Changes in smoking status among substance abusers: baselinecharacteristics and abstinence from alcohol and drugs at 12-month

follow-up

Carolynn S. Kohn a,b,*, Janice Y. Tsoh a, Constance M. Weisner a,b

a University of California San Francisco, 401 Parnassus Avenue, San Francisco, CA 94143-0984, USAb Division of Research at Kaiser Permanente, 2000 Broadway, 3rd Floor, Oakland, CA 94612-2304, USA

Received 9 April 2002; received in revised form 19 July 2002; accepted 6 August 2002

Abstract

The impact of change in smoking status on 12-month substance abuse (SA) treatment outcomes was examined among an HMO

population seeking SA treatment. Of the 749 participants who entered the study at baseline, 649 (86.9%) were retained at the 12-

month follow-up. At treatment entry, 395 participants were smokers and 254 were nonsmokers. At 12-month follow-up, 13% of the

395 baseline smokers reported quitting smoking and 12% of the 254 baseline nonsmokers reported starting/relapsing to smoking.

Those who quit smoking were less likely to be diagnosed as alcohol dependent compared to those that remained smokers. Those

who started/resumed smoking were more likely to be diagnosed as both alcohol and drug dependent at treatment entry compared to

all other groups. Total days abstinent from alcohol and illicit drugs was greatest for individuals who quit smoking (adjusted M�/

310.6) or who were nonsmokers (adjusted M�/294.7) and lowest for those who started/resumed smoking (adjusted M�/246.6) or

remained smokers (adjusted M�/258.2), even after controlling for demographic (i.e. age, income), psychosocial (ASI psychiatric

severity), and other treatment characteristics (length of treatment stay, prescribed bupropion) that were associated with days

abstinent at 12 months. Self-initiated smoking cessation does not appear to be detrimental to SA treatment outcomes, and may be

beneficial. Starting/resuming smoking after entering SA treatment may be a clinical marker for individuals at greater risk of relapse.

Future studies may want to measure the smoking status of all participants at all time points in order to include this higher-risk group

of substance using smokers.

# 2002 Elsevier Science Ireland Ltd. All rights reserved.

Keywords: Smoking; Alcohol; Drugs; Treatment outcome; Managed care; Tobacco

1. Introduction

Nearly three times as many individuals with alcohol

and drug problems smoke cigarettes compared to the

general population, and smoking-related diseases are a

leading cause of premature death among substance users

(Hurt et al., 1996). Despite these statistics, few substance

abuse (SA) treatment programs or providers offer

structured smoking cessation services (Burling et al.,

1997; Sees and Clark, 1993), even though studies suggest

a substantial proportion of clients in SA treatment state

a desire to quit (Ellingstad et al., 1999; Irving et al.,

1994) and attempt to quit smoking and/or participate in

smoking treatment during or after SA treatment (Bern-

stein and Stoduto, 1999; Bobo et al., 1996b; Burling et

al., 2001). Continued smoking has been linked to worse

SA treatment outcomes (Frosch et al., 2000) while there

is some evidence that attempting to quit smoking is not

harmful to SA treatment outcomes (Lemon et al., in

press), or may improve SA treatment outcomes (Bobo et

al., 1998; Hughes, 1993; Shoptaw et al., 1996) and is a

feasible goal (Burling et al., 2001). Barriers for imple-

menting formal, standardized smoking cessation inter-

ventions in SA treatment programs include beliefs that

cigarette smoking is more of a ‘habit’ compared to other

addictions (Karan, 1993; Sees and Clark, 1993) and a

lack of information about patient populations in SA

* Corresponding author. Tel.: �/1-510-891-3583; fax: �/1-510-891-

3606

E-mail addresses: carolynn.s.kohn@kp.org,

carolynnkohn@hotmail.com (C.S. Kohn).

Drug and Alcohol Dependence 69 (2003) 61�/71

www.elsevier.com/locate/drugalcdep

03765-8716/02/$ - see front matter # 2002 Elsevier Science Ireland Ltd. All rights reserved.

PII: S 0 3 7 6 - 8 7 1 6 ( 0 2 ) 0 0 2 5 6 - 9

programs who attempt to quit smoking (Ellingstad et

al., 1999), particularly among non-community/non-re-

sidential treatment populations. In addition, limited

information exists regarding the impact of change insmoking status on SA treatment outcomes.

Currently, conflicting data exist regarding whether

smoking status is associated with worse (Toneatto et al.,

1995) or better (e.g. Bobo et al., 1998; Frosch et al.,

2000) SA treatment outcomes and may have to do with

study limitations. With few exceptions (Bobo et al.,

1998; Burling et al., 2001), previous studies have

examined only alcohol (Bobo et al., 1996a; Toneatto etal., 1995) or only drug outcomes (Frosch et al., 2000;

Shoptaw et al., 1996), but rarely both; have had limited

follow-up periods, (Bobo et al., 1996a; Frosch et al.,

2000); significant attrition (Toneatto et al., 1995) and

small samples (Shoptaw et al., 1996); and have almost

exclusively examined community or residential treat-

ment samples (Burling et al., 2001; Frosch et al., 2000).

To the best of our knowledge, no naturalisticobservational data published to date document the

change in smoking status over time among clients in

SA treatment settings. A 3-year naturalistic observation

study of 254 polydrug users who were not in SA

treatment found that a significant subsample of smokers

reported a change in smoking status despite an overall

stability in the prevalence of smoking within this sample

(McCarthy et al., 2001). This study suggests thatsubstance abusers do change their smoking statuses

over time, with shifts to both nonsmoker as well as

smoker statuses. However, the goals of this study did

not include providing descriptive characteristics of those

who change their smoking statuses, nor was it con-

ducted within the context of SA treatment, so treatment

outcomes were not reported.

