Masterthesis
Master Clinical Psychology
Name Rachel Heikamp
E-Mail rachelheikamp@hotmail.com
Student number 5829038
University University of Amsterdam (UvA)
Course Clinical Psychology
Supervisor UvA Arjen Noordhof
Institution of internship AMC Psychiatry, department of Anxiety Disorders Supervisor of institution Nienke Vulink and Rianne Blom
Index
1. Summary 3
2. Introduction 3
Background 3
Previous studies 4
Defining age of onset 6
Scientific relevance of this study 7
Clinical/social relevance 7 3. Methods 8 Participants 8 Procedure 8 Materials 9 4. Data-analysis 10 5. Results 10
Demographic and clinical features 10
Choosing cut-offs 10
Content of obsessions and compulsions 11 Comorbidity of obsessions and compulsions 11
6. Discussion 16
7. References 19
SUMMARY
Obsessive compulsive disorder (OCD) is a psychiatric disease characterized by obsessions and/or compulsions. The nature of OCD is heterogeneous, with multiple symptom subtypes and comorbidity patterns. It is still unknown how OCD develops exactly. In order to further understand the etiology it is important to recognize the heterogeneity of OCD. To do this, many different authors attempted to define subgroups. Previous studies on heterogeneity focused on three main topics, namely comorbidity, content of symptoms and age of onset. In this study we extended the literature on age of onset by investigating both subfactors and comorbidity conditions, which has previously not been done in a joint analysis. Using a sample of 1152 outpatients with primary OCD who were referred to the Anxiety Clinic of the University Medical Centre Utrecht and to the Anxiety Clinic of the Academic Medical Centre in Amsterdam, the Netherlands, we explored the differences between the early AOO group and the late AOO group. To distinguish between early and late age of onset, we used two cut-of points frequently discussed in literature, namely age 10 and 17. The first purpose cut-of the present study was to investigate whether there are any differences in content of the obsessions and/or compulsions between early AOO and late AOO OCD. The second purpose was to find associations between AOO and current comorbidity with axis I, axis II and OC-spectrum disorders. For both variables we found differences between early and late AOO.
Focusing on the content of symptoms, both comparisons (AOO ≤ 10 vs. AOO > 10 and AOO > 17 vs. AOO ≤ 17) showed that late AOO is associated with
contamination/cleaning. Only the second comparison (AOO > 17 vs. AOO ≤ 17) showed an association between early AOO and symmetry/ordering/counting. Focusing on comorbidity, both comparisons (AOO ≤ 10 vs. AOO > 10 and AOO > 17 vs. AOO ≤ 17) showed that late AOO is associated with psychotic disorders and that early AOO is associated with ‘other’ psychiatric disorders. Only the first comparison (AOO ≤ 10 vs. AOO > 10) showed us an association between early AOO and personality disorders.
These results suggest that early onset OCD differs from late onset OCD. This means that there is more evidence to assume that the various subtypes of OCD arise from various etiological factors. Results and clinical implications are further discussed.
INTRODUCTION Background
Obsessive-compulsive disorder (OCD) is a psychiatric disease characterized by obsessions and/or compulsions. Obsessions are intrusive thoughts, images, impulses or fears and compulsions are repetitive behaviours or mental acts that are impossible to ignore or suppress. Obsessions cause severe anxiety or distress and compulsions are actions performed to prevent or reduce distress or prevent some dreaded event or situation. The nature of OCD is heterogeneous, with multiple symptom subtypes (e.g. cleaning/contamination, aggressive or sexual obsessions, hoarding, symmetry). OCD is also frequently comorbid with other psychiatric disorders (Mathews, 2009).
To be diagnosed with OCD, one could have both obsessions and compulsions, but it is also possible to have only one of these. To meet the diagnostic criteria of OCD the obsessions and/or compulsions have to be time consuming, taking at least an hour a day and causing marked distress or significant impairment (Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.; American Psychiatric Association, 2000)). OCD is a relatively common disorder with a lifetime prevalence of 2-3% of the population worldwide (World Health Organization 2001, cited in Antony & Stein, 2009).
It is still unknown how OCD develops exactly. We do know that there are a number of risk factors that increase the chance of developing OCD. First, genetic factors play an
important role. For example, OCD seems to be more common in children with lower
intelligence than in children with higher intelligence (Heyman et al., 2001). OCD also seems to be more common in adult women, in male children and in male adolescents. Late
adolescence seems to be a period of increased vulnerability for the development of OCD (Fontenelle & Hasler, 2008). Social and environmental factors can also be influential in the development of OCD. These environmental risk factors occur already during pregnancy: a prolonged labor and edema during pregnancy increases the risk for the development of OCD (Vasconcelos et al., 2007). Further, it is known that OCD is more common in people from lower socio-economic class (Karno, Golding, Sorenson, & Burnam, 1988) and in unmarried people and drug users (Fontenelle & Hasler, 2008). Finally, the experience of major life events contributes to the development (Karno et al., 1988).
The above mentioned factors, namely psychological, biological and environmental factors can interact and can directly or indirectly result in the onset of an anxiety disorder (Furr, Tiwari, Suveg, & Kendall, 2009). In order to understand the development of OCD there are several approaches. These approaches posit that obsessions arise from normal thoughts that are experienced by most of the people. However, there is no single accepted explanation of how these normal thoughts turn into a psychiatric disorder (Purdon, 2009). These
approaches also assume that the development of OCD is gradual, which means that there is often a slow progression from the onset of symptoms to meeting criteria for the disorder. Changes in daily routines and general stress are important factors in the transition from symptoms to full OCD (Coles, Johnson, & Schubert, 2011).
