Subtypes of depression in a nationally representative sample

Journal of Affective Disorders 113 (2009) 88 – 99 www.elsevier.com/locate/jad

Research report

Subtypes of depression in a nationally representative sample Natacha Carragher ⁎, Gary Adamson, Brendan Bunting, Siobhan McCann Psychology Research Institute, University of Ulster, N. Ireland, United Kingdom Received 28 January 2008; received in revised form 18 May 2008; accepted 18 May 2008 Available online 21 July 2008

Abstract Background: Continued research efforts aim to elucidate the heterogeneity in depression. The identification of meaningful and valid subtypes has implications for research and clinical practice. Based on patterns of depressive symptomatology, this study identified a typology of depressive syndromes using data from a large, nationally representative survey. Methods: Analyses were based on a subsample of 12,180 respondents from the 2001–2002 Wave of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). Latent class analysis was applied to the DSM-IV ‘A’ criteria for major depression to identify homogenous subtypes or classes of depressive syndromes. Associations between the emergent latent classes and demographic and clinical characteristics were assessed. Results: Three clinically relevant subtypes were identified, in addition to a class who reported few depressive symptoms: severely depressed (40.9%), psychosomatic (30.6%), cognitive–emotional (10.2%) and non-depressed (18.3%). The odds of experiencing negative life events, psychiatric disorders, and having a family background of major depression were significantly higher for the severely depressed, psychosomatic and cognitive–emotional classes, compared to the non-depressed class. Several unique differences between the latent classes also emerged. Limitations: Methodological shortcomings included: reliance on lay interviewer-administered structured interviews to determine diagnoses; basing sample selection on the endorsement of screener items; and, using measures of ‘any anxiety disorder’, ‘any mood disorder’, and ‘any personality disorder’ to determine psychiatric disorder prevalence rates. Conclusions: Significant heterogeneity in depressive symptomatology exists in this U.S. sample. Profiling symptom patterns is potentially useful as a first step in developing tailored intervention and treatment programmes. © 2008 Elsevier B.V. All rights reserved. Keywords: Latent class analysis; Depression; NESARC; Heterogeneity

1. Subtypes of depression in a nationally representative sample Depression is generally considered to be an etiologically and clinically heterogeneous condition. It is asso⁎ Corresponding author. School of Psychology, University of Ulster at Magee, Northland Road, Derry BT48 7JL, Northern Ireland, United Kingdom. Tel.: +44 28 71375367. E-mail address: [email protected] (N. Carragher). 0165-0327/$ - see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.jad.2008.05.015

ciated with a wide range of risk factors and affected individuals vary markedly in their symptom profiles, age at onset, and response to treatment (Kendler et al., 1999). Recognition of such heterogeneity in depression has long motivated research interest in the identification of meaningful and valid subtypes. Subtyping holds promise for guiding research on etiology and better informing clinical management by improving diagnostic practices and prevention approaches, and refining differential treatment (Haslam and Beck, 1994). The identification of meaningful

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typologies is particularly appealing in the context of depression given the public health significance and economic burden of the condition (Murray and Lopez, 1997). Heterogeneity is said to be present when a population can be separated into distinct subpopulations or clusters. More specifically, heterogeneity is observed when it is possible to identify the subpopulations based on an observed variable. Conversely, in the case of unobserved heterogeneity, the variables giving rise to heterogeneity are unknown and subpopulation membership must be inferred from the data. In this context, the subpopulations are termed latent classes since subpopulation membership is unobserved (Lubke and Muthén, 2005). Research efforts to elucidate the heterogeneity in depression have utilised a wide variety of latent variable techniques including factor analysis (Aggen et al., 2005; Muthén, 1989; Simon and von Korff, 2006); discriminant function analysis (Sen, 1987); cluster analysis (Andreasen et al., 1980; Blashfield and Morey, 1979; Cox et al., 2001; Scotte et al., 1997); grade of membership analysis (Blazer et al., 1988, 1989; Davidson et al., 1988); and, more recently, latent class analysis (Chen et al., 2000; Crum et al., 2005; Eaton et al., 1989; Kendler et al., 1996; Sullivan et al., 1998, 2002). Across these typology studies, different assessment instruments have been employed (i.e., self-report checklists, clinician rating scales, and structured psychiatric interviews) and the resultant subtypes have been validated by reference to comorbid psychopathology, demographic variables, familial liability to psychiatric illness, and clinical features, including treatment response and relapse rate. On a final methodological note, these studies have utilised data both from the general population and clinical samples derived from psychiatric hospitals, outpatient clinics, and primary care practices. Basing a typology on data from treated populations may, however, introduce selection bias since individuals in clinical settings do not represent a random sample of those affected in the population and are likely to display greater symptom severity. It follows that such typologies may not generalise to the wider population, thereby limiting their utility in terms of public health initiatives (Kendler et al., 1996; Moss et al., 2007). Based on this rationale, this paper is based on data from the general population. A number of variables have been identified as important in capturing the heterogeneity in depression. One of the most replicated findings in psychiatric epidemiology and cross-national surveys is the increased prevalence of major depression among women compared to men (e.g., Weissman et al., 1996). Similarly, investigations consistently demonstrate that psychiatric disorders such as major depression are “over-represented in the lower social strata” (Miech et al., 1999: 1096). Several lines of

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research garner support for a significant association between major depression and the experience of stressful or negative life events (Leskela et al., 2006) and a familial background of major depression (Weissman et al., 2005). Several studies in the U.S. suggest that racial or ethnic minority groups have a lower prevalence of psychiatric disorders, despite the higher levels of social adversity encountered (Breslau et al., 2006). Research also suggests that having a partner acts as a protective buffer against depression (Dehle et al., 2001). In contrast, the literature on geographic associations with major depression is inconclusive. Some mental health surveys lend support to the idea of a rural advantage (Bijl et al., 1998), whereas other researchers cite lower depression rates in urban areas (Probst et al., 2006), and others fail to find significant differences (Kovess-Masfety et al., 2005). Inconsistent findings are also evident in relation to age (see Jorm, 2000). Comorbidity represents another potential source of heterogeneity and has received considerable attention in the literature in recent years. Research has documented a high rate of comorbidity between major depression and anxiety disorders (Wittchen et al., 2000); substance use disorders, involving alcohol and illicit drugs (Davis et al., 2005; Weissman et al., 1996); and personality disorders (Corruble et al., 1996; Farabaugh et al., 2004). Comorbidity between major depression and nicotine dependence is similarly well established (Fergusson et al., 2003). Epidemiological studies, as used in this paper, are particularly suited to the investigation of comorbidity since they are not confounded by treatment seeking status (Barry et al., 2008). In current nomenclature, unipolar and bipolar disorders are conceptualised as being qualitatively different and categorised as “separate branches on the mood disorder diagnostic tree” (Cuellar et al., 2005: 309). However, in recent years several converging lines of research have garnered support for a continuum between bipolar disorders and major depressive disorder, in line with Kraepelin's classification of mood disorders (for an overview see Benazzi, 2005). Longitudinal analysis, for instance, suggests that patients initially hospitalised for unipolar depression have a significant risk of developing bipolar symptoms (e.g., Goldberg et al., 2001). At the unipolar end of this spectrum, epidemiological surveys and clinical samples provide evidence for a progression over time from dysthymia to major depression, with the two disorders often occurring concurrently — termed ‘double depression’ (Alonso et al., 2004; Bell et al., 2004). This paper employed latent class analysis to identify latent classes or subtypes of depressive syndromes. The

