Does insight have specific correlation with symptom dimensions in OCD?

Journal of Affective Disorders 138 (2012) 352–359

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Does insight have specific correlation with symptom dimensions in OCD?☆ Anish V. Cherian, Janardhanan C. Narayanaswamy, Ravindra Srinivasaraju, Biju Viswanath, Suresh B. Math, Thennarasu Kandavel, Y.C. Janardhan Reddy ⁎ National Institute of mental health and Neurosciences (NIMHANS), Bangalore, India

a r t i c l e

i n f o

Article history: Received 15 July 2011 Received in revised form 15 January 2012 Accepted 15 January 2012 Available online 12 February 2012 Keywords: Obsessive–compulsive disorder Insight Co-morbidity Symptom dimensions

a b s t r a c t Objective: To study relationship between insight and clinical characteristics in subjects with obsessive–compulsive disorder (OCD). Method: Sample included 545 consecutive patients with a primary diagnosis of DSM-IV OCD who consulted a specialty OCD Clinic at a tertiary psychiatric hospital in India between January 2004 and December 2009. They had been evaluated with the Yale-Brown Obsessive Compulsive Scale (Y-BOCS) symptom checklist, severity rating scale and the item 11 for insight, the Mini International Neuropsychiatric Interview (MINI) and the Clinical Global Impression scale (CGI). Regression analyses were performed to identify predictors of insight. Results: The sample had 498 (91%) subjects with good insight (score ≤ 2) and 47 (9%) subjects with poor insight (score > 2) as per the Y-BOCS item11. Poor insight group had a significantly higher score on the Y-BOCS compulsions (p b 0.001) and total score (p = 0.001), the CGISeverity (p = 0.001) and a higher rate of contamination fears (p b 0.001) and washing compulsions (p b 0.001). Good insight group had a significantly higher frequency of aggressive obsessions (p b 0.001). In linear regression, contamination dimension (p = 0.007) and Y-BOCS total score (p b 0.001) predicted poorer insight and presence of forbidden thoughts (p = 0.006) predicted better insight. Limitations: Study sample is from a specialty OCD clinic of a major psychiatric hospital in India and therefore, generalizability to other clinical settings may be limited. Conclusion: Poor insight is associated with severe form of OCD, and is associated with contamination dimension. That degree of insight has specific correlation with certain symptom dimensions adds to the growing knowledge on the dimensional aspect of OCD. Insight has to be systematically assessed in all OCD subjects particularly in those with contamination fears. Failure to systematically assess insight may have treatment implications. © 2012 Elsevier B.V. All rights reserved.

1. Introduction Insight can defined as an understanding of the motivation behind one's thoughts or behaviors, as a recognition of one's ☆ An earlier version of this paper was presented as a poster at the 10th International Forum on Mood and Anxiety Disorders held in Vienna, November 2010. The poster was awarded the best poster prize of the conference. ⁎ Corresponding author at: Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore 29, India. Tel.: + 91 80 26995278; fax: + 91 80 26564822. E-mail addresses: [email protected], [email protected], [email protected] (Y.C.J. Reddy). 0165-0327/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.jad.2012.01.017

emotional or mental problems or may even connote a sudden understanding of a problem-based on various organizational strategies (Neziroglu and Stevens, 2002). Insight, however, is somewhat vague and certainly complex construct that appears multi-dimensional and influenced by many internal and external factors (Neziroglu and Stevens, 2002). In the typical clinical evaluation of psychiatric disorders, insight is however assessed more conceptually by asking patients whether they believe they are ill, what possibly caused illness and whether or not they feel that treatment is necessary. Insight in to the experience of symptoms is just one aspect of a broader concept of insight. In essence, there is a distinction

