J Autism Dev Disord DOI 10.1007/s10803-013-1979-4

ORIGINAL PAPER

Can Autism Spectrum Disorders and Social Anxiety Disorders be Differentiated by the Social Responsiveness Scale in Children and Adolescents? Hannah Cholemkery • Laura Mojica • Sonja Rohrmann • Angelika Gensthaler Christine M. Freitag

•

Ó Springer Science+Business Media New York 2013

Abstract Autism spectrum disorder (ASD) as well as social phobia (SP), and selective mutism (SM) are characterised by impaired social interaction. We assessed the validity of the Social Responsiveness Scale (SRS) to differentiate between ASD, and SP/SM. Raw scores were compared in 6–18 year old individuals with ASD (N = 60), SP (N = 38), SM (N = 43), and typically developed (N = 42). Sensitivity and specificity were examined. The three disorders showed overlapping SRS scores. Especially in boys with SM (ROC–AUC = .81), presence of ASD was overestimated by the SRS. A combination of three disorder specific questionnaires resulted in marginally improved diagnostic accuracy. For the clinically very relevant differential diagnosis of SP/SM, SRS results must be interpreted with caution. Keywords Psychometric assessment
Differential diagnosis
Child psychiatric disorder
Autism spectrum disorder
Social anxiety disorders

H. Cholemkery (&)
L. Mojica
A. Gensthaler
C. M. Freitag Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, JW Goethe University Hospital, Deutschordenstraße 50, 60528 Frankfurt am Main, Germany e-mail: [email protected] L. Mojica e-mail: [email protected] A. Gensthaler e-mail: [email protected] C. M. Freitag e-mail: [email protected] S. Rohrmann Department of Psychology, JW Goethe University, Frankfurt am Main, Germany e-mail: [email protected]

Introduction Impairments in reciprocal social interaction, communication skills, and the presence of stereotyped behaviour, interests, and activities are core features of autism spectrum disorders (DSM-IV-TR; American Psychiatric Association 2000). Prevalence estimates of ASDs have increased over the last decades (Baird et al. 2006; Fombonne 2003), likely due to greater ascertainment of mild to moderate ASD. This is also reflected in the current concept of an autism spectrum disorder continuum (DSM-5 2013), which only includes one diagnosis of ASD with varying degrees of severity. Because of many overlapping symptoms with other psychiatric disorders (White et al. 2012), diagnosis is especially complicated in the high functioning (HFA) (i.e. IQ [ 70) and milder range of ASD (Hartley and Sikora 2009). For instance, high functioning individuals frequently show social worries or anxiety as the logical endproduct of negative feedback or rejections from others due to their lack of social skills. Especially in adolescents, an increasing awareness of their social disabilities can be observed (White et al. 2009, 2012). Also, several symptoms of ASD, like preoccupations, repetitive behaviour, and rituals, or irritability, withdrawal, avoidance of social situations, and problems due to talking in specific situations are commonly seen in children with other psychiatric disorders, namely anxiety disorders like social phobia (SP) or selective mutism (SM) (van Steensel et al. 2013; Towbin et al. 2005; Hartley and Sikora 2009). This overlap in psychopathology can lead to difficulties in differentiating both, ASD and social anxiety disorders. In addition, high prevalence rates (40 %) of anxiety disorders in children and adolescents with ASD were reported in a recent metaanalysis (van Steensel et al. 2011). Also, numerous studies

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in children and adolescents with ASD showed high scores in parent rated social anxiety scales (Kuusikko et al. 2008; Melfsen et al. 2006; Strang et al. 2012, Sukhodolsky et al. 2008; White et al. 2009). On the other hand, recent studies have also found elevated scores of ASD in children with anxiety disorders (Pine et al. 2008; Settipani et al. 2012; Towbin et al. 2005). Hartley and Sikora (2009) compared 55 ASD, 27 ADHD (attention deficit hyperactivity disorder), and 23 children and adolescents with anxiety disorder based on the results of a semi-structured parent interview. Communication and social interaction difficulties differentiated high-functioning ASD from anxiety disorders, and ADHD, whereas restricted, repetitive and stereotyped behaviour did not distinguish both groups. Most studies concentrated on SP, whereas the overlap, respectively differentiating psychopathology of ASD and SM has rarely been investigated. However, comparable verbal and nonverbal language impairments were observed in individuals with SM and ASD (Carbone et al. 2010). Kristensen (2000) reported, in line with Andersson and Thomsen (1998), that 7.4 % of the 54 evaluated 3–17 years old individuals with SM (compared with 0 % in a group of 108 TD) fulfilled the diagnostic criteria of Asperger’s Syndrome. Unfortunately, SM is under-researched, and little information about the disorder is available. Still, it has recently been conceptualised as an anxiety disorder which may be a variant of SP (Melfsen and Warnke 2009; Sharp et al. 2007). SM seems to be a precursor of SP (Bergman et al. 2002), or a very severe form of SP (Black and Uhde 1992, 1995). Both share several features (Chavira et al. 2007), as a high rate of mutual comorbidity, phenotypic similarity, a rather strong genetic background, the same underlying temperamental risk factors, and improvement by SSRIs (Carbone et al. 2010). They also have some psychopathological aspects like impairments in reciprocal social interaction or reduced eye contact with HFASD (high functioning autism spectrum disorder) in common (Tyson and Cruess 2012). These aspects may result in misclassification of the described disorders. Because ASD diagnostic instruments like the autism diagnostic interview-revised (ADI-R), and the autism diagnostic observation schedule (ADOS) are very timeconsuming (Constantino et al. 2003a), there is a pressing need to develop and evaluate reliable, economic and quickly accomplishable screening-instruments for clinical settings (Towbin et al. 2005). Screening is a brief method for selecting individuals for further assessment (Glascoe 2005), and then confirm the diagnosis in positively screened individuals by an in-depth diagnostic procedure (Towbin et al. 2005). For a good screening tool it is essential, that it picks up individuals with ASD correctly (sensitivity), but also does not overestimate ASD in

