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Protocol for the development and testing of the schiZotypy Autism Questionnaire (ZAQ) in adults: a new screening tool to discriminate autism spectrum disorder from schizotypal disorder

BMC Psychiatry volume 23, Article number: 200 (2023) Cite this article

Abstract

Background

Autism spectrum disorder (ASD) and schizotypal disorder (SD) both have a heterogenous presentation, with significant overlaps in symptoms and behaviour. Due to elevated recognition and knowledge of ASD worldwide, there is a growing rate of referrals from primary health professionals to specialised units. At all levels of assessment, the differential diagnostic considerations between ASD and SD exert major challenges for clinicians. Although several validated screening questionnaires exist for ASD and SD, none have differential diagnostic properties. Accordingly, in this study, we aim to develop a new screening questionnaire, the schiZotypy Autism Questionnaire (ZAQ), which provides a combined screening for both conditions, while also indicating the relative likelihood of each.

Methods

We aim to test 200 autistic patients and 100 schizotypy patients recruited from specialised psychiatric clinics and 200 controls from the general population (Phase 1). The results from ZAQ will be compared to the clinical diagnoses from interdisciplinary teams at specialised psychiatric clinics. After this initial testing phase, the ZAQ will be validated in an independent sample (Phase 2).

Conclusions

The aim of the study is to investigate the discriminative properties (ASD vs. SD), diagnostic accuracy, and validity of the schiZotypy Autism Questionnaire (ZAQ).

Funding

Funding was provided by Psychiatric Centre Glostrup, Copenhagen Denmark, Sofiefonden (Grant number: FID4107425), Trygfonden (Grant number:153588), Takeda Pharma.

Trial registration

Clinical Trials, NCT05213286, Registered 28 January 2022, clinicaltrials.gov/ct2/show/NCT05213286?cond = RAADS&draw = 2&rank = 1.

Peer Review reports

Background

The psychiatric burden in people with Autism Spectrum Disorder (ASD) [1] is considerably higher compared to neurotypical individuals, with 70% experiencing at least one co-occurring psychiatric disorder and 40% exhibiting two or more psychiatric disorders [2]. In a clinical setting and at all levels of assessment, one of the most difficult differential diagnostic challenges is schizotypal disorder (SD), which shares considerable features with ASD both at the symptomatic level and in the diagnostic criteria (see Fig. 1). Thus, discriminating ASD from SD in a clinical setting is a time-consuming task and demands a high level of expertise of both disorders. An audit of 319 patient cases at our clinic (specialized unit for the assessment of ASD) from January-June 2019 showed that about 37% of the adults referred to our clinic for the assessment of ASD, did not meet diagnostic criteria. Of these, almost 40% were diagnosed on the schizophrenia spectrum (predominantly SD). To improve diagnostic accuracy, it is therefore not only pertinent but also necessary to investigate discriminating features between ASD and SD and to develop tools to aid clinicians in the assessment.

Fig. 1
figure 1

Diagnostic criteria for Autism Spectrum Disorder (ASD) and Schizotypal Disorder (SD). The encircled criteria mark the highest degree of clinical overlap

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Autism spectrum disorder

ASD and SD historically and practically have been grounded in two different scientific traditions, which has had a significant impact on the way clinicians assess patients and how the diagnostic criteria have developed (see Fig. 2).

Fig. 2
figure 2

Development of concepts and criteria (figure provided by Maria Nilsson)

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ASD has its roots in the relatively young field of child and adolescent psychiatry, whereas SD comes out of a more than 100-year-old tradition and is primarily based on clinical presentations in adults. Consequently, professionals involved in assessment of ASD and SD often come from different backgrounds and make use of different diagnostic methods. The prevalence estimates of ASD display a high variability across nations worldwide. In most western countries the prevalence is estimated to be 1–2% [3]. Danish data suggest a prevalence of 1.65% or approximately 95,000 persons in Denmark [4]. The case identification and assessment of ASD is challenged by a high rate of comorbidity with depression (50%), anxiety (40%) and ADHD/ADD (40–60%) [5, 6]. The prevalence of comorbidity with SD has to our knowledge not been established. Nevertheless, some indications of the relation between ASD and SD can be estimated by looking at studies comparing ASD with psychosis or schizophrenia spectrum disorders (SSD). Sub-clinical psychotic symptoms occur frequently in ASD patients [7,8,9], and the prevalence of SSD in adults with ASD are in the range of 4–12% [5].

