Alcohol and Alcoholism Advance Access published online on October 8, 2008
Alcohol and Alcoholism, doi:10.1093/alcalc/agn083
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Assessing the General Health of Diagnostic Orphans Using the Short Form Health Survey (SF-12v2): A Latent Variable Modelling Approach
School of Psychology, University of Ulster, Magee Campus, Northland Road, County Londonderry BT48 7JL, Northern Ireland
* Corresponding author: Room MC223, School of Psychology, University of Ulster, Magee Campus, Northland Road, County Londonderry BT48 7JL, Northern Ireland. Tel: +44-28-71375367; Fax: +44-28-71375315; E-mail: mcbride-o{at}email.ulster.ac.uk
Received 8 May 2008; first review notified 3 July 2008; in revised form 26 July 2008, 22 August 2008; accepted 18 September 2008
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ABSTRACT |
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Aims: Research has demonstrated that diagnostic orphans (i.e. individuals who experience only one to two criteria of DSM-IV alcohol dependence) can encounter significant health problems. Using the SF-12v2, this study examined the general health functioning of alcohol users, and in particular, diagnostic orphans. Methods: Current drinkers (n = 26,913) in the National Epidemiologic Survey on Alcohol and Related Conditions were categorized into five diagnosis groups: no alcohol use disorder (no-AUD), one-criterion orphans, two-criterion orphans, alcohol abuse and alcohol dependence. Latent variable modelling was used to assess the associations between the physical and mental health factors of the SF-12v2 and the diagnosis groups and a variety of background variables. Results: In terms of mental health, one-criterion orphans had significantly better health than two-criterion orphans and the dependence group, but poorer health than the no-AUD group. No significant differences were evident between the one-criterion orphan group and the alcohol abuse group. One-criterion orphans had significantly poorer physical health when compared to the no-AUD group. One- and two-criterion orphans did not differ in relation to physical health. Conclusion: Consistent with previous research, diagnostic orphans in the current study appear to have experienced clinically relevant symptoms of alcohol dependence. The current findings suggest that diagnostic orphans may form part of an alcohol use disorders spectrum severity.
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Introduction |
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The DSM-IV (American Psychiatric Association, 1994
) defines two mutually exclusive alcohol use disorders (AUD) known as abuse and dependence. Described as experiencing a loss of control over drinking despite the occurrence of negative consequences (Grant et al., 2007), alcohol dependence is defined by seven criteria including unsuccessful attempts at cutting down on drinking, tolerance (defined as either needing increased amounts of alcohol to achieve intoxication or experiencing a diminished effect using the same amount of alcohol) and withdrawal (defined as either the withdrawal syndrome or using alcohol or a related substance to relieve or avoid withdrawal symptoms). According to DSM-IV, at least three criteria must be present for a diagnosis of dependence. Individuals are diagnosed with abuse if the three-criterion threshold for dependence is not met, and at least one of four criteria, relating to adverse social, legal or interpersonal consequences of alcohol use, is present. Although the DSM-IV Substance Use Disorders Work Group considered various thresholds for the AUD, the current thresholds were deemed appropriate for identifying individuals who were at high risk for future morbidity and who would benefit most from treatment (Cottler et al., 1995).
Since the mid-1990s, concerns have been raised regarding the conceptualization of alcohol use disorders in the DSM-IV (Muthén et al., 1993; Heath et al., 1994; Grant et al., 2007). For example, although the reliability and validity of the dependence diagnosis has been reported consistently within the literature, diagnoses of alcohol abuse tend to have lower reliability and validity (Hasin, 2003). Recent empirical evidence has revealed that the DSM-IV AUD criteria fall along a single latent continuum (Krueger et al., 2004; Kahler and Strong, 2006; Proudfoot et al., 2006). More importantly, this research has demonstrated that the abuse criteria are not confined to the lower ranges of severity but are spread out along this continuum, with some being positioned at the more severe end (Kahler and Strong, 2006; Saha et al., 2006; Ray et al., 2008a). These results are noteworthy because they contradict the common assumption that alcohol abuse is a less severe disorder when compared to alcohol dependence. One major problem associated with the conceptualization of an AUD continuum, however, is the difficultly in defining clinically relevant thresholds (Krueger et al., 2004; Proudfoot et al., 2006; Saha et al., 2006).