A.V. Cherian et al. / Journal of Affective Disorders 138 (2012) 352–359

between the ‘concept’ of insight and the ‘phenomenon’ of insight; the former refers to broader structure of insight, whereas the latter refers to clinical aspect of insight(Markova and Berrios, 1995).It is the ‘phenomenon’ of insight that is often studies in empirical research. The phenomenon refers to the part of the mental state comprising an aspect of selfknowledge that is elicited clinically (Markova et al., 2009). The phenomenon of insight is determined by the concept of insight held by clinician/researcher, measures employed to capture insight and the ‘object’ of insight assessment (Markova and Berrios, 1995). In obsessive–compulsive disorder (OCD), insight in to senselessness of symptoms is treated equivalent to insight into illness. Since the time of Esquirol, presence of insight has been intrinsic to the diagnosis of OCD (Berrios, 1985). In other words, patients with OCD have been traditionally described as having good insight into their symptoms; they perceive obsessive–compulsive (OC) symptoms as excessive, unreasonable and distressing (Kozak and Foa, 1994). Although there is no consensus on what should be the object of insight in OCD, whether it should be symptoms, need for treatment or general awareness of illness (Markova et al., 2009), the DSM IV field trial demonstrated that about a quarter of patients were uncertain about whether their symptoms were unreasonable or excessive, indicating that a broad range of insight exists among patients with OCD (Foa and Kozak, 1995). It is now recognized that insight may lie on a continuum of full awareness of senselessness or absurdity at one end to a total lack of any such awareness at the other end. As a result, DSM-IV defines poor insight as inability to recognize the excessiveness or unreasonableness of symptoms (APA, 1994). In this study we examine this specific aspect of insight that reflects the patient's ability to recognize excessiveness and unreasonableness of OC symptoms. It is now well recognized that patients with OCD may present with varying degree of insight including poor and complete lack of insight in to their OC symptoms (Foa et al., 1995; Insel and Akiskal, 1986; Ravi Kishore et al., 2004). About 15 to 36% of patients may have poor insight in to their symptoms (Alonso et al., 2008; Catapano et al., 2001; Foa et al., 1995; Frost and Gross, 1993; Kozak and Foa, 1994; Lelliott et al., 1988; Matsunaga et al., 2002; Ravi Kishore et al., 2004; Robinson et al., 1976; Solyom et al., 1985; Turksoy et al., 2002). The DSM-IV (APA, 1994) has included ‘poor insight’ as a specifier, which involves a lack of recognition by the patients that the obsessions and compulsions are excessive or unreasonable. The DSM-V draft has included ‘good or fair insight’ (recognition that OCD beliefs are definitely or probably not true, or that they may or may not be true); ‘poor insight’ (OCD beliefs are probably true); and ‘absent insight’ (completely convinced OCD beliefs are true) reflecting the growing awareness that insight is dimensional in nature (APA, 2011). Despite the recognition that insight may be poor in a significant proportion of patients, the relationship between insight as a clinical construct and other clinical characteristics is not well studied. Poor insight has been associated with greater severity of OCD in some studies (Bellino et al., 2005; Catapano et al., 2001; Matsunaga et al., 2002; Ravi Kishore et al., 2004; Solyom et al., 1985; Turksoy et al., 2002) but not others (Eisen et al., 2004; Marazziti et al., 2002). Poor

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insight is also associated with earlier age of onset of symptoms, (Catapano et al., 2010; Matsunaga et al., 2002; Ravi Kishore et al., 2004) longer duration of illness, (Catapano et al., 2010; Matsunaga et al., 2002; Ravi Kishore et al., 2004) and greater psychiatric co-morbidity, especially depression (Catapano et al., 2010; Ravi Kishore et al., 2004; Solyom et al., 1985; Turksoy et al., 2002) and schizotypal and anankastic personality disorders(Alonso et al., 2008; Bellino et al., 2005; Fear et al., 2000; Matsunaga et al., 2002; Rodrigues Torres and Del Porto, 1995). Certain symptom dimensions like hoarding (Alonso et al., 2008; De Berardis et al., 2005; Jakubovski et al., 2011; Matsunaga et al., 2002; Pino Alonso et al., 2008; Ravi Kishore et al., 2004; Samuels et al., 2007; Storch et al., 2007) have been consistently associated with poor insight and there is some data to suggest that need for symmetry too could be associated with poor insight (Elvish et al., 2010; Jakubovski et al., 2011). However, there is conflicting data on the relation between contamination/washing symptoms and insight. Fear of contamination/washing has been associated with both good (Pino Alonso et al., 2008) and poor insight (Jakubovski et al., 2011). In the latter study, a positive association between poor insight and contamination/washing dimension lost significance when overall severity was adjusted for in the analysis and only hoarding remained significant with poor insight. Poor insight is reported to predict poor response to pharmacological (Catapano et al., 2001; Erzegovesi et al., 2001; Ravi Kishore et al., 2004) and psychological treatments (Foa, 1979; Foa et al., 1983). However, some studies have failed to find any association between levels of insight and specific clinical characteristics including treatment response (Eisen and Rasmussen, 1993; Eisen et al., 2001; Marazziti et al., 2002). Studying clinical correlates of insight has important clinical implications. Firstly, insight is a consistently recognized prognostic indicator, particularly indicative of poor response to treatment (Catapano et al., 2010); therefore there is a need to further examine the clinical correlates of insight in OCD. Secondly, OCD is not a unitary disorder (Mataix-Cols et al., 2005) and insight might vary across symptom dimensions as has been reviewed previously (Jakubovski et al., 2011). However, the relation between specific symptom dimensions and insight is rather inconsistent (reviewed previously). It is evident from the review that association between level of insight and specific clinical characteristics is not all that well established and that there is a need for further research in this area. Since poor insight is present in only a proportion of OCD patients, a larger sample may adequately represent a range of insight seen in patients. Many studies have examined insight in OCD as a dichotomous entity (good vs. poor) instead of treating insight as a continuous variable. It has been suggested that instead of viewing insight as a dichotomous variable it may be appropriate to view it as a dimensional construct (Fontenelle et al., 2010). This study therefore systematically examines insight and its relationship with specific clinical characteristics including symptom dimensions from both dichotomous and dimensional perspective in a large cohort of subjects who attended the specialty OCD clinic at a major psychiatric hospital in India. Based on the literature review, we hypothesized that those with poorer insight will have a more severe illness compared to those with well preserved insight and that poor insight may