To aid in case identification of ASD, several screening questionnaires have been developed, including the Autism Spectrum Quotient (AQ) [10], the Ritvo Adult Asperger Diagnostic Scale Revised (RAADS-R) [11], and the Social Responsiveness Scale (SRS) [12]. Importantly, however, several studies indicate that such questionnaires do not differentiate well between autistic individuals and individuals with SSD [13, 14].

Schizotypal disorder

SD is also a heterogeneous disorder with an estimated prevalence of 4.6% and a high rate of comorbidity [15, 16]. The diagnosis can be made if at least 4 out of 9 ICD-10 criteria are met (see Fig. 1). Although debated [17], SD is considered a crucial construct in the development of SSD [18]. Thus, early diagnosis is important for prognosis, as 25–48% of SD patients are prodromal and go on to develop SSD [17, 19]. The prevalence of comorbidity between ASD and SD, which is the main focus of this study, has to our knowledge not been described in any detail. However, the prevalence of ASD in individuals with SSD seems to be substantially increased relative to the general population, although estimates vary widely [20].

Several psychometrically robust questionnaires are available to assess features of and screen for SD. In adult populations these notably include the Schizotypal Personality Questionnaire Brief Revised (SPQ-BR) [21], the Oxford-Liverpool Inventory of Feelings and Experiences (O-LIFE) [22], the Multidimensional Schizotypy Scale (MSS) [23], the Cardiff Anomalous Perception Scale (CAPS) [24], and the Community Assessment of Psychic Experience (CAPE) [25]. In children, these notably include the Melbourne Assessment of Schizotypy in Kids (MASK) [26] and the Schizotypy Personality Questionnaire in Children (SPQ-C) [27], (see Table 2). To our knowledge, none of these have significant psychometric power to discriminate SD from ASD.

Given significant difficulties in discriminating between ASD and SD and the serious consequences that case misclassification can have for treatment and prognosis, we have sought to develop the schiZotypy Autism Questionnaire (ZAQ) to aid clinicians in the assessment of and discrimination between ASD and SD in adults at the case identification stage. A pilot version of this novel screening questionnaire ZAQ, containing 130 questions, has been developed at the Mental Health Centre, Capital Region of Copenhagen. This will be tested in 300 psychiatric patients and 200 healthy controls from May 2022 – April 2023, which constitutes Phase 1 of this study. Psychometric data analysis from this study will constitute the empirical foundation for the development of the final version of ZAQ. This final version, which pending statistical recommendations is expected to contain approximately 60 questions, will undergo subsequent validation in an independent sample (Phase 2).

Methods

Description of the creation of ZAQ

To construct the ZAQ, we first performed an exhaustive literature search to identify potential discriminating features between ASD and SD. Complementing the literature search, we interviewed three experienced clinicians who identified discriminating factors based on their clinical knowledge. Altogether, we identified 38 features which appeared to differentiate ASD and SD. These features constituted 11 subscales, subsumed under 6 subdomains (see Table 1). Identification of potentially discriminating features has been the primary focus. Once identified these features were grouped together into well-defined subscales. To present an overview, these subscales are grouped together in subdomains, highlighting the areas showing potential for discrimination [28, 29].

Table 1 Summary of the domains, subdomain and subscales serving as the theoretical framework for ZAQa
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Secondly, the research group identified several psychometrically-validated assessment and screening questionnaires for schizotypy and ASD (see Table 2). From this list of questionnaires, questions were extracted based on their clinical face validity, factor loadings from the validity studies of these questionnaires, and reliability scores (e.g., test–retest reliability). This process yielded approximately 950 questions (version 1 ZAQ).

Table 2 Scales and questionnaires used in the development of the schiZotypy Autism Questionnaire (ZAQ)
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In a first round of evaluation of their face-validity, these 950 questions were presented to six experienced clinicians—each with extensive (+ 5 years) clinical knowledge in either ASD or SD and who were not associated with the research group. These clinicians assessed how their patients would answer these 950 questions. Importantly, questionnaires measuring schizotypy were assessed by clinicians with ASD experience and vice versa. This evaluation retained 195 questions (version 2 ZAQ). All items of the version 2 ZAQ then underwent a second round of evaluation by 5 autistic patients and 2 schizotypy patients. Of these 195 questions, judged on the basis of their unambiguity and clarity, the final 130 questions entering Phase 1 of the study were selected. The resulting subscales ranged from 5–19 items.

