RECURRENCE RATE AND PHENOTYPICAL SIMILARITIES IN SIBLINGS DIAGNOSED
WITH AUTISM SPECTRUM DISORDERS by
ALEXANDRA MARIE VOHS B.S. Mississippi College, 2014
A thesis submitted to the University of Colorado in partial fulfillment of the requirements for the degree of Doctor of Psychology School Psychology Program
This thesis for the Doctor of Psychology degree by Alexandra Marie Vohs has been approved for the School Psychology Program by
M. Franci Crepeau-Hobson, Chair Bryn Harris Rachel Stein
Date: May 18, 2019
Vohs, Alexandra Marie (PsyD, School Psychology Program)
Recurrence Rate and Phenotypical Similarities in Siblings with Autism Spectrum Disorders Thesis directed by Professor M. Franci Crepeau-Hobson
Autism Spectrum Disorder (ASD) is the fastest growing neurodevelopmental disorder, with recent surveillance efforts indicating that 1 in 59 American children meet criteria for ASD (Baio et al., 2018). Studies of twins and siblings with autism have revealed an increased risk for autism, representing a 60-100% higher risk when a child in the family has already been diagnosed with the disorder (Rutter, Silber, Oâ€™Connor, & Simonoff, 1999). Recurrence rates of autism in siblings, however, have thus far have been remarkably inconsistent, ranging from 3% to 18.7% (Ozonoff et al., 2011). This study examined the recurrence rate of sibling pairs diagnosed with ASD in a community clinic where families sought services for their children.
This study also sought to examine the factors that contribute to sibling diagnosis of ASD by examining the phenotypical similarities of the sibling pairs. Via a retrospective review of over two thousand records, 37 sibling sets in which both or all siblings were diagnosed with ASD. Results indicate the recurrence rate of ASD within this sample was 47%. A significant difference was found between the recurrence rate in this study and the 18.7% recurrence rate reported by Ozonoff et al. (2011) in their prospective study. The severity of autism did not predict the order in which siblings were diagnosed and there were no significant phenotypical characteristics that contribute to sibling diagnosis of ASD. These findings suggested increased education and services is likely warranted to promote timely screening, diagnosis, and treatment among siblings of children with ASD.
The form and content of this abstract are approved. I recommend its publication.
Approved: M. Franci Crepeau-Hobson
TABLE OF CONTENTS
I. INTRODUCTION 1
II. LITERATURE REVIEW 4
III. METHODS 11
IV. RESULTS 15
V. DISCUSSION 19
CHAPTER I INTRODUCTION
In the 1940s, Kanner (1943) and Asperger (1944) separately described small samples of children with severe developmental disorders. Kanner defined the symptoms in eleven children as â€œextreme autistic aloneness, abnormal speech with echolalia, pronominal reversal, literalness, and inability to use language; and monotonous repetitive behaviors with an anxiously obsessive desire for the maintenance of samenessâ€ (Wolff, 2004, p. 203). Asperger described four children who had cognitive gifts in mathematics or science, but struggled with social and emotional functioning; specifically, they lacked social reciprocity, had stereotypic behaviors, and exhibited restricted interests (Wolff, 2004). Kanner and Aspergerâ€™s early definitions of Autism Spectrum Disorders (ASD) have not changed much over time, as ASD is now defined as a neurodevelopmental disorder that effects the domains of social interaction, communication, and repetitive or stereotypic behavior.
Factors causing ASD have been hypothesized since its discovery, with attempts to link ASD to numerous childhood disorders and experiences, such as childhood schizophrenia, highly intelligent parents, â€œrefrigerator parentsâ€, and children with brain damage or intellectual disabilities (Wolff, 2004). However, several studies failed to find evidence to support a relationship between child-rearing practices and broad personality attributes and the development of ASD (Bailey, Palferman, Heavey, & LeCouteur, 1998). Folstien and Rutterâ€™s 1977 study of twins provided the first evidence of a genetic basis for autism. If one twin had the disorder, the other was far more likely to have it if he or she was identical rather than fraternal, since identical twins share their DNA while fraternal twins share only half their genes. A disease that tends to co-occurs more frequently in identical twins indicates genetic influence (Folstien & Rutter,
1977). Once the significance of the genetic liability to autism was recognized, the possibility that certain familial characteristics might represent milder phenotypic expression attracted increasing attention (Bailey et al., 1998). Although the concordance rate for identical twins is high, ranging from 60% to 90%, it is less than 100% which indicates that environmental factors also influence the expression of ASD (Kroncke, Willard, & Huckabee, 2016). The variability in the traits associated with conditions on the spectrum suggests that there are multiple genes and environmental factors involved in the expression of ASD.
Research has found some early genetic makers that could be implicated in ASD (Kroncke et al., 2016); however, the genetic code for the disorder has yet to be identified and this effect is further confounded by the fact that children with similar genes do no express autism symptoms at the same rate or intensity. Continued research on the genetic-environmental interaction is needed to better advise families of the risk of ASD in their children and to minimize and treat symptoms that arise.