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have specific clinical correlation with certain symptom dimensions and co-morbidity patterns. Our study may yield more robust results in view of large sample size, assessment of insight from both dichotomous and dimensional perspective, and examination of relationship between insight and symptoms by using factor-analyzed symptom dimensions and not just the symptom categories of the Yale-Brown Obsessive Compulsive Scale (YBOCS) checklist. In this study, we have used the YBOCS item 11 (for insight) which specifically measures the unreasonableness and excessiveness of symptoms and is reflective of the DSM-IV OCD specifier for poor insight (APA, 1994). 2. Method We analyzed clinical records of all 545 consecutive patients with a primary diagnosis of DSM-IV OCD (APA, 1994) who consulted the specialty OCD Clinic at the National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, India during the period January 2004 to December 2009. The study was approved by the institute Ethics Committee. Each patient registered in the clinic had been evaluated in detail by a trained post graduate junior resident in psychiatry using the OCD clinic work up proforma. The proforma captures socio-demographic data, age of onset of OCD, duration of illness, duration of untreated illness, presence or absence of precipitating factors, detailed history of present illness including presence of common co-morbid disorders — especially mood, anxiety, tic and other spectrum disorders, family history of OCD and major psychiatric disorders, and treatment details. In addition, all patients were evaluated with the YBOCS that includes symptom checklist, severity rating scale and item 11 for insight (Goodman et al., 1989a, 1989b), the Mini International Neuropsychiatric Interview (MINI) (Sheehan et al., 1998) and the Clinical Global Impression scale (CGI)(Guy, 1976). Diagnosis and associated features were confirmed by a senior consultant psychiatrist of the OCD clinic (YCJR/SBM) by reviewing all the available information. Family history of OCD in first-degree relatives was determined by obtaining history from the proband and at least one immediate family member (usually parents and siblings). On the YBOCS item-11 insight scale, the insight is graded as follows: 0 = excellent, 1 = good insight, 2 = fair insight, 3 = poor insight (overvalued ideas), 4 = lacks insight (delusional). A higher score on the Y-BOCS item-11 indicates poorer insight. In the present study a score 0, 1 or 2 was considered as indicative of good insight and a score of 3 or 4 was considered as poor insight (Matsunaga et al., 2002). Statistical analysis was conducted using the Statistical Package for Social Sciences (SPSS) version 13.0 (SPSS Inc., Chicago, IL, USA). The independent sample t test and the Chi-square/Fisher's exact test were used for comparison of continuous and categorical variables respectively. All tests were two tailed and significance was set at a conservative p value of ≤0.001 after Bonferroni correction for 52 tests of associations. Binary logistic regression backward stepwise (Wald) was used to identify significant variables in differentiating good and poor insight groups. All variables which were significant or showed a trend toward significance with p ≤ 0.01 in the univariate comparisons and certain clinically

