Predictors of Dropout From Alcoholism Treatment
Multiple
Reginald G. Smart, PhD, Gaye Gray \s=b\ A common problem in treating alcoholics is the high dropout rate. Many studies have identified individual factors associated with dropout, eg, poor motivation and previous dropout. We believe the present study reports the first major effort to use multivariate analyses to predict dropout in a large (792), oneyear follow-up study of alcoholics, and examines the possibility that medical and nonmedical treatments lead to differential dropout rates. A multiple classification analysis technique showed that treatment variables as opposed to client characteristics were the best predictors of dropout. Patients remaining in treatment were more likely to have a variety of medical interventions, eg, medication and medical assessment, than those who dropped out. Results were similar to studies using other techniques and have interesting implications for the treatment of alcoholics, raising questions about current trends toward nonmedical treatment of alcoholism. (Arch Gen Psychiatry 35:363-367, 1978)
A keeping problem
for those treating alcoholics is them in treatment. Dropout rates for many types of treatment, eg, hospital treatment of medical conditions, methadone maintenance of heroin addiction, and general psychiatric outpatient care are higher1 than one would wish. However, the dropout rate for alcoholics getting outpatient care is also very high; anywhere between 52% and 75% of patients drop out by the fourth session. Much evidence suggests that alcoholics who stay in treatment for long periods have higher abstinence rates.1 We- found that matched groups of alcoholics staying in treatment more than one interview were more likely to be abstinent at follow-up than those having only one inter¬ view. Many alcoholics do not stay in treatment long enough to sample treatment adequately or decide on its value to them. Dropouts also derive less benefit from their treat¬ ment than those who persist. It is, therefore, important to understand as much as possible about their reasons for dropping out of treatment. Numerous studies have identified the characteristics of alcoholic dropouts from treatment, but a number of diffi¬ culties still exist. Some factors associated with dropout are social isolation, poor motivation for treatment, previous treatment dropouts, nonabstinence on admission, and field dependence, although some inconsistencies have been found in different studies. For example, some studies have found that social isolation and motivation are related to treatment outcome,1 but others have not.3 One extensive review of dropout studies4 has pointed out that these studies have typically not used multiple predictors and that constant
Accepted
for publication April 20, 1977. From the Addiction Research Foundation, Toronto. Reprint requests to Addiction Research Foundation, 33 Russell Toronto, Canada (Dr Smart).
St,
most "if they have more than one predictor do not include variables from both the patient and the therapist at the same time." This is particularly true of studies of dropouts from alcoholism treatment: most employ only one or a few predictors and tend to treat each separately. Most studies use only variables from the patient and not from the therapist or clinic. Without a multivariate approach, it is difficult to identify the separate or unique importance of variables. For example, poor motivation for treatment, social isolation, previous treatment dropout, and nonabsti¬ nence on admission are likely to be characteristic of the same patients. Analysis of each variable separately will not aid in identifying the unique contribution of each. The purpose of this study was to conduct a multivariate analysis of therapy and patient variables most associated with dropout and with staying in treatment. The method used is multiple classification analysis, which allows the identification of 1. How well all variables taken together explain dropout; 2. How each predictor and class of predictors (eg, patient and treatment related) considered separately relate to dropout when the others are included or removed; ie, which have unique explanatory power; 3. Whether dropout can be explained better by patient characteristics or by treatment experiences.
So
far, studies of this type of dropout from alcoholism answer these questions. The present study examined a variety of demographic, employment, social stability, and drinking variables as well as the location of treatment, type of treatment received, profession of therapist, medications received, and occurrence of a medical assessment. These aspects of treatment have not been able to
selected because they could be determined patients, even those who had only one or a few contacts. Other features of treatment such as number of treatments obtained would be correlated with the depen¬ dent variable (length of stay) and were not used here. A major interest was shown in examining the value of medical vs nonmedicai interventions. METHOD treatment
were
for all
The alcoholics included were being admitted for the first time for an alcohol problem (or were being readmitted after a one-year absence) to one of five alcoholism services in Ontario. All were seen at or soon after admission for an intensive interview. An indication of their degree of alcoholism was provided by their admission scores on the Alcoholic Involvement Scale. Only 15% of this group had scores at or below a mean level for social drinkers. The majority of clients reported having a high frequency of heavy drinking and loss of control over drinking habits, suffering negative effects (eg, blackouts, loss of jobs, craving), and feeling
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more
dependent on alcohol as a "coping" device, especially in social
situations.
Approximately 90% of those seeking treatment were inter¬ viewed. The remainder dropped out before the information required could be obtained in full. In all, 792 alcoholics were obtained from five treatment facilities. Of this group, roughly 80% were men.
Most were in their late 30s to mid 40s and
were
married
living with spouses. More than half had some high school or university education and were employed, many in skilled trades or in white collar jobs. In general, the sample was mostly middle and and
lower class alcoholics with almost no skid row persons. The treatment facilities covered a variety of therapies and approaches including individual and group psychotherapy, protec¬ tive drugs, tranquilizers and antidepressants, family therapy, day care, occupational therapy, and inpatient care. Not all facilities offered all types of treatment to all patients, and the patients' exposure to any treatment variable or set of variables was the result of a combination of clinical judgment, availability, and patient motivation. Patients were not randomly assigned to different treatment. The facilities offered a range of therapeutic persuasions: in one, individual therapy and medical management predominated; in another, group psychotherapy; in another, family therapy; and in another, a mixture of individual and group approaches. Admission interviews provided information about the patient during the pretreatment phase. Items included were demographic characteristics, eg, age, sex, employment; drinking habits according to the Alcoholic Involvement Scale—a reliable and valid measure of drinking''; a variety of scales covering 14 dimensions; ie, physical health, drug use, social stability, marital stability, drinking assessment, problems due to drinking, attitudes toward abstinence, motivation for treatment, patient's resources (people), resources in terms of interests and activities, isolation from such interests and activities, isolation (people), an assessment of life conditions, and a measure of patient self-satisfaction. All of these scales have been devised so that correlations of each item with its total scale score are high and intercorrelations among total scale scores are low. The scoring for all scales is consistent: the higher the score, the worse the status on the dimension being measured. A record was also kept of certain conditions pertaining to each patient's treatment. The relevant items were the location of the clinic and, hence, the orientation of the treatment staff since this varied from clinic to clinic; the type of treatment received, eg, individual, group, or family therapy; the occurrence of a medical assessment; the type and dosage of medications, if any were administered; and the profession of the patient's principal thera¬
pist.
