Psychiatric telephone interview with parents for screening of childhood autism – tics, attention-deficit hyperactivity disorder and other comorbidities (A–TAC)
Preliminary reliability and validity
Sara Lina Hansson, Annika Svanström Röjvall, Maria Rastam, Carina Gillberg, Christopher Gillberg, Henrik Anckarsäter


Background Reliable, valid and easily administered screening instruments would greatly facilitate large-scale neuropsychiatric research.

Aims To test a parent telephone interview focused on autism – tics, attention-deficit hyperactivity disorder (ADHD) and other comorbidities (A–TAC).

Method Parents of 84 children in contact with a child neuropsychiatric clinic and 27 control children were interviewed. Validity and interrater and test – retest reliability were assessed.

Results Interrater and test – retest reliability were very good. Areas under receiver operating characteristics curves between interview scores and clinical diagnoses were around 0.90 for ADHD and autistic spectrum disorders, and above 0.70 for tics, learning disorders and developmental coordination disorder. Using optimal cut-off scores for autistic spectrum disorder and ADHD, good to excellent kappa levels for interviews and clinical diagnoses were noted.

Conclusions The A–TAC appears to be a reliable and valid instrument for identifying autistic spectrum disorder, ADHD, tics, learning disorders and developmental coordination disorder.

Telephone interviews with good psychometric properties have been developed for attention-deficit hyperactivity disorder (ADHD) and general psychopathology in childhood (Nadder et al., 1998; Rohde et al., 1998; Holmes et al., 2004), but one assessing traits related to autistic spectrum disorders and comorbid psychiatric problems has been lacking. A number of paper screening instruments for autistic spectrum disorder exist, including the Checklist for Autism in Toddlers (CHAT; Baron-Cohen et al., 1992), the Asperger Syndrome Screening Questionnaire (ASSQ; Ehlers & Gillberg, 1993), the Autism Screening Questionnaire (ASQ; Berument et al, 1999) and the Autism Quotient (AQ; Baron-Cohen et al, 2001), but these assess only narrow autism/Asperger syndrome and do not take into account the most common coexisting problems. The Autism–Tics, ADHD and Other Comorbidities Inventory (A–TAC) is a comprehensive screening interview, evaluated for reliability and validity as a parent telephone interview for autistic spectrum disorders, ADHD, tic disorders, developmental coordination disorder and specific learning disorders. Results from parent interviews blinded to clinical diagnoses are compared with parent interviews regarding healthy control children.


Development and design of the interview

The telephone interview is based on a screening questionnaire developed at the Department of Child and Adolescent Psychiatry, Göteborg University, Sweden, for the purpose of screening general populations in research and mental health surveys. The 178-item A–TAC questionnaire contains all symptoms listed in the DSM–IV (American Psychiatric Association, 1994) symptom criteria of childhood-onset neuropsychiatric disorders, a selection of DSM–IV symptoms listed for other psychiatric disorders, and additional items including symptoms listed in the Gillberg & Gillberg (1989) algorithm for Asperger syndrome, and questions or aspects included in published questionnaires for screening or diagnosis of autistic spectrum disorders and general psychiatric disorders such as the ASSQ (Ehlers & Gillberg, 1993), the Asperger Syndrome Diagnostic Interview (ASDI; Gillberg et al, 2001) and the Five to Fifteen Questionnaire (Kadesjo et al, 2004).

The telephone interview is highly structured, with four possible ratings for each item: ‘yes’; ‘yes, previously’ (both scored as 1 in this study); ‘yes, to some extent’ (scored as 0.5 in this study); and ‘no’. It is intended for use with parents as informants and lay persons as interviewers. The interview is preceded by a short introduction to inform the parent that the interview concerns problems or difficulties that the child is either experiencing now or has experienced earlier in life. These problems or difficulties must be pronounced compared with other children of the same age. The parent is also asked to write down the four response alternatives, to have them visually available throughout the interview. In this validation study, the parents were also specifically asked to provide no more facts about the child than those that the interviewer enquired about. This was in order to assure masking of the interviewer to the child’s diagnostic status. The time for completing the interview varied from 15 min to 35 min.


