on research projects sponsored
Wisconsin; Mark E. Zehner, Department of Industrial and Systems Engi-
by pharmaceutical companies, including Pfizer, Glaxo Wellcome, Sanofi,
neering, University of Wisconsin College of Engineering. Cathlyn J. Le-
and Nabi. Over the past 3 years, Michael C. Fiore has served as an
itzke is now at the Department of Medicine, University of Wisconsin
investigator in research studies at the University of Wisconsin that were
School of Medicine and Public Health.
funded by Pfizer, GlaxoSmithKline, and Nabi Biopharmaceuticals. In
This research was conducted at the University of Wisconsin—Madison
1998, the University of Wisconsin appointed Michael C. Fiore to a named
and was supported by Grant P50 DA019706 from the National Institutes of
chair funded by an unrestricted gift to the University of Wisconsin from
Health and the National Institute on Drug Abuse and by Grant M01
Glaxo Wellcome.
RR03186 from the General Clinical Research Centers Program of the
Correspondence concerning this article should be addressed to Megan
National Center for Research Resources, National Institutes of Health.
E. Piper, Center for Tobacco Research and Intervention, 1930 Monroe
Megan E. Piper was supported by an Institutional Clinical and Transla-
Street, Suite 200, Madison, WI 53711. E-mail: mep@ctri.medicine
tional Science Award (University of Wisconsin—Madison; KL2 Grant
.wisc.edu
13

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14
PIPER ET AL.
show that smokers with psychiatric disorders are less likely to quit
we used both structured psychiatric interviews for diagnosis and
smoking than are other smokers (Covey, 1999; Glassman, 1993;
prospective biochemically confirmed cessation outcomes. In sum-
Glassman et al., 1990; Haas, Munoz, Humfleet, Reus, & Hall,
mary, this research combines the internal validity of a large ran-
2004). These observations raise questions about whether psychi-
domized placebo-controlled comparative efficacy cessation trial
atric disorders are associated with severity or type of tobacco
with the use of structured interviews prior to a quit-smoking
dependence. However, little is known about whether smokers with
attempt to establish psychiatric diagnoses (ever in a lifetime and
psychiatric disorders differ from other smokers (those without
past year).
psychiatric comorbidity) in terms of the nature of their tobacco
A second major question addressed in this article is whether
dependence.
smokers with and without psychiatric disorders (specifically, anx-
Given that individuals with psychiatric disorders constitute such
iety disorders, mood disorders, and/or SUDs) differ in severity or
a large portion of the smoker population (in one study, 62% of
form of nicotine dependence. As discussed earlier, it is important
smokers seeking treatment had a history of psychiatric disorders;
to determine whether psychiatric comorbidity is related to depen-
Keuthen et al., 2000), it is critical to learn more about how
dence because dependence can affect cessation success and may
psychiatric disorders may affect nicotine dependence and ability to
have implications for treatment. Previous research in this area
quit (cf. Morissette et al., 2008; Ziedonis et al., 2008). The goal of
often used dependence measures such as the Fagerstro¨m Test of
this research is to answer two primary questions about smoking
Nicotine Dependence (FTND; Heatherton, Kozlowski, Frecker, &
and comorbid psychiatric disorders: (a) whether smokers with
Fagerstro¨m, 1991) or the number of cigarettes smoked per day
psychiatric diagnoses (the ever diagnosed, who have ever been
(Baker et al., 2007). These measures focus on smoking pattern and
diagnosed in their lifetime, including in the past year, and those
do not comprehensively assess the multiple dimensions of nicotine
with past-year diagnoses, who were diagnosed in the past year and
dependence (Hudmon et al., 2003; Piper et al., 2004; Shiffman,
may or may not have been diagnosed previously) have less success
Waters, & Hickcox, 2004). Research with such measures suggests
with quitting smoking than smokers without such diagnoses and
that, in general, smokers with psychiatric disorders are more
(b) whether smokers with psychiatric diagnoses report different
dependent on nicotine than are other smokers (i.e., smokers with-
levels of nicotine dependence motives than do smokers without
out comorbidities; American Psychiatric Association, 1994;
such comorbid diagnoses.
Breslau, Kilbey, & Andreski, 1994; Breslau, Novak, & Kessler,
At present, the literature is somewhat inconsistent regarding
whether psychiatric disorders are, in fact, related to cessation
2004a; Covey, 1999; Dierker & Donny, 2008; Heatherton et al.,
success (Agrawal, Sartor, Pergadia, Huizink, & Lynskey, 2008;
1991; John et al., 2004). However, such research provides little
Breslau, Novak, & Kessler, 2004b; Lasser et al., 2000). Given the
insight into the nature of the differences in dependence beyond the
high prevalence of psychiatric disorders among smokers, there are
fact that smokers with psychiatric comorbidity tend to smoke more
important public health implications if certain types of psychiatric
than do smokers without such comorbidity. We used the Wiscon-
diagnoses hinder cessation. This research addresses several ques-
sin Inventory of Smoking Dependence Motives (WISDM-68;
tions about the relation between psychiatric disorders and cessa-
Piper et al., 2004) to achieve a multidimensional assessment of
tion. First, whereas much of the prior work has focused on depres-
nicotine dependence (Piper et al., 2004; Shiffman et al., 2004). The
sion, the current data set also contains diagnoses of anxiety disorders
WISDM-68 targets 13 different dimensions of smoking depen-
and substance use disorders (SUDs). Second, some prior research has
dence motives (i.e., the different influences that may cause people
suggested that vulnerability to relapse or cessation failure may be
to smoke in a dependent manner). We examine five specific
related to symptom recency, with only recent or current symptoms
WISDM-68 motivation constructs that we predicted would differ
increasing risk (Breslau et al., 2004b; Burgess et al., 2002; Covey,
on the basis of psychiatric diagnostic status.