The dependent variable, length of stay in treatment, was categorized according to the method used by Baekeland and associates": Immediate visit.
Dropouts.-Those
who did not return after the first
Rapid Dropouts.-Those who dropped out after one to four weeks of treatment. Slow Dropouts.-Those who dropped out during the second to sixth month of treatment. Clinic Attenders.-Those who attended for seven months or more.
In this particular sample of outpatients, there were 96 imme¬ diate dropouts, 116 rapid dropouts, 379 slow dropouts, and 201 clinic attendere. RESULTS Selection of Variables
The identification of variables associated with treat¬ dropout was achieved by the joint use of two multivariate techniques: automatic interaction detector (AID) and multiple classification analysis (MCA). No detailed description of the AID analysis will be given ment
a full explanation of AID see Sonquist et al.7) For our purposes, AID was used to prepare the data for the MCA. Although the MCA allows predictor variables that may be measured on nominal, ordinal, or interval scales, they must be categorized, preferably with not more than six categories. The problem lies in categorizing to give the maximum amount of variance explained by the variable, ie, the largest reduction in predictive error. The AID tech¬ nique partitioned the sample into a series of nonoverlapping subgroups whose means explained more variance than any other competing partition. Using the guidelines of the AID program, predictors with numerous classes (greater than seven) were reduced for the MCA purposes. Multiple classification analysis is described in considerable detail by Andrews et al." Basically, it is a technique for examining the interrelation¬ ships between a set of predictor variables and a dependent variable. It uses multiple regression with dummy variables. A series of MCAs were carried out, beginning with all reported predictors and gradually eliminating predictors in order to find the smallest set of admission and treatment variables that would account for maximum variance in explaining dropouts. Variables were eliminated on the basis of F tests. Nonsignificant F-test results indicated that the predictor in question was not able to explain, by itself, a significant proportion of the variance and, there¬ fore, was not important enough to retain for further
here. (For
analyses.
Admission Variables
Only
four of the admission variables had
values:
significant
F
Motivation for Treatment Scale.—This six-item scale, with ranging from 0 to 24, measures what behavioral changes and what treatment measures a client is willing to undergo to ameliorate an alcohol problem. Each item correlates with the total scale score upwards from .43 and the largest (absolute value) interscale correlation is .32 scores
with the Problems Due to Problems Due to
Drinking Scale.
Drinking Scale.—This scale also contains
six items and examines how frequently a client's drinking leads to daily living problems with family members, friends, work, the law, finances, and health. Again, scores range from 0 to 24, and item-total scale correlatons range upwards from .56. This scale correlates .56 with the Assess¬ ment of Life Experiences Scale, .72 with the Alcoholic Involvement Scale, and .63 with the Drinking Assessment Scale. Assessment of Life Experiences Scale.—This scale contains four items (scores from 0 to 16) that examine a client's degree of satisfaction with four dimensions of daily life: accommodation, work experience, finances, and actively meaningful relationships with others. Item-total scale score correlations are above .55 while interscale correla¬ tions are below .62. The Length of Time.-Number of years that the patient feels drinking has been a problem. The admission variables that were not significant (with < .05) included 1. All demographic variables (sex, age, patient status, marital status, education, religion, rural/urban childhood,
employment status, present occupation);
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Table 1.—Bivariate Relationship of Four Admission Variables to Length of Stay in Treatment* %of
Sample
Deviations From Grand Mean
in Class
Predictor Variable Motivation for treat¬ ment
scale Problems due to
drinking scale
Life
experi¬
ence
scale Time drink¬
ing a problem,
a)
Class Score 0
68
1 to 2 3 to 8
9 to 13 to Score Oto 9 to
12 24 8
14 15 to 19 20 to 24
Score 0 3 6 12
80
395 173 66 309 308 134 31
to 2
42
to 5
223 433 84 41 215
to 11 to 16 < 1 1 to 4 5 to 9 > 10
174
352
% 8.7 10.2 50.5 22.1 8.4 39.5 39.4 17.1 4.0 5.4 28.5 55.4 10.8 5.2 27.5 22.3 45.0
"Mean length of stay for entire sample
=
Unadjusted -.068 .121 .091 -.123 -.300 .105 -.031 -.093 -.343 .177 -.035 .079 .225 .379 .034 .097 -.025
2.89
or
Adjusted -.009 .145
.099
-.167 -.320 .185 -.073 -.179 -.345
i
° .c
2-6 Months 1-4 Weeks
Least .
Most
.
problems with drinking
problems
with
drinking
>
I Contact'
-1-1Good Poor
•247
-.082
.095 -.151 352
Life experience scale
Fig 1.—Interaction effect between Life Problems Due to Drinking Scale.
Experience Scale and
-.012
.994 001
about four weeks In
treatment.
2. Five
.£
drinking variables—(age at regular drinking, length
of drinking career, usual beverage, type of drinker, average alcohol consumption); 3. Eleven of the 14 scales covering physical health, drug abuse, social stability, marital stability, drinking assess¬ ment, alcoholic involvement, attitudes toward abstinence, patient's resources (people), resources in terms of interests and activities, isolation from such interests and activities, and isolation (people).