Problem and Significance
Research has shown that early identification and intervention mitigates ASD severity (Kroncke et al., 2016). This study aims to explore the relationship between recurrence rate of sibling pairs diagnosed with ASD in a community clinic where families sought services for their children. It was hypothesized that the incidence of sibling pairs diagnosed in such a clinic would be more than the 18.7% recurrence rate found in a prospective study of recruited children because the current study sampled siblings whose parents self-selected their children for evaluation. In addition, this study aims to identify the factors that contribute to sibling diagnosis of ASD by examining the phenotypical similarities of the sibling pairs. It was hypothesized that the time between sibling diagnoses would be less if the phenotypic characteristics of the siblings
are similar. It was also hypothesized that parents would be more likely to seek an evaluation for their second child if the severity of ASD in the first diagnosed child was less severe. With improved clarity, resources, systems, and/or communication that fosters earlier diagnoses may be identified to allow for more timely intervention for siblings with ASD and support their families. The present study is guided by the following research questions:
1. What is the recurrence rate of Autism Spectrum Disorder (ASD) in sibling pairs evaluated at a community clinic where families sought services for their children?
2. What is the relationship of severity of autism in siblings to the order in which they are diagnosed?
3. Is there an association between the amount of time between evaluations and phenotypic characteristics for siblings diagnosed with ASD?
CHAPTER II LITERATURE REVIEW
Autism Spectrum Disorder
Individuals with Autism Spectrum Disorder (ASD) struggle primarily with social communication and social reciprocity, as well as restricted and repetitive behaviors. The diagnostic criteria for ASD as published in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Editionâ€™s (DSM-5; American Psychological Association [APA], 2013) are divided into two factors: criterion A associated with social and communication domains and criterion B pertaining to restricted or repetitive behavior domain. Criterion A states a person must exhibit â€œpersistent deficits in social communication and social interaction across multiple contexts, as manifested by the followingâ€: (1) â€œDeficits in social-emotional reciprocityâ€, (2) â€œDeficits in nonverbal communicative behaviors used for social interactionâ€, (3) â€œDeficits in developing, maintaining, and understanding relationshipsâ€ (APA, 2013, p. 50). Criterion B includes â€œrestricted, repetitive patterns of behavior, interests, or activities, as manifested by at least two of the followingâ€: â€œStereotyped or respective motor movements, use of objects or speechâ€, (2) â€œInsistence on sameness, inflexible adherence to routines, or ritualized patterns of verbal or nonverbal behaviorâ€, (3) â€œHighly restricted, fixed interests that are abnormal in intensity or focusâ€, and (4) â€œHyper- or hypo-reactivity to sensory input or unusual interest in sensory aspects of the environmentâ€ (APA, 2013, p. 50). The DSM-5 also states that symptoms must be present in early development, though they may not be obvious until social demands increase (APA, 2013).
Additional specification regarding severity in both criteria A and B is required for a diagnosis of ASD. Social communication and restricted or repetitive patterns of behavior can be
qualified using the severity rating of Level 1, 2, or 3. Level 1 represents â€œRequiring supportâ€, Level 2 represents â€œRequiring substantial supportâ€, and Level 3 represents â€œRequiring very substantial support.â€ The DSM-5 also encourages assessing and describing both language and cognitive skills and functioning in a thorough manner (APA, 2013). Clinically, it is best practice to provide a specific statement in the diagnosis, for example â€œwithout cognitive impairment, with language impairmentâ€ (Kroncke Willard, & Huckabee, 2016, p. 40).
Prevalence of Autism Spectrum Disorder
Autism Spectrum Disorder (ASD) is the fastest growing neurodevelopmental disorder, with recent surveillance efforts indicating that 1 in 59 American children meet criteria for ASD (Baio et al., 2018). The global estimates for ASD indicate that rates have increased 20- to 30-fold since the 1970s (Centers for Disease Control; CDC, 2012). Rates of autism grew from 1 in 500 by 1995; to 1 in 150 by 2002; then to 1 in 110 by 2006; then to 1 in 88 by 2008 (CDC, 2012).
The Centers for Disease Control reports that â€œComparison of the 2008 findings with those for previous surveillance years showed an increase of approximately 23% compared to the 2006 estimates and 78% when compared with 2002â€ (CDC, 2014, p. 2). It is estimated by some that children identified with autism in schools in the United States rose by over 800% between 1993 and 2003 (Kroncke et al., 2016). The statistics of ASD prevalence highlight the need for continuing surveillance as the rate continues to rise, as well as a need to examine what might be behind the increasing rate of ASD in children.
Scientists have found that autism has an extremely high heritability rate of 0.80-0.90, which indicates that 80-90% of the causal variance is genetic (Bailey, Le Couteur, Gottesman, & Bolton, 2009). Approximately 90 to 100 genes have been identified and associated with autism
(Broad Institute of MIT & Harvard, 2012). Scientists are currently making progress in identifying gene combinations that may increase the risk for certain forms of autism. For example, genomic copy-number variants in genes involved in synaptic cell adhesion and related pathways have been identified in as many as 7% to 10% of people with ASD (Cook & Scherer, 2008). However, the genetic code for autism is not well understood. It is recognized that autism is highly heterogeneous from a genetic perspective and likely to be polygenetic, but there is no clear genetic pattern for this congenital neurologic disorder (Kroncke et al., 2016). Additionally, many individuals with autism have no known genetic abnormalities at all (Kroncke et al., 2016). Although the precise genetic variants have yet to be identified for all individuals with autism, ASD heritability is thought to be based on a complex combination of genes, mutations, and chromosomal abnormalities (Veenstra-VanderWeele, Christian, & Cook, 2004).