Bomback, & Yan, 2006; Gilbert, Crauthers, Mooney, McClernon,
Internalizing disorders, such as generalized anxiety disorder and
& Jensen, 1999; Hitsman, Borrelli, McChargue, Spring, &
depression, are linked by an underlying trait of neuroticism, which
Niaura, 2003; John, Meyer, Rumpf, & Hapke, 2004; Johnson &
manifests as a tendency to experience negative affect (e.g.,
Breslau, 2006; Niaura et al., 2001). We used the Composite
Watson, 2005). First, we hypothesized that smokers with internal-
International Diagnostic Interview (CIDI; Kessler & Ustun, 2004;
izing disorders, relative to smokers without such disorders, would
World Health Organization, 1990) in this research, which allowed
be especially likely to experience affective distress and would
us to compare ever qualifying for a diagnosis (ever being diag-
come to rely on smoking’s perceived ability to ameliorate such
nosed) with having a recent (i.e., past-year) diagnosis.1 Our goal
distress. Thus, we hypothesized that smokers with internalizing
was to determine the extent to which increased vulnerability to
disorders would report unusually strong negative reinforcement
cessation failure is related to factors associated with ever receiving
motives to smoke (i.e., a tendency to smoke to reduce distress),
a comorbid diagnosis (e.g., genetic and personality factors) and/or
which would be assessed with the Negative Reinforcement sub-
to factors related to symptom recency (e.g., distress, acute stress).
scale of the WISDM-68.
The former is indexed by the ever diagnosed category, whereas the
Second, we anticipated that smokers with internalizing disor-
latter is indexed by the past-year category. Third, previous re-
ders, relative to smokers without such disorders, would score
search in this area comprises cessation trials that typically use
especially highly on the Affiliative Attachment subscale of the
symptom checklists to infer diagnoses, rather than formal diagnos-
WISDM-68, which elicits ratings of the extent to which the smoker
tic interviews (e.g., Niaura et al., 1999; Smith et al., 2003), and
epidemiologic studies that use diagnostic interviews tend to use
retrospective reports of cessation with no biochemical validation
1 The CIDI does not assess current symptomatology and therefore does
(Breslau et al., 2004b; John et al., 2004). In the current research,
not distinguish between current versus recent (past year) symptoms.

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SPECIAL SECTION: PSYCHIATRIC DISORDERS IN SMOKERS SEEKING TREATMENT
15
has developed an emotional attachment to the cigarette that resem-
Method
bles a close personal friendship. We hypothesized that these smok-
ers would be especially emotionally attached to their cigarettes
Recruitment and Inclusion/Exclusion Criteria
because these smokers tend to be high in social anxiety and
experience social distress (Bifulco, Moran, Jacobs, & Bunn, 2009;
Participants were recruited in Madison and Milwaukee, WI, to
Burnette, Davis, Green, Worthington, & Bradfield, 2009). Re-
participate in the Wisconsin Smokers’ Health Study. Recruitment
search has shown that nicotine effectively reduces social anxiety
methods included TV, radio, and newspaper advertisements, com-
and distress by directly activating brain regions that mediate per-
munity flyers, and earned media (e.g., radio and TV interviews,
ceived social attachment (Sahley, Panksepp, & Zolovick, 1981). In
press releases). Primary inclusion criteria included smoking at
addition, the alleviation of social distress has been implicated in
least 10 cigarettes per day on average for the past 6 months and
the development of drug dependence motivation (i.e., the social
being motivated to quit smoking ( 8 on a 1–10 scale where 10 is
highly motivated to quit). Exclusion criteria included current use
attachment adheres to the cigarette; Nocjar & Panksepp, 2007;
of any medications contraindicated for use with any of the study
Panksepp, Knutson, & Burgdorf, 2002).
pharmacotherapies (this included monoamine oxidase inhibitors,
Third, we hypothesized that the cognitive enhancement pro-
bupropion, lithium, anticonvulsants, and antipsychotics); any his-
duced by nicotine (see Heishman, Taylor, & Henningfield, 1994,
tory of psychosis, bipolar disorder, or an eating disorder (the latter
for a review) might especially motivate smoking in smokers with
was exclusionary because one of the study medications, bupro-
internalizing disorders. This would occur if nicotine’s cognitive
pion, is contraindicated); consuming six or more alcoholic bever-
actions were effective (or perceived to be effective) in ameliorat-
ages daily 6 or 7 days a week (when drinking at this rate, bupro-
ing the repetitive and intrusive thoughts experienced by individu-
pion may be contraindicated); pregnancy or breastfeeding; and
als with internalizing disorders. Therefore, smokers with internal-
serious health conditions that would prevent participation in or
izing disorders might endorse the Cognitive Enhancement
completion of the study. Women of childbearing age had to agree
WISDM-68 subscale more highly than smokers without internal-
to take steps to prevent pregnancy during the medication treatment
izing disorders. Smokers with externalizing disorders (e.g., SUDs)
phase of the study. This study received human subjects approval
might also endorse the Cognitive Enhancement subscale more
from the University of Wisconsin Health Sciences Institutional
highly than smokers without externalizing disorders given that an
Review Board.
underlying dimension of externalizing disorders manifests as at-
tention deficit (e.g., Krueger et al., 2002).