Recruitment and testing

We have designed a multicenter, prospective, non-randomized experimental study, where adult patients will be recruited from May 2022 – April 2023. A group of 300 patients will be recruited from 2 outpatient clinics located at Mental Health Centre Copenhagen: 200 patients (see power calculation) will be recruited from the pool referred to the specialised clinic for diagnostic assessment of ASD at Mental Health Centre Copenhagen, and 100 patients will be recruited from the specialised psychosis clinic at Mental Health Centre with an SD diagnosis. Additionally, a group of 200 adults from the general population without a psychiatric diagnosis will be recruited as a control group. Inclusion and exclusion criteria are detailed in Table 3. The data will be collected on-line and managed through a secure server with full compliance with Personal Data Handling laws of Denmark.

Table 3 Inclusion and exclusion criteria
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In all three outpatient clinics, the diagnostic assessment will be carried out as usual and will follow gold standard guidelines in their respective fields. The diagnostic assessment of ASD will be performed by clinicians who are not involved in the study. Using DSM 5, the assessment will be based on clinical interviews (minimum 4 h, including childhood history and differential diagnostic assessment) and supplemented by a semi-structured interview (Adult Asperger Assessment, AAA). Video recordings from AAA assessment and the clinical assessment will be presented at a multidisciplinary meeting, with at least 2 experienced clinicians, who will finalize the diagnostic conclusion. A schematic showing the flow of patients is depicted in Fig. 3. The result from this initial phase of the study will undergo psychometric analysis and items displaying adequate validity and discriminating power will be selected for the final ZAQ questionnaire (Phase 2).

Fig. 3
figure 3

Flow diagram Phase 1 of the ZAQ-study. Patients will undergo clinical evaluation and be divided into 3 groups: Patients with an ASD (Autism Spectrum Disorder) diagnosis, SD (schizotypal Disorder) diagnosis, neither ASD nor SD diagnosis. In parallel and independent from the clinical evaluation, participants will be assessed with the ZAQ schiZotypy Autism Questionnaire), RAADS-R-DK (Ritvo Adult Aspergers diagnostic Schedule – Revised – Danish translation) and SPQ (Schizotypy Personality Questionnaire)

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Considerations related to the statistical analysis

Statistical models and analyses

Factorial evaluation

First, the dimensionality of the ZAQ will be evaluated with confirmatory factor analysis (CFA). If modification indices suggest a better fit from altered subscales, e.g., by relocating individual items to other subscales, these hypotheses will be included in the subsequent unidimensional tests.

Unidimensional construct evaluation

Responses to the proposed subscales of the ZAQ will each be analysed with a unidimensional item response theory (IRT) model. The 1-parameter logistic Rasch model represents the gold standard of construct validation and offers stronger inferences than less restricted models. If applicable, violations of the assumption of local dependence and no differential item functioning (DIF) may be accommodated by the extended graphical log-linear Rasch model [49]. If the assumption of tau equivalence is violated and this cannot be solved — e.g., by excluding a minimal number of under-discrimination items — a more flexible 2-parameter logistic model may be adapted.

Deriving a general factor

The results of the unidimensional results will be used to select an appropriate multidimensional model in which all subscales reflect a general ZAQ trait. If no indications of DIF were found, a higher-order CFA model will be used, specified in line with the validated unidimensional model (e.g., item interaction terms and/or loading restrictions if applicable). Otherwise, a multidimensional IRT model will be used, ensuring an unbiased trait estimation across subgroups.

Concurrent validity and screening accuracy

The latent ZAQ estimate will be evaluated against the diagnostic outcome using logistic regression and a receiver operating characteristic (ROC) curve. The predictive power is evaluated by the area under the curve (AUC) and the sensitivity and specificity at their equilibrium, reflecting equal importance of correct identification in both groups. To promote the generalisability of the results, K-fold cross-validation will be used to dynamically divide the sample into training and testing subsamples.

Post-hoc analysis

A post-hoc analysis driven by the diagnostic data can increase model performance, while also posing a risk of overfitting the model to the data and thereby reducing the generalizability of the results. To counteract the latter, only modifications considered clinically meaningful by the clinical experts of the research group will be incorporated, and results will either serve purely as hypotheses or will be stabilised with bootstrap aggregation.

In Phase 1, the diagnostic discrimination of each item is evaluated with logistic regression on the diagnostic outcome. Where clinically meaningful, the model will be modified to promote the influence of highly predictive items while also retaining parsimony. The resulting model will serve as a hypothesis in Phase 2.