In order to further understand the etiology it is important to recognize the
heterogeneity of OCD. This heterogeneity can be apparent in the type of symptoms, their severity and duration, the comorbidity and the age of onset. Apparently heterogeneous conditions may still have a shared etiology, but it may also be the case that different subtypes exist with different etiologies. For this reason, many different authors attempted to define subgroups. The identification of these groups can help in understanding variations in comorbidity patterns, symptom severity and psychological symptoms of family members (Janowitz et al., 2009; Leckman et al., 2010; Nestadt et al., 2000) and is important for the search of etiological factors and more effective treatment methods (Mathis de et al., 2008). Previous studies
Given the heterogeneous character of OCD the question arises whether it is one disorder or more. There might be a single common pathway resulting in various
manifestations of the disorder. In this case, all of the variations have a shared etiology. It is also possible that different pathways exist from which different disorders emerge that share some features of OCD. The latter would advocate the assumption that the different
manifestations of OCD cannot be seen as the same disorder. To understand the etiology it is important to explore the heterogeneity of the disorder. In this way, differences and similarities between people having the same disorder would become visible. It is possible that the more similarities there are in the way the disorder manifests, the more similarities there are in the way the disorder develops. Controversely, the more differences there are in the way the disorder manifests, the less similarities there are in the way the disorder develops.
Studies on heterogeneity focused on three main topics, namely comorbidity, content of symptoms and age of onset. Patterns of comorbidity are informative as to whether OCD has a shared etiology with comorbids. Content of symptoms may indicate whether different
manifestations of OCD have a shared etiology. Finally, by investigating age of onset it can be investigated whether an early age of onset has the same or different etiology as a late age of onset.
Comorbidity
Recent studies have shown that OCD is a disorder with a high frequency of various life time comorbidities of Axis I and Axis II disorders (Holland LaSalle et al., 2004; Pinto, Mancebo, Eisen, Pagano, & Rasmussen, 2006; Nestadt et al., 2009; Denys, Tenney, Megen van, Geus de, & Westenberg, 2004). In the National Comorbidity Servey Replication of American adults with OCD (Ruscio, Stein, Chiu, & Kessler, 2010), OCD was associated with anxiety (75.8%), mood (63.3%), impulse-control (55.9%) and substance use disorders
(38.6%).
Pure OCD patients (patients without any comorbid condition) are the minority. They have less frequent severe depressive and anxious symptoms, less suicidal thoughts and less use of psychotherapy (Torres et al., 2013). This means that comorbidity is associated with more severe OCD, anxiety and depressive symptoms and more suicidal thoughts.
Comorbidity in OCD is also associated with greater chronicity and more negative consequences in daily life (Srivastava, Bhatia, Thawani & Jhanjee, 2011).
Nestadt et al., (2009) tried to explain the relationship between OCD and its comorbid conditions. They investigated the hypothesis that comorbid disorders are expressions of one or more underlying classes. A model with three latent classes showed optimal fit; in the first group major depressive disorder was the most frequent comorbid disorder, in the second group tics were prominent and in the third group affective and personality disorders were highly represented. The results of Nestadt et al., (2009) show that, given the same diagnosis of OCD, two individuals can differ in their pattern of comorbid conditions.
Content of symptoms
OCD is not only heterogeneous because of its various comorbid disorders, but also due to the various patterns of symptoms. The symptoms for defining OCD are diverse and include a wide variety of obsessions and compulsions. Different authors have attempted to identify factors based on the content of symptoms in order to describe heterogeneity. Most studies identified four (Feinstein, Fallon, Petkova, & Liebowitz, 2003; Leckman et al., 1997;
Summerfeldt, Richter, Antony, & Swinson, 1999; Stewart et al., 2008) or five (Cavallini, Di Bella, Siliprandi, Malchiodi, & Bellodi, 2002; Denys et al., 2004; Mataix-Cols, Marks, Greist, Kobak, & Baer, 2002) factors. Given the controversy with regards to the correct number of factors and the content of the factors, most studies have used self-defined factors. This has made it very difficult to compare research studies. To clarify this, Bloch,
Landeros-Weisenberger, Rosario, Pittenger, & Leckman et al. (2008) conducted a meta-analysis of 21 factor analytic studies to determine the factor structure of the Yale-Brown Obsessive-Compulsive Scale Symptom Checklist (Y-BOCS CL). The first factor was composed of symptoms concerning symmetry, ordering and counting. The second factor included obsessive thoughts and images associated with aggressive, sexual, and/or religious (forbidden) content and somatic obsessions related with checking compulsions. The third factor included contamination obsessions and cleaning compulsions and the fourth factor included hoarding obsessions and compulsions. This factor structure will also be used in this thesis.
When we take a closer look at this four factor structure, females appear more likely to have more contamination/cleaning (Zhang, Liu, Cui, & Liu (2013) and hoarding (Mataix-Cols, Pertusa, & Leckman, 2007) compulsions. Male appear to be more often associated with sexual obsessions (Mataix-Cols et al., 2007). An early age of onset is associated with
symmetry/arranging/repeating/counting (Zhang et al., 2013; Kichuk et al., 2013). Finally, people with checking compulsions score lower on spatial working memory (Nedeljkovic et al., 2009) and people with hoarding compulsions lower on the level of global functioning (Mataix-Cols et al., 2007).
The heterogeneous character of the disorder indicates that there is probably not one explanatory model but more. According to Purdon (2009) there is a growing consensus that the development of OCD has to be explained by different models according to symptom subtypes.
Age of onset
A third form of heterogeneity of OCD is based on the age of onset (AOO). This stems from the observation that the beginning of OCD has two peaks: one in childhood and another one in middle adulthood. (Geller et al., 1998; Swedo, Rapoport, Leonard, Lenane, & Cheslow, 1989). The hypothesis arose that early and late onset OCD were two different disorders. Studies that followed investigated whether patients with an early age of onset differ from those with a late age of onset.
Studies that focused on heterogeneity in other psychiatric disorders, have already shown that an early age of onset can be seen as a separate or more severe subtype of the disorder. For example, early onset bipolar disorder is associated with higher frequencies of comorbid conditions (Schurhoff et al., 2000) and early onset schizophrenia with more severe disease than late onset schizophrenia (Gordon et al., 1994).