For every subclass, the MCA computes two coefficients. The first coefficient is the amount by which the mean value of the dependent variable calculated for observations in that subclass deviates from the grand mean for the whole sample. This coefficient is given in Table 1 as the unadjusted deviation from the mean and indicates to what extent persons in a given subclass are below or above the average stay in treatment for this sample. The unadjusted deviations show the simple bivariate relationship between the dependent and independent variables. The second coefficient is the adjusted deviation from the grand mean, and represents the deviation of each class mean from the grand mean while controlling for the effects of all other variables in the analysis. For the entire sample, the mean length of time in treatment was approximately one month. For every admission variable, except the Problems Due to Drinking Scale, there exists a curvilinear relationship between the dependent variable and the predictors. This means that persons most likely to drop out from treatment have scores on these three admission variables at either extreme. The early dropouts either had problems with their drinking for less than one year or for more than ten years. Their life experiences with alcohol have either been good (score 0 to 2) or extremely poor (score 12 to 16); they are either very highly motivated or have virtually no motiva¬ tion to change their drinking behavior. Those most likely to stay in treatment have scores on the three variables somewhere in the middle range. Clinic attenders have had problems with their drinking for one to nine years but, although still fairly motivated to change drinking behavior and to take the necessary treatment (score or motivation scale of 1 to 8), they have had at some
time poor drinking experiences (score on life experience scale of 6 to 11). The relationship between problems due to drinking and staying in treatment is straightforward. The more prob¬ lems a person experienced due to his drinking behavior, the less likely he is to stay in a treatment program. Although (for some classes of all predictors) the adjusted deviations show the same patterned relationship to the criterion dimension as do the unadjusted deviations, the difference between the unadjusted and adjusted devia¬ tions is great enough to suspect that the variables are intercorrelated. For example, the differences in length of stay in treatment are not explainable in terms of just the Motivation for Treatment Scale only, but in terms of the other intake variables. Multivariate
Analyses
of Admission Variables
The MCA model assumes that the effect of the predictor variables combine additively without interactions among predictors. A procedure using the MCA allowed the researcher to detect and eliminate predictor interactions. The strategy is outlined as follows: For the variables suspected of interacting, a single variable was created that contained a category for each possible combination of the set of variables. The new combination variable contained all the main additive effects plus the effects due to interaction. A one-way analysis of variance performed on the combination variable resulted in an if measure. This measure indicates the amount of variance that can be accounted for by both the main and interaction effect. This figure compares with the multiple r- from an MCA run that uses the set of suspected variables separately. Differ¬ ence between r2 and n- is attributable to interaction. When this strategy for detecting interaction was applied to the four admission variables, two main interaction effects were discovered. There existed interaction both between the Life Experience Scale and the Problems Due to Drinking Scale and between the Motivation for Treat¬ ment Scale and the length of time drinking has been a
problem.
The interaction effect between the Life Experience Scale and Problems Due to Drinking Scale is shown graph¬ ically in Fig 1. The effect that varied life experiences have on staying in treatment is very different for the subgroup
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Table 2.—Bivariate Relationship of Five Treatment Variables to Length of Stay in Treatment*
S
2-6 Months
Middle Motivation (Score 3-8) High Motivation (Score O) Low Motivation (Score 13-24)
%of ¡n Class
Predictor Variable
1-4 Weeks
Class
Group I Contoct
c
Type
E
Deviations From Grand Mean
Sample
of treat¬
ment
Unadjusted
%
Adjusted
=
individual Individual
26
3.3
-1.54
-1.12
mostly Group mostly
43 70
5.5 9.0
.55
.37 .57
642 317 210 121
82.2 40.6 26.9 15.5
133 54 125 147 196 259 490 291
17.0 6.9 16.0 18.8 25.1 33.2 62.7 37.3
676
99 5
.48
Individual
only
- -1-1-1—
I
1-4
5-9
Length of time drinking
No medication
10 + a
problem (
in
years)
Type of medi¬ cation
Fig 2.—Interaction effect between Motivation for Treatment Scale length of time drinking has been a problem.
more
and
people who have had minimal problems with their drinking. Those with the least problems are more likely to remain in treatment if their life experiences are poor. However, the opposite is true for those with many prob¬ lems. The latter group are more likely to remain in treatment when their life experiences with alcohol are fairly good and to drop out when their life experiences of
become poorer. Similarly, the effect of varying lengths of drinking problems on time in treatment is different for those who are highly motivated than it is for other groups with lower motivation levels. The interaction between the Motivation for Treatment Scale and the length of time drinking has been a problem is graphically presented in Fig 2. For those with high or low motivation, the longer drinking has been a problem, the more likely it is that a person will stay in treatment up to a point. Once a person has more than nine years of problems due to drinking, the longer his problems persist, the less likely he is to stay in treatment. However, for those with medium motivation, the relationship between staying in treatment and the length of time drinking has been a problem is not curvilinear but linear. The longer drinking is a problem, the more likely the person is to drop out of treatment. Treatment Variables
Each of the treatment
variables, by itself, was able to explain a significant proportion of the variance (ie, signif¬ icant F values). The five treatment variables are as follows:
1. Type of treatment received (individual therapy only, group therapy mostly, individual therapy mostly, or equal amounts of group and individual therapy); 2. Type of medication received while in treatment, be it disulfiram (Antabuse), calcium carbimide (Temposil), seda¬ tives (of any kind), or tranquilizers (of any kind). Categories of this variable describe whether all or none of these drugs were prescribed, singly or in combination; 3. Treatment location (Toronto OPD, Hamilton, Ottawa,
London, or East Toronto); 4. Medical assessment (received medical assessment or did not receive medical assessment); 5. Profession of principal therapist (physician, nurse,
psychologist, psychiatrist).
Table 2 shows the bivariate relationship each predictor has with the dependent variable. Four fifths of the outpa-
One type only Two types Three or
Treatment location
Medical
as¬
sessment
types
East Toronto Hamilton London Ottawa Toronto OPD Yes No
-
.04
.03 -.35
-
-.29
.05 .33
.06 .23
.47
.41 .25
.33 .26 .37 -.25 .20
.20 -.45 -.02 .12
.08 .13
.13
86.6
.04
.01
12.7 0.6
-.29 -.29
-.01 -.42
.21
Physician/ nurse/
Profession of
principal therapist
social worker
Psychologist/ psychiatrist/ lay therapist Other
"Mean length of stay for entire treatment.
sample
2.89
or
about four weeks in
tients received individual treatment only. Approximately three fifths received medication while in treatment and underwent medical assessment. It is not surprising, then, that most patients had a physician or a nurse as their
principal therapist. Those patients whose treatment program had a medical orientation were most likely to remain in treatment, namely, those who underwent medical assessment and received medication and who had either a physician or nurse as their principal therapist. Patients were m.Qxe_ likely to stay in treatment when their treatment program was mainly one type, either individual treatment or group treatment, rather than an equal mixture of group and individual treatment. These five variables were checked for interaction effects. Only one important interaction was found—the double interaction of type of treatment and medical assess¬ ment, which was incorporated into a multicategory, "treat¬ ment" variable. Thus, multivariate analyses were per¬ formed on the combination variable treatment, type of medication, treatment location, and profession of principal
therapist.