Since the late 1970s, many researchers have studied the phenotypic characteristics of relatives of individuals with autism (Bailey, Palferman, Heavey, & LeCouteur, 1998). The earliest study to investigate and generate a list of the inherited genetic characteristics in siblings with ASD was Folstien and Rutter (1977). These researchers concluded that in twin pairs concordant for autism, 50% appeared phenotypically congruent in terms of cognitive and language skills. However, when the authors examined concordance for cognitive and language among the twins discordant for autism (i.e., one twin had a clinical diagnosis for autism, but the other twin did not meet criteria according to the authorâ€™s definition), they discovered that 82% of the monozygotic (MZ) twin pairs and 10% of the dizygotic (DZ) pairs were concordant for cognitive and language impairments (Folstien & Rutter, 1977). In a follow up study using an expanded twin sample, results supported the earlier findings with 92% of MZ twins and 10% of
the DZ twins concordant for cognitive and/or social deficits (Folstien & Rutter, 1977). These findings suggest considerable genetic effects for a broader autism phenotype.
Subsequent research focusing on sibling concordance for level of functioning in autism has generated mixed results (Goin-Kochel & Mazefsky, 2008). With the exception of ritualistic and repetitive actions, Spiker et al. (1994) found little to no evidence for familial aggregation of IQ scores, verbal ability, or specific behaviors in their sample of 37 multiplex families (i.e., two or more children with autism or ASD). A similar study conducted two years later with MZ twins reported significant sibling concordance but for only verbal and nonverbal IQ scores (Le Couteur et al., 1996). The results of these studies suggest that twins and non-twin siblings with autism are phenotypically more dissimilar for most characteristics than unrelated individuals with autism.
However, other researchers have reported contradictory findings. Szatmari et al. (1996) compared measures of intelligence, social and communication skills, and autism characteristics among 23 multiplex families. Using Intraclass Correlation Coefficients (ICC) to determine the extent to which there was variability within sibling pairs relative to variability between sibling pairs, the authors concluded that there was significantly high ICCs for virtually all domains measured (Szatmari et al., 1996). MacLean et al. (1999) conducted a similar study using data from 46 families, further analyzing data by sibling pairs meeting strict criteria for autism versus those meeting criteria for ASD. IQ and adaptive behaviors in socialization and communication scores showed significant familial aggregation in both autism-only and ASD groups (MacLean et al., 1999).
More recently, Silverman et al. (2002) investigated familial effects for ASD using the Autism Diagnostic Interview-Revised (ADI-R; Lord et al., 1994) domains and select item scores among 212 sibling pairs. The authors reported significantly reduced variance for the severity of
repetitive behaviors, the level of deficits in nonverbal communication, and age and presence of phrase speech (Silverman et al., 2002). These authors did not investigate additional measures of functioning such as intelligence and adaptive behaviors. Goin-Kochel, Mazefsky, and Riley (2008), extended the Szatmari et al. (1996) and MacLean et al. (1999) studies on phenotypic congruence among siblings with ASD with respect to level of functioning. These authors concluded that siblings with ASD were more similar on measures of verbal and nonverbal intelligence, as well as adaptive functioning (Goin-Kochel et al., 2008).
The most statistically significant findings of familial similarity in the ASD phenotype in extent research are based on data from the ADI-R as it captures information on the specific characteristics indicative of ASD but fails to quantify severity. However, the ADI-R is not appropriate for examining phenotypic congruence for overall level of functioning. To capture the complete ASD phenotype, researchers must use the specific characteristics indicative of ASD from the ADI-R, as well as IQ and adaptive functioning scores.
Studies of twins and siblings with autism have revealed a significant risk for autism, representing a 60-100% higher risk when a child in the family has already been diagnosed with the disorder (Rutter, Silber, Oâ€™Connor, & Simonoff, 1999). According to Ozonoff et al. (2011), an important measure of genetic contribution is the risk of recurrence in siblings. Recurrence rates are operationalized as the occurrence of autism spectrum disorder in one or more siblings of an index case, using a denominator of all additional children in the family (Constantino, Zhang, Frazier, Abbacchi, & Law, 2010). Studies of recurrence rates of autism in siblings thus far have been remarkably inconsistent.
Research studies examining the rate of ASD in families with one affected child have found recurrence rates ranging from 3% to 18.7% (Charkrabarti & Fombonne, 2001; Icasiano, Hewson, Machet, Cooper, & Marshall, 2004; Lauritsen, Pedersen, & Mortensen, 2005; Ozonoff et al., 2011). For example, a large epidemiologic survey of autism in Utah in the late 1980s reported a recurrence rate of 8.6% in siblings born after an affected child (Ritvo, Jorde, & Mason-Brothers, 1989). The Utah study found that the risk to later bom children was approximately twice as high if the first affected child was female (Ritvo et al., 1989). Twenty-one years later, Constantino, Zhang, Frazier, Abbacchi, & Law (2010) reported an even higher risk to later-born siblings of 14.2% in a large U.S. registry of children with ASD. However, there are several limitations to the Constantio et al. (2010) study, including parental report of diagnosis and the use of several identification/diagnostic sources (i.e., schools and doctoral level practitioners), and thus, the recurrence rate could be skewed. Further, both the Utah and Canstantio et al. studies examined only the recurrence rate for siblings who were born after a sibling with ASD. Additionally, the research was limited by small sample sizes and biases related to ascertainment of reporting and stoppage factors.