Also research focused on OCD showed differences between early and late onset of the disorder. Fontenelle, Mendlowics, Marques, & Versiani (2003) found that an early age of onset was characterized by male gender predominance, more clinically significant obsessions and compulsions and a higher frequency of rituals repetition. Several studies have also associated early AOO with a higher degree of familiarity (Pauls et al., 1995; Nestadt et al., 2000; Fyer, Lipsitz, Mannuzza, Aronowitz, & Chapman, 2005; Delorme et al., 2004), a higher comorbidity of OCD-spectrum disorders (Janowitz et al., 2009) and personality disorders (Maina, Albert, Salvi, Pessina, & Bogetto, 2008; Pinto, Mancebo, Eisen, Pagano, &
Rasmussen, 2006). Studies have also consistently found higher comorbidity with tics (Mathis 2008; Chabane et al., 2005; Millet et al., 2004; Rósario-Campos do et al., 2001), eating (Mathis de et al., 2008; Pinto et al., 2006) and somatoform disorders (Mathis de et al., 2008; Rósario-Campos do et al., 2001). Finally, Mathis de et al. (2008) associated early AOO with a higher probability of having an impulse-control or an anxiety disorder.
Defining age of onset
The above section showed that research focused on AOO has been most
comprehensive. There are clear indications that early OCD differs from late OCD. However, there is little consensus about the definition of AOO. Due to this inconsistency in previous studies, the results are difficult to compare.
In the literature, AOO is defined either as the moment that OC symptoms are first recognized (Rosario-Campos do et al., 2000; Diniz et al., 2004), or the moment that OC symptoms cause significant distress or impairment (Fontenelle et al., 2003), or the moment at which the OCD DSM-IV criteria are met (Chabane et al., 2005). Another source of
inconsistency between previous studies was the use of different cut-off points for early and late-onset OCD. There is no consensus about what is the best threshold for defining ‘early’ and ‘late’ onset. Proposed cut-off point are; before age 10 (Rósario-Campos do et al., 2001; Maina, Albert, Salvi, Pessina, & Bogetto, 2008), 14 (Bellodi, Sciuto, Diaferia, Ronchi, & Smeraldi, 1992), 15 (Millet et al., 2004), 16 (Chabane et al., 2005) and 17 (Albert, Maina, Ravizza, & Bogetto, 2002; Fontenelle et al., 2003; Pinto et al., 2006; Fyer et al., 2005) and 18 (Pauls et al., 1995; Nestadt et al., 2000). Another controversy in literature is the way cut-off points are chosen for defining early and late onset groups. Some studies defined categories of age according to the cut-off point previously chosen in studies (Millet et al., 2004), validated or not, other compared more cut-off points and recommend one on the basis of their results
(Janowitz et al., 2009). Delorme et al. (2005) used an admixture analysis to determine the best-fitting model for the observed AOO. Last, Chabane et al. (2005) choose the AOO cut off based on the onset distribution observed in the sample.
Scientific relevance of this study
In this study we investigated differences between early onset and late onset OCD. We defined AOO as the moment when the first symptom was first experienced. We did so
because clinical experience at the Academic Medical Centre has shown a great delay between start of OC-symptoms and the official diagnoses of OCD. The diagnosis is often made late because people do not initially seek help. We compared multiple cut-off points for defining early and late onset group. Like Janowitz et al. (2009) we choose the thresholds most frequently discussed in literature: age 10 and 17. This decision makes it possible to compare several groups. The first comparison is between the group before or at age 10 and the group after age 10. The second comparison is between the group before or at age 17 and the group after age 17.
In this study we extended the literature on age of onset by investigating both
subfactors and comorbidity conditions, which has previously not been done in a joint analysis. The aim is to explore the differences between the early AOO group and the late AOO group in a large sample of OCD patients. The first purpose of the present study is to investigate whether there are any differences in content of the obsessions and/or compulsions between early and late onset OCD. Most previous studies were focused only on hoarding and associated it with early AOO (Samuels et al., 2002; Fontenelle, Mendlowicz, Soares, & Versiani, 2004). We focused on all four subfactors: 1) symmetry, ordering and counting, 2) obsessive thoughts and images associated with aggressive, sexual, and/or religious
(forbidden) content and somatic obsessions related with checking compulsions, 3) contamination obsessions and cleaning compulsions and 4) hoarding obsessions and compulsions. Kichuk et al. (2013) found an association between AOO and symptom dimension: symmetry symptoms have an earlier onset than symptoms in other dimensions. We aimed to replicate this finding. The second purpose is to find associations between AOO and current comorbidity with axis I, axis II and OC-spectrum disorders. We hypothesize that specific comorbidity patterns may be related to the different AOO groups.
Clinical/social relevance
For the treatment of OCD there are both pharmacological and psychological methods which are supported by research evidence. Although they are highly effective in reducing symptoms, the treatment outcomes vary a lot between individual patients (Knopp, Knowles, Bee, Lovell, & Bower, 2013). Moreover it is known that most patients show residual
symptoms and impairments. Several studies showed also a very high relapse rate (between 24%-89%) (Abramowitz, Braddock, & Moore et al. 2009).
The suboptimal outcomes of current treatment methods also appear from studies investigating quality of life (QoL) in persons with OCD who had received treatment.
Compared to controls these people showed more problematic peer relations, more academic difficulties and severe sleep problems. Also when compared to other anxiety and mood disorders people with OCD are less likely to be married, more likely to be unemployed and more likely to have impaired social functioning (Fontenelle et al., 2010). With the current study we hope to get more insight in OCD, which may contribute to the development of more efficient treatment methods. Knowing OCD as a very heterogeneous disorder, subtype specific treatment methods may be developed. For such developments, adequate distinction between subtypes is a prerequisite.
