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Focus on Autism and Other
Developmental Disabilities
1 –14
© Hammill Institute on Disabilities 2015
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DOI: 10.1177/1088357615610547
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Article
More than 100 treatments are available to treat core and
associated features of autism spectrum disorder (ASD;
Green et al., 2006). These treatments have varying degrees
of empirical support; recently, Wong et al. (2013) identified
27 ASD treatments for children and adolescents that were
classified as “evidence-based practice.” Although multiple
professionals may be involved with families of children
with ASD, parents often assume primary responsibility for
seeking, choosing, and even coordinating services for their
children. Treatment selection can be overwhelming (Green,
2007), and different professionals may suggest conflicting
treatment recommendations (Mandell, Novak, & Zubritsky,
2005). Further, parents may rely less on direct communica-
tion with professionals as an informational resource and
more on various media (e.g., books, websites) and other
parents of children with ASD (Mackintosh, Myers, & Goin-
Kochel, 2005; Miller, Schreck, Mulick, & Butter, 2012), yet
the validity of information shared through these sources
often is questionable.
With many choices and competing recommendations,
how do parents choose what types of treatments to pursue
for their children with ASD? Though “evidence-based prac-
tice” is a prominent construct for researchers and practitio-
ners, whether a treatment has empirical support may not be
the most salient factor in parents’ decisions about what to
use for either diagnosed or children suspected to have ASD
(Green, 2007; Regehr & Feldman, 2009). Not only is an
evidence basis for the efficacy of most treatments for ASD
lacking, varying criteria are used to evaluate the efficacy of
an intervention (Wong et al., 2013). In addition, journal
articles that contain evidence-based results may be difficult
for parents to access and/or interpret (Thomas, Ellis,
McLaurin, Daniels, & Morrissey, 2007).
Factors That Influence Treatment
Choices
Overall, little is known about how families choose treat-
ments for their children with ASD (Patten, Baranek, Watson,
& Schultz, 2013). However, child’s age, cognitive function-
ing, and individual ASD symptoms—as well as the family’s
610547FOAXXX10.1177/1088357615610547Focus on Autism and Other Developmental DisabilitiesMire et al.
research-article2015
1University of Houston, TX, USA
2Baylor College of Medicine, Houston, TX, USA
Corresponding Author:
Sarah S. Mire, College of Education, University of Houston, Farish Hall
#467, Houston, TX 77204-5029, USA.
Email: ssmire@central.uh.edu
Parent Perceptions About Autism
Spectrum Disorder Influence
Treatment Choices
Sarah S. Mire, PhD1, Whitney Gealy, MSEd1, Tom Kubiszyn, PhD1,
Andrea Backscheider Burridge, PhD1, and Robin P. Goin-Kochel, PhD2
Abstract
Parents of children with autism spectrum disorder (ASD) must identify, select, and even implement treatments. Child
age, cognitive functioning, ASD symptoms, family income, parent education, and cultural background, all may influence
treatment selection. Parents’ perceptions about ASD also may contribute. We explored whether parents’ perceptions
of ASD, along with family- and child-specific characteristics, predicted use of various ASD treatment categories. Sixty-
eight families from the Simons Simplex Collection completed the Revised Illness Perception Questionnaire (IPQ-R). Logistic
regression results indicated that when parent perceptions predicted use of a treatment category, relative contribution
of perceptions was somewhat stronger than child- and family-specific factors (i.e., demographics, functioning). Moreover,
predictive factors differed between treatment categories. Overall, treatment category use was influenced by parents’
perceptions of control over ASD treatment, behaviors perceived to be related to ASD, and beliefs about chronicity of
the diagnosis. These findings may contribute to broader understanding of parents’ ASD treatment selection and enhance
professionals’ ability to guide families’ decision-making.
Keywords
autism spectrum disorders, parent, treatment choice
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2 Focus on Autism and Other Developmental Disabilities
socioeconomic status (i.e., income, parent education level),
geographic locale, and even racial and ethnic background—
may influence parents’ treatment choices. Parents of young
children often use a greater number of treatments than those
whose children are older (Green et al., 2006; Mire, Raff,
Brewton, & Goin-Kochel, 2015)—perhaps related to enroll-
ment in many services after an initial diagnosis since
early intervention is considered key to a more favorable
prognosis—and many intervention programs are targeted for
young children (i.e., Treatment and Education of Autistic
and related Communication Handicapped Children
[TEACCH], pivotal response treatment [PRT], applied
behavior analysis [ABA], Floortime, Early Start Denver
Model).
