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Research in Autism Spectrum Disorder&
Research in Autism Spectrum Disorders 6 (2012) 871–880
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Research in Autism Spectrum Disorders
J o u r n a l h o m e p a g e : h t t p : / / e e s . e l s e v i e r . c o m / R A S D / d e f a u l t . a s p
An examination of the relationship between communication and socialization deficits in children with autism and PDD-NOS
Megan A. Hattier, Johnny L. Matson *
Louisiana State University, United States
A R T I C L E I N F O A B S T R A C T
Article history: Received 7 August 2011
Accepted 15 November 2011
Keywords: Autism
BISCUIT
Battelle Developmental Inventory
Communication
Infants
Autism Spectrum Disorders (ASDs) are characterized by pervasive impairments in
repetitive behaviors or interests, communication, and socialization. As the onset of these
features occurs at a very young age, early detection is of the utmost importance. In an
attempt to better clarify the behavioral presentation of communication and socialization
deficits to aid in early assessment and intervention, impairments in these areas were
examined among infants and toddlers (17–36 months) with Autistic Disorder (AD),
Pervasive Developmental Disorder-Not Otherwise Specified (PDD-NOS), and non-ASD
related developmental delay. The Baby and Infant Screen for Children with aUtIsm Traits- Part1 (BISCUIT-Part1) and the Battelle Developmental Inventory, 2nd Edition (BDI-2) were utilized to examine communication and socialization levels, respectively, among these
groups. All groups significantly differed on level of socialization impairment with the
Autism group displaying the greatest impairment and the non-ASD related developmental
delay group evincing the least impairment. In regards to communication deficits, the non-
ASD related developmentally delayed group differed significantly in comparison to the
Autism and PDD-NOS groups; however, no significant differences were found between
children with AD and PDD-NOS. While communication and socialization impairments
were found to significantly correlate for all participants with the exception of those with
PDD-NOS, these correlations were not found to significantly differ from one another across
groups. The implications, limitations, and future directions of these results are discussed.
� 2011 Elsevier Ltd. All rights reserved.
In the past decade there has been an increase in the public’s interest in Autistic Disorder (AD), more commonly known as autism (Ban Itzchak, Lahat, & Zachor, 2011; Evans et al., 2001; Levy & Perry, 2011; Lord & Luyster, 2006; Matson, Wilkins, & Gonzales, 2008; Suzuki, 2011; Worley, Matson, Sipes, & Kozlowski, 2011). Autism is a neurodevelopmental disorder characterized by pervasive deficits in socialization and communication, as well as the presence of repetitive or restricted behaviors or interests (Horovitz & Matson, 2010; Lugnegård, Hallerbäck, & Gillberg, 2011; Matson, 1994, 2008; Matson, Dempsey, & Fodstad, 2009; Matson, Dempsey, & LoVullo, 2009; Matson, Fodstad, Hess, & Neal, 2009; Meindl & Cannella- Malone, 2011; Worley & Matson, 2011). For purposes of this study, the focus will remain on the former two impairments. Approximately, 25–50% of all children with an ASD diagnosis never develop a functional language (Dawson & Murias, 2009; Howlin, 2006; Rutter, 1978). Many are stigmatized socially as well, particularly those who exhibit stereotypic behavior (Cunningham & Schreibman, 2008; Matson, Shoemaker, et al., 2011; Matson, Kozlowski, et al., 2011; Rivet & Matson, 2011; Smith & Matson, 2010a). Therefore, optimizing skills in communication and socialization is of great importance (Matson, Kozlowski, et al., 2011; Matson, Sipes, et al., 2011).
* Corresponding author at: Department of Psychology, Louisiana State University, Baton Rouge, LA 70803, United States.
E-mail address: [email protected] (J.L. Matson).
1750-9467/$ – see front matter � 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.rasd.2011.12.001
M.A. Hattier, J.L. Matson / Research in Autism Spectrum Disorders 6 (2012) 871–880872
Deficits in the area of socialization can be detrimental to a child’s quality of life for many reasons, as these children tend to isolate themselves from others and have impaired social relationships (Chan, Hu, Cui, Wang, & McAlonan, 2011; Ghuman, Leone, Lecavalier, & Landa, 2011; Mahan & Matson, 2011b; Matson, Fodstad, & Rivet, 2009; Matson, Matson, & Rivet, 2007; Smith & Matson, 2010c). The three most explicit social impairments of those with ASDs identified by Rutter (1978) include uncooperativeness while playing with other children, the inability to form friendships, and the failure to recognize others’ feelings. Communication deficits also lead to negative consequences, such as elevated levels of problem behaviors (Barnes, Dunning, & Rehfeldt, 2011; Beitchman, 2006; Matson & LoVullo, 2008; Smith & Matson, 2010a, 2010b, 2010c; Sturmey, Laud, Cooper, Matson, & Fodstad, 2010a, 2010b). More importantly, Newborg (2005) hypothesizes that children with higher deficits in communication may also exhibit greater socialization deficits, particularly, because the inability to communicate with adults and/or peers can create social strain (Matson, Fodstad, et al., 2009; Matson & Wilkins, 2007; Matson, Wilkins, & Gonzales, 2008).
Over the past 30 years there reportedly has been a 16-fold increase in the diagnosis of autism, although this increase may not be completely attributed to a genuine growth in the disorder. Rather, other factors like greater diagnostic precision, more expansive diagnostic criteria, and more public attention to the disorder may be potential causes (Bertoglio & Hendren, 2009; Matson & Kozlowski, 2011). Nevertheless, more children are in need of services and accommodations, thus making early detection and intervention a top priority among today’s diagnosticians (Matson & Boisjoli, 2007; Matson, Dixon, & Matson, 2005; Volkmar & Pauls, 2003). While much research has been conducted on the presence of communication and socialization impairments in this population, the amount of literature is much less for younger children, especially for children under the age of 3. This factor may be attributed to the fact that the average age for diagnosis of ASDs is approximately 3–4 years of age, although it is dropping (DeGiacomo & Fombonne, 1998; Matson, 2005). The current study serves as a means to provide such an analysis as all participants were 17–36 months of age. The majority of studies which have been conducted to analyze core ASD symptomatology among this very young population have done so through the utilization of retrospective analyses (e.g., inspecting old home videos; Brown, Dawson, Osterling, & Dinno, 1998; Osterling & Dawson, 1994). The current study is more robust since deficits in communication and socialization were examined using real time, objective measures.
In 2010, Horovitz and Matson found children 17 to 36 months of age with PDD-NOS possess significantly more communication deficits than those with non-ASD related developmental delays. Children with a diagnosis of Autistic Disorder were found to display significantly more deficits in communicative skills than children with PDD-NOS and non-ASD related developmental delay. The current study extends the Horovitz and Matson (2010) study and adds additional children to the sample. The updated sample included newly recruited participants and only children that have been administered the Battelle Developmental Inventory, 2nd Edition (BDI-2). Additionally, the present study examines not only the presence of communication deficits but the presence of socialization impairments and the relationship of the deficits seen in these two areas.
Since Kanner’s (1943, 1944) original description, many researchers have conducted studies in an attempt to better define the three core features of ASD on an individual basis (Kanai et al., 2011; Sipes, Matson, Worley, & Kozlowski, 2011; Tseng, Fu, Cermak, Lu, & Shieh, 2011). However, understanding the relationship between the core features of autism is equally important when attempting to detect them among young children. For a diagnosis of AD, all three core features must be present to a large degree, which suggests there are strong associations between these symptoms (Dworzynski, Happé, Bolton, & Ronald, 2009; Kuenssberg & McKenzie, 2011; Matson, Matson, & Beighley, 2011). Some, however, have questioned these associations. For instance, family studies have found that relatives of those with autism often display milder forms of communication and socialization deficits without having repetitive and restricted behaviors and interests (Bishop et al., 2004; Piven, Palmer, Jacobi, Childress, & Arndt, 1997). More recently, Dworzynski et al. (2007) found significant correlations between communication and socialization impairments among children of this ASD population; however, there was no significant correlation between communication impairment and repetitive and restricted behaviors and interests. Elsewhere, Howlin and Moore (1997) state that communication and socialization deficits are the first signs suggesting a child is developing atypically.
The aim of the current study was to build upon these abovementioned findings. Fortunately, due to recently developed instruments, screening for autism and other developmental delays has proved to be less problematic than in the past. The Baby and Infant Screen for Children with aUtIsm Traits-Part1 (BISCUIT-Part1) has recently been designed to aid in the early detection of ASDs among children from 17 to 37 months of age (Matson, Wilkins, Sevin, et al., 2008). Conversely, the BDI-2 (Newborg, 2005) is intended to identify developmental skills of children from birth to 7 years 11 months. This study aimed to utilize two portions of these two measures (the communication domain of the BISCUIT-Part 1 and the Personal-Social domain of the BDI-2) in establishing if a relationship exists, and if so determining where the correlation lies between the level of communication deficits and the level of socialization deficits among those with diagnoses of AD, PDD-NOS, and non-ASD related developmental delays.
First, it was hypothesized that the autism group would have significantly greater levels of impairment in communication and socialization than the PDD-NOS and non-ASD related developmental delay groups. Those with PDD-NOS were also expected to show significantly greater levels of deficits in these areas compared to the non-ASD related developmental delay group. Second, it was also thought that correlations between level of communication deficit and socialization impairment would be significant for the AD group; however, it was believed that non-significant differences would be found for the PDD- NOS and non-ASD related developmental delay groups. Finally, in comparison of these correlations for each diagnostic group, it was hypothesized that significant differences would be found between the communication–socialization (C–S)
M.A. Hattier, J.L. Matson / Research in Autism Spectrum Disorders 6 (2012) 871–880 873
correlations for those with autism and those with PDD-NOS and for those with autism and those with non-ASD related developmental delay. It was not believed, however, that the C–S correlations would significantly differ for the PDD-NOS group and the non-ASD related developmental delay group.
1. Method
1.1. Participants
Five hundred ninety-one children served as participants. Ranging from 17 to 37 months of age (M = 26.03; SD = 4.71) these infants and toddlers were recruited through the Early Steps program funded by the State of Louisiana. Early Steps is Louisiana’s Early Intervention System housed under the Individuals with Disabilities Education Act, Part C. Infants and toddlers from birth to 36 months of age who had developmental delays or a medical condition likely to result in a developmental delay qualify for services. Participants were classified into one of these three conditions: Autism, PDD-NOS, or controls with non-ASD related developmental delay. These assignments were established by a licensed doctoral level psychologist who was blind to the BISCUIT scores (Matson, Boisjoli, Hess, & Wilkins, 2010). A portion of participants from the original sample recruited for this study (n = 197) also received diagnoses from a second doctoral level clinical psychologist, and inter-rater reliability between these independent diagnoses was found to be excellent (k = .935). The non-ASD related developmental delay group consisted of children who did not meet criteria for an ASD but their previous family pediatrician determined that they were either atypically developing, had a genetic disorder, or had a physical disability (Matson, Fodstad, et al., 2009).
Originally, a total of 2214 participants were recruited. All participants with missing or improperly coded data were removed from inclusion in this study (n = 818). Consequently, the PDD-NOS group was the smallest diagnostic group with 197 participants. Field (2009) suggests generating equal sample sizes among all groups to ensure robustness of statistical tests; thus, all three diagnostic groups were made equal, each with 197 participants. This process was conducted by utilizing the select random cases function in SPSS. Therefore, one participant was randomly deleted from the AD group and 804 participants were deleted from the non-ASD related developmentally delayed group to achieve group totals of 197 for each group.