In addition to the importance of distinguishing subtypes, it is also of importance to recognize the early onset of OCD. In major depression early response to treatment is highly associated with final outcome. Da Conceicao Costa et al. (2013) showed the same for OCD: early improvement during treatment predicted treatment response. The degree of symptom reduction during the first four weeks of treatment predicted the degree of symptom reduction after the whole period of treatment. Micali et al. (2010) showed with their study that
paediatric OCD can be a chronic condition that persists into adulthood. To prevent this, they point to, just as Da Conceicao Costa et al. (2013), the importance of early recognition and treatment. To advance early recognition it is important to map out the characteristics of early onset OCD. Our study aims to contribute to this.
By understanding OCD better, it becomes possible to detect risk factors of developing OCD. Does having a certain disorder increase the risk of having OCD? Or vice versa: does having OCD increase the risk of having a comorbid disorder? This study takes a step toward answering these questions. In the long term the development of preventive treatment methods might be possible.
Our current study could also contribute to research focused on underlying mechanisms of psychiatric disorders. When early and late onset OCD differ in their comorbid and content patterns, could this be an indication for different etiological mechanisms? This information could also contribute to the early recognition of the disorder.
In summary, our study is designed to explore the differences in content of the
obsessions and compulsions and in comorbidity between early AOO and late AOO in a large sample of OCD patients. We investigated this question by comparing two thresholds most frequently used in literature. We expect an association between AOO and symptom
dimension: symmetry symptoms have an earlier onset than symptoms in other dimensions. We also expect that specific comorbidity patterns may be related to the different AOO groups. METHODS
Participants
Subjects were 1152 outpatients with primary obsessive-compulsive disorder (OCD) according to DSM-IV criteria (American Psychiatric Association Diagnostic and Statistical Manual of Mental Disorders, 1994), who were referred between 1997-2006 to the Anxiety Clinic of the University Medical Center Utrecht, the Netherlands (UMCU) and between 2006-2010 to the Anxiety Clinic of the Academic Medical Center in Amsterdam, the Netherlands.
Inclusion criteria were 1. 18 years or more at intake assessment. 2. a diagnosis of DSM-IV OCD and 3. having sought treatment for OCD. This means that the treatment the patients receive will focus on reduction of OCD-symptoms. All patients met full DSM-IV criteria for current OCD. This report includes data from the intake assessment only.
For our analyses we used the Mann-Whitney test, but this analysis is not available in G*power. For this reason we chose to base power-analysis on the t-test. We chose a high power (0.95) and did a sensitivity-analysis to compute the minimal effect-size needed to detect an effect: 0.25, a medium effect size.
A non-parametric test will yield less power, so it should be expected that the effect-sizes need to be somewhat higher to be detected. However, given our large sample we are quite confident that statistical power is sufficient.
Procedure
The assessment and study were part of a large-scale research of the University Medical Centre Utrecht (UMC) and Academic Medical Centre Amsterdam (AMC). The study was approved by AMC, the medical faculty of the University of Amsterdam. It is a
non-experimental research, without a randomisation and manipulation. At the start of the study all 8
patients were part of the same group. According to their age at onset (AOO) the patients were assigned to either the early age at onset group or the late age at onset group. The dependent variables are the comorbidity and the content of the obsessions and/or compulsions. Age at onset is the independent variable. The early and late age at onset groups were compared to see if there are differences between these two groups.
All patients signed informed consent to use these data for research purposes. The patients were not compensated for participating in this research. The interview and all ratings were completed by psychiatrists and psychiatrists in training working at the section Mood Disorders at the AMC. At the intake assessment, the psychiatrists completed a semistructured clinical interview, rater-administered assessments and self-report questionnaires to collect detailed information on demographic (age, sex, marital status and education) and clinical features (age of onset, course and family history). After completion of all this, the
psychiatrists wrote a detailed narrative history that included all important information and the DSM-IV diagnosis.
Materials
OCD symptom severity was assessed with the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) (Goodman et al., 1989). The Y-BOCS is a reliable and valid, rater-administered which assesses current severity of obsessions and compulsions (Grant et al., 2007). The scale measures obsessions separately from compulsions with two 5 item scales, each item rated from 0 (no symptoms) to 4 (extreme symptoms). The items contain: time spending on obsessions/compulsions, amount of impairment, distress, resistance and level of control of obsessions/compulsions Higher scores indicate greater severity, with a score of 0-7 is subclinical and 32-40 is extreme.
The content of the obsessions and compulsions was assessed using an extended version of the Y-BOCS checklist (Goodman et al. 1989) dividing the symptoms in five main groups: 1) contamination and cleaning, 2) aggressive, sexual and religious obsessions, 3) somatic obsessions and checking 4) symmetry and counting / arranging obsessions and 5) high risk assessment and checking. Each group provides a different amount of symptoms, each with 3 possible responses; never, past and this week. An example of a symptom is: “excessive hand washing”.
The severity of a patient’s depressive symptoms was assessed with the Hamilton Depression Rating Scale (HDRS) (Hamilton, 1960), a rater-administered 21-item scale assessing depressive symptoms experienced over the past week. The HDRS scale’s internal reliability is adequate, but the interrater and retest reliability is poor. The content validity is poor, while the convergent and discriminant validity are adequate (Bagby, Ryder, Schuller, & Marschall, 2004). Each item has between 3-5 possible responses which increase in severity. The first 17 questions contribute to the total score, the questions 18-21 are recorded to give further information about the depression. An example of an item is: ‘‘Suicide’’ with 0 (absent) to 4 (attempts at suicide). Higher scores on the HDRS indicate greater severity, with a score of 7 or lower is accepted to be within the normal range and a score of 20 or higher indicates at least moderate severity.
