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Autism spectrum disorder: investigating predictive diagnostic relationships in children 3 years of age and younger

Kaitlin Juergensen, Rhonda Mattingly, Teresa Pitts & Alan F. Smith

To cite this article: Kaitlin Juergensen, Rhonda Mattingly, Teresa Pitts & Alan F. Smith (2018): Autism spectrum disorder: investigating predictive diagnostic relationships in children 3 years of age and younger, Early Years, DOI: 10.1080/09575146.2018.1490891

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Autism spectrum disorder: investigating predictive diagnostic relationships in children 3 years of age and younger Kaitlin Juergensena, Rhonda Mattinglya, Teresa Pittsa,b and Alan F. Smith a

aDepartment of Otolaryngology-Head/Neck Surgery-and Communicative Disorders, University of Louisville, Louisville, KY, USA; bDepartment of Neurological Surgery; Kentucky Spinal Cord Research Centre, University of Louisville, Louisville, KY, USA

ABSTRACT Autism spectrum disorder (ASD) is a heterogeneous neurodeve- lopmental disorder whose symptoms may involve deficits across three domains: communication, socialization and atypical beha- viors or interests. With a high prevalence across populations and a tendency to impact males more than females, early and accurate diagnosis appears critical. The most current literature on ASD provides a myriad of difficulties associated with diagnosis under the age of 3 years. The purpose of this study was to determine if a predictive relationship exists between a child’s individual develop- mental domain standard deviation (SD) subscale scores (motor, language, cognitive, social-emotional and adaptive skills) on the Bayley-III assessment instrument and whether a diagnosis of ASD was applied. A retrospective file review of 151 children participat- ing in Kentucky’s early intervention program – First Steps – was completed. The children ranged in age from 18 to 35 months. Individual binary logistic regressions were used to assess the association between the Bayley-III subscale scores and whether or not an ASD diagnosis was applied following multidisciplinary evaluation. The results indicated that individual lower subscale scores in the cognitive, language, adaptive, and social-emotional domains on the Bayley-III were predictive of an autism diagnosis.

ARTICLE HISTORY Received 26 February 2018 Accepted 16 June 2018

KEYWORDS Autism; early intervention; diagnosis; assessment; Bayley-III

Introduction

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder char- acterized by impairments in social communication and restricted interests (Landa 2007). Delayed language, abnormal language development, joint attention deficits and atypical eye contact are commonly noticed within the first 18 months to 2 years of life (Charman et al. 1997; Chawarska et al. 2007; Iverson and Wozniak 2007). Parents, teachers and clinicians need to be aware of these behavioral characteristics so that diagnosis can be determined as early as possible. Early diagnosis leads to early intervention which may establish a pathway for reducing symptomology and improvement in the quality of life of many children and their families (Camarata 2014; Koegel et al. 2014).

CONTACT Alan F. Smith [email protected] University of Louisville, Medical Dental Apartments, 627 South Preston Street, Suite 220, Louisville, KY 40292 USA.

EARLY YEARS https://doi.org/10.1080/09575146.2018.1490891

© 2018 TACTYC

Diagnostic criteria

Kanner (1943) first described autism in 1943 as impairments in three categories: com- munication difficulties, social deficits and restricted interests/repetitive behaviors. In 1980, autism was officially identified as a clinical diagnosis by the American Psychiatric Association and published in the Diagnostic Statistical Manual of Mental Disorders-III (DSM-III)(American Psychiatric Association, 1980). The DSM-III included criteria for infan- tile autism and pervasive developmental disorder (PDD). In 1994, the autism criteria were again revised and published in the Diagnostic Statistical Manual of Mental Disorders, fourth edition (DSM-IV); this time including five subtypes of autism: Autistic Disorder (AD), Asperger’s Disorder, Rett’s Disorder, Pervasive Developmental Disorder – Not Otherwise Specified (PDD-NOS), and Child Disintegrative Disorder(American Psychiatric Association, 1994). In May 2013, the Diagnostic Statistical Manual of Mental Disorders, fifth edition (DSM-V) released new diagnostic criteria reducing the three distinguishable categories into two categories: social communication impairments and restricted inter- ests(American Psychiatric Association, 2013).