The children within the autism group ranged in age from 18 to 36 months of age (M = 26.59; SD = 4.75). For this diagnostic group, 47.2% were Caucasian, 43.1% were of African American descent, 2.5% were of Hispanic ethnicity, and 7.1% were recorded as other. Additionally, 75.1% of the autism group was male. In regards to the PDD-NOS group, children from 17 to 35 months of age (M = 25.54; SD = 4.48) met inclusion criteria. The ethnicities of these children with a PDD-NOS diagnosis were recorded as Caucasian (48.7%), African American (44.7%), Hispanic (.5%), or other (6.1%). Within this group, 72.6% were male. Within the non-ASD related developmental delay group, the children were between the ages of 18 and 36 months (M = 25.96; SD = 4.86) with 66% being male. In regards to ethnicity, 51.3% were Caucasian, 44.2% were African American, 1.5% were Hispanic, and 3.0% were recorded as other. Demographic information is presented within Table 1.
To determine if the diagnostic groups differed significantly on demographic variables (i.e., gender, ethnicity, or age) a priori analyses were conducted (Matson, Rivet, Fodstad, Dempsey, & Boisjoli, 2009). The results from chi-square analyses revealed that the groups did not differ significantly in terms of gender or ethnicity. A one-way between-subjects analysis of variance (ANOVA) also found no significant differences between groups in terms of age. While non-significant differences among these variables exist, it is believed that this variability would not significantly affect the findings from this study.
1.2. Measures
1.2.1. Baby and infant screen for children with autism traits – Part 1
The BISCUIT has recently been designed to aid in the early detection of ASDs among children from 17 to 37 months of age (Matson et al., 2010; Matson, Wilkins, Sevin, et al., 2008). It is a battery of assessments designed to assess autism in young
Table 1
Demographic characteristics (N = 591).
Demographic characteristics Diagnostic group
Autism (n = 197) PDD-NOS (n = 197) Non-ASD Developmentally Delayed (n = 197)
Age (in months)
Mean (SD) 26.59 (4.75) 25.54 (4.48) 25.96 (4.86)
Range 18–36 17–35 18–36
Gender, %
Male 75.1% 72.6% 66.0%
Female 24.9% 27.4% 34.0%
Race/ethnicity, %
Caucasian 47.2% 48.7% 51.3%
African–American 43.1% 44.7% 44.2%
Hispanic 2.5% 0.5% 1.5%
Other 7.1% 6.1% 3.0%
M.A. Hattier, J.L. Matson / Research in Autism Spectrum Disorders 6 (2012) 871–880874
children along with PDD-NOS, comorbid psychopathology, and challenging behaviors. The BISCUIT-Part 1 is the section concerned with diagnostic criteria and consists of 62 questions. The parents and/or caregivers rate the child’s impairments in comparison to typically developing children of the same age. Items are scored on a 3-point scale: 0 indicating no difference or no impairment; 1 indicating different or mild impairment; and 2 indicating very different or severe impairment in comparison to their peers. Inspection of these items with a factor analysis revealed three separate factors: socialization/ nonverbal communication, repetitive behaviors/restricted interest and communication (Matson et al., 2010). The seven items that fall under the communication factor will be the focus of this study. These items include ‘‘use of language to communicate,’’ ‘‘language development,’’ and ‘‘communicates effectively.’’ This communication domain has been determined to have good internal consistency (0.83) and item-scale correlations ranging from .34 to .90 (Matson et al., 2010).
Internal reliability for this 62 question component was found to be high .97 (Matson, Wilkins, Sevin, et al., 2008). Item content for autism and PDD-NOS was successfully established. Validity studies found that the BISCUIT-Part 1 was able to effectively distinguish between those with and without ASDs. Furthermore, as previously mentioned when differentiating between those without a diagnoses and those with PDD-NOS the sensitivity and specificity was established as .847 and .864, respectively (Matson, Wilkins, et al., 2009). The sensitivity and specificity was found to be slightly lower (.844 and .833, respectively) when distinguishing between diagnoses of autism and PDD-NOS (Matson, Wilkins, et al., 2009). Lastly, the overall classification rate was found to be 88.8 for the BISCUIT-Part 1 (Matson, Wilkins, et al., 2009).
1.2.2. Battelle developmental inventory, second edition
The BDI-2 (Newborg, 2005) is a revision of the original BDI. The revisions include, but are not limited to, relocating the placement of some items into different domains, fewer subtrials on many items for efficiency purposes, and an easier-to- administer design of the interview. It is intended to identify developmental skills of children from birth to 7 years 11 months. Administration of the full BDI-2 usually lasts approximately 1–2 h. The five domains that this 450-item assessment addresses are Adaptive (ADP), Personal–Social (P–S), Communication (COM), Motor (MOT), and Cognitive (COG). The items are scored on a 3-point Likert scale: a score of 0 indicates that the child has no ability in this skill; a score of 1 indicates that they possess an emerging ability; and a score of 2 indicates that they have ability with this skill. A total developmental quotient (DQ) is calculated by combining the scores of each of the five domains. This combined score has a mean of 100 and a standard deviation of 15. Using a sample of 2500 children between the ages of birth to 7 years, 11 months, acceptable levels of test retest reliability and excellent internal consistency were found along with appropriate content and criterion validity (Newborg, 2005).
For the purposes of this study, the score for the Personal–Social domain was used as the dependent variable. This particular domain consists of 100 items that assess the child’s ability to interact with adults and peers and their self-concept and self role (Newborg, 2005). The test is made up of 3 subdomains: Adult Interaction (AI), Peer Interaction (PI), and Self- Concept and Social Role (SR). The AI subdomain includes 30 items that are administered to only children younger than 6 years of age. Attachment to and interaction with adults during infancy is assessed along with initiation and maintenance of social contact and the use of adults to assist themselves with solving problems. The assessment of the 25-item PI subdomain begins at 2 years of age and ends at 6 years of age. Behaviors including, but not limited to, forming friendships, interacting with peers, responding to and initiating social contact with peers, playing well in a small group, and cooperation are among the abilities assessed in this subdomain. The SR subdomain consists of 45 items that are administered to all ages that the BDI- 2 assesses (birth to 7 years, 11 months). The child’s self-awareness, self-worth, morals, sensitivity to the feelings of others, and coping skills are among the skills addressed in this subdomain. The quality and frequency of the abilities mentioned above are also measured for each item on each subdomain.
Good internal reliability has been found for the BDI-2 total score and for the Personal–Social Domain, .99 and .96, respectively (Newborg, 2005). All subdomains within the P–S domain also reached adequate levels in regards to internal reliability. The test–retest was calculated using a sample of 126 two-year-old children. The stability coefficient for the P-S domain and the total DQ were both very high, .90 and .93, respectively (Newborg, 2005). Inter-rater reliability was also found to be quite high. Consistency between scorers ranged from 94% to 99% agreement (Newborg, 2005). Convergent validity was also established with a number of different scales measuring development in young children. BDI-2 scores were also able to effectively distinguish between typically developing children with children with autism evidenced by sensitivity and specificity levels of .91 (Newborg, 2005).
1.3. Procedure
Parental interviews and child observations were conducted by individuals whose training qualified them to screen children that might benefit from services provided by EarlySteps. Physical therapy, occupational therapy, social work, education, speech–language pathology, and psychology were the various disciplines represented (Matson, Wilkins, et al., 2009). In addition to their prior training, the assessors received education on ASDs, the measures used throughout the screening process, and the correct standardized administration methods. The screening process involves an entire battery of assessments which include the BISCUIT and the BDI-2. The parents or legal guardians of the children participating in this study served as the informants on all measures and have provided informed consent for participation. Furthermore, the Louisiana State University Institutional Review Board and Louisiana’s Office for Citizens with Developmental Disabilities provided prior approval for this study.
M.A. Hattier, J.L. Matson / Research in Autism Spectrum Disorders 6 (2012) 871–880 875
2. Results
2.1. Main analyses
All statistical analyses were carried out using SPSS 16.0. A 3 � 2 MANOVA was conducted to test for differences among the three groups (Autism, PDD-NOS, and non-ASD related developmental delay) on communication and socialization scores (i.e., the overall communication score on the BISCUIT-Part 1 and the developmental quotient on the P–S domain of the BDI-2, respectively). Determined to be robust in cases of equal sample sizes (Field, 2009), the Pillai–Bartlett trace statistic indicated significant differences between the three diagnostic groups in regards to communication and socialization scores, F(4, 1176) = 71.69, p < .001.
Significant results of the MANOVA were followed by 2 one-way between subjects ANOVAs to determine if these significant group differences lie among the P–S domain score of the BDI-2 or the overall communication score of the BISCUIT- Part 1. Again, the diagnostic groups served as the independent variable. Separate univariate ANOVAs on the outcome variables were significant across diagnostic group effects on communication, F(2, 588) = 117.22, p < .001, and socialization, F(2, 588) = 86.08, p < .001.
To account for the inflation of type-I errors, Tukey post hoc tests were conducted following significant ANOVAs. In regard to communication, the Autism group did not differ significantly when compared to the group with PDD-NOS, p = .062. Significant differences were found for communication, however, between the Autism group and the non-ASD related developmental delay group, p < .001, and between the PDD-NOS group and the non-ASD related developmental delay group, p < .001. See Fig. 1 for a depiction of the mean scores on the communication domain of the BISCUIT-Part 1 for all diagnostic groups.
For socialization, all comparisons (i.e., Autism vs. PDD-NOS; Autism vs. non-ASD related developmental delay; and PDD- NOS vs. non-ASD related developmental delay) were found to differ significantly, p < .001, universally. See Fig. 2 for a depiction of the mean scores for children with autism, PDD-NOS, and non-ASD related developmental delay on the P–S domain of the BDI-2.
The next set of analyses involved identifying the relationship between communication and socialization differs significantly between diagnostic groups. Pearson’s correlation coefficients were obtained for the overall communication score on the BISCUIT-Part 1 and the developmental quotient of P–S domain on the BDI-2 to determine whether or not relationships between communication and socialization level existed. These analyses resulted in three correlation coefficients, with one for each of the diagnostic groups (i.e., Autism, PDD-NOS, and non-ASD related developmental delay), and of the three correlations analyzed, two were found to be significant. The overall communication score on the BISCUIT- Part 1 was strongly correlated with the BDI-2 P–S domain developmental quotient for the Autism group, r = �.207, p < .01, and for those with non-ASD related developmental delays, r = �.187, p < .01. There was a weaker relationship, however, between communication and socialization for the PDD-NOS group at the .02 level, r = �.137, p = .05. It should be noted that all correlations were found to be negative as a higher score on the BISCUIT-Part 1 indicates a greater impairment and a higher score on the BDI-2 indicates fewer deficits.
These three correlations were then compared with one another to test for any significant differences. To control for the inflation in the type-I error rate, a significance level of .05 divided by the number of simultaneous tests (n = 3) was chosen.
Fig. 1. Mean score on the communication domain of the BISCUIT-Part 1 for autism, PDD-NOS, and non-ASD related developmental delay.
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0.00 Autism POD-NOS
Diagnosis
non-ASD related Developmental Delay
Fig. 2. Mean score on the P-S domain of the BDI-2 for autism, PDD-NOS, and non-ASD related developmental delay.
M.A. Hattier, J.L. Matson / Research in Autism Spectrum Disorders 6 (2012) 871–880876
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Autism POD-NOS
Diagnosis
non-ASD related Developmental Delay
Therefore, a = .02 was used for all correlation comparisons. When comparing the Autism and PDD-NOS groups, the relationship between communication and socialization was not significantly stronger in either group; thus, significant differences were not found, z = �.711, p = 0.48. Non-significant differences were found when comparing the correlations for communication and socialization for the Autism and non-ASD related developmental delay groups, z = �.205, p = 0.84, as well. Finally, significant differences were not found for the communication/socialization relationship when comparing the PDD-NOS and non-ASD related developmental delay group, z = .506, p = 0.61.
