I health-related quality of life (HRQOL) has emerged as

0145-6008/97/2105-0899$03.00/~ Vol. 21, No. 5 August 1997 ALCOHOLISM: CLINICAL AND EXPERIMENTAL RESEARCH Alcohol Use Disorders, Consumption Pattern...
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Vol. 21, No. 5 August 1997


Alcohol Use Disorders, Consumption Patterns, and Health-Related Quality of Life of Primary Care Patients Robert J. Volk, Scott B. Cantor, Jeffrey R. Steinbauer, and Alvah R. Cass

This study examined the association of alcohol use disorders and consumption patterns with various dimensions of Health-Related Quality of Life (HRQOL) in primary care patients, as measured by the SF-36 Health Survey. A probability sample of 1333 primary care patients completed the Alcohol Use Disorder and Associated Disabilities Interview Schedule to determine the presence of alcohol abuse or dependence disorders, and answeredquestions about patterns of alcohol consumption. Physical and Mental Health Component Summaries and primary scales of the SF-36 were used as measures of HRQOL. Patients meeting criteria for alcohol dependence scored lower (poorer HRQOL) on the Mental Health Component Summary and each primary scale of the SF-36, whereas no differences were observed for alcohol abusers compared with patients not meeting criteria for a disorder. The association of alcohol dependence with diminished mental health functioning was mediated by its co-occurrence with mood and anxiety disorders. Patients who drank in a Frequent, Low-Quantity pattern generally had better overall HRQOL than patients from other consumption groups. Binge drinkers and Frequent, High-QuantityDrinkers showed markedly lower scores in the areas of Role Functioning and Mental Health. In contrast to recent studies of mental health problems in primary care, alcohol use disorders and consumption patterns seem to have a modest impact on patients’ HRQOL. These effects, though, vary by dimension of functioning, the presence of alcohol dependence rather than abuse, and pattern of alcohol consumption. Global measures of HRQOL such as the SF-36 HealthSurvey may provide important indicators of treatment effectiveness in primary care interventionstudies for patients with drinking problems. Key Words: Health-Related Quality of Life, Alcohol Abusemependence, Primary Care.


THIS era of consumer-based health care evaluation, health-related quality of life (HRQOL) has emerged as an important indicator of treatment effectiveness in outcomes research.’*2The definition of health has been broadFrom the Department of Family Medicine (R.J.V., J.RS., A.R.C.), the University of Terns Medical Branch at Galveston, Galveston, Texas; and the Section of General Internal Medicine, Department of Medical Specialties (S.B.C.), the University of Texas M. D. Anderson Cancer Center, Houston, Texas. Received for publication September 26, 1996; accepted February 21, I997 This study was supported by grants from the National Institute on Alcohol Abuse and Alcoholism (AA09496), and the Bureau of Health Professions, Health Resources and Services Administration (D32-PE16033 and D32PE10158-01). A version of this paper was presented at the 1996 Research Society on Alcoholism Scientific Meeting, Washingion, D.C., June 24, 1996. Reprint requests: Robert J. Volk, Ph.D., Department of Family Medicine, the University of Texas Medical Branch, 301 University Boulevard, Galveston, TX 77555-I 123. Copyright 0 1997 by The Research Society on Alcoholism. Alcohol Clin Erp Res, Vol21, No 5, 1997 pp 899-905

ened by the World Health Organization to include not only as the absence of disease, but as a state of complete physical and social well-being. This change in conceptualization of health has led to research emphasizing the outcomes of health care, where direct measures of disease process (“hard” endpoints) are complemented by consideration of how treatment affects patients’ quality of life, because such outcomes may be those of greatest interest to patients. Improvement in personal health and social functioning has also been identified as outcome criteria for evaluating the effectiveness of treatment for addiction^.^ Longabaugh et a1.4 have offered the following definition of quality of life in the context of alcohol abuse treatment: ‘‘Quality of life can be viewed as the totality of characteristics of the way of life of an individual or group with particular reference to (1) clinical status with respect to substance use, (2) problems specific to the disorder, and (3) generic health measures focusing on general functioning and health perceptions usually valued regardless of a person’s age, or health state.” Reductions in alcohol consumption have often been the principal goal of primary care treatment studies, where interventions for hazardous or problem drinking have been e v a l ~ a t e d . ~Yet, - ~ the association between alcohol consumption and HRQOL is not well understood. In the primary care setting, where alcohol problems are common, the association of HRQOL and alcohol use problems is complicated by physical comorbidity and the high co-occurrence of alcohol use disorders with other mental health problems.9-’’ The unique association of alcohol use problems is particularly difficult to estimate without concurrent assessment of physical and psychiatric comorbidities. With the growing emphasis on the outcomes of health care for patients has come a need for the development of quality of life instruments. Numerous measures of quality of life have been developed as indicators of global functioning and disease-specific functioning.’* Of the global measures, the SF-36 Health Survey has been among the most widely used and well validated in clinical The SF-36 Health Survey is a self-report measure of general health status in eight areas: Physical Functioning, Role Functioning, Bodily Pain, General Health, Vitality, Social Functioning, Role-Emotional Functioning, and Mental Health. These eight health concepts were selected as representing those most frequently measured in health surveys 899