Social communication and interaction deficits, including nonverbal communicative impairments, manifest across multiple contexts. Deficiencies may include the following characteristics: abnormal social approach; reciprocity difficulties; reduced sharing of inter- ests and emotions (or affect); abnormal eye contact; poor interpretation of body language; and lack of facial expressions (DSM-V, 2013). Difficulties engaging in imaginative play and making friends, or a general lack of interest in peers also fall under the umbrella of social communication and interaction deficits (DSM-V, 2013). Pragmatic deficits may include difficulty initiating or maintaining a topic, lack of presuppositional skills, or inability to communicate intentions using a variety of communicative modalities (Landa 2007).

Restricted and repetitive patterns of behavior or interests include stereotyped or repetitive motor movements, use of objects or speech (DSM-V, 2013). A child with autism may line up toys in a particular order, insist on sameness or resist change. Other symptoms include echolalia and/or ritualized patterns of verbal or nonverbal behavior. Hyper- or hypo-reactivity to sensory input are also included in the restricted interest domain. Some children with autism may demonstrate adverse response to specific sounds or textures or engage in excessive smelling or touching of objects (DSM-V, 2013). For autism to be diagnosed the symptoms from the social communica- tion and restricted interest domains must be present in the early developmental period (DSM-V, 2013) and must not be better explained by intellectual disability or global developmental delay (DSM-V, 2013).

Prevalence and diagnostic difficulty

When Kanner (1943) first described autism, it was thought to be an extremely rare disorder. In the 1960s and 1970s, research reported approximately four-to-five cases per 10,000 children (Christensen, Baio, and Braun et al. 2016). As awareness increased and diagnostic criteria changed, prevalence continued to rise. From 2000 to 2002, studies reported one in 150 children, and in 2008, studies reported one in 88 children across the United States (Christensen, Baio, and Braun et al. 2016). According to findings from Autism and Developmental Disabilities Monitoring (ADDM) Network the estimated

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percentage of children identified with ASD is currently one in 68, or about 1.5% of children across the United States (Christensen, Baio, and Braun et al. 2016). ADDM also reports ASD is about 4.5 times more common among boys (one in 42) than among girls (one in 189) (Christensen, Baio, and Braun et al. 2016).

Currently, there is a lack of understanding of the symptomology from birth to 3 years of age in children with autism, contributing to the lack of early diagnosis (Camarata 2014; Koegel et al. 2014; Landa 2007). The first diagnostic criteria included the three core symptoms/ categories discussed earlier (communication difficulties, social deficits, and restricted inter- est/repetitive behaviors) as evident from birth (Camarata 2014). The DSM-III also recognized autism as beginning at birth with the inclusion of an infantile autism diagnosis. The pre- valence of ASD is further complicated by the addition of a regressive form known as regressive autism. In this form, children appear to develop normally until around 15 to 30 months of age at which point they begin to lose previously acquired developmental skills including social and/or communication abilities (Duffy et al. 2014). Barbeau (2017) suggests that one-in-three children with autism will have this regressive form.

Turner and Stone (2007) point out that the difficulty in diagnosis before 30 months may be due to the variability of symptoms across age: children at 4 or 5 years of age differ in their behavior from their 2- and 3-year-old counterparts. For example, it is typical for 2-year-old children to demonstrate repetitive motor patterns with their hands (Camarata 2014). However, a 5-year-old child engaging in this motor behavior would be abnormal making it easier to recognize as a possible symptom of ASD (Camarata 2014). This example provides evidence of the importance of further inquiry into the core symptomology in young children (infants and toddlers) with autism. Given that autism may be present in children from birth or due to a regression of skills, a missed diagnosis or lack thereof during the toddler years may negatively influence the therapeutic process and delay any potential gains.