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emergent typology was subsequently validated by reference to covariates cited in the extant literature, reviewed above, as playing an important role in the patterning of depression. This flexible modelling approach is a popular tool in the subtyping literature used to capture unobserved population heterogeneity, and is particularly useful since it can simultaneously accommodate mixed levels of measurement as well as descriptive and predictive models. Muthén and Asparouhov (2006) advocate the use of large samples in investigations of population heterogeneity to ensure that sufficient numbers of respondents endorse the respective criteria. Large nationally representative surveys also have the added advantage of yielding stable parameter estimates. Accordingly, this study utilised data from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). Given that the NESARC is the largest prevalence study of psychiatric disorders conducted to date, the findings hold promise of providing a clearer picture of depression. Furthermore, this paper extended earlier efforts to subtype depression by basing analyses on current DSMIV criteria. 2. Method 2.1. Sample This study is based on data from the 2001–2002 Wave of the NESARC, conducted by the National Institute on Alcohol Abuse and Alcoholism (NIAAA; Grant et al., 2003a). The NESARC is a nationally representative survey, which targeted the civilian, non-institutionalised population living in the United States, including the District of Columbia, Alaska and Hawaii. Face-to-face personal interviews were conducted with 43,093 respondents, aged 18 years and older. The overall response rate was 81%. Various subpopulations, including Blacks, young adults and Hispanics were over-sampled, with data adjusted to reflect over-sampling and household- and person-level non-response. The weighted data were then adjusted to represent the U.S. civilian population based on the 2000 Census. For further details regarding the NESARC methodology see Grant et al. (2005). 2.2. Assessment of depressive symptoms Depressive symptoms were assessed using the Alcohol Use Disorder and Associated Disabilities Interview Schedule-DSM-IV (AUDADIS-IV: Grant et al., 2001), a fully structured diagnostic interview for use by non-clinician interviewers. Test–retest reliability of the

AUDADIS-IV measures of major depression has been shown to be good (kappa = 0.64–0.67; Canino et al., 1999; Chatterji et al., 1997; Grant et al., 2003b) and a clinical reappraisal study reported good correspondence between AUDADIS-IV diagnoses and psychiatrist evaluations (kappa = 0.64–0.68; Canino et al., 1999). The AUDADIS-IV included twenty-one dichotomous symptom item questions that separately operationalised the nine DSM-IV ‘A’ criteria for major depression (American Psychiatric Association, 2000). Similar to other diagnostic interviews (e.g., Composite International Diagnostic Interview: World Health Organization, 1990), the depression module in the AUDADIS-IV was curtailed to those respondents who endorsed the lifetime occurrence of a 2-week period of depressed mood or loss of interest in activities. Basing the sample on the endorsement of screener items precluded the use of these non-independent questions as indicators of depression in the analyses (cf. Slade and Andrews, 2005). As screeners, the items offer little in the way of variability of responses and therefore their exclusion would be unlikely to significantly impact on the findings. In consequence, the analyses focused on seven, rather than nine, aggregated diagnostic criteria: (i) appetite/weight change; (ii) sleep disturbance; (iii) psychomotor difficulties; (iv) fatigue; (v) feelings of worthlessness/excessive guilt; (vi) impaired concentration/indecision; (vii) death/suicidal ideations. A binary coding system indicated the presence or absence of each criterion; where a criterion had multiple symptom items, endorsement of any one of the symptoms meant the criterion was considered to be present. We used the aggregated diagnostic criteria (or item parcels in other words) because they offer a number of advantages over over-disaggregated, individual symptom items. As Little et al. (2002) point out, parcels provide more reliable latent variables by reducing random error and requiring fewer estimated parameters. In turn, this increases the reliability of the model's structural coefficients and provides a more parsimonious model. This analytic strategy has been used in previous typology studies of depression (e.g., Chen et al., 2000). A diagnosis of major depressive disorder (MDD) was not required for inclusion in the analyses (cf. Kendler et al., 1996; Sullivan et al., 1998) in light of research documenting the clinical and public health significance of subthreshold depressive symptomatology (e.g., Goldney et al., 2004). Finally, analyses were restricted to those respondents who had complete information on all items relating to depressive symptomatology (n = 12,180).

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2.3. Latent class analysis Latent class analysis (LCA) was used to empirically identify subtypes of depressive syndromes. Often described as a categorical variant of factor analysis, LCA assumes that observed variables are indicators of an unobserved, latent variable and attempts to explain this relationship in terms of a small number of subgroups or classes. Succinct introductions to LCA are available elsewhere (e.g., Hagenaars and McCutcheon, 2002). As Nylund (2007) points out, there is currently no consensus in the literature regarding a single statistical index that identifies the most appropriate number of classes in a given population. Thus, a series of models with a successive number of classes were specified and selection of the optimal model that combines goodness of fit and parsimony was based on conceptual considerations and various statistical fit indices. Statistical indices reported here include: the Akaike Information Criterion (AIC; Akaike, 1974); Bayesian Information criterion (BIC; Schwartz, 1978); samplesize adjusted BIC (SSABIC; Sclove, 1987); Lo– Mendell–Rubin likelihood ratio test (LMR-LRT; Lo et al., 2001); and an entropy measure (Ramaswamy et al., 1993). The AIC, BIC and SSABIC are goodness of fit measures commonly used for comparison across competing models: the lowest value on each criterion indicates the best best-fitting model. The BIC has been recently found to be one of the most reliable indicators in determining the number of latent classes (Nylund, 2007). Another useful tool for class enumeration is the LMR-LRT, which assesses the improvement in fit between competing models: a non-significant value (p N 0.05) suggests that the model with one fewer class provides a more parsimonious fit to the data. Based on the posterior class membership probabilities, entropy evaluates how well each of the classes is separated and represented by the data; values range from 0 to 1, with high values preferred. The average conditional probabilities for class assignment similarly assess classification quality and accuracy; values approaching or exceeding 0.80 are preferred. Finally, selection of the best-fitting model was based on whether the model reflected coherent, conceptually meaningful subgroups and adequately accounts for the heterogeneity in the population. All analyses were implemented using Mplus version 5.1, a statistical modelling programme which can accommodate complex design methodology (Muthén and Muthén, 1998–2008). Specifically, the NESARC

