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Compr Psychiatry. Author manuscript; available in PMC 2012 May 1.
Published in final edited form as:
PMCID: PMC3086656
NIHMSID: NIHMS219318

Latent Class Analysis of YBOCS Symptoms in Obsessive Compulsive Disorder

Abstract

Objective

Obsessive-compulsive disorder (OCD) is phenomenologically heterogeneous, and findings of underlying structure classification based on symptom grouping have been ambiguous to date. Variable-centered approaches, primarily factor analysis, have been used to identify homogeneous groups of symptoms, but person-centered latent methods have seen little use. This study was designed to uncover sets of homogeneous groupings within 1611 individuals with OCD, based on symptoms.

Method

Latent class analysis (LCA) models using 61 obsessive-compulsive symptoms (OCS) collected from the Yale-Brown Obsessive-Compulsive Scale were fit. Relationships between latent class membership and treatment response, gender, symptom severity and comorbid tic disorders were tested for relationship to class membership.

Results

LCA models of best fit yielded three classes. Classes differed only in frequency of symptom endorsement. Classes with higher symptom endorsement were associated with earlier age of onset, being male, higher YBOCS symptom severity scores, and comorbid tic disorders. There were no differences in treatment response between classes.

Conclusions

These results provide support for the validity of a single underlying latent OCD construct, in addition to the distinct symptom factors identified previously via factor analyses.

Keywords: obsessions, compulsions, latent class

Obsessive-compulsive disorder (OCD) is a common neuropsychiatric disorder, affecting 2% of adults and between 1% and 2% of children (13). Although the DSM-IV-TR definition is straightforward, OCD is phenomenologically heterogeneous and etiologically complex (49). OCD-affected individuals exhibit a wide variety of symptoms (e.g., contamination, sexual, religious, or aggressive fears, hoarding, checking behaviours, repeating rituals) and a range of comorbid neuropsychiatric conditions (including tic disorders, depression, generalized anxiety, grooming disorders, eating disorders and others) (1013).

In attempting to better understand OCD, investigators have used data reduction methods, most commonly factor analysis, to identify subgroups of symptoms (as defined by the Yale Brown Obsessive Compulsive Scale (YBOCS) symptom checklist) that may be amenable to etiological and treatment studies (6, 1432). However, while factor analysis seeks the underlying structure in variables, other approaches, such as latent class analysis (LCA), can be used to find latent homogeneous groups of individuals and provide an additional dimension of analysis. This has the advantage of potentially refining the OCD phenotype, ultimately increasing our ability to identify the underlying causes of OCD, provide specific targeted treatments, and predict outcomes . While LCA has been used on symptom data from other neuropsychiatric disorders, most notably attention deficit-hyperactivity disorder (3340), studies examining the latent class structure of OCD are scarce, and there has only been one study published that examines the latent class structure of obsessive-compulsive symptoms (21, 27, 41, 42).

In a pioneering application of LCA to OCD published in 1991, Thomsen and Jensen (27) used four variables in an LCA (i.e. neurological signs, EEG abnormalities, attention deficit and developmental disorder) to examine birth complications and neurological abnormalities in individuals with OCD and controls. With only four measures, they were limited to at most a two-class model, which they identified as an “organic class” and a “non-organic class”; individuals with OCD primarily fell into the “non-organic class”, suggesting that OCD is not likely to be the result of organic brain disease.

Two further studies of OCD using LCA were published in 2003 and 2008 by Nestadt et al., who conducted two analyses examining latent classes in OCD-affected individuals and their family members based on patterns of comorbidity (21, 41). The first study examined 80 OCD-affected individuals, 73 control subjects, and family members from both the cases and controls (total N=450), and identified four latent classes, a minimal disorders class, a recurrent major depression and generalized anxiety class, a highly comorbid class, consisting of individuals with multiple comorbid psychiatric disorders, and a tic disorder, panic, and agoraphobia class (21). The authors suggested that the first three classes represented a single subgroup distributed ordinally along a severity spectrum, while the fourth class, the tic disorders class, represented a separate subgroup. In the second study, Nestadt et al. assessed a larger group of OCD-affected individuals (N=706), again using comorbid disorders, and examined the relationship of the resulting latent classes to specific clinical characteristics such as gender, age at onset, and OCD symptom type (41). This study identified two possible solutions, a two-class solution that was characterized by lesser and greater comorbidity classes (similar to the classes identified in the 2003 study), and a three-class solution that consisted of an OCD-only class (±major depression), an OCD+tics class, and an OCD+affective disorders (highly comorbid) class (41). When plotted according to prevalence of comorbid disorders, the three-class solution was also consistent with a severity spectrum profile, with individuals in class 1 endorsing low rates of comorbid disorders and individuals in class 3 endorsing high rates of comorbid disorders. The exception to this was the pattern of tic disorders, which were more prevalent in class 2 than in the other classes (41). In general, individuals in the more severe classes had a younger age at onset, more OCPD features, and more ordering/symmetry and taboo symptoms; individuals in class 2 were more likely to be male compared with the other classes.