Ozonoff et al. (2011) sought to address the limitations of previous research in this area by studying sibling recurrence with prospective methods. The authors studied a large cohort of infants and used a population-based epidemiological method that was representative of all families who have a child with ASD (Ozonoff et al., 2011). The authors also included direct assessment of the children by experts to supplement parent-reports to reduce reporting biases. The authors observed a substantially higher rate of ASD in infant siblings of children with ASD than previously documented. Earlier investigations reported recurrence estimates ranging from 3% to 14%, whereas Ozonoff et al. (2011) reported a recurrence rate of 18.7% for infants with at
least one older sibling with ASD. This study provided evidence that the recurrence rate of autism in siblings is higher than previously estimated. However, an important limitation of this study, as well as the previous studies is that study participants were recruited by the researchers. As such, participants were previously diagnosed with autism and not assessed and identified by qualified experts as part of study procedures. Previous recurrence estimate studies were also limited in that they were conducted prior to the prevalence rate increase cited by the Centers for Disease Control (CDC, 2012). Furthermore, all of the research examining recurrence rates was conducted before the publication of the DSM-5 which included Aspergerâ€™s Syndrome and Pervasive Developmental Disorder under the Autism Spectrum Disorder (ASD) umbrella (APA, 2013) and therefore may have used somewhat different diagnostic criteria.
The present study expands and improves upon this previous work by using retrospective data to provide an incidence estimate of siblings who have been formally diagnosed with ASD in a community-based clinic. In addition, this study sought to reduce the limitations of previous studies by identifying sibling pairs which include cases where a younger child is diagnosed first. The most recent recurrence estimate studies (Ozonoff et al., 2011; Constantio et al., 2010) recruited their subjects, while the present study obtained an estimate for siblings who have previously been diagnosed with ASD by qualified professionals. The prospective methodology used by Ozonoff et al. (2011) may provide a theoretical estimate of the recurrence rate since the authors identified and evaluated siblings before a natural diagnosis. This eliminated the opportunity for the parents to have the child evaluated at a later time. The present study provides an estimate of the recurrence rate for children who are formally diagnosed with ASD without researcher interference subsequent to self-referring for an evaluation.
By linking phenotypical characteristics to recurrence estimates, this study sought to identify the factors that may be related to parents self-selecting their children for evaluation which could result in diagnosis of ASD in siblings. It was hypothesized that parents are more likely to seek an evaluation for their second child if the severity of ASD in their first child is less severe. It was also hypothesized that the time in between evaluations will be less for siblings with more similar phenotypic characteristics. Research has not yet investigated the phenotypical characteristics versus diagnostic interval.
CHAPTER III METHODS
The Colorado Multiple Institutional Review Board (COMIRB) approved this study for exemption on June 26, 2018.
Study participants were identified through a record review at a private community clinic in a large city in the western United States. Records for clients evaluated across 9 years and 2 months from June 1, 2009 until August 1, 2018 were reviewed. Inclusion criteria were: pairs or sets of siblings in which at least one of the children met the diagnostic criteria for ASD or Phenotypical Characteristics of ASD as defined by the DSM-IV or DSM-V as appropriate, depending upon the date of the evaluation. The DSM-V was published in May 2013. Participants with a preexisting diagnosis of ASD were considered for this study as long as the original evaluation date was available and the first concerns (i.e., language delay, stereotypic behaviors, restricted interests) were noted in the psychological or neuropsychological report. Participants with a diagnosis of Asperger Disorder, Pervasive Developmental Disorder- Not Otherwise Specified (PDD-NOS), ASD in the DSM-IV, or ASD in the DSM-V were all included in the present analysis.
Demographics. Upon evaluation at the clinic, parents were provided a neuropsychological or psychoeducational report. For the purposes of this study, these reports provided the following data: date of birth, age, gender, languages spoken at home, with whom the child currently lives, and diagnoses. Within the report, parents reported initial age at which first concerns were observed, referral agency or individual, family history (e.g., psychological/
emotional problems, autism, speech concerns), behavioral issues (e.g., aggression, repetitive behaviors, social interactions) and any prior speech therapy, occupational therapy, ABA therapy, psychotherapy, and/or special education services. Data from assessments administered were also included in the reports. For this study, results from a cognitive measure (Mullen, DAS, WISC-IV, WISC-V), social communication measure (ADOS and ADOS-2), and adaptive measure (VABS-2 and VABS-3) were recorded.
Phenotypic Data. In addition to coding for the recurrence rate of ASD in sibling sets, clinician reviewers systematically recorded additional information for each case including: (a) concerns regarding the childâ€™s development by age 3 years focusing on social, language, and imaginative play skills; (b) functional skills, including scores on tests of intellectual ability and adaptive skills; (c) autism discriminators, which referred to descriptions of behaviors that are hypothesized to distinguish children with ASD from children with other developmental disabilities; (d) associated features (Esler, Hall-Lande, & Hewitt, 2017). Autism discriminators included behaviors reflecting lack of awareness of others, lack of interest in social interaction, rare or absent social interactions or response, and the presence of mark restricted, repetitive behaviors. Discriminators were identified through a review of the literature and were collected using the ADOS/ADOS-2 codes.