A final but noteworthy issue relating to the validity of the DSM-IV AUD criteria concerns the existence of ‘diagnostic orphans’—individuals who do not meet the diagnostic criteria for either DSM-IV AUD but experience—one to two criteria of alcohol dependence (Eng et al., 2003). Diagnostic orphans have been identified in adult treatment seeking (Olfson et al., 1996; Ray et al., 2008b), population (Hasin and Paykin, 1999), community (Hasin and Paykin, 1998; Sarr et al., 2000; Shankman et al., 2008) and adolescent or young adult samples (Kaczynski-Pollock and Martin, 1999; Rohde et al., 2001; Schuckit et al., 2008). The identification of this group of individuals has proven controversial. For example, it has been argued that the problems experienced by diagnostic orphans are too mild to warrant a diagnosis (Hoffman and Hoffman, 2003) and their existence does not pose a threat to the validity of the DSM-IV AUD criteria (Wells et al., 2006). Research has demonstrated, however, that diagnostic orphans tend to experience more severe alcohol-related problems when compared to those with no-AUD, but less severe than individuals with abuse or dependence (Sarr et al., 2000; Eng et al., 2003). Studies have demonstrated that concern over diagnostic orphans as an at-risk group is warranted. For example, Hasin and Paykin (1998) reported that diagnostic orphans frequently endorsed the ‘using more alcohol than usual or for a longer than expected time period’ criterion, which is indicative of a high-risk drinking pattern (Saha et al., 2006). Indeed, there is a general consensus that being a diagnostic orphan indicates a greater likelihood of future alcohol-related problems compared to those with no AUD (Rohde et al., 2001; Schuckit et al., 2008).
The complex process to revise the current substance use disorders diagnostic criteria is now underway [see Schuckit and Saunders (2006)]. Clearly, revising current diagnostic threshold levels is high on the research agenda (Cottler and Grant, 2006). For example, in addition to the existing binary diagnostic categories, the feasibility and utility of a severity measure, based on a count of the number of diagnostic criteria endorsed, are being considered (Hasin et al., 2006). Presently, there is scope to investigate whether diagnostic orphans experience significant levels of impairment in different life domains (i.e. physical health functioning, psychological distress) that may warrant their inclusion, at some level, in future diagnostic classification systems.
The aim of the current study, therefore, is to investigate the general health and well-being of alcohol users experiencing different alcohol symptoms. Specifically, using data from the 2001–2002 National Epidemiologic Survey on Alcohol and Related Conditions (NESARC; Grant et al., 2003a), alcohol users were classified as having a DSM-IV AUD, being a diagnostic orphan or experiencing no AUD. Consistent with a continuum viewpoint of psychopathology that hypothesizes that greater impairment is associated with experiencing more severe symptoms (Krueger et al., 2005), this study differentiated between diagnostic orphans experiencing one- or two-criterion of alcohol dependence. Using Short Form Health Survey Version II (SF-12v2; Ware et al., 2002) as a measure of general health, latent variable modelling was used to investigate the associations between each of the groups and physical and mental health. The analysis controlled for the effects of important demographic and clinical variables (e.g. comorbid psychiatric disorders) that have been shown to influence the relationship between general health and alcohol use (Romeis et al., 1999). In light of previous research, it was hypothesized that the general health of diagnostic orphans would be poorer when compared with the no-AUD group, but possibly better than the DSM-IV alcohol abuse and dependence groups.
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Methods |
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Sample
The 2001–2002 NESARC is a nationally representative face-to-face survey of 43,093 non-institutionalized adults residing in the United States and the Districts of Columbia [see Grant et al. (2003a)
for full details]. Blacks, Hispanics and young adults (aged 18–24 years) were over-sampled. The overall response rate was 81%. Data were weighted to adjust for probabilities of selecting one person per household and over-sampling of young adults (Grant et al., 2003a). The current analysis focuses on a sample (n = 26,946) of alcohol users who had consumed at least one alcoholic drink in the year prior to the interview. Individuals with complete missing data on the SF-12v2 items were excluded (n = 33). Missing data for the remaining respondents was minimal (0.2–0.4%). The statistical software Mplus version 5 (Muthén and Muthén, 1998–2007) uses the full-information maximum likelihood algorithm for handling missing data. Respondents were divided into five diagnosis groups: (i) no-AUD (i.e. did not endorse any AUD criteria, n = 20743); (ii) one-criterion orphans (i.e. endorsed one dependence criterion, n = 2116); (iii) two-criterion orphans (i.e. endorsed two dependence criteria, n = 787); (iv) DSM-IV alcohol abuse (n = 1918); and (v) DSM-IV alcohol dependence (n = 1349). The five diagnosis groups were compared in terms of demographic and clinical characteristics (refer to Table 1).