For the Phase 2 post-hoc analysis, the suitable procedure will depend on the consistency of the ZAQ across Phase 1 and 2. If the ZAQ was mainly shortened, with limited modifications to the items carried over to Phase 2, it will allow for a supervised machine learning based-model on a combined Phase 1 and Phase 2 sample. This analysis will use a random forest algorithm and overfitting will be controlled by bootstrap aggregation and by splitting the sample into training and validation sets. Otherwise, the post-hoc analysis procedure of Phase 1 will be repeated, to inform hypotheses for future studies.

Power calculation

Acceptable diagnostic prediction by the ZAQ would manifest as an AUC ≥ 0.7 in the ROC analysis [50]. Establishing this with confidence relies on the variance of the ZAQ trait estimate, which can only be estimated at Phase 1 completion. For the factor analysis, no formal consensus exists for the minimum sample size [51]. While a minimum of n = 200 is a popular rule of thumb, Kline [52] suggests an ideal sample-to-item ratio of 20:1 in CFA. This corresponds to n ≤ 360 for the separate subscales (≤ 18 items) and 2600 at the aggregate level (130 items). Assuming the ZAQ is abbreviated to 60 items for Phase 2 and taking into account the stabilising effect of pilot testing, we may reasonably consider a combined clinical and non-clinical sample of 500 to provide a ratio of 8:1. This ensures reasonable factorial stability at the aggregate level while offering excellent statistical power at the subscale level.

Ethical considerations

The study will be conducted in accordance with the Helsinki Declaration 1964, including subsequent revisions. Participants will only be included after signing an informed consent based on oral and written information. The participants may, at any time, choose to withdraw from the study without being required to explain and without affecting the person's future treatment. The study is approved by the Ethical Committee of the Capital Region of Denmark (approval number H-21039423), and the project is reported to the Danish Data Protection Agency. The study investigators are under the impression that the questionnaires and investigations will not lead to any discomfort for the subjects. There are no known expected short- or long-term risks associated with the present study.

Outcome

We hypothesize that the ZAQ will provide acceptable discrimination between ASD and SD, as indicated by an AUC ≥ 0.7. The ZAQ will retain a clinically meaningful factor structure, instructing further research on distinct sub-constructs. Lastly, the positive predictive value of the cut-off score will have strong clinical power regarding the selection of which patients need further diagnostic examination in either a specialised clinic for the assessment of psychotic disorders or a clinic for the assessment of ASD.

Discussion

The main incentive to conduct this study has arisen from a clinical need to improve case identification of patients before referring to either ASD or SD assessment. Wrong initial case identification can lead to unacceptable clinical trajectories for the patients. An overlooked prodromal SSD in an autism clinic can delay onset of necessary treatment. A misdiagnosis of a patient in a unit for assessment of SSD can lead to treatment with antipsychotics when not warranted. Thus, incorrect referral can have severe negative consequences for the patient and is cost inefficient. ZAQ is designed to help avoid these unsatisfying trajectories and is primarily developed to alleviate difficulties in the initial case identifying phase of clinical assessment.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

ZAQ:

SchiZotypi Autism Questionnaire

ASD:

Autism Spectrum Disorder

SD:

Schizotypal Disorder

ADHD:

Attention Deficit Hyperactivity Disorder

ADD:

Attention Deficit Disorder

SSD:

Schizophrenia Spectrum Disorder

RAADS-R:

Ritvo Adult Asperger Diagnostic Scale Revised

SRS:

Social Responsiveness Scale

SPQ-BR:

Schizotypal Personality Questionnaire Brief Revised

O-LIFE:

Oxford-Liverpool Inventory of Feelings and Experiences

MMS:

Multidimensional Schizotypy Scale

CAPS:

Cardiff Anomalous Perception Scale

CAPE:

Community Assessment of Psychic Experience

MASK:

Melbourne Assessment of Schizotypy in Kids (MASK)

SPQ-C:

Schizotypy Personality Questionnaire in Children

Tom:

Theory of Mind

CFA:

Confirmatory Factor Analysis

IRT:

Item Response Theory

DIF:

Differential Item Functioning

RUC:

Receiver Operating Characteristics

AUC:

Area under the Curve

References

  1. Lai MC, Baron-Cohen S. Identifying the lost generation of adults with autism spectrum conditions. Lancet Psychiatry. 2015;2(11):1013–27.