Procedure
Fourth, we hypothesized that smoking among friends and fam-
ily, assessed by the Social/Environmental Goads subscale, would
Participants who passed a phone screen were invited to an
be an especially important motivator of smoking for smokers with
information session where written informed consent was obtained.
SUDs compared with those without SUDs. This hypothesis stems
Next, participants completed an individual orientation session
from research showing that (a) a high percentage of current and
where they underwent multiple screenings, including a medical
past substance abusers smoke (Dawson, 2000; Grant et al., 2004),
history screening, vital signs measurements, and a carbon monox-
(b) substance abusers tend to be concentrated in social networks
ide breath test. Participants also completed demographic, smoking
(Buchanan & Latkin, 2008; Homish & Leonard, 2008), and (c)
history, and tobacco dependence questionnaires. If participants
high rates of smoking in a social network expose members to
met the inclusion criteria assessed in this session, they then com-
smoking cues and sustain smoking (e.g., Christakis & Fowler,
pleted three baseline visits. At the first baseline visit, participants
2008).
completed additional questionnaires and were interviewed with the
Fifth, on the basis of past research (Breslau et al., 1994, 2004a;
World Mental Health Survey Initiative version of the CIDI
(Kessler & Ustun, 2004; World Health Organization, 1990). At the
Covey, 1999; Dierker & Donny, 2008; Heatherton et al., 1991;
second baseline visit, physical health (e.g., lipid profile, diabetes
John et al., 2004), we predicted that having a psychiatric diagnosis
screen) measures were assessed. At the third baseline visit, partic-
versus having no diagnosis would be associated with higher scores on
ipants completed additional questionnaires, and eligible partici-
dependence measures that primarily reflect smoking heaviness
pants were randomized to one of six treatment conditions: bupro-
(e.g., the FTND and the Primary Dependence Motives subscales
pion sustained release (n
264); nicotine lozenge (n
260);
on the WISDM-68).
nicotine patch (n
262); nicotine patch plus nicotine lozenge (n
Against this backdrop, the current study used structured psychi-
267); bupropion sustained release plus nicotine lozenge (n
262),
atric diagnostic interviews to determine psychiatric diagnoses
or placebo (five placebo conditions that matched the five active
(ever diagnosed and past-year diagnoses) in smokers who volun-
conditions; n
189). At this visit, participants set a quit date for
teered for a formal cessation treatment study. The efficacy findings
the following week. Participants had study visits on their quit day
from this research are reported in a separate article (Piper et al.,
and at 1, 2, 4, and 8 weeks after quitting. All participants received
2009) and revealed that all active treatments were significantly
individual counseling at the third baseline visit, on the quit day,
efficacious relative to placebo (ORs
1.63–2.34, p
.05) but that
and at each subsequent study visit. Each counseling session was
nicotine patch plus nicotine lozenge appeared to be especially
10 –20 min and provided intratreatment social support and training
efficacious (OR
2.34, p
.001). The analyses reported in this
in problem solving and coping skills, as recommended in the U.S.
article focus on whether ever diagnosed or past-year psychiatric
Public Health Service Guidelines (Fiore, Bailey, & Cohen, 2000;
comorbidity is (a) related to cessation success and (b) related to
Fiore et al., 2008). Counselors were bachelor’s-level case manag-
level and type of nicotine dependence.
ers supervised by a licensed clinical psychologist. All medications

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16
PIPER ET AL.
were provided for 8 weeks after quitting except the nicotine
to analyze separately. The rates of diagnoses were evenly distrib-
lozenge, which was provided for 12 weeks after quitting (consis-
uted across treatment conditions. Compared with participants with-
tent with prescribing instructions). Randomization was conducted
out each specific diagnosis, rates of study withdrawal by 6 months
in a double-blind fashion; we used a blocked randomization
after quitting were significantly higher for smokers who had ever
scheme with blocking on gender and race (White vs. non-White).
(including in the past year) had a mood disorder or anxiety disor-
der but not for those who had ever had an SUD (see Figure 1).
Measures
Analytic Plan
Carbon monoxide assessment.
Participants provided a
breath sample at all study visits to permit alveolar carbon monox-
All analyses were conducted with PASW Statistics 17.0. We
ide analysis, using a Bedfont Smokerlyzer (Bedfont Scientific,
used descriptive statistics to examine the rates of exclusions due to
Rochester, England). A carbon monoxide value of less than 10
severe and chronic psychiatric disorders and use of exclusionary
ppm was considered to be confirmatory of self-reported abstinence
medications and to examine the rates of psychopathology in the
from smoking.
eligible study sample. Cessation outcomes were defined as bio-
Demographics and smoking history.
A questionnaire as-
chemically confirmed (carbon monoxide
10) 7-day point-
sessed demographic characteristics, such as gender, ethnicity, age,
prevalence abstinence at 8 weeks and 6 months after quitting. We
education level, and employment. The smoking history question-
used the intent-to-treat principle such that smokers who did not
naire included items such as the number of cigarettes smoked per
provide outcome data were assumed to be smoking. To determine
day, age at smoking initiation, number of prior quit attempts, and
how psychiatric diagnoses are related to cessation outcome, we
other smokers in the household.
used cessation outcomes as the dependent variable and treatment,
Tobacco dependence measures.