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Measures
The Alcohol Use Disorder and Associated Disabilities Interview Schedule (AUDADIS-IV; Grant et al., 2001
) is a diagnostic interview that enquires about the occurrence of 32 alcohol-related symptoms during the last 12 months. These binary symptom questions are used to operationalize the 11 DSM-IV AUD criteria. As described elsewhere (Grant et al., 2003b), the reliability and validity of abuse and dependence diagnoses derived from the AUDADIS-IV were fair to excellent (intraclass correlations: 0.71–0.75; kappa values: 0.61–0.74).
The SF-12v2 is a measure of general health functioning (Ware et al., 2002). The 12 items reflect eight sub-domains: self-perceived general health (item 1); bodily pain (item 2); physical functioning (items 3 ‘engagement in moderate activities’ and 4 ‘ability to climb a flight of stairs’); physical role (items 5 ‘accomplishment’ and 6 ‘limitation’); vitality (item 7); social functioning (item 8); mental health (items 9 ‘feeling calm and peaceful’ and 10 ‘feeling downhearted and depressed) and emotional role (items 11 ‘accomplishment’ and 12 ‘limitation’).
Prior to conducting the analysis, dummy variables were created to compare each diagnosis group to a referent group. Given that the present study aimed to investigate the impact of experiencing different levels of severity of alcohol symptoms on general health, the one-criterion orphan group (i.e. the group with the lowest level of alcohol symptoms) was deemed to be the most appropriate referent group. The following variables were included in the analysis: gender, age, family history of alcoholism (defined as having any first- or second-degree relatives who were alcoholics), treatment-seeking behaviour (defined as ever having sought treatment for alcohol-related problems), age of first drink, current smoking status and past year diagnoses of any mood, anxiety or drug use disorder. Finally, dummy variables were created to represent frequency of binge drinking, comparing the time frames everyday or nearly everyday, 3–4 times a week, 1–3 times a month, 3–11 times a year and 1–2 times or never in the last year, to a referent group (i.e. 1–2 times a week).
Analytic plan
Several alternative factor structures of the SF-12v2 have been reported within the literature (as outlined below). A preliminary stage of the analysis, therefore, involved specifying and estimating four confirmatory factor analytic (CFA) models with categorical indicators (Fig. 1). The boxes represent the SF-12v2 items and the circles represent the SF-12v2 physical and mental health factors. The arrows connecting the factors to the items represent factor loadings and the curved connector between the factors represents a correlation. A curved arrow linking items indicates a residual correlation. Model 1 specified a correlated two-factor structure with each item loading on either the physical or mental health factor (Ware et al., 1996; Keller et al., 1998). Model 2 was identical to Model 1 but included correlated residuals between pairs of items within the same sub-domain (Wilson et al., 2002; Maurischat et al., 2006). Model 3 specified a correlated two-factor structure with items 1, 7 and 8, cross-loading on both factors, and also included five pairs of correlated residuals (Fleishman and Lawerence, 2003). Model 4 specified a correlated two-factor structure with item 7 loading on the physical health factor and item 8 cross-loading on both factors (Resnick and Nahm, 2001). Mplus (Muthén and Muthén, 1998–2007) takes into account NESARC sampling weights and design effects and uses a robust, weighted, least-squares estimator. Goodness-of-fit indices were used to compare the factor models: chi-square, the comparative fit index (CFI; Bentler, 1990), the Tucker–Lewis index (TLI; Tucker and Lewis, 1973) and the root mean square error of approximation (RMSEA; Steiger, 1990). Hoyle and Panter (1995) advocate that a non-significant chi-square, TLI and CFI values of 
0.95, or a RMSEA value of 
0.06, indicate acceptable model fit. The best fitting model was used to specify a multiple indicators and multiple causes (MIMIC) model.