Identifying characteristics of individuals whose smok-ing status changes during treatment and examining the

impact that this may have on SA outcomes are

important steps towards developing smoking cessation

interventions for this population (Ellingstad et al.,

1999). Characteristics that may be associated with

change in smoking status include demographic charac-

teristics (e.g. gender, ethnicity, income), smoking char-

acteristics (e.g. cigarettes smoked per day), SAcharacteristics (type of dependence or abuse diagnosis),

psychosocial characteristics (e.g. depression, anxiety,

problems with family or the legal system), and treatment

characteristics (e.g. length of stay (LOS) in treatment,

adjunct/self-help treatment, smoking cessation aids).

1.1. Current study

In this study, we examined demographic, smoking,substance use, psychosocial, and treatment-related char-

acteristics of individuals in SA treatment in a private

managed-care setting who at 12 months after SA

treatment admission: (a) remained smokers; (b) stopped

smoking; (c) resumed or started smoking after a period

of abstinence from smoking; or (d) remained nonsmo-

kers. We also examined the relationship of changes insmoking status to SA treatment outcomes at 12 months

(i.e. total days of abstinence). This study addresses some

of the limitations of past research studies by using a

large sample (n�/649), minimizing attrition (86.9% at

12-month follow-up), including measures of both alco-

hol and drug use, and examining how change in

smoking status is related to longer-term abstinence

from alcohol and/or drugs. Additionally, our samplewas drawn from a large, heterogeneous managed-care

SA treatment program; this program is typical of many

managed care programs in which a large segment of our

population receives treatment in that, while smoking is

not encouraged, there are few structured, formal,

standardized treatment inventions in place to assist

with smoking cessation.

2. Method

The study site was Kaiser Permanente’s Chemical

Dependency Recovery Program (CDRP) in Sacra-

mento, CA, where SA treatment services are provided

within the same health plan. The CDRP is an outpatient

treatment program, in which the first 8 weeks of

treatment are considered the ‘rehabilitation phase’ andthe following 10 months are considered the ‘aftercare

phase.’ The CDRP has both a day and an evening

program to allow flexibility for working patients. The

CDRP allows smoking in designated outside areas only.

Smoking is not allowed inside the buildings or during

treatment sessions. While smoking was not encouraged,

there were few structured, formal, standardized treat-

ment interventions in place to assist with smokingcessation. The issues of the risks of smoking were

more generally addressed in an educational group

format within the CDRP as part of a Medical Education

series. Patients had free access to nicotine replacement

products (i.e. nicotine patches and gum) and to a low

cost smoking cessation program provided off-site.

This study was part of a primary study in which

participants were randomized to receive their primaryhealth care from physicians within the CDRP or off-site

in the primary care clinics (Weisner et al., 2001).

Analyses to determine if this randomization procedure

may have had an effect on participants’ smoking

behaviors found no group differences.

2.1. Participants

Participants were 747 men and women aged 18 and

over who were admitted to the program between April

1997 and December 1998 and who met DSM-IV criteria

C.S. Kohn et al. / Drug and Alcohol Dependence 69 (2003) 61�/7162

(American Psychiatric Association, 1994) for current

drug (13 substance types) or alcohol abuse or depen-

dence at the time of intake as assessed by trained

research staff conducting the Diagnostic InterviewSchedule (DIS) for Psychoactive Substance Dependence

(Robins et al., 1989). For each substance, the ques-

tionnaire determines whether the DSM-IV diagnostic

criteria for abuse or dependence was present or absent

during the previous 30 days. Substances of dependence

for the sample as a whole, in order by most to least

prevalent, were alcohol (58.9%), stimulants (24.5%),

marijuana (16.9%), narcotic analgesics (8.9%), cocaine(8.3%), tranquilizers (3.7%), heroin (1.1%), and PCP

(0.3%). Substances of abuse (without dependence) for

the sample as a whole, in order by most to least

prevalent, were alcohol (3.5%), marijuana (2.8%), sti-

mulants (2.6%), narcotic analgesics (0.9%), cocaine

(0.8%), tranquilizers (0.3%), heroin (0%), and PCP (0%).

2.2. Procedure and self-report data validity

Informed consents were obtained at baseline when the

interviews were conducted. Baseline interviews lasted

approximately 1 hour and participants did not receive

monetary reimbursement at that time. Follow-up tele-

phone interviews assessing treatment outcome took

place 12 months after intake and were conducted by

telephone interviewers located at the Division of Re-

search in Oakland, CA who were blind to participantclassification and who were not employed by the CDRP

in Sacramento. Telephone interviews lasted about 30

minute and participants were mailed a $30 reimburse-

ment check for completing the phone interview. A

validity test of self-report data using urinalysis was

conducted on-site at the CDRP at the 6-month follow-

up on all 361 randomly selected participants (55.6% of

the entire sample), each of whom received a reimburse-ment of $20 after completion of the urinalysis. The

urinalysis (using the Hitachi microparticle immuno

assay with gas chromatography mass spectrometry,

thin layer chromatography, and high pressure liquid

chromatography confirmation) tested for alcohol, bar-

biturates, benzodiazepines, cocaine, cannabis/THC, opi-

ates, phencyclidine, and amphetamines, and

comparisons yielded rates of reporting no use but testingpositive for a substance of 2.5% for alcohol, and

