Transitions In and Out of Alcohol Use Disorders: Their Associations with Conditional Changes in Quality of Life Over a 3-Year Follow-Up Interval

doi:10.1093/alcalc/agn094

Alcohol Alcohol. 2009 Jan-Feb; 44(1): 84–92.

Published online 2008 November 28. doi: 10.1093/alcalc/agn094

PMCID: PMC2605522

Transitions In and Out of Alcohol Use Disorders: Their Associations with Conditional Changes in Quality of Life Over a 3-Year Follow-Up Interval^†

Deborah A. Dawson,¹^* Ting-Kai Li,² S. Patricia Chou,¹ and Bridget F. Grant¹

¹Laboratory of Biometry and Epidemiology, Division of Intramural Clinical and Biological Research, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA and

²Office of the Director, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, USA

^*Corresponding author: NIAAA/LEB, 5635 Fishers Lane, Room 3071, MSC 9304, Bethesda, MD 20892-9304, USA. Tel: Phone: +1-301-435-2244; Fax: +1-301-443-1400; E-mail: ddawson/at/mail.nih.gov

^†The views and opinions expressed in this paper are those of the authors and should not be construed to represent the views of any of the sponsoring organizations, agencies or the US government.

Received July 21, 2008; Revisions requested September 24, 2008; Revised October 6, 2008; Accepted October 22, 2008.

This article has been cited by other articles in PMC.

Abstract

Aims: The aim of this study was to investigate longitudinal changes in quality of life (QOL) as a function of transitions in alcohol use disorders (AUD) over a 3-year follow-up of a general US population sample. Methods: The analysis is based on individuals who drank alcohol in the year preceding the Wave 1 National Epidemiologic Survey on Alcohol and Related Conditions and were reinterviewed at Wave 2 (n = 22,245). Using multiple linear regression models, changes in SF-12 QOL were estimated as a function of DSM-IV AUD transitions, controlling for baseline QOL and multiple potential confounders. Results: Onset and offset of AUD were strongly associated with changes in mental/psychological functioning, with significant decreases in mental component summary (NBMCS) scores among individuals who developed dependence and significant increases among those who achieved full and partial remission from dependence. The increases in overall NBMCS and its social functioning, role emotional and mental health components were equally great for abstinent and nonabstinent remission from dependence, but improvements in bodily pain and general health were associated with nonabstinent remission only. Onset of abuse was unrelated to changes in QOL, and the increase in NBMCS associated with nonabstinent remission from abuse only was slight. Individuals with abuse only or no AUD who stopped drinking had significant declines in QOL. Conclusions: These results suggest the possible importance of preventing and treating AUD for maintaining and/or improving QOL. They are also consistent with the sick quitter hypothesis and suggest that abuse is less a mental disorder than a maladaptive pattern of behavior.

Introduction

Despite the well-documented adverse effects of alcoholism and heavy drinking on physical and mental functioning (World Health Organization, 2003; Room et al., 2005; Rehm et al., 2006; Taylor et al., 2007), only recently have studies begun to explicitly address quality of life (QOL) as an outcome measure in relation to alcohol use disorder (AUD). In a review of articles published between 1993 and 2004, Donovan et al. (2005) cited just 36 studies that either explicitly focused on QOL or used instruments designed to measure various aspects of QOL. The findings generally demonstrated that (1) individuals with an AUD had a diminished QOL relative to the general population, (2) heavy episodic drinkers had a lower QOL than other drinkers, (3) QOL improved in relation to short- and long-term abstinence in treated alcoholics, (4) reduction of drinking was associated with improved QOL in harmful/hazardous drinkers and (5) the associations of AUD and heavy drinking with QOL were mediated in part by sociodemographic characteristics and comorbidity (Donovan et al., 2005).