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children with other psychiatric disorders like social anxiety disorders (specificity), particularly in present of overlapping symptomatology like described above. A recently developed and commonly used screening tool for ASD is the Social Responsiveness Scale (SRS) (Constantino and Gruber 2005). The SRS has been developed as a quantitative measure of autistic traits in children and adolescents. It was originally designed as a continuously distributed measure of autism-related severity in population based samples (Constantino and Gruber 2005; Constantino et al. 2000, 2003b). The SRS focusses on reciprocal social behaviour and social-communicative abilities. Items examine core symptoms of ASD as communication deficits and stereotyped behaviour as well as difficulties which are not exclusively related to ASD. Whereas the majority of the items was deduced from DSMIV-TR autism symptoms, several items were selected additionally as non-specific but frequently observed symptoms in children and adolescents with ASD (Bo¨lte and Poustka 2008; Grzadzinski et al. 2011). The SRS demonstrated good psychometric properties and cross-cultural validity (Bo¨lte et al. 2008; Wang et al. 2012). Applied as a screening tool for ASD, the parent rated SRS differentiated well between ASD, and TD in receiver operating characteristic (ROC) analyses (Bo¨lte et al. 2011), but sensitivity and specificity were considerably lower, when children with ASD were compared to children with different psychiatric disorders (Bo¨lte et al. 2008). Constantino and Gruber (2005) reported a sensitivity of .70 and a specificity of .90 (cut-off score = 85, AUC = .85) in children with ASD aged 4–18 years old when compared to another clinical sample. In a German study of 480 participants (aged 4–18 years) the ROC analyses showed an AUC = .81 (cut-off score = 85) when ASD was compared with mixed child psychiatric disorders (Bo¨lte et al. 2011). In a British sample of 119 children (9–13 years old), Charman et al. (2007) reported an almost similar sensitivity (.78) but a reduced specificity (.67) with a cut-off score of 75 in children with special educational needs with and without ASD (AUC = .77). Accuracy was especially low for intellectually disabled children and for children with additional behaviorial problems measured by the Strengths and Difficulties Questionnaire (AUCboth = .67). Cholemkery et al. (2013) aimed to assess the diagnostic validity of the SRS to differentiate ASD from disruptive behaviour disorders, which are also characterised by difficulties in reciprocal social interaction in a sample of 165 children and adolescents aged 6–18 years. Again, the SRS differentiated excellently between ASD and TD (ROC– AUC = 1.00), but sensitivity (.76) and specificity (.82) were considerably lower when ASD was compared to ODD/CD (oppositional defiant disorder/conduct disorder) (ROC–AUC = .82; cut-off score = 80). A combination of

J Autism Dev Disord

three disorder specific parent rated questionnaires resulted in an improved validity to differentiate ASD and ODD/CD. Cholemkery et al. (2013) concluded that the SRS should be used in combination with additional disorder specific questionnaires for clinical practise. Although the SRS explicitly aims at measuring autistic traits, many items describe symptoms which are not exclusively related to ASD, for example ‘‘anxious in social interaction’’, ‘‘strange bizarre behaviors’’ or ‘‘confident when engaging with others’’ (Grzadzinski et al. 2011). Towbin et al. (2005) reported that a significant number of children with mood and anxiety disorders obtained scores on the SRS within the range typically found in children with ASD diagnoses. The frequency of the children with anxiety disorder (n = 61) screening positive for a possible ASD on SRS was 61 % (n = 37), although none of these children had ever been diagnosed with ASD before (Aldridge et al. 2012). Despite the great overlap in pathology, no previous study has compared SRS scores in children with HFASD and social anxiety disorders. As a consequence we aimed at assessing the diagnostic validity of the SRS to differentiate ASD from SP/SM, which are also characterised by difficulties in social interaction, and communication abilities. Evaluating the screening accuracy and gathering more information with regard to the usage of the SRS in different cases is of particular interest due to the high demand in clinicians and researchers in using shorter screening questionnaires. To study the diagnostic validity of the SRS in differentiating child psychiatric disorders, we compared parent rated SRS scores in 6–18 years old children and adolescents with SP/SM, ASD without comorbid intellectual disability (ID), and well matched TD. We expected that children and adolescents with ASD scored highest, followed by individuals with SP/SM, and TD. We also hypothesized that the SRS total score will better differentiate ASD and TD than ASD and SP or SM. Additionally we explored the diagnostic validity of SRS sub-scales. We also expected an increased correct classification rate by adding different standardized disorder specific parent questionnaires. For an in-depth comprehension and a better sample description, we assessed the comorbid diagnoses as well as the family history of psychiatric disorders. Results for concurrent and convergent validity of the SRS are available in Cholemkery et al. (2013).

Methods

comparability, the groups were matched for IQ, age and gender (Table 1). Diagnoses in the clinical groups were established by independent, and experienced clinicians (psychologists, psychiatrists) according to DSM IV-TR, approximately 6–24 months prior to the questionnaire based study. Diagnoses were revised for the study by Kinder-Dips. The ASD sample included 40 individuals with High Functioning Autism, and 20 with Asperger’s Syndrome. Fourty-three (72 %) male, and 17 (28 %) female participants with an average IQ of 102.15 (SD 16.23) and a mean age of 12.28 (SD 3.03) years were included. The following comorbidities were defined as exclusion criteria: SM, SP, and schizophrenia, as diagnosed by a standardized parentinterview on child psychiatric disorders (Kinder-DIPS; Schneider, Unnewehr and Margraf 2009). The anxiety group consisted of 38 individuals with SP and 43 with SM. The SP group comprised 18 (47 %) females, and 20 (53 %) males with an average IQ of 104.06 (SD 14.33), aged 12.12 (SD 3.63). The SM group included 17 (40 %) females and 26 (60 %) males with a mean IQ of 98.18 (SD 16.26) and an age of 11.09 (SD 3.85) years. On suspicion of an ASD diagnosis, the ADOS (Ru¨hl et al. 2004) was performed, and individuals with SP/SM scoring in the ASD spectrum or above were excluded from the study. Additionally, schizophrenia was excluded as well. The typically developing group (N = 42) consisted of 18 (43 %) female and 24 (57 %) male participants, with an average IQ of 105.32 (SD 11.62) and a mean age of 11.18 years (SD 3.32). This group had no psychiatric symptoms (all first and second order scale T-scores \ 60) according to the Child Behavior Checklist (CBCL) (Achenbach 1998). Some data were also included in the study of Cholemkery et al. (2013). In particular, n = 31 individuals in the TD (22 males, 9 females) and n = 47 of the ASD group (39 males, 8 females) took part in both studies. SM/SP individuals are only included here. The study protocol was approved by the ethical committee of the medical faculty, Goethe-University, Frankfurt am Main, Germany. Informed consent was obtained by the parents and children prior to participation. For the clinical groups, recruitment took place between 2011 and 2012 at the Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, Frankfurt am Main, after completion of the diagnostic process, and before any kind of treatment. Participants of the typically developing group were recruited from regional schools and local advertisements. All families taking part in the study received a moderate fee for participation.

Participants Measures The sample consists of 183 children and adolescents aged 6–18 years old, including 60 individuals with ASD, 38 with SP, 43 with SM, as well as 42 TD. To ensure a better

To confirm the clinical diagnosis of ASD, the German versions of the Autism Diagnostic Interview-Revised (ADI-

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J Autism Dev Disord Table 1 Sample description: age, IQ, gender ASD

SM

SP

TD

Statistical group differences F/v2 (df)

p value

1.53 (3/179)

.21

2.93 (3/66)

.04

.72 (3/109)

.54

1.57 (3/165)

.20

.25 (3/61)

.86

3.53 (3/100)

.02

4.24 (3)

.24

Post hoca

Age All (n)

60

43

38

42

M (SD)

12.28 (3.03)

11.09 (3.85)

12.12 (3.63)

11.18 (3.32)

Female (n)

17

17

18

18

M (SD)

12.53 (3.35)

10.28 (3.64)

13.34 (3.47)

11.23 (2.80)

Male (n)

43

26

20

24

12.18 (2.93)

11.62 (3.96)