between 0.9% (barbiturates) and 5.8% (marijuana) for

other substances. The results for marijuana are con-

servative; it was the most commonly reported, and the

test detects tetrahydrocannabinol levels as low as 40 ng/

ml. Chronically using individuals can test positive for 30

days after use, but the self-report question asked before

the test measures use for the past 3 days. Participantswere informed that information obtained during the in-

person and telephone interviews as well as the urinalyses

results are used for research purposes only and are not

reported to the CDRP. The health plan’s automated

databases, generally an accurate measure of clinical

activities, were used to obtain information about

clinician-generated diagnoses of anxiety and depressivedisorders, LOS in treatment, and prescriptions for

nicotine replacement therapies (i.e. nicotine patch or

gum) or bupropion, and attendance to smoking cessa-

tion treatment (Selby, 1997).

2.3. Measures

2.3.1. Smoking status classification

Smoking status was determined by response to the

question ‘Are you currently smoking cigarettes?’ asked

at Baseline and at 12-month follow-up. Participants

were classified into one of four groups: SMOKERS,QUITTERS, STARTERS/RESUMERS, and NON-

SMOKERS. Participants who answered ‘yes’ at baseline

and follow-up were classified as SMOKERS (n�/342),

those who answered ‘yes’ at baseline and ‘no’ at follow-

up were classified as QUITTERS (n�/53), those who

answered ‘no’ at baseline and ‘yes’ at follow-up were

classified as STARTERS/RESUMERS (n�/30), and

those who answered ‘no’ at baseline and follow-upwere classified as current NONSMOKERS (n�/224).

Although nicotine dependence and biochemical verifica-

tion of smoking status were not assessed, self-report of

cigarette smoking has been shown to be a valid indicator

of actual use (Velicer et al., 1992).

2.3.2. Cigarette use and demographic characteristics

At baseline, participants were asked, ‘Have you ever

smoked cigarettes regularly for at least one year, meaning

at least 5 cigarettes per week, almost every week?’ and

‘How many years did you smoke before you stopped?’ At

the 12-month follow-up, they were asked how many

cigarettes per day on average they smoked in the

previous 6-month period. Age, gender, race, income,education, and marital status were obtained from the

baseline questionnaire.

2.3.3. Substance use and psychosocial characteristics at

treatment entry

The Addiction Severity Index (ASI, McLellan et al.,1992a) was used to assess severity of substance use and

psychosocial problems at admission by asking about the

number, frequency, and duration of problem symptoms

in the past 30 days. The ASI provides separate severity

composite scores from 0 to 1.0 for each of the following

seven areas, with higher scores indicative of greater

problem severity: alcohol use, drug use, employment,

medical, psychiatric, family or social, and legal pro-blems. We report means and standard deviations for all

baseline ASI composite scores (i.e. scores obtained

during baseline/treatment entry interview).

C.S. Kohn et al. / Drug and Alcohol Dependence 69 (2003) 61�/71 63

The Diagnostic Interview Schedule for Psychoactive

Substance Dependence (American Psychiatric Associa-

tion, 1994) was administered by trained research staff as

part of the baseline interview in order to obtain DSM-IV diagnoses for current (i.e. symptoms occurring within

the past 30 days) alcohol and other drug (13 substance

types) abuse and dependence diagnoses. Because the

percentage of participants that received a dependence

diagnosis for any one specific drug category was

relatively low, we examined participants based on the

following diagnoses: alcohol dependent only, drug

dependent only, both alcohol and drug dependent, andalcohol and/or drug abuse without dependence.

Baseline subscales from the shortened version of the

Symptom Checklist-90 (Alvir et al., 1988) were used as

continuous measures of anxiety and depressive symp-

toms. Subscales scores range from 0 (no symptoms) to 4

(extreme symptoms).

DSM-IV depressive and anxiety disorder diagnoses

made during the year before treatment entry (i.e. bylicensed mental health and/or primary care providers)

were identified using the health plan’s automated

diagnostic databases (Selby, 1997). These diagnoses

were based on physician and clinician assessment of

the patient. While a limitation of this method is that

structured clinical interviews and reliability checks of

diagnoses between raters are not used, a strength of this

method lies in its ‘real world’ value, in which clinicians’diagnoses are used in the manner in which they are

reported in patients’ medical charts.

2.3.4. Treatment characteristics: LOS, adjunct/self-help

SA treatment, smoking cessation aids

The health plan’s automated appointment registration

database was used to measure LOS in treatment by

calculating participants’ number of days in treatment

before being discharged or dropping out. Informationabout attendance to Alcoholics Anonymous (AA) and

Narcotics Anonymous (NA) meetings was measured

using the Treatment Services Review (TSR; McLellan et

al., 1992b). The health plan’s databases were used to

identify variables (i.e. ‘smoking cessation aids’) from

treatment entry through 12-month follow-up that might

influence the rate of smoking cessation. Smoking cessa-

tion aids included in this study were participation insmoking cessation psychoeducational groups, prescrip-

tions for nicotine replacement therapies (i.e. the patch or

gum), and prescriptions for bupropion (Jarvis and

Sutherland, 1998). We were unable to ascertain whether

bupropion was being prescribed primarily as an anti-

depressant or a smoking cessation aid. Only one person

participated in a smoking cessation group and five (2

SMOKERS and 3 QUITTERS) were prescribed nico-tine patches, likely an accurate assessment since parti-

cipants receive them free through their health plan but

must pay for them out-of-pocket if they purchase them

elsewhere. Because other smoking cessation aids were

used infrequently, only bupropion was included in the

analyses.