Many of the studies of alcohol-related QOL have employed versions of the Medical Outcomes Study Short Form (SF): the SF-36, SF-20 or SF-12, reflecting the number of items included in the form. These instruments measure eight subscales of mental and physical functioning that can be combined to yield mental and physical component summary scores (Ware et al., 2002). In studies of primary care patients, Volk et al. (1997) found that alcohol dependence, but not abuse, was associated with lower scores on all eight SF-36 subscales, and Johnson et al. (1995) found that patients with alcohol abuse or dependence had poorer functioning than patients without any psychiatric disorders; however, Spitzer et al. (1995) reported that patients with AUD did not differ on any of the eight SF-20 subscales from those with no mental disorder. In studies of treatment samples, individuals with AUD generally demonstrated poorer QOL relative to general population norms or controls, although the differences were smaller with respect to physical functioning (McKenna et al., 1996; Daeppen et al., 1998; Stein et al., 1998; Morgan et al., 2003, 2004; Smith and Larson, 2003; Mansell et al., 2006; Senbanjo et al., 2006).

In the few SF-based studies that employed prospective designs, a reduction of 30% or more in volume of consumption was associated with improved physical and mental component summary scores for hazardous drinkers over a 1-year follow-up (Kraemer et al., 2002), and baseline impairment in physical and mental functioning among alcoholics was significantly reduced following treatment (Morgan et al., 2003, 2004; Feeney et al., 2004). These findings are consistent with the results of a study of discordant twins (one alcoholic, one not), in which there were fewer significant differences in SF-36 subscales among the pairs in which the alcoholic twin had been asymptomatic for the preceding 5 years than among the pairs with more recent symptoms (Romeis et al., 1999).

Few of the longitudinal studies following treatment samples have examined whether changes in QOL differ for abstinent and nonabstinent recovery. In one study that followed 187 individuals enrolled in a clinical trial of alternative AUD outpatient treatments, Maisto et al. (2002) found that those who had been abstinent in the first year following treatment had less negative affect after 24 months than those who had consumed any alcohol. Similarly, a 5-year study of 850 Catalonian AUD patients (Gual et al., 1999) indicated that those who were abstinent at follow-up had lower morbidity rates, less emergency room utilization, fewer sick days and lower levels of psychosocial stress than controlled drinkers. However, data from a 9-year follow-up of 60 alcoholic patients treated in a residential inpatient program showed that abstainers and controlled drinkers were virtually indistinguishable with respect to neuroticism, self-esteem, life satisfaction, outpatient visits, hospitalizations and physical complications, with both groups demonstrating better functioning than improved or unchanged drinkers (Shaw et al., 1997). To our knowledge, no studies have compared abstinent and nonabstinent recovery in terms of SF-12 QOL scores.

The literature examining associations between AUD and QOL has a number of significant limitations. Many existing studies are based on cross-sectional designs, precluding inference as to how changes in the AUD status affect QOL. Few studies have employed categories of AUD that correspond to and distinguish DSM-IV (American Psychiatric Association, 1994) alcohol abuse and dependence. Most longitudinal studies have followed treatment samples, precluding generalization to the general population, and most have failed to distinguish abstinent from nonabstinent recovery. Control for confounders has been sparse. Finally, to our knowledge, no nationally representative data are available on changes in QOL in relation to the development or progression of AUD.

Accordingly, the aim of this study is to examine the relationship between transitions in the AUD status and changes in QOL over a 3-year interval in a longitudinal study of a nationally representative sample of US adults. The analysis is restricted to individuals who were past-year drinkers at the time of the Wave 1 interview (n = 22,245) in order to avoid biasing comparisons by the inclusion of ‘sick quitters’, i.e. individuals who had already stopped drinking at baseline because of physical or mental health problems. Analyses control for sociodemographic factors, family history of alcoholism, lifetime personality disorders, baseline risk factors (tobacco and illicit drug use, mood and anxiety disorders and medical conditions) and changes over the follow-up interval in the baseline risk factors.

Although this is essentially an exploratory study, we anticipate on the basis of past research findings that developing an AUD will be associated with a decrease in QOL and that remission from an AUD will be associated with an increase in QOL. We further anticipate that the increase in QOL will be smaller for partial than full remission. Reflecting inconsistent findings on the impact of type of recovery, we do not anticipate any differential change in QOL for abstinent as opposed to nonabstinent recovery. However, based on the DSM-IV hierarchy of abuse and dependence, in which abuse is prodromal to dependence and thus a presumably milder disorder, we anticipate that changes related to transitioning into or out of abuse will be smaller than those associated with transitioning into or out of dependence and that progression from abuse only to dependence will be associated with a decrease in QOL.