3. Discussion
Although ASDs have recently been receiving great attention among researchers in the scientific community (Evans et al., 2001; Lord & Luyster, 2006; Mahan & Matson, 2011a), few studies examine symptomotology prior to age 3 (Kishore & Basu, 2011; Matson, Wilkins, & Gonzales, 2008). Given that the general consensus among researchers is that ASDs are present from birth, infancy and toddlerhood is a crucial developmental period to study (Kanner & Eisenberg, 1957; Matson, Reiske, & Tureck, 2011; Nyden, Hagberg, Gousse, & Rastam, 2011; Rogers, 2000). This current study compared the socialization and communication deficits in infants and toddlers with AD, PDD-NOS, and non-ASD related developmental delay using real time measures of assessment through four major sets of analyses.
First, children with autism were not found to have significantly more communication impairments than the children with PDD-NOS in this current study. This is not surprising as some researchers argue that communication impairments are the main deficit of ASDs and tend to be the most pervasive (Rutter, 1968; Rutter & Bartak, 1971). Also, deficits in communicative abilities have previously been found to be the most common first concern of parents who seek assessment of an ASD for their young child (DeGiacomo & Fombonne, 1998; Goin-Kochel & Myers, 2004; Howlin & Moore, 1997; Kishore & Basu, 2011; Kozlowski, Matson, Horovitz, Worley, & Neal, 2011; Volkmar & Pauls, 2003). Furthermore, the non-significant findings between the Autism group and the PDD-NOS group in regard to communication deficits need to be interpreted with care. With no distinct cut-offs (i.e., diagnostic criteria) delineating between a diagnosis of Autistic Disorder and PDD-NOS, the diagnostic picture can become quite unclear for clinicians (Walker et al., 2004). Researchers have begun to gain headway in determining the line that distinguishes communicative abilities for these two disorders. Many researchers have found a significantly greater number of deficits among those with a diagnosis of autism in relation to those with PDD-NOS (Anderson et al., 2007; Matson, Fodstad, et al., 2009; Myhr, 1998). Walker et al. (2004) attempted to better define PDD-NOS and determined that this diagnosis is most often given for the presence of atypical autism. The authors, however, suggest that ‘‘atypical autism’’ be used to describe children with the presence of communication and socialization impairments but lacking repetitive and restricted behaviors and interests. This implies that children with a diagnosis of PDD-NOS will often possess impairment in both communicative abilities and social skills, providing a rationale for the non-significant findings between these two groups.
Communicative impairments were also found to be significantly greater in children with a diagnosis of PDD-NOS or AD than children with non-ASD related developmental delays. These results are in agreement with the findings of Horovitz and Matson (2010). In line with the rationale for the previous finding, communication impairments are thought by some to be the main deficit for all ASDs (Rutter, 1968; Rutter & Bartak, 1971). While problems in this area are not required for a diagnosis
M.A. Hattier, J.L. Matson / Research in Autism Spectrum Disorders 6 (2012) 871–880 877
of PDD-NOS, researchers can be certain that all informed assessments of ASDs will include an examination of the child’s communicative abilities. These results suggest that deficits in communication can aid in differentiating between not only those with ASDs and typically developing children but also between children with ASD and non-ASD related developmental delays prior to age 3. However, it proves to be more difficult to distinguish between those with autism and those with PDD- NOS when only taking into account communication deficits.
Secondly, socialization deficits were found to be greatest in the AD group and were least evident in the non-ASD related developmental delay group, with the PDD-NOS group falling in between. These findings implicate and reaffirm that socialization deficits are integral to the diagnosis of ASDs. Future research and future diagnostic conceptualizations of this disorder should continue to consider impairments in this area a primary factor, and the treatment of ASDs should place emphasis on this deficit when trying to attain a higher level of functioning.
Third, the relationship between communicative and social impairments was examined in each of these three diagnostic groups (i.e., AD, PDD-NOS, and non-ASD related developmental delay), and these relationships were compared to test for significant differences. It should be noted that these correlations do not imply causality as there may be a third variable influencing both communication and socialization simultaneously, or if these two constructs are causal, the direction of this causality is still unknown (Field, 2009). The strong correlation between communication and socialization for children with a diagnosis of Autistic Disorder substantiates prior research which found close relationships between these two constructs for this population (Dworzynski et al., 2007). As a result, these findings support not only the diagnostic presentation for Autistic Disorder (i.e., a presence of both communication and socialization deficits), but also supports this diagnostic presentation in children prior to age 3, when most ASD diagnoses are made. The strong correlation between communication and socialization problems for children with non-ASD related developmental delays can be explained by the fact that children with developmental delays may have low levels of impairment in both areas which would result in a significant correlation even though deficits were not clinically significant. The weaker relationship between socialization and communication for children with a PDD-NOS diagnosis was expected as a diagnosis of PDD-NOS does not require the presence of impairment in both of these areas (APA, 2000). However, if the child does present with communication and socialization deficits, problems in one of these areas may be subthreshold (Walker et al., 2004), weakening the link between these two areas of impairment.
Fourth, the correlation between communication and socialization impairment was not found to be significantly different in children with autism, PDD-NOS, and non-ASD related developmental delay. These findings suggest that while some diagnostic groups may have a significant relationship between communication and socialization impairments and others do not, these relationships are still too similar to distinguish between Autistic Disorder, PDD-NOS, and non-ASD related developmental delay. Therefore, researchers should proceed with caution when studying diagnostic differences between these groups as many similarities exist. More research in this area is needed, possibly on a more micro level looking at specific abilities.
One should consider the possible limitations of this current study while interpreting the results. The inability to account for intelligence is one limitation. Researchers have shown that level of ID affects adaptive functioning, including communication and socialization abilities (Matson & Shoemaker, 2009). However, the participants recruited for this study consisted of infants and toddlers 17–37 months of age, and intelligence has been found to be difficult to assess and unstable at this young age (Ho, Foch, & Plomin, 1980). Another limitation involves the nature of the BDI-2. The domain important to this study was the Personal-Social Domain which consists of three subdomains: Adult Interaction, Self-Concept and Social Role, and Peer Interaction. While the two former subdomains were administered to all participants, the Peer Interaction subdomain was only administered to children between the ages of 24 and 71 months (Newborg, 2005). This subdomain assesses the child’s ability to develop appropriate friendships, to effectively interact with others, to cooperate, and to initiate social interaction. There were 193 children in the sample for this study between the ages of 17 and 23 months who were lacking this third subdomain of the Personal–Social subdomain of the BDI-2. It was decided to include these participants in the analyses since Newborg (2005) found the overall Personal–Social Domain Developmental Quotient score to have excellent reliability for children 17 months of age (r = .96) and for children 18–23 months of age (r = .95).
These data help diagnosticians and parents understand the behavioral presentation of young children with an ASD. Communication and socialization deficits were clearly distinguishable between those with and without an ASD diagnosis and partially distinguishable between those with Autistic Disorder and PDD-NOS even prior to age 3. These findings support and strengthen the argument for early detection and diagnosis of ASDs (Baird et al., 2001; Matson, Wilkins, & Gonzales, 2008). Although many support earlier detection of behavioral symptoms because there currently are no biological markers for ASDs, little research has been done to substantiate this argument (Barbaro & Dissanayake, 2009). Nevertheless, researchers have shown that the earlier intensive behavioral intervention is implemented, the better the prognosis (Matson, 2007; Matson, Wilkins, & Gonzales, 2008). Early identification, diagnosis, and treatment can improve the long-term functioning of children with an ASD including social skills, communication skills, adaptive behaviors, and even IQ (Manning- Courtney et al., 2003; Matson, 2007; Martinez-Pedraza & Carter, 2009; Wainer & Ingersoll, 2011). Ben Itzchak and Zachor (2007) noted that approximately half of their participants in early behaviorally based interventions were able to perform considerably better on standardized tests, adequately function in mainstream classes, and may even become impossible to tell apart from their peers of typical development. This current study has supported the cause for researchers to develop measures, identify early symptoms, and ultimately diagnose children with ASDs at an earlier age.
In regards to future directions of study, researchers should also examine the presence of restricted and repetitive behaviors and interests among infants and toddlers and its relationship with other core features of ASDs. It is not suitable to
M.A. Hattier, J.L. Matson / Research in Autism Spectrum Disorders 6 (2012) 871–880878
base an ASD diagnosis solely on inspection of communication and socialization impairments; therefore, it is essential to also study this third core diagnostic feature of ASDs. Since restricted or repetitive behaviors and interests are not typically among the first noticed in infancy or toddlerhood (Kishore & Basu, 2011), it is important to outline when these behaviors first manifest and in what form. This data will assist in identifying young infants and toddlers with ASDs, which will ultimately allow parents to seek the recommended interventions as soon as possible. Secondly, since social skills, specifically negative ones, possess a strong positive relationship with problem behaviors among adults diagnosed with an ASD (Matson, Fodstad, & Rivet, 2009), future studies could also examine how the interaction of socialization and communication impairments affects challenging behaviors. While challenging behaviors are not a diagnostic feature of ASDs, they are very common (Kozlowski & Matson, 2012); therefore, this type of study would allow for treatment plans to be better modified for the population as a whole and better individualized for each specific child with autism allowing for better outcomes. Lastly, to build upon the findings of this study, researchers should consider the relationship of communication and socialization impairments across the lifespan, which may instruct the scientific community on if this relationship either strengthens or diminishes as these young children age.
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- An examination of the relationship between communication and socialization deficits in children with autism and PDD-NOS
- Method
- Participants
- Measures
- Baby and infant screen for children with autism traits - Part 1
- Battelle developmental inventory, second edition
- Procedure
- Results
- Main analyses
- Discussion
- References
Development and Psychopathology 25 (2013), 1455–1472 # Cambridge University Press 2013 doi:10.1017/S0954579413000710
A quarter century of progress on the early detection and treatment of autism spectrum disorder
GERALDINE DAWSONa AND RAPHAEL BERNIERb aDuke University; and bUniversity of Washington, Seattle
Abstract
The last 25 years have witnessed tremendous changes in our ability to detect autism very early in life and provide interventions that can significantly influence children’s outcomes. It was once questioned whether autism could be recognized before children had developed language and symbolic play skills; now changes in early behaviors, as well as structural brain changes, have been documented in infants 6–12 months of age who later develop autism. Advances in brain imaging and genetics offer the possibility of detecting autism before the syndrome is fully manifest, thereby reducing or preventing symptoms from developing. Whereas the primary mode of behavioral intervention a few decades ago relied on operant conditioning, recent approaches integrate the methods of applied behavioral analysis within a developmental, relationship-focused intervention model that are implemented by both parents and clinicians. These interventions have been found to have positive effects on children’s developmental trajectory, as measured by both behavioral and neurophysiological assessments. Future approaches will likely combine both behavioral and pharmacological treatments for children who have less robust responses to behavioral interventions. There has been a paradigm shift in the way that autism is viewed, evolving from a lifelong condition with a very poor prognosis to one in which significant gains and neuroplasticity is expected, especially when the condition is detected early and appropriate interventions are provided. The grand challenge for the future is to bridge the tremendous gap between research and the implementation of evidence-based practices in the broader community, both in the United States and worldwide. Significant disparities in access to appropriate health care for children with autism exist that urgently require advocacy and more resources.