The parents of 118 children and adolescents (aged 7–18 years) were asked to participate in the study, and parents of 112 accepted. One of these had to be excluded because of language difficulties. Of the 111 children, 84 (32 girls and 52 boys, mean age 11.5 years) were patients at the Child Neuropsychiatric Clinic in Göteborg. They were either under investigation at the time of the study or had recently been investigated. Children with any diagnosed or suspected chromosomal or genetic medical disorder – other than high-functioning individuals with fragile X or CATCH 22 (cardiac defects, abnormal facies, thymic hypoplasia, cleft palate, hypocalcaemia and a deletion on chromosome 22) – were excluded.

Twenty-seven children (10 girls, 17 boys, mean age 12.2 years, range 9–17) constituted a comparison group of healthy children without any known assessment or treatment for child and adolescent mental health problems. The comparison cases were children of staff at the Child Neuropsychiatric Clinic, the Department of Child and Adolescent Psychiatry and the Department of Forensic Psychiatry in Göteborg, and of their acquaintances. After all the interviews had been completed, parents were again contacted and asked for information about earlier psychiatric problems or contacts with child psychiatry or psychology departments.

Interview procedure

Two medical students (one 4th year, one 5th year) completed the 111 telephone interviews. They were masked to diagnosis of the target cases and to possible psychiatric history of the comparison cases. The two interviewers conducted ten of the interviews together, during which they took turns, interviewing five parents each (all target cases) while the other listened and filled in the questionnaire independently. The results obtained were then compared in order to analyse interrater reliability. Ten of the interviewees (eight target cases, two comparison cases) were contacted again 6–8 weeks after the first interview and asked to participate in a second interview; they were informed that the purpose of the second interview was to determine if responses would vary over time. These parents had not been informed at the first interview that they would be contacted again. The interviewers were still masked to diagnoses (target group) as well as to prior psychiatric problems (comparison group). All clinical information was collected after all the interviews had been completed.

Diagnostic process

Diagnoses assigned during investigations at the clinic were based on medical history, physical examination (including a neuromotor assessment) by a physician with expertise in neuropsychiatry, and psychological examination by a trained neuropsychologist. In all children, an assessment of cognitive level was made with a test battery appropriate for the child’s mental age (Doll, 1965; Griffiths, 1970; Leiter, 1980; Wechsler, 1992). Children with significant school achievement problems were also examined by an educational specialist using tests of reading and writing skills, observation of the child at school, and interviews with the child’s teachers about school performance and behaviour. Structured instruments, such as the Autism Diagnostic Interview–Revised (ADI–R; Lord et al, 1994), the Diagnostic Interview for Social and Communication Disorders (DISCO; Leekam et al, 2002; Wing et al, 2002), the Childhood Autism Rating Scale (Schopler et al, 1988), the ASDI (Gillberg et al, 2001) and the ADHD Rating Scale (DuPaul et al, 1998) were used as appropriate, although not the sole basis for a diagnosis. For each case that fulfilled DSM–IV criteria for a specific condition, the physician in charge was asked to complete a diagnostic protocol specifying other possible comorbid diagnoses.

Attrition analysis

Six of the initially contacted 118 parents declined to participate in the study: two lacked motivation for further exploration following the clinical investigation and diagnosis of their children; one declined owing to a difficult life situation; and three parents did not supply a reason. One interview could not be completed owing to language difficulties. All seven cases of non-completion were from the target group.