Importance of early intervention

Under the Individuals with Disabilities Education Improvement Act (IDEIA), specifically Part C, the law in Kentucky defines the age range for children eligible for early intervention services as birth to 3 years of age (First steps policy and procedure manual 2015). Children who are not appropriately diagnosed before the age of 3 years miss crucial opportunities to access early intervention services, which may lead to devastating consequences. These unidentified children are likely to miss important learning cues from parents, siblings, and teachers (Camarata 2014). The absence of such learning cues can be detrimental because brain development is greatly influenced by environmental conditions and experiences (Owens 2016). According to Owens (2016), the types of experience children encounter during cognitive and perceptual sensitive periods is extremely important for learning and development. This can be demonstrated by the development of joint attention and its impact on language development. At an early age, joint attention skills are used for communicating wants and needs and they also contribute to word-learning (Landa 2007). Children with autism may lack these skills and without early intervention this may inhibit the development of further language skills (Landa 2007).

Koegel (2014) emphasized the importance of the type of experiences children encoun- ter at an early age in a study that used motivational techniques in therapy for children

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below the age of five. In total, 85% to 90% of children with autism who received early intervention involving motivational techniques learned to use verbal communication as the primary mode of communication (Koegel 2014). In addition, nonverbal children who began intervention in the early preschool years were more likely to become verbal than children who began therapy after the age of five. Furthermore, the age a child begins therapy greatly impacts and predicts future developmental skills (Koegel 2014). The majority of research agrees that early intervention for children with autism must occur at the earliest possible point in time in order to combat the symptomology and obtain optimal outcomes (Landa 2007; Koegel et al. 2014; Camarata 2014).

Early intervention may also have a positive impact on the prevention and/or reduc- tion of secondary symptoms in children with autism. Secondary symptoms include self- injury, tantrums, and aggression, most likely due to communication breakdown (Koegel et al. 2014). With early intervention, the function/underlying cause of the disruptive behavior can be addressed and likely reduced or replaced with other functional beha- viors. According to Koegel et al. (2014), ‘early intervention techniques may prevent these secondary symptoms and reduce the need for more substantial and expensive interven- tions later in life’ (p. 52).

Early intervention evaluation and assessment

In the state of Kentucky, the program that provides early intervention services for children with developmental disabilities under the age of 3 years is known as First Steps (First steps policy and procedure manual 2015). These services are rendered under the auspices of federal law, PL 108–446, IDEIA of 2004, Part C (First steps policy and procedure manual 2015). In order for program eligibility to be determined and for intervention to begin, a child must have been diagnosed with a physical or mental condition that has a high probability of resulting in a developmental delay (e.g. ASD, hearing loss, Down syndrome) or evidence a significant developmental delay in one or more of the following developmental domains: motor, cognitive, language, social-emo- tional and/or adaptive skills (First steps policy and procedure manual 2015).

Children with documented established risk conditions are eligible to receive services until the age of 3 years. For those children without a documented established risk condition, eligibility is determined based on significance of delay. A delay is considered significant if a child scores two standard deviations (SD) (−2.00) below the mean in one area (e.g. cognitive) or one-and-a-half SD (−1.50) below the mean in two areas (e.g. cognitive and language) (First steps policy and procedure manual 2015). One commonly used norm-referenced instrument is the Bayley Scales of Infant and Toddler Development, Third Edition (Bayley-III) (Bayley 2006).

Per First Steps policy (in Kentucky), children who are suspected to have autism are generally evaluated and a differential diagnosis determined before early intervention services are provided. The intent is to ‘gain in-depth information so that the child’s team can develop effective interventions and services’ (First steps policy and procedure manual 2015, p.91). The researchers acknowledge that the aforementioned process may be unique to the state of Kentucky and may not be representative of all early intervention programs in other states or countries.

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Specific aims

As research indicates, the symptomology of autism in young children (infants and toddlers) needs to be fully examined to facilitate accurate and early diagnosis in order to implement effective and appropriate intervention strategies. The specific aim of this study was to determine if a statistically significant association exists among the devel- opmental domain SD subscale scores (motor, language, cognitive, social-emotional, and adaptive skills) on the Bayley-III and whether a diagnosis of ASD was applied following multidisciplinary evaluation. Knowledge of the predictive value of each domain, or combination thereof, may contribute to an increase in confidence when diagnosing ASD in children under the age of 3 years.