This 25th Anniversary Special Issue of Development and Psychopathology provides an opportunity to look back at the last quarter century of progress in autism research in the areas of early detection and intervention with the goal of in- forming future directions and priorities. The last two and a half decades have involved significant changes in prevalence, early detection, and intervention methods for autism spec- trum disorder (ASD). In 1989, the prevalence of autism was estimated to be 4 per 10,000 individuals, and 66% of the autism population scored below 70 on standardized IQ tests (Ritvo et al., 1989). In comparison, ASD is currently es- timated to occur in about 1% of children in the United States (1 in 88), with 1 in 54 boys affected. The distribution of in- tellectual disability among individuals with ASD has also changed significantly, with only 38% of individuals with ASD
The authors acknowledge Autism Speaks for its efforts in autism advocacy, science, services, and awareness and the many families and individuals with autism spectrum disorders, whose partnership in research has made the prog- ress of the past 25 years possible. Elizabeth Sturdivant provided helpful edi- torial assistance on this manuscript. This paper is dedicated to Dr. Marian Sigman, mentor, colleague, and friend, whose pioneering efforts in the area of developmental psychopathology transformed our understanding and treatment of autism spectrum disorders.
Address correspondence and reprint requests to: Geraldine Dawson, De- partment of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, NC 27701; E-mail: [email protected].
now classified in the range of intellectual disability (IQ � 70; Centers for Disease Control and Prevention [CDC], 2012). The reported increase in prevalence of ASD has been demonstrated across multiple studies (Cavagnaro, 2009; CDC, 2009, 2012; Hertz-Picciotto & Delwiche, 2009; King & Bearman, 2009; Newschaffer, Falb, & Gurney, 2005). Al- though it is clear that some of the increase in prevalence of ASD is related to improved identification and broadening definitions, a true increase in prevalence cannot be ruled out (Rice et al., 2012). Current research is focusing on a variety of prenatal and early postnatal environmental risk factors that could help explain some of the increase in prevalence. Multiple risk factors, including genetic and environmental factors and their interaction, contribute to risk for autism (Newschaffer et al., 2007).
Regardless of the reasons for the increases in prevalence, it is clear that ASD now represents a major public health chal- lenge. It is estimated that the annual cost of caring for indi- viduals with ASD in the United States is $137 billion, with the lifetime cost per individual estimated to be $2.4 million for those with co-occurring intellectual disability and $1.4 million for those without intellectual disability (Buescher, Ci- dav, Knapp, & Mandell, 2013). These estimates are based on services and supports received, as well as opportunity costs and productivity losses. Given that early detection and early behavioral intervention has been shown to ameliorate the in- tellectual impairment associated with autism, thus leading to
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better long-term outcomes, improvements in the ability to recognize autism early in life and access to effective interven- tions can help reduce the costs of autism and increase quality of life (Peters-Scheffer, Didden, Korzilius, & Matson, 2012). This paper provides a perspective on the considerable prog- ress that has been made over the past quarter of a century in the ability to identify children at risk for autism and the devel- opment of evidence-based early interventions that can lead to improved outcomes. There has been a paradigm shift in the way that autism is viewed, evolving from a lifelong condition with very poor prognosis to one in which significant gains and neuroplasticity is expected, especially when the condi- tion is detected early and appropriate interventions are pro- vided. The field of developmental psychopathology has been a significant contributing factor in this shift in perspec- tive on autism and long-term outcome, particularly in demon- strating the dynamic and developmental nature of autism and the important role of the environmental in shaping develop- mental outcomes.
The Changing Landscape of Early Detection of Autism
Identification of autism in the 1980s
The landscape of early detection of autism has changed con- siderably over the past quarter century. Formative work con- ducted in the 1980s helped to define the core distinguishing early characteristics of autism. This foundational understand- ing set the stage for the systematic examination of autism in infancy, originally through home videotape studies and more recently through studies of high-risk infants, which has led to tools for early screening. Looking ahead, the sci- ence of early detection of autism will increasingly rely on the use of genetics, neuroimaging, and other biomarkers.
There is a clear and significant increase in the ability to diagnose autism at younger ages, with the current national average age of diagnosis for children with autistic disorder estimated to be 3.1 years of age (Mandell, Novak, & Zubritsky, 2005). This shift can be largely traced back to the seminal work conducted in the 1980s examining the early distinguishing characteristics of autism from a developmental psychopathology perspective. Only with the identification of these key early features could we consider how autism may look in early development and therefore develop methods to accurately identify young children with autism. The foun- dational work in identifying these early characteristics high- lighted behaviors that were not necessarily part of the diag- nostic nomenclature but that over the course of early development would result in the social communicative im- pairments that serve as the hallmark diagnostic criteria.
Pioneering work in the 1980s clarified the nature of the impairments in affective reciprocity shown by young children with autism. Although children with autism show similar fre- quency and duration of facial expressions of positive affect overall, they show less positive affect in conjunction with at- tention to others, such as mothers and teachers, or when en-
gaged in interactions (Dawson, Hill, Spencer, Galpert, & Watson, 1990; Kasari, Sigman, Mundy, & Yirmiya, 1990; Yirmiya, Kasari, Sigman, & Mundy, 1989). It is this pattern of intact positive affect in general but reduced facial expres- sions in conjunction with engagement with others that con- tributes to the impairments in affective reciprocity. Dawson and colleagues (1990) coded videotaped observations of nat- uralistic, face-to-face interactions between children with au- tism and their caregivers. Raters unaware of diagnosis status found that the frequency and duration of smiles and positive affect did not differ between children with autism and typi- cally developing peers but that the children with autism were much less likely to combine smiles with eye contact in acts to convey affective reciprocity (Dawson et al., 1990). Further, although the groups did not differ in the fre- quency with which they smiled at social (mother’s verbaliza- tion) and nonsocial (playing with a chair) actions, the chil- dren with autism were much less likely to smile in response to mother’s smile than were the typically developing children. In addition, results indicated that the mothers of the children with autism smiled less frequently overall and in response to their children’s smiles than did the mothers of the typical chil- dren, highlighting a critical, developmental interaction: the behavior of children with autism can influence the behavior of those with whom they interact. Through careful and sys- tematic coding of behaviors and facial expressions using the Maximally Discriminative Movement Coding System, Sigman and colleagues found that children with autism were more neutral in their facial expressions and showed more ambiguous facial expressions relative to typically devel- oping children and children with mental retardation, thereby disrupting the sense of emotional reciprocity (Yirmiya et al., 1989). Further, coding of facial expressions elicited during a semistructured interaction between a child and experimenter in which joint attention and requests are elicited, the Early So- cial-Communication Scales (Mundy, Sigman, Ungerer, & Sherman, 1986) highlighted that when jointly attending to toys or making requests of others, children with autism show significantly less positive affect than typical peers or peers with intellectual disability (Kasari et al., 1990). This early work identified that although the display of affect over- all differs little from comparison children, the affect displayed during interactions with others is significantly impaired in au- tism, highlighting the disruption to affective reciprocity in young children with autism.
The observation that children with autism show marked re- ductions in orienting to social information was another crit- ical finding that helped improve early detection and establish tools for screening young children with autism. Dawson and colleagues documented a failure to orient to social stimuli and introduced the term “social orienting impairment” as a core early feature of autism. A social orienting impairment was documented in preschool age children with autism (Dawson, Meltzoff, Osterling, Rinaldi, & Brown, 1998) and subse- quently noted in videotapes of 10-month-old infants who go on to develop autism as well (Werner, Dawson, Osterling,
Early detection and treatment of autism spectrum disorder 1457
& Dinno, 2000). In the “social orienting task,” a child seated across from an experimenter while playing quietly is pre- sented with a series of auditory stimuli that are either social (e.g., the child’s name being called, clapping hands) or non- social (e.g., car horn honking, kitchen timer). Using this para- digm, Dawson and colleagues (1998) found that children with autism more frequently failed to orient to all stimuli on the social orienting task, with greater impairment for the social stimuli compared to typical peers and children with Down syndrome. Further, those children with autism who oriented to the social stimuli were delayed in doing so relative to the comparison children. Subsequent work examining so- cial orienting in young children with autism has found that impairments on the social orienting task, in conjunction with impairments in joint attention, best discriminate children with autism from their same age typically developing and de- velopmentally delayed peers (Dawson, Toth, et al., 2004). These and other findings led to the introduction of the “social motivation hypothesis” (Dawson, Webb, & McPartland, 2005), which posited that autism is associated with reduced social reward sensitivity that manifests in a failure to affec- tively tag socially relevant stimuli. This failure to attend to so- cial stimuli was further hypothesized to disrupt the neural and behavioral development of a wide range of social and com- municative skills, further compounding the impairments as- sociated with autism (Dawson, 2008; Grelotti, Gauthier, & Schultz, 2002).
Impairments in imitation were also viewed as a fundamen- tal impairment that broadly affected social learning in young children with autism. These imitation impairments were elu- cidated through a series of studies that explored their preva- lence and nature (Dawson & Adams, 1984; Rogers, Bennetto, McEvoy, & Pennington, 1996; Rogers, Hepburn, Stack- house, & Wehner, 2003). A deficit in joint attention, the abil- ity to jointly share a common point of reference or coordinate attention with a social partner, is another distinguishing char- acteristic of children with autism that was influential in shap- ing our understanding of autism and early detection efforts. Mundy, Sigman and colleagues first demonstrated the critical contribution of joint attention deficits to autism by comparing children with autism to typically developing children and children with intellectual disability and observing a much lower frequency of sharing, showing, and pointing despite similar general levels of responsiveness to their caregivers among groups (Mundy et al., 1986). Further, they found these deficits in nonverbal communicative behaviors, such as pointing, better discriminated children with autism from the comparison groups than did other behaviors, such as object play. Finally, this early work highlighted the contributions of joint attention behaviors to subsequent language develop- ment in young children with autism (Mundy, Sigman, Un- gerer, & Sherman, 1987). Research has shown that autism is marked by impairments in initiating joint attention (i.e., spontaneously sharing and directing others’ attention), as op- posed to requesting (Mundy et al., 1986) or responding to joint attention bids (i.e., following others’ gaze and gestures
to share a common point of reference; Mundy, Sigman, & Ka- sari, 1994). Further exploration of these deficits has revealed that these impairments are consistent over time (Mundy, Sig- man, & Kasari, 1990), correlate with subsequent language use (Mundy et al., 1990), and are related to the intensity of subse- quent social symptoms and outcomes (Mundy et al., 1994; Sigman et al., 1999).
Subsequent experimental work examining young chil- dren’s responses to others’ affective cues revealed differences associated with autism. In a series of three experiments, Sig- man and colleagues observed the behavior of children with autism in response to experimenter and parent displays of dif- ferent emotions and compared this to the behavior of children with mental retardation and typical development (Sigman, Kasari, Kwon, & Yirmiya, 1992). In the first experiment, ex- aminers and parents pretended to hurt themselves with a plas- tic hammer during play and then proceeded to display facial and vocal expressions of distress. Overall the 3- to 4-year- old children with autism often failed to notice or ignored the affective displays of the adults, whereas the comparison children were very attentive to the emotional displays, regard- less of the type of affective display. Further, when the adults showed a hurt expression, the children with autism were much more likely to stay engaged with playing with a toy than to attend to the adult in distress. Taken together with findings on social orienting and joint attention, these findings led to a general picture of autism involving a global impair- ment in social attention (Dawson, Bernier, & Ring, 2012; Dawson, Toth, et al., 2004).