Autism Severity. The Autism Diagnostic Observation Schedule-Second Edition (ADOS-2; Lord, Rutter, DiLavore, Risi, Gotham, & Bishop, 2012) is a semi-structured measure that clinicians use to assess a childâ€™s ability to play and communicate through naturalistic observation with the use of defined sets and activities and assessment criteria (Kroncke et al., 2016). For this research project, ADOS/ADOS-2 Overall Score and the ADOS/ADOS-2 Comparison Scores were used as an indicator of autism severity. Additional autism discriminators were considered.
These include impairment in use of nonverbal behaviors, peer relationships, lack of sharing enjoyment and interests, lack of social reciprocity, lack of response to approach others, impairment in conversation, stereotyped and repetitive language, echolalia, lack of make believe or social imitative play, restricted interests, inflexible routines and rituals, motor mannerisms, and sensory preoccupations.
Data collection began by pulling client files in alphabetical order. The files were first reviewed to see if a neuropsychological or psychoeducational report was present. If so, the next step was to determine if the client had one or more siblings who had participated in a neuropsychological or psychoeducational evaluation and if the report was present. The siblingâ€™s reports were then reviewed to determine if they qualified for inclusion. If a sibling pair or multiplex family did meet inclusion criteria, de-identified data were recorded into a password-protected server.
Descriptive statistics were conducted to investigate Research Question 1: What is the incidence of Autism Spectrum Disorder (ASD) in sibling pairs diagnosed in a community clinic where families sought services for their children? Incidence rate was calculated by dividing the total number of families with more than one sibling diagnosed with ASD by the total number families with only one sibling with an ASD diagnosis. A chi square analysis was conducted to determine if the recurrence rate found in the current study was significantly different from that found in previous research.
One-way ANOVAs were conducted to investigate Research Question 2: What is the relationship of severity of autism in siblings and the order in which they are diagnosed?
Regression analyses were conducted to investigate Research Question 3: Are phenotypical characteristics associated with time in between evaluations for siblings identified with autism were examined first? The phenotypical characteristics included: functional skills (IQ Score, Verbal IQ Composite, Adaptive Behavior Composite, Communication Composite, Daily Living Skills Composite, Socialization Composite and Motor Skills Composite) and autism discriminators (ADOS/ADOS-2 Overall Score, ADOS/ADOS-2 Comparison Score, Impairment in use of nonverbal behaviors, peer relationships, lack of sharing enjoyment and interests, lack of social reciprocity, lack of response to approach others, impairment in conversation, stereotyped and repetitive language, echolalia, lack of make believe or social imitative play, restricted interests, inflexible routines and rituals, motor mannerisms, and sensory preoccupations.
CHAPTER IV RESULTS
A total of 79 sibling sets were evaluated at the small private clinic from June 1, 2009 to August 1, 2018. Of these, none of the siblings were diagnosed with ASD in 23% (n = 18) of the sibling sets, leaving 61 sibling sets with at least two siblings diagnosed with ASD (n=78 children). Thirty percent (n = 24) of sibling sets had only one sibling with an ASD diagnosis, while both siblings were diagnosed with ASD in 46.8% (n = 37) of the sibling sets. Five percent (n = 4) of the sibling sets were composed of 3 siblings; all three siblings in these families were diagnosed with ASD (see Table 1).
Evaluations with Multiple Familial Siblings
Total number of sibling sets evaluated 79
Total number of sibling sets with 1 or more ASD: 61
Total number of sibling sets with 2 or more ASD: 37
Percentage of sibling pairs with no ASD Dx: 23%
Percentage of sibling pairs with 1 ASD Dx: 30%
Percentage of sibling pairs with 2 or more ASD Dx: 47%
Percentage of sibling pairs with 3 ASD Dx: 5%
As presented in Table 2, 38% of the sibling sets with ASD were all male, 10% of the sibling sets were all female, 59% of the sibling sets were 1 male/1 female, and 0.2% of the sibling sets were 2 males/1 female. The majority of subjects identified as Caucasian (88%; n = 64), while 12% of subjects identified as Other or Multiple ethnicities (n= 8). Six percent of
participants identified as Hispanic (n = 4), 3% identified as African-American (n = 2), and 3% identified as Asian-Pacific (n = 2)
Table 2 Gender of Sibling Sets (total N=78)
Number Number Number sibling Number
sibling sets sibling sets sets sibling sets
all male all female 1 male/1 female 2 males/1 female
2 siblings with ASD (n=33) 12 3 22 0
3 siblings with ASD (n=4) 2 1 0 1
Table 3 includes demographic characteristics of the sample. The age of these 78 siblings at diagnosis ranged from 1 year, 1 month and 17 years, 0 months, with a mean age of 7 years, 11 months (SD= 3.8). The time in between evaluation of the siblings in each set ranged from 0 months to 61 months, with a mean time in between evaluations of 10.4 months. The average maternal age at the birth of participants was 31 years, 8 months and the average paternal age was 34 years. The IQ (standard score) of these siblings ranged from 51 to 133, (mean=99.7).
Standard scores on measures of adaptive functioning ranged from standard score 51 to 118 (mean=77.7).
Table 3. Descriptive Data (N=78)
Mean Standard Deviation N
Mean age at diagnosis (months) 95 3.8 78
Mean months time between evaluations 10.4 15 37
Mean maternal age (years) 31yr 8mo 5.5 50
Mean paternal age (years) 34yr 5.1 50
Mean IQ (WISC-IV, WISC-V, DAS-II) 99.7 21.6 74
Mean Adaptive (VABS-II, VABS-3, ABC) 77.7 15.4 67
Recurrence Rate of ASD in Siblings
Of the 79 sibling sets who underwent an evaluation, both or all of the siblings were diagnosed with ASD in 37 (46.8%) of them. This recurrence rate of ASD in siblings is significantly higher than the highest recurrence rate of ASD in siblings reported in published studies (A2=18.79, df=l,/>< 0001).