Method

Procedures

This study utilized a retrospective file review of children (n = 151) that participated in Kentucky’s early intervention program, First Steps, between 1/1/2014 and 1/31/2017. Tabachnick and Fidell (2013) recommend a sample size of at least 80 where n > 50 + 8m (m is the number of predictor variables). Children with and without ASD diagnosis were represented. The data collected did not identify co-morbid conditions such as attention- deficit-hyperactivity disorder (ADHD), sleep disturbances, epilepsy, or metabolic abnorm- alities. ASD diagnosis was previously determined by multidisciplinary evaluation by a team from the University of Louisville. The multidisciplinary evaluation typically includes: a Speech-Language Pathologist, Psychologist, and Developmental Pediatrician. An Occupational Therapist may also be involved on a case-by-case basis. Approval for this study was granted by the Institutional Review Boards (IRB) of the University of Louisville and the Kentucky Cabinet for Health and Family Services.

Temporary statewide access was granted to First Steps’ Technology-assisted Observation and Teaming Support (TOTS) database, an electronic record used by the Kentucky Department of Public Health to track children as they are referred, evaluated, and, in some cases, receive services through the early intervention program. The researchers used TOTS to query children referred to and evaluated by First Steps between the aforementioned dates. Specific interest centered on ASD diagnosis and the developmental domain SD subscale scores per the Bayley- III. Demographic information included each child’s age (in months) at evaluation and gender. The above information was compiled in a Microsoft Excel spreadsheet and then exported to SPSS Version 24 for statistical analyses. Gender was coded where 1 = male and 2 = female. ASD diagnosis was coded in the same manner where 1 = not diagnosed and 2 = diagnosed. No identifying information was recorded.

Data analysis

Individual binary logistic regression analyses were completed to assess if an association exists among the developmental domain SD subscale scores (motor, language, cogni- tive, social-emotional, and adaptive skills) on the Bayley-III and whether a diagnosis of ASD was applied. Individual binary logistic regression analyses were used, as the criterion variable – ASD diagnosis – is dichotomous (Warner 2013).

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Results

Descriptive statistics

This study comprised a retrospective file review of 151 children in the state of Kentucky; 79.5% (n = 120) were male and 20.5% (n = 31) were female. The ages ranged from 18–35 months (M = 27.50, SD = 3.99). Fifty-seven percent (n = 86) of the children were diagnosed with ASD; 43% (n = 65) did not have an ASD diagnosis. Table 1 presents the mean and SD for the predictor variables from the Bayley-III subscales (Bayley 2006). ASD diagnosis served as the criterion variable.

Assumption testing and correlation matrix

Logistic regressions are sensitive to multicollinearity. As such, a correlation matrix (Spearman’s Rho) was calculated to assess multicollinearity presence. The results are presented in Table 2 and evidence violation of this assumption test. Therefore, the continuous variables and the dichotomous variable (ASD diagnosis) were mean cen- tered. The dichotomous variable, ASD diagnosis, was also centered. This was completed by changing the values of 0 to -.5 and 1 to .5. Variables were centered as a strategy to prevent errors in statistical inference.

Additionally, the SD for the language and social-emotional subscales from the Bayley- III were skewed (i.e. non-normally distributed). Given the presence of negative values, the skew was corrected by making the values positive prior to log-transformation of the data. Therefore, a constant was added to the mean centered variables (2.28 to the SD for language and 2.22 to SD for social-emotional), so that the minimum for both variables scored 1. The variables were then log-transformed and the skew successfully addressed.