The severity of a patient’s anxiety symptoms was assessed with the Hamilton Anxiety Scale (HAS) (Hamilton, 1959), a semi-structures series of 14 questions related to anxiety. The HAS scale’s reliability and concurrent validity is adequate, but the internal validity tested by latent structure analysis proved to be poor (Maier, Buller, Philipp, & Heuser, 1988). It provides 7 items about psychic anxiety and 7 items about somatic anxiety, each item rated from 0 (no anxiety) to 4 (very severe anxiety). Examples of psychic symptoms include feelings of tension and difficulty concentrating and examples of somatic anxiety include
muscular pain and restlessness. Higher scores on the HAS indicate greater severity, with a score of 0-17 is mild severity and a score of 30-56 is severe anxiety.
The Sheehan Disability Scale (SDS), a brief self-report scale, was used to assess functional impairment in three inter-related domains; work/school, social and family life. The SDS internal consistency reliability is high and there is empirical support for the construct validity (Leon, Olfson, Portera, Farber, & Sheehan, 1997). Each scale rated from 0
(unimpaired) to 10 (highly impaired). The three scores can be summed into a measure of global functional impairment, rated from 0 (unimpaired) to 30 (highly impaired). There is no recommended cut-off score.
The current psychological functioning of a patient was assessed using the interviewer-rated DSM-IV-TR Axis V 100-point scales, the Global Assessment of Functioning (GAF). The GAF is used to rate overall psychological, social and occupational functioning, excluding physical and environmental impairment (Pinto, Mancebo, Eisen, Pagano & Rasmussen, 2006).
DATA-ANALYSES
Predictive Analytic Software (PWAS) for Windows version 18.0 was used to conduct the analyses (SPSS inc, Chicago, IL, 2010).
Differences in demographic features (age, sex, marital status and education) between the two groups were checked by using an independent sample t-test for continuous variables and Pearson’s chi-squared analysis (or Fisher’s exact test when cell expected frequencies are less than 5) for dichotomous and categorical dependent variables.
In order to test the null hypotheses that the early- and late-onset groups would not differ with regard to symptom dimensions, comorbidity, family history and symptom severity, we perform independent t-tests and Mann-Whitney tests. A p value of 0.05, two tailed, was used to determine statistical significance when rejecting null hypotheses.
Finally, if significant differences between the groups were found, we performed post hoc analyses to view the differences in more detail.
RESULTS
Demographic and clinical features
The studied population consisted of 1032 patients (416 males and 616 females) with a mean age at interview of 36.1 (SD=12.0) years. The mean age of the onset of obsessive-compulsive symptoms was 18.8 (SD=9.8) years. The distribution of marital status and education level is shown in Table 1 and 2.
Choosing cut-offs
First, we compared groups with an AOO before (early10) vs after (Late10) the age of 10. Early10 was composed of 205 patients (74 males and 131 females) had a mean age at interview of 33.1 (SD=11.6) years. Late10 was composed of 827 patients (342 males and 485 females) had a mean age at interview of 36.8 (12.1) years. There were no differences between the two groups regarding mean age at interview, distribution by sex, marital status or
education level.
Second, we compared groups with an AOO before (early17) vs after (Late17) the age of 17. Early17 was composed of 528 patients (221 males and 307 females) and had a mean age at interview of 33.0 (11.6) years. Late17 was composed of 504 patients (195 males and 309 females) and had a mean age at interview of 39.3 (11.7) years. There was a difference in age at interview between these groups; the mean age at interview at Late17 was statistically higher than at Early17 (39.3 vs. 33.0, Χ²= 146.193, df=57, P<0.001). There was an overall
difference in education level between the groups, Χ²= 25.118, df=8, P=0.001 and in marital status, Χ²= 25.579, df=6, P<0.001. No differences were found regarding distribution by sex.
Content of obsessions and compulsions
First, we compared groups with an AOO before (early10) vs after (Late10) the age of 10. There was a difference between the two groups in distribution of the content of the
symptoms (Χ²=8.159, df=3, p=0.043). In Early10, there was a significant lower percentage of subjects with contamination and/or cleaning obsessions and/or compulsions compared to Late10 (17.6 vs. 25.8, Χ²=6.302, df=1, p=0.012). There were no differences between the two groups regarding symmetry, ordering, counting obsessions and/or compulsions. The same applies to the obsessions and/or compulsions of forbidden/somatic thoughts and checking and to hoarding.
In summary, it can be said that contamination/cleaning obsessions/compulsions are more common in people with an age of onset older than ten years than in people with an age of onset at or below age ten. This means that people who got OCD after their tenth year suffer more often from fear of contamination or cleaning compulsions than do people who got the decease before age or at age ten.
Second, we compared groups with an AOO before (early17) vs after (Late17) the age of 17. There was a difference between the two groups in distribution of the content of the symptoms (Χ²=12.178, df=3, p=0.007). In Late17, there was a significant lower percentage of subjects with symmetry, ordering, counting obsessions and/or compulsions compared to Early17 (20.4 vs 26.5, Χ²=4.889, df=1, p=0.027). In Early17, there was a significant lower percentage of subjects with contamination and/or cleaning obsessions and/or compulsions compared to Late17 (20.5 vs 28.0, Χ²=8.593, df=1, p=0.003). There were no differences between the two groups regarding obsessions and/or compulsions of forbidden/somatic thoughts and checking. The same applies to the obsessions and/or compulsions of hoarding.
The above results show that contamination/cleaning obsessions/compulsions are more common in people with an age of onset above seventeen than in people with an age of onset at or below age seventeen. The results show that for symmetry, ordering and counting the
reverse is true, namely that these symptoms are more common in people with an age of onset at or below seventeen than in people with an age of onset above seventeen.