Finally, within the last few decades, the notion of a deficit in theory of mind was proposed and has played a key role in the advancement of our understanding of the characteristics of autism in children. By using Wimmer and Perner’s Sally and Anne puppet scenario (Wimmer & Perner, 1983), Baron-Cohen and colleagues demonstrated that, despite cog- nitive ability greater than that of comparison children, chil- dren with ASD failed to make inferences about another’s be- liefs (Baron-Cohen, Leslie, & Frith, 1985). Charman and Baron-Cohen further clarified that this deficit was specific to the imputation of other’s mental states and beliefs and not only a metarepresentation impairment by demonstrating intact performance on false drawing but not false belief tasks (Charman & Baron-Cohen, 1992). These studies identified and clarified the disruption in theory of mind present in chil- dren with ASD and underscored that autism is a disorder of social cognition.
Concurrent to the illumination of the distinguishing char- acteristics of children with ASD, examination of the early manifestations of ASD in infancy was taking place. By col- lecting home videotapes recorded by parents of children who went on to receive an ASD diagnosis, these studies es- tablished a relatively consistent picture of few symptoms ap- parent at 6 months of age followed by a loss of social behav- iors and the emergence of symptoms between 6 and 12 months. By coding behaviors observed on the videotape clips of children who later were diagnosed with ASD and children
1458 G. Dawson and R. Bernier
with typical development while unaware of the child’s diag- nostic status, Osterling and Dawson (1994) found that chil- dren with ASD, even at 1 year of age, showed a failure to ori- ent to their name and demonstrated reduced eye contact, pointing, and showing. Further, by examining these behav- iors in first birthday party videotapes, Dawson and colleagues were able to reliably distinguish children who subsequently received an ASD diagnosis from those who later were diag- nosed with intellectual disability without autism (Osterling, Dawson, & Munson, 2002). Examination of videotapes of in- fants between the ages of 8 and 10 months of age showed that a failure to orient to name and reduced social smiling accu- rately discriminated children with ASD from those with typ- ical development (Werner et al., 2000). The findings from these early studies highlighted the key early identifying fea- tures of autism and underscored the idea that autism can be reliably observed as early as the first year of life. The findings that emerged from home videotapes, summarized by Ozonoff and colleagues (Palomo, Belinchon, & Ozonoff, 2006) were consistent with the first case study of an infant who was fol- lowed prospectively from birth through diagnosis, which was published in 2000 (Dawson, Small, Logan, & Geringer, 2000). The development of this infant was documented in medical records made by a pediatric neurologist who noted that the infant was socially engaged at 6 months but then be- gan to withdraw and show distress reactions between 6 and 12 months. By 13 months of age, this toddler showed many symptoms of autism and eventually received an autism diag- nosis.
The identification of the early emerging distinguishing characteristics of autism, such as deficits in joint attention and affective reciprocity, paved the way for the development of toddler screening tools. The Checklist for Autism in Tod- dlers (CHAT) emerged as an early screening tool for autism, which combined parent reports with clinical observation to examine the presence or absence of these distinguishing au- tism characteristics. Through nine short parent-report yes and no questions and five short yes and no validation items used by the clinician to cross-check the parent report, the CHAT allows a clinician in the community to screen for ASD in 18-month-old children in the typical population. In a study of 91 18-month-old toddlers, 40 of which were younger siblings of children with ASD, Baron-Cohen and colleagues found that 4 of the 91 failed the CHAT, and of the 4 toddlers that failed, all went on to receive a diagnosis of ASD (Baron-Cohen, Allen, & Gillberg, 1992). Results from a population-based study of the CHAT suggested that screening of autism in the population is not only important but also possible through quick assessment of the core behav- iors first reported by seminal work in the 1980s highlighting the social deficits in ASD (Baron-Cohen et al., 2000).
Current approaches to the identification of autism
Building on work conducted in the 1980s and 1990s, screen- ing parameters were developed and implemented and a new
wave of screening tools was introduced in the community. The American Academy of Neurology issued practice param- eters highlighting a two-tiered screening approach in which level 1 consists of routine developmental surveillance at all well-child visits to identify children at risk for atypical devel- opment, followed by identifying those specifically at risk for autism, and Level 2 consists of formal diagnostic procedures by expert evaluators (Filipek et al., 2000). In addition to the recommendation that surveillance occur during all well-child visits, the practice parameters stipulated that further evalu- ation was required whenever a child failed to meet certain milestones (babbling by 12 months, gesturing by 12 months, using single words by 16 months, using spontaneous two- word phrases by 24 months) if there was a loss of language or social skills at any age. The practice parameters high- lighted the importance of screening instruments, such as the CHAT, for any child failing routine developmental surveil- lance. More recently, the American Academy of Pediatrics highlighted that although surveillance, the process of identi- fying children at risk for developmental delay (Council on Children With Disabilities, Section on Developmental Be- havioral Pediatrics, Bright Futures Steering Committee, & Medical Home Initiatives for Children With Special Needs Project Advisory Committee, 2006), should be undertaken in an ongoing manner at every visit, specific screening should take place using an autism screening tool at 18 and 24 months of age regardless of whether any risks have been identified through ongoing surveillance (Johnson, Myers, & American Academy of Pediatrics Council on Children With Disabil- ities, 2007).
There are several screening measures for infants at Level 1 screening that are currently available to meet the American Academy of Pediatrics’ recommendations: modified CHAT (M-CHAT), the Pervasive Developmental Disorders Screen- ing Test and the First Year Inventory and Infant Toddler Checklist. The M-CHAT (Robins, Fein, Barton, & Green, 2001) and Pervasive Developmental Disorders Screening Test (Siegel, 2004) offer Level 1 screening for toddlers, which are parent-report screeners that provide clinicians with key information through quickly completed question- naires. The First Year Inventory (Baranek, Watson, Crais, & Reznick, 2003) increases the lower age boundary for screening through parent report of behaviors in children as young as 12 months old. Level 2 screening measures include the Screening Tool for Autism in Toddlers (Stone, Coonrod, & Ousley, 2000) and the Communication and Symbolic Be- havior Scales Developmental Profile (Wetherby & Prizant, 2002). Both are interactive tools that, in a 20-min play-based interaction, provide the clinician with information regarding the presence of autism along with key targets for intervention. In addition to the short play-based interaction, the Communi- cation and Symbolic Behavior Scales Developmental Profile includes a general developmental screener, the Infant Toddler Checklist, and a follow-up caregiver questionnaire.
At the same time that screening tools have increased in so- phistication, so, too, has our understanding of early defining
Early detection and treatment of autism spectrum disorder 1459
characteristics of autism in young children. Studies of high- risk infants, the younger siblings of children with ASD, have painted a fuller yet more complicated picture of autism in early childhood. The sibling recurrence rate of autism is about 20%, much higher than the general population risk of about 1% (Ozonoff et al., 2011). This makes this population of high-risk siblings fertile ground for examining the early emerging traits of autism. By following younger children from very early on, 20% of whom will go on to develop ASD, greater insight into the developmental course and tra- jectory of autism can be gained. Even the 80% of siblings who do not go on to develop ASD provide valuable contribu- tions to our understanding of the disorder because many share some of the characteristic features of ASD but to a lesser de- gree, termed the broader autism phenotype. In this way, pro- spective studies of at-risk infants provide a mechanism for in- creasing our understanding of etiology and course as well as enhance methods for early detection and indicate avenues for intervention.
Prospective studies of high-risk infants are consistent with the case study that was reported in 2000 showing that, during the earliest months of life, young infant siblings exhibit only subtle differences from low-risk infants, often displaying clear social engagement (Ozonoff et al., 2010; Rogers, 2009; Tager-Flusberg, 2010). However, these prospective studies demonstrate that by 12 months of age the young chil- dren that ultimately develop ASD show notable differences from those that do not. These children begin to show motor delays (Landa & Garrett-Mayer, 2006), demonstrate unusual repetitive behaviors (Iverson & Wozniak, 2007), display atypical visual attention (Ozonoff et al., 2008) and disengag- ing and shifting attention (Zwaigenbaum et al., 2005), as well as display characteristic deficits in social communication, such as reduced social orienting, joint attention skills, eye contact, imitation abilities, and use of gestures (Mitchell et al., 2006; Nadig et al., 2007; Ozonoff et al., 2010; Pres- manes, Walden, Stone, & Yoder, 2007). However, despite these clear differences observed at 1 year of age, there is no one single atypical behavior that differentiates those children who go on to develop ASD, reflecting the complexity of the disorder and highlighting that it is the constellation of these behaviors that indicates increased risk, not any one single be- havioral deficit (Tager-Flusberg, 2010).
Even those young siblings who do not go on to develop ASD show differences from low-risk comparison infants, which suggests that these observed behavioral differences could be phenotypic risk markers for autism, or endopheno- types. Enhanced performance on working memory tasks fo- cused on nonsocial stimuli (Noland, Reznick, Stone, Walden, & Sheridan, 2010), increased latencies to disengage from a central stimulus (Elsabbagh et al., 2009), decreased prefer- ence for infant-directed speech (Nadig et al., 2007), reduced affective facial expressions (Yirmiya et al., 2006), and re- duced smiling (Cassel et al., 2007) have all been observed in high-risk younger siblings relative to low-risk younger sib- lings. Recent work suggests that 19% of high-risk siblings
who do not go on to develop ASD by 3 years of age show the presence of broader autism phenotype traits by 1 year of age (Georgiades et al., 2013).
The development of the Autism Observation Scale for In- fants (AOSI) stemmed from the work on infant siblings. The AOSI was initially designed to identify and monitor early emerging autism signs as observed in high-risk infant siblings of children with ASD (Bryson, Zwaigenbaum, McDermott, Rombough, & Brian, 2008). The goal of its development was to provide developmentally appropriate activities for in- fants so that through 20 min of direct play interaction and cod- ing behaviors in several domains, putative signs of autism can be detected. The interactive approach requires an examiner skilled with both infants and autism to administer a series of presses during the interactive play through which behav- iors in the social, affective, communication, visual, and motor domains can be coded. The contribution of this measure to our understanding is underscored by findings resulting from its use. Longitudinal studies utilizing the AOSI has revealed increased repetitive motor mannerisms in at-risk siblings at 12 and 18 months (Loh et al., 2007), differences in sensory responsivity evidenced at 12 months (Zwaigenbaum et al., 2005), atypical levels of behavioral activity and motor control (Brian et al., 2008), and increased presence of the broader au- tism phenotype in high-risk siblings who do not develop ASD (Georgiades et al., 2013).
The work of the last few decades ultimately paved the way for the development of screening instruments and tools to identify and diagnose autism earlier and earlier. The study of at-risk, younger siblings of children with autism has yielded critical information leading to breakthroughs in our understanding of early symptom emergence and course and development of ASD and has provided insight into etiologi- cal mechanisms. Recent advances in technology and genetics suggest that we are on the crest of a wave of advances in our ability to detect autism through the use of biomarkers and new screening tools.
Looking ahead
The future of the early identification of autism will see in- creased application of neuroimaging and genetics. The iden- tification of biomarkers for autism is a high priority for the scientific community; the National Institutes of Health Inter- agency Autism Coordinating Committee Strategic Plan calls for the identification of biological markers that separately, or in combination with behavioral markers, “accurately identify, before age 2, one or more subtypes of children at risk for de- veloping ASD” (Interagency Autism Coordinating Commit- tee, 2011). Developmental perspectives increasingly incorpo- rate multiple levels of analysis (Cicchetti & Dawson, 2002) as a means of exploring the early indices of autism.