2.3.5. Treatment outcome: abstinence rates at 12 months

Longest period of abstinence since treatment admis-

sion was obtained from 12-month follow-up interviews

by asking participants to report in number of days theirlongest period of abstinence since beginning treatment.

Percent of individuals in each group that reported

complete abstinence during the past 30 days was also

obtained from 12-month follow-up interviews. Also, as

mentioned in Section 2.2, just over half of the partici-

pants were randomly tested using urinalysis that resulted

in a fairly high rate of corroboration between self-report

use and urinalysis results, and participants were in-formed that information provided in the context of this

study was confidential and not made available to the

CDRP or individual treatment providers.

2.4. Data analyses

Baseline demographic characteristics, psychosocial

characteristics (i.e. SCL-90 anxiety and depression

scales, ASI psychiatric and family problem severity

composite scores, and clinician generated diagnoses of

depressive and anxiety disorders), substance use char-

acteristics (i.e. age at first use, DSM-IV alcohol anddrug dependence diagnoses and ASI alcohol and drug

severity composite scores), and treatment characteristics

(i.e. LOS in treatment, adjunct/self-help SA treatment,

and smoking cessation aids) were compared across the

four groups: SMOKERS, QUITTERS, STARTERS/

RESUMERS, and NONSMOKERS using one-way

Analyses of variance (ANOVAs) for continuous vari-

ables and chi-square tests for categorical variables.Analysis of covariance (ANCOVA) was used to examine

whether number of days abstinent at 12 months differed

between the groups. Variables that were significantly

correlated with number of days abstinent at 12 months

for the whole sample were used as preliminary covari-

ates, and included age, income, ASI psychiatric severity

composite score, LOS treatment and being prescribed

bupropion. An initial ANCOVA was conducted bysimultaneously entering these covariates along with

smoking group status as potential contributors to the

outcome variable of total days abstinent at 12-month

follow-up. The initial ANCOVA was significant,

although age, income, psychiatric severity, and being

prescribed bupropion did not contribute significantly to

the model, while LOS and smoking group did contribute

significantly. Therefore, in order to present a moreparsimonious model, the final ANCOVA included only

LOS and smoking group status, which were again

entered simultaneously.

C.S. Kohn et al. / Drug and Alcohol Dependence 69 (2003) 61�/7164

3. Results

A total of 649 participants (86.9%) were retained at

12-month follow-up. Demographic differences betweenparticipants who were retained at follow-up versus those

who were not examined by one-way ANOVAs and Chi-

Square analyses and revealed that participants who were

retained at follow-up were somewhat older (M�/37.9

years, SD�/10.4) compared to those not retained at

follow-up (M�/35.6 years, SD�/9.9; F (1, 747)�/4.36,

P B/0.05). Also, participants with some post high school

education were more likely than those with none to beretained at follow-up (89.3% versus 83.9%, X2�/4.64,

P B/0.05) and participants prescribed bupropion were

more likely than those not prescribed bupropion to be

retained at follow-up (96.1% versus 85.4%, X2�/8.77,

P B/0.01). No differences were found for gender,

ethnicity, income, marital status, smoking status (i.e.

smoking at baseline versus not), diagnoses of depression

or anxiety in the past year, diagnoses of alcohol-onlydependence, drug-only dependence, or polysubstance

dependence at admission, ASI drug scores, ASI alcohol

scores, number of cigarettes smoked per day, and

depressive and anxiety symptoms at baseline. In general,

this HMO’s membership is insured primarily through

employment; income and employment levels are higher,

and addiction severity is somewhat lower than in public

populations (Weisner et al., 2000).

3.1. Demographic and smoking characteristics (Table 1)

Changes in smoking behaviors observed at 12-month

follow-up resulted in classifying 14% of the 395 baseline

smokers as QUITTERS and 12% of the 254 baseline

nonsmokers as STARTERS/RESUMERS. Thus, the

sample consisted of 342 (52.7%) SMOKERS, 53 (8.2%)

QUITTERS, 30 (4.6%) STARTERS/RESUMERS, and224 (34.5%) NONSMOKERS. Over half of the QUIT-

TERS (56.6%) reported having quit smoking for at least

6 months. All 30 STARTERS/RESUMERS reported

smoking during the prior 6 months. However, 36.7%

appeared to be ‘‘starters’’ as they reported never

smoking regularly for at least 1 year whereas the other

63.3% appeared to be ‘‘resumers’’ as they reported

having smoked regularly for at least 1 year and reportedsmoking an average of 11.9 years before quitting. One-

way ANOVAs revealed no differences between ‘‘star-

ters’’ and ‘‘resumers’’ on ASI composite variables and

SCL-90 measures of depression and anxiety, all P ’s�/

0.05; therefore they were combined in the STARTERS/

RESUMERS group. Of the NONSMOKERS, 35.3%

were ‘‘former smokers’’ because they reported having

previously smoked for at least 1 year and had smoked anaverage of 11.6 years prior to quitting; the remaining

64.7% were ‘‘never smokers.’’ One-way ANOVAs re-

vealed no differences between ‘‘never smokers’’ and

‘‘former smokers’’ on ASI composite variables and

SCL-90 measures of depression and anxiety, all P ’s�/

0.05; therefore they remained in the combined in the

NONSMOKERS group.At baseline, QUITTERS reported smoking fewer

cigarettes per day (M�/10.3, SD�/11.1) compared to

SMOKERS (M�/16.7, SD�/11.6), F (1, 392)�/14.22,

P B/0.0001. At follow-up, STARTERS/RESUMERS

reporting smoking fewer cigarettes (M�/9.4, SD�/8.3)

compared to SMOKERS (M�/16.9, SD�/9.3), F (1,

392)�/18.23, P B/0.0001. The groups differed to varying

degrees on the demographic variables of age, ethnicity,income, and education. NONSMOKERS were older

(M�/40.9 years, SD�/10.3) and STARTERS/RESU-

MERS were younger (M�/31.1 years, SD�/8.6) com-

pared to all other groups, F (1, 646)�/13.21, P B/0.0001.