11.03 (3.50)

11.14 (3.72)

All (n)

59

33

36

41

M (SD)

102.15 (16.23)

98.18 (16.26)

104.06 (14.33)

105.32 (11.62)

Female (n)

16

14

17

18

M (SD) IQ

M (SD)

99.75 (16.49)

101.50 (17.18)

103.00 (15.03)

99.00 (10.54)

Male (n)

43

19

19

23

M (SD)

103.05 (16.23)

95.74 (15.55)

105.00 (14.02)

110.26 (10.08)

–

4[2

Gender N

60

43

38

42

Female/male

17/43

17/26

18/20

18/24

ASD autism spectrum disorder, SM selective mutism, SP social phobia, TD typically developed, IQ intelligence quotient, n sample size, M mean, SD standard deviation a

Mann–Whitney-U Test with Bonferroni-adjustment

R) (Bo¨lte et al. 2006), and the Autism Diagnostic Observation Schedule (ADOS) (Ru¨hl et al. 2004) were performed with all ASD individuals by clinical experts (psychologists, psychiatrists) who were trained to research standards. Both are based on ICD-10/DSM-IV-TR criteria and provide empirically derived diagnostic algorithms for three subdomains of autism: social interaction, communication, and stereotyped behaviors. The combined usage is generally accepted as gold-standard in diagnosing ASD (Risi et al. 2006). The ADI-R is a detailed interview with the primary caretaker on lifetime ASD symptoms. The ADOS is a direct observation schedule with four different modules for children, adolescents, and adults with varying developmental age and language abilities. In this study, modules 2, 3, and 4 were applied. Primary and comorbid psychiatric diagnoses in children and adolescents with anxiety disorders, and ASD were confirmed by The Diagnostic Interview for Children and Adolescents, parent version (Kinder-DIPS; Schneider, Unnewehr and Margraf 2009) for almost all individuals of the clinical sample (n = 135). N = 6 (ASD = 3; SM = 2; SP = 1) parents declined participation in the Kinder-DIPS. The Kinder-DIPS is a structured diagnostic interview, designed to assess all anxiety disorders, depression, attention-deficit/hyperactivity disorder, oppositional defiant, and conduct disorders, sleeping and eating disorders as well as

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obsessive–compulsive and tic disorders according to ICD10 and DSM-IV-TR criteria. Symptom frequency and/or severity is assessed on a four point Likert scale varying from 0 (never) to 4 (very often). The Kinder-DIPS is widely used in German speaking populations. It has shown good retest and inter-rater reliability (for anxiety disorder j = .85, for other axis I disorders j = .85–.94, both parent-version) (Adornetto et al. 2012). The SRS (Constantino and Gruber 2005) is a 65-item rating scale that measures symptoms indicative of ASD over the previous 6 months in 4- to 18-year-olds. It is a parent/teacher questionnaire and can be quickly completed within 15 min. Each item is scaled from 0 (never true) to 3 (almost always true), generating a total score ranging from 0 to 195. For comparability, the usage of SRS raw scores is recommended for research (Bo¨lte and Poustka 2008; Hus et al. 2013). Scores can also be generated for five symptom domains: social awareness, social cognition, social communication, social motivation, and autistic mannerisms. For this study, the German adaption (Bo¨lte and Poustka 2008) was applied. Reliability and validity findings for this version were similar to the data of the US original sample (Bo¨lte et al. 2008). The following questionnaires were collected to revise a combined diagnostic approach to differentiate ASD and social anxiety disorders.

J Autism Dev Disord

The German version (Bo¨lte and Poustka 2006) of the Social Communication Questionnaire (SCQ) as a second parent-report screening questionnaire for autism, widely available and with good psychometric properties was obtained in this study. The 40-item SCQ is based on ADI-R items (Rutter et al. 2003). General psychopathology was assessed by the German version of the Child Behavior Checklist (CBCL 4-18) (Achenbach, 1998; Do¨pfner et al. 1994). The CBCL is an internationally validated and widely used parent report form with 113 items, computing a total score, second order scores for internalizing and externalizing problems, and the following first order syndrome scales for behaviorial and emotional problems: withdrawn, somatic complaints, anxious/depressed, social problems, thought problems, attention problems, delinquent behaviour and aggressive behaviour. Responses are coded on a Likert Scale from 0 (not true), 1 (sometimes true) to 2 (often true). To further quantify anxiety and attention deficit symptoms, two parent rated questionnaires from the Diagnostic System for Mental Disorders in Children and Adolescents (DISYPS-II) (Do¨pfner et al. 2008) were applied. The DISYPS-II are German diagnostic symptom checklists according to DSM-IV TR, and ICD-10. Anxiety and obsessive–compulsive behaviours (FBB-ANZ) are obtained for four anxiety disorders (33 items): separation anxiety (10 items), generalized anxiety (7 items), social phobia (7 items) and specific phobia (7 items), and two additional items for obsessive–compulsive symptoms. ADHD symptoms (FBB-ADHD) are measured on three scales: attention problems (9 items), hyperactivity (7 items), and impulsivity (4 items). Symptoms can be classified on a Likert scale from 0 (no problems) to 3 (most severe problems). Validation studies of the DISYPS-II have demonstrated good reliability (a = .71–.94) and appropriate validity for the parent rating questionnaires (Do¨pfner et al. 1994). In the present study, the total raw score of both questionnaires (FBB-ANZ, FBB-ADHS) was used to explore their diagnostic validity to distinguish autism spectrum and anxiety disorders. IQ was obtained by using the age appropriate German version of the Wechsler Intelligence Scales for children and adolescents (Hamburg–Wechsler-Intelligence Test for children, HAWIK-IV) (Petermann and Petermann 2010), adults (Wechsler Intelligence Test for adults, WIE) (Aster et al. 2006), or the current version of the revised Culture Fair Intelligence Test (CFT 20-R) (Weiss 2006). For a more comprehensive understanding of disorder related differences, information on the familiar predisposition for psychiatric disorders was obtained from the parents by a semi-standardized medical history. Data collection for the present study took place after the clinical diagnosis (ASD, anxiety disorders) had been