Electrophysiological studies of toddlers and preschoolers with ASD have demonstrated the utility of the study of event-related potentials (ERPs) and electroencephalography (EEG) in elucidating differential brain activity in infants
1460 G. Dawson and R. Bernier
with autism. Because electrophysiological paradigms do not rely on language or behavioral responses beyond passive viewing they are excellent for studying neurophysiological processes in infants. EEG, with its temporal sensitivity, pro- vides insight into aspects of brain activity that functional magnetic resonance imaging studies are unable to illuminate. ERPs, which can be derived from EEG recordings, reflect the averaged brain response to the repeated presentation of a sin- gle stimulus event. Atypical ERPs have been observed in young children with autism in response to the observation of faces and facial expressions (Dawson et al., 2002; Dawson, Webb, et al., 2004; Webb, Dawson, Bernier, & Panagiotides, 2006) as well as to speech sounds (Kuhl, Cof- fey-Corina, Padden, & Dawson, 2005). These findings have been replicated in high-risk infants. In an ERP paradigm in which infants viewed pictures of faces and toys, 10-month- old high-risk infants showed slower responses to faces and faster responses to objects and failed to show the hemispheric specialization that the low-risk infants did (McCleery, Ak- shoomoff, Dobkins, & Carver, 2009). High-risk infants be- tween 6 and 10 months of age who go on to develop ASD also show decreased amplitude of the ERP signal to eye- gaze stimuli relative to high-risk siblings who do not develop ASD and control infants (Elsabbagh et al., 2012). Nine- month-old high-risk infants have also shown reduced habi- tuation to repeated pure tone stimuli and attenuated amplitude responses to deviant auditory stimuli using an ERP paradigm (Guiraud et al., 2011). These downward extensions of the electrophysiological work conducted with children with ASD suggest that atypical neurological functioning in re- sponse to specific social stimuli such as faces and speech sounds, measured using EEG and ERP paradigms, may prove to be useful biomarkers for the early detection of autism.
Although electrophysiological measures provide fine tem- poral resolution and insight into the brain’s activity, magnetic resonance imaging and diffusion tensor imaging, provide in- sight into the brain’s structure, circuitry, and connectivity. By following toddlers from 12 months to 4 years, Courchesne and colleagues examined the hypothesis that there is an ab- normal brain growth trajectory in autism (Schumann et al., 2010). They identified significant differences in brain enlar- gement between 41 children diagnosed with ASD and 44 typically developing peers. By 30 months of age, toddlers who were ultimately diagnosed with ASD showed significantly greater enlargement of cerebral gray and white matter, which was most pronounced in the frontal, temporal and cingulate cortices. Further, in the toddlers with ASD, all gray matter re- gions, except for that in the occipital cortex, showed an abnor- mal growth rate. Early developmental abnormalities to white matter pathways in the brain may also serve to illuminate po- tential biomarkers for autism. In infants who later developed ASD, the development of white matter fiber tracts between 6 and 12 months of age was characterized by increases in frac- tional anisotropy, which indexes axonal diameter, fiber den- sity, and myelination at 6 months; but by 24 months the de- velopment was characterized by significant slowing in
development (Wolff et al., 2012). The observation of atypical development of white matter pathways using diffusion tensor imaging provides further evidence that autism is marked by aberrant neurodevelopmental connectivity very early in life. Taken together, these findings highlight that altered patterns and rates of brain growth could potentially serve as a bio- marker for autism.
The rapid advances in genetics over the past decade have led to significant leaps in our understanding of the etiological heterogeneity of autism. The number of genes believed to confer autism risk has reached far into the 100s with predic- tions of close to 1,000 different genes being implicated in the disorder (O’Roak, Vives, Girirajan et al., 2012; Sanders et al., 2012; State & Sestan, 2012). Multiple studies have confirmed the role of rare and de novo chromosomal structural rearrange- ments and point mutations in increasing autism risk. The collaborations that have developed over the past decade, in- cluding the Autism Genetic Resource Exchange, the Simons Simplex Collection, and the Autism Genome Project, as well as repositories such as NIMH’s Autism Genetics Initiative, have provided valuable resources for geneticists and have radically quickened the pace and advanced efforts toward gene discovery in autism. Further collaborations with increasingly larger samples, in conjunction with increased numbers of identified risk genes, will allow for the quantification of risk in the near future. This will provide medical geneticists and genetic counselors the necessary tools to offer meaning- ful and relevant information to families impacted by autism.
Following the successes of gene discovery, the role of pro- teomics (the study of protein structure and function) in autism has dramatically increased. The identification of specific pro- teins implicated in autism, ranging from synaptic adhesion molecules (NRXN1) to chromatin modifiers (CHD8), has pro- vided insight into the biology of autism whereas the elucida- tion of networks of implicated proteins has suggested that common molecular pathways underlie the phenotypic expres- sion from a multitude of genotypic presentations (Geschwind, 2011; Sakai et al., 2011; Voineagu et al., 2011). Illumination of molecular pathways, such as the highly interconnected beta-catenin/chromatin remodeling protein network revealed through large-scale exome sequencing (O’Roak, Vives, Fu, et al., 2012; O’Roak, Vives, Girirajan, et al., 2012), provides new avenues for understanding the biological pathways in au- tism and identifying risk earlier than before.
The increased understanding of the genetic contributors to autism has led to closer scrutiny of the phenotypic expression of those specific mutations or rearrangements, each of which accounts for no more than 1% of ASD cases. The focus on identifying meaningful phenotypic subtypes that reflect this genetic heterogeneity has become an important research priority (Geschwind, 2011). Atypical physical features (dys- morphologies), such as macro- and microcephaly, have been shown to reflect the divergent genetic etiologies in ASD (O’Roak, Vives, Fu, et al., 2012). Children with autism have significantly greater numbers of major and minor dys- morphologies relative to typically developing control chil-
Early detection and treatment of autism spectrum disorder 1461
dren (Ozgen et al., 2011). Recent work assessing unusual physical characteristics, such as a prominent forehead, asym- metrical face, and hair whorls, suggests that the presence of these three dysmorphologies alone can significantly differ- entiate children with autism from comparison children with typical development (Ozgen, Hellemann, de Jonge, Beemer, & van Engeland, 2013). Therefore, the presence of particular dysmorphologies could serve as a useful biomarker and aid in the detection of ASD.
Early detection through the use of biomarkers has the po- tential to allow us to intervene much earlier than we do cur- rently (Glatt et al., 2012; Kong et al., 2012). Studies employ- ing multiple levels of analysis will likely reveal risk profiles for autism that incorporate genetic, brain structural, physio- logical, and behavioral information (Cicchetti & Dawson, 2002). With earlier detection, families can begin treatments that we know are effective, such as behavior-based therapies, prior to a full syndrome being present so that atypical devel- opmental trajectories can be recalibrated and, ideally, diagno- ses even averted (Dawson, 2008).
The Changing Landscape of Early Autism Intervention
Early autism intervention in the 1980s
With the advent of Skinnerian principles in psychological re- search, as early as the 1960s and 70s, practitioners began using operant conditioning to address impairments associated with autism (Hingtgen, Coulter, & Churchill, 1967; Leff, 1968; Lovaas, Schreibman, & Koegel, 1974; Mazuryk, Barker, & Harasym, 1978) and taught parents to use these methods as well (Berkowitz & Graziano, 1972). With the publication of Lovaas’ 1987 controlled trial of intensive early intervention based in applied behavior analysis (Lovaas, 1987), the notion that autism is a treatable condition that re- sponds to early intervention was embraced by many in the professional and parent community. Particularly compelling was the finding that significant changes in cognitive abilities, as reflected on IQ tests, resulted from early intensive behav- ioral intervention. Furthermore, Lovaas later showed that in- itial gains achieved through early intensive intervention were sustained in later life (McEachin, Smith, & Lovaas, 1993). From the beginning, parents were viewed as important partic- ipants in the intervention, and parent-training methods typi- cally accompanied therapist-delivered treatment programs (Berkowitz & Graziano, 1972). This tenant has continued as part of the most recent approaches to early intervention in autism (Vismara & Rogers, 2010).
Lovaas’s model of intervention used discrete trial training as its primary intervention strategy, a method that involves presentation of a stimulus, a child response, and a conse- quence, followed by repeated trials of those steps. Soon after the Lovaas method was developed, variations began to be cre- ated that attempted to increase children’s motivation and en- gagement in the treatment (Koegel & Mentis, 1985). These
new approaches were based on studies that had explored ways of increasing motivation through stimulus variation (Dunlap & Koegel, 1980), novel prompting strategies (Schreibman, Charlop, & Koegel, 1982), optimal response– reinforcer contingencies (Koegel & Mentis, 1985), and use of child-preferred activities (Koegel, Dyer, & Bell, 1987). Approaches that incorporated these new strategies included natural language teaching paradigms (Koegel, O’Dell, & Koe- gel, 1987) and pivotal response training (Pierce & Schreib- man, 1995; Stahmer, 1995). These approaches incorporated natural rather than artificial reinforcers and emphasized child choice of materials, reinforcement of approximations and communicative attempts, and trials that occurred within the context of a natural exchange. The concept of teaching “pivo- tal” behaviors was introduced; these behaviors are those that impact multiple areas of functioning (Koegel & Koegel, 1988), leading to response covariation, generalization, and improvements in response classes. These modifications were consistent with developmentally oriented behavioral in- terventions which were emerging at about the same time.
The integration of developmental principles into methods of early intervention was motivated by an explosion of new developmental science that elucidated the core develop- mental impairments in autism, such as joint attention and so- cial orienting, as well as a better understanding of the devel- opmental precursors and pathways that led to many of the core autism symptoms, such as impairments in imaginary and symbolic play. Several core developmental principles be- gan to influence treatment approaches with young children with autism. First, the importance of prelinguistic develop- ment in the form of communicative babbling, imitation, toy play, and joint attention for setting the stage for language de- velopment was underscored by several studies of both infants and toddlers with typical and atypical development (Love- land & Landry, 1986; Mundy et al., 1990; Tomasello & Far- rar, 1986; Toth, Munson, Meltzoff, & Dawson, 2006), lead- ing to increased emphasis on teaching these skills as a way of promoting language development. In addition, these stud- ies suggested that language should be promoted within the context of joint activities involving shared participation and control between the therapist and child that allow for oppor- tunities to promote skills such as imitation, shared toy play, and joint attention.
Second, research findings revealed that infants are active participants and constant hypothesis testers who are involved in cocreating their learning experiences (Baldwin, 1991; Meltzoff, Kuhl, Movellan, & Sejnowski, 2009), which em- phasized the need to provide opportunities for children with autism to initiate and explore within the therapeutic context rather than be passive recipients of antecedent requests and prompts and reinforcers. Research suggested that infants are capable of detecting statistical patterns and rely on statistical learning to detect such patterns and make predictions (Saf- fran, Aslin, & Newport, 1996). Thus, interventions began to incorporate strategies that helped direct children’s attention to relevant stimuli, making key information such as language,
1462 G. Dawson and R. Bernier
faces, and gestures more salient to the child. Third, develop- mental research shed light on the important role of affective engagement between the child and his or her social partner in the promotion of learning, including language, social, cog- nitive, and perceptual development. Studies demonstrated that learning is facilitated when it occurs within the context of an affectively rich social relationship (Kuhl, 2007; Kuhl, Tsao, & Liu, 2003). In light of studies that demonstrated that children with autism show deficits in affective sharing (Dawson et al., 1990) and have reduced sensitivity to the re- ward value of social information (Dawson, Bernier, et al., 2012), developmental interventions began to incorporate in- tervention strategies that promote affective engagement and increased social motivation on the part of the child with its so- cial partner (Rogers & Pennington, 1991). Concurrent studies that demonstrated reduced neural responses to social and af- fective stimuli by young children with autism further sup- ported the need to focus directly on social engagement as part of the intervention program (Dawson et al., 2002; Dawson, Webb, et al., 2004).