The FTND, a six-item scale,
gender, race, and age as covariates. Psychiatric diagnoses (either
has fair internal consistency (
.61; Heatherton et al., 1991).
ever diagnosed or past-year diagnosis) were used as predictors, and
The WISDM (Piper et al., 2004) comprises 68 items designed to
smokers with no psychiatric diagnosis were the reference condi-
assess 13 different theoretically derived motivational domains,
tion. We did not control for all variables that differed significantly
with subscales possessing good internal consistency (
.87 or
among diagnostic groups so that we would not partial out variance
greater; Piper et al., 2004). We present data for selected WISDM
in the naturally occurring diagnostic groups that was intrinsic to
subscales based on theorized relations between psychiatric diag-
the nature of the diagnoses examined (Cohen, Cohen, West, &
nostic status and targeted constructs: for the Negative Reinforce-
Aiken, 2003). We also examined the role of ever having had a
ment, Affiliative Attachment, Cognitive Enhancement, Social/
diagnosis with past-year diagnoses removed from the model to
Environmental Goads subscales and for the Primary Dependence
assess the impact of lifetime but not recent diagnosis. Relations
Motives composite scale (a combination of the Automaticity,
between dependence and psychiatric disorders were examined
Craving, Loss of Control, and Tolerance subscales), which, like
through seven separate linear regression models in which one of
the FTND, appears to primarily reflect a heavy smoking motive
the seven dependence measures (the FTND, one of the five
(Piper et al., 2008).
WISDM subscales, or the WISDM total score) served as the
World Mental Health Survey Initiative version of the CIDI.
dependent variable in a model; gender, race, and age served as
The CIDI (Kessler & Ustun, 2004; World Health Organization,
covariates in each model. All three lifetime diagnoses (ever diag-
1990) is a structured clinical interview administered with Com-
nosed mood, anxiety, and SUD disorders) were simultaneously
puter Assisted Personal Interviews (CAPI), Version 20 by trained
entered into the regression models as predictors of the dependence
study personnel who were certified by a CIDI trainer. The CIDI
facets.
provides both past-year diagnoses (i.e., within the past 12 months)
as well as lifetime diagnoses (i.e., ever in the participant’s lifetime,
which would include anyone with a past-year diagnosis) for the 12
Results
modules administered: Screening, Depression, Mania, Panic Dis-
order, Social Phobia, Generalized Anxiety Disorder, Substance
Participants
Use, Attention Deficit Disorder, Services, Chronic Conditions,
30-Day Functioning, and 30-Day Symptoms. Although other di-
Of the 5,269 smokers who were phone screened to recruit this
agnostic interviews assess current psychiatric illness (e.g., occur-
sample, 2,120 did not meet eligibility requirements. The most
ring within the past 2 weeks; Structured Clinical Interview for
common reasons for exclusion on the phone screen were reporting
DSM–III–R; Spitzer, Williams, Gibbon, & First, 1992), the CIDI
insufficient motivation ( 7 on a 10-point scale; 26%), smoking
used in this research did not allow this assessment. Therefore, a
fewer than 10 cigarettes per day (13%), being unwilling to commit
smoker with a past-year diagnosis may or may not have been
to the study (13%), and not planning to remain in the area for 12
experiencing clinically significant symptoms at the time of study
months (13%). Of specific relevance to this study, 4.5% reported
participation.
psychosis or bipolar disorder, 1.2% reported an eating disorder,
We calculated the number of diagnoses (ever and past year)
and 1.7% reported currently drinking six or more drinks a day at
participants had that fit into three main diagnostic categories:
least 6 days a week, all of which were exclusionary. In addition,
mood disorders (primarily major depression), anxiety disorders
14.9% of the excluded smokers reported that they were currently
(panic disorder, social phobia, generalized anxiety disorder), and
taking exclusionary medications, including 87 (4.3%) who were
alcohol and other nonnicotine SUDs. Other diagnoses, such as
taking bupropion for either smoking cessation or depression.
attention deficit disorder (5.2% of the sample), were too rare
Smokers may have met more than one exclusionary criterion (e.g.,

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SPECIAL SECTION: PSYCHIATRIC DISORDERS IN SMOKERS SEEKING TREATMENT
17
Participants who
responded to recruitment
efforts (n = 8531)
Unable to contact
(n = 2003)
Assessed for eligibility
(n = 6528)
Excluded (n = 5024)
•Declined (n = 1259)
•Failed phone screen (n = 2120)
•Formally withdrew or discontinued
Randomized
participation (n = 1645)
(n = 1504)
Did not complete
diagnostic interview
(n = 34)
Ever Diagnosed
Never Diagnosed
(n = 1080)
(n = 390)
•Withdrew by 8 weeks (n = 11)
•Withdrew by 6 months (n = 16)
•Completed 8-week follow-up
(n = 337)
Ever Diagnosed with a
Ever Diagnosed with an
Ever Diagnosed with a
•Completed 6-month follow-up
Mood Disorder
Anxiety Disorder
Substance Use Disorder
(n = 347)
(n = 263)
(n = 579)
(n = 817)
•Analyzed (n = 390)
•Withdrew by 8 weeks (n = 23)
•Withdrew by 8 weeks (n = 35)
•Withdrew by 8 weeks (n = 40)
•Withdrew by 6 months (n = 27)
•Withdrew by 6 months (n = 42)
•Withdrew by 6 months (n = 53)
•Completed 8-week follow-up (n = 213)
•Completed 8-week follow-up (n = 475)
•Completed 8-week follow-up (n = 693)
•Completed 6-month follow-up (n = 219)
•Completed 6-month follow-up (n = 484)
•Completed 6-month follow-up (n = 704)
•Analyzed (n = 263)
•Analyzed (n = 579)
•Analyzed (n = 817)
Figure 1.