and as most sensitive to treatment-related change in functioning. The developers of the SF-36 recommend it be used as a “generic core” battery of measures for comparison with other studies and patient populations, as opposed to serving as the principal outcome measure in treatment studies. Few studies have examined SF-36 health profiles for alcohol use disorders, whether among patient or nonpatient p~pulations.’~~’~ Such information is particularly important in planning outcome studies, because potential treatment effects must be considered against baseline functioning. Furthermore, because reductions in alcohol consumption are often the primary indicators of treatment efficacy in primary care-based intervention studies, the association of alcohol consumption with HRQOL should be more carefully explored. This study examined the impact of alcohol use disorders and patterns of alcohol consumption by primary care patients on their HRQOL, as measured by the SF-36 Health Survey. METHODS This study represents a secondary analysis of data from a study comparing the efficacy of alcoholism screening instruments when used with primary care patients. Subjects for this study included a probability sample of 1333 adult primary care patients presenting to the Family Practice Center of the University of Texas Medical Branch, located in Galveston, TX.Minority and female patients were oversampled, due to the lower prevalence of alcohol use disorders among women, and proportionately fewer minority patients in the clinic patient base. Details of the sampling plan and procedures can be found elsewhere.I6 Before scheduled office visits, subjects completed self-administered questionnaires, including sociodemographic indicators, questions on cigarette use, and the SF-36 Health Survey. After office visits, subjects participated in a diagnostic interview, including questions on alcohol consumption over the past 30 days. Informed, written consent was obtained, and subjects were compensated $10 for participation. Characteristics of the sample are given in Table 1.


SF-36 Health Survey. As described previously, the SF-36 is a well-

known, validated, and standardized general measure of HRQOL.I3 The instrument measures the respondent’s subjective impression of healthrelated functioning in eight areas. Scale labels and sample items are given in Table 2. Norms for many health conditions are a~ailable.’~ The instrument is meant to be self-administered,as was done in this study. Recent scoring developments include derivation of the Physical Component Summary (PCS) and the Mental Health Component Summary (MCS),from orthogonal principal components analysis of the eight scale^.'^ The summary components have the advantage of higher reliability than the individual scales and are standardized as T-scores (mean of 50 and standard deviation of 10) to aid in interpretation. For each scale, a higher score indicates better functioning. Alcohol Use Disorders. The Alcohol Use Disorder and Associated Disabilities Interview Schedule (AUDADIS) was used as the diagnostic schedule in this study. The AUDADIS is a structured diagnostic interview schedule developed for use in the National Longitudinal Alcohol Epidemiologic Survey (NLAES)initiated by the National Institute on Alcohol Abuse and Alcoholism in 1992, and is designed to be administered by nonclinicians.18The test-retest reliability of the AUDADIS for alcohol use disorders is e~cellent.’~ In this study, we used DSM-IV criteria for alcohol abuse and alcohui dependence in the past year.”

Table 1. Sample Characteristics Gender Males Females Age Range Mean (SD) Ethnicity White African-American Mexican-American Daily cigarette use Among all patients Among patients with an Alcohol Abuse Disorder Among patients with an Alcohol Dependence Disorder Alcohol Use Disorder (past year) No Disorder Abuse Dependence Alcohol consumption patterns* Abstainer Infrequent Drinker Less Frequent, Low-Quantity Less Frequent, High-Quantity Frequent, Low-Quantity Frequent, High-Quantity * See text for definitions of

399 (29.9%) 934 (70.1%) 18-86 43.2 (15.7) 512 (38.4%) 471 (35.3%) 350 (26.3%) 382 25 54

(28.7%) (52.1%) (52.4%)

1182 (88.7%) 48 (3.6%) 103 (7.7%) 435 (32.6%) 349 (26.2%) 242 (18.2%) 37 (2.8%) 198 (14.9%) 72 (5.4%)

alcohol consumption groups.