    Article  PubMed  Google Scholar 

  2. DeFilippis M. Depression in children and adolescents with autism spectrum disorder. Children. 2018;5(9):112.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Chiarotti F, Venerosi A. Epidemiology of autism spectrum disorders: a review of worldwide prevalence estimates since 2014. Brain Sci. 2020;10(5):274.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Schendel DE, Thorsteinsson E. Cumulative incidence of autism into adulthood for birth cohorts in Denmark, 1980–2012. JAMA. 2018;320(17):1811.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Hossain MM, Khan N, Sultana A, Ma P, McKyer ELJ, Ahmed HU, et al. Prevalence of comorbid psychiatric disorders among people with autism spectrum disorder: an umbrella review of systematic reviews and meta-analyses. Psychiatry Res. 2020;287:112922.

    Article  PubMed  Google Scholar 

  6. Mannion A, Leader G. Comorbidity in autism spectrum disorder: a literature review. Res Autism Spectr Disord. 2013;7(12):1595–616.

    Article  Google Scholar 

  7. Sullivan S, Rai D, Golding J, Zammit S, Steer C. The association between autism spectrum disorder and psychotic experiences in the Avon Longitudinal Study of Parents and Children (ALSPAC) Birth Cohort. J Am Acad Child Adolesc Psychiatry. 2013;52(8):806-814.e2.

    Article  PubMed  Google Scholar 

  8. Goto A, Miyawaki D, Kusaka H, Okada Y, Asada N, Iwakura Y, et al. High prevalence of non-psychotic delusions in children with high-functioning pervasive develonmental disorder. Osaka City Med J. 2015;61(2):73–80.

    PubMed  Google Scholar 

  9. Eussen MLJM, de Bruin EI, Van Gool AR, Louwerse A, van der Ende J, Verheij F, et al. Formal thought disorder in autism spectrum disorder predicts future symptom severity, but not psychosis prodrome. Eur Child Adolesc Psychiatry. 2015;24(2):163–72.

    Article  PubMed  Google Scholar 

  10. Baron-Cohen S, Wheelwright S, Skinner R, Martin J, Clubley E. The autism-spectrum quotient (AQ): evidence from Asperger syndrome/high-functioning autism, males and females, scientists and mathematicians. J Autism Dev Disord. 2001;31(1):5–17.

    Article  CAS  PubMed  Google Scholar 

  11. Ritvo RA, Ritvo ER, Guthrie D, Ritvo MJ, Hufnagel DH, McMahon W, et al. The Ritvo Autism Asperger Diagnostic Scale-Revised (RAADS-R): a scale to assist the diagnosis of autism spectrum disorder in adults: an international validation study. J Autism Dev Disord. 2011;41(8):1076–89.

    Article  PubMed  Google Scholar 

  12. Constantino JN, Davis SA, Todd RD, Schindler MK, Gross MM, Brophy SL, et al. Validation of a brief quantitative measure of autistic traits: comparison of the social responsiveness scale with the autism diagnostic interview-revised. J Autism Dev Disord. 2003;33(4):427–33.

    Article  PubMed  Google Scholar 

  13. Spek AA, Wouters SGM. Autism and schizophrenia in high functioning adults: behavioral differences and overlap. Res Autism Spectr Disord. 2010;4(4):709–17.

    Article  Google Scholar 

  14. Lugnegård T, Hallerbäck MU, Gillberg C. Asperger syndrome and schizophrenia: Overlap of self-reported autistic traits using the Autism-spectrum Quotient (AQ). Nord J Psychiatry. 2015;69(4):268–74.

    Article  PubMed  Google Scholar 

  15. Kirchner SK, Roeh A, Nolden J, Hasan A. Diagnosis and treatment of schizotypal personality disorder: evidence from a systematic review. NPJ Schizophr. 2018;4(1):20.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Pulay AJ, Stinson FS, Dawson DA, Goldstein RB, Chou SP, Huang B, et al. Prevalence, Correlates, Disability, and Comorbidity of DSM-IV Schizotypal Personality Disorder: Results From the Wave 2 National Epidemiologic Survey on Alcohol and Related Conditions. Prim Care Companion J Clin Psychiatry. 2009;11(2):53–67.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Debbane M, Eliez S, Badoud D, Conus P, Fluckiger R, Schultze-Lutter F. Developing psychosis and its risk states through the lens of schizotypy. Schizophr Bull. 2015;41(suppl 2):S396-407.