Methods

Sample

The data used in this analysis came from Waves 1 and 2 of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC), designed by the National Institute on Alcohol Abuse and Alcoholism (Grant et al, 2003b, 2007). The 2001–2002 Wave 1 NESARC sample represented US adults 18 years or older residing in households and noninstitutional group quarters in all 50 states and the District of Columbia (n = 43,093, response rate = 81.0%). In Wave 2, the reinterview rate among eligibles (those who had not died, become incapacitated or institutionalized or left the country and were not in the military for the duration of the Wave 2 interviewing) was 86.7%, yielding a cumulative response rate of 70.2% (n = 34,653). Wave 1 and 2 data were weighted to reflect design characteristics, oversampling of Blacks, Hispanics and young adults, and nonresponse relative to sociodemographic characteristics. Wave 2 weights also adjusted for nonresponse relative to Wave 1 lifetime substance use and other psychiatric disorders. In both waves, the weighted data were then adjusted to match the civilian, noninstitutionalized population of the United States with respect to the distribution by age, sex, race, ethnicity and region based on the 2000 Decennial Census (see details of weighting adjustment in Grant et al, 2003b, 2007). Data were collected in personal interviews. All potential respondents were informed in writing about the nature of the survey, the statistical uses of the survey data, the voluntary aspect of their participation and the Federal laws that rigorously provide for the confidentiality of identifiable survey information. Only respondents consenting to participate after receiving this information were interviewed. The research protocol, including informed consent procedures, received full ethical review and approval from the US Census Bureau and the US Office of Management and Budget. This analysis is based on a subsample of respondents who had consumed at least one drink in the year preceding the Wave 1 interview (n = 22,245).

Measures

Quality of life

Quality of life (QOL) was measured using two summary scales from Version 2 of the Short Form 12 Health Survey (SF-12v2), the norm-based physical component summary score (NBPCS) and the norm-based mental component summary score (NBMCS), in addition to the eight individual scales representing physical functioning, role physical (the extent to which health interferes with regular activities), bodily pain, general health, vitality, social functioning, role emotional (the extent to which emotional problems interfere with regular activities) and mental health. All scales were standardized to a mean of 50 (range 0–100) using standard norm-based scoring techniques, with higher scores indicative of better functioning. Alternate-form reliabilities of these scales are excellent in the general US population, 0.89 and 0.86 (Ware et al., 2002). Studies in clinical populations also have indicated excellent alternate-form reliability relative to the SF-36 (Globe et al., 2002; Singh et al., 2006) and have shown that the SF-12 scales have high levels of test–retest reliability (Bohannon et al., 2004), internal consistency (Resnick and Nahm, 2001; King et al., 2005) and convergent validity (Salyers et al., 2000). Changes in QOL between Waves 1 and 2 were calculated by subtracting the Wave 1 from the Wave 2 score.

AUD transitions

At Waves 1 and 2, AUD were defined in accordance with the DSM-IV criteria (American Psychiatric Association, 1994), using the Alcohol Use Disorders and Associated Disabilities Interview Schedule—DSM-IV Version (AUDADIS-IV, Grant et al., 2001). To be classified with past-year alcohol dependence, respondents had to satisfy at least three of the seven dependence criteria. To be classified with past-year abuse, they had to satisfy at least one of the four abuse criteria. Consistent with the DSM-IV hierarchy, abuse hereafter refers to abuse only (no prior or concurrent dependence), and dependence includes dependence with or without abuse (Grant et al., 2004).

AUD transitions were based on the AUD status at Waves 1 and 2. Individuals who did not have an AUD at either wave were divided to distinguish those who continued and stopped drinking. The resulting transition categories were (1) no AUD at either time and continued drinking; (2) no AUD at either time and stopped drinking; (3) no AUD at baseline and developed abuse; (4) no AUD at baseline and developed dependence; (5) abuse at both times; (6) abuse at baseline and nonabstinent remission (NR, i.e. drinking with no AUD symptoms) at Wave 2; (7) abuse at baseline and abstinent remission (AR) at Wave 2; (8) abuse at baseline and progressed to dependence; (9) dependent at both times; (10) dependent at baseline and NR at Wave 2; (11) dependent at baseline and AR at Wave 2 and (12) dependent at baseline and partial remission (i.e. did not meet criteria for dependence but had one or more symptoms of abuse or dependence) at Wave 2.