Alcohol Consumption Patterns. A combined measure of quantity and frequency of alcohol use was adapted from definitions used by Cahalan et alF1 and Cherpitel” as follows: Abstainers, never more than 12 drinks of any kind of alcohol in a given year; Znfiquent Drin&ers, did not drink in the past 30 days and did not meet criteria for an abstainer; Less Frequent, [email protected] Drinkers, drank 5 days in the past 30 days and drank 5 or more drinks/episode. Psychiatric [email protected] The Primary Care Evaluation of Mental Disorders (PRIME-MD) is a recently developed and validated diagnostic schedule for the diagnosis of common mental disorders in the primary care setting.= We administered the Mood and Anxiety Disorder Modules in this study, each yielding diagnostic criteria consistent with DSM-111-R. Although the PRIME-MD was originally developed for administrationby primary care physicians, we administered the modules using trained lay interviewers. The highly structured interview format, lack of physician judgment in assessment of mood and anxiety symptoms, and consistency of our prevalence estimates with those observed in the PRIME-MD loo0 studyL3 suggested this approach was acceptable. Physical Comorbidify. We developed a measure of physical comorbidity based on a simple count of chronic health problems, using PCS scores as the criterion measure. Our approach was similar to Deyo et al.’~’~ modification of an index developed by Charlson et al.= with hospitalized patients. Problem lists from patients’ medical records were reviewed for chronic health problems present before or diagnosed at the time of the study visit. Diagnostic clusters were identified using ICD-9-CM codes, representing the common chronic health problems seen in primary care patients, including an “other” category for rarer but important problems. Separate analyses suggested a ceiling affect at three chronic diagnostic clusters. The range of scores was from “0” (no chronic health problems) to “3” (three or more chronic health problems). Data Analysis

Conventions set forth in previous studies using the SF-36 were used to guide the analyses and presentation of the r e s ~ l t s . ’ ~ ~Results ~ ~ * ’ ~are presented first for alcohoi use disorders and then for consumption pat-


901 Table 2 SF-36 Scales, Components, and

Sample Items SF-36 Scales’





Physical Functioning (10 items) “lifting or carrying groceries” Role Functioning-F‘hysical (4 items) “limited in the kind of work or other activities. . .” Bodily Pain (2 items) “How much bodily pain have you had during the past 4 weeks?” General Health (5 items) “In general, would you say your health is. . . .” Vitality (4 items) “Did you feel tired?” Social Functioning (2 items) ”. . .health or emotional problems interfered with your normal social activities. . . .” Role Functioning-Emotional (3 items) ”. . .problems with your work or other regular daily activities as a result of any emotional problems. . . .” Mental Health (5 items) “.. .have you felt downhearted and blue?” SF-36 Component Summaries7


Physical Health Component Summary Mental Health Component Summary

The SF-36 Health Survey is copyrighted by the Medical Outcomes Trust. Scales are scored on a 0 to 100 scale, with higher scores indicating better health and functioning. t Components derived from orthogonal principal components analysis, using a T-score transformation (mean of 50, SD of 10).Higher scores indicate better health and functioning. Table 3. SF-36 Component Means, by Current Alcohol Use Disorders

terns. The strategy was to consider initially the SF-36 components, because they represent summary measures of mental and physical health, and then examine in more detail the eight scales to identify domains of functioning impacted by alcohol use problems. First, analysis of variance was used to examine PCS and MCS scores for patients grouped by diagnosis: those not meeting criteria for an alcohol use disorder (No Disorder), those meeting criteria for Alcohol Abuse, and those meeting criteria for Alcohol Dependence. Means were adjusted for covariates, including age, sex, race, and daily cigarette use, using least-squaresregression (least-squares means are reported in the tables). Daily cigarette use was included as a covariate due to the association of cigarette and alcohol use, and the potential health burden of regular cigarette use. Contrasts were specified, and significancetests reported. Analyses were rerun with physical comorbidity as an additional covariate and compared with the previous results. Given the high co-occurrence of alcohol use disorders with mood and anxiety we used hierarchical linear regression to investigate how the effect of alcohol use disorders on MCS might be moderated by their co-occurrence with mood/anxiety disorders. Alcohol use disorders were entered first into the regression equation, followed by the relevant covariates, and then the mood and anxiety disorder variables. By modeling the relationship in this way, it was possible to examine change in the magnitude of the regression coefficients for the alcohol use disorder variables before and after adjustments for mental health disorders cooccurring with alcohol use disorders. We then plotted least-squares SF-36 scale means for the three diagnostic groups. The second series of analyses examined the association of alcohol consumption patterns with SF-36 scores. Again, analysis of variance was used to estimate PCS and MCS means for each of the alcohol consumption groups, adjusted for the same covariates mentioned above. Contrasts with Lifetime Abstainers serving as the comparison group were specified and significancetests reported. We then used multiple linear regression to estimate average differences between Lifetime Abstainers and patients in the other alcohol consumption groups, for each SF-36 scale. These regressions were adjusted for the same covariates previously mentioned. Analyses were also rerun with physical comorbidity as an additional covariate and then compared with the previous results.


No Disorder

Alcohol Abuse

Alcohol Dependence





43.1 40.5

Note: Componentsare in units of T-scores, where the mean is 50 and SD is 10. Means are adjusted for sex, age, racdethnicity, and cigarette use. Contrast with Alcohol Dependence significant at p < 0.001. Main effect for PCS not significant.


Alcohol Use Disorders and SF-36 Component Summaries Table 3 gives PCS and MCS adjusted means for the three alcohol use disorder groups: No Disorder, Alcohol Abuse, and Alcohol Dependence. PCS was not statistically significantly different across alcohol use disorder groups, whereas No Disorder and Alcohol Abuse patients scored significantly higher on MCS than Alcohol Dependence patients (a difference of slightly

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