established. The questionnaire data were analysed by an independent researcher, who was not involved in the diagnostic process, using computer based calculation algorithms due to the respective manual criteria. Statistical Analysis Descriptive measures were compared by v2 test, parametric ANOVA, or non-parametric Kruskal–Wallis tests, as appropriate. Frequency distribution of gender was assessed by v2 test, group differences in age and IQ were tested by ANOVA followed by Scheffe´ tests after verifying normal distribution with Kolmogorov–Smirnoff Test and variance homogeneity with Levene Test. The rates of comorbid diagnoses and familiar predisposition relating to psychiatric disorders were compared by v2 test. Group differences of the SRS total and sub-scale scores were compared by two-factor covariance analysis (ANCOVA; factors: group affiliation, gender, group x gender; covariate: IQ), and subsequent Sidak post hoc test for pair-wise differences, after examining normal distribution (Kolmogorov–Smirnoff Test) and variance homogeneity (Levene Test and Fmax-Ratio) (Tabachnick and Fidel 2007; Field 2009). To eliminate the potential confounding effect of gender, and IQ, analyses were conducted adjusting for IQ as covariate, and for gender as a second factor, due to results of previous studies showing an influence of both variables on SRS total scores (Bo¨lte and Poustka 2008; Constantino and Gruber 2005). Significance level was set at a = .05 (uncorrected). With a power of 1 - b C .8, a medium effect of f = .25 can be observed by ANOVA with a total sample size of 180 individuals in a four-group sample. Diagnostic validity and calculation of the optimal cut-off score was studied by ROC comparing ASD versus anxiety disorders (SP, SM), and ASD versus TD for the SRS total score and sub-scales. Because of significant gender differences in the mean comparisons above, ROC analyses were done separately for males and females to prevent the confounding effect of sex. ROC illustrates the performance of a binary classifier system. The area under the curve (AUC), sensitivities (true positives) and specificities (1-true negatives) for recommended SRS cut-offs were calculated as determined by the Youden-score. Test accuracy is measured by AUC with an area of 1 representing perfect classification and an area of .5 showing a random result. Finally, the predictive value of a positive and a negative test was calculated for the clinical groups. Similar analyses (group differences, ROC–AUC calculation) were also performed with the CBCL, FBB-ANZ, FBB-ADHD, and the SCQ, to assess their validity to differentiate between the three groups. In subsequent analyses studying the diagnostic validity by a combination of questionnaires, binary logistic regression analyses were calculated in females and males separately. At

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J Autism Dev Disord Table 2 Parent rated behavioral measures between all groups

SCQ

ASD

SM

SP

TD

Statistical group differences

M (SD)

M (SD)

M (SD)

M (SD)

H (3)

p value

98.28

\.001

n = 55

n = 42

N = 37

n = 42

19.07 (7.59)

8.86 5.24)

7.49 (5.16)

2.86 (3.02)

Post hocb

1 , 2 , 3[4 1[2 , 3

FBB-ANZ

n = 45

n = 42

n = 35

n = 42

5.04 (3.81)

8.07 (5.27)

7.37 (6.08)

.40 (1.35)

72.11

\.001

1 , 2 , 3[4 2[1

FBB-ADHD

n = 51 6.84 (4.69)

n = 41 3.10 (4.13)

n = 36 3.67 (4.01)

n = 42 .64 (1.34)

CBCLa

n = 58

n = 42

n = 36

n = 42

Total score

69.36 (7.05)

64.10 (9.13)

65.81 (8.80)

45.26 (8.15)

54.72

\.001

1 , 2 , 3[4 1[2 , 3

89.89

\.001

1 , 2 , 3[4 1[2

Externalizing

61.10 (8.43)

54.69 (9.94)

57.89 (9.86)

44.69 (8.18)

56.76

\.001

1 , 2 , 3[4

Internalizing

67.79 (7.87)

69.36 (8.81)

67.97 (8.81)

46.45 (8.30)

85.05

\.001

1 , 2 , 3[4

Withdrawn

70.53 (9.98)

72.95 (10.55)

66.44 (10.63)

51.79 (3.43)

86.05

\.001

2[3

Somatic complaints

60.91 (10.63)

61.10 (11.45)

62.64 (9.25)

53.21 (5.88)

24.48

\.001

1 , 2 , 3[4

Anxious/depressed

64.45 (8.22)

65.69 (7.85)

67.56 (8.92)

51.83 (3.50)

77.17

\.001

1 , 2 , 3[4

Social symptoms

72.66 (9.84)

63.88 (7.98)

63.94 (9.84)

52.17 (4.15)

84.70

\.001

1[2 , 3

Thought problems

68.29 (10.45)

57.02 (9.18)

59.89 (11.82)

51.67 (4.81)

61.14

\.001

1[2 , 3

Attention symptoms

69.24 (9.02)

60.24 (7.67)

63.14 (9.32)

52.67 (4.14)

74.02

\.001

1[2 , 3

Delinquent behavior Aggressive behavior

59.64 (6.40) 62.21 (9.07)

55.52 (6.84) 56.95 (7.50)

57.86 (8.10) 59.72 (7.71)

51.40 (3.11) 51.57 (3.05)

44.61 45.38

\.001 \.001

1[2 1[2

1[2

ASD autism spectrum disorder, SM selective mutism, SP social phobia, TD typically developed, M mean, SD standard deviation, SCQ Social Communication Questionnaire, FBB-ANZ external assessment for anxiety and obsessive–compulsive behavior, FBB-ADHD external assessment for attention deficit hyperactivity disorder, CBCL Child Behavior Checklist [means significant differences a b

T scores Mann-Whitney-U Test with Bonferroni-adjustment

the respective cut-off dichotomised questionnaire data were included stepwise into the model, depending on their AUC values, predicting ASD (= 1) versus anxiety disorder (= 0). Contribution of the predictors to the model was tested by Wald statistic. Models were compared by likelihood ratio test.

Results Sample Characteristics Table 1 displays the descriptive data of the four groups. Groups did not differ in age, IQ, or gender distribution. Data on parent rated questionnaires were analysed by nonparametric Kruskal–Wallis Test, followed by Mann– Whitney-U Test with Bonferroni-adjustment because of

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normal distribution violation (Table 2). The highest scores in the SCQ were found for ASD, followed by SM, SP, and TD. In the DISYPS-II scales, all clinical groups showed higher scores than TD. Highest scores for anxiety (FBBANZ) were found for Selective Mutism whereas ASD scored highest in attention problems (FBB-ADHD). Comparing CBCL scales between groups, children with ASD scored highest in total score, externalizing problems, social problems, thought problems, attention problems, delinquent, and aggressive behavior. No differences between clinical groups were found for internalizing problems, somatic complaints, and anxious/depressed. All of the three clinical groups (n = 135) showed a similar rate of comorbid diagnoses (Table 3) according to the Kinder-Dips (Schneider et al. 2009), 52.6 % (n = 30) of the ASD group, 58.5 % (n = 24) in the SM group and 67.6 % (n = 25) in the SP group showed at least one

J Autism Dev Disord Table 3 Psychiatric comorbid diagnoses and psychiatric family history ASD n = 57 N (%)

SM n = 41 N (%)

SP n = 37 N (%)

v2 (2)

p value

Post hoca

Psychiatric comorbid disorders Total rate*

30 (52,6 %)

24 (58,5 %)

25 (67,6 %)

2,06

.38

ADHD ODD

19 (33,3 %) 8 (14,0 %)

0 (0 %) 1 (2,4 %)

3 (8,1 %) 2 (5,4 %)

21,93 4,24

\.001 .11

1 [ 2, 3

Separation anxiety

1 (1,8 %)

2 (4,9 %)

7 (18,9 %)

8,64

.008

3[1

Specific phobia

4 (7,0 %)

14 (34,1 %)

6 (16,2 %)

12,09

.002

2[1

Agoraphobia

0 (0 %)

3 (7,3 %)

1 (2,7 %)

3,99

.07

Depression

0 (0 %)

4 (9,8 %)