Groups differed in their proportion of Caucasians, X2(3,

N�/649)�/8.29, P B/0.05, and Hispanics, X2(3, N�/

649)�/10.54, P B/0.05. Groups differed in their income

level, such that NONSMOKERS (39.0%) and STAR-TERS/RESUMERS (43.3%) were more likely to report

a yearly income level above $40 000 compared to

SMOKERS (29.0%) and QUITTERS (19.6%), X2(3,

N�/649)�/11.60, P B/0.01. Lastly, groups differed in

their education level such that NONSMOKERS (68.8%)

and QUITTERS (62.3%) were more likely than SMO-

KERS (52.9%) and STARTERS (50.0%) to report

having obtained education beyond a high schooldiploma or diploma equivalent, X2(3, N�/649)�/

15.26, P B/0.01. No group differences were found for

gender, employment status, or marital status. After

examining all demographic and smoking variables,

only age, r�/0.10, P B/0.01, and income, t(632)�/

�/2.16, P B/0.05, were significantly associated with the

outcome variable of total days of abstinence. Thus,

these two variables were included as covariates in theinitial ANCOVA analysis described below.

3.2. Substance use and psychosocial characteristics at

treatment entry (Table 2)

NONSMOKERS reported being older at time of first

drug use compared to all other groups, X2(3, N�/

649)�/13.10, P B/0.001. At baseline, there were no

differences between the groups for ASI alcohol scores;although NONSMOKERS had lower ASI drug scores

compared to the other groups, F (1, 648)�/3.42, P B/

0.05. QUITTERS and STARTERS/RESUMERS were

less likely to be diagnosed as Alcohol Dependent-only

compared to the other three groups, X2(3, N�/649)�/

11.34, P B/0.01. STARTERS/RESUMERS were more

likely to be diagnosed as both Alcohol and Drug

Dependent compared to the other three groups, X2(3,N�/649)�/14.57, P B/0.001. However, none of these

substance use variables were significantly associated

with the outcome measure of total days abstinent at

C.S. Kohn et al. / Drug and Alcohol Dependence 69 (2003) 61�/71 65

12-month follow-up (all P ’s�/0.05) and thus they were

not included as covariates in the ANCOVA analysis

described below.

STARTERS/RESUMERS appeared to have thepoorest overall baseline psychosocial functioning com-

pared to SMOKERS and NONSMOKERS, including

higher SCL-90 depression scores, F (1, 648)�/6.64, P B/

0.001, higher SCL-90 anxiety scores, F (1, 648)�/7.59,

P B/0.001, higher ASI psychiatric severity composite

scores, F (1, 648)�/3.71, P B/0.05, and a greater like-

lihood of being diagnosed with a depressive disorder

during the year prior to treatment entry, X2(3, N�/

649)�/9.45, P B/0.05. QUITTERS also reported rela-

tively higher SCL-90 depression scores compared to

NONSMOKERS, F (1, 648)�/6.64, P B/0.001, and

higher anxiety scores compared to both SMOKERS

and NONSMOKERS, F (1, 648)�/7.59, P B/0.001. The

groups did not differ on the ASI family or social,

employment, legal, or medical problem severity scores,

or on the likelihood of having received an anxietydisorder diagnosis during the year prior to treatment

entry, all P ’s�/0.05. Only ASI psychiatric severity at

treatment entry was significantly associated with the

outcome measure of total days abstinent at 12-month

follow-up, r�/�/0.08, P B/0.05, and therefore this vari-

able was included as a covariate in the ANCOVA

analysis described below.

3.3. Treatment characteristics: LOS, adjunct/self-help

SA treatment, smoking cessation aids

The four groups were similar in their LOS in SA

treatment, M�/80.9 days, SD�/104.6, Range�/0�/428

days, for all participants. At 12-month follow-up, no

group differences were found for percent of participants

who attended the CDRP during the previous 6 months,

or for percent who attend AA or NA meetings duringthe previous 30 days; therefore all percentages and

means reported are for the whole sample. Twenty-seven

percent of participants reported they had attended the

CDRP during the previous 6 months and 62.9%

reported they had attended an AA or NA meeting

during the previous 30 days, M�/5.8 days of atten-

dance, SD�/7.7, Range�/0�/30 days. Because other

smoking cessation aids were used infrequently (i.e.nicotine replacement therapies), only bupropion was

examined for its potential role as a smoking cessation

aid. QUITTERS (35.8%) were more likely to have been

prescribed bupropion compared to SMOKERS (14.0%),

STARTERS/RESUMERS (16.7%), and NONSMO-

KERS (11.6%), X2(3, N�/649)�/20.29, P B/0.001.

However, days of bupropion use, milligrams prescribed,

and percent prescribed bupropion who were diagnosedwith Depressive Disorder did not differ by groups.