Logistic regression analyses

Individual logistic regression analyses were used to assess whether an association existed among the developmental domain SD subscale scores (motor, language,

Table 1. Descriptive statistics for the Bayley-III Scales (n = 151). Subscale M SD Motor −1.17 0.88 Cognitive −1.66 0.84 Language −2.72 0.72 Social-emotional −1.78 0.91 Adaptive −1.89 0.91

Table 2. Spearman’s Rho correlation matrix (n = 151). Motor Cognitive Language Social-em. Adaptive

Motor – Cognitive .59** – Language .32** .56** – Social-em. .25** .37** .43** – Adaptive .38** .42** .42** .55** –

**Correlation is significant at the 0.01 level (two-tailed). *Correlation is significant at the 0.05 level (two-tailed).

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cognitive, social-emotional, and adaptive skills) on the Bayley-III, and whether a diag- nosis of ASD was applied. The complete results of the logistic regression analyses are presented in Table 3.

Bayley-III motor subscale and ASD diagnosis

Logistic regression – step 1a – entered the Bayley-III motor SD subscale score as a predictor of ASD diagnosis. The results were not significant (odds ratio = .70, 95% CI = .48–1.02, p = .06). Although statistical significance was not achieved, the model explained 3.1% (Nagelkereke R2) of the variance of ASD diagnosis.

Bayley-III cognitive subscale and ASD diagnosis

Logistic regression – step 1b – entered the Bayley-III cognitive SD subscale score as a predictor of ASD diagnosis. The results were statistically significant (odds ratio = .41, 95% CI = .27–.64, p = < .001) and explained 14.9% (Nagelkereke R2) of the variance of ASD diagnosis per this sample.

Bayley-III language subscale and ASD diagnosis

Logistic regression – step 1c – entered the Bayley-III language SD subscale score as a predictor of ASD diagnosis. One outlier (> 4 SD) was observed and subsequently removed from the analysis. The results were statistically significant (odds ratio < .001, 95% CI = < .00001–< .001, p = < .001) and explained 42.8% (Nagelkereke R2) of the variance of ASD diagnosis per this sample.

Bayley-III social-emotional subscale and ASD diagnosis

Logistic regression – step 1d – entered the Bayley-III social-emotional SD subscale score as a predictor of ASD diagnosis. The results were statistically significant (odds ratio = .01, 95% CI = .001–.099, p = < .001) and explained 47.1% (Nagelkereke R2) of the variance of ASD diagnosis per this sample.

Bayley-III adaptive skills subscale and ASD diagnosis

Logistic regression – step 1e – entered the Bayley-III adaptive skills SD subscale score as a predictor of ASD diagnosis. The results were statistically significant (odds ratio = .48, 95% CI = .326–.721, p = < .001) and explained 12.1% (Nagelkereke R2) of the variance of ASD diagnosis per this sample.

Table 3. Predictors of ASD diagnosis using the Bayley-III. Subscale Odds ratio 95% (CI) % Variance p Motor .70 .48–1.02 3.1% .06 Cognitive .41 .28–.64 14.9% < .001 Language .000001 .00000001–.0001 42.8% < .001 Social-emotional .01 .001–.01 47.1% < .001 Adaptive .48 .33–.72 12.1% < .001

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Discussion

In the United States, the average age of autism diagnosis is between 3 and 4 years (Chawarska et al. 2007; Filipek et al. 1999). A common belief is that the lack of diagnoses of autism before the age of 3 years is due to the variability and uncertainty of symptoms at such a young age (Turner and Stone 2007; Camarata 2014). Nonetheless, early diagnosis is important in order to obtain the benefits of early intervention services (Landa 2007; Koegel et al. 2014; Camarata 2014). This study aimed to contribute to an increase in confidence when diagnosing ASD in children under the age of 3 years.

In this study, individual binomial logistic regression analyses determined that lower scores on the Bayley-III subscales of cognitive, language, social-emotional, and adaptive domains were significant predictors of ASD diagnosis. It is no surprise that lower SD scores for social-emotional and language domains were associated with an increased likelihood of ASD diagnosis as both are encompassed in the diagnostic criteria for autism. Additionally, there has been an abundance of research indicating the presence of language and social- emotional deficits in children with autism under the age of 3 years.