4 (10,8 %)

7,24

.02

Tic disorder

8 (14,0 %)

2 (4,9 %)

1 (2,7 %)

4,03

.12

Enuresis/Encopresis

2 (3,5 %)

3 (7,3 %)

2 (5,4 %)

,89

.72

Sleeping disorders

4 (7,0 %)

0 (0 %)

4 (10,8 %)

4,52

.08

1=2=3

Family history of psychiatric disorders Total rate*

25 (58,1 %)

26 (70,3 %)

27 (77,1 %)

3,34

.19

ADHD

5 (11,6 %)

2 (5,4 %)

0 (0 %)

4,31

.10

Anxiety disorders

3 (7,0 %)

12 (32,4 %)

9 (25,7 %)

8,52

.01

2[1

Depression

6 (14,0 %)

14 (37,8 %)

11 (31,4 %)

6,27

.04

2[1

* Total means at least one diagnosis ASD autism spectrum disorder, SM selective mutism, SP social phobia, ADHD attention deficit and hyperactivity disorder, ODD oppositional deviant disorder a

v2 Test with Bonferroni-adjustment

comorbid diagnosis (v2 = 2.06; p = .36, n.s.). Children with ASD most often suffered from ADHD (n = 19; 33.3 %). In the SM group specific phobia was most prevalent (n = 14; 34.1 %). The SP group presented separation anxiety (n = 7; 18.9 %) as the most often observed comorbidity. Families of the analyzed clinical groups showed a high grade of familiar predisposition related to psychiatric disorders: 58.1 % of the ASD, 70.3 % of the SM, and 77.1 % of the SP group had at least one mentally ill family member (v2 = 3.34, p = .19, n.s.). For all groups, depression was the most often observed comorbidity (ASD: n = 6, 14 %; SM: n = 14, 37.8 %; SP: n = 11; 31.4 %). Participants of the SM group showed a significant higher proportion of family members with anxiety disorders (v2 = 8.46, p = .004, Odds Ratio = 6.4) and depression (v2 = 6.05, p = .01, Odds Ratio = 3.81) than the ASD group. SRS Raw Scores and Moderating Effects In two-factor co-variance analysis IQ negatively predicted the SRS total raw score (F = 7.67, p = .006; r = -.20). Females showed higher SRS total raw scores than males (F = 7.62, p = .006) over all groups. No interaction effect of gender and group was present (F = 1.71, p = .17).

Main effect analyses detected a difference in SRS total raw score between groups (F = 101.42, p \ .001). Post hoc comparisons including Sidak correction showed that the ASD group (M = 98.63, SD = 26.19) differed from children with SM (p \ .001, d = 1.36), with SP (p \ .001, d = 1.73), and TD (p \ .001, d = 3.56). Also, SM and SP differed from TD (pall \ .001, dSM = 2.24, dSP = 1.82). Children and adolescents with SM (M = 64.14, SD = 24.05) and SP (M = 55, SD = 23.63) showed comparable total SRS raw scores (p = .66, d = .038). Within the ASD group no differences (F = .52, p = .48) were observed for Autism (M = 99.03, SD = 28.72) and Asperger‘s Syndrome (M = 97.85, SD = 20.88). Similar results were found for SRS sub-scales (Table 4): ASD scored highest, followed by anxiety disorders and healthy controls (pall \ .001). Post-hoc tests showed significant higher scores by ASD in contrast to all other groups for social awareness (F = 56.88, p \ .001), social cognition (F = 65.23, p \ .001), social communication (F = 86.55, p \ .001), and autistic mannerisms (F = 79.16, p \ .001), but not for social motivation in the one-to-one comparison with SM (p = .86; d = .02). Participants of the SM and SP groups showed slightly similar scores on all sub-scales, except for social motivation (p = .03; d = .72; SM [ SP) and social communication (p = .04; d = .67; SM [ SP). For most of the sub-scales (social cognition, social

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J Autism Dev Disord Table 4 Social Responsiveness Scale (SRS) total and sub-scale scores between all groups ASD (n = 60) M (SD)

SM (n = 43) M (SD)

SP (n = 38) M (SD)

TD (n = 42) M (SD)

F (df)

p value

98.63 (26.19)

64.14 (24.05)

55.00 (23.63)

21.45 (12.54)

101.42

\.001

Post hoc*

Total score All

1,2,3 [ 4 1 [ 2,3

Femalesa

113.00 (24.20)

68.35 (23.72)

58.61 (24.59)

22.94 (12.75)

Malesb

92.95 (24.98)

61.38 (24.32)

51.75 (22.86)

20.33 (12.54)

7.62

.006

12.33 (3.34)

6.35 (3,12)

6.68 (3,52)

4.29 (2,34)

56.88

.001

a[b

Social awareness All

1,2,3 [ 4 1 [ 2,3

Femalesa Males

b

13.71 (3.26)

6.71 (3,08)

6.83 (3,75)

4.33 (1,97)

11.79 (3.26)

6.12 (3,19)

6.55 (3,40)

4.25 (2,63)

18.28 (6.32)

9.28 (5.66)

9.95 (5.83)

3.93 (2.98)

2.01

.16

65.23

.001

a=b

Social cognition All

1,2,3 [ 4 1 [ 2,3

Femalesa Males

b

21.24 (5.80)

10.94 (5.14)

11.78 (5.73)

4.28 (3.16)

17.12 (6.19)

8.19 (5.82)

8.30 (5.55)

3.67 (2.87)

9.34

.003

a[b

Social communication All

33.95 (9.64)

23.12 (9.76)

16.92 (8.53)

6.36 (5.12)

86.55

.001

1[2[3[4

Femalesa Malesb

38.00 (7.98) 32.35 (9.85)

24.71 (9.80) 22.08 (9.79)

18.06 (8.52) 15.90 (8.64)

6.72 (5.51) 6.08 (4.91)

6.32

.01

a[b

17.35 (4.97)

13.89 (4.55)

5.67 (3.76)

61.97

.001

Social motivation** All

17.25 (5.51)

1,2,3 [ 4 1,2 [ 3

Femalesa

21.12 (4.66)

17.88 (5.29)

13.94 (4.73)

6.44 (3.99)

Malesb

15.72 (5.09)

17.00 (4.83)

13.85 (4.50)

5.08 (3.55)

8.05 (5.01)

7.55 (5.34)

1.21 (1.52)

7.87

.006

79.16

.001

a[b

Autistic mannerisms All

16.82 (6.20)

1,2,3 [ 4 1 [ 2,3

Femalesa

18.94 (7.06)

8.12 (4.64)

8.00 (5.84)

1.17 (1.62)

Malesb

15.98 (5.70)

8.00 (5.34)

7.15 (4.97)

1.25 (1.48)