Overall participants used bupropion (n�/98) an average

of 3.5 months, SD�/3.6, Range�/0�/11.5 months, and

were prescribed an average of 210.7 mg, SD�/87.2,

Range�/75�/600 mg. Of those already diagnosed with a

Depressive Disorder (n�/192), 19% were prescribed

bupropion. Only being prescribed bupropion was sig-nificantly associated with the outcome measure of total

days abstinent at 12-month follow-up, t (647)�/�/2.03,

P B/0.05, and therefore this variable was included as a

covariate in the ANCOVA analysis described below.

3.4. Treatment outcome: abstinence at 12 months (Table

3)

Although the groups differed on a number of vari-ables (Tables 1 and 2), most were not used as covariates

because they were not significantly correlated with the

treatment outcome variable of days abstinent at 12-

month follow-up (all P ’s�/0.05), with the exception of

age, income, ASI psychiatric severity composite score,

and being prescribed bupropion. In addition to these

covariates, LOS in SA treatment was included as a

covariate because it is consistently highly correlated withabstinence outcome (e.g. Goldstein et al., 2000), as was

the case in this study, r�/0.42, P B/0.0001. Initially

entering LOS, age, income, ASI psychiatric severity, and

being prescribed bupropion as covariates allowed for a

more accurate examination of the variance in abstinence

rates accounted for by changes in smoking status.

An initial ANCOVA was conducted with age, income,

psychiatric severity, LOS and prescribed bupropion ascovariates along with smoking group as potential

contributors to the outcome variable of total days

abstinent at 12-month follow-up. Although the overall

ANCOVA was significant, F (3, 625)�/3.75, P B/0.05,

age, income, ASI psychiatric severity, and being pre-

scribed bupropion did not significantly contribute to the

model, F (3, 644)�/0.173, F (3, 644)�/0.752, F (3, 644)�/

2.42, F (3, 644)�/0.306, respectively, all P ’s�/0.05, whileLOS, F (1, 634)�/120.55, P B/0.0001 and smoking

group, F (3, 634)�/3.75, P B/0.05 did significantly con-

tribute to it. Therefore, in order to obtain a more

parsimonious model, we re-ran the model to include

only LOS as the covariate and smoking group as the

independent variable. The final overall ANCOVA was

significant, F (3, 644)�/4.03, P B/0.01, and total days

abstinent differed significantly by smoking group, F (3,644)�/4.03, P B/0.01, above and beyond the variance

contributed by LOS, F (3, 644)�/134.3, P B/0.0001. The

adjusted and unadjusted marginal means (Table 3) show

that at 12 months QUITTERS and NONSMOKERS

had the greatest number of days abstinent and SMO-

KERS and STARTERS/RESUMERS had the fewest

days abstinent. This finding also corresponds with the

group differences in the percent of participants atfollow-up who reported being abstinent from alcohol

and (non-nicotine) drugs at 12 months. At follow-up,

QUITTERS (69.8%) and NONSMOKERS (62.9%)

C.S. Kohn et al. / Drug and Alcohol Dependence 69 (2003) 61�/7166

were more likely to report abstinence for the previous 30

days compared to SMOKERS (52.3%) and STAR-

TERS/RESUMERS (56.7%), X2(3, N�/649)�/9.75,

P B/0.05.

4. Discussion

This study documented naturalistic observations of

the associations between change in smoking status and

12-month SA treatment outcomes, and to our knowl-

edge it is the first study to examine both those who quit

smoking in addition to those who start/resume smoking.

Without implementing a formal smoking cessation

program, 13% of Smokers enrolled in the SA treatment

program quit smoking at 12-month follow-up and over

half succeeded in quitting smoking for at least 6 months.

This quit rate is much higher than is found among the

general population (Hughes, 1993). Perhaps more strik-

ing was the finding that 12% of baseline Nonsmokers

(i.e. former- or never-smokers) had started or resumed

smoking at 12-month follow-up.

4.1. Demographic, substance use, and psychosocial

characteristics

The groups differed on demographic characteristics,

but similar to other studies, there were few patterns to

help identify those more likely to attempt to quit or to

start smoking (e.g. Bobo et al., 1996a). On average,Quitters and Starters/Resumers smoked fewer cigarettes

per day compared to Smokers, similar to previous

findings (Lemon et al., in press, McCarthy et al.,

2001). Consistent with other findings, Quitters were

less likely to be diagnosed as alcohol dependent while

Smokers were more likely to be diagnosed as alcohol

dependent (Campbell et al., 1995; Dawson, 2000).

Starters/Resumers were more likely to be diagnosed asboth alcohol and drug dependent, indicating a greater

likelihood of having more severe substance dependence

problems. This study could not detect the role that

Table 1

Demographic and smoking characteristics of persons entering substance abuse treatments

Group F or X2

values

Post hoc

SM SMOKERS

(n�342)

QU QUITTERS

(n�53)

S/R STARTERS/RE-

SUMERS (n�30)

NON NONSMOKERS

(n� 224)

M (SD) M (SD) M (SD) M (SD)

Smoking characteris-

tics

Years smoked before

quittinga

n/a n/a 11.9 (11.2) 11.7 (8.5) 0.016

Ever smoked �1

year (%)

97.9% 94.3% 63.3% 35.3% 292.2# NONBSTBQU,

SM

Demographic charac-

teristics

Age in years 36.8 (10) 36.4 (10) 31 (8.5) 41 (10) 13.21# STBSM,

QUBNON

Female 42.1% 45.3% 56.7% 42.4% 2.53

Ethnicity 19.01*

Caucasian 77.5% 67.9% 70.0% 66.5%

African American 8.5% 13.2% 13.3% 10.3%

Hispanic 7.5% 3.8% 10.0% 14.2%

Other 6.5% 14.1% 6.7% 9.0%

Income �40 K 29.0% 19.6% 43.3% 39.0% 11.60** SM, QUBS/R,

NON

Employed (full-or

part-time)

61% 62% 67% 72% 6.81

Education after HS 52.9% 62.3% 50.0% 68.8% 15.26** SM, S/RBQU,

NON

Married/living as

married

41.2% 41.5% 30.0% 46.4% 3.58

Note: aAnalysis conducted between STARTERS/RESUMERS and NONSMOKERS only, *** P B0.001.