Multivariate
Analyses
of Treatment Variables
Table 3 shows the four treatment variables in order of
importance in predicting dropouts from treatment. The first column gives the proportion of the total variance in dropping out of treatment accounted for by each predictor separately (r¡2). This measure includes the unique effect of the predictor, plus the effect of the predictor that results from its correlation with other predictors in the analysis. The adjusted value of v'1 is used instead of the unadjusted value in order that the variance
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explained
is
an
unbiased
Table 3—Multiple Classification Analysis of Four Treatment Variables Predicting Dropouts From Treatment*
Predictor Treatment
Type
Location of clinic Profession of princi¬
pal therapist
•Multiple
r1
(unadjusted)
.153
Variance .122
Shared Variance .031
.124
.055
.069
.082
.037
.045
.013
.000
n', Adjusted
of medication
=
Marginal Proportion of
.310; multiple
r*
(adjusted)
.013 =
.295.
estimate. The second and third columns break down the variance explained by each predictor into the total variance in the criterion that can be attributed solely to that particular variable (marginal proportion of variance explained) and the variance explained by this variable that is shared by the other predictors in the analysis (shared variance), respectively. The multiple r- provides an esti¬ mate of the total variance in the criterion explainable by the entire set of predictors operating together. The multiple r- is adjusted for degrees of freedom. In combination, the four treatment variables can account for approximately 30% of the variance in dropping out of treatment. The combination variable labeled treatment is shown to account for most of the variance. By itself, the combination variable treatment accounts for 15% of the variance, which is half of the total variance (30%) explained by the entire set of variables. Most (12.2%) of the variance that treatment can account for in predicting dropouts is unique as only 3.1% is shared with the other predictors. Type of medication accounts for 12.4% of the variance of which 5.5%' is unique. Location of clinic also explains 8.2% of the variance; early dropout was least common in clinics with a strong medical orientation (East Toronto, Hamil¬ ton) as opposed to those with psychological or social work orientations (London, Ottawa). The profession of the prin¬ cipal therapist accounted for only 1% of the variance, all of which was shared with other predictors. Little would be lost by eliminating this last variable from consideration. In summary, all treatment variables but one-profession of principal therapist-had unique explanatory power. Those dropping out early, ie, immediate or rapid dropouts, were more often patients who received treatment without a medical orientation (no medical assessment and no medication) who received either individual therapy or an equal amount of individual and group therapy, and who were treated at clinics with largely nonmedicai orienta¬ tions. COMMENT
The results of the multivariate analysis of dropout indicate that some admission and treatment variables are important predictors. Among admission variables, only four were significant: motivation for treatment, problems due to drinking, life experiences with alcohol, and length of time alcohol has been a problem. However, three of these variables had curvilinear relationships with dropout. Those most likely to stay in treatment had intermediate scores on life experiences with alcohol, length of drinking problems, and motivation for treatment. All of the treatment variables taken together explained 30% of the variance in dropout rates. Each of the
of treatment received, medication, treat¬ medical assessment, and profession of ther¬ location, apist—had a significant bivariate relationship to dropout; all but the last variable had unique explanatory power. Strong indications were found that medical as opposed to nonmedicai approaches led to lower dropout rate. Multivariate analyses also provide interesting informa¬ tion about what variables are least important. This study failed to find significant relationships between dropout and any demographic or drinking variable or the scales dealing with physical health, social stability, marital stabil¬ ity, drinking assessment, alcoholic involvement, attitudes to abstinence, resources, and isolation. The greatest depar¬ ture from earlier studies is the lack of significance for social stability and isolation." However, none of the earlier studies used a complex multivariate analysis, and many of them used much smaller samples than the 792 subjects used here. However, other studies' have found that poor motiva¬ tion measured in a variety of terms is associated with
variables—type ment
dropout. The findings concerning treatment variables related to dropout are more in keeping with expectations from earlier studies. This study found that patients remaining in treatment were more likely to have had a variety of medical interventions than those who dropped out early, eg, medication, medical assessment, and treatment in a medically oriented facility. In a study of several clinics, Gerard and Saenger3 found that alcoholics who had medical assessments and medication were more likely to stay in treatment. Both their results and
our own
suggest that
medical interventions for alcoholics could maintain alcoholics in treatment. This is somewhat contrary to the growing trend of involvement of nonprofessionals in the treatment of alcoholics and represents an expensive solution to the dropout problem. Perhaps true experimental studies where alcoholics are randomly assigned to medical and nonmedicai treatment should be done before a complete reversion of alcoholism treatment to the medical profession is made. A further problem is that perhaps because the patient population examined in this study was predominantly lower and lower middle class, medical management is most desired by them. Other approaches might be desired by alcoholics of higher social classes.
providing
more
References 1. Baekeland F, Lundwall L: Dropping out of treatment: A critical review. Psychol Bull 82:738-783, 1975. 2. Smart RG, Gray G: Minimal, moderate and long-term treatment for alcoholism: Addiction Research Foundation Substudy 742. Br J Addict, to be
published.
3. Gillies M, Laverty SG, Smart RG, et al: Outcomes in treated alcoholics: Patient and treatment characteristics in a one-year follow-up study. J Alcoholism 9:125-134, 1974. 4. Luborsky L, Chandler M, Auerback AH, et al: Factors influencing the outcome of psychotherapy: A review of quantitative research. Psychol Bull 75:145-185, 1971. 5. Gillies M, Aharan CH, Smart RG, et al: The Alcoholic Involvement Scale: A method of measuring change in alcoholics. J Alcoholism 10:142-147, 1975. 6. Baekeland F, Lundwall L, Shanahan TJ: Correlates of patient attrition in the outpatient treatment of alcoholism. J Nerv Ment Dis 157:99-107, 1973. 7. Sonquist JA, Baker EL, Morgan JN: Searching for Structure. Ann Arbor, Mich, Institute for Social Research, 1973. 8. Andrews FM, Morgan JW, Sonquist JA, et al: Multiple Classification Analysis. Ann Arbor, Mich, Institute for Social Research, 1967. 9. Gerard DL, Saenger G: Outpatient Treatment of Alcoholism. Toronto, University of Toronto Press, 1966.