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The MIMIC model extends the CFA model by examining two additional relationships, which are particularly useful in addressing the aims of the current study. Firstly, the relationship between important covariates (i.e. diagnosis groups, demographic and clinical variables) and the latent variables (i.e. physical and mental health factors) can be investigated. This analysis is valuable because it can reveal whether the diagnosis groups differ in relation to the reference group regarding estimates on the physical and mental health factors, whilst controlling for the effects of all other covariates in the model. The second part of the MIMIC model investigates whether there are associations between the observed variables (i.e. the SF-12v2 items) and specific covariates (i.e. the diagnosis groups), whilst controlling for other covariates in the model. These associations are referred to as direct effects. The presence of direct effects indicates that there are differences in the measurement parameters of the factor model based on the covariates (i.e. measurement non-invariance). In the current study, direct effects would imply that the SF-12v2 items function differently for the diagnosis group compared to the referent group.
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Results |
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The results of the CFA are presented in Table 2. The highly significant chi-square values suggest that none of the four factor models were good fitting models; however, Bollen (1989)
noted that the chi-square statistic is highly sensitive to large sample sizes and may overestimate the lack of fit of a structural model. Thus, inspection of the other fit indices revealed that both Models 2 and 3 provide a good fit to the data. A chi-square difference test for nested models estimated using a robust, weighted, least-squares estimator revealed that Model 3 provided a significantly better fit to the data than Model 2 (
2 diff = 1650.159, df diff = 4, P < 0.001). Model 2, however, was chosen to be a superior theoretical model when compared to Model 3, for a number of reasons. Firstly, Model 3 had a complex factor structure, with several items loading on more than one factor. The current results revealed that although item 1 was specified to load on both factors, the loading for this item on the mental health factor was extremely low (–0.02), suggesting that this item was not a good indicator of the factor. It should be noted that the correlated residuals in this complex factor model were derived from post hoc model modification analysis (Fleishman and Lawerence, 2003). In contrast, however, the correlated residuals in Model 2 were only included because it is hypothesized that correlated items with a specific sub-domain are likely to have some specific element in common that is not accounted for in the overall factor solution (Wilson et al., 2002). The addition of these correlated residuals does not affect the estimates of the key parameters of the model (i.e. structural regression coefficients) but enhances the model fit by taking into account a variety of minor factors that are not included in the two-factor solution (Grant et al., 2007). Previous research has demonstrated that Model 2 is an appropriate factor model for the SF-12v2 (Wilson et al., 2002; Maurischat et al., 2006). Thus, Model 2 was chosen as the structural model for the MIMIC analysis.
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The MIMIC model involved the regression of the physical and mental health factors on the diagnosis groups and the other covariates (see Table 3). These structural regression equations are interpreted as partial regression coefficients. The factor loadings, factor correlations and residual correlations remained stable following the introduction of the covariates and the fit of the model improved (
2 = 1588.958, df = 69, P < 0.0001; CFI = 0.974; TLI = 0.980; RMSEA = 0.029).
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Compared to the one-criterion orphan group, the abuse and the no-AUD groups experienced significantly better physical health. Two-criterion orphans and the dependence group did not differ significantly from the reference group in terms of physical health. When mental health was considered, one-criterion orphans had significantly better health when compared to two-criterion orphans and the dependence group, but significantly poorer health than the no-AUD group. No significant differences were evident between the one-criterion orphan group and the alcohol abuse group.
Males and younger adults (18–29 years) were more likely to report experiencing better overall health. Individuals who binge drank on a frequent basis (i.e. everyday or nearly everyday) or rarely (i.e. 1–2 times a year) were significantly more likely to report experiencing poorer physical health when compared to those individuals who engaged in binge drinking once or twice a week. Frequency of binge drinking was not associated with mental health. Experiencing poorer physical and mental health was associated with seeking treatment for alcohol problems, having a family history of alcoholism, starting to drink at 12 years or younger, being a smoker and having a diagnosis of an anxiety, mood or drug use disorder.
The MIMIC analysis revealed no significant direct effects between the diagnosis groups and the SF-12v2 items, indicating that the factor model for the SF-12v2 was the same for the different groups.