1.34

.25

a=b

ASD autism spectrum disorder, SM selective mutism, SP social phobia, TD typically developed, M mean, SD standard deviation; a = females, b = males * Sidak correction ** significant Interaction effect for group 9 gender

communication, social motivation) a significant gender effect was observed. Discriminant Validity of the SRS Alone Because of the observed gender differences, ROC-analyses were calculated for males and females separately. For greater clarity, results are summarized for SP/SM as one anxiety group in the text below, as differences between both groups were small. All single results are additionally presented in Table 5. The SRS differentiated ASD and TD in females (95 % CI .1.00–1.00) marginally better than in males (95 % CI .99–1.00). For males, an SRS score of 43 resulted in the

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optimal sensitivity (.98) and specificity (.96). For females, an SRS score of 64, calculated by Youden-score, showed the best sensitivity, and specificity (both = 1.00). Predicting ASD and anxiety disorders by SRS resulted in an ROC– AUC = .84 (95 % CI .76-.92) for males (Fig. 1). An SRS total score of 75 showed a sensitivity of .79 and a specificity of .83. Predicting ASD and anxiety disorders in the female group (Fig. 2) resulted in an optimal SRS cut-off of 71, calculated by Youden-Score, with a ROC–AUC = .92 (95 % CI .85–.99, sensitivity = 1.00, specificity = .69). Confidence Intervals for both, male and female anxiety groups did not overlap with the corresponding CIs for ASD versus TD. ROC–AUC was slightly higher when

J Autism Dev Disord Table 5 Receiver operating characteristics (ROC) with area under the curve (AUC), optimal cut-off scores, sensitivity, and specificity for all groups in Social Responsiveness Scale (SRS)

All Females Males

ASD versus TD

ASD versus SM

ASD versus SP

ROC–AUC [95 % CI] Cut-off (sensitivity/ specificity)

ROC- AUC [95 % CI] Cut-off (sensitivity/ specificity)

ROC–AUC [95 % CI] Cut-off (sensitivity/ specificity)

ASD versus social anxiety disorders ROC–AUC [95 % CI] Cut-off (sensitivity/ specificity)

.996 [.99–1.00]

.83 [.75–.91]

.88 [.82–.95]

.85 [.79–.92]

43 (.98/.95)

75 (.83/.74)

78 (.80/.84)

75 (.83/.77)

1.00 [1.00–1.00]

.90 [.80–1.00]

.94 [.87–1.00]

.92 [.85–.99]

64 (1.00/1.00)

88 (.82/.82)

71 (1.00/.72)

71 (1.00/.69)

.995 [.99–1.00]

.81 [.71–.92]

.88 [.78–.97]

.84 [.76–.92]

43 (.98/.96)

75 (.79/.81)

80 (.74/.90)

75 (.79/.83)

ASD autism spectrum disorder, TD typically developed, SM selective mutism, SP social phobia, social anxiety disorders = Selective Mutism and Social Phobia together, CI confidence interval

Fig. 1 Receiver operating characteristics (ROC) curve (AUC = .84, CI .76–.92) of the Social Responsiveness Scale for autism spectrum disorder versus anxiety disorders in the male group

Fig. 2 Receiver operating characteristics (ROC) curve (AUC = .92, CI .85–.99) of the Social Responsiveness Scale for autism spectrum disorder versus anxiety disorders in the female group

differentiating between ASD and SP, especially in females (ROC–AUCall = .88, 95 % CI .82–.95, males: AUC = .88, females: AUC = .94) than for differentiating between ASD and SM (ROC–AUCall = .83, 95 % CI .75–.91, males: AUC = .81, females: AUC = .90). Using the Likelihood Ratio method, the predictive value (PV ?) is the probability that a positive test result really identifies an individual with ASD. Here, the PV ? was 81 % for our clinical male sample (ASD vs. anxiety disorders) with a best cut-off score of 75. The predictive value of the negative test (PV -) also showed a probability of 81 % that individuals really do not show the condition ASD when the test is negative. For the female group, an SRS total score of 71 resulted in a PV ? of 61 % while the PV - showed a result of 99 %. Regarding the validity of the SRS sub-scales to differentiate between ASD and anxiety disorders, the best performance was shown by the scale social awareness for both,

males (ROC–AUC = .89, 95 % CI .82–.96) and females (ROC–AUC = .93, 95 % CI .87–1.00). For males, best results besides the social awareness scale were found for autism mannerism (ROC–AUC = .85, 95 % CI .77–.93), followed by social cognition (ROC–AUC = .85, 95 % CI .77–.93), and social communication (ROC–AUC = .82, 95 % CI .72–.91). Lowest ROC–AUC was, equally to the female group, observed for social motivation (ROC– AUC = .52, 95 % CI .39–.64), for both, SM (ROC– AUC = .44) and SP (ROC–AUC = .61). For females social communication (ROC–AUC = .91, 95 % CI .83–.98), autism mannerism (ROC–AUC = .90, 95 % CI .81–.99), and social cognition (ROC–AUC = .88, 95 % CI .79–.98) also showed better validity than the sub-score social motivation (ROC–AUC = .79, 95 % CI .65–.92). Only studying SM, social awareness, social cognition, and autism mannerism distinguished ASD and SM well in females (ROC– AUC [ .90), but only social awareness reached an ROC–

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AUC = .90 in males. Studying SP, best validity was found for social awareness and social communication for males (ROC–AUC = .88) and for females (ROC–AUC [ .90). In summary, the discriminant validity of the SRS was almost perfect while distinguishing ASD and TD (ROC– AUC = .996), better in differentiating ASD and SP (ROC– AUC = .88) than ASD and SM (ROC–AUC = .83), and higher AUCs could be observed for females (ROC– AUC = .92) than for males (ROC–AUC = .84) when differentiating ASD versus SP/SM by SRS total score. The best differentiation for ASD versus both anxiety disorders together was demonstrated for social awareness (AUC = .89 males; ROC–AUC = .93 females), the worst for social motivation (ROC–AUC = .52 males; ROC–AUC = .79 females). Discriminant Validity of the SRS in Combination With Cther Questionnaires To analyze if a combination of questionnaires would result in a better validity than the classification by the SRS alone, binary regression analyses were calculated. Due to significant gender differences in SRS total scores (p = .006), binary regression analyses were calculated for males and females separately. Since SP and SM showed comparable SRS raw scores (p = .66), both were summarized as one social anxiety group. Besides the SRS, all other parent rating scales showed differing mean scores between both clinical groups: CBCL, SCQ, FBB-ANZ, and FBB-ADHD (Table 2). First, for all of these questionnaires, ROCanalyses with AUC, sensitivity, specificity, and respective cut-offs were calculated to explore their validity to discriminate between anxiety and autism spectrum disorders in the male, female, and total group (CBCL: AUCfemale = .71, AUCmale = .63, AUCtotal = .65.; SCQ: AUCfemale = .94, AUCmale = .84, AUCtotal = .88; FBB-ANZ: AUCfemale = .55, AUCmale = .68, AUCtotal = .64; FBB-ADHD: AUCfemale = .70, AUCmale = .75, AUCtotal = .73). To assess the specific contribution of each questionnaire to