Study flow diagram. The numbers of mood disorder, anxiety disorder, and substance use disorder
diagnoses do not sum to 1,080, the number of participants ever diagnosed, because there were 668 participants
who received more than one diagnosis ever in their lifetime.
reporting both serious mental illness and taking exclusionary med-
263 smokers ever diagnosed with a mood disorder received a
ication).
past-year mood disorder diagnosis. A total of 205 (35.4%) of the
A total of 1,504 participants (876 of whom were women;
579 smokers who ever had an anxiety disorder received a past-year
58.2%) were randomized into the study. The majority of partici-
anxiety disorder diagnosis. Finally, 87 (10.7%) of the 816 smokers
pants were White (83.9%); 13.6% were African American, and
who ever qualified for an SUD diagnosis qualified for an SUD
2.5% reported another race. In the entire sample, 2.8% reported
diagnosis in the past year.
that one of their parents was of Hispanic origin. In addition, 44.3%
were married, and 23.6% had a high school education, but no more
Cessation Outcomes
and 21.9% had a 4-year college degree. On average, participants
were 44.67 (SD
11.08) years old and smoked 21.43 (SD
8.93)
Logistic regression was used to predict 8-week and 6-month
cigarettes per day. Study outcomes and additional study details are
cessation outcomes with psychiatric disorders (the ever diagnosed
reported in a separate article (Piper et al., 2009). See Figure 1 for
and past-year diagnosis of mood disorders, anxiety disorders, and
the study flow diagram.
SUDs) as the primary predictors and with gender, race, age, and
treatment condition as covariates. For all cessation analyses, smok-
ers with no history of mental illness served as the comparison
Frequency of Psychiatric Diagnoses
condition to provide the strongest contrast. Outcome variables
Of the 1,504 study participants randomized, 34 did not complete
were carbon monoxide confirmed 7-day point-prevalence intent-
the CIDI interview. Of the 1,080 who had ever received a diag-
to-treat abstinence 8 weeks and 6 months after quitting in distinct
nosis in their lifetime (including in the past year), the most com-
analyses for each diagnosis.
mon diagnosis was SUD (see Figure 1). With respect to diagnoses
Compared with smokers who had no history of psychiatric
in the past year, 1,165 (77.5%) did not receive any past-year
disorders, smokers who had ever had a mood disorder or an
psychiatric diagnoses (other than tobacco dependence), 213
anxiety disorder (including those who had one in the past year) and
(14.2%) received one past-year diagnosis, 92 (6.1%) received two
those with a past-year mood or anxiety disorder were all less likely
or more past-year diagnoses, and 34 did not complete the CIDI
to be abstinent 8 weeks after quitting when controlling for treat-
interview. With respect to specific diagnoses, 71 (27.0%) of the
ment, age, gender, and ethnicity. Table 1 shows the percentage of

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18
PIPER ET AL.
Table 1
Logistic Regression Comparing Participants With Diagnoses and Participants With No History of Psychiatric Disorders, Controlling
for Treatment, Gender, and Race, and Percentage of Smokers Who Were Abstinent 8 Weeks and 6 Months After Quitting by
Psychiatric Diagnosis (N
1,470)a
Diagnosis and outcome assessment time
Percentage abstinent
Wald
p
OR
95% CI
No history of psychiatric disorders (n
390; 26.5% of total sample)
8 weeks
47.4
6 months
35.4
Ever diagnosed with a mood disorder (n
263; 17.9% of total sample)
8 weeks
39.5
4.87
.03c
0.69
[0.50, 0.96]
6 months
31.2
1.46
.23
0.81
[0.58, 1.14]
Past-year mood disorder (n
71; 4.8% of total sample)
8 weeks
28.2
7.61
.01
0.45
[0.25, 0.79]
6 months
23.9
2.37
.12
0.62
[0.34, 1.14]
Ever diagnosed with an anxiety disorder (n
579; 39.4% of total sample)
8 weeks
39.9
5.89
.02
0.72
[0.55, 0.94]
6 months
28.7
5.31
.02
0.72
[0.54, 0.95]
Past-year anxiety disorder (n
205; 13.9% of total sample)
8 weeks
37.6
3.79
.05
0.70
[0.49, 1.00]
6 months
29.8
1.21
.27
0.81
[0.55, 1.18]
Ever diagnosed with a substance use disorder (n
816; 55.5% of total sample)
8 weeks
43.4
2.62
.11
0.81
[0.63, 1.05]
6 months
32.8
1.43
.23
0.85
[0.65, 1.11]
Past-year substance use disorder (n
87; 5.9% of total sample)
8 weeks
46.0
0.004
.95
1.02
[0.62, 1.68]
6 months
34.5
0.01
.91
1.03
[0.61, 1.75]
Only one lifetime diagnosis (n
410; 27.9% of total sample)
8 weeks
43.9
1.08
.30
0.86
[0.64, 1.15]
6 months
36.2
0.08
.77
1.05
[0.77, 1.41]
Two or more lifetime diagnoses (n
667; 45.4% of total sample)b
8 weeks
41.9
3.97
.05
0.77
[0.59, 1.00]
6 months
29.9
4.52
.03
0.74
[0.57, 0.98]
a The percentages do not sum to 100% because some participants had more than one diagnosis. b Compared with smokers with only one lifetime diagnosis,
smokers with two or more lifetime diagnoses were less likely to be abstinent at 6 months after quitting (Wald
6.00, p
.01, OR
.72, 95% CI
0.55– 0.94), but the two groups were equally likely to be abstinent 8 weeks after quitting.