Current approaches to autism intervention
The evidence base for the efficacy of interventions based on the principles of applied behavioral analysis (ABA) has con- tinued to grow, such that treatments based on ABA principles are now widely acknowledged as those with the most empir- ical support (National Research Council, 2001). One review of 13 studies including a total of 373 children with ASD in- dicated significant gains in cognitive ability following ABA treatment using the Lovaas model (Reichow & Wolery, 2009). Since Lovaas’ 1987 report, many studies have replica- ted findings of efficacy (Cohen, Amerine-Dickens, & Smith, 2006; Howard, Sparkman, Cohen, Green, & Stanislaw, 2005; Sallows & Graupner, 2005), demonstrated long-standing ef- fects (McEachin et al., 1993) and shown that treatment inten- sity alone does not account for the gains (Eikeseth, Smith, Jahr, & Eldevik, 2002; Howard et al., 2005). Further effec- tiveness studies of ABA-based approaches to treat communi- cation deficits, social skills impairments, and problematic be- havior have yielded positive support (Cohen et al., 2006; Horner, Carr, Strain, Todd, & Reed, 2002; McConnell, 2002; Sallows & Graupner, 2005). In the multiple systematic reviews or meta-analyses of early comprehensive behavioral- ly based interventions, the consistent conclusion is that be- haviorally based interventions resulted in gains in cognition, language skills, and adaptive behaviors for children with au- tism and showed some evidence of sustained benefit (see Ta- ble 1 and Table 2). This strong scientific support for ABA- based intervention as an effective treatment for ASD has prompted significant policy changes, including substantial insurance reform. State insurance programs and many private agencies have revised policies to provide insurance coverage for ABA-based treatment approaches for ASD.
Although traditional ABA-intervention models based on discrete trial training remain in wide use today, several natu-
ralistic, developmental models have been created to incorpo- rate ABA-based principles into a developmental framework, taking into account the significant gains in knowledge about infant learning and developmental trajectories that have occurred over the past two decades.
One such developmental intervention, the Social Commu- nication, Emotional Regulation, and Transactional Support model, incorporates a developmental framework to address the specific learning styles of children with ASD (Prizant, Wetherby, Rubin, & Laurent, 2003). According to the model, the developmental dimensions of social communication, emotional regulation, and transactional support must be ad- dressed within a comprehensive treatment approach. The treatment priority goals therefore fall into these three primary domains. For example, in the social communication domain, the focus is on enhancing joint attention skills and increasing symbolic behavior, such as spontaneously communicating. Another prioritized treatment goal is in the ability to regulate emotional arousal to support learning and engagement with others. The third priority domain concerns the incorporation of transactional supports, such as the use of environmental modifications, support for family members, and supporting developing relationships. The Social Communication, Emo- tional Regulation, and Transactional Support model proposes a developmental framework to address the core deficits in ASD through the integration of an individual’s strengths and weaknesses into evidence-based treatment planning (Pri- zant, Wetherby, Rubin, Laurent, & Rydell, 2005).
Another model, joint attention training, has also emerged as a developmental approach in the treatment of ASD. As demonstrated through pioneering work into the early charac- teristics of autism, joint attention is impaired in ASD (Sigman, Mundy, Sherman, & Ungerer, 1986). The ability to share at- tention with others about a common event or object is a prelin- guistic skill that affects later language development because it affords a child an opportunity to share attention in a social in- teraction, thereby facilitating socially acquired skills such as language (Adamson, Bakeman, & Deckner, 2004). Because the development of speech prior to age 5 has been proposed to be a strong indicator of later positive outcomes in children with ASD (Billstedt, Gillberg, & Gillberg, 2005; Venter, Lord, & Schopler, 1992), by focusing on joint attention, a de- velopmental precursor to language, Kasari and colleagues (Kasari, Freeman, & Paparella, 2006) proposed to improve language and overall outcomes in ASD. In a randomized con- trolled trial in which interventionists provided joint attention skills training for 30 min each day over 6 weeks to 3- to 4- year-old children with ASD, the children undergoing the inter- vention demonstrated improved joint attention skills and greater spoken language compared to the control group. At follow-up five years later, the children who began intervention earlier and those showing improved joint attention skills and higher levels of play exhibited wider use of spoken vocabulary (Kasari, Gulsrud, Freeman, Paparella, & Hellemann, 2012). Moreover, following the 6-week administration of this joint at- tention intervention in a preschool setting by trained public
Early detection and treatment of autism spectrum disorder 1463
Table 1. Reviews of evidence of efficacy of early intensive behavioral intervention
Authors Publication Reviewed Conclusion
Reichow et al., 2012 Cochrane Library 1 RCT and 4 CCTs Only studies of the Lovaas method that used treatment as the usual comparison group are included. Some evidence that early intensive behavioral intervention is effective for some children with ASD.
Kuppens & Research in Autism Spectrum Sequential meta-analysis Sufficient cumulative knowledge to Onghena, 2012 Disorders of 14 studies draw convincing statistical
conclusions favoring a treatment benefit for intellectual, language, and adaptive behavior
Reichow, 2012 Journal of Autism and Overview of 5 meta- Four of five meta-analyses concluded Developmental Disorders analyses that EIBI was effective.
Dawson & Burner, Current Opinion in Pediatrics 34 studies The EIBI RCT for toddlers with ASD 2011 demonstrated gains in language,
cognitive abilities, and adaptive behavior. Targeted, brief behavioral interventions are efficacious for improving social communication. Several studies show that social skills interventions are efficacious for improving peer relationships and social competence.
Peters-Scheffer Research in Autism Spectrum Meta analysis, 11 studies EIBI resulted in large and clinically et al., 2011 Disorders significant effect sizes compared to
other treatments on cognitive ability, receptive language, expressive language, and significant improvements on adaptive skills. EIBI outperformed other groups.
Warren et al., 2011 Pediatrics 34 studies Studies of Lovaas-based approaches and early intensive behavioral intervention variants and the ESDM resulted in some improvements in cognitive performance, language skills, and adaptive behavior, although the literature is limited by methodological concerns.
Young et al., 2010 IMPAQ Final Report on 271 publications Fifteen behavioral interventions were Environmental Scan found to be evidence based,
including EIBI. Eldevik et al., 2010 American Journal on Intellectual 16 studies, 11 with An individual data meta-analysis
and Developmental Disabilities comparison showed intensive behavioral intervention had the greatest improvements in IQ and adaptive behaviors. The authors conclude that intensive behavioral intervention is an evidence-based intervention for children with autism.
Makrygianni & Research in Autism Spectrum Meta-analysis, 14 studies Behavioral intervention programs were Reed, 2010 Disorders effective in improving intellectual
and language abilities and adaptive behavior. The effects are most evident through a meta-analytic approach.
Vismara & Rogers, Annual Review of Clinical Review of behavioral ABA is an educational–behavioral 2010 Psychology interventions intervention for children that has
generated the most extensive research and has been identified as the treatment of choice to address learning deficits.
1464 G. Dawson and R. Bernier
Table 1 (cont.)
Authors Publication Reviewed Conclusion
Virues-Ortega, 2010 Clinical Psych Review Meta-analysis of 26 trials The results suggested that long-term, of EIBI comprehensive ABA intervention
leads to positive medium to large effects in intellectual functioning, language development, acquisition of daily living skills, and social functioning in children with autism.
Eikeseth, 2009 Research in Developmental 25 studies, systematic Evidence from several high quality Disabilities review studies demonstrated that children
receiving ABA made significantly more gains than control group children on standardized measures of IQ, language, and adaptive functioning.
Eldevik et al., 2009 Journal of Clinical Child and 9 controlled studies A meta-analysis of 9 studies revealed a Adolescent Psychology large effect on IQ after EIBI and
medium to large improvement in adaptive behavior.
Granpeesheh, Annals of Clinical Psychiatry Review of behavioral ABA treatment programs for Tarbox & Dixon, interventions individuals with autism are 2009 supported by a significant amount of
scientific evidence and are recommended for use.
Howlin, Magiati, & American Journal on Intellectual 11 studies This review provides evidence for the Charman, 2009 and Developmental Disabilities effectiveness of EIBI for some, but
not all, preschool children with autism.
National Autism http://www.nationalautismcenter. 5,978 articles Eleven specific behavioral treatments Center, 2009 org/pdf/NAC%20Standards% were established as effective,
20Report.pdf including EIBI. Reichow & Wolery, Journal of Autism and 14 samples, 13 research The findings suggest that EIBI is an
2009 Developmental Disorders reports effective treatment, on average, for children with autism.
Spreckley & Boyd, Journal of Pediatrics 13 studies More research is needed to establish 2009 that EIBI has better outcomes than
standard care for children with autism.
Seida et al., 2009 Developmental Medicine and 30 high quality studies There were positive findings on Child Neurology reviewed intellectual abilities, communication,
and problem behavior following behavioral interventions. Psychosocial interventions were reviewed positively.
Technology BlueCross, BlueShield report 16 studies The authors felt that more research was Evaluation needed with larger sample sizes and Center, 2009 longer follow-up.
Ospina et al., 2008 PLoS ONE 101 studies, 55 RCTs Evidence was found for positive outcomes in intellectual abilities, language abilities, and adaptive behaviors for Lovaas, TEACCH. Lovaas seemed to be superior to special education.
Rogers & Vismara, Journal of Clinical Child and Studies published since Lovaas treatment met APA criteria for 2008 Adolescent Psychology 1998 “well established.”
Note: RCT, Randomized controlled trial; CCT, clinical controlled trial; ASD, autism spectrum disorder; EIBI, Early Intensive Behavioral Intervention; ABA, applied behavioral analysis; ESDM, Early Start Denver Model; TEACCH, Treatment and Education of Autistic and Related Communication Handicapped Children.
Early detection and treatment of autism spectrum disorder 1465
Table 2. Studies on long-term outcomes following early intensive behavioral intervention
Authors Publication Participants Follow-Up Conclusion
Kovshoff, Hastings, & Remington, 2011
Behavior Modification 41 2 years after 24–month
intervention
There is a slight difference between university and parent mediated EIBI. However, overall, EIBI was associated with greater likelihood of mainstream placement. There was evidence that the delivery model, higher program intensity, and higher initial skill set affected the outcome in EIBI.
Magiati et al., 2011 Research in Autism Spectrum Disorders
36 6–7 years Follow-up was from 2007 study to 7 years of age. Most children were in specialist provision. Expressive and receptive language skills increased. Initial IQ, adaptive behavior, and language skills predicted long-term outcomes.
Magiati, Charman, & Howlin, 2007
Sallows & Graupner, 2005
Harris & Handleman, 2000
Smith, Groen, & Wynn, 2000
Journal of Child Psychology and Psychiatry
American Journal on Mental Retardation
Journal of Autism and Developmental Disorders
American Journal on Mental Retardation
44
23
27
28
2 years
4 years
4–6 years
4–5 years
Not a RCT, follow-up was based on choice of intervention. Home-based EIBI in a community setting resulted in slightly higher adaptive skills scores compared to a nursery provision. There were large individual differences. Both groups showed improvements over time, with few group differences.
There were significant gains in IQ, receptive language, and adaptive skills for the EIBI group. Outcomes were improved for “rapid learners” compared to “moderate learners.”
Children enrolled in EIBI before 48 months of age were more likely (11/ 27) to be placed in a mainstream classroom than kids who began after 48 months. Higher IQ at intake also associated with IQ gains and school placement.