improve classification, the predictors were entered hierarchically into the model, comparing each new model with the previous one. As binary classifier, cut-offs were chosen accordingly to the best sensitivity and specificity calculated by Youden-score for this population. Only three of the questionnaires improved the respective fit (likelihood ratio test: p \ .01) of the binary regression model for males, females, and the combined sample: the SRS, the SCQ, and the FBB-ANZ. Because sample size of female participants was too small for calculating a separately regression analysis, binary regressions were calculated for the total sample on the one hand, and for the male sample on the other hand. In the combined sample, A combination of SRS (cut-off 75), SCQ (cut-off 14), and FBB-ANZ (cut-off 9) as independent predictors (likelihood ratio tests: pall \ .01) showed the best model accuracy (v2 = 82.10, p \ .001; -2 Log Likelihood = 73.60) and explanatory value (Nagelkerkes R2 = .68) for the total sample. Nagelkerkes R2 summarizes how much of the ‘‘variability’’ of the dependent variable is explained by the independent variables. The rate of correctly classified individuals increased from 80.7 % using the SRS alone to 84.9 % using all three instruments with the respective cut-offs. Table 6 presents a summary overview. Binary regression analyses were additionally calculated for males with similar findings. A combination of SRS, SCQ, and FBB-ANZ as independent predictors improved again the respective fit of the binary regression model (likelihood ratio tests: pall \ .05) and showed the best model accuracy (v2 = 48.66, p \ .001; -2 Log Likelihood = 49.44) as well as the explanatory value (Nagelkerkes R2 = .66). The right classification increased from 82.2 % (SRS alone) to 83.6 %.

Discussion The validity of screening tools for ASD needs to be especially studied with regard to other psychiatric disorders

Table 6 Binary logistic regression results comparing SRS alone and in combination with SCQ and FBB-ANZ classifying ASD Model fit

Model 1

v2 (df)

R2

-2 LL

Correct classification (%)

48.40 (1)

.46

107.29

80.7

.61

85.99

84.9

Variables

b (SE)

Wald (df = 3.84)

p value

Odd’s ratio

SRS (cut-off 75)

3.06 (.52)

34.83

\.001

21.40 8.68

(p \ .001) Model 2

69.70 (2) (p \ .001)

Model 3

82,10 (3) (p \ .001)

.68

73.60

84.9

SRS (cut-off 75)

2.16 (.59)

13.61

\.001

SCQ (cut-off 14)

2. 52 (.57)

19.59

\.001

12.45

SRS (cut-off 75)

2.78 (.68)

16.94

\.001

16.06

SCQ (cut-off 14)

2.69 (.65)

17.26

\.001

14.74

FBB-ANZ (cut-off 9)

2,52 (.80)

9.90

.002

12.38

LL log likelihood, R2 Nagelkerkes R2, b regression coefficient, SE standard error, SRS Social Responsiveness Scale, SCQ Social Communication Questionnaire, FBB-ANZ external assessment questionnaire for anxiety and obsessive–compulsive disorders

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with overlapping symptomatology (Norris and Lecavalier 2010). Due to marked impairments in social interaction and communication skills of both, ASD and social anxiety disorders (Tyson and Cruess 2012), this study explicitly focused on analyzing the diagnostic accuracy of the widely used autism screening tool SRS. We therefore aimed at answering the question if ASD and social anxiety disorders can be differentiated by the SRS. When comparing mean SRS total scores between groups, we observed—as expected—highest SRS total scores in children with ASD, followed by SP/SM and TD. Within the ASD subgroups, no differences were observed as shown by previous studies (Bo¨lte and Poustka Bo¨lte and Poustka 2008; Charman et al. 2007). Similarly, both social anxiety disorders did not differ with regard to the SRS total score. These findings present the first tendency that the SRS do not encompass more subtle psychopathological differences. When studying the validity of the SRS to differentiate between disorders, we confirmed the results of previous studies (Bo¨lte et al. 2011; Cholemkery et al. 2013; Constantino and Gruber 2005). The SRS is an excellent screening tool when differentiating ASD from typically developing children. However, in clinical practice the core aim is to differentiate between children with ASD and children with overlapping psychopathological symptoms but different underlying psychiatric disorders. Focusing on social anxiety disorders, in our study the SRS showed a sensitivity of .83 (.80), and a specificity of .74 (.84) for differentiating ASD and SM (SP) in the combined sample. The respective sensitivities and specificities are in line with previous studies comparing ASD with a mixed group of other mental disorders (sensitivity = .70, specificity .90 in Constantino and Gruber 2005; .73, .81 in Bo¨lte and Poustka 2007; .66, .89 in Wang et al. 2012). Sensitivity rates of 70–80 % are an accepted minimum standard for screening purposes, whereas specificity values should be close to 80 % or higher (Norris and Lecavalier 2010). In our sample, sensitivity results (.83 for SM, .80 SP) just met these criteria, while specificity rates (.74 for SM, .84 for SP) were somewhat lower in the SM sample. Both values are important, but greater consideration should be given to sensitivity as a critical aspect of a screening tool (Norris and Lecavalier 2010). Concerning the purpose and independent of the calculated Youden-score, the cut-off values can be decreased (with a higher sensitivity in consequence) or increased (with a higher specificity at the cost of sensitivity). Looking at the summarizing AUC-scores, a comparison of ASD versus social anxiety disorders (AUC = .79–.92) and ASD versus TD (.99–1.00) resulted in non-overlapping 95 %-confidence intervals. These results proof our second hypothesis of a lower validity of the SRS to differentiate between ASD and social anxiety

disorders compared to the validity in differentiating ASD and TD. In consequence, a need for caution when interpreting SRS results is recommended, even if sensitivity is just sufficient for screening purposes. Particularly in the male SM comparison group, the SRS did not differentiate sufficiently. Similar findings have been demonstrated for differentiating ASD and ODD/CD by the SRS (Cholemkery et al. 2013). To translate ROC values into the requirements of clinical diagnostics, we additionally calculated the predictive value of a positive or a negative test by the Likelihood Ratio method with the respective ‘‘best’’ cut-off. These results underline the difficulties of the SRS to differentiate adequately between ASD and social anxiety disorders especially in the male sample. This is of strong clinical interest as both anxiety disorders represent important differential diagnoses of ASD. Other studies have likewise shown that children with mood and anxiety disorders, who never received an ASD diagnosis, obtained scores on the SRS within the range typically found in children with ASD diagnoses (Towbin et al. 2005; Pine et al. 2008; Aldridge et al. 2012). Without consideration of the overlapping and differentiating symptoms, incorrect diagnoses may be a consequence. To date, only a few studies have examined the overlapping symptomatology of ASD and SP in a clinical comparison study (Tyson and Cruess 2012), and no comparison of ASD and SM is available. Both disorders are difficult to differentiate by behavioral ratings (White et al. 2012). Based on a semistructured interview to identify the DSM-IV-TR criteria for ASD that distinguished high functioning ASD from ADHD and anxiety disorders, Hartley and Sikora (2009) found that communication abilities followed by social relatedness aspects were the strongest predictors of ASD. In that study, none of the stereotyped domain criteria determined ASD membership reliably. In contrast, in our study autism mannerism, which reflect some stereotyped and repetitive behavior, distinguished well between groups. Comparable to previous studies (Hartley and Sikora 2009; Koyama et al. 2006) social awareness, defined as a competence of recognizing socially relevant key stimuli, differentiated best between the clinical groups. Thus, a closer look on sub-scale scores may be helpful in the differential diagnostic process. Due to the reported difficulties of the SRS, we explored if a combination of disorder specific questionnaires for ASD, and social anxiety disorders will improve the rate of correct classification. Although each of the questionnaires improved the respective fit of the binary regression model, a combination of SRS, SCQ, and FBB-ANZ only lead to a marginally increased correct classification from 81 to 85 % to differentiate ASD from SP/SM in the combined male and female groups. Even if comorbid SM/SP diagnoses were excluded in the ASD group, the marginally improved