Model covariates

Proper estimation of the associations between AUD transitions and change in QOL requires adjustment for potential confounders (Dawson et al., in press). In addition to baseline sociodemographics (age, sex, race/ethnicity, marital status, education and children under 18 years in the home), covariates included baseline and lifetime risk factors. Family history of alcoholism (any versus none) was based on 14 types of first and second degree relatives. Lifetime personality disorders (PD) included antisocial, borderline, avoidant, paranoid, dependent, schizoid, obsessive-compulsive, histrionic, narcissistic and schizotypal. Because we believed that the volatility associated with borderline PD had particularly strong potential for confounding estimates of change in QOL, it was considered separately from the other nine PD, which were combined into a single category. Baseline smoking reflected past-year use of cigarettes, cigars, pipe tobacco, snuff or chewing tobacco and illicit drug use comprised past-year non-prescription use of sedatives, tranquilizers, painkillers or stimulants or past-year use of marijuana, cocaine/crack, hallucinogens, inhalants/ solvents, heroin or other illicit drugs. Past-year mood disorders included major depressive disorder (MDD), dysthymia, bipolar disorders and hypomania, and past-year anxiety disorders included panic disorder (with or without agoraphobia), specific and social phobias and generalized anxiety. These disorders ruled out exclusively illness- or substance-induced episodes. MDD also ruled out bereavement. As an independent indicator of baseline physical health, we constructed a count of past-year medical conditions confirmed by a doctor, comprising arteriosclerosis, hypertension, cirrhosis, other liver disease, angina pectoris, tachycardia, myocardial infarction, other heart disease, stomach ulcer, gastritis and arthritis (range 0–11). To this count, we added one if the individual had a body mass index (calculated from self-reported height and weight) of ≥30, indicative of obesity.

To control for other potentially confounding health-related changes between Waves 1 and 2, we constructed four-level measures representing change in smoking (stopped, started and continued smoking versus not smoking at either wave) and change in illicit drug use (stopped using, started using and continued using versus not using illicit drugs at either wave). These were used as alternatives to the measures of baseline use of tobacco and illicit drugs in separate sets of models. Changes in mood and anxiety disorders and medical conditions were not controlled, because these changes were thought to represent critical intervening variables through which changes in physical and mental functioning would occur.

Reliability and validity

The AUDADIS-IV alcohol diagnoses have demonstrated good reliability and validity in test–retest and other studies, including clinical reappraisals (Muthen et al., 1993; Cottler et al., 1997; Hasin et al., 1997; Pull et al., 1997; Canino et al., 1999; Nelson et al., 1999; Grant et al., 2003a). Test–retest reliability for family history, use of tobacco, marijuana and cocaine, and diagnoses for other Axis I disorders have been good to excellent, and personality disorders have demonstrated levels of reliability comparable to those reported in the clinical literature (Grant et al., 1995, 2003a; Ruan et al., 2008).

Analysis

Multiple linear regression models were estimated using SUDAAN (Research Triangle Institute, 2001), a software package that uses Taylor series linearization to adjust variance estimates for complex survey designs. These models were stratified within categories of baseline AUD (no AUD, abuse only and dependence) in order to yield the most meaningful comparisons. Because all models controlled for the baseline QOL score, beta parameters from the models can be interpreted as either (a) the difference in the change in QOL over the follow-up interval between the AUD transition category in question and the reference category, or (b) the difference in the actual Wave 2 QOL score between the AUD transition category in question and the reference category. Positive beta coefficients represent better QOL and negative beta coefficients represent poorer QOL than in the reference transition category. Because of the large number of models tested, a P-value of <0.005 was required for significance, with P-values lying between 0.005 and 0.05 described as representing marginally significant.

Results

Looking at the unadjusted SF-12 scores from Waves 1 and 2 (Table (Table1),1), there were more changes in mental than physical QOL over the course of the 3-year follow-up interval. There were small but significant declines in overall physical and mental function among individuals who did not have an AUD at either wave, both for those who continued and stopped drinking. These groups represented the majority of the adult population (67.9 and 11.9%, respectively). There were no significant changes in overall physical function (NBPCS) for the other AUD transition categories. However, there were significant declines in mental function for individuals who developed an AUD or progressed from abuse to dependence. There was also a marginally significant increase in mental function among individuals who achieved NR from alcohol dependence.