The language domain encompasses both receptive (i.e. understanding of words and sounds) and expressive (i.e. production of words and sounds) skills. Landa and Garret- Mayer (2006) used the Mullen Scales of Early Learning (MSEL) to assess 87 participants at six, 14, and 24 months of age. Participants consisted of those with ASD, language delay, and a control group of children without any impairments. Results showed that children with ASD demonstrated a progressive receptive and expressive language regression around 14 months of age. Furthermore, deficits in the frequency and prosodic features of vocalizations (Chawarska et al. 2007), delayed onset of babbling (Iverson and Wozniak 2007) and usage of gestures and pointing as a communicative tool (Chawarska et al. 2007) have all been documented between 1 and 2 years of age.

The social-emotional domain consists of skills including social reciprocity and related- ness to others. In children with autism under the age of 3 years, the absence of social smiling, lack of facial expression, poor eye contact (Zwaigenbaum et al. 2005), failure to orient to name, limited interest in other children, and limited empathy and imitation (Chawarska et al. 2007), have all been documented. Even parents of children with autism, when asked to recall their child’s differences in the first year of life, refer to extremes of temperament and behavior ranging from passivity to marked irritability (Zwaigenbaum et al. 2005).

As development is a simultaneous process, it is not surprising that lower SD scores in both adaptive and cognitive domains presented as predictors of possible ASD. The likelihood appears increased when considering corresponding deficits involving both language and social-emotional skills. Cognition, even in infancy, involves comprehen- sion of information, organization, storage, memory, problem-solving, sequencing and the use of knowledge/executive functioning. Stone et al. (2007) used the MSEL to test cognitive functioning of infant siblings at high risk for ASD. The results indicated that siblings at risk for ASD performed significantly lower on nonverbal problem solving (visual reception) and attentional domains than siblings of typical development. Children with autism also demonstrate cognitive deficits through symbolic play as early as 18 months (Toth et al. 2006).

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Adaptive skills encompass those abilities that foster independence and self-care. In essence, this domain addresses a child’s ability to participate in activities of daily living. For children under the age of 3 years, parents or caregivers tend to perform/aid many of these tasks. Green and Carter (2014) conducted a 3-year longitudinal study of 162 children with autism between 18–33 months of age and their adaptive behaviors. To assess change, the children were given a series of tests including the MSEL, the Vineland Adaptive Behavior Scales (VABS), and the Brief Infant Toddler Social and Emotional Assessment (BITSEA) annually over the course of 2 years. Results indicated that partici- pants with autism learned daily living skills, but did so at a slower rate than typically developing children. In addition, developmental level and autism symptom severity predicted acquisition of daily living skills. Children who were older with a higher developmental level at the beginning of the study made more rapid gains in daily living skills than those who were younger or had a lower developmental level at the beginning of the study. Children with more severe autism symptoms made slower gains in daily living skills than those children with less severe autism symptoms (Green and Carter 2014). This is supportive of our findings that lower scores on the adaptive domain subscale of the Bayley-III may be predictive of an autism diagnosis.

The results of the current study were not significant for the Bayley-III motor SD subscale score as an individual predictor of ASD diagnosis. The motor domain of the Bayley-III includes assessment of both fine (e.g. visual tracking, reaching, grasping) and gross motor skills (e.g. static positioning, dynamic movements, coordination, balance). Autism is not perceived as a syndrome with obvious motor impairment. The literature, however, seems controversial. Some literature contained supporting evidence of motor impairments in children with autism (Provost, Heimerl, and Lopez 2009) and other literature contained evidence supporting the motor skills domain as a strength in children with autism (Ming, X, Brimacombe, M., & Wagner, G.C., 2007). In addition, there seems to be a paucity of research completed involving children below the age of 3 years.