meaningful variables (age, age at onset, gender, duration of illness, current major depression, and number of Axis I comorbid conditions) were included in the regression analysis. A linear regression analysis was also performed to identify the predictors of insight with score on Y-BOCS item-11 as a dependent variable. In the linear regression analysis, we used same clinical variables as in logistic regression (mentioned above) along with factor scores (symptom dimensions) that were significantly correlated with insight score instead of just symptom categories of the Y-BOCS checklist. To generate factors (symptom dimensions), we performed principal component analyses with a Varimax rotation on the 14 symptom categories of the Y-BOCS checklist (excluding miscellaneous symptoms). The criterion used to select the number of factors was an eigenvalue of greater than 1. Factor loadings of greater than 0.50 were considered robust. The factor scores were then correlated with Y-BOCS item-11 insight score using Pearson correlation. 3. Results The sample consisted of 332 men (61%) and 213 women (39%). The mean age (SD) and age-at-onset (SD) of illness of the sample was 29.33 (10.56) and 21.79 (9.4) years respectively. Mean (SD) total Y-BOCS score was 23.31 (8.19) and the mean (SD) score on obsessions and compulsions were 12.47 (3.96) and 10.86 (5.36) respectively. The sample had 498 (91%) subjects with good insight and 47 (9%) subjects with poor insight. The two groups did not differ significantly with regard to gender, marital status, family history of any major psychiatric illness including OCD, age at onset, duration of illness and duration of untreated illness (Table 1). Poor insight group had a significantly higher score on YBOCS compulsions, Y-BOCS total score, and Clinical Global Impression (CGI)-severity subscale (Table 1). The poor insight group had significantly higher rate of contamination fears and washing compulsions (Table 2). On the other hand, good insight group had significantly higher frequency of aggressive obsessions. There were no differences in terms of co-morbidity (Table 3). For binary regression (backward Wald), we included the following variables: age, age at onset, gender, duration of illness, current major depression, number of Axis I comorbid conditions, Y-BOCS (total, obsession and compulsions scores), CGI-S, and certain obsessions (contamination, aggression, sexual, religious, pathological doubts, need for symmetry) and compulsions (washing, collecting). Severity as measured by the CGI-S, and presence of washing compulsions predicted poor insight whereas Y-BOCS obsessions sub score and presence of aggressive obsessions was associated with good insight (Table 4). The overall prediction was 92%. Factor analyses resulted in 5 factor solutions: factor 1 (doubts and checking) with robust loadings on pathological doubts (.844), checking (.823) and repeating compulsions (.609); factor 2 (contamination) with loadings on contamination fears (.911) and washing/cleaning compulsions (.893); factor 3 (hoarding) with robust loading on hoarding obsession (.863) and collecting compulsions (.894); factor 4 (symmetry) with need for symmetry (.843) and ordering compulsions (.805); and factor 5 (forbidden thoughts) with robust loadings on sexual (.706), religious (.731) and aggressive (.553)

A.V. Cherian et al. / Journal of Affective Disorders 138 (2012) 352–359 Table 1 Demographic and clinical characteristics of poor (n = 47) and good insight (n = 498) subgroups. Poor insight Good insight χ2/t N (%)/mean (SD) N (%)/mean (SD) Gender, male 26 (55.3) 28.6(10.7) Age at assessment in years 21.5(10.1) Age at onset of OCD in years Juvenile onset 23(48.9) 7.2 (6.0) Duration of OCD in years 5.4(5.2) Duration of untreated OCD in years Drug naïve at 30 (63.8) assessment Referral status Self36 (76.6) referred 1 (2.1) General practitioner/ physician Psychiatrist 7 (14.9) Others 3 (6.4) Marital status Single/ 28(59.6) unmarried Married 19(40.4) Widow/ 0 widower Divorce/ 0 separated 21(44.7) Family history of psychiatric illness OCD 11(23.4) Psychosis 6 (12.8) Mood 4 (8.5) disorders Anxiety 2 (4.3) disorders 13.1 (4.55) Y-BOCS obsession score 13.9 (4.51) Y-BOCS compulsion score Y-BOCS total 27 (7.34) score CGI-severity 4.96 (1.0)

0.677 0.460

0.411 0.646

21.8(9.3)

0.230

0.811

202(40.6) 7.6 (7.2)

1.242 0.391

0.265 0.696

5.1(5.8)

0.343

0.732

316 (63.5)

0.003

0.959

347 (70)

0.101

0.799

13 (2.6)