Table 1

Percentage distribution of population and baseline and Wave 2 SF-12 measures of physical and mental functioning, by change in the AUD status between Wave 1 and Wave 2

Table Table22 shows the differential net changes in overall physical functioning (NBPCS) associated with AUD transitions. Successive models demonstrated the effects of sequentially adjusting for a growing list of covariates. Compared to individuals who remained without an AUD and continued drinking, those who remained without an AUD but stopped drinking had a significantly greater decrease in NBPCS. Although the magnitude of the decrease was attenuated with increasing levels of adjustment, it remained significant even after controlling for all covariates. The transition to abuse only was associated with an increase in NBPCS relative to continued drinking without an AUD after adjusting solely for the baseline level of physical functioning, but this increase fell short of significance after adjusting for the full range of covariates. Adjusting only for baseline NBPCS, there was an increase in physical function associated with partial remission from alcohol dependence, relative to remaining dependent, but it lost significance when additional controls were added.

Table 2

Changes in the SF-12 norm-based physical component summary scale (NBPCS) over a 3-year follow-up interval, with successive levels of adjustment, as a function of change in the AUD status

A comparison of Tables Tables22 and and33 bears out the contents of Table Table11 in demonstrating that transitions in the AUD status were more often associated with changes in mental than physical functioning. Compared to those who continued drinking, individuals who remained without an AUD but stopped drinking showed a decrease in overall mental/psychological functioning (Table (Table3),3), but the magnitude was smaller than the decrease in physical function and became marginally significant after adjusting for psychopathology, medical conditions and other substance use. Developing alcohol dependence and progressing from abuse to dependence were both associated with decreased mental/psychological functioning (NBMCS), regardless of the level of control. There was no consistent evidence of change in mental function associated with the development of abuse only.

Table 3

Changes in the SF-12 norm-based mental component summary scale (NBMCS) over a 3-year follow-up interval, with successive levels of adjustment, as a function of change in the AUD status

A decrease in NBMCS associated with AR from alcohol abuse lost significance after adjusting for sociodemographic characteristics and family history of alcoholism, but regained a marginal level of significance after accounting for changes in other substance use. In contrast, all forms of remission from dependence, including partial remission, were associated with substantial increases in NBMCS that remained significant, although reduced in magnitude, after controlling for all covariates. These increases were equally large for NR and AR, and about half as large for partial remission.

Table Table44 presents results from the fully adjusted models for associations between AUD transitions and the eight individual SF-12 scales. Among individuals with no AUD at baseline, those who remained without an AUD but stopped drinking showed significant net decreases in scores for all scales except vitality and mental health, for which there were marginally significant decreases. Note that decreases in the scores for role physical, role emotional and bodily pain reflect worsened functioning in these domains, i.e. more limitation in activities due to physical or emotional problems and more bodily pain. Developing abuse was not associated with changes in any of the scores, but developing dependence was associated with significant decreases in social functioning, role emotional and mental health scores and with marginally significant worsening of bodily pain and general health.

Table 4

Adjusted^a changes in individual SF-12 norm-based scales over a 3-year follow-up interval, as a function of change in the AUD status: past-year drinkers at baseline

Compared to individuals who remained with alcohol abuse only, those who achieved AR from alcohol abuse demonstrated marginally decreased levels of performance in four areas: bodily pain, vitality, social functioning and role emotional. Progression from abuse to dependence was associated with a significant decrease only in the mental health score, but it also had marginally significant negative associations with vitality and role emotional scores.

Among individuals with dependence at baseline, all forms of full and partial remission were associated with significantly improved mental health scores. NR also had a significant positive association with bodily pain (i.e. reduced pain), and partial remission had a significant positive association with social functioning. In addition, NR and partial remission were marginally associated with improved general health and role emotional scores, and AR and NR were marginally associated with improved social functioning.