With regard to cognition, children with ASD and comor-
bid intellectual disability (ID) are more likely to be treated
with psychotropic medications (Aman, Lam, & Collier-
Crespin, 2003; Witwer & Lecavalier, 2005), whereas higher
functioning children may be more likely to attend social
skills groups (Reichow, Steiner, & Volkmar, 2012). Core
symptoms of ASD (i.e., social communication deficits,
restricted interests/repetitive behaviors), however, may not
be related to types of treatments chosen. For example, in one
recent study, a significant but very small relationship
emerged between ASD core symptoms and parents’ endorse-
ment of psychotropic medication use, suggesting that factors
other than ASD symptoms contribute to choosing this type
of treatment (Mire, Nowell, Kubiszyn, & Goin-Kochel,
2014).
Higher parent education level may increase the likeli-
hood of pursuing complementary and alternative medicine
(CAM) treatments (Akins, Krakowiak, Angkustsiri, Hertz-
Picciotto, & Hansen, 2014), including increased use of spe-
cial diets and/or vitamin therapy (Patten et al., 2013).
Medicaid-eligible children with ASD are much more likely
to be treated with psychotropic medications (Mandell et al.,
2008). Families from non-White backgrounds are less likely
to attribute their children’s chronic illnesses, developmental
delays, or behavior problems to underlying health-related
reasons and subsequently, are less likely to pursue tradi-
tional medical treatments (Akins et al., 2014; Levy, Mandell,
Merhar, Ittenbach, & Pinto-Martin, 2003; Yeh, Hough,
McCabe, Lau, & Garland, 2004).
Contribution of Perceptions on
Treatment Decisions
Parents make sense of their children’s ASD through the filter
of their own knowledge of development and their own expe-
riences, such that they “construct” the meaning of the diag-
nosis and interpret how the diagnosis will affect their child
and family (Avdi, Griffin, & Brough, 2000). These percep-
tions, or cognitive processes—including beliefs about causes
of ASD—are likely to affect treatment selection (Al Anbar,
Dardennes, Prado-Netto, Kaye, & Contejean, 2010; Mandell
& Novak, 2005). How parents conceptualize their child’s
ASD diagnosis influences the treatments they choose
(Dardennes et al., 2011), as do parents’ feelings about their
own role in helping their children (Hebert & Koulouglioti,
2010).
How parents conceptualize the severity of their chil-
dren’s impairments in social, communication, and/or behav-
ioral domains may particularly influence treatment choices.
Greater perceived severity of diagnosis by parents was
associated with higher usage of special diets (e.g., gluten-
free [GF] and/or casein-free [CF]) and other CAM therapies
(Christon, Mackintosh, & Myers, 2010; Goin-Kochel,
Myers, & Mackintosh, 2007; Green et al., 2006). However,
parents’ perception of more severe ASD symptomatology
has also been linked with increased likelihood of using
interventions based on the principles of ABA (Green et al.,
2006). In some research, use of psychotropic medications
was comparably endorsed by parents of children with vary-
ing degrees of parent-perceived ASD severity (Green et al.,
2006); yet in other studies, greater perceived severity was
associated with increased likelihood of psychotropic use
(Aman et al., 2003).
Applying Leventhal, Nerenz, and Steele’s (1984) illness
representation model to ASD, Al Anbar et al. (2010) found
that parents’ perceptions about the severity of their child’s
ASD was associated with educative methods of treatment.
Further, parental perceptions that the course of their child’s
ASD is unpredictable were related to use of psychotropic
medication treatment, whereas parents who perceived some
degree of control over the ASD were less likely to pursue
either psychopharmacological or nutritional (i.e., special
diets, vitamins/supplements) treatments (Al Anbar et al.,
2010). This study team also found that parents’ beliefs about
cause of their children’s ASD were also associated with
treatment choice (Dardennes et al., 2011). To date, these
appear to be the only studies that have examined how vari-
ous aspects of parent perceptions about their children’s
ASD may predict treatment choices.