Current obsessions Contamination Somatic Aggression Sexual Religious Hoarding Doubt Symmetry Miscellaneous obsessions

Current compulsions Washing/cleaning Checking Repeating rituals Counting Collecting Ordering Miscellaneous compulsions

Poor insight

Good insight

χ2

p

40 (85.1) 2 (4.3) 3 (6.4) 6 (12.8) 9 (19.1) 9 (19.1) 20 (42.6) 20 (42.6) 62 13 (27.7)

273 28 184 142 80 80 276 142 218

(54.8) (5.6) (36.9) (28.9) (16.1) (16.1) (55.4) (28.5) (43.8)

16.114 – 17.801 5.615 4.374 0.299 2.866 4.052 4.567

b 0.001 1.000 b 0.001 0.018 0.036 0.584 0.090 0.044 0.033

42 22 17 2 10 19 26

264 259 215 61 52 161 303

(53) (52) (43.2) (12.2) (10.4) (32.3) (60.8)

23.046 0.465

b 0.001 0.495 0.44 0.101 0.025 0.259 0.459

(89.4) (46.8) (36.2) (4.3) (21.3) (40) (55.3)

2.684 5.001 1.273 0.548

Bold values indicate statistically significant difference.

102 (20.5) 34 (6.9) 287(57.6)

Table 2 Symptom profile in poor (n = 47) and good insight (n = 498) subgroups.

p

306 (61.4) 29.4(10.6)

355

0.878

0.831

191(38.4)

0.723

0.395

87(17.5) 45 (9) 36 (7.2)

1.026 – 0.104

0.311 0.498 0.747

14 (2.8)

–

0.640

12.4 (3.9)

0.694

0.233

10.6 (5.3)

− 4.705 b 0.001

202(40.6) 5(1.0) 4(0.8)

23 (8.19)

− 3.262

0.001

4.4 (1.1)

− 3.273

0.001

Bold values indicate statistically significant difference. Y-BOCS: Yale-Brown Obsessive Compulsive Scale. CGI: Clinical Global Impression.

obsessions. The five factor structure accounted for 62% of the variance. The Y-BOCS item 11 score correlated significantly with factor score on dimension 2 (contamination) (r = .214, p b .001) and 5 (forbidden thoughts) (r = −.119, p = 0.005) and a trend toward significance on dimension 3 (hoarding) (r= .078, p = 0.068). The insight score correlated positively with the CGI-S (r = .188, p b 0.001), the Y-BOCS [total score (r= 0.246, p b 0.001), obsessions (r= 0.141, p = 0.001) and compulsions (r= 0.270, p b 0.001) sub scores] and with time

(r= 0.188, p b 0.001), interference (r= 0.210, p b 0.001), distress (r= 0.133, p = 0.002), resistance (r= 0.313, p b 0.001) and control (r= 0.300, p b 0.001) of compulsions. The insight also correlated with resistance (r = 0.264, p b 0.001) and control (r= 0.219, p b 0.001) of obsessions. In the linear regression, factor scores 2 (contamination), 3 (hoarding) and 5 (forbidden thoughts) were entered in to linear regression along with other clinical variables (age, age at onset, gender, duration of illness, current major depression, number of Axis I comorbid conditions, the Y-BOCS total, obsession and compulsions scores, and the CGI-S) with score on the Y-BOCS item 11 as a dependent variable. Table 5 shows similar results with contamination dimension

Table 3 Comorbid patterns in poor (n = 47) and good insight (n = 498) subgroups.

Major depressive episode Dysthymia Hypomania Mania Panic disorder Agora phobia Social phobia Post-traumatic stress disorder Generalized anxiety disorder Alcohol dependence Non-alcohol dependence Psychosis Body dysmorphic disorder Tic disorders Pain disorder Hypochondriasis Somatization disorder Eating disorders

Poor insight

Good insight

χ2

p

13 (27.7) 5 (10.6) 1 (2.1) 1 (2.1) 0 2 (4.3) 3 (6.4) 0

152 (30.5) 84 (16.9) 8 (1.6) 5 (1.0) 10 (2.0) 2 (0.4) 68 (13.7) 0

0.117 1.220 – – – – 2.004 –

0.269 0.269 0.559 0.419 1.000 0.039 0.157 –

2 (4.3) 0 1 (2.1) 2 (4.3) 0 0 0 1 (2.1) 1 (2.1) 0

32 (6.4) 2 (0.04) 14 (2.8) 7 (1.4) 3 (0.6) 21 (4.2) 1 (0.2) 3 (0.6) 1 (0.2) 0

– – – – – – – – – –

0.758 1.000 1.000 0.178 1.000 0.243 1.000 0.304 0.165 –

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Table 4 Predictors of poor insight using logistic regression analysis. Predictor variable