Discussion

Transitions in the AUD status, though not associated with changes in physical functioning, were strongly associated with changes in mental/psychological functioning—driven primarily by changes in the SF-12 domain representing mental health. Although many of our initial expectations were supported by the results of this study, a number of findings were unexpected. First, there were no significant decreases in functioning associated with the onset of alcohol abuse only, suggesting that abuse might more appropriately be considered a maladaptive behavioral pattern than a disorder characterized by physical and psychological disability. In addition, whereas NR from abuse was associated with a small and marginally significant degree of improvement in mental functioning, the opposite was true for AR from abuse. The worsened pain, vitality, social functioning and limitation in activities due to emotional problems that were associated (albeit marginally) with AR from abuse signal the need for a better understanding of the factors that might lead to this AUD transition.

The net decline in mental/psychological functioning associated with the development of dependence was substantially reduced by controlling for the excess baseline levels of mental and physical problems experienced by individuals who developed dependence. This suggests that much of the reduction in QOL associated with becoming dependent reflects the worsening of pre-existing mental conditions. Although these findings preclude causal attribution, they do suggest that treatment and prevention of alcohol dependence should address the issues surrounding comorbid or precursor mood and anxiety disorders.

The improvements in QOL associated with remission from dependence in this study were smaller than those reported in some previous studies of treatment samples (e.g. Garg et al., 1999; Feeney et al., 2004; Morgan et al., 2003, 2004). This discrepancy is not surprising. Treatment samples are likely to be selective not only of individuals with high baseline levels of severity and impairment (thus with a great deal of room for improvement in QOL) but also of those who through their entry into treatment have indicated a commitment or willingness to change their drinking patterns. Moreover, the follow-up interval for these treatment studies was in the range of 3 months to a year, shorter than the period examined in this study, and they did not adjust for the full range of covariates examined in this study. Additional analyses of the longitudinal NESARC sample could examine changes among individuals who had received treatment in the follow-up interval or include treatment/12-step participation as a covariate in the models used to assess the magnitude and significance of changes in QOL.

The one transition that was strongly associated with a change in physical functioning in this study was drinking cessation among individuals who did not meet the criteria for an AUD at baseline. This change in drinking behavior, although not a transition in the AUD status per se, was strongly associated with decreases in numerous aspects of physical and mental/psychological functioning, and thus is consistent with the ‘sick quitter’ hypothesis, i.e. the argument that individuals who stop drinking are selective of those with adverse physical and mental conditions and, accordingly, that inclusion of former drinkers with lifetime abstainers may bias tests of the health effects of moderate drinking (Shaper et al., 1988; Fillmore et al., 2007). However, the exclusion of individuals who had already stopped drinking at baseline means that only a few of the sick quitters would be observed in this analysis, so it should not be considered a formal test of that hypothesis. In addition, because changes in the AUD status and QOL were measured in the same time period, the analysis cannot rule out the possibility that the decreases were the result rather than the cause of the drinking cessation.

One of the unique contributions of this study was its inclusion of changes in QOL associated with developing alcohol dependence, a subject that to our knowledge has not received attention in the previous literature. In association with the onset of dependence, we found a net change in the NBMCS score for mental/psychological functioning of −2.48 for individuals with no baseline AUD and of −3.48 for those with abuse only at baseline. To put these changes into perspective, they exceeded the net changes associated both with starting to smoke (−1.54 and −1.05, respectively) and with starting to use illicit drugs (−1.70 and −1.84, respectively), as estimated in the models from which the QOL changes were derived (full models not shown).

In a study of primary care patients that used measures of alcohol dependence and QOL comparable to those used in this study (Volk et al., 1997), the cross-sectional difference in the mean NBMCS score between individuals with no AUD and those with dependence was more than three times as great as the longitudinal decline in NBMCS associated with developing alcohol dependence in the current study (−8.5 versus −2.48). Several factors may account for this discrepancy. First, the scores reported by Volk et al. adjusted for age, sex, race/ethnicity and cigarette use, but they did not control for other mental and medical conditions, nor for marital status and education. Prior to these adjustments, the decrease in QOL found in this study was greater, although still only about half as large as the difference reported by Volk et al. More importantly, individuals who developed alcohol dependence during the 3-year follow-up interval between Waves 1 and 2 of the NESARC represent individuals in the early stages of dependence, among whom consumption levels were still relatively low (Dawson et al., 2008). With a longer follow-up interval, dependence likely would have increased in severity and thus have been associated with a greater decline in mental and psychological functioning. Indeed, when the unadjusted baseline NBMCS scores for individuals with dependence are compared with those for individuals with no AUD (averaging across the appropriate transition categories in Table Table1),1), it is evident that these cross-sectional differences at baseline are greater than the longitudinal changes in QOL associated with the onset of dependence.