    Article  PubMed  Google Scholar 

  18. Debbane M, Barrantes-Vidal N. Schizotypy from a developmental perspective. Schizophr Bull. 2015;41(suppl 2):S386–95.

    Article  PubMed  Google Scholar 

  19. Nordentoft M, Thorup A, Petersen L, Øhlenschlæger J, Melau M, Christensen TØ, et al. Transition rates from schizotypal disorder to psychotic disorder for first-contact patients included in the OPUS trial. A randomized clinical trial of integrated treatment and standard treatment. Schizophr Res. 2006;83(1):29–40.

    Article  PubMed  Google Scholar 

  20. Chisholm K, Lin A, Abu-Akel A, Wood SJ. The association between autism and schizophrenia spectrum disorders: a review of eight alternate models of co-occurrence. Neurosci Biobehav Rev. 2015;55:173–83.

    Article  PubMed  Google Scholar 

  21. Cohen AS, Matthews RA, Najolia GM, Brown LA. Toward a more psychometrically sound brief measure of schizotypal traits: introducing the SPQ-Brief Revised. J Pers Disord. 2010;24(4):516–37.

    Article  CAS  PubMed  Google Scholar 

  22. Mason O, Linney Y, Claridge G. Short scales for measuring schizotypy. Schizophr Res. 2005;78(2–3):293–6.

    Article  PubMed  Google Scholar 

  23. Kwapil TR, Gross GM, Burgin CJ, Raulin ML, Silvia PJ, Barrantes-Vidal N. Validity of the multidimensional schizotypy scale: associations with schizotypal traits and normal personality. Personal Disord Theory Res Treat. 2018;9(5):458–66.

    Article  Google Scholar 

  24. Bell V, Halligan PW, Ellis HD. The Cardiff Anomalous Perceptions Scale (CAPS): a new validated measure of anomalous perceptual experience. Schizophr Bull. 2006;32(2):366–77.

    Article  PubMed  Google Scholar 

  25. Konings M, Bak M, Hanssen M, van Os J, Krabbendam L. Validity and reliability of the CAPE: a self-report instrument for the measurement of psychotic experiences in the general population. Acta Psychiatr Scand. 2006;114(1):55–61.

    Article  CAS  PubMed  Google Scholar 

  26. Jones HP, Testa RR, Ross N, Seal ML, Pantelis C, Tonge B. The Melbourne assessment of Schizotypy in kids: a useful measure of childhood schizotypal personality disorder. Biomed Res Int. 2015;2015:1–10.

    Article  CAS  Google Scholar 

  27. Liu J, Wong KKY, Dong F, Raine A, Tuvblad C. The Schizotypal Personality Questionnaire – Child (SPQ-C): psychometric properties and relations to behavioral problems with multi-informant ratings. Psychiatry Res. 2019;275:204–11.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Crespi B, Badcock C. Psychosis and autism as diametrical disorders of the social brain. Behav Brain Sci. 2008;31(3):241–61.

    Article  PubMed  Google Scholar 

  29. Crespi BJ, Go MC. Diametrical diseases reflect evolutionary-genetic tradeoffs: evidence from psychiatry, neurology, rheumatology, oncology and immunology. EMPH. 2015;2015(1):216–53.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Raine A. The SPQ: a scale for the assessment of schizotypal personality based on DSM-III-R Criteria. Schizophr Bull. 1991;17(4):555–64.

    Article  CAS  PubMed  Google Scholar 

  31. Bosco FM, Gabbatore I, Tirassa M, Testa S. Psychometric properties of the theory of mind assessment scale in a sample of adolescents and adults. Front Psychol. 2016;7. Available from: http://journal.frontiersin.org/Article/10.3389/fpsyg.2016.00566/abstract. [Cited 2022 Apr 22].

  32. Vallat-Azouvi C, Pradat-Diehl P, Azouvi P. The Working Memory Questionnaire: a scale to assess everyday life problems related to deficits of working memory in brain injured patients. Neuropsychol Rehabil. 2012;22(4):634–49.

    Article  PubMed  Google Scholar 

  33. Wastler HM, Lenzenweger MF. Self-referential hypermentalization in schizotypy. Personal Disord Theory Res Treat. 2019;10(6):536–44.

    Article  Google Scholar 

  34. Roberts ME, Barthel FMS, Lopez C, Tchanturia K, Treasure JL. Development and validation of the Detail and Flexibility Questionnaire (DFlex) in eating disorders. Eat Behav. 2011;12(3):168–74.