B

SE

p

Odds ratio 95% CI for OR (OR)

Y-BOCS obsessions score CGI-S Aggressive obsession Washing/ cleaning

− 0.090 0.052

0.081 0.914

0.826–1.011

0.570 0.202 − 2.091 0.611

0.005 1.769 0.001 0.124

1.190–2.628 0.037–0.410

1.773 0.494 b0.001 5.889

2.234–15.520

Y-BOCS: Yale-Brown Obsessive Compulsive Scale. CGI-S: Clinical Global Impression-Severity.

and Y-BOCS total score predicting poorer insight and presence of forbidden thoughts being associated with better insight. Obsessions sub-score of the Y-BOCS tended to correlate negatively with insight score indicating that those with predominant obsessions may have good insight. 4. Discussion Our study reports data on insight and its relationship with other clinical variables in a large sample of subjects with a primary diagnosis of OCD. To the best of our knowledge, this is one of the largest studies on insight in OCD; the only other larger study involved a sample of over 800 subjects (Jakubovski et al., 2011). Our results suggest that poor insight is present in 9% of the OCD patients and that poor insight is associated with a more severe illness and with fear of contamination and related washing compulsions. Those with forbidden thoughts tend to have good insight. 4.1. Occurrence of poor insight In our sample, 9% had poor insight, which is lesser than 15%–36% rate that has been previously reported (De Berardis et al., 2005; Eisen and Rasmussen, 1993; Eisen et al., 2001; Insel and Akiskal, 1986; Marazziti et al., 2002; Matsunaga et al., 2002; Ravi Kishore et al., 2004; Turksoy et al., 2002). Differing rates across studies could be a function of the nature of instrument used to assess insight. Alternatively, it may be due to differences in sample characteristics. 4.2. Insight and severity of OCD Poor insight was associated with a more severe illness in this study as indicated by a higher YBOCS-total score, Table 5 Predictors of insight score on Y-BOCS item-11 using linear logistic regression analysis. Predictor variable

Beta

p

95% CI

Y-BOCS obsessions score Y-BOCS total score Contamination dimension Forbidden thoughts

− 0.131 0.310 0.123 − 0.113

0.083 b 0.001 0.007 0.006

− 1.065 to 0.004 0.017 to 0.052 0.032 to 0.194 − 0.178 to − 0.029

Y-BOCS: Yale-Brown Obsessive Compulsive Scale.

compulsions score and CGI-Severity scores. This is in agreement with previous studies which have reported higher YBOCS scores and a greater number of symptoms (De Berardis et al., 2005; Matsunaga et al., 2002; Ravi Kishore et al., 2004; Solyom et al., 1985; Turksoy et al., 2002) or at least more severe compulsive behaviors among poor insight group (Bellino et al., 2005). However, some studies have failed to show an association between symptom severity and insight (Eisen et al., 2004; Marazziti et al., 2002). Previous studies have argued that relationship between poor insight and severity score on the Y-BOCS may be at least partially tautological since definition of poor insight includes lesser resistance against obsessions and compulsions and therefore poorer control (Alonso et al., 2008; Catapano et al., 2010). Our findings partially support this hypothesis since resistance and control over obsessions and compulsions significantly correlated with insight score. However, degree of insight significantly correlated with other items under compulsions of the Y-BOCS severity scale such as ‘time spent’, ‘interference’ and ‘distress’. In addition, those with poor insight had significantly greater severity of compulsions. In our regression analyses, higher score on Y-BOCS obsessions correlated with good insight (Tables 4 and 5) suggesting a possibility that those with predominantly compulsions, particularly those with washing/cleaning compulsions may be associated with poorer insight and those with predominantly obsessions of forbidden nature may have good insight. 4.3. Insight and symptom dimensions The relationship between the degree of insight and OCD symptom dimensions remains inconclusive. Poor insight has been associated mainly with hoarding obsessions and related collecting compulsions (De Berardis et al., 2005; Jakubovski et al., 2011; Matsunaga et al., 2002; Ravi Kishore et al., 2004; Samuels et al., 2002; Storch et al., 2007) and to some extent with somatic obsessions, (De Berardis et al., 2005; Marazziti et al., 2002) dysmorphophobic worries (Solyom et al., 1985) and need for symmetry and exactness (Elvish et al., 2010; Matsunaga et al., 2002). A recent study linked good insight with fear of contamination and washing compulsions (Pino Alonso et al., 2008). Our study is the only one to report strong association between poor insight and contamination/washing dimension. Our finding is also supported by the finding of another recent study of 203 OCD subjects from our center (L. Prabhu. Symptom dimensions in OCD and their association with clinical variables and comorbid disorders. Thesis submitted to NIMHANS University, 2011), which reported a strong positive correlation between score on the Y-BOCS 11-item and contamination and cleaning dimension of the Dimensional Yale Brown Obsessive– Compulsive Scale (Rosario-Campos et al., 2006). A study by Jakubovski et al. (2011) reported association between poor insight and contamination dimension but the significance disappeared after controlling for overall severity of illness. Individuals who reported fear of harming self or others via overwhelming impulse and those with religious obsessions had poorer insight in a previous study (Tolin et al., 2001). In contrast, good insight group in our study exhibited more of aggressive obsessions when compared to the poor insight