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complex survey features of stratification, clustering and sampling weights were taken into account in the parameter estimations and the standard error calculations. Methods appropriate for subpopulation analyses were employed and models were specified using maximum-likelihood estimation with robust standard errors (MLR). The MLR estimator is well suited for analysing data based on complex survey designs. Finally, in mixture analysis, models are vulnerable to converging on local rather than global solutions. In order to avoid the issue of local maxima and to ensure that all values converged on identical solutions, 500 random sets of starting values were used initially and 10 final stage optimisations. 2.4. Inclusion of covariates in the measurement model Following identification of the best-fitting latent class solution, several covariates were included in the model to help describe the heterogeneity in depression and to substantiate the validity of the emergent classes or subtypes. Odds ratios (ORs) and accompanying confidence intervals (CIs) were calculated to evaluate these associations. 2.4.1. Demographic variables Demographic variables of interest included: age; educational attainment; total personal income in the last 12 months; race/ethnicity; current marital status; urbanicity; and, gender. 2.4.2. Family history of major depression Respondents were asked a series of questions enquiring whether any of their first- and second-degree relatives had ever experienced major depression. First-degree relatives comprised biological parents or children, and full siblings. Second-degree relatives related to full siblings of both biological parents, and biological paternal and maternal grandparents. For the purposes of the present study, family background of major depression was treated as a continuous measure1: i.e., ‘no family history’, ‘second seconddegree relatives only’, ‘first degree relatives only’, and ‘both first and second second-degree relatives’ (cf. National Institute on Alcohol Abuse and Alcoholism, 2006). 2.4.3. Negative life events Respondents were asked whether they had experienced 12 different types of negative life events in the 1

The analysis was also performed treating family background of major depression as an ordinal categorical variable. Apart from being computationally demanding, there was little, if any, difference in the findings.

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12 months prior to interview. A continuous measure of stress was created based on the number of negative life events reported (cf. Dawson et al., 2007).

tomatology and the remaining NESARC sample are displayed in Table 1. 3.2. Estimation of the number of latent classes

2.4.4. Psychiatric disorders Several disorders were diagnosed in the NESARC, the reliability and validity of which have been documented in several studies (e.g., Grant et al., 2003b). For the purposes of this paper, three mood disorder diagnoses (i.e., dysthymia, mania, hypomania) were aggregated to create a single variable indicating the presence of ‘any other mood disorder’. Similarly, a dichotomous measure of ‘any anxiety disorder’ was coded as positive for individuals who met criteria for panic disorder with/without agoraphobia, social phobia, specific phobia, or generalised anxiety disorder. A dichotomous measure of ‘any personality disorder’ was created to reflect a diagnosis of antisocial, avoidant, dependent, obsessive–compulsive, paranoid, schizoid, or histrionic personality disorder (cf. Dawson et al., 2007). These measures all reflect lifetime estimates. Finally, respondents were asked a series of questions relating to alcohol use and, on this basis, past-year diagnoses of alcohol abuse and/or dependence were made. 2.4.5. Illicit drug use and nicotine The NESARC enquired into the use of ten different types of illicit drugs in the past year: amphetamines, opioids, sedatives, tranquillisers, cocaine, inhalants/ solvents, hallucinogens, cannabis, heroin, and other drugs (e.g., antidepressants). Illicit drug use was considered to be positive if the respondent reported using any of these drugs (cf. Dawson et al., 2007). Finally, respondents answered an extensive list of symptom questions assessing nicotine dependence and a lifetime diagnosis was made accordingly. 2.5. Further validation of the measurement model In a recent paper based on the NESARC, Hasin et al. (2005) reported a MDD lifetime prevalence of 13.23%. In an effort to further substantiate the validity of the measurement model, we examined the lifetime MDD prevalence rates of the emergent latent classes. 3. Results 3.1. Demographic characteristics The demographic distribution of the subsample of respondents who screened positive for depressive symp-

Five latent class models were fitted to the data, beginning with the most parsimonious one-class model

Table 1 Demographic distribution of the subsample of respondents who screened positive for depressive symptomatology and the remaining NESARC sample Variable

Subsample

Remaining NESARC

(n = 12,180)

(n = 30,913)

Unweighted Weighted Unweighted Weighted n Sex Male 4190 Female 7990 Race/ethnicity White 7514 Black 2071 American 260 Indian/Alaska Native Asian/Native 273 Hawaiian/ Pacific Islander Hispanic 2062 Age, y 18–29 2492 30–44 3865 45–64 4026 ≥65 1797 Educational attainment Less than 749 high school High school 4660 Some college 6771 or higher Total personal income in the last ≤ 19,999 6352 20,000–34,999 2706 35,000–69,999 2395 ≥70,000 727 Current marital status Married 5486 or cohabiting Widowed, 3784 separated, or divorced Never married 2910 Urbanicity Rural 2256 Urban 9924

%

n

%

38.3 61.7

14328 16585

51.7 48.3

75.1 9.7 2.7

16993 6174 441

69.2 11.6 1.9

3.1

1059

4.9

9.4

6246

12.4

22.0 31.6 34.2 12.2

6174 9517 8814 6408

21.7 30.6 29.8 17.8

4.7

2582

6.8

37.6 57.7

12405 15926

39.2 54.0

12 months, US$ 51.3 14749 21.7 7269 20.4 6634 6.7 2261

45.7 23.0 22.6 8.7

56.2

16595

63.7

22.4

7333

15.5

21.3

6985

20.8

20.0 80.0

5540 25373

19.6 80.4

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Table 2 Fit indices for a one-class model through to a five-class model Model

Fit statistics AIC

BIC

SSABIC

Entropy

LRT-LMR

1-class 2-class 3-class 4-class 5-class

111,186.755 96,940.697 95,486.036 95,094.991 95,029.562

111,238.608 97,051.811 95,656.409 95,324.626 95,318.456

111,216.363 97,004.142 95,583.318 95,226.111 95,194.519

N/A .78 .68 .67 .60

N/A b.0001 b.0001 b.0001 .3367

Note. AIC, Akaike Information Criteria; BIC, Bayesian Information Criteria; SSABIC, Sample-Size Adjusted Bayesian Information Criteria; LRT, p-value for the Lo–Mendell–Rubin Likelihood Ratio Test. Best-fitting model in bold type.

and four latent classes, are presented in Table 3. To aid interpretation, the estimated probabilities are plotted in Fig. 1. Latent class one comprised the majority of respondents (40.9%) who highly endorsed each of the depressive criteria, with probabilities ranging from 0.69 for death/suicidal ideations to 0.98 for sleep disturbances. This class is best characterised as a ‘severely depressed’ class. Members of the second group, which comprised 30.6% of the sample, had high probabilities of experiencing appetite (0.61) and sleep disturbances (0.86), psychomotor complaints (0.70), and impaired concentration/indecision (0.70). This class was labelled as a ‘psychosomatic’ class. In contrast, the third class (10.2%) consisted of individuals with relatively high probabilities of experiencing feelings of worthlessness/ excessive guilt (0.81), impaired concentration/indecision (0.67), and death/suicidal ideations (0.50). This class was termed as a ‘cognitive–emotional’ class. Finally, latent class four (18.3%) was characterised by respondents who screened positive for depressive symptomatology but displayed relatively low endorsement rates on the DSM-IV criteria, as the conditional probabilities are all below 0.26. As such, this class was considered as a ‘non-depressed’ class.