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Multiple
Reginald G. Smart, PhD, Gaye Gray \s=b\ A common problem in treating alcoholics is the high dropout rate. Many studies have identified individual factors associated with dropout, eg, poor motivation and previous dropout. We believe the present study reports the first major effort to use multivariate analyses to predict dropout in a large (792), oneyear follow-up study of alcoholics, and examines the possibility that medical and nonmedical treatments lead to differential dropout rates. A multiple classification analysis technique showed that treatment variables as opposed to client characteristics were the best predictors of dropout. Patients remaining in treatment were more likely to have a variety of medical interventions, eg, medication and medical assessment, than those who dropped out. Results were similar to studies using other techniques and have interesting implications for the treatment of alcoholics, raising questions about current trends toward nonmedical treatment of alcoholism. (Arch Gen Psychiatry 35:363-367, 1978)
A keeping problem
for those treating alcoholics is them in treatment. Dropout rates for many types of treatment, eg, hospital treatment of medical conditions, methadone maintenance of heroin addiction, and general psychiatric outpatient care are higher1 than one would wish. However, the dropout rate for alcoholics getting outpatient care is also very high; anywhere between 52% and 75% of patients drop out by the fourth session. Much evidence suggests that alcoholics who stay in treatment for long periods have higher abstinence rates.1 We- found that matched groups of alcoholics staying in treatment more than one interview were more likely to be abstinent at follow-up than those having only one inter¬ view. Many alcoholics do not stay in treatment long enough to sample treatment adequately or decide on its value to them. Dropouts also derive less benefit from their treat¬ ment than those who persist. It is, therefore, important to understand as much as possible about their reasons for dropping out of treatment. Numerous studies have identified the characteristics of alcoholic dropouts from treatment, but a number of diffi¬ culties still exist. Some factors associated with dropout are social isolation, poor motivation for treatment, previous treatment dropouts, nonabstinence on admission, and field dependence, although some inconsistencies have been found in different studies. For example, some studies have found that social isolation and motivation are related to treatment outcome,1 but others have not.3 One extensive review of dropout studies4 has pointed out that these studies have typically not used multiple predictors and that constant
Accepted
for publication April 20, 1977. From the Addiction Research Foundation, Toronto. Reprint requests to Addiction Research Foundation, 33 Russell Toronto, Canada (Dr Smart).
St,
most "if they have more than one predictor do not include variables from both the patient and the therapist at the same time." This is particularly true of studies of dropouts from alcoholism treatment: most employ only one or a few predictors and tend to treat each separately. Most studies use only variables from the patient and not from the therapist or clinic. Without a multivariate approach, it is difficult to identify the separate or unique importance of variables. For example, poor motivation for treatment, social isolation, previous treatment dropout, and nonabsti¬ nence on admission are likely to be characteristic of the same patients. Analysis of each variable separately will not aid in identifying the unique contribution of each. The purpose of this study was to conduct a multivariate analysis of therapy and patient variables most associated with dropout and with staying in treatment. The method used is multiple classification analysis, which allows the identification of 1. How well all variables taken together explain dropout; 2. How each predictor and class of predictors (eg, patient and treatment related) considered separately relate to dropout when the others are included or removed; ie, which have unique explanatory power; 3. Whether dropout can be explained better by patient characteristics or by treatment experiences.
So
far, studies of this type of dropout from alcoholism answer these questions. The present study examined a variety of demographic, employment, social stability, and drinking variables as well as the location of treatment, type of treatment received, profession of therapist, medications received, and occurrence of a medical assessment. These aspects of treatment have not been able to
selected because they could be determined patients, even those who had only one or a few contacts. Other features of treatment such as number of treatments obtained would be correlated with the depen¬ dent variable (length of stay) and were not used here. A major interest was shown in examining the value of medical vs nonmedicai interventions. METHOD treatment
were
for all
The alcoholics included were being admitted for the first time for an alcohol problem (or were being readmitted after a one-year absence) to one of five alcoholism services in Ontario. All were seen at or soon after admission for an intensive interview. An indication of their degree of alcoholism was provided by their admission scores on the Alcoholic Involvement Scale. Only 15% of this group had scores at or below a mean level for social drinkers. The majority of clients reported having a high frequency of heavy drinking and loss of control over drinking habits, suffering negative effects (eg, blackouts, loss of jobs, craving), and feeling
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more
dependent on alcohol as a "coping" device, especially in social
situations.
Approximately 90% of those seeking treatment were inter¬ viewed. The remainder dropped out before the information required could be obtained in full. In all, 792 alcoholics were obtained from five treatment facilities. Of this group, roughly 80% were men.
Most were in their late 30s to mid 40s and
were
married
living with spouses. More than half had some high school or university education and were employed, many in skilled trades or in white collar jobs. In general, the sample was mostly middle and and
lower class alcoholics with almost no skid row persons. The treatment facilities covered a variety of therapies and approaches including individual and group psychotherapy, protec¬ tive drugs, tranquilizers and antidepressants, family therapy, day care, occupational therapy, and inpatient care. Not all facilities offered all types of treatment to all patients, and the patients' exposure to any treatment variable or set of variables was the result of a combination of clinical judgment, availability, and patient motivation. Patients were not randomly assigned to different treatment. The facilities offered a range of therapeutic persuasions: in one, individual therapy and medical management predominated; in another, group psychotherapy; in another, family therapy; and in another, a mixture of individual and group approaches. Admission interviews provided information about the patient during the pretreatment phase. Items included were demographic characteristics, eg, age, sex, employment; drinking habits according to the Alcoholic Involvement Scale—a reliable and valid measure of drinking''; a variety of scales covering 14 dimensions; ie, physical health, drug use, social stability, marital stability, drinking assessment, problems due to drinking, attitudes toward abstinence, motivation for treatment, patient's resources (people), resources in terms of interests and activities, isolation from such interests and activities, isolation (people), an assessment of life conditions, and a measure of patient self-satisfaction. All of these scales have been devised so that correlations of each item with its total scale score are high and intercorrelations among total scale scores are low. The scoring for all scales is consistent: the higher the score, the worse the status on the dimension being measured. A record was also kept of certain conditions pertaining to each patient's treatment. The relevant items were the location of the clinic and, hence, the orientation of the treatment staff since this varied from clinic to clinic; the type of treatment received, eg, individual, group, or family therapy; the occurrence of a medical assessment; the type and dosage of medications, if any were administered; and the profession of the patient's principal thera¬
pist.