Aim of Current Study
Considering the diagnostic prevalence of ASD and the
broad impact of the diagnosis, understanding decision-
making for children’s treatment is critical. Parent percep-
tions about their children’s ASD are important consider-
ations for understanding treatment selection (Al Anbar
et al., 2010; Dardennes et al., 2011), but study in this area is
just emerging and more study is needed about how percep-
tions and other factors influence treatment decision-making
(Christon et al., 2010; Mandell & Novak, 2005). Perceptions
are amenable to change (Leventhal, Leventhal, & Cameron,
2001), and if particular aspects of parent perceptions are
demonstrated to have an effect on the types of treatments
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Mire et al. 3
parents choose, then researchers and practitioners may be
able to tailor dissemination of psychoeducational and treat-
ment information to better align parents’ treatment choices
with evidence-based practices (Al Anbar et al., 2010;
Dardennes et al., 2011). Toward this end, the primary ques-
tion we sought to answer within the current study was the
following: Are parents’ perceptions of their child’s ASD
more predictive of treatment selection relative to child-spe-
cific and demographic factors?
Methods
Participants
Participants were from the Simons Simplex Collection
(SSC) at the Baylor College of Medicine (BCM) site who
had agreed to being recontacted about other research proj-
ects. At the time of data collection for the current project,
there were 2,115 SSC participants, 199 of whom were from
the BCM site. The SSC was a multi-site project funded by
the Simons Foundation Autism Research Initiative (SFARI)
with the goal of developing a permanent repository of
genetic samples from families of children with ASD.
Genetic and phenotypic (i.e., clinical) data were collected
from families with a single child diagnosed with ASD.
Details about the development of the genetic repository of
the SSC, including recruitment strategies and clinical
assessment, are available in Fischbach and Lord (2010). Of
the 199 families who participated at the BCM site, 148 con-
sented to being recontacted, and the response rate for the
current study was 46% (N = 68).
Procedures
Appropriate Institutional Review Board (IRB) and SFARI
approval was granted, including waiver of documentation
of informed consent to prevent a link between participants
and the study; return of completed questionnaires indicated
the families’ agreement to participate in the current study.
Potential participants were mailed a packet that included a
welcome letter, statement of informed consent, a perception
questionnaire (Revised Illness Perception Questionnaire for
Autism [IPQ-RA]; described in subsequent section), and an
updated treatment-history form. These data were linked to
the SSC data set using the family’s unique four-digit local
identification number.
Measures
The SSC employed a standard research protocol that was
used across all 12 data-collection sites. For the current
study, measures of interest collected during the SSC
included demographic variables, cognitive functioning, and
information related to ASD symptomatology. New data
regarding parent perceptions about the course and nature of
their children’s ASD were collected from recontacted
participants.
Parent educational and family income level. Demographic data
were collected on families via the SSC-created Background
History Form, including age of the child at the time of data
collection, mothers’ and fathers’ education levels, and
annual household income. Mother and father education
level were highly correlated (r = .66, p < .001), so the arith-
metic mean of these was used to represent parent education
level in our study. Income was reported categorically by
ranges (less than $20k, $21–35k, $36–50k, $51–65k, $66–
80k, $81–100k, $101–130k, $131–160k, and more than
$161k).
SSC measures. Age of problem onset was captured via item
2 from the Autism Diagnostic Interview–Revised (ADI-R;
Rutter, Le Couteur, & Lord, 2003). The calibrated severity
score (CSS; Gotham, Pickles, & Lord, 2009) derived from
the Autism Diagnostic Observation Schedule (ADOS; Lord
et al., 2000; administered by research-reliable ADOS exam-
iners per the SSC study protocol) was used as indicator of
overall ASD severity. Verbal cognitive ability was mea-
sured with one of several options (based on child develop-
mental level) within the SSC: the Mullen Scales of Early
Learning (MSEL; Mullen, 1995), the Differential Ability
Scales–Second Edition (DAS-II; Elliott, 2007), or the
Wechsler Intelligence Scales for Children–Fourth Edition
(WISC-IV; Wechsler, 2003). The majority of participants in
the current study (88%) had been administered the DAS-II.