through to a five-class model. The goodness of fit indices, provided in Table 2, suggested that the bestfitting model was a four-class solution. The AIC, BIC, and SSABIC were markedly lower for the four-class model compared to the earlier models. There were only small decreases in these indices thereafter, providing weak support for a five-class solution. The LMR-LRT further confirmed that the five-class model was not a significant improvement over the four-class model (p N 0.05). Classification quality, based upon the average conditional probabilities for most likely class membership, indicated that the four classes were reasonably well defined: 0.85 for latent class one, 0.77 for latent class two, 0.73 for latent class three, and 0.87 for latent class four. The entropy measure (0.67) similarly suggested that the data was adequately defined by a four-class solution. 3.3. Symptom profiles On a more substantive note, the four-class solution appeared to be conceptually meaningful. Individuals were assigned to the latent classes on the basis of their response profile and the estimated probabilities of endorsing the seven DSM-IV criteria, for the subsample

Table 3 Estimated probabilities of endorsing each of the DSM-IV criteria for the four latent classes DSM-IV criterion

Observed proportion Probability of endorsing each criterion endorsing each criterion Severely depressed Psychosomatic Cognitive–emotional Non-depressed

Appetite/weight change Sleep disturbance Psychomotor difficulties Fatigue Feelings of worthlessness/excessive guilt Impaired concentration/indecision Death/suicidal ideations

0.637 0.735 0.493 0.630 0.557 0.702 0.426

0.876 0.984 0.815 0.889 0.927 0.959 0.692

0.609 0.857 0.394 0.704 0.294 0.702 0.237

0.442 0.390 0.312 0.350 0.814 0.670 0.504

0.257 0.168 0.040 0.081 0.025 0.142 0.102

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Fig. 1. Latent class profile plot of the seven DSM-IVSSS major depression criteria.

3.4. Validity of the latent classes To validate the measurement model and to help describe the heterogeneity in depression, covariates were included in the model (see Table 4 for a list of the covariates). This resulted in an improvement in fit over the original unrestricted model: AIC = 90877.905, BIC = 91685.328, SSABIC = 91338.938. The entropy result was similar (0.68) and the LMR-LRT remained significant (p = 0.007). Additionally, the average conditional probabilities and prevalence rates were similar: e.g., class 1 = 37.0%, class 2 = 30.3%, class 3 = 13.1%, and class 4 = 19.6%. It would seem prudent to compare the four- and five-class solutions with the inclusion of covariates in order to ensure stability of model fit. The LMR-LRT indicated that the five-class solution was not a significant improvement over the four-class solution (p = 0.064). Associations between the covariates and latent classes are presented in Table 4, with the ‘non-depressed’ class treated as the reference class. The OR describes the proportionate change in the odds associated with a oneunit change in the independent variable. As Table 4 indicates, the odds of experiencing negative life events and having a family background of major depression were significantly elevated for members of the ‘severely depressed’, ‘psychosomatic’ and ‘cognitive–emotional’ classes, compared to the ‘non-depressed’ class. The ‘severely depressed’, ‘psychosomatic’ and ‘cognitive–

emotional’ classes were all significantly more likely to experience psychiatric disorders, though the magnitude of risk varied accordingly. For example, the odds ratio associated with a personality disorder diagnosis was highest for the ‘cognitive–emotional’ class (OR = 4.06, CI = 2.88–5.71), whereas the odds ratio associated with an anxiety disorder diagnosis was highest for the ‘severely depressed’ class (OR = 4.54, CI = 3.57–5.78). The odds ratio associated with a mood disorder diagnosis was highest for the ‘severely depressed’ class (OR = 16.54, CI = 10.52–26.01); in fact, this was the largest odds ratio observed in the study. Those individuals with a high school education and American Indians/Alaska Natives were significantly less likely to be present in the ‘severely depressed’, ‘psychosomatic’ and ‘cognitive–emotional’ classes, compared to the nondepressed class. Having a lifetime diagnosis of nicotine dependence (OR = 1.39, CI = 1.12–1.73 and OR = 2.47, CI = 1.98– 3.09) or being female (OR = 0.41, CI = 0.32–0.53 and OR = 0.49, CI = 0.38–0.63) were associated with increased risks of being in the ‘psychosomatic’ and ‘severely depressed’ classes. Belonging to the highestearning income bracket signalled a decreased risk of being in the ‘severely depressed’ (OR = 0.75, CI = 0.57– 0.97) or ‘cognitive–emotional’ class (OR = 0.66, CI = 0.45–0.98). Interestingly, there were no significant differences among the classes in respect of a drug use disorder diagnosis.

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Table 4 Odds ratios and 95% confidence intervals for the latent class model with covariates Covariate

Urbanicity Gender Age, y 18–29 (referent) 30–44 45–64 ≥65 Education attainment Less than high school (referent) High school Some college or high Income in the last 12 months, US$ ≤19,999 (referent) 20,000–34,999 35,000–69,999 ≥70,000 Race/ethnicity White (referent) Black American Indian/Alaska Native Asian/Native Hawaiian/Pacific Islander Hispanic Current martial status Married or cohabiting (referent) Widowed, separated or divorced Never married Negative life events (last 12 months) Family history of major depression Lifetime nicotine dependence diagnosis Any lifetime mood disorder Any other lifetime personality disorder Any lifetime anxiety disorder Alcohol abuse diagnosis (last 12 months) Alcohol dependence diagnosis (last 12 months) Alcohol abuse and dependence diagnosis (last 12 months) Any drug use disorder diagnosis (last 12 months)

Latent classes Severely depressed

Psychosomatic

Cognitive–emotional

OR (95% CI)

OR (95% CI)

OR (95% CI)

0.73 (0.56–0.94) 0.49 (0.38–0.63)

0.99 (0.79–1.23) 0.41 (0.32–0.53)

0.97 (0.65–1.45) 1.94 (1.36–2.78)

0.84 (0.63–1.12) 1.23 (0.96–1.57) 1.04 (0.82–1.31)

0.70 (0.54–0.91) 1.06 (0.83–1.35) 0.85 (0.67–1.06)

0.80 (0.50–1.27) 0.85 (0.56–1.28) 0.74 (0.52–1.05)

0.40 (0.29–0.57) 0.92 (0.63–1.36)

0.58 (0.43–0.79) 0.94 (0.65–1.36)

0.44 (0.25–0.76) 1.73 (0.73–4.07)

0.93 (0.62–1.41) 0.94 (0.74–1.19) 0.75 (0.57–0.97)