The dependent variable, length of stay in treatment, was categorized according to the method used by Baekeland and associates": Immediate visit.
Dropouts.-Those
who did not return after the first
Rapid Dropouts.-Those who dropped out after one to four weeks of treatment. Slow Dropouts.-Those who dropped out during the second to sixth month of treatment. Clinic Attenders.-Those who attended for seven months or more.
In this particular sample of outpatients, there were 96 imme¬ diate dropouts, 116 rapid dropouts, 379 slow dropouts, and 201 clinic attendere. RESULTS Selection of Variables
The identification of variables associated with treat¬ dropout was achieved by the joint use of two multivariate techniques: automatic interaction detector (AID) and multiple classification analysis (MCA). No detailed description of the AID analysis will be given ment
a full explanation of AID see Sonquist et al.7) For our purposes, AID was used to prepare the data for the MCA. Although the MCA allows predictor variables that may be measured on nominal, ordinal, or interval scales, they must be categorized, preferably with not more than six categories. The problem lies in categorizing to give the maximum amount of variance explained by the variable, ie, the largest reduction in predictive error. The AID tech¬ nique partitioned the sample into a series of nonoverlapping subgroups whose means explained more variance than any other competing partition. Using the guidelines of the AID program, predictors with numerous classes (greater than seven) were reduced for the MCA purposes. Multiple classification analysis is described in considerable detail by Andrews et al." Basically, it is a technique for examining the interrelation¬ ships between a set of predictor variables and a dependent variable. It uses multiple regression with dummy variables. A series of MCAs were carried out, beginning with all reported predictors and gradually eliminating predictors in order to find the smallest set of admission and treatment variables that would account for maximum variance in explaining dropouts. Variables were eliminated on the basis of F tests. Nonsignificant F-test results indicated that the predictor in question was not able to explain, by itself, a significant proportion of the variance and, there¬ fore, was not important enough to retain for further
here. (For
analyses.
Admission Variables
Only
four of the admission variables had
values:
significant
F
Motivation for Treatment Scale.—This six-item scale, with ranging from 0 to 24, measures what behavioral changes and what treatment measures a client is willing to undergo to ameliorate an alcohol problem. Each item correlates with the total scale score upwards from .43 and the largest (absolute value) interscale correlation is .32 scores
with the Problems Due to Problems Due to
Drinking Scale.
Drinking Scale.—This scale also contains
six items and examines how frequently a client's drinking leads to daily living problems with family members, friends, work, the law, finances, and health. Again, scores range from 0 to 24, and item-total scale correlatons range upwards from .56. This scale correlates .56 with the Assess¬ ment of Life Experiences Scale, .72 with the Alcoholic Involvement Scale, and .63 with the Drinking Assessment Scale. Assessment of Life Experiences Scale.—This scale contains four items (scores from 0 to 16) that examine a client's degree of satisfaction with four dimensions of daily life: accommodation, work experience, finances, and actively meaningful relationships with others. Item-total scale score correlations are above .55 while interscale correla¬ tions are below .62. The Length of Time.-Number of years that the patient feels drinking has been a problem. The admission variables that were not significant (with < .05) included 1. All demographic variables (sex, age, patient status, marital status, education, religion, rural/urban childhood,
employment status, present occupation);
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Table 1.—Bivariate Relationship of Four Admission Variables to Length of Stay in Treatment* %of
Sample
Deviations From Grand Mean
in Class
Predictor Variable Motivation for treat¬ ment
scale Problems due to
drinking scale
Life
experi¬
ence
scale Time drink¬
ing a problem,
a)
Class Score 0
68
1 to 2 3 to 8
9 to 13 to Score Oto 9 to
12 24 8
14 15 to 19 20 to 24
Score 0 3 6 12
80
395 173 66 309 308 134 31
to 2
42
to 5
223 433 84 41 215
to 11 to 16 < 1 1 to 4 5 to 9 > 10
174
352
% 8.7 10.2 50.5 22.1 8.4 39.5 39.4 17.1 4.0 5.4 28.5 55.4 10.8 5.2 27.5 22.3 45.0
"Mean length of stay for entire sample
=
Unadjusted -.068 .121 .091 -.123 -.300 .105 -.031 -.093 -.343 .177 -.035 .079 .225 .379 .034 .097 -.025
2.89
or
Adjusted -.009 .145
.099
-.167 -.320 .185 -.073 -.179 -.345
i
° .c
2-6 Months 1-4 Weeks
Least .
Most
.
problems with drinking
problems
with
drinking
>
I Contact'
-1-1Good Poor
•247
-.082
.095 -.151 352
Life experience scale
Fig 1.—Interaction effect between Life Problems Due to Drinking Scale.
Experience Scale and
-.012
.994 001
about four weeks In
treatment.
2. Five
.£
drinking variables—(age at regular drinking, length
of drinking career, usual beverage, type of drinker, average alcohol consumption); 3. Eleven of the 14 scales covering physical health, drug abuse, social stability, marital stability, drinking assess¬ ment, alcoholic involvement, attitudes toward abstinence, patient's resources (people), resources in terms of interests and activities, isolation from such interests and activities, and isolation (people).