c It should be noted that this effect is no longer significant if
smokers with a diagnosis in the past year are removed from the analysis.
smokers with different types of psychiatric diagnoses who were
ever having an SUD diagnosis were not significant predictors.
abstinent at each measurement time. Ever having a diagnosis of an
Specifically, ever having an anxiety disorder was related to poorer
anxiety disorder was also related to a decreased likelihood of
cessation outcome at 8 weeks and 6 months, independent of the
maintaining abstinence 6 months after quitting. Table 1 results
effects of ever having a mood disorder or an SUD diagnosis.
were similar when no covariates were included.
We used logistic regression, controlling for gender, race, age,
To focus on residual risk due to lifetime diagnoses per se, we
and treatment, to analyze the relation of number of diagnoses in a
removed smokers with a past-year diagnosis from the ever diag-
lifetime (coded as 0, 1, and 2 or more) with cessation outcome to
nosed group. Results showed that having a history of an anxiety
model the cumulative impact of diagnoses. Number of diagnoses
disorder without a past-year diagnosis continued to be a significant
did not predict abstinence 8 weeks after quitting, but it did predict
predictor of cessation outcome 8 weeks and 6 months after quitting
abstinence 6 months after quitting (see Table 1). Specifically, 6
(OR
.73, p
.04 and OR
.68, p
.02, respectively). When
months after quitting, smokers with no diagnoses or smokers with
smokers with a past-year mood disorder (n
71) were removed
only one lifetime diagnosis were significantly more likely to be
from the analyses for the participants ever diagnosed with a mood
abstinent than were smokers ever diagnosed with two or more
disorder, the effect on 8-week outcome was no longer statistically
disorders.
significant ( p
.25), and the effect size decreased from OR
.69
to OR
.81.
Tobacco Dependence and Psychiatric Disorders
To examine the relative effects of specific diagnoses on out-
come, we included ever having a mood disorder, anxiety disorder,
We examined the relation of different ever diagnosed categories
or SUD (including a past-year diagnosis) as three predictors in a
(i.e., ever diagnosed with a mood disorder, anxiety disorder, or
logistic model, controlling for treatment, gender, age, and race.
SUD, including past-year diagnoses) to the tobacco dependence
Results revealed that ever having an anxiety disorder remained a
measures (the FTND and selected WISDM subscales). We con-
significant predictor of 8-week and 6-month abstinence (8 weeks:
ducted a series of linear regression analyses with seven different
OR
.78, p
.04, 95% CI
.62–.99; 6 months: OR
.72, p
dependence measures as the dependent variable (the five theoret-
.01, 95% CI
.57–.92), whereas ever having a mood disorder and
ically targeted WISDM subscales, the total WISDM score, and the

---

SPECIAL SECTION: PSYCHIATRIC DISORDERS IN SMOKERS SEEKING TREATMENT
19
FTND score) and with gender, race, age, and ever being diagnosed
with a mood disorder, anxiety disorder, or SUD as predictors (the
CI
three diagnoses were dummy coded as 0
no diagnosis and 1
diagnosis). For each disorder, smokers without that particular
95%
0.06–0.30]
0.10–0.26]
0.03–0.26]
0.09–0.37]
[0.01–0.26]
[
[
[
[0.19–0.57]
[0.17–2.91]
[
disorder served as the comparison group (e.g., smokers ever diag-
nosed with a mood disorder vs. smokers never diagnosed with a
SUD
.03
.18
.39
.12
.001
.03
.22
mood disorder). We chose to enter all the diagnostic categories
Analyses
no
into the same model to control for individuals who had more than
the
Bp
0.14
0.12
0.08
0.12
0.38
1.54
0.14
one diagnosis. We did not use past-year disorder as a predictor
in
versus
because of the smaller sample sizes of the past-year groups.
)
Results indicated that diagnostic category was not related to the
SUD
(
SD
(1.17)
(1.72)
(1.68)
(1.38)
(1.74)
(12.95)
(2.07)
disorder
FTND—the dependence measure most heavily influenced by
Included
M
4.88
3.23
3.50
4.50
3.18
5.28
smoking heaviness per se (see Table 2). When dependence was
No
53.32
Lifetime
analyzed with the multidimensional WISDM, results were fairly
)
consistent with predictions. Smokers with any psychiatric diagno-
Dependence.