    Article  PubMed  Google Scholar 

  35. Happé FGE. An advanced test of theory of mind: Understanding of story characters’ thoughts and feelings by able autistic, mentally handicapped, and normal children and adults. J Autism Dev Disord. 1994;24(2):129–54.

    Article  PubMed  Google Scholar 

  36. Blais AR, Weber EU. Domain-Specific Risk-Taking Scale. American Psychological Association; 2012. Available from: http://doi.apa.org/getdoi.cfm?doi=10.1037/t13084-000. [Cited 2022 Apr 22].

  37. Bagby RM, Parker JDA, Taylor GJ. The twenty-item Toronto Alexithymia scale—I. Item selection and cross-validation of the factor structure. J Psychosom Res. 1994;38(1):23–32.

    Article  CAS  PubMed  Google Scholar 

  38. Baron-Cohen S, Wheelwright S. The empathy quotient: an investigation of adults with asperger syndrome or high functioning autism, and normal sex differences. J Autism Dev Disord. 2004;34(2):163–75.

    Article  PubMed  Google Scholar 

  39. Frederick S. Cognitive reflection and decision making. J Econ Perspect. 2005;19(4):25–42.

    Article  Google Scholar 

  40. Fach W, Atmanspacher H, Landolt K, Wyss T, Rössler W. A comparative study of exceptional experiences of clients seeking advice and of subjects in an ordinary population. Front Psychol. 2013;4. Available from: http://journal.frontiersin.org/article/10.3389/fpsyg.2013.00065/abstract. [Cited 2022 Apr 22].

  41. Lord C, Rutter M, Le Couteur A. Autism diagnostic interview-revised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. J Autism Dev Disord. 1994;24(5):659–85.

    Article  CAS  PubMed  Google Scholar 

  42. Lam KSL, Aman MG. The repetitive behavior scale-revised: independent validation in individuals with autism spectrum disorders. J Autism Dev Disord. 2007;37(5):855–66.

    Article  PubMed  Google Scholar 

  43. Carson SH, Peterson JB, Higgins DM. Reliability, validity, and factor structure of the creative achievement questionnaire. Creat Res J. 2005;17(1):37–50.

    Article  Google Scholar 

  44. Tavassoli T, Hoekstra RA, Baron-Cohen S. The Sensory Perception Quotient (SPQ): development and validation of a new sensory questionnaire for adults with and without autism. Mol Autism. 2014;5(1):29.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Lefly DL, Pennington BF. Reliability and validity of the adult reading history questionnaire. J Learn Disabil. 2000;33(3):286–96.

    Article  CAS  PubMed  Google Scholar 

  46. Parnas J, Møller P, Kircher T, Thalbitzer J, Jansson L, Handest P, et al. EASE: Examination of Anomalous Self-Experience. Psychopathology. 2005;38(5):236–58.

    Article  PubMed  Google Scholar 

  47. Møller P. Schizofreni en forstyrrelse av selvet: forståelsens betydning for klinisk virksomhet. Oslo: Universitetsforl; 2018.

    Google Scholar 

  48. Cicero DC, Neis AM, Klaunig MJ, Trask CL. The Inventory of Psychotic-Like Anomalous Self-Experiences (IPASE): development and validation. Psychol Assess. 2017;29(1):13–25.

    Article  PubMed  Google Scholar 

  49. Kreiner S, Christensen KB. Graphical Rasch Models. In: Mesbah M, Cole BF, Lee MLT, editors. Statistical Methods for Quality of Life Studies. Boston, MA: Springer US; 2002. p. 187–203. Available from: http://link.springer.com/10.1007/978-1-4757-3625-0_15. [Cited 2022 Apr 22].

    Chapter  Google Scholar 

  50. Hosmer DW, Lemeshow S. Applied logistic regression. 2nd ed. New York: Wiley; 2000. p. 373 (Wiley series in probability and statistics).

    Book  Google Scholar 

  51. DeVellis RF. Scale development: theory and applications. 4th ed. Los Angeles: SAGE; 2017. p. 262.

    Google Scholar 

  52. Kline RB. Principles and practice of structural equation modeling. New York: Guilford Press; 2004. p. 366 (Methodology in the social sciences).

    Google Scholar 

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Acknowledgements

We would like to extend our sincere gratitude to Lise Nyholm, Casper Buchardt, Benjamin Sneider, Ane-Kristine Klemmensen, Peter Winning Iepsen, Lennart Bertil Jansson, Anja Sønderskov Sesler, Daniel Martin Godthjælp Nielsen, Maja Zandersen, Kim Berg Engersgaard Johannessen and Simon Mølgaard for their hard work, time and assessments of the questions proposed for the first versions of ZAQ.