Comorbidity of obsessions and compulsions
First, we compared groups with an AOO before (early10) vs after (Late10) the age of 10. In Late10, there was a significant higher percentage of subjects with a psychotic disorder compared to Early10 (3.1 vs 0.5, X2=4.519, df=1, P=0.034). In Early10, there was a
significant higher percentage of subjects with other psychiatric disorders (6.3 vs 2.9, X2=5.700, df=1, P=0.017) and axis II disorders (61.5 vs. 52.7, X2=5.471, df=1, P=0.019). There were no differences between the two groups regarding mood-, anxiety-, substance abuse-, eating-, impulse control and OC-spectrum disorders.
This means that psychotic disorders are more common in people with an age of onset above ten than in people with an age of onset at or below age ten. The results show that for other psychiatric disorders and axis II disorders the reverse is true, namely that these disorders are more common in people with an age of onset at or below ten than in people with an age of onset above ten.
Second, we compared groups with an AOO before (early17) vs after (Late17) the age of 17. In Late17, there was a significant higher percentage of subjects with a psychotic disorder compared to Early17 (4.0 vs 1.3, X2=6.993, df=1, P=0.008). In Early17, there was a significant higher percentage of subjects with any other psychiatric disorder compared to Late17 (4.9 vs 2.2, X2=5.688, df=1, P=0.017). There were no differences between the two
groups regarding mood-, anxiety-, substance abuse-, eating-, impulse control, OC-spectrum-, and axis-II disorders.
Here shows that psychotic disorders are more common in people with an age of onset above seventeen than in people with an age of onset at or below age seventeen. The results show that for other psychiatric disorders the opposite is true, namely that these disorders are more common in people with an age of onset at or below seventeen than in people with an age of onset above seventeen.
Table 1
Demographic and clinical characteristics (means (SD), number of patients (%) and statistics): comparison between group 1 and 2.
Total (N= 1032) Early10 (N= 205) Late10 (N=827) Statistics t or Χ² df p Age, mean (SD) 36.1 (12.0) 33.1 (11.6) 36.8 (12.1) 70,451 57 0,109 Education level, n (%) 13.071 8 0.109 Marital status, n (%) 4,879 6 0.559 Single 396 (38.4) 83 (40.5) 313 (37.8) Married 319 (30.9) 53 (25.9) 266 (32.2) Divorced 52 (5.0) 12 (5.9) 40 (4.8) Widowed 7 (0.7) 1 (0.5) 6 (0.7) Living together 141 (13.7) 24 (11.7) 117 (14.1) LAT-relationshi p 78 (7.6) 19 (9.3) 59 (7.1) Unknown 39 (3,7) 13 (6.2) 24 (2.9) Gender, n (%) 1,887 1 0.170 Males 416 (40.3) 74 (36.1) 342 (41.4) Females 616 (59.7) 131 (63.9) 485 (58.6) Age at onset, mean (SD) 18.8 (9.8) 7.4 (2.1) 21,6 (8.8) 1032 51 <0.001 12
Table 2
Demographic and clinical characteristics (means (SD), number of patients (%) and statistics): comparison between Early17 and Late17.
Total (N= 1032) Early17 (N=528) Late17 (N=504) Statistics t or Χ² df p Age, mean (SD) 36.1 (12.0) 33.0 (11.6) 39.3 (11.7) 146.193 57 <0.001 Education level, n (%) 25.118 8 0.001 Marital status, n (%) 25.579 6 <0.001 Single 396 (38.4) 237 (44.9) 159 (31.5) Married 319 (30.9) 141 (26.7) 178 (35.3) Divorced 52 (5.0) 22 (4.2) 30 (6.0) Widowed 7 (0.7) 3 (0,6) 4 (0.8) Living together 141 (13.7) 63 (11.9) 78 (15.5) LAT-relationshi p 78 (7.6) 37 (7.0) 41 (8.1) Unknown 39 (3,7) 25 (4.8) 14 (2.8) Gender, n (%) 1.074 1 0.300 Males 416 (40.3) 221 (41.9) 195 (38.7) Females 616 (59.7) 307 (58.1) 309 (61.3) Age at onset, mean (SD) 18.8 (9.8) 11.5 (3.8) 26.4 (8.2) 1032.000 51 <0.001 13
Table 3
Content of obsessions and compulsions (number of patients (%) and statistics): comparison between Early10 and Late10.
Total (N= 1032) Early10 (N=202) Late10 (N=809) Statistics t or Χ² df p Content obsessions and compulsions, N (%) 1) symmetry, ordering, counting 243 (23.5) 57 (27.8) 186 (22.5) 2.418 1 0.120 2) forbidden/somatic thoughts + checking 507 (49.1) 108 (52.7) 399 (48.2) 1.111 1 0.292 3) contamination, cleaning 249 (24.1) 36 (17.6) 213 (25.8) 6.302 1 0.012 4) hoarding 12 (1.2) 1 (0.5) 11 (1.3) 1.030 1 0.478 Missing 21 (2.0) 3 (1.5) 18 (2.2) Table 4
Content of obsessions and compulsions (number of patients (%) and statistics): comparison between Early17 and Late17.