Statistical analyses

The interview ratings were coded on a three-point scale: 0 indicating normality (‘no’), 0.5 indicating some abnormality (‘yes, to some extent’) and 1.0 indicating abnormality or earlier abnormality (‘yes’ or ‘yes, previously’). Sum scores were calculated for each diagnostic category. Interrater and test – retest reliability was assessed through intraclass correlations between dimensional ratings within each category. The intraclass correlation coefficient (ICC), defined as (variance between subject)/(variance between subject+variance of error), includes both random errors and systematic differences, but is also dependent on the range of the variable measured. The ICC ranges from 0 (no agreement) to 1 (perfect agreement); values above 0.75 indicate excellent reliability, 0.4–0.75 indicate fair to poor reliability, and values below 0.4 indicate poor reliability (Fleiss, 1986). Diagnostic validity for the neuropsychiatric disorders, where the prevalence of disorders was sufficiently high for these calculations, were assessed first through a receiver operating characteristics (ROC) curve, where clinical diagnosis was the dependent variable and the telephone interview sum score the independent predictor. The area under the curve (AUC) is a measure of the overall predictive validity of the instrument where AUC=0.50 signals random prediction, 0.60<AUC≤0.70 poor, 0.70<AUC≤0.80 fair, 0.80<AUC≤0.90 good and AUC>0.90 excellent validity (Tape, 2004). The inflection point of the curve is the optimal cut-off value of the dimensional independent variable for a categorical decision in the dependent variable with maximal sensitivity and specificity. These cut-offs were then used for calculating four-field tables comparing the diagnostic results for the telephone interviews and the clinical assessments through Cohen’s kappa, values above 0.60 indicating good correspondence (Altman, 1991). All statistics were calculated with the Statistical Package for the Social Sciences, version 11.0, using a significance level of P<0.05.


The interrater reliability was excellent overall (Table 1). The test – retest reliability (Table 2) was highly significant for all assessed dimensions, and good for most aspects of the neuropsychiatric disorders, although slightly lower for attention deficits and anxiety problems and considerably lower for some of the less common conditions, such as obsessive – compulsive disorder, sleeping problems and eating disorders.

View this table:
Table 1

Results of interrater reliability analysis

View this table:
Table 2

Results of test – retest analyses

Validity in screening and establishing cut-off scores

A ROC curve (Fig. 1) plotting the sum of the DSM–IV criteria (independent variable) and a diagnosis within the autism spectrum (dependent variable) yielded an AUC of 0.88. The addition of the Gillberg & Gillberg (1989) criteria for Asperger syndrome did not improve the screening for any diagnosis in the autism spectrum, yielding a ROC curve plot with an AUC of 0.88. The best match was achieved with a cut-off score of 4.5, yielding a four-field table with 34 (31%) true positives, 57 (51%) true negatives, 16 (14%) false positives and 4 (4%) false negatives. Cohen’s κ for this model was 0.63 (P<0.001). The sensitivity was 0.89, the specificity 0.78, the positive predictive value 0.68 and the negative predictive value 0.93. A cross-tabulation of all specific diagnostic categories within the autism spectrum with their respective DSM–IV criteria in the interview (without any adjustment of cut-off levels) showed much poorer performance; for autism κ =0.22 (P=0.011), for Asperger syndrome κ=0.27 (P=0.002) and for pervasive developmental disorders not otherwise specified κ=0.07 (P=0.418).

Fig. 1

Receiver operating characteristics curve for the relationship between the sum of DSM–IV autism items and a diagnosis in the autism spectrum (area under curve 0.88). Diagonal segments are produced by ties.

For ADHD the AUC was 0.90 for the DSM–IV symptoms and increased to 0.91 with the addition of the A–TAC questions ‘Does he/she alternate between exaggerated activity and passivity?’ and ‘Does he/she get excited by having a number of persons around?’ (Fig. 2). The optimal cut-off was eight A–TAC symptoms, which yielded a distribution of 58 (52%) true positives, 36 (32%) true negatives, 12 (11%) false positives and 5 (5%) false negatives; Cohen’s κ=0.68 (P<0.001). The sensitivity was 0.92, the specificity 0.75, the positive predictive value 0.83 and the negative predictive value 0.88.

Fig. 2

Receiver operating characteristics curve for the relationship between the sum of DSM–IV attention-deficit hyperactivity disorder (ADHD) items and a diagnosis of ADHD (area under curve 0.91).

For tic disorders (Tourette syndrome or chronic tics) the AUC was 0.84 (Fig. 3) and the optimal cut-off was two symptoms, which yielded a distribution of 7 (6%) true positives, 86 (77%) true negatives, 13 (12%) false positives and 5 (5%) false negatives; κ=0.35 (P<0.001). The sensitivity was 0.58, the specificity 0.87, the positive predictive value 0.35 and the negative predictive value 0.95.