The most recent study, published in 2009 by Althoff et al., used LCA to examine the latent structure of the eight-item obsessive-compulsive scale (OCS) of the Child Behavior Checklist (CBCL) in several unselected community-based samples of children, including twin pairs (42). The authors hypothesized that the LCA would parallel the known factor structure of the CBCL-OCS, and would identify two latent classes. However, the results instead suggested a four-class solution, including a “no symptoms” class, with low endorsement across all eight items, a “worries and has to be perfect” class, which the authors hypothesized consists of individuals with anxiety unrelated to OCD, a “thought problems” class, consisting of individuals who endorsed repeating behaviors, strange ideas, strange behaviors, and obsessions, and an “OCS items” class, which consisted of individuals who highly endorsed all eight items. All of these classes were heritable in the twin samples, and the authors concluded that the OCS items class represented a latent class for obsessive-compulsive behavior that may be a useful alternative to DSM-based diagnoses of OCD for genetic studies. The paucity of OCD studies using a person-centered analysis is surprising given the wide range of symptom manifestations that fall under OCD as such an analysis would indicate which patients tend to have similar symptoms. One reason for this may be that few studies have had sufficient sample sizes for such work.

Given this lack of research on the grouping of OCD-affected individuals by symptomatology, the primary aim of this study was to examine latent groups based on obsessive-compulsive (OC) symptoms using LCA in a large heterogeneous sample, and to examine the relevance of the derived latent classes by assessing their relationships to clinically relevant variables such as gender, symptom severity, presence or absence of comorbid tic disorders, family history of OCD symptoms, and treatment response. We hypothesized that the LCA would parallel the observed clinical heterogeneity as well as the results of the variable-centered data reduction analyses (i.e., factor analyses), that is, that we would identify latent classes characterized by groups of items which were highly endorsed for one class and not for the others (i.e, a cleaning/contamination class, a hoarding class, a taboo[aggressive, sexual, and religious obsessions] class, a doubts and checking class, and a repeating rituals and superstitions class) (10, 31, 4346). Further, we hypothesized that the latent classes would have specific clinical and/or demographic profiles. That is, the hoarding class would have a poorer treatment response, the contamination class would be more prevalent in women, and the taboo class would have an earlier age of onset (16, 47, 48).

Method

Participants

The study samples used in these analyses have been described in detail previously (19, 4952). Briefly, subjects who met criteria for a lifetime diagnosis of OCD according to DSM-IV criteria (N=1611) were recruited from six sites, the Department of Psychiatry of the University of California, San Francisco (n=124), the Department of Psychiatry of the McLean/Massachusetts General Hospital (n=329), the Department of Psychiatry of the VU Medical Center and the Outpatient Academic Clinic for Anxiety Disorders, GGZ Buitenamstel in Amsterdam (n=229), the Department of Psychiatry of the Utrecht Medical Center (n=387), the MRC Unit on Anxiety & Stress Disorders, Department of Psychiatry, University of Stellenbosch, University of Cape Town, South Africa (n=393) and the Department of Psychiatry of the University Medical Center Groningen (n=149) (53). These subjects were originally recruited for genetic, phenomenological and treatment response studies. Study participants varied in demographic characteristics and symptom severity across sites, adding to the generalizability of the final sample (Table 1).

Table 1
Subject demographics and clinical characteristics by recruitment site.