(1.19)
(1.72)
(1.69)
(1.41)
(1.87)
(13.22)
(2.20)
sis had higher WISDM total scores than smokers with no diag-
(
SD
Categories
noses. Smokers with a history of anxiety disorders and SUDs had
Disorder
M
4.97
3.28
3.59
4.56
3.60
5.47
54.30
significantly higher scores on the WISDM Primary Dependence
Nicotine
of
Motives scale, which assesses heavy, automatic smoking that is
CI
out of control, than did smokers without such disorders. Smokers
Diagnosed
Test
with a lifetime mood or anxiety disorder scored significantly
disorder
95%
0.06–0.41]
¨
m
higher on the WISDM Affiliative Attachment, Cognitive Enhance-
Ever
[0.09–0.34]
[0.25–0.62]
[0.16–0.53]
[0.13–0.43]
[0.04–0.44]
[1.85–4.66]
[
ment, and Negative Reinforcement subscales than did smokers
anxiety
.001
.001
.001
.001
.02
.001
.15
Fagerstro
without those respective diagnoses (see Table 2). As predicted,
Three
no
smokers with a lifetime history of SUD had significantly higher
All
Bp
scores than did smokers with no history of SUD on the Social/
0.21
0.43
0.35
0.28
0.24
3.25
0.17
versus
FTND
Environmental Goads subscale. We were surprised to find that
With
)
smokers with a history of an anxiety disorder also had significantly
(
SD
(1.19)
(1.66)
(1.64)
(1.40)
(1.82)
(12.86)
(2.11)
higher scores on this subscale than did smokers with no history of
disorder
disorder
Scores
M
Motives;
an anxiety disorder.
No
4.83
3.05
3.38
4.36
3.28
5.31
52.18
anxiety
)
Discussion
(
SD
(1.15)
(1.77)
(1.72)
(1.36)
(1.81)
(13.06)
(2.19)
Dependence
Dependence
Lifetime
Disorder
M
The first major question we addressed in this research was
5.09
3.58
3.81
4.79
3.61
5.50
56.46
whether psychiatric disorder diagnosis was related to cessation
Smoking
success. The results suggest that smoking cessation outcome is
CI
of
related to internalizing (mood and anxiety) disorders, such that
Predicting
smokers who have ever had a mood or anxiety disorder were less
in
95%
0.03–0.29]
0.25–0.26]
0.03–0.57]
disorder
[
[0.09–0.55]
[0.07–0.54]
[0.16–0.54]
[
[0.57–4.14]
[
likely to be abstinent 8 weeks after quitting. The influence of
Age,
Inventory
past-year mood disorder seemed especially important in this rela-
mood
.11
.006
.01
.001
.97
.01
.07
tion as reflected by the odds ratio magnitude and the fact that the
and
no
ever diagnosed condition was no longer significantly related to
Bp
Wisconsin
cessation outcome once smokers with past-year diagnoses were
Race,
versus
0.13
0.32
0.31
0.35
0.005
2.35
0.27
removed from the sample (see Table 1). Past research has also
)
found that recent depressive symptoms are especially predictive of
(1.17)
(1.69)
(1.65)
(1.39)
(1.83)
(12.84)
(1.17)
cessation outcomes (Breslau et al., 2004b; Burgess et al., 2002;
Gender,
disorder
(
SD
disorder
WISDM
M
Covey et al., 2006; Gilbert et al., 1999; Hitsman et al., 2003; John
for
No
4.88
3.16
3.47
4.44
3.39
4.88
53.09
et al., 2004; Johnson & Breslau, 2006; Niaura et al., 2001). In
mood
addition, in the current study, smokers who had ever had an
)
disorder;
anxiety disorder and those with two or more lifetime psychiatric
(
SD
(1.17)
(1.81)
(1.81)
(1.57)
(1.79)
(13.70)
(2.25)
Lifetime
use
diagnoses were less likely than smokers who had never had psy-
Controlling
Disorder
M
5.15
3.72
3.92
4.96
3.53
5.64
chiatric diagnoses to be abstinent 6 months after quitting. These
57.39
findings suggest that both mood and anxiety disorders place smok-
substance
ers at increased risk for tobacco cessation failure, although ever
having had an anxiety disorder was unique in this study in that it
Regressions,
Primary
Affiliative
Cognitive
Negative
Social/
Total
was associated with differences in abstinence levels at both 8
2
Scale
Total
SUD
weeks and 6 months after quitting. In addition, if psychiatric
Dependence
Motives
Attachment
Enhancement
Reinforcement
Environmental
Goads
disorders are modeled as cumulative impact of the number of
Table
Linear
WISDM
WISDM
WISDM
WISDM
WISDM
WISDM
FTND
Note.

---

20
PIPER ET AL.
diagnoses, the more psychiatric diagnoses ever assigned, the less
that those with SUD diagnoses be provided with evidence-based
likelihood there is of long-term cessation success.
smoking cessation interventions (Fiore et al., 2008).
It should be noted that the small size (n
71) of the past-year
The second major question targeted in this research was whether
mood disorder group reduced power to detect a significant effect
smokers with and without psychiatric disorders differ on dimen-
(post hoc power
.45). Therefore— despite having the greatest
sions of nicotine dependence. In general, psychiatric diagnostic
effect size at 6 months after quitting on the basis of the odds ratio
status was not significantly related to dependence measures that
(OR
.62)—the effect was not statistically significant, whereas an
narrowly reflect smoking heaviness (i.e., cigarettes per day, the
effect of OR
.72 was statistically significant for the lifetime
FTND). These findings stand in contrast to large sample epidemi-
anxiety disorder group (n
579). Modified post hoc power
ological studies that have found such differences (e.g., Grant et al.,
analyses revealed that significantly larger effect sizes (i.e., a dif-
2004; cf. Breslau & Johnson, 2000). It may be that psychiatric
ference of 15 percentage points in abstinence rates) would be
disorders confer risk for especially heavy smoking when a large,
needed to detect a significant effect with the current sample size of
population-based sample of smokers is studied. In other words,
the past-year mood disorder group.
this effect may depend on the presence of light smokers in the
Psychiatric diagnosis (i.e., mood and anxiety disorders) was
analyzed sample; such smokers tend not to seek out formal ces-
more consistently associated with early versus late cessation out-
sation treatment (Pierce & Gilpin, 2002; Shiffman, Di Marino, &
comes. These results raise the question of why the effect of
Sweeney, 2005), and smokers were excluded from this study if
psychiatric disorders decreased from the 8-week to the 6-month
they smoked fewer than 10 cigarettes a day.
follow-up, as indicated by odds ratio magnitude (see Table 1).