This study has a number of methodological strengths that increase our confidence in the validity of these findings. Because of the longitudinal design of the NESARC, both transitions in the AUD status and changes in QOL were measured directly by comparison of past-year data at two points in time. Accordingly, they were not subject to the level of recall error that might bias retrospective reports of AUD and QOL, nor were they subject to potential bias arising from asking respondents to directly report subjective changes in QOL. In addition, the past-year AUD and QOL measures, as well as the measures used as model covariates, have demonstrated high levels of reliability in test–retest and validity studies.

Despite these methodological strengths, there are limitations to this analysis. First, because this study conditioned its outcome measure of change in QOL upon baseline QOL, it shares limitations common to lagged dependent variable (LDV) models, including possible bias resulting from a measurement error in the outcome variable and from insufficient control for all possible time-invariant confounders (Norström, 2008). One method for assessing the impact of these limitations is comparison of the LDV model parameters with those from within-subjects change in score (CS) models (Johnson, 2005). Because CS models have their own limitations, including statistical inefficiency and failure to address genuine causal relationships between the outcome measures at times 1 and 2, including possible ceiling effects, they should not be considered the gold standard for validating this study's results; however, comparison of the two models may reveal differences that can enrich the interpretation of this study's findings.

When the changes in QOL examined in this analysis were reanalyzed using CS models, many of the marginally significant changes in Tables 2–4 became nonsignificant. Although this is to be expected given the relative inefficiency of the CS models, it underscores the need for caution in interpreting these marginally significant findings. The great majority of significant changes in QOL remained significant or marginally significant, and their magnitudes were within the sampling error of those presented in Tables 2–4, varying in both directions (further details of the model comparison available upon request).

Another possible limitation of this study is that it did not consider the potentially confounding effects of changes over the follow-up period in all model covariates. Changes in mental disorders and physical conditions were excluded by design, because they were considered intervening variables, but changes in the marital or educational status are more problematic. They might represent intervening variables too, but they could also be seen as potential confounders that could be controlled. It would be interesting to see what effect it would have on the results of this study to control for marital and educational transitions.

Finally, the design of this analysis precluded causal inferences with respect to the associations between changes in QOL and AUD status, as these occurred within the same 3-year time frame and we have no information as to their temporal ordering within that period. This is important to bear in mind when considering possible explanations for the decreased psychological functioning that was observed in association with AR from alcohol abuse, and it extends to any conclusions that onset and offset of AUDs lead to (as opposed to result from) changes in psychological functioning. Moreover, our inability to study changes in QOL among individuals who died or became incapacitated over the follow-up interval limits our ability to draw inferences as to the magnitude of the changes or their associations with the course of AUD in the full Wave 1 sample. However, because the analysis examined changes conditional upon baseline mental and physical functioning, any selectivity in terms of sample attrition with respect to baseline QOL should have little impact on the findings.

Despite these limitations, this study is an important complement to earlier studies of clinical and patient samples. It demonstrates that even among the less severely dependent members of the general population, many of whom may not have realized that they had recently met the criteria for an AUD, the development of alcohol dependence is accompanied by significant reductions in mental and psychological functioning. Its findings suggest that AR and NR are associated with comparable levels of improvement in the general population, and that even partial remission is associated with some improvement. By documenting the wide range of psychological and general health harms that were at least marginally associated with developing dependence, it illustrates some of the potentially important implications of preventing and treating of alcohol dependence—implications that need to be verified using a design permitting causal attribution. A greater understanding of this process might be gained through future analyses examining changes in QOL as a function of the first incidence of specific dependence criteria or as a function of the number of new symptoms experienced.

Acknowledgments

The study on which this paper is based, the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC), is sponsored by the National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, US Department of Health and Human Services, with supplemental support from the National Institute on Drug Abuse. This work was supported, in part, by the Intramural Program of the National Institutes of Health, National Institute on Alcohol Abuse and Alcoholism.

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