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J Autism Dev Disord

validity may originate from the frequently mentioned (e.g. White et al. 2009) co-occurring anxiety symptoms in ASDs. As indicated by many studies (e.g. Towbin et al. 2005) there is not only a high prevalence of children with anxiety disorders scoring in the typically range of autistic traits on screening tools. Anxiety-related concerns are as well among the most common presenting problems in children and youths with ASD (Ghaziuddin 2002; White et al. 2009). In order to screen with more precision and accuracy, more research on a better combination of instruments is crucially needed. This is particularly important because valuable time for intervention can be lost for a misclassified child during screening procedures (Norris and Lecavalier 2010). By analyzing all other questionnaires, we also found high scores of ADHD symptoms and several sub-scores of the CBCL in the children with ASD. These results fit well with the frequently mentioned relationship between ASD and attention deficits (Taylor et al. 2012), and underline the widely described additional psychopathologic symptom variety besides the core phenomenology in ASD (Gadow et al. 2005; Bo¨lte et al. 2008). Highest scores for anxiety (FBB-ANZ) were found for SM, closely followed by SP (Carbone et al. 2010; Black and Uhde 1992, 1995). Despite these mean differences between groups, other symptom scales could not differentiate satisfyingly between clinical groups, likewise indicating a strong overlap especially of internalizing symptoms in ASD and social anxiety disorders. This is also reflected by the psychiatric comorbidity pattern as assessed by the Kinder-DIPS. Even if the international classification systems currently do not allow a large number of comorbid diagnoses in children with ASD, and notwithstanding the exclusion criteria for ADHD, we assessed all of them by the Kinder-DIPS. By this approach, over 50 % of all participating children fulfilled the criteria of at least one comorbid diagnosis. Whereas the total rate did not differ between groups, the type of comorbidities was different. In close agreement with the literature, children with ASD often suffered from ADHD, and ODD (Grzadzinski et al. 2011; Simonoff et al. 2008; Mazzone et al. 2012). Given the high rates of psychiatric comorbidity in children with ASD, an extension of our current diagnostic systems seems to be advisable (White et al. 2009). Nevertheless, our primary object in assessing the comorbid diagnoses was to describe the sample very carefully. Due to the low sample size, these results cannot be generalized to other samples of ASD. Several studies have also shown a familial aggregation of some psychiatric disorders in families with autistic children (Piven and Palmer 1999). High incidences of affective disorders in family members suggest a clinically and possibly genetically relation (DeLong 2004). To gain additional knowledge on differences in the prevalence of

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psychiatric disorders in parents ascertained through an autistic or anxious child, we analyzed the familiar predispositions among the groups. Parents of the clinical groups did not differ in total rates of psychiatric disorders, but frequencies of the single disorders were different yet. Depression was the most common disorder in all three groups. As major depression is one of the most common mental disorders (DSM-IV-TR; American Psychiatric Association, 2000) it is not astonishing that it was the most frequently observed one. The familial aggregation of depression and anxiety as observed in our study was also found by Piven and Palmer (1998). Besides depression, the most frequently observed psychopathology in parents of children with SM was an anxiety disorder. These results are in line with those from Chavira et al. (2007). Chavira et al. (2007) reported highest lifetime rates in depression (29 %), social phobia (37 %), and avoidant personality disorder (17.5 %) in a sample of seventy parent dyads of children with SM. Also a substantial degree of familial aggregation of social anxiety disorders in families with a child with SP was demonstrated before (Merikangas et al. 2003). These results emphasize the representativeness of our sample. Finally, limitations of the study have to be addressed. The first shortcoming is that children’s diagnoses were known by parents before filling in the questionnaires, what may have lead to a rater bias. As a consequence, a possible overestimation of diagnostic validity from our sample should be considered. Additionally, we only assessed parent-rated questionnaires and interviews. This approach was suggested by Russell and Sofronoff (2005) as more accurate and reliable concerning the differences between children with ASD and social phobia than child self-report information. Second, the cut-offs used for binary regression analyses were derived from the same sample, what may again have caused an overestimation of diagnostic validity. For this reason inferences about cut-off values for clinical screening purposes need to be replicated in further studies. Due to selection effects according to regional and motivational aspects, the TD group may possibly not be representative of the general population. Thus, recruitment bias cannot be fully excluded. Therefore, replication of this study in a prospectively collected clinical population with unknown prior diagnoses, equally sized samples, and different combination of questionnaires would provide further information about the clinical usefulness of the SRS. Despite these restrictions, the findings of this study have meaningful implications for clinical practices and highlight the importance of being aware of overlapping symptomatology between ASD on the one hand, and social anxiety disorders on the other side. Considering demands of previous studies (Norris and Lecavalier 2010; Bo¨lte et al. 2011; Steensel et al. 2012), standardized and comprehensive clinical assessment was done very carefully. Both

J Autism Dev Disord

ADI-R and ADOS were used, comorbid diagnoses were obtained by an extensive parent interview, and data on general pychopathology and IQ were collected. In contrast to many previous studies (Aldridge et al. 2012; Corsello et al. 2007; Towbin et al. 2005) our sample was derived from a general child psychiatry clinic confirming the representativeness of the affected children. Few studies have directly examined the overlapping symptomatology of social anxiety disorders, especially SM and ASD, and their impact on screening validity. We have demonstrated that several children, carefully diagnosed with ASD, were not correctly classified by SRS and participants with anxiety disorders were misleadingly labelled as suffering from ASD. As a consequence we alert to the possibility of reduced screening accuracy of the SRS in clinical purposes while distinguishing ASD and social anxiety, especially in males. A closer look on the SRS sub-scales is certainly worthwhile, especially with focus on the sub-scale social awareness. Further studies are certainly necessary to evaluate a better combination of screening questionnaires for an increased screening accuracy. Those who are interested in using the SRS should keep in mind about the symptom overlap and be aware that social and communication impairments are not exclusively observed in ASD, but also in other psychiatric disorders. Acknowledgments The authors gratefully acknowledge all families and their children who have dedicated their time and commitment to this study. The authors also thank Janina Kitzerow for helping with data collection, and Heiko Zerlaut for data preparation. This research was made possible by the grant Heinrich and Fritz Riese foundation of the Medical Faculty of the JW Goethe University Frankfurt / Main awarded to Hannah Cholemkery. Conflict of interest

None.

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