1.24 (0.85–1.80) 0.90 (0.72–1.13) 1.07 (0.86–1.33)

2.11 (0.86–5.18) 0.91 (0.65–1.28) 0.66 (0.45–0.98)

0.95 (0.75–1.21) 0.52 (0.40–0.67) 0.95 (0.57–1.60) 0.77 (0.43–1.41)

0.92 (0.74–1.16) 0.68 (0.53–0.88) 0.87 (0.47–1.62) 0.40 (0.23–0.72)

0.77 (0.53–1.13) 0.57 (0.36–0.90) 0.53 (1.18–1.56) 0.84 (0.34–2.09)

0.81 (0.51–1.28) 1.47 (1.21–1.78) 1.26 (1.20–1.33) 1.60 (1.46–1.74) 2.47 (1.98–3.09) 16.54 (10.52–26.01) 3.49 (2.61–4.67) 4.54 (3.57–5.78) 1.23 (0.80–1.92) 2.12 (0.78–5.79) 1.93 (0.92–4.06) 1.03 (0.50–2.13)

1.58 (1.07–2.35) 1.17 (0.99–1.39) 1.11 (1.05–1.17) 1.35 (1.25–1.46) 1.39 (1.12–1.73) 3.19 (1.98–5.14) 1.47 (1.06–2.03) 2.26 (1.81–2.82) 1.29 (0.86–1.93) 2.59 (0.94–7.17) 0.97 (0.44–2.15) 1.03 (0.47–2.25)

1.27 (0.70–2.32) 0.89 (0.62–1.28) 1.11 (1.01–1.21) 1.55 (1.38–1.73) 1.26 (0.90–1.77) 5.84 (3.38–10.07) 4.06 (2.88–5.71) 2.47 (1.70–3.58) 1.59 (0.91–2.79) 3.40 (1.20–9.67) 2.30 (1.01–5.27) 1.61 (0.69–3.76)

Bold type indicates a significant odds ratio.

Among individual factors, being male (OR = 1.94, CI = 1.36–2.78) or having a past-year alcohol dependence diagnosis (OR = 3.40, CI = 1.20–9.67) or a pastyear alcohol abuse and dependence diagnosis (OR = 2.30, CI = 1.01–5.27) were consistent with increased risks of being in the ‘cognitive–emotional’ class. The likelihood of being widowed, separated, or divorced (OR = 1.58, CI = 1.07–2.35) was significantly higher for the ‘psychosomatic’ class, compared to the ‘nondepressed’ class. Individuals who were aged 30– 44 years (OR = 0.70, CI = 0.54–0.91) or Hispanic (OR = 0.40, CI = 0.23–0.72) were significantly less likely to be present in the ‘psychosomatic’ class. Finally, individuals who resided in a rural area (OR = 0.73,

CI = 0.56–0.94) or who were never being married (OR = 1.47, CI = 1.21–1.78) had significantly higher odds of being in the ‘severely depressed’ class, compared to the ‘non-depressed’ class. 3.5. Major depressive disorder (MDD) diagnoses Using the posterior probabilities for most likely class membership, the lifetime prevalence estimates of MDD were examined for each latent class to further substantiate the emergent typology. As one might expect, a considerable proportion (92.1%) of the severely depressed class had a lifetime diagnosis of MDD, whilst the prevalence rate was zero in the non-depressed class.

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The psychosomatic and cognitive–emotional classes had a MDD lifetime prevalence rate of 48.4% and 35.5%, respectively. 4. Discussion In this paper, we applied LCA to a nationally representative sample and identified three clinically relevant homogeneous subtypes of depressive syndromes. A fourth subgrouping of respondents reported few, if any, depressive symptoms. The ‘severely depressed’ subtype comprised the majority of respondents who exhibited high endorsement rates across the DSM-IV criteria. The profiles of the ‘psychosomatic’ and ‘cognitive–emotional’ subtypes were comparatively less uniform. Specifically, the ‘psychosomatic’ subtype was characterised by appetite and sleep disturbances, psychomotor complaints, and impaired concentration/ indecision. Finally, respondents belonging to the ‘cognitive–emotional subtype’, the smallest subgroup, reported feelings of worthlessness/excessive guilt, impaired concentration/indecision, and death/suicidal ideations. The subtypes share a number of characteristics in common with other typologies, particularly that by Chen et al. (2000) who identified ‘psychomotor’, ‘severely depressed’ and ‘non-depressed’ latent classes in their analysis of data from the Epidemiologic Catchment Area Study. Similar to the present study, Eaton et al. (1989) identified a class resembling severe depression. However, the present paper failed to find evidence in support of a gradient of severity, as reported by Garrett and Zeger (2000) in the their LCA using Markov chain Monte Carlo techniques. Having said that, detailed comparisons with typology studies are limited, due to methodological issues such as differences in psychological measurements. The inclusion of covariates provided a valuable insight into the subtypes. For instance, a significant association was observed between the ‘severely depressed’ subtype and rural residence. As Probst et al. (2006) point out, rural residents are more likely than their urban counterparts to experience circumstances, conditions, and behaviours that contribute towards depression. Specifically, they have poorer physical health and typically have less access to primary health care, specialists, health-related technologies, and other health and social services. Other factors, including changes in the traditional characteristics of rural life may contribute to the significant association observed in this study. In light of research documenting a relationship between social isolation and suicide (e.g., Singh and Siahpush, 2002), the present finding has important implications in terms of allocation of resources towards the

alleviation of the public health burden of depression in rural areas. On a related note, the ‘cognitive–emotional’ subtype was characterised by suicidal ideation symptomatology and was significantly associated with males with a lifetime diagnosis of alcohol dependence or abuse and dependence. This gender and alcohol use disorder finding is consistent with previous research (Sher, 2005). Indeed, given that suicide was the eighth leading cause of death for males in the U.S. in 2004 (Centers for Disease Control and Prevention, CDC, 2005), this current findings further underscores the need for an increased understanding of the associations between alcohol use and suicidality and the targeting of resources to address these issues. A number of similarities were apparent across the ‘severely depressed’, ‘psychosomatic’ and ‘cognitive– emotional’ latent classes. Relative to the ‘non-depressed’ class, all three subtypes were significantly more likely to have a family background of depression, to experience negative life events, and to be vulnerable to developing psychiatric disorders. The latter finding, in particular, has public health and economic implications as comorbidity is significantly associated with functional status and quality of life (Gijsen et al., 2001); increases in service utilisation and health care costs (Druss and Rosenheck, 1999); and negatively influences treatment outcome, prognosis, and course as individuals typically display a more chronic and treatment-resistant course (Myrick and Brady, 2003; Swendsen and Merikangas, 2000). Relative to respondents with the lowest formal educational attainment, high school graduates, on the whole, were less likely to report depressive symptomatology. Further consistent with the literature linking socially disadvantaged groups to mental illness (Miech et al., 1999), individuals earning ≥ $70,000 had a decreased risk of reporting severely depressed or cognitive–emotional symptoms. As Shavers (2007) suggests, individuals with a higher level of education are likely to possess superior information processing and problem solving skills and able to interact effectively with healthcare providers. Moreover, they are more likely to be socialized to healthpromoting behaviour and lifestyles, and have better occupational and economic conditions, as well as psychological resources. Due to the disabilities and economic cost incurred by poor mental health, intervention strategies targeting improvements in education appear most worthwhile. Significant health disparities also emerged, with Hispanics significantly less likely to report psychosomatic symptoms relative to their White counterparts, and American Indians/Alaska Natives had a decreased risk,