Measures

The Yale-Brown Obsessive-Compulsive Scale (YBOCS) was used to assess obsessive-compulsive symptoms and their resulting clinical impact or severity. The YBOCS, a well-known, widely used instrument, consists of a 74-item clinician-rated symptom checklist and a quantitative 40-point severity scale with demonstrated validity and reliability (54, 55). Obsessive-compulsive symptoms on the YBOCS were coded as 0 (never present) or 1 (ever present). Severity was coded as worst-ever (San Francisco, South Africa) or current (Groningen, Amsterdam, Utrecht, and Boston). Because we had no a priori reason to exclude specific symptoms, all were used in the analyses (including those that are not clearly OCD symptoms, such as compulsive hair pulling, excessive concern with an aspect of appearance or body part, and excessive fear of illness), except for thirteen non-specific or open-ended items (e.g., “other contamination obsessions”, “other checking behaviors”), that were not reliably endorsed across sites. Assessments were conducted by psychologists or psychiatrists at all sites.

Data on treatment response, defined by a 25% decrease in YBOCS severity score at treatment discharge (endpoint defined by the referenced treatment study), were available on 320 subjects from the McLean/Massachusetts General Hospital site who were part of a study on the effectiveness of intensive inpatient treatment for treatment refractory OCD (51). The intervention was an intensive residential treatment program designed for severe, refractory, OCD. Further details are given in Stewart, et al. (56) Percent decrease in YBOCS severity scores at the end of treatment was also measured. Because data were collected retrospectively from a variety of sites and a variety of study types, data on additional clinical or demographic characteristics, such as comorbid tic disorders (Tourette Syndrome or chronic motor or vocal tic disorder) and family history, were not available for all subjects. Data on gender, age of onset of any OCD symptoms, and age at interview were available for the majority of subjects (Table 1). Data on comorbid tic disorders was available for 455 subjects from all participating sites except the South Africa, Utrecht and Groningen sites. The presence, duration, and impact of tics was assessed as part of a semi-structured clinical interview administered by the research team. At the Amsterdam site a more intensive screening was done. Data on family history of OCD or clinically significant obsessive-compulsive symptoms (OCS) were available on 783 subjects from the Boston, Amsterdam and Utrecht sites. A positive family history was recorded when the subject reported the presence of clinically significant OC symptoms in at least one first- or second-degree family member. Studies were approved by the Medical Ethical Review Boards of the participating centers. All subjects (and in the case of minors, their parents) gave written informed consent for participation in the study. Children under age 18 years gave assent.

Statistical Analyses

Latent Class Analyses (LCA)

A series of latent class models were estimated using 61 YBOCS items in the entire sample of 1611 using MPLUS version 5.2 (57). As we had no basis for pre-defining the number of classes that we expected to identify, we fit models ranging from two to seven classes and summarized the results before considering models with more classes. To identify the model with best fit, two fit criteria were examined: the sample-size adjusted Bayesian information criteria (BIC) (58), Akaike’s Information Criterion (AIC) (59). The size of the smallest class was taken into account because a solution with a relatively small-sized class would not be useful in further analysis and is likely to be an anomaly of the sample. The Lo-Mendell-Rubin (LMR) adjusted likelihood ratio test adjusted likelihood ratio test was used to identify whether the fit of the model with K classes was better than the fit of a model with K-1 classes (an alternative test, the Vuong-Lo-Mendell-Rubin likelihood ratio, gave nearly identical p-values). We note that as these tests are relatively new, their performance under various conditions has not been studied. While not a measure of fit, entropy is an index of the orderliness of the classifications and helps to describe the model. Subjects received a weight reflecting the probability of belonging to each fitted class, so the average of the classification probabilities for each class was also examined. Finally, for the comparisons among the clinical variables, subjects were assigned to their most-likely class to minimize interdependencies.

To search for effects of heterogeneity including differences in ascertainment, assessment, or clinical demographics by site, we re-fit the models, first dropping subjects under age 18 (6% of the total sample), second dropping those subjects who endorsed fewer than five symptoms, third by systematically removing each site one-by-one from the analysis, and fourth by dropping the San Francisco site and the South Africa site, both of which examined worst-ever rather than current symptoms.

Correlations with Clinical Variables

Associations among the most likely latent class membership derived from the item-level LCA with gender, age of symptom onset, symptom severity as measured by YBOCS total severity score, presence of comorbid tic disorders (Tourette Syndrome or chronic motor or vocal tic disorder), family history of OCS/OCD, and treatment response were assessed in all available subjects using chi-square analyses, t-tests, and analyses of variance. A p-value < 0.05 was considered to be significant. As this is an exploratory study, we did not correct for multiple comparisons.