* P B0.05.

** P B0.01.# P B0.0001.

C.S. Kohn et al. / Drug and Alcohol Dependence 69 (2003) 61�/71 67

alcohol dependence played in the changes in smoking,

but one speculation is that continued alcohol (and other

drug) use may lower inhibitions to smoke or provide

environmental, social, or physiological cues that rein-

force smoking (Dawson, 2000; Gulliver et al., 1995).

Alternatively individuals may continue to smoke in

order to decrease the sedating effects of continued

alcohol use, or conversely they may continue to use

alcohol in order to moderate the stimulant affects of

smoking (Friday’s Progress Notes, 2001). Among Star-

ters/Resumers, smoking prior to substance use may have

served as a cue that led to substance use relapse;

Table 2

Substance use and psychosocial characteristics at treatment entry

Group F or X2

values

Post hoc

SM SMOKERS

(n�342)

QU QUITTERS

(n�53)

S/R STARTERS/RE-

SUMERS (n�30)

NON NONSMO-

KERS (n�224)

M (SD) M (SD) M (SD) M (SD)

Substance use characteris-

tics

Age first used any drug 13.2 (4.4) 13.9 (2.8) 12.9 (2.7) 15.8 (6.6) 13.10*** SM, QU, S/

RBNON

DSM-IV substance use di-

agnoses

Drug dependence only 30.4% 41.5% 23.3% 26.3% 5.42

Alcohol dependence only 39.8% 24.5% 26.7% 46.4% 11.34** QU, S/RBSM,

NON

Alcohol and drug depen-

dence

20.5% 18.9% 40.0% 12.9% 14.57** NON, SM,

QUBS/R

Substance abuse only 8.3% 15.1% 10.0% 14.4% 4.24

ASI scores (range)

Alcohol (0�/0.97) 0.38 (0.31) 0.30 (0.30) 0.37 (0.31) 0.37 (0.29) 1.11

Drug (0�/0.55) 0.14 (0.13) 0.14 (0.13) 0.17 (0.14) 0.11 (0.13) 3.42* NONBSM, QU,

S/R

Psychosocial characteristics

DSM-IV depressive disor-

der and anxiety diagnosesa

Depressive disorder 27.5% 34.0% 53.3% 28.6% 9.45* SM, NONBS/R

Anxiety disorder 16.1% 18.9% 23.3% 18.3% 1.35

SCL-90 scores (range)

Depression (0�/4) 1.7 (1.1) 2.0 (1.3) 2.4 (1.1) 1.5 (1.1) 6.64*** SM, NONBS/R

NONBQU

Anxiety (0�/4) 1.3 (1.0) 1.8 (1.2) 1.9 (1.1) 1.2 (1.1) 7.59*** SM, NONBQU,

S/R

ASI scores (range)

Psychiatric (0�/1.0) 0.39 (0.26) 0.44 (0.27) 0.53 (0.25) 0.37 (0.27) 3.71* SM, NONBS/R

Social/Family (0�/1.0) 0.35 (0.27) 0.35 (0.27) 0.42 (0.28) 0.32 (0.27) 1.66

Employment (0�/1.0) 0.41 (0.24) 0.40 (0.23) 0.42 (0.23) 0.37 (0.22) 1.21

Legal (0�/0.84) 0.09 (0.18) 0.08 (0.17) 0.09 (0.19) 0.07 (0.17) 0.906

Medical (0�/1.0) 0.20 (0.32) 0.22 (0.31) 0.25 (0.36) 0.22 (0.32) 0.233

Note: aInformation about other DSM-IV diagnoses is available from the first author, but is not reported because occurrences averaged less than

4%.# P B0.0001.

* P B0.05.

** P B0.01.

*** P B0.001.

Table 3

Adjusted and unadjusted mean days abstinent from substance use at

12-month follow-up by smoking group status

Groups Adjusted mean (95% con-

fidence interval)a,b

Unadjusted

mean (SD)

QUITTERS 310.6 (270.1�/351.2) 316.9 (151.1)

NONSMOKERS 294.7 (274.9�/314.4) 297.8 (169.4)

SMOKERS 258.2 (242.3�/274.2) 257.3 (164.0)

STARTERS/RESU-

MERS

246.6 (192.6�/300.7) 223.4 (167.3)

aAdjusted means with covariate LOS, bSMOKERS, STARTERS/

RESUMERSBQUITTERS, NONSMOKERS.

C.S. Kohn et al. / Drug and Alcohol Dependence 69 (2003) 61�/7168

conversely, relapse to substance use may have provided

potent social and environmental cues that led to

resuming or starting smoking.

Examination of psychosocial factors indicated thatthe groups differed little on their family or social,

employment, legal, and medical ASI composite scores.