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in general, of reporting depressive symptomatology. Congruent with recent findings from Breslau et al. (2006) and Beals et al. (2005), this finding underscores the importance of exploring factors which offer protection to these ethnic minority populations. The ‘severely depressed’ and ‘psychosomatic’ subtypes were significantly more likely to comprise females. This gender disparity in depression in well established in the literature, possibly reflecting biological, environmental, or psychological differences between genders (Kuehner, 2003). Similarly, research consistently indicates that having a partner has a protective effect for mental and physical health (Dehle et al., 2001), providing a valuable source of companionship, as well as emotional and financial support (Waite and Lehrer, 2003). However, a surprising feature of the present analyses was the failure to detect a significant association between the subtypes and a drug use disorder. This is inconsistent with community and clinical studies which frequently cite a significant association between depression and drug use (Davis et al., 2005; Weissman et al., 1996). Some notes of caution, however, should temper the above findings. Firstly, due to the structure of the AUDADIS-IV, this study was curtailed to individuals who reported a 2-week period of depressed mood/loss of interest in activities. This may limit the generalisability of the findings to individuals in the general population who do not endorse these questions. Secondly, the NESARC utilised lay interviewer-administered structured interviews to determine mental health diagnoses. Having said that, the interviews were conducted by professional interviewers from the U.S. Bureau of the Census and adhered to strict quality control procedures. In addition, the reliability and validity of the diagnoses have been established (Grant et al., 2003b, 2005). A final limitation concerns our reliance on measures of ‘any anxiety disorder’, ‘any mood disorder’, and ‘any personality disorder’ to determine psychiatric disorder prevalence rates. Albeit a somewhat crude measure for analyses involving common disorders, this metric facilitated examination of less prevalent disorders and interpretation of class structures (cf. Whitesell et al., 2006). Notwithstanding these limitations, this paper highlights the utility of latent class analysis in elucidating the heterogeneity in depression in the general population. Rather than a generic one size fits all approach, profiling patterns of depressive symptomatology is a potentially useful first step in informing tailored intervention and treatment strategies (cf. Moss et al., 2007). The second wave of the NESARC holds promise for examining

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class transitions over time and contributing to the prediction of outcome. Role of funding source Nothing declared. Conflict of interest All authors declare that they have no conflict of interests.

References Aggen, S.H., Neale, M.C., Kendler, K.S., 2005. DSM criteria for major depression: evaluating symptom patterns using latent-trait item response models. Psychol. Med. 35, 475–487. Akaike, H., 1974. A new look at the statistical model identification. IEEE Trans. Automat. Contr. 19, 716–723. Alonso, J., Angermeyer, M.C., Bernert, S., Bruffaerts, R., Brugha, T.S., Bryson, H., de Girolamo, G., Graaf, R., Demyttenaere, K., Gasquet, I., Haro, J.M., Katz, S.J., Kessler, R.C., Kovess, V., Lépine, J.P., Ormel, J., Polidori, G., Russo, L.J., Vilagut, G., Almansa, J., Arbabzadeh-Bouchez, S., Autonell, J., Bernal, M., Buist-Bouwman, M.A., Codony, M., Domingo-Salvany, Ferrer, M., Joo, S.S., Martínez-Alonso, M., Matschinger, H., Mazzi, F., Morgan, Z., Morosini, P., Palacín, C., Romera, B., Taub, N., Vollebergh, W.A., 2004. 12-Month comorbidity patterns and associated factors in Europe: results from the European Study of the Epidemiology of Mental Disorders (ESEMeD) project. Acta Psychiatr. Scand., Suppl. 420, 28–37. American Psychiatric Association, 2000. Diagnostic and Statistical Manual of Mental Disorders, Text Revision, 4th Ed. American Psychiatric Association, Washington. Andreasen, N.C., Grove, W.M., Maurer, R., 1980. Cluster analysis and the classification of depression. Br. J. Psychiatry 137, 256–265. Barry, D., Pietrzak, R.H., Petry, N.M., 2008. Gender differences in associations between body mass index and DSM-IV mood and anxiety disorders: results from the National Epidemiologic Survey on Alcohol and Related Conditions. Ann. Epidemiol. 18, 458–466. Beals, J., Novins, D.K., Whitesell, N.R., Spicer, P., Mitchell, C.M., Manson, S.M., 2005. Prevalence of mental disorders and utilization of mental health services in two American Indian reservation populations: mental health disparities in a national context. Am. J. Psychiatry 162, 1723–1732. Bell, B., Chalklin, L., Mills, M., Browne, G., Steiner, M., Roberts, J., Gafni, A., Byrne, C., Wallik, D., Kraemer, J., Webb, M., Jamieson, E., Whittaker, S., Dunn, E., 2004. Burden of dysthymia and comorbid illness in adults in a Canadian primary care setting: high rates of psychiatric illness in the offspring. J. Affect. Disord. 78, 73–80. Benazzi, F., 2005. The relationship of major depressive disorder to bipolar disorder: continuous or discontinuous? Curr. Psychiatry Rep. 7, 462–470. Bijl, R.V., Ravelli, A., van Zessen, G., 1998. Prevalence of psychiatric disorder in the general population: results of the Netherlands Mental Health Survey and Incidence Study (NEMESIS). Soc. Psychiatry Psychiatr. Epidemiol. 33, 587–595. Blashfield, R.K., Morey, L.C., 1979. The classification of depression through cluster analysis. Compr. Psychiatry 20, 516–527. Blazer, D., Woodbury, M., Hughes, D.C., George, L.K., Manton, K.G., Bachar, J.R., Fowler, N., 1989. A statistical analysis of the classification of depression in a mixed community and clinical sample. J. Affect. Disord. 16, 11–20.