Total (N=1032) Early17 (N=520) Late17 (N=491) Statistic t or Χ² df p Content obsessions and compulsions, N (%) 1) symmetry, ordering, counting 243 (23.5) 140 (26.5) 103 (20.4) 4.889 1 0.027 2) forbidden/somatic thoughts + checking 507 (49.1) 368 (50.8) 239 (47.4) 0.828 1 0.363 3) contamination, cleaning 249 (24.1) 108 (20.5) 141 (28.0) 8.593 1 0.003 4) hoarding 12 (1.2) 4 (0.8) 8 (1.6) 1.593 1 0.207 Missing 21 (2.0) 8(1.5) 13 (2.6) 14
Table 5
Comorbidity (number of patients (%) and statistics): comparison between Early10 and Late10. Total (N= 1032) Early10 (N=205) Late10 (N=819) Statistics Χ² df p Comorbid condition, N (%) Axis I disorder 485 (47.0) 101 (49.3) 384 (46.4) 0.957 1 0.328 Mood 263 (25.5) 53 (25.9) 210 (25.4) 0.030 1 0.862 Anxiety 135 (13.1) 32 (15.6) 103 (12.5) 1.506 1 0.220 Substance Abuse 37 (3.6) 8 (3.9) 29 (3.5) 0.082 1 0.775 Psychotic 27 (2.6) 1 (0.5) 26 (3.1) 4.519 1 0.034 Eating 28 (2.7) 7 (3.4) 21 (2.5) 0.493 1 0.482 Impulse control 16 (1.6) 4 (2.0) 12 (1.5) 0.279 1 0.598 OC-spectrum 176 (17.1) 42 (20.5) 134 (16.2) 2.230 1 0.135 Other 37 (3.6) 13 (6.3) 24 (2.9) 5.700 1 0.017 Axis II disorder 562 (54.5) 126 (61.5) 436 (52.7) 5.471 1 0.019 Table 6
Comorbidity (number of patients (%) and statistics): comparison between Early17 and Late17. Total (N= 1032) Early17 (N=528) Late17 (N=504) Statistics Χ² df p Comorbid condition, N (%) Axis I disorder 485 (47.0) 239 (45.3) 246 (48.8) 1.132 1 0.287 Mood 263 (25.5) 125 (23.7) 138 (27.4) 1.736 1 0.188 Anxiety 135 (13.1) 76 (14.4) 59 (11.7) 1.728 1 0.189 Substance Abuse 37 (3.6) 16 (3.0) 21 (4.2) 0.931 1 0.335 Psychotic 27 (2.6) 7 (1.3) 20 (4.0) 6.993 1 0.008 Eating 28 (2.7) 14 (2.7) 4 (2.8) 0.012 1 0.912 Impulse control 16 (1.6) 11 (2.1) 5 (1.0) 2.043 1 0.153 OC-spectrum 176 (17.1) 93 (17.6) 83 (16.5) 0.280 1 0.597 Other 37 (3.6) 26 (4.9) 11 (2.2) 5.688 1 0.017 Axis II 562 (54.5) 285 (54.0) 277 (55.0) 0.050 1 0.823 15
DISCUSSION
Our study was designed to explore the differences in content of the obsessions and compulsions and in comorbidity between early AOO and late AOO in a large sample of OCD patients. We investigated this question by comparing two thresholds most frequently used in literature: age 10 and 17. For both variables we found differences between early and late AOO.
We found significant differences in content of the obsessions/compulsions between the early AOO and the late AOO groups. The first comparison (AOO ≤ 10 vs. AOO > 10) showed that late AOO is associated with contamination/cleaning. The second comparison (AOO > 17 vs. AOO ≤ 17) showed the same association. We can conclude that the later the onset of OCD the more common the content of contamination/cleaning. This means that
contamination/cleaning obsessions/compulsions appear more likely in older than in younger age. In addition to the association above, the second comparison (AOO > 17 vs. AOO ≤ 17) showed an association between early AOO and symmetry/ordering/counting. The first
comparison (AOO ≤ 10 vs. AOO > 10) did not show this association. These results mean that symmetry/ordering/counting is more common in people with an AOO before age 17 than in people with an AOO after age 17. Probably symmetry/ordering/counting
obsessions/compulsions appear more likely before adulthood.
The second purpose was to find any differences in current comorbidity with axis I, axis II and OC-spectrum disorders between the groups. Consistent with the hypothesis that specific comorbidity patterns may be related to the different AOO groups, we found
associations between the presence of some comorbid psychiatric disorders and AOO. The first comparison (AOO ≤ 10 vs. AOO > 10) showed that late AOO is associated with psychotic disorders. The second comparison (AOO > 17 vs. AOO ≤ 17) showed also that association. This means that the later the onset the more common a comorbid psychotic disorder. This means that psychotic disorders appear more likely in older than in younger age. In addition, both comparisons showed that early AOO is associated with ‘other’ psychiatric disorders. This means that ‘other’ disorders appear more likely in younger than in older age. Finally, the results of the first comparison (AOO ≤ 10 vs. AOO > 10) showed us an association between early AOO and personality disorders. This means that personality disorders are more common in people with an AOO before age 10 than in people with an AOO after age 10.
There are several methodological limitations to the current study. First, the results could be influenced by the subjectivity of perception. In other words, this study is partly based on retrospective information that can cause bias. To mitigate this bias studies with
longitudinal samples are needed. These studies start soon after the onset or at an age before the onset of the disorder. The bias will also be mitigated by using multiple informants, parents or spouses for example.
Second, this study does not examine the onset of the comorbid conditions. It therefore isn’t clear which of the disorder developed first. Questions like ‘what causes what?’ cannot be answered with such a design. To solve this problem also studies with longitudinal samples are needed.
Third, the data used in this study came from patients who were treated in hospital. This means in general that these patients report more severe symptoms than patients who are treated elsewhere. Possibly for this reason, this study paints a distorted picture of OCD. To get a more reliable picture of OCD, a follow-up study should be conducted in a wider setting. Patients should not only be recruited from hospitals, but also from other mental health
institutions. In this way also the patients with less severe symptoms are included.
Fourth, the group of early AOO (≤ 10) was quite small in comparison to the other groups. Although the power was large enough for the given sample size, the differences in
size between the groups was relatively large. This may have had an effect on the power. Possibly could the power be increased by using a larger early AOO (≤ 10) group.
The first purpose of this study was to demonstrate whether there are any differences in content of the obsessions and/or compulsions between patients with early and late AOO. In line with previous literature (Kichuk et al., 2013) we found an association between early AOO and symmetry/ordering/counting. Unexpectedly, we found late AOO is associated with
contamination/cleaning. There are several explanations for this association. At first,
contamination and cleaning seem to be daily proceedings performed by adults and in general not by children. Since compulsions arise from normal daily actions, the option of compulsive cleaning and/or contaminations thoughts doesn’t seem obvious for children. It is puzzling why previous studies did not find such an association between late AOO and
contamination/cleaning. Probably most of the studies focused on early onset instead of both early and late onset. Most studies only discussed the early AOO group in their conclusions. They didn’t consider the late AOO group. In this study we analyzed both groups. Another explanation for the association we found is the fact that the use of more cut-off points increases the chance of finding differences. Previous studies mostly focused on one cut-off point thereby decreasing the chance of finding differences.