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group and when factor analyzed, forbidden thoughts dimension was negatively correlated with insight score. Hoarding has been consistently associated with poor insight, but we failed to replicate this finding because hoarding was not a predominant symptom or a clinically significant symptom in our sample. Using a specific measure of hoarding such as the Saving Inventory-Revised (Frost et al., 2004) may help clarify the relationship between insight and hoarding. We hypothesize that contamination fears are often understandable even if excessive, resulting in less ego-dystonicity and poorer insight unlike forbidden thoughts of sexual, aggressive and religious nature which are often perceived as alien to one's nature and therefore more ego-dystonic and less acceptable. Cultural factors may have played a role in the perception that contamination fears are not unreasonable or excessive. In the traditional Indian system of medicine, the Ayurveda, hygiene occupies a central role; hygienic living involves regular bathing, cleansing of teeth, skin care, and eye washing (Underwood and Rhodes, 2008). In the traditional Indian society, there is special significance to purity and cleanliness as the basic prerequisites to religious practices and to even enter living, dining and Puja (worshipping) rooms in homes. “Suchi-bai” syndrome is culturally accepted in certain parts of India, which is characterized by exaggeration of concern with cleanliness and is similar to OCD (Chakraborty and Banerji, 1975a, 1975b). Considering the primacy of cleanliness, hygiene and purity in Indian culture, it is perhaps not unusual that concerns with contamination are considered reasonable. Sex is still a taboo in Indian society and religion still occupies a central role in the society. Considering this, it is perhaps understandable that obsessions related to religion, god, sex and harm are perceived as egodystonic and hence with well-preserved insight. Although our study demonstrated relationship between certain symptom dimensions and insight, it has not examined the relationship between insight and beliefs (Obsessive Compulsive Cognitions Working Group, 1997) and metacognitions (Wells and Papageorgiou, 1998) that underlie the symptoms. For example, it is possible that beliefs related to threat estimation, responsibility, perfectionism, tolerance for uncertainty, and control over thoughts may have specific relation with insight. Metacognitions include beliefs concerning the meaning and power of thoughts and beliefs about rituals. It is therefore, important to measure not just symptoms but also underlying beliefs and metacognitions with instruments such as Obsessional-Beliefs Questionnaire (Obsessive Compulsive Cognitions Working Group, 2003) and Metacognitions Questionnaire (Wells and Cartwright-Hatton, 2004). Examining relationship between beliefs and insight may have important clinical implication in that these beliefs may first have to be modified before traditional behavioral techniques are employed in poor insight OCD. That degree of insight has specific correlation with certain symptom dimensions also adds to the growing interest in the dimensional aspect of OCD. It is possible that good insight is typical of certain dimensions (e.g., forbidden thoughts) but not others (e.g., hoarding, contamination). OCD is considered as a disorder with multiple unique but overlapping symptom dimensions (Mataix-Cols et al., 2005) which possibly have distinct neural correlates (Hashimoto et al., 2011; MataixCols et al., 2004).