When prevalence of depressive and anxiety disorder

diagnoses, ASI psychiatric severity scores, and severity

of depressive and anxiety symptoms (as measured by the

SCL-90) were examined, some interesting findings

emerged. Although Starters/Resumers were most likely

to have been diagnosed with a depressive disorder in theyear prior to SA treatment entry and to report more

severe psychiatric problems on the ASI, they did not

differ from Quitters in severity of SCL-90 anxiety and

depressive symptoms reported at treatment entry. Both

Starter/Resumers and Quitters reported higher baseline

anxiety symptoms compared to Nonsmokers and Smo-

kers. Although this study can not test a causal relation-

ship between smoking status and symptoms of anxietyand depression, the relationship found indicate the

importance of examining this in future studies using a

different research design.

4.2. Treatment characteristics: LOS, adjunct/self-help

SA treatment, smoking cessation aids

The four groups did not differ on LOS in treatment,

nor in attendance to the CDRP, NA, or AA. Quittersdid not attend more treatment compared to the other

groups, which suggests that factors other than treatment

were influential on their greater days of abstinence.

Because smoking cessation aids were used infrequently

(i.e. nicotine replacement therapies), only bupropion

was examined for its potential role as a smoking

cessation aid. Quitters were more likely to be prescribed

bupropion compared to the other three groups.Although this lends some support to observations that

bupropion may aid in smoking cessation (Jarvis and

Sutherland, 1998), we cannot assess the role bupropion

may have played, particularly because it was not shown

to be significantly associated with abstinence at 12

months follow-up. Quitters’ underlying depression may

have been addressed through extra attention from their

physicians and/or clinicians in the SA treatment pro-gram, placebo effect, (Khan et al., 2001) or nicotine-like

effects (Young and Glennon, 2002), all of which tend to

increase motivation and/or ability to quit smoking.

4.3. Treatment outcome: abstinence at 12 months

Self-initiated quitting of smoking during SA treat-

ment did not appear to be detrimental to drug andalcohol abstinence among managed care recipients, a

finding similar to that found in previous research

(Burling et al., 2001; Frosch et al., 2000; Jospeh et al.,

1993; Lemon et al., in press). In fact, those who

attempted to quit smoking had more days abstinent

than those who either remained smokers or started/

resumed smoking after treatment entry, even whencontrolling for LOS in treatment. The smoking status

of substance abusers is not static (McCarthy et al.,

2001), and in fact, a fairly large minority of individuals

in SA treatment appear to successfully quit smoking

(Lemon et al., in press), suggesting that SA treatment

may present a prime opportunity to encourage smoking

cessation.

Moreover, although the findings that Smokers tend tobe more at-risk for relapse compared to Nonsmokers

and Quitters is not new (e.g. Frosch et al., 2000), this

study’s contributions to the literature include providing

corroborating evidence for this among the privately

insured managed-care population, using a large sample

size, and examining long-term drug and alcohol use

outcomes. Perhaps most unique to this study was the

examination of individuals who started or resumedsmoking after beginning SA treatment. To the best of

our knowledge, this group of Starters/Resumers has not

been discussed in the empirical literature, even though in

this study they were nearly as large a group as the

Quitters, the group that is generally examined in the

literature. By focusing on only smokers identified at

baseline measurement, we miss a potentially high-risk

group of nonsmokers or former smokers who startsmoking at a later point (e.g. after treatment initiation),

and show poorer SA treatment outcomes.

4.4. Limitations

This study was not without limitations. Although the

sample was drawn from a private, managed-care pro-

gram which is representative of a major organization of

current manner in health care, it is a sample of limitedethnic diversity and higher SES compared to commu-

nity, public, or non-insured populations. While self-

report of smoking status and smoking behavior during

the previous 6 months can generally be considered valid

(e.g. Velicer, et al., 1992), biochemical validation and a

measure of specific number of nonsmoking days at

follow-up would have provided additional independent

validation to this self-report data. Finally, although arelatively understudied group of individuals who

started/resumed smoking after treatment entry were

identified, qualitative data regarding why these indivi-

duals started/resumed smoking were not available.

4.5. Implications

This study provides additional support for researchindicating the importance of addressing and/or offering

smoking cessation within SA treatment programs

(Bernstein and Stoduto, 1999), and provides evidence

C.S. Kohn et al. / Drug and Alcohol Dependence 69 (2003) 61�/71 69

that privately insured managed-care patients appear to

have a relatively high rate of smoking cessation. These

findings highlight the clinical importance of identifying

individuals who are at-risk for starting or resumingsmoking during or after SA treatment, as they are more

likely to have difficulty remaining abstinent from illicit

drugs and alcohol. These findings also underscore the

empirical importance of measuring smoking status of all

study participants at all time points during the study.

Future smoking research should include a focus on

separating starters/resumers from continuous smokers

as this may shed some light on otherwise complex andconfusing impacts of anxiety and depression on smoking

cessation outcomes. It may also be important for future

research within substance using populations to focus on

separating starters/resumers from continuous smokers

and quitters in order to achieve a better understanding

of the relationship between smoking and SA treatment

outcomes.

Acknowledgements

This study was supported by the National Institute on

Drug Abuse (RO1 DA10572). Preparation of this

manuscript was supported by training grants from the

National Institute on Drug Abuse (T32 DA07250 and

K23 DA00468). We would like to thank Charles Moore,

MD and anonymous reviewers for reviewing earlierdrafts of this manuscript. Portions of the results were

presented at the 25th Annual Research society on

Alcoholism Scientific Meeting in San Francisco, CA in

June/July 2002. Previous studies from this database

include Weisner et al., (2001) and in Kohn et al., (2002).

This study is based on results no previously published.

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