Further we would like to acknowledge and appreciate the leadership at Psychiatric Center Glostrup, especially Head of Psychiatric Center Mette Bertelsen Fredsgaard, Chief physician Darius Mardosas, Development manager Kirsten Tofterup, Chief consultant Bolette Søndergaard and Team leader Nina Ginnerup for encouraging our work and providing funding for the project.

Heartfelt thanks to all the members of Team ADHD and Autism at Psychiatric Center Glostrup for supporting and embracing the extra clinical workload created by this research project and finally a huge gratitude to Research assistant Stine Algart for her dedicated and invaluable contribution in all aspects of the project.

Funding

Funding was provided by Psychiatric Centre Glostrup, Copenhagen Denmark, Sofiefonden (Grant number: FID4107425) and Trygfonden (Grant number:153588). Apart from the financial support the sponsors had no further involvement in the study or publication. The protocol has not been peer reviewed during the funding process.

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Authors and Affiliations

  1. Department of ADHD and Autism, Mental Health Services, Capital Region of Denmark, Copenhagen, Denmark

    Rizwan Parvaiz, Zainab Al-Sayegh & Ida-Marie Danielsen

  2. Competence Centre for Transcultural Psychiatry, Mental Health Centre Ballerup, Copenhagen, Denmark

    Erik Vindbjerg

  3. Department of Biological Sciences, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada

    Bernard Crespi

  4. Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK

    Francesca Happe

  5. Department of Psychiatry and Neuropsychology, Maastricht University, Maastricht, Netherlands

    Rik Schalbroeck

  6. Centre for Developmental Psychiatry and Psychology, Monash University, Melbourne, Australia

    Bruce Tonge

  7. Center for Neuropsykiatrisk Depressionsforskning Psykiatrisk Center Glostrup, Nordstjernevej 41, Glostrup, Copenhagen, 2600, Denmark

    Poul Videbech

  8. School of Psychological Sciences, University of Haifa, 3498838, Haifa, Israel

    Ahmad Abu-Akel

Authors
  1. Rizwan Parvaiz
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  2. Erik Vindbjerg
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  3. Bernard Crespi
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  4. Francesca Happe
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  5. Rik Schalbroeck
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  6. Zainab Al-Sayegh
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  7. Ida-Marie Danielsen
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  8. Bruce Tonge
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  9. Poul Videbech
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  10. Ahmad Abu-Akel
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Contributions

RP initiated the study, procured the funding conceptualized and constructed the original manuscript, to which all authors contributed significant intellectual input. AAA contributed significantly to paper conceptualization and the final editing of the paper. ZAS edited the paper and procured funding. IMD edited the paper. EV contributed to paper conceptualization and writing the statistical analysis. FH contributed to paper conceptualization and editing the paper. RS contributed to paper conceptualization and editing the paper. BC contributed to paper conceptualization and editing the paper. BT contributed to paper conceptualization and editing the paper. PV contributed to paper conceptualization and editing the paper. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Rizwan Parvaiz.

Ethics declarations

Ethics approval and consent to participate

The study is approved by the Ethical Committee of the Capital Region of Denmark (approval number H-21039423), and the project is reported to the Danish Data Protection Agency. The study will be conducted in accordance with the Helsinki Declaration 1964, including subsequent revisions. Participants will only be included after signing an informed consent based on oral and written information. The participants may, at any time, choose to withdraw from the study without being required to explain and without affecting the person's future treatment. The study investigators are under the impression that the questionnaires and investigations will not lead to any discomfort for the subjects. There are no known expected short- or long-term risks associated with the present study.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Signed ICMJE forms have been obtained and can be provided upon request.

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Supplementary Information

Additional file 1:

Supplementary file 1. ZAQ English Version. schiZotypy Autism Questionnaire (ZAQ) in English version.

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Parvaiz, R., Vindbjerg, E., Crespi, B. et al. Protocol for the development and testing of the schiZotypy Autism Questionnaire (ZAQ) in adults: a new screening tool to discriminate autism spectrum disorder from schizotypal disorder. BMC Psychiatry 23, 200 (2023). https://doi.org/10.1186/s12888-023-04690-3

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  • DOI: https://doi.org/10.1186/s12888-023-04690-3

Keywords

BMC Psychiatry

ISSN: 1471-244X

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