98

N. Carragher et al. / Journal of Affective Disorders 113 (2009) 88–99

Blazer, D., Swartz, M., Woodbury, M., Manton, K.G., Hughes, D., George, L.K., 1988. Depressive symptoms and depressive diagnoses in a community population: use of a new procedure for analysis of psychiatric classification. Arch. Gen. Psychiatry 45, 1078–1084. Breslau, J., Aguilar-Gaxiola, S., Kendler, K.S., Su, M., Williams, D., Kessler, R.C., 2006. Specifying race-ethnic differences in risk of psychiatric disorder in a USA national sample. Psychol. Med. 36, 57–68. Canino, G., Bravo, M., Ramirez, R., Febo, V.E., Rubio-Stipec, M., Fernandez, R.L., Hasin, D., 1999. The Spanish Alcohol Use Disorder and Associated Disabilities Interview Schedule (AUDADIS): reliability and concordance with clinical diagnoses in a Hispanic population. J. Stud. Alcohol 60, 790–799. Centers for Disease Control and Prevention, 2005. Web-based Injury Statistics Query and Reporting System (WISQARS), National Center for Injury Prevention and Control, CDC. Retrieved 14 September 2007, from http://www.cdc.gov/ncipc/ wisqars. Chatterji, S., Saunders, J.B., Vrasti, R., Grant, B.F., Hasin, D.S., Mager, D., 1997. Reliability of the Alcohol Use Disorder and Associated Disabilities Interview Schedule-Alcohol/Drug Revised (AUDADIS-ADR): an international comparison. Drug Alcohol Depend. 47, 171–185. Chen, L., Eaton, W.W., Gallo, J.J., Nestadt, G., 2000. Understanding the heterogeneity of depression through the triad of symptoms, course and risk factors: a longitudinal population-based study. J. Affect. Disord. 59, 1–11. Corruble, E., Ginestet, D., Guelfi, J.D., 1996. Comorbidity of personality disorders and unipolar major depression: a review. J. Affect. Disord. 37, 157–170. Cox, B.J., Enns, M.W., Larsen, D.K., 2001. The continuity of depression symptoms: use of cluster analysis for profile identification in patient and student samples. J. Affect. Disord. 65, 67–73. Crum, R.M., Storr, C.L., Chan, Y.F., 2005. Depression syndromes with risk of alcohol dependence in adulthood: a latent class analysis. Drug Alcohol Depend. 79, 71–81. Cuellar, A.K., Johnson, S.L., Winters, R., 2005. Distinctions between bipolar and unipolar depression. Clin. Psychol. Rev. 25, 307–339. Davidson, J., Woodbury, M.A., Pelton, S., Krishnan, R., 1988. A study of depressive typologies using grade of membership analysis. Psychol. Med. 18, 179–189. Davis, L.L., Rush, J.A., Wisniewski, S.R., Rice, K., Cassano, P., Jewell, M.E., Biggs, M.M., Shores-Wilson, K., Balasubramani, G.K., Husain, M.M., Quitkin, F.M., McGrath, P.J., 2005. Substance use disorder comorbidity in major depressive disorder: an exploratory analysis of the Sequenced Treatment Alternatives to Relieve Depression cohort. Compr. Psychiatry 46, 81–89. Dawson, D.A., Grant, B.F., Li, T.K., 2007. Impact of age at first drink on stress-reactive drinking. Alcohol. Clin. Exp. Res. 31, 69–77. Dehle, C., Larsen, D., Landers, J.E., 2001. Social support in marriage. Am. J. Fam. Ther. 29, 307–324. Druss, B.G., Rosenheck, R.A., 1999. Patterns of health care costs associated with depression and substance abuse in a national sample. Psychiatr. Serv. 1999, 214–218. Eaton, W.W., Dryman, A., Sorenson, A., McCutcheon, A., 1989. DSM-III major depressive disorder in the community: a latent class analysis of data from the NIMH Epidemiologic Catchment Area Programme. Br. J. Psychiatry 155, 48–54. Farabaugh, A., Mischoulon, D., Fava, M., Guyker, W., Alpert, J., 2004. The overlap between personality disorders and major depressive disorder (MDD). Ann. Clin. Psychiatry 16, 217–224.

Fergusson, D.M., Goodwin, R.D., Horwood, L.J., 2003. Major depression and cigarette smoking: results of a 21-year longitudinal study. Psychol. Med. 33, 1357–1367. Garrett, E.S., Zeger, S.L., 2000. Latent class model diagnosis. Biometrics 56, 1055–1067. Gijsen, R., Hoeymans, N., Schellevis, F.G., Ruwaard, D., Satariano, W.A., van den Bos, G.A., 2001. Causes and consequences of comorbidity: a review. J. Clin. Epidemiol. 54, 661–674. Goldberg, J.F., Harrow, M., Whiteside, J.E., 2001. Risk for bipolar illness in patients initially hospitalized for unipolar depression. Am. J. Psychiatry 158, 1265–1270. Goldney, R.D., Fisher, L.J., Dal Grande, E., Taylor, A.W., 2004. Subsyndromal depression: prevalence, use of health services and quality of life in an Australian population. Soc. Psychiatry. Psychiatr. Epidemiol. 39, 293–298. Grant, B.F., Hasin, D.S., Stinson, F.S., Dawson, D.A., Chou, P.S., Ruan, W.J., Huang, B., 2005. Co-occurrence of 12-month mood and anxiety disorders and personality disorders in the US: results from the National Epidemiologic Survey on Alcohol and Related Conditions. J. Psychiatr. Res. 39, 1–9. Grant, B.F., Kaplan, K., Shepard, J., Moore, T., 2003a. Source and Accuracy Statement for Wave 1 of the 2001–2003 National Epidemiologic Survey on Alcohol and Related Conditions. National Institute on Alcohol Abuse and Alcoholism, Bethesda. Grant, B.F., Dawson, D.A., Stinson, F.S., Chou, P.S., Kay, W., Pickering, R., 2003b. The Alcohol Use Disorder and Associated Disabilities Interview Schedule-IV (AUDADIS-IV): reliability of alcohol consumption, tobacco use, family history of depression and psychiatric diagnostic modules in a general population sample. Drug Alcohol Depend. 71, 7–16. Grant, B.F., Dawson, D.A., Hasin, D.S., 2001. The Alcohol Use Disorder and Associated Disabilities Interview Schedule-DSM-IV Version (AUDADIS-IV). National Institute on Alcohol Abuse and Alcoholism, Bethesda. Hagenaars, J., McCutcheon, A., 2002. Applied Latent Class Analysis Models. Cambridge University Press, Cambridge. Hasin, D.S., Goodwin, R.D., Stinson, F.S., Grant, B.F., 2005. Epidemiology of major depressive disorder: results from the National Epidemiologic Survey on Alcoholism and Related Conditions. Arch. Gen. Psychiatry 62, 1097–1106. Haslam, N., Beck, A.T., 1994. Subtyping major depression: a taxometric analysis. J. Abnorm. Psychol. 103, 686–692. Jorm, A.F., 2000. Does old age reduce the risk of anxiety and depression? A review of epidemiological studies across the adult life span. Psychol. Med. 30, 11–22. Kendler, K.S., Gardener, C.O., Prescott, C.A., 1999. Clinical characteristics of major depression that predict risk of depression in relatives. Arch. Gen. Psychiatry 56, 322–327. Kendler, K.S., Eaves, L.J., Walters, E.E., Neale, M.C., Heath, A.C., Kessler, R.C., 1996. The identification and validation of distinct depressive syndromes in a population-based sample of female twins. Arch. Gen. Psychiatry 53, 391–399. Kuehner, C., 2003. Gender differences in unipolar depression: an update of epidemiological findings and possible explanations. Acta Psychiatr. Scand. 108, 163–174. Kovess-Masfety, V., Lecoutour, X., Delavelle, S., 2005. Mood disorders and urban/rural settings: comparisons between two French regions. Soc. Psychiatry Psychiatr. Epidemiol. 40, 613–618. Leskela, U., Rytsala, H., Komulainen, K., Melartin, T., Sokero, P., Lestela-Mielonen, P., Isometsa, E., 2006. The influence of adversity and perceived social support on the outcome of major