Results

Demographic and clinical characteristics

Table 1 shows demographic and clinical characteristics of the sample. Fifty three percent of the sample was female, and the mean age at assessment was 34.3 years (SD=12.5, range 4 to 80). The mean age of onset of OC symptoms was 16.9 (SD=9.5, range 0 to 59). Of the 783 patients for whom there were family history data, 37.6% had a family history of OCD/OCS and of the 455 patients for whom there were tic related data, 46.4% had a comorbid tic disorder.

Latent class analyses

As seen in Table 2, the BIC and AIC values continued to improve (i.e., move towards zero) as the number of classes increased and the estimated number of subjects in each class remained reasonably large, indicators consistent with improved model fit. The LMR p-value indicated that a model containing three latent classes was the best fit. The diagonal of the matrix of the average latent class probabilities for the most likely class membership by latent class (data not shown) was above .92 in all models, indicating clear class distinctions. A plot of the item endorsement rates for each class in the three-class model, plotted in Figure 1, clearly showed three groups separated by level of severity as measured by the frequency of items endorsed. No overlap in symptom endorsement rates was seen among the three classes. When we systematically analyzed subsets of the data to assess for the effects of sample heterogeneity (as described above), we obtained the same three- class solution (data not shown).

Figure 1
Proportion of class member endorsing each item by most likely class for 3-class model, sorted by class 1 endorsement rate.
Table 2
Summary of latent class models from 2 through 7 class models.

Relationship of latent class membership to age of onset and symptom severity

In examining the three-class model, which was the best fit for the data across all analyses, the greater the level of symptom endorsement by a latent class, the significantly lower the age of symptom onset (Table 3). A similar pattern was seen for the YBOCS total severity score, with higher endorsement classes associated with higher YBOCS symptom severity scores (Table 3). This was also true for all of the individual YBOCS severity items, with the exception of resistance against and control over compulsions, which were not significantly different among the three classes (Table 3).

Table 3
Means (standard deviations) and percentages of clinical and demographic characteristics by latent class*.

Relationship of latent class membership to gender, comorbid tic disorders, and family history of OCD/OCS

Although the proportion of males and females in the overall sample was similar, the ratios of males to females differed between latent classes, with the proportion of males increasing in the higher symptom endorsement classes (Table 3). The relationship between latent class membership and presence of comorbid tic disorders was similar, with class 1 having proportionally more individuals with comorbid tic disorders, and class 3 having proportionally fewer (Table 3). There was no statistically significant relationship between class membership and family history of OCD/OCS. We also compared the classes on the factor scores based on these items as given by Katerberg, et al. (43). Individuals in the higher symptom endorsement classes had higher mean factor sum scores (taboo, contamination, doubts, rituals, and hoarding) than those in lower symptom endorsement classes on all symptom factors.

Class Membership and Treatment Response

There were no significant associations between treatment history (history of behavioral treatment or number of medications tried prior to the treatment study) and class membership or between class membership and treatment response for any of the outcome variables measured (Table 4).

Table 4
Means (standard deviations) and percentages of treatment history and treatment response by latent class.

Discussion

The aim of this study was to elucidate the underlying symptom structure of OCD by looking for homogenous groups of subjects based on their patterns of symptom endorsement, and to relate the resulting classes to clinically relevant subject characteristics. We originally hypothesized that, although LCA uses individuals as the unit of analysis rather than the symptom variables used in factor analysis, the classes derived from LCA would be similar to the symptom categories that have been previously identified using factor analyses, providing further support for the symptom category model. Contrary to our expectations, the best-fit results from the global analysis and from the sub-analyses differentiated individuals with OCD into classes based on the frequency with which they endorsed symptoms, rather than by the type of symptoms they endorsed, suggesting an underlying spectrum of OCD severity ranging from uniformly low endorsement rates across symptom subtypes to uniformly high endorsement rates across symptom subtypes.