The intensive treatment group outperformed parent training on IQ measures; There were more IT participants in regular education. They also outperformed on language abilities, but there were no differences for adaptive behavior. Intake and follow-up were not related.
McEachin et al., 1993 American Journal on Mental Retardation
38 10 years The EIBI group showed higher IQ than control and more likely to be placed in regular classes. They also showed improvement in adaptive functioning and had fewer maladaptive behaviors. Many children in EIBI were “indistinguishable” from nonaffected individuals.
Note: EIBI, Early Intensive Behavioral Intervention; RCT, randomized controlled trial.
school teachers, the preschoolers with ASD undergoing the focusing on the improvement of developmental precursors training used more joint attention skills compared to control of language to improve outcomes in children with ASD. preschoolers who did not (Lawton & Kasari, 2012). The find- A comprehensive developmental intervention model ap- ings of these intervention trials demonstrate the utility of propriate for children as young as 12 months of age is the
1466 G. Dawson and R. Bernier
Early Start Denver Model (ESDM). ESDM is a downward extension of the Denver Model (Rogers et al., 2006; Rogers & Lewis, 1989) such that it meets the needs of infants and toddlers. Results of studies testing the Denver Model using pre–post test study designs demonstrated significant language and social emotional developmental gains in children with ASD undergoing the treatment (Rogers & DiLalla, 1991; Rogers, Herbison, Lewis, Pantone, & Reis, 1986; Rogers & Lewis, 1989; Rogers, Lewis & Reis, 1987). In ESDM, ASD treatment is conceptualized from a multidisciplinary ap- proach encompassing all aspects of development with a spe- cific focus on social reciprocity, affective engagement, social attention and motivation. A randomized, controlled trial of ESDM, in which 48 toddlers with ASD were randomized ei- ther to 2 years of ESDM intervention or to treatment as usual in the community, showed significantly greater gains in cognitive, language, social, and adaptive behavior for the children who received the ESDM intervention (Dawson, Jones, et al., 2012; Dawson et al., 2010). Although at baseline the groups did not differ on cognitive ability, ASD severity, gender or socioeconomic status, following treatment the ESDM group showed an average gain of 17.6 points in overall cognitive abilities relative to 7 points in the community inter- vention group. Further, although adaptive skills in communi- cation, daily living, and motor ability remained stable in the ESDM group, there was a decline in the community interven- tion group, providing further support for the efficacy of ESDM.
The impact that behaviorally based interventions have had on cognitive and adaptive functioning has prompted the ex- amination of the effect of the intervention on brain activity, especially those neural systems that support social process- ing. Animal studies suggest that early enrichment has a sig- nificant impact on brain structure and activity. Changes in the weight and thickness of the cortex (Diamond, Rosen- zweig, Bennett, Lindner, & Lyon, 1972), increases in neuro- transmitter receptor density (Bredy, Humpartzoomian, Cain, & Meaney, 2003), increases in synapse number and density (Kleim, Lussnig, Schwarz, Comery, & Greenough, 1996), and diminished effects of early injury or genetic risk (Nithia- nantharajah & Hannan, 2006) have been noted in mice, rat, and nonhuman primate studies. These findings suggest the utility of behaviorally based interventions in altering the course of both behavioral and brain development. By inte- grating biological measures into the design and evaluation of the ESDM model (Cicchetti & Gunnar, 2008), the impact of early intervention on brain activity could be assessed. Fol- lowing 2 years of intervention using ESDM, children un- dergoing treatment showed normalized spectral power in the alpha and theta ranges using an EEG paradigm comparing faces and houses. Control children who received a range of community interventions during the same interval failed to show that normalization. Further, in this study, increased cor- tical activation during the viewing of faces correlated with improved social communication outcomes, underscoring the contribution of this treatment approach to changes in brain
activity (Dawson, Jones, et al., 2012). A second EEG study ex- amining the effects of adult intervention on brain activity sug- gests that neural plasticity in response to intervention may exist throughout the life span. This study assessed the impact of face training on ERP responses to faces in adults with ASD (Faja et al., 2012). Specifically, adults with ASD were randomly as- signed to a computerized training program focusing on either faces or houses. Participants were tested pre- and posttraining in behavioral measures of face and house recognition as well as on electrophysiological indices of face processing. Follow- ing training, participants demonstrated behavioral expertise with the specific stimuli to which they were randomly assigned, but only the group with face training showed more normalized behavioral and electrophysiological responses to faces. Taken together, these two studies provide strong support that behavio- rally based treatments are associated not only with behavioral improvement, but can also normalize some aspects of brain ac- tivity. Furthermore, they provide evidence of neural plasticity throughout the life span in individuals with ASD.
Looking ahead
The future of intervention research holds promise for improv- ing the lives of individuals with autism. Although a quarter of a century ago the conversation about autism treatment was in its infancy, through advances in our understanding of early detection and behaviorally based treatments, the possibility of intervening prior to the development of ASD symptoms is now within reach. The evidence that behavioral interven- tions are effective at changing both behavior and brain func- tioning suggests that intervening prior to the emergence of behavioral symptoms, which only hint at the underlying atypical brain development already underway, will allow for significant neural plasticity and adaptation and the possi- bility of avoiding or ameliorating the presentation of ASD symptoms altogether (Cicchetti & Gunnar, 2008; Dawson, 2008). A recent study found that a minority of children with autism lose their autism symptoms altogether and dem- onstrate an overall level of function within normal limits (Fein et al., 2013). It is clear that parents play a central role in pro- viding intervention, especially when those interventions are being provided to infants and toddlers. Parents spend more time with their children than any therapist can and thus can be the most effective therapists. In this way, every interaction becomes a learning opportunity for the child. By training par- ents how to intervene with their infants and toddlers who are at risk of developing autism, such as younger siblings of chil- dren with autism or young children presenting with early warning signs, infants and toddlers can be directed to a more typical developmental trajectory.
Another primary focus of future autism intervention re- search will include an understanding of the variability in re- sponse to intervention. Although all children with ASD ben- efit from early intervention, some children make extremely rapid progress whereas progress for others is slower. Im- proved understanding of the mediating and moderating fac-
Early detection and treatment of autism spectrum disorder 1467
tors will allow for a more comprehensive understanding of the mechanisms of change and how best to intervene with each individual. Given that autism is a group of disorders with wide heterogeneity in terms of etiology, course, response to treatment, and outcome, advances in understanding the biological processes underlying the heterogeneity of the disorder and their interaction with treatment and prevention approaches will allow for more targeted interventions that are specific and appropriate for differing individuals with autism.
In order to understand the biological mechanisms at play in autism and evaluate the effectiveness of interventions, identifying meaningful biomarkers is a necessary step for the field. Neurophysiological indicators such as cortical acti- vation in response to viewing faces (Dawson, Bernier, et al., 2012) or functional brain responses to rewarding stimuli (Scott-Van Zeeland, Dapretto, Ghahremani, Poldrack, & Bookheimer, 2010) provide insight both into the biological mechanisms underlying autism but may also serve as avenues for identifying what tailored approaches are needed for each individual. Further, the assessment of neurophysiological change following onset of an intervention can serve as an in- formation-rich index of the anticipated behavioral change.
Finally, the future of autism intervention likely includes greater understanding of the efficacy and contribution of bio- medical treatments. Only two drugs have been approved by the FDA for the treatment of ASD, and those treat associated symptoms of irritability rather than core autism symptoms (McPheeters et al., 2011). There are few studies demonstrat- ing clear support for the efficacy of any of the full array of biomedical interventions that have been proposed to treat au- tism and that are currently in use (Warren et al., 2011). Initial clinical trials evaluating the efficacy of biomedical treatments that address the core social impairments of autism, such as ar- baclofen (Gurkan & Hagerman, 2012) and oxytocin (Domes et al., 2013), have yielded encouraging results. The next sev- eral years of ASD intervention research likely will lead to the discovery of a number of novel biomedical treatments that will address core autism symptoms (Farmer, Thurm, & Grant, 2013). These can be used in combination with behavioral in- terventions to enhance social motivation (Dawson, Bernier, et al., 2012) and neural plasticity (Smith & Ehlers, 2012), per- haps allowing higher rates of improvement in those children whose response to behavioral intervention alone has not been robust. By tailoring treatment approaches to meet the specific biological and behavioral needs of individuals presenting with specific autism subtypes and using well-defined bio- markers to examine and assess response to treatment, the ul- timate hope is that the lives of all individuals impacted by ASD will be markedly improved (Hammock et al., 2012).
The grand challenge we face: Dissemination and implementation of evidence-based practices
At the same time that we applaud the significant advances that have taken place in the development of methods for earlier
detection and intervention for children with ASD, we are sorely aware of the significant challenge in disseminating and implementing such interventions within community set- tings, especially in those settings that have low resources. Even though it is possible to reliably diagnose autism by 18 to 24 months of age (Johnson et al., 2007) and despite the availability of evidence-based, efficacious early interven- tions, diagnosis lags behind in many cases (CDC, 2012; Shat- tuck et al., 2009). The CDC reported that the average age at diagnosis for autism in the United States is approximately 48 months and is 53 and 75 months for ASD/pervasive devel- opmental disorder and Asperger disorder, respectively (CDC, 2012). Without a diagnosis, children are not able to access the early interventions in a timely manner.
Several factors have been found to be associated with a de- lay in autism diagnosis in the United States, including lower socioeconomic status and racial/ethnic minority background (Mandell et al., 2009). Several studies have documented the lower prevalence of ASD among African American and Latino children (Boyle et al., 2011; CDC, 2012; Jarquin, Wiggins, Schieve, & Van Naarden-Braun, 2011; Kogan et al., 2008).
Many children with autism do not have access to high- quality, intensive, early behavioral interventions. Only half of the states in the United States mandate insurance coverage for behavioral health interventions for children with autism, and thus parents often must pay out of pocket for intervention. Research has shown that mothers of children with autism earn 35% less than mothers of children with other chronic health conditions, and family earnings are 28% lower (Cidav, Mar- cus, & Mandell, 2012). Parents of children with autism report higher levels of unmet health care needs and family support and difficulty receiving appropriate referral information, as compared to parents of children with other chronic health conditions (Kogan et al., 2008).
A global perspective only accentuates the scale of the chal- lenge we face in disseminating and implementing evidence- based practices across the globe, especially in low-resource countries (Patel, Kieling, Maulik, & Divan, 2013). There is an urgent need to scale up services for developmental disorders both in the United States and abroad (Lancet Global Mental Health et al., 2007). Two strategies for scaling up services in remote and low-resource communities have received recent at- tention. First, clinical services that can be delivered by persons who are not trained professionals, including both parents and paraprofessionals, will allow communities greater access to screening and some forms of treatment (Lancet Global Mental Health et al., 2007). Second, the use of eLearning and tele- health programs that can provide both professionals and par- ents training from remote locations promises to expand access to expertise and support (Szeftel, Federico, Hakak, Szeftel, & Jacobson, 2012; Vismara, Young, & Rogers, 2012). In order for such strategies to be effective, sustained collaboration and dedication of a variety of stakeholders, including government, professionals, parents, donors, and nongovernment organiza- tions, will be necessary (Wallace et al., 2012).
1468 G. Dawson and R. Bernier
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- A quarter century of progress on the early detection and treatment of autism spectrum disorder
- Abstract
- The Changing Landscape of Early Detection of Autism
- Identification of autism in the 1980s
- Current approaches to the identification of autism
- Looking ahead
- The Changing Landscape of Early Autism Intervention
- Early autism intervention in the 1980s
- Current approaches to autism intervention
- Looking ahead
- The grand challenge we face: Dissemination and implementation of evidence-based practices
- References