Although not the main focus of this study, interesting findings emerged in the MIMIC analysis regarding the effects of frequency of binge drinking on physical and mental health. Follow-up analyses were subsequently conducted to compare groups of individuals engaging in binge drinking at different frequencies in terms of demographic and clinical characteristics (see Table 4). The results revealed that compared to individuals who regularly binge drank (i.e. 1–2 times a week), those who binge drank more frequently were generally more likely to be younger and male, have a younger age of drinking onset, have sought treatment for alcohol problems, have a family history of alcoholism, likely to smoke, have experienced prior alcohol problems and have experienced comorbid mood and drug use disorders in the last year. Alternatively, those who engaged in binge drinking less frequently were more likely to be female, older, have a later drinking onset, less likely to have sought treatment or have a family history of alcoholism, less likely to smoke and less likely to have experienced prior alcohol problems or comorbid psychiatric disorders.
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Discussion |
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The current study examined the general health of alcohol users and, in particular, diagnostic orphans in a recent national survey. Firstly, in relation to the SF-12v2, the results revealed that a simple two-factor structure, with correlated residuals between pairs of items in the same sub-domains, was a good fitting model. The absence of significant direct effects in the MIMIC analysis demonstrated that the SF-12v2 is invariant across groups experiencing different alcohol-related problems. Thus, these findings suggest that in the future, researchers and clinicians in the alcohol field should consider using the SF-12v2 in situations where a measure of general health status is required.
Several findings in relation to the main aim of the study warrant discussion. Firstly, experiencing only one criterion of alcohol dependence was associated with significantly poorer mental health when compared to the no-AUD group. Consistent with previous research (Olfson et al., 1996) therefore, experiencing supposedly mild symptoms of alcohol dependence can have a detrimental effect on mental health. One- and two-criterion orphans differed in relation to mental health. Specifically, the results revealed that experiencing an additional criterion of alcohol dependence translated into an increased risk for poorer mental health for two-criterion orphans. The current data are cross-sectional and therefore causality in relation to the poorer mental health of diagnostic orphans cannot be established. Previous research, however, has suggested that diagnostic orphans are significantly more likely to experience comorbid drug dependence when compared to individuals with no-AUD (Ray et al., 2008b; Schuckit et al., 2008). Thus, in addition to experiencing alcohol dependence symptoms, diagnostic orphans may be at greater risk for other mental health problems that could contribute to their poor mental health. The alcohol dependence group had significantly poorer mental health when compared to one-criterion orphans. It has been previously reported that alcohol dependence is not associated with mental disability (Romeis et al., 1999; Proudfoot and Teesson, 2002). The present results, however, concur with other research investigations that have demonstrated a negative association between alcohol dependence and mental health, even after accounting for the effects of important demographic and clinical covariates (Volk et al., 1997; Daeppen et al., 1998). Interestingly, alcohol abuse was not associated with poorer mental health, which is consistent with the research that has documented that individuals with alcohol abuse generally do not view their problems as serious or debilitating (Volk et al., 1997; Biji and Ravelli, 2000).
Two important findings emerged in relation to physical health. Firstly, one-criterion orphans were significantly more likely to experience poorer physical health when compared to the no-AUD and the alcohol abuse groups. Conversely, however, no significant physical health differences were evident either between the two diagnostic orphan groups or between the one-criterion orphan group and the alcohol dependence group. These findings are inconsistent with the consensus that experiencing more alcohol symptoms is associated with greater impairment. The reasons for these findings are not entirely clear; however, one plausible explanation could relate to the type of alcohol symptoms being endorsed by each diagnosis group. As previously mentioned, recent research has demonstrated that some of the diagnostic criteria for DSM-IV alcohol use disorders reflect more severe problems than others (Krueger et al., 2004; Kahler and Strong, 2006; Ray et al., 2008a). For example, Kahler and Strong (2006) revealed that particular items relating to the alcohol abuse criterion ‘recurrent alcohol use in hazardous situations’ were positioned at the milder end of the AUD continuum. On the other hand, items relating to the alcohol dependence criterion ‘use despite knowledge of physical or psychological harm caused or worsened by drinking’, however, were located at the more severe end of the continuum (Kahler and Strong, 2006). Although the aim of this investigation was not to explore the type of symptoms experienced by the different diagnosis groups, the current findings suggest that the one-criterion orphans could be experiencing more severe alcohol symptoms when compared to the other diagnosis groups. This is an important area for future research and the findings from such endeavours could have significant implications for the selection of symptoms to operationalize the AUD criteria in DSM-V (Kahler and Strong, 2006). Finally, in addition to the type and number of symptoms experienced, it will be important for future research to explore the role of other important clinical and socio-demographic factors (e.g. SES, ethnicity, social support) in contributing to the poorer general health of diagnostic orphans relative to both AUD and no-AUD groups.