Severity of Autism and Order Diagnosed
A one-way analysis of variance (ANOVA) was conducted to answer the research question: â€œWhat is the relationship between the order in which siblings are diagnosed and the severity of autism in each sibling?â€ Based on DSM-5 criteria, three severity categories were defined and labeled as follows: (1) Level 1 represents â€œRequiring supportâ€, (2) Level 2 represents â€œRequiring substantial supportâ€, and (3) Level 3 represents â€œRequiring very substantial support.â€ Participants were assigned to one of the three severity categories based on the diagnosis provided in the evaluation. The severity category is based on results of the ADOS as well as other data collected during the evaluation.
Assumptions were tested and normality was violated. However, ANOVA is robust to this violation because the sample size was large (N= 65). No significant differences were observed in the order in which siblings were diagnosed based on autism severity.
Time between Diagnoses and Phenotypical Characteristics
Regression analyses were conducted to answer the research question: â€œIs there an association between time in between evaluations and phenotypic characteristics for siblings diagnosed with ASD?â€ Phenotypic characteristic categories were defined and labeled as follows:
IQ Score, Verbal IQ Composite, Adaptive Behavior Composite, Communication Composite, Daily Living Skills Composite, Socialization Composite and Motor Skills Composite, ADOS/ADOS-2 Overall Score, ADOS/ADOS-2 Comparison Score, impairment in use of nonverbal behaviors, peer relationships, lack of sharing enjoyment and interests, lack of social reciprocity, lack of response to approach others, impairment in conversation, stereotyped and repetitive language, echolalia, lack of make believe or social imitative play, restricted interests, inflexible routines and rituals, motor mannerisms, and sensory preoccupations. Results indicated that none of the phenotypical characteristics was a significant predictor of the amount of time that elapsed between sibling evaluations.
CHAPTER V DISCUSSION
Recent surveillance efforts indicate that 1 in 59 American children meet the criteria for Autism Spectrum Disorder (ASD), making it the fastest growing neurodevelopmental disorder in the U.S., (Baio et al., 2018). Studies of twins and siblings with autism indicate heritability rates of 60-100% (Rutter et al., 1999) suggesting that genetic factors may explain most of the risk for the disorder. Research examining recurrence rates of autism in siblings have resulted in remarkably inconsistent findings (Ozonoff et al., 2011). These previous studies had a number of limitations, including small sample sizes, stoppage, over-reporting, inconsistent diagnostic procedures and prospective methodology. The present study sought to minimize the range of scope of these limitations.
The current study is the largest known retrospective investigation of ASD sibling recurrence, with 37 sets of siblings participating. The primary finding was a significantly higher rate of ASD in siblings than previously documented. Earlier investigations reported recurrence estimates ranging from 3% to 18.7% (Charkrabarti & Fombonne, 2001; Icasiano, et al., 2004;
Lauritsen, Pedersen, & Mortensen, 2005; Constantio et al., 2010; Ozonoff et al., 2011), whereas within this sample, 46.8% of children with at least 1 sibling with ASD were also diagnosed with ASD. In other words, when one child was diagnosed with ASD, there is a 46.8% chance one or more of their siblings was also diagnosed with the disorder.
The design of the current investigation minimized many of the limitations of earlier research such as overreporting, stoppage factors, sibling diagnosis order, and prospective design by using a retrospective design with diagnostic methodology. The biases related to overreporting in Constantion et al. (2010) and Ritvo et al. (1989) studies were mitigated in the methodology of this study with the diagnostic methodology of the clinic using structured and reliable assessments. All participants in this study were diagnosed by a License Psychologist using a consistent test battery (e.g., assessment of cognitive ability adaptive behavior, ADOS/ADOS-2, and social-emotional measures) rather than relying on parent report or the use of several identification/ diagnostic sources.
Another limitation of earlier research this study addressed through methodology was stoppage factors. Stoppage is the tendency of couples with an affected child to stop reproducing which leads to an underestimate of the recurrence rate (Ritvo et al., 1989, Constantio et al., 2010; Ozonoff et al., 2011). In the Ozonoff et al. (2011) study, parents were educated about the recurrence rate of autism for future siblings, which could have led to stoppage. Stoppage was addressed in the design of the current investigation by retrospectively examining families where both siblings were previously identified in a natural setting.
Additionally, earlier research examined the recurrence rate for siblings who were born after a sibling with ASD (e.g., Ritvo et al., 1989; Constantion et al., 2010; Ozonoff et al. 2011). This study mitigated this limitation in the inclusion factors, allowing for variety of birth order
and order of diagnosis to be included. For example, there were several sibling sets in this study where the second bom sibling was diagnosed before the first bom sibling. In previous studies, these sibling sets would not have been included, leading to lower observed recurrence rates.