N. Carragher et al. / Journal of Affective Disorders 113 (2009) 88–99 depressive disorder in subjects with different levels of depressive symptoms. Psychol. Med. 36, 779–788. Little, T.D., Cunningham, W.A., Shahar, G., Widaman, K.F., 2002. To parcel or not to parcel: exploring the question, weighing the merits. Struct. Eq. Modeling 9, 151–173. Lo, Y., Mendell, N., Rubin, D.B., 2001. Testing the number of components in a normal mixture. Biometrika 88, 767–778. Lubke, G.H., Muthén, B., 2005. Investigating population heterogeneity with factor mixture models. Psychol. Methods 10, 21–39. Miech, R.A., Caspi, A., Moffitt, T.E., Wright, B.E., Silva, P.A., 1999. Low socioeconomic status and mental disorders: a longitudinal study of selection and causation during young adulthood. Am. J. Sociol. 104, 1096–1131. Moss, H.B., Chen, C.M., Yi, H.Y., 2007. Subtypes of alcohol dependence in a nationally representative sample. Drug Alcohol Depend. 91, 149–158. Murray, C.J., Lopez, A.D., 1997. Alternative projections of mortality and disability by cause 1990–2020: Global Burden of Disease Study. Lancet 349, 1498–1504. Muthén, B.O., 1989. Dichotomous factor analysis of symptom data. Sociol. Methods Res. 18, 19–65. Muthén, B., Asparouhov, T., 2006. Item response mixture modeling: application to tobacco dependence criteria. Addict. Behav. 31, 1050–1066. Muthén, L.K., Muthén, B.O., 1998–2008. Mplus User's Guide, (5th Ed). Muthén & Muthén, Los Angeles. Myrick, H., Brady, K., 2003. Current review of the comorbidity of affective, anxiety, and substance use disorders. Curr. Opin. Psychiatry 16, 261–270. National Institute on Alcohol Abuse and Alcoholism, 2006. Alcohol Use and Alcohol Use Disorders in the United States: Main Findings from the 2001–2002 National Epidemiological Survey on Alcohol and Related Conditions (NESARC), U.S. Alcohol Epidemiologic Data Reference Manual Volume 8, Number 1. National Institute on Alcohol Abuse and Alcoholism, Bethesda. Nylund, K.L., 2007. Latent transition analysis: modeling extensions and an application to peer victimization. PhD Dissertation, University of California, Los Angeles. Probst, J.C., Laditka, S.B., Moore, C.G., Harun, N., Powell, M.P., Baxley, E.G., 2006. Rural–urban differences in depression prevalence: implications for family medicine. Fam. Med. 38, 653–660. Ramaswamy, V., DeSarbo, W., Reibstein, D., Robinson, W., 1993. An empirical pooling approach for estimating marketing mix elasticities with PIMS data. Marketing Sci. 12, 103–124. Schwartz, G., 1978. Estimating the dimension of a model. Ann. Stat. 6, 461–464. Sclove, S.L., 1987. Application of model-selection criteria to some problems in multivariate analysis. Psychometrika 52, 333–343.

99

Scotte, C.K., Maes, M., Cluydts, R., Cosyns, P., 1997. Cluster analytic validation of the DSM melancholic depression. The threshold model: integration of quantitative and qualitative distinctions between unipolar depressive subtypes. Psychiatry Res. 71, 181–195. Sen, B., 1987. An analysis of the nature of depressive phenomena in primary health care utilising multivariate statistical techniques. Acta Psychiatr. Scand. 76, 28–32. Shavers, V.L., 2007. Measurement of socioeconomic status in health disparities research. J. Natl. Med. Assoc. 99, 1013–1023. Sher, L., 2005. Alcohol use and suicide rates. Med. Hypotheses 65, 1010–1012. Simon, G.E., von Korff, M., 2006. Medical co-morbidity and validity of DSM-IV depression criteria. Psychol. Med. 36, 27–36. Singh, G.K., Siahpush, M., 2002. Increasing rural–urban gradients in U.S. suicide mortality, 1970–1997. Am. J. Public Health 92, 1161–1167. Slade, T., Andrews, G., 2005. Latent structure of depression in a community sample: a taxometric analysis. Psychol. Med. 35, 489–497. Sullivan, P.F., Kessler, R.C., Kendler, K.S., 1998. Latent class analysis of lifetime depressive symptoms in the National Comorbidity Survey. Am. J. Psychiatry 155, 1398–1406. Sullivan, P.F., Prescott, C.A., Kendler, K.S., 2002. The subtypes of major depression in a twin registry. J. Affect. Disord. 68, 273–284. Swendsen, J.D., Merikangas, K.R., 2000. The comorbidity of depression and substance use disorders. Clin. Psychol. Rev. 20, 173–189. Waite, L.J., Lehrer, E.L., 2003. The benefits from marriage and religion in the United States: a comparative analysis. Popul. Dev. Rev. 29, 255–275. Weissman, M.M., Wickramaratne, P., Nomura, Y., Warner, V., Verdeli, H., Pilowsky, D.J., Grillon, C., Bruder, G., 2005. Families at high and low risk for depression: a 3-generation study. Arch. Gen. Psychiatry 62, 29–36. Weissman, M.M., Bland, R.C., Canino, G.J., Faravelli, C., Greenwald, S., Hwu, H.G., Joyce, P.R., Karam, E.G., Lee, C.K., Lellouch, J., Lepine, J.P., Newman, S.C., Rubio-Stipec, M., Wells, J.E., Wickramaratne, P.J., Wittchen, H., Yeh, E.K., 1996. Cross-national epidemiology of major depression and bipolar disorder. JAMA 276, 293–299. Whitesell, N.R., Beas, J., Mitchell, C.M., Novins, D.K., Spicer, P., Manson, S.M., 2006. Latent class analysis of substance use: comparison of two American Indian reservation populations and a national sample. J. Stud. Alcohol 67, 32–43. Wittchen, H.U., Kessler, R.C., Pfister, H., Lieb, M., 2000. Why do people with anxiety disorders become depressed? A prospectivelongitudinal community study. Acta Psychiatr. Scand., Suppl. 406, 14–23. World Health Organization, 1990. Composite International Diagnostic Interview. World Health Organization, Geneva.