For every subclass, the MCA computes two coefficients. The first coefficient is the amount by which the mean value of the dependent variable calculated for observations in that subclass deviates from the grand mean for the whole sample. This coefficient is given in Table 1 as the unadjusted deviation from the mean and indicates to what extent persons in a given subclass are below or above the average stay in treatment for this sample. The unadjusted deviations show the simple bivariate relationship between the dependent and independent variables. The second coefficient is the adjusted deviation from the grand mean, and represents the deviation of each class mean from the grand mean while controlling for the effects of all other variables in the analysis. For the entire sample, the mean length of time in treatment was approximately one month. For every admission variable, except the Problems Due to Drinking Scale, there exists a curvilinear relationship between the dependent variable and the predictors. This means that persons most likely to drop out from treatment have scores on these three admission variables at either extreme. The early dropouts either had problems with their drinking for less than one year or for more than ten years. Their life experiences with alcohol have either been good (score 0 to 2) or extremely poor (score 12 to 16); they are either very highly motivated or have virtually no motiva¬ tion to change their drinking behavior. Those most likely to stay in treatment have scores on the three variables somewhere in the middle range. Clinic attenders have had problems with their drinking for one to nine years but, although still fairly motivated to change drinking behavior and to take the necessary treatment (score or motivation scale of 1 to 8), they have had at some
time poor drinking experiences (score on life experience scale of 6 to 11). The relationship between problems due to drinking and staying in treatment is straightforward. The more prob¬ lems a person experienced due to his drinking behavior, the less likely he is to stay in a treatment program. Although (for some classes of all predictors) the adjusted deviations show the same patterned relationship to the criterion dimension as do the unadjusted deviations, the difference between the unadjusted and adjusted devia¬ tions is great enough to suspect that the variables are intercorrelated. For example, the differences in length of stay in treatment are not explainable in terms of just the Motivation for Treatment Scale only, but in terms of the other intake variables. Multivariate
Analyses
of Admission Variables
The MCA model assumes that the effect of the predictor variables combine additively without interactions among predictors. A procedure using the MCA allowed the researcher to detect and eliminate predictor interactions. The strategy is outlined as follows: For the variables suspected of interacting, a single variable was created that contained a category for each possible combination of the set of variables. The new combination variable contained all the main additive effects plus the effects due to interaction. A one-way analysis of variance performed on the combination variable resulted in an if measure. This measure indicates the amount of variance that can be accounted for by both the main and interaction effect. This figure compares with the multiple r- from an MCA run that uses the set of suspected variables separately. Differ¬ ence between r2 and n- is attributable to interaction. When this strategy for detecting interaction was applied to the four admission variables, two main interaction effects were discovered. There existed interaction both between the Life Experience Scale and the Problems Due to Drinking Scale and between the Motivation for Treat¬ ment Scale and the length of time drinking has been a
problem.
The interaction effect between the Life Experience Scale and Problems Due to Drinking Scale is shown graph¬ ically in Fig 1. The effect that varied life experiences have on staying in treatment is very different for the subgroup
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Table 2.—Bivariate Relationship of Five Treatment Variables to Length of Stay in Treatment*
S
2-6 Months
Middle Motivation (Score 3-8) High Motivation (Score O) Low Motivation (Score 13-24)
%of ¡n Class
Predictor Variable
1-4 Weeks
Class
Group I Contoct
c
Type
E
Deviations From Grand Mean
Sample
of treat¬
ment
Unadjusted
%
Adjusted
=
individual Individual
26
3.3
-1.54
-1.12
mostly Group mostly
43 70
5.5 9.0
.55
.37 .57
642 317 210 121
82.2 40.6 26.9 15.5
133 54 125 147 196 259 490 291
17.0 6.9 16.0 18.8 25.1 33.2 62.7 37.3
676
99 5
.48
Individual
only
- -1-1-1—
I
1-4
5-9
Length of time drinking
No medication
10 + a
problem (
in
years)
Type of medi¬ cation
Fig 2.—Interaction effect between Motivation for Treatment Scale length of time drinking has been a problem.
more
and
people who have had minimal problems with their drinking. Those with the least problems are more likely to remain in treatment if their life experiences are poor. However, the opposite is true for those with many prob¬ lems. The latter group are more likely to remain in treatment when their life experiences with alcohol are fairly good and to drop out when their life experiences of
become poorer. Similarly, the effect of varying lengths of drinking problems on time in treatment is different for those who are highly motivated than it is for other groups with lower motivation levels. The interaction between the Motivation for Treatment Scale and the length of time drinking has been a problem is graphically presented in Fig 2. For those with high or low motivation, the longer drinking has been a problem, the more likely it is that a person will stay in treatment up to a point. Once a person has more than nine years of problems due to drinking, the longer his problems persist, the less likely he is to stay in treatment. However, for those with medium motivation, the relationship between staying in treatment and the length of time drinking has been a problem is not curvilinear but linear. The longer drinking is a problem, the more likely the person is to drop out of treatment. Treatment Variables
Each of the treatment
variables, by itself, was able to explain a significant proportion of the variance (ie, signif¬ icant F values). The five treatment variables are as follows:
1. Type of treatment received (individual therapy only, group therapy mostly, individual therapy mostly, or equal amounts of group and individual therapy); 2. Type of medication received while in treatment, be it disulfiram (Antabuse), calcium carbimide (Temposil), seda¬ tives (of any kind), or tranquilizers (of any kind). Categories of this variable describe whether all or none of these drugs were prescribed, singly or in combination; 3. Treatment location (Toronto OPD, Hamilton, Ottawa,
London, or East Toronto); 4. Medical assessment (received medical assessment or did not receive medical assessment); 5. Profession of principal therapist (physician, nurse,
psychologist, psychiatrist).
Table 2 shows the bivariate relationship each predictor has with the dependent variable. Four fifths of the outpa-
One type only Two types Three or
Treatment location
Medical
as¬
sessment
types
East Toronto Hamilton London Ottawa Toronto OPD Yes No
-
.04
.03 -.35
-
-.29
.05 .33
.06 .23
.47
.41 .25
.33 .26 .37 -.25 .20
.20 -.45 -.02 .12
.08 .13
.13
86.6
.04
.01
12.7 0.6
-.29 -.29
-.01 -.42
.21
Physician/ nurse/
Profession of
principal therapist
social worker
Psychologist/ psychiatrist/ lay therapist Other
"Mean length of stay for entire treatment.
sample
2.89
or
about four weeks in
tients received individual treatment only. Approximately three fifths received medication while in treatment and underwent medical assessment. It is not surprising, then, that most patients had a physician or a nurse as their
principal therapist. Those patients whose treatment program had a medical orientation were most likely to remain in treatment, namely, those who underwent medical assessment and received medication and who had either a physician or nurse as their principal therapist. Patients were m.Qxe_ likely to stay in treatment when their treatment program was mainly one type, either individual treatment or group treatment, rather than an equal mixture of group and individual treatment. These five variables were checked for interaction effects. Only one important interaction was found—the double interaction of type of treatment and medical assess¬ ment, which was incorporated into a multicategory, "treat¬ ment" variable. Thus, multivariate analyses were per¬ formed on the combination variable treatment, type of medication, treatment location, and profession of principal
therapist.