Fig. 3

Receiver operating characteristics curve for the relationship between the sum of tic disorder questions and a diagnosis of chronic tic disorder (area under curve 0.84).

For learning disorders the AUC of the ROC curve was 0.74 (Fig. 4) and the optimal cut-off was 3.5 symptoms, which yielded a distribution of 8 (7%) true positives, 88 (80%) true negatives, 5 (5%) false positives and 10 (9%) false negatives; κ=0.44 (P<0.001). The sensitivity was 0.44, the specificity 0.95, the positive predictive value 0.62 and the negative predictive value 0.90.

Fig. 4

Receiver operating characteristics curve for the relationship between the sum of learning disorder questions and a diagnosis of learning disorder (area under curve 0.74).

For developmental coordination disorder the AUC of the ROC curve was 0.71 (Fig. 5) and the optimal cut-off was 1.5 symptoms, which yielded a distribution of 14 (13%) true positives, 63 (57%) true negatives, 27 (24%) false positives and 7 (6%) false negatives; κ=0.27 (P=0.002). The sensitivity was 0.67, the specificity 0.70, the positive predictive value 0.34 and the negative predictive value 0.90.

Fig. 5

Receiver operating characteristics curve for the relationship between the sum of developmental questions and a diagnosis of development coordination disorder (area under curve 0.71).


This preliminary validation and reliability study showed that the A–TAC telephone interview was reliable in terms of interrater agreement (as expected, since the interview is highly structured and the ratings were simultaneous) and also test – retest agreement. Because of the low prevalence of general child psychiatric diagnoses in the study group, it was not possible to assess the interview’s capacity for identifying conditions such as depression, anxiety, eating disorders or obsessive – compulsive disorder. For the neuropsychiatric disorders, however, particularly for autistic spectrum disorders and ADHD, the instrument appeared to work well. Kappa values over 0.60 when comparing two entirely different diagnostic procedures (a lay person administering a structured interview v. comprehensive neuropsychiatric assessment by a team of clinical specialists) can be considered very good. It is also open to argument which gold standard should be chosen for this kind of study. In order to validate a telephone interview, it might seem to be more appropriate to use rating scores from DISCO and ADI–R algorithms rather than clinical diagnosis as an external validation criterion. Kappa values for tics, learning disorders and developmental coordination disorder were lower, with AUCs in the fair range of prediction, probably reflecting too narrow a range of possible responses, resulting in poor resolution. A possibly less stringent clinical diagnostic assessment might also be at the root of this problem.

We are now pursuing the further development of this instrument through the incorporation of more questions under each domain, to provide both screening questions and a wider set of more detailed questions with dimensional symptom ratings for those who screen positive. This instrument will be further validated in other neuropsychiatric patient groups, in general child and adolescent psychiatry groups, and in the normal population.

Clinical Implications and Limitations


  1. The Autism–Tics, Attention-Deficit Hyperactivity Disorder and Other Comorbidities Inventory (A–TAC) telephone interview may be used for screening in research and mental health surveys to assess autistic spectrum disorders and common comorbid conditions.

  2. The A–TAC does not require expert interviewers.

  3. The number of symptoms affirmed in the A–TAC may be used as a dimensional measure of the probability of a clinical diagnosis.


  1. The study group was small, and the controls were not randomly recruited from the general population because of ethical considerations.

  2. It is unclear whether clinical diagnoses or results on established instruments should be used as the gold standard in validation studies such as this.

  3. Parents waiting for clinical investigations may be more prone to describe problems in their children than other parents.


The study was supported by research grants from Alcohol Research Council of the Swedish Alcohol Retailing Monopoly, the Wilhelm and Martina Lundberg Research Foundation, the Frimurare Barnhusdirektionen Research Foundation and the Swedish National Research Council.

  • Received March 19, 2004.
  • Revision received September 14, 2004.
  • Accepted September 29, 2004.


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