While not the main focus of this paper, a number of other findings are worth mentioning briefly. Firstly, consistent with the literature on the SF-12v2, males reported better overall general health when compared to women (Fleishman and Lawerence, 2003). Not surprisingly, an early onset of alcohol use and having a family history of alcoholism impacted negatively on both physical and mental functioning (Grant and Dawson, 1997). The finding that individuals seeking treatment for alcohol problems often are in poorer physical and mental health compared to those who do not seek treatment is also consistent with the existing literature (Stein et al., 1998). Despite comparing binge drinkers in terms of demographic and clinical characteristics, the associations between frequency of binge drinking and physical and mental health are not entirely clear. For example, the finding that individuals who rarely binge drink have poorer physical health than those who binge drink once or twice a week is somewhat surprising. Previous research has explained this association by proposing that individuals who rarely binge drink are often former heavy drinkers who have health problems but currently only drink infrequently (Volk et al., 1997). Although the current study did not support this hypothesis, the results demonstrated that the poorer health of infrequent binge drinkers maybe partly explained by the fact that this group was more likely to be older and female compared to regular binge drinkers. The lack of a significant association between frequency of binge drinking and mental health could be related to the fact that individuals with substance use problems often under-report negative psychosocial consequences associated with consumption (Chan, 1991). Alternatively, it may be the case that after accounting for the effects of important demographic and clinical variables, including comorbid psychiatric disorders, the frequency of binge drinking did not significantly impact on the mental health of alcohol users in this sample.
The current study has several major strengths including the use of a theory-driven structural equation-modelling framework to conduct analysis, using data from a large national survey. Furthermore, a limitation of previous epidemiologic research (Hasin and Paykin, 1999) was addressed by comparing the levels of impairment of diagnostic orphans to both DSM-IV AUD groups and to a no-AUD group. This study advanced the literature on diagnostic orphans by differentiating between those experiencing one and two criteria of dependence (Harford et al., 2005). Nevertheless, all of the limitations associated with cross-sectional survey research apply to this study. Specifically, the formation of the alcohol diagnosis groups relied on retrospective, self-reported data, which may be subject to social desirability biases. Responses to the SF12-v2 items are susceptible to similar limitations. Efforts were made in the current study to control for the effects of important factors that could influence the association between general health functioning and alcohol symptoms (e.g. cigarette use, comorbid psychiatric disorder, etc.). Nevertheless, the possibility that other confounding variables could have had an impact on this relationship cannot be ruled out. Finally, this study explored the association between general health status and alcohol use. Further research is needed to investigate if similar findings are evident for other substances.
Conclusion and implications for future diagnostic classification systems
Findings from this and other research investigations suggest that by overlooking diagnostic orphans, clinicians and researchers may be excluding an important subset of individuals who are experiencing clinically relevant symptoms of dependence. Presently, there is uncertainty as to how diagnostic orphans should be dealt with. Early research in this area suggested that diagnostic orphans could be included under the abuse category (Sarr et al., 2000); however, recent studies have advised against this practice arguing that it could dilute the prognostic meaning of abuse (Ray et al., 2008b; Schuckit et al., 2008). It has been suggested that a dimensional diagnostic approach may be more appropriate for capturing a holistic view of the psychopathology associated with alcohol use (Krueger et al., 2004; Proudfoot et al., 2006). This would mean that diagnostic orphans could be included in the spectrum of AUD, possibly being positioned at the milder end. Longitudinal epidemiologic research is now needed to investigate the long-term course of diagnostic orphans and to explore whether the subthreshold dependence symptoms remit or worsen over time. Research of this nature could be beneficial in the development of cost-effective intervention programmes designed to reduce the risk of diagnostic orphans escalating to more severe symptoms in the future.
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