When dependence was measured with the multidimensional
Reasons for this are unknown, but it may be that the high rates of
WISDM-68, psychiatric diagnostic status was associated with the
relapse at 8 weeks in comorbid groups left relatively few partici-
Primary Dependence Motives composite scale and with theoreti-
pants with psychiatric disorders available to relapse over the next
cally relevant subscales. Psychiatric diagnosis may be associated
4 months. This pattern of susceptibility to early relapse among
with the Primary Dependence Motives scale because this compos-
smokers with psychiatric diagnoses replicates previous findings
ite assesses more than smoking heaviness per se (i.e., it assesses
regarding depression history (Japuntich et al., 2007). These results
automatic smoking, strong urges, and a sense that smoking is out
suggest that smokers who have ever had internalizing psychiatric
of control). Smokers with internalizing disorders also scored
disorders may benefit from intensive treatment early in the cessa-
higher than smokers without internalizing disorders on negative
tion process.
reinforcement motives for smoking. As noted earlier, such smok-
Among the individual psychiatric diagnoses, ever having had an
ers struggle to cope with affective distress, and smoking may
anxiety disorder was unique in that it significantly predicted smok-
constitute a more valued means of affective coping for these
ing status 6 months after quitting, and this effect was not depen-
smokers. Smokers with lifetime anxiety disorders also reported a
dent on smokers with a past-year diagnosis. This relation was
strong emotional bond with their cigarettes and reported that
found after controlling for mood disorder and SUD diagnoses,
smoking enhanced their cognitive processing. Contrary to our
suggesting that specific features of anxiety may impede cessation
hypothesis, however, smokers with lifetime mood disorders did
success. Multiple studies have focused on the role of depression in
not differ from smokers without lifetime mood disorders on the
relapse, perhaps because negative mood and anhedonia are symp-
Affiliative Attachment and Cognitive Enhancement WISDM sub-
toms of both depression and nicotine withdrawal (Hughes &
scales. Compared with smokers who did not have a history of
Hatsukami, 1986; Welsch et al., 1999). Few studies have examined
SUD, those with a history of SUD reported that social and envi-
the role of anxiety in cessation, although anxiety has been linked
ronmental influences were especially likely to affect their smok-
to smoking (e.g., Strine et al., 2008). Several theories address the
ing. In summary, these results suggest that particular smoking
role of anxiety in the maintenance of dependence, including the
dependence motives may be especially influential for smokers
theory that smokers with anxiety disorders have increased anxiety
with particular psychiatric disorders. These findings may be help-
sensitivity (Brown, Kahler, Zvolensky, Lejuez, & Ramsey, 2001;
ful in guiding the development of treatment for smokers with
Zvolensky et al., 2005) and believe that their anxiety symptoms,
particular psychiatric diagnoses because these dependence motives
which may be a product of withdrawal, will have profound nega-
can confer added risk of cessation failure.
tive physical, social, or psychological consequences (Reiss, 1991).
Although these findings concerning psychiatric disorders and
Additional research is needed to understand factors and mecha-
dependence motives are provocative, certain caveats must be borne
nisms that contribute most to the heightened relapse vulnerability
in mind. One is that the self-report of a dependence motive may
of smokers with anxiety disorders.
reflect only attributions or beliefs that are associated with the
Of interest, SUD diagnosis was not associated with smoking
various psychiatric disorders. (Although recent research suggests
cessation failure in this population (i.e., treatment-seeking smokers
that motives reported on the WISDM-68 do predict the behaviors
with an SUD diagnosis who were not currently drinking more than
and symptoms that smokers report with real-time experience sam-
six drinks on 6 or 7 days a week). Thus, a significant proportion of
pling techniques; Japuntich, Piper, Schlam, Bolt, & Baker, in
smokers who have a history of substance abuse and dependence
press). In addition, aspects of this work raise questions about
are able to quit smoking; other research has suggested that quitting
sample representativeness. We found strikingly high levels of
smoking does not cause relapse back to other substances (Bobo,
psychiatric diagnoses in this clinical sample of smokers. Although
McIlvain, Lando, Walker, & Leed-Kelly, 1998; Burling, Burling,
fewer than 25% of the sample received a past-year DSM–IV
& Latini, 2001; Hurt et al., 1994, 1995; Myers & Brown, 2005;
diagnosis, approximately 75% had a lifetime history of at least one
Prochaska, Delucchi, & Hall, 2004; Shoptaw et al., 2002). An
DSM–IV diagnosis, and almost 50% had two or more lifetime
absence of heightened SUD-linked risk supports recommendations
diagnoses. These rates are higher than in other research (Keuthen

---

SPECIAL SECTION: PSYCHIATRIC DISORDERS IN SMOKERS SEEKING TREATMENT
21
et al., 2000), which found that 62% of treatment-seeking smokers
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