Multivariate
Analyses
of Treatment Variables
Table 3 shows the four treatment variables in order of
importance in predicting dropouts from treatment. The first column gives the proportion of the total variance in dropping out of treatment accounted for by each predictor separately (r¡2). This measure includes the unique effect of the predictor, plus the effect of the predictor that results from its correlation with other predictors in the analysis. The adjusted value of v'1 is used instead of the unadjusted value in order that the variance
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explained
is
an
unbiased
Table 3—Multiple Classification Analysis of Four Treatment Variables Predicting Dropouts From Treatment*
Predictor Treatment
Type
Location of clinic Profession of princi¬
pal therapist
•Multiple
r1
(unadjusted)
.153
Variance .122
Shared Variance .031
.124
.055
.069
.082
.037
.045
.013
.000
n', Adjusted
of medication
=
Marginal Proportion of
.310; multiple
r*
(adjusted)
.013 =
.295.
estimate. The second and third columns break down the variance explained by each predictor into the total variance in the criterion that can be attributed solely to that particular variable (marginal proportion of variance explained) and the variance explained by this variable that is shared by the other predictors in the analysis (shared variance), respectively. The multiple r- provides an esti¬ mate of the total variance in the criterion explainable by the entire set of predictors operating together. The multiple r- is adjusted for degrees of freedom. In combination, the four treatment variables can account for approximately 30% of the variance in dropping out of treatment. The combination variable labeled treatment is shown to account for most of the variance. By itself, the combination variable treatment accounts for 15% of the variance, which is half of the total variance (30%) explained by the entire set of variables. Most (12.2%) of the variance that treatment can account for in predicting dropouts is unique as only 3.1% is shared with the other predictors. Type of medication accounts for 12.4% of the variance of which 5.5%' is unique. Location of clinic also explains 8.2% of the variance; early dropout was least common in clinics with a strong medical orientation (East Toronto, Hamil¬ ton) as opposed to those with psychological or social work orientations (London, Ottawa). The profession of the prin¬ cipal therapist accounted for only 1% of the variance, all of which was shared with other predictors. Little would be lost by eliminating this last variable from consideration. In summary, all treatment variables but one-profession of principal therapist-had unique explanatory power. Those dropping out early, ie, immediate or rapid dropouts, were more often patients who received treatment without a medical orientation (no medical assessment and no medication) who received either individual therapy or an equal amount of individual and group therapy, and who were treated at clinics with largely nonmedicai orienta¬ tions. COMMENT
The results of the multivariate analysis of dropout indicate that some admission and treatment variables are important predictors. Among admission variables, only four were significant: motivation for treatment, problems due to drinking, life experiences with alcohol, and length of time alcohol has been a problem. However, three of these variables had curvilinear relationships with dropout. Those most likely to stay in treatment had intermediate scores on life experiences with alcohol, length of drinking problems, and motivation for treatment. All of the treatment variables taken together explained 30% of the variance in dropout rates. Each of the
of treatment received, medication, treat¬ medical assessment, and profession of ther¬ location, apist—had a significant bivariate relationship to dropout; all but the last variable had unique explanatory power. Strong indications were found that medical as opposed to nonmedicai approaches led to lower dropout rate. Multivariate analyses also provide interesting informa¬ tion about what variables are least important. This study failed to find significant relationships between dropout and any demographic or drinking variable or the scales dealing with physical health, social stability, marital stabil¬ ity, drinking assessment, alcoholic involvement, attitudes to abstinence, resources, and isolation. The greatest depar¬ ture from earlier studies is the lack of significance for social stability and isolation." However, none of the earlier studies used a complex multivariate analysis, and many of them used much smaller samples than the 792 subjects used here. However, other studies' have found that poor motiva¬ tion measured in a variety of terms is associated with
variables—type ment
dropout. The findings concerning treatment variables related to dropout are more in keeping with expectations from earlier studies. This study found that patients remaining in treatment were more likely to have had a variety of medical interventions than those who dropped out early, eg, medication, medical assessment, and treatment in a medically oriented facility. In a study of several clinics, Gerard and Saenger3 found that alcoholics who had medical assessments and medication were more likely to stay in treatment. Both their results and
our own
suggest that
medical interventions for alcoholics could maintain alcoholics in treatment. This is somewhat contrary to the growing trend of involvement of nonprofessionals in the treatment of alcoholics and represents an expensive solution to the dropout problem. Perhaps true experimental studies where alcoholics are randomly assigned to medical and nonmedicai treatment should be done before a complete reversion of alcoholism treatment to the medical profession is made. A further problem is that perhaps because the patient population examined in this study was predominantly lower and lower middle class, medical management is most desired by them. Other approaches might be desired by alcoholics of higher social classes.
providing
more
References 1. Baekeland F, Lundwall L: Dropping out of treatment: A critical review. Psychol Bull 82:738-783, 1975. 2. Smart RG, Gray G: Minimal, moderate and long-term treatment for alcoholism: Addiction Research Foundation Substudy 742. Br J Addict, to be
published.
3. Gillies M, Laverty SG, Smart RG, et al: Outcomes in treated alcoholics: Patient and treatment characteristics in a one-year follow-up study. J Alcoholism 9:125-134, 1974. 4. Luborsky L, Chandler M, Auerback AH, et al: Factors influencing the outcome of psychotherapy: A review of quantitative research. Psychol Bull 75:145-185, 1971. 5. Gillies M, Aharan CH, Smart RG, et al: The Alcoholic Involvement Scale: A method of measuring change in alcoholics. J Alcoholism 10:142-147, 1975. 6. Baekeland F, Lundwall L, Shanahan TJ: Correlates of patient attrition in the outpatient treatment of alcoholism. J Nerv Ment Dis 157:99-107, 1973. 7. Sonquist JA, Baker EL, Morgan JN: Searching for Structure. Ann Arbor, Mich, Institute for Social Research, 1973. 8. Andrews FM, Morgan JW, Sonquist JA, et al: Multiple Classification Analysis. Ann Arbor, Mich, Institute for Social Research, 1967. 9. Gerard DL, Saenger G: Outpatient Treatment of Alcoholism. Toronto, University of Toronto Press, 1966.
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