The second purpose was to find any differences in current comorbidity with axis I, axis II and OC-spectrum disorders between the groups. Previous studies associated early AOO with a content of symmetry/arranging/counting, higher comorbidity with OCD spectrum disorders, personality disorders, tic related disorders, eating disorders, somatoform disorders, impulse control disorders and anxiety disorders. In our study, we found an association between an earlier disease onset and a content of symmetry/arranging/counting and
personality disorders. We also confirmed the association between early AOO and tic related and somatoform disorders cause both disorders were covered by ‘other disorders’ which were related to early AOO. In our study, we did not find an earlier disease onset in people with comorbid OCD spectrum disorders, eating disorders, impulse control disorders and anxiety disorders.
An explanation for the discrepancy between our findings and the findings of previous studies may lie in maintaining different criteria for having a comorbid disorder; Probably some studies define a comorbid condition a meeting symptoms of the disorder rather than meeting all DSM-criteria. It is also well known that OCD has some overlapping symptoms with other disorders. For example, it is difficult to distinguish tics from compulsions and eating disorder thoughts from obsessions. We also know that in some studies life time
comorbidity was examined as opposed to current comorbid conditions in other studies. In this study current comorbidity was used while in most other studies life time comorbidity was used. This could be an explanation for the discrepancy between our results and those of previous studies.
By just looking at concurrent comorbid conditions longitudinal transitions between disorders are not included. It is conceivable that the underlying problem will be revealed as disorder A and later revealed as a disorder B. When looking at current comorbidity only disorder B becomes visible, while disorder A remains invisible. When looking at life time comorbidity both disorders become visible. That means, there is access to more data over a greater period of time, which means there is a higher chance of transitions between disorders and a higher chance of a larger number of comorbid conditions. This could be an explanation for the large amount of associations between early AOO and its comorbid conditions in previous studies.
In summary, this study on the one hand confirms the findings of previous studies that specific content and comorbidity patterns may be related to AOO, but on the other hand some previously reported patterns were not replicated.
It could mean that early and late AOO have differences in their genetic backgrounds. Early AOO could share genetic vulnerability factors with personality disorders and ‘other’ disorders, whereas late AOO could share genetic factors with psychotic disorders. This implies that people with early onset OCD do have more often a personality or ‘other’ disorder than people with late onset OCD do. Psychotic disorders occur more often in people with late onset OCD than in people with early onset OCD.
These comorbidity findings can be interpreted in various ways. One possible
explanation for the patterns observed for early AOO is that personality and ‘other’ disorders have shared genetic vulnerability with early onset OCD. Alternatively, the disorders do not necessarily have common genes. It can be the case that the disorders do have common non-genetic pathways, common personality traits for example. These traits are first expressed as OCD and then as a personality disorder. Another possibility is that people with early onset OCD may have a higher risk of developing a personality or an ‘other’ disorder. Finally, the reverse may also be the case: people with a personality disorder or an ‘other’ disorder may have a higher risk of developing OCD. In this case OCD could serve as coping mechanism.
With regard to late AOO this can mean the following: psychotic disorders have shared genes with late onset OCD. However, shared genes do not necessarily have to be responsible for the association between psychotic disorders and late onset OCD. The higher risk of developing one of the disorders while having the other, can also be caused by other factors. It is interesting to investigate the order in which the disorders occur.
We intended to make statements about the role of genes in comorbidity of OCD. However, most of the statements are speculative. Further research should be conducted before reliable statements can be made.
We have to take the current treatment methods into consideration. The results of our study imply that there are differences between people with early AOO OCD and late AOO OCD. This means that there is more evidence to assume that the various subtypes of OCD arise from various etiological factors. The following hypothesis would be interesting to investigate: people with different phenotypes may have various etiological factors and may respond differently on treatment. The latter should be investigated in future studies.
Subsequently there should be different ways of treating these people.
To improve the quality of this type of studies, future studies should focus on the following: first, research of the underlying physiological markers is needed. Studies focused on genetics, neuroimaging and immunology could be helpful to determine whether early onset patients differ from late onset patients. This would allow to further understand etiological differences between early and late onset OCD. If there are differences in genes detectable, we can expand research focused on biological markers. If there are not, we have to shift our focus. From here on, it is also possible to develop studies focused on interactions between biological markers and environmental factors. To gain insight into these interactions, long-term research will be important. Through this type of research a clear image of all
environmental factors ( e.g. stressors, social support, education) can be obtained. By following people for a long period, we can pay more attention to the order in which the disorders manifest. This can potentially lead to a better understanding of the relationship between OCD and other psychiatric disorders. The advantage of longitudinal research is also more accurate data and therefore less bias by retrospective reporting. Another advantage of longitudinal research is the possibility of early recognition of the disorder. Risk factors can be identified and treatment methods can be applied earlier.
Except for the above improvements, it is important to expand the research to other mental health institutions. Also research in non-clinical settings is needed to better understand the presentation of the disorder. In this way also people with a less severe form of OCD and
people who don’t receive treatment can be included in future research. This will give a more realistic view whereby the results can be generalized.
Despite the mentioned limitations, the findings reported provide insight into OCD which in the long term can contribute to the improvement of treatment methods. These new methods will in turn increase quality of life in people with OCD. The findings also added to our knowledge of markers of early onset OCD. This may contribute to early recognition of the disorder and possibly also to early treatment. Finally, we identified possible risk factors of developing OCD. This brings us a step closer to the development and application of preventive treatment methods.
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