The retrospective design of the present study also minimized the limitations of previous studies where a prospective design was utilized. The Ozonoff et al. (2011) study determined outcomes of siblings (e.g., ASD diagnosis or no ASD diagnosis) before the age at which milder forms of ASD are accurately diagnosed. The authors concluded that â€œthe true recurrence rate may in fact be higher than reported here (18.7%; Ozonoff et al., 2011, p. 492).â€ The retrospective design of the current research mitigated this risk because outcomes were not determined at very young ages. The mean age of diagnosis of siblings with ASD in the present study was 7 years, 11 months which is significantly higher than the age at which milder forms of ASD can detected.
Previous studies have shown that ASD can be reliably diagnosed at 24 months of age (Kroncke et al., 2016). In a large surveillance study, the median age of earliest ASD diagnosis ranged from 46 months to 67 months and the proportion of children who received a comprehensive evaluation by age 3 was 42% (Baio et al., 2018). With an average age of sibling ASD diagnosis of 7 years, 11 months, results of the current study suggest that siblings are identified at a later age than that previously reported.
Surprisingly, the severity of ASD in the first child diagnosed was not predictive of parents seeking an evaluation for their second or third child. Phenotypical characteristics also did not predict the time between the siblingâ€™s evaluation, though this was supported by some earlier investigations (Szatmari et al., 1996; MacLean et al. 1999). Previous studies have provided inconsistent findings on similarities of phenotypical characteristics in siblings with ASD (Szatmari et al., 1996; MacLean et al. 1999; Silverman et al. 2002; Goin, Kochel, & Mazfesky,
2008). The findings from the current study support the Goin et al., (2008) conclusion that siblings with ASD are similar in terms of level of verbal and nonverbal intelligence, as well as adaptive functioning. However, the sample size of the sibling sets (n = 37) participating in the current study may not have been robust enough to produce significant differences. A larger sample size may have the power to detect significant differences between severity and parents seeking an evaluation for their second or third child, as well as phenotypical characteristics predicating the time between siblingâ€™s evaluation.
In addition, this study examined all of quantifiable and measurable characteristic variables of the sibling sets. The only variable not examined in the study but shared by both siblings that could contribute to the time in between evaluations was parents. The order in which siblings are diagnosed and the amount of time between diagnoses may be related to parental variables such as the capacity to grieve the diagnosis of their first child and have another child evaluated, and/or parental education levels of the genetic components of ASD.
While the results from this study contribute to the professional literature, there are several limitations that should be noted. First, the small data set (n = 79 families) limited the scope of this research study in terms of the regression analyses. Additionally, there is a lack of diversity in the study sample. The majority of participants identified as Caucasian and all were English speaking. Similarly, the socioeconomic status was not considered in this study, as it was not collected. The majority of the evaluations completed at the small private clinic were insurance funded with a small number payed out of pocket. The generalizability of this study to other groups, including those who rely on Medicaid, is limited.
Another limitation relates to the population of the private community clinic where the data was collected. The clinic averages 233 neuropsychological and psychoeducational evaluations per year, with approximately 67% of those evaluations resulting in an ASD diagnosis. This rate translates to a high level of expertise in ASD and consequently, this clinic receives a large number of referrals for ASD evaluations from insurance companies, primary care physicians, and the local childrenâ€™s hospital. This could lead to ascertainment bias and the high rate of recurrence of ASD in sibling sets. Ascertainment bias may affect recurrence rate estimates because of the overinclusion of families who have developmental concerns about a sibling.
Future studies should continue to focus on the recurrence rate of ASD in siblings to determine if the high rate of recurrence rate found in this study holds true in other contexts. In order to further assess the relationship between time in between diagnoses of siblings with ASD, a qualitative study using surveys and parent interviews is suggested. Parent input is crucial in identifying understanding the factors that contribute to the order siblings diagnosed and the time in between diagnoses. By identifying the factors that contribute to the order siblings are diagnosed and the time in between diagnoses, researchers will provide clinicians and physicians in the field with information to quickly identify ASD in siblings sets where one has previously been diagnosed with ASD. Research shows that the younger a child is diagnosed with ASD, the sooner they receive intervention that softens the ASD symptoms, allowing them to access their community, school and home more effectively.
This study highlights the importance of routine surveillance and rapid referral for siblings of children with ASD. Given that study results indicate a greater than 46.8% chance that a sibling of the child diagnosed with ASD could also meet criteria for the disorder, it is critically important for primary care physicians and diagnostic clinicians to closely monitor the development of children with siblings with ASD, screening them routinely. If red flags are identified, immediate intervention referral should be made because early specialized intervention is considered to be best practice in ASD (Kroncke et al., 2016). This will decrease the time between evaluations and siblings will receive intervention earlier, which mitigates ASD risk.
American Psychiatric Association (2013). Diagnostic and statistics manual of mental disorders (5th ed.) Arlington, VA: American Psychiatric Publishing.
Bailey, A., Le Couteur A., Gottesman, I., & Bolton, P. (2009). Autism as a strongly genetic disorder: evidence from a British twin study. Psychological Medicine, 25(1), 63-77.
Bailey, A., Palferman, S., Heavey, L., & Le Couteur, A. (1998). Autism: The phenotype in relatives. Journal of Autism and Developmental Disorders, 25(5), 369-392.