These classes were positively correlated with YBOCS severity scores, further supporting the severity spectrum concept. We note that some of the YBOCS severity scores in our study are current scores rather than worst-ever scores, and as such, may be a reflection of treatment effects. However, this, and the inclusion of patients with lower levels of severity helps to generalize the findings. In addition, although the LMR suggested that the three-class model was the best fitting model, the fit criteria (BIC and AIC) continued to improve with increasing numbers of classes in the model, and did not level out at any point before the last tested (seven-class) model, also perhaps suggesting an underlying spectrum of OCD severity.

These findings have potential relevance for etiological studies, in particular genetic studies, and for studies aimed at further elucidating OCD pathophysiology using approaches such as neuroimaging. For genetic studies, our results suggest that there may be an underlying susceptibility to OCD that exists across symptom subtypes, as evidenced by the fact that in the LCA individuals were not classified based on their symptoms, as predicted, but rather according to a severity spectrum. Our previous heritability studies have also suggested that this may be the case; these studies found that both total symptom endorsement counts and OCD symptom severity (along with symptom-based factor-derived scores) are heritable within families (43). Therefore, it may be of utility to classify OCD-affected families or individuals in genetic studies based on frequency or severity of symptoms.

Similarly, these results may be relevant for the design of neuroimaging studies of OCD. The majority of neuroimaging studies of OCD have examined structural or functional differences based on symptom type or treatment response rather than on severity (60, 61). However, a recent meta-analysis of structural neuroimaging studies found increased gray matter volumes associated with increased OCD severity(62). To our knowledge, functional or structural brain changes have not been examined in conjunction with different levels of symptom endorsement rates. Such studies may be of interest, and may identify different patterns of connectivity or reactivity than have been identified in neuroimaging studies to date.

While this study is the first to use LCA in a large sample of OCD-affected individuals to classify patients based on their symptoms, the findings are consistent with the previous studies that have utilized an LCA approach to further characterize OCD. Although differing in the numbers of latent classes identified, the results of the previous studies could also be consistent with an OCD severity spectrum, with the least severe class in each study characterized by few to no psychiatric comorbidities, and the most severe class characterized by multiple comorbid conditions (21, 41). LCA studies of other psychopathology, including nicotine dependence, patterns of violence among male batters, and borderline personality disorder, have also identified latent classes most consistent with levels of severity (6365). While reassuring that models estimated from various subgroups of the data produced parallel results, the principal limitation of this study is that it utilizes data collected from multiple sources using a variety of ascertainment and assessment techniques, resulting in variation in the demographic and clinical characteristics of the subjects, as well as in incomplete data for some variables. Although it is possible that this variation results in decreased precision of the findings, the heterogeneity of the sample has the important advantage of improving the generalizability of the findings. The differences in data collection approaches resulted in variation in the age and severity of illness of individuals in the sample across sites, introducing the possibility of recall or other types of bias. However, the results of the analysis did not change when children, who made up 6% of the sample, were excluded, or when low responders on the YBOCS were excluded, nor did they change when each site was systematically excluded. Similarly, we did not have data on the presence of comorbid tic disorders or family history of OCD/OCS in the entire sample, potentially introducing a bias, and the treatment response data were collected at a single site (MGH) in a subset of patients with severe, refractory OCD who may not be representative of OCD patients in general. Also, inter-rater reliability was not assessed. Finally, we assigned the study participants to the most likely latent class, necessitated by the lack of full data on all covariates. While the mean probability of assignment to class was very high, it is possible that the significance associated with the differences among classes shown in Tables 3 and and44 may be sub-optimal.

In summary, the results of this first latent class analysis of YBOCS symptoms conducted to date in a large group of OCD patients provides evidence for a latent OCD construct defined as a single spectrum based on severity or symptom endorsement rates in addition to the distinct subtypes of symptoms that have been identified via factor analysis. As with any analysis, replication would strengthen this finding and future work should include clinical studies focused on the relevance of the LCA severity level classes to features such as treatment resistance and comorbid disorders such as other anxiety disorders.

Acknowledgments

The authors are grateful to the families with obsessive compulsive disorder in all of the participating centers who generously agreed to be part of this study. This work was partially supported by funds from NARSAD (CAM), the Obsessive Compulsive Foundation (CAM, SES), the Harvard Scholar in Medicine Program (SES) and the Canadian Institutes of Health Research (SES).

Footnotes

Financial Disclosures

The authors have no conflicts to disclose.

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