In one of the few studies examining motor delay in preschool children with autism, Provost, Heimerl, and Lopez (2009) found the motor skills to be a relative weakness. In addition, Provost, Heimerl, and Lopez (2009) found that motor signs may be difficult to distinguish from other types of disorders. The study was conducted using two groups of children, one group with autism and one group with developmental delay, all aged from 21–41 months. Fine motor and gross motor skills of both groups of children were assessed using the Peabody Developmental Motor Scales – 2nd Edition (PDMS-2). Results demonstrated that children with autism have motor skills ranging from average to very poor. These motor skills vary in degree as some children displayed equal development of gross and fine motor skills, some displayed greater development of gross motor skills than fine motor skills and some showed greater development of fine motor skills than gross motor skills (Provost, Heimerl, and Lopez 2009). The overall motor skills of children with autism and children with developmental delay were very similar to each other (Provost, Heimerl, and Lopez 2009).

Conversely, Ming et al. (2007) found gross motor skills in children with autism to be a strength. One-hundred and fifty-four children with autism were assessed and results indicated only 12 of the children (10%), between the ages of two and six, displayed a history of gross motor delay but all had reached the target milestones of walking

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independently, walking steps or ramps, and jumping by initiation of the study (Ming et al. 2007). With such scarce research regarding motor development in children with autism below the age of 3 years, it would be advantageous to further study this developmental domain.

Our findings may have important implications regarding early intervention treatment strategies. Developing treatment plans that target skills not only in the language and social-emotional domains but also in the cognitive and adaptive skills domains may benefit children with autism. Specifically, early interventions targeting play skills, imita- tion skills, and joint attention skills have been documented to show improvement in communication development in children with autism (Kasari, Freeman, and Paparella 2006; Ingersoll and Schreibman 2006). In fact, evidence shows that intervention in just one area of communication can positively improve other areas of communication as well (Ingersoll and Schreibman 2006). With the abundance of research demonstrating the remarkable results of early intervention, it is important that children at risk for autism under the age of 3 years should receive these services.

Limitations

Although this study was supported by current literature, the researchers acknowledge the study is not without limitations. The Bayley-III is only one option for developmental assessment of children under the age of 3 years. Future research may utilize other valid assessment tools. Additionally, the current study patterned the assessment protocol after the Kentucky First Steps Program. Other states may have different government directed procedures in place for diagnosing children with autism under the age of 3 years.

Future research opportunities may be warranted using the Bayley-III. The Bayley-III compartmentalizes the language, motor, and adaptive developmental domains into further subgroups that are scored individually (e.g. language subgroups include receptive and expressive language). These subgroups contribute to the overall developmental domain score. Obtaining the individual subgroup scores may provide more descriptive information regarding which specific aspects/deficits of the aforementioned domains are more likely to appear in a child with autism under the age of 3 years.

Conclusion

The overarching intent of this study was to contribute to the knowledge base on ASD diagnosis. The focus centered specifically on young children under the age of 3 years. The results obtained are consistent with the current body of literature on ASD with respect to deficits in the areas of cognitive, language, social-emotional and adaptive skills (Cognition: Stone et al. 2007; Toth et al. 2006; Language: Chawarska et al. 2007; Iverson and Wozniak 2007; Landa and Garret-Mayer 2006; Social-emotional: Chawarska et al. 2007; Zwaigenbaum et al. 2005; Adaptive: Green and Carter 2014).

While ASD is a heterogeneous neurodevelopmental disorder whose symptoms may involve deficits across three primary domains – communication, socialization, and beha- viors or interests – it is vitally important that researchers, physicians, and therapists alike routinely consider secondary related deficits in the areas of cognitive and adaptive development. For this sample and this context, use of the Bayley-III proved to be a

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beneficial tool for use in evaluating children under the age of 3 years who were suspected to have ASD. It is important to note, however, that the Bayley-III is but one of the available options. The researchers are also quick to stress the importance of thorough and routine screening for ASD. Lastly, timely intervention necessitates timely evaluation and diagnosis. It is our hope that the limited knowledge base on early ASD diagnosis in young children has been increased and the gap in the available literature narrowed.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Alan F. Smith http://orcid.org/0000-0001-5665-1134

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12 K. JUERGENSEN ET AL.

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