Baio, J., Wiggins, L., Christensen, D. L., Maenner, M. J., Daniels, J., Warren, Z., . . . Dowling,
N. F. (2018). Prevalence of autism spectrum disorder among children aged 8 years -autism and developmental disabilities monitoring network, 11 sites, united states,
2014. Morbidity and Mortality Weekly Report. Surveillance Summaries (Washington,
D.C. : 2002), 67(6), 1-23. doi:10.15585/mmwr.ss6706al
Broad Institute of MIT and Harvard (2012, April 4). DNA sequencing consortium unveils. Patterns of mutations in autism. Science Daily. Retrieved from http://www.sciencedailv.com/releases/2012/94/1204Q4133658.htm
Centers for Disease Control and Prevention. (2012). Prevalence of autism spectrum disorders-Autism and developmental disabilities monitoring network. Morbidity and Mortality Weekly Report (MMWR),61, 1-19.
Chakrabarti, S., & Formbonne E., (2001). Pervasive developmental disorders in preschool children. Journal of American Medical Association, 255(24), 3093-3099.
Constantino, J.N., Zhang, Y., Frazier, T., Abbacchi, A., & Law, P. (2010). Sibling Recurrence and the genetic epidemiology of autism. American Journal of Psychiatry, 167, 1349-1356.
Cook, E.H., & Scherer, S.W. (2008) Copy number variations associated with neuropsychiatric conditions. Nature, 76(7215), 919-923.
Duvall, J.A., Lu, A., Cantor, R.M., Todd, R.D., Constantino, J.N., & Geschwind, D.H. (2007). A quantitative trait locus analysis of social responsiveness in multiplex autism families. American Journal of Psychiatry, 164, 656-662.
Esler, A.N., Hall-Lande, J., & Hewitt, A. (2017). Phenotypic characteristics of autism spectrum disorder in diverse sample of Somali and other children. Journal of Autism and Developmental Disorders, 47,3150-3165.
Folstein, S., & Rutter, M. (1977). Infantile autism: A genetic study of 21 twin pairs. Journal of Child Psychology and Psychiatry, 18, 297-321.
Goin-Kochel, R.P., Mazefsky, C.A., & Riley, B.P. (2008). Level of function in autism spectrum disorders: Phenotypic congruence among affected siblings. Journal of Autism and Developmental Disorders, 38, 1019-1027. DOI: 10.1007/sl0803-007-0476-z.
Icasiano, F., Hewson, P., Machet, P., Cooper, C., & Marshall, A. (2004). Childhood autism
spectrum disorder in the Barwon region: A community based study. Journal of Pediatric Child Health, 40(12), 696-701.
Kanner, L. (1943). Autistic disturbances of affective contact. Nervous Child 2, 217-250.
Kroncke, A.P., Willard, M., & Huckabee, H. (2016). Assessment of Autism Spectrum Disorder: Critical Issues in Clinical, Forensic and School Settings. New York, NY: Springer International Publishing.
Le Couteur, A., Bailey, A., Goode, S., Pickles, A., Gottesman, I., Robertson, S., & Rutter, M. (1996). A broader phenotype of autism: the clinical spectrum in twins. Journal of Child Psychology and Psychiatry, 37(7), 785-801.
Lord, C., Rutter, M. DiLavore, P. C., Risi, S., Gotham, K., & Bishop, S. (2012). The Autism Diagnostic Observation Schedule-Second Edition (ADOS-2). Torrance, CA: Western Psychological Services.
Lord, C., Rutter, M., & Le Couteur, A. (1994). Autism Diagnostic Interview-Revised: A revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. Journal of Autism and Developmental Disorders, 24(5), 659-685.
Losh, M., Trembath, D., & Piven, J. (2008). Current developments in the genetics of autism. Journal of Neuropathology, 67(9), 829-837.
Ozonoff, S., Young, G. S., Carter, A., Messinger, D., Yirmiya, N., Zwaigenbaum, L.,... Stone, W.L. (2011). Recurrence risk for autism spectrum disorders: A baby siblings research consortium study. Pediatrics, 128, 488-495.
Ritvo, E. R., Jorde, L. B., & Mason-Brothers, A. (1989). The UCLA-University of Utah epidemiologic survey of autism: recurrence risk estimates and genetic counseling. American Journal of Psychiatry, 746(8), 1032-1036.
Silverman, J. M., Smith, C. J., Schmeidler, J., Hollander, E., Lawlor, B. A., Fitzgerald, M.,... Glavin, P., Autism Genetic Research Exchange Consortium. (2002). Symptom domains in autism and related conditions: Evidence for familiality. American Journal of Medical Genetics. Part B Neuropsychiatric Genetics, 114, 64-73.
Spiker, D., Lotspeich, L., Kraemer, H. C., Hallmayer, J., McMahon, W., & Peterson, P. B.
(1994). Genetics of autism: Characteristics of affected and unaffected children form 37 multiplex families. American Journal of Medical Genetics, 54(1), 27-35.
Szatmari, P., Jones, M. B., Holden, J., Bryson, S., Mahoney, W., Tuff, L.,... Hoult, L. (1996). High phenotypic correlations among siblings with autism and pervasive developmental disorders. American Journal of Medical Genetics. Part B Neuropsychiatric Genetics, 67, 354-360.
Veenstra-VanderWeele, J., Christian, S. L., & Cook, E. H. (2004). Autism as a paradigmatic
complex genetic disorder. Annual Review of Genomics and Human Genetics, 5, 379-405. DOI: 10.1146/annurev.genom. 5.061903.180050.
Wolff, S. (2004). The history of autism. European Child & Adolescent Psychiatry, 75(4),
201 -208. doi: 10.1007/s00787-004-0363-5.