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4.4. Insight and its relation with comorbid disorders In previous studies poor insight has been associated with presence of depression, (Alonso et al., 2008; Catapano et al., 2001, 2010; De Berardis et al., 2005; Matsunaga et al., 2002; Ravi Kishore et al., 2004; Solyom et al., 1985; Turksoy et al., 2002) schizotypal personality disorder (Alonso et al., 2008; Catapano et al., 2010; Matsunaga et al., 2002) and schizophrenia-spectrum disorders in first-degree relatives (Catapano et al., 2010; Poyurovsky et al., 2008). We did not assess Axis II disorders but there was no elevation of psychotic disorders in first-degree relatives of patients with poor insight. Similarly, depression was not particularly predictive of poor insight and our findings are supportive of two previous studies which failed to support a relationship between depression and poor insight (Bellino et al., 2005; Marazziti et al., 2002). 4.5. Strengths and limitations Our study has certain strengths. Assessment of clinical profile was fairly extensive although done as part of standard clinical evaluation in a specialty OCD clinic. The factor structure is similar to what has been reported from other parts of the world (Bloch et al., 2008) including Asian countries (Girishchandra and Khanna, 2001; Matsunaga et al., 2008). There are obvious limitations, which needs to be acknowledged. First, we assessed insight using the single item 11 of the Y-BOCS which only measures the perception of unreasonableness and excessiveness of symptoms and not other dimensions of insight in OCD. As mentioned previously in the introduction, insight varies from total awareness to denial of illness. Instruments such as the Brown Assessment of Beliefs Scale (BABS) (Eisen et al., 1998) and the Overvalued Ideas Scale (OVIS) (Neziroglu et al., 1999) may be more valid and reliable measures of insight in OCD. They tap dimensional nature of insight more effectively. The BABS has seven items that comprise conviction, perception of other's view of beliefs, explanation of differing views, fixity of ideas, attempts to disprove beliefs, insight, and ideas/delusions of reference. The score of each item ranges from delusional to non-delusional. The OVIS is a ten-item scale that reflects strength, reasonableness and accuracy of belief; the extent to which others share beliefs; attribution of similar or differing views; the effectiveness of compulsions; the extent to which the disorder has caused the belief; and their resistance to the belief. The BABS and the OVIS measure somewhat different although overlapping phenomenon of insight; the former measures delusionality and the latter the overvalued ideas. Second, study sample is from a specialty OCD clinic of a major psychiatric hospital in India and therefore, generalizability to other clinical settings may be limited. Third, study is cross-sectional in nature with no data on Axis II disorders and treatment response and follow-up. Finally, although senior consultants with expertise in assessing OCD subjects confirmed diagnosis and associated features, no reliability exercises were performed. 4.6. Conclusion To conclude, poor insight is associated with severe form of OCD, and is associated with contamination symptom dimension.

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Our study demonstrates the need to assess insight in all OCD subjects particularly in those with washing and cleaning dimension. Failure to systematically assess insight may have treatment implications (Catapano et al., 2001, 2010; Ravi Kishore et al., 2004). For example, those with poor insight may benefit from “belief modification” based exposure than just “habituation” based exposure and response prevention. To understand the relationship between insight and symptom dimensions better, future studies should employ dimensional measures such as the DYBOCS. In addition, there is a need to study the long-term course and outcome of poor insight OCD. 4.7. Future directions Study of insight in OCD, is influenced by several aspects that include the object of insight, the measures employed to capture it, and the dynamic nature of insight that tends to fluctuate over time and in relation to specific context. It is important to recognize that insight has various aspects to it and not just phenomenological symptom level awareness. Future studies should consider examining insight in OCD from both ‘conceptual’ and ‘phenomenon’ perspective. This is important because it is unclear which aspect of insight is of most clinical utility. The existing instruments such as the Y-BOCS item 11, the BABS and the OVIS mostly measure the phenomenon of insight and not the broader self-awareness. To study the broader theoretical structure of insight, there is a need to develop instruments that can capture the ‘concept’ of insight and not just the ‘phenomenon’ of insight. Role of funding source The study was non-funded. Conflict of interest None for any of the authors. Acknowledgment An earlier version of this paper was presented as a poster at the 10th International Forum on Mood and Anxiety Disorders held in Vienna, November 2010. The poster was awarded the best poster prize of the conference. Authors thank Dr. Jagadisha Thirthalli of National Institute of Mental Health and Nuero-sciences, Bangalore, for his valuable comments in improvising the manuscript.

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