Parent perceptions about child’s ASD. Parent perceptions
about their child’s ASD were measured using a modified
version of the Revised Illness Perception Questionnaire
(IPQ-R; Moss-Morris et al., 2002). The IPQ-R measures the
five components of illness representation (e.g., identity,
consequences, timeline, control/cure, and cause), from Lev-
enthal’s Self-Regulatory Model of Illness Representation
(Leventhal et al., 1997; Leventhal et al., 1984). Seven sub-
scales comprise the IPQ-R: Timeline–Acute/Chronic,
Timeline–Cyclical, Consequences, Personal Control, Treat-
ment Control, Illness Coherence, and Emotional Represen-
tations. Two additional subscales—Identity and Cause—are
presented separately.
High scores on the Identity, Timeline, Consequences,
and Cyclical subscales indicate (respectively) strong beliefs
about number of symptoms attributed to the illness, chronic
nature of the condition, negative consequences of the ill-
ness, and the cyclical nature of the illness. High scores on
the Personal Control, Treatment Control, and Coherence
subscales indicate positive beliefs about how controllable
the illness is and how well the illness is understood (Using
and Scoring the IPQ-R, n.d.).
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4 Focus on Autism and Other Developmental Disabilities
Researchers are encouraged to modify the IPQ-R to fit
diagnoses being studied to enhance understanding of how
perceptions may be related to treatment choice and adher-
ence outcomes (Moss-Morris et al., 2002). It has been
widely used in studies about the role perceptions play in
outcomes related to a variety of chronic illnesses and diag-
noses (e.g., Covic, Seica, Gusbeth-Tatomir, Gavrilovici, &
Goldsmith, 2004; Jopson & Moss-Morris, 2003). It also has
been used in assessing the perceptions of spouses and care-
givers of persons with chronic health-related conditions
(e.g., Hews, de Ridder, & Bensing, 1999; Salewski, 2003).
Though not an “illness,” ASD is a chronic condition,
and the pervasive impact of this diagnosis requires differ-
ent approaches to assessment and subsequent treatment
across the life span (Aman, 2005; Shea & Mesibov, 2009).
Al Anbar et al.’s (2010) creation of the IPQ-R for Autism
(IPQ-RA) included 70 items. The first 14 items query
observations of symptoms (Identity), and the last section
(Cause) asks for degree of agreement with each of 18 pos-
sible causes. The subscales in between comprise the
remaining items. Al Anbar et al.’s study of the IPQ-RA for
use with parents of children with ASD yielded respectable
to very good internal-consistency reliability (α = .69–.81)
on six of the seven IPQ-RA subscales within their ASD-
caregiver sample, although internal-consistency reliability
was lower (α = .62) on one subscale (Treatment Control).
As described previously, results of their study suggested
relationships between parent perceptions about their chil-
dren’s ASD and subsequent treatment decisions (Al Anbar
et al., 2010).
For our study, we used the IPQ-RA but changed the
wording in Al Anbar et al.’s version from “his/her illness”
to “your child’s ASD” to use person-centered language
(American Psychological Association [APA], 2010), and to
acknowledge that parents may not view ASD as either an
illness or a “disorder.” “Hereditary–Runs in my family” as
a potential causal factor was changed to “Genetics” (fami-
lies were included in the SSC specifically because their
child’s ASD was not thought to be [i.e., simplex study]).
Finally, four additional potential causes of ASD were
included: “in utero stress or accident,” “my child’s brain
structure,” “toxins found in vaccines/immunizations,” and
“stress at birth,” as these are additional causes sometimes
considered by parents of children with ASD (Hebert &
Koulouglioti, 2010). The original IPQ-R is accessible at
http://www.uib.no/ipq/, and our modified version (per these
descriptions) may be obtained by emailing the first author.
(Table A1 in Appendix A includes the descriptive character-
istics of the IPQ-RA for our subsample.)
Treatment categories chosen by parents. Educational, behav-
ioral, and biomedical categories of treatment use were que-
ried from a child’s second birthday up to his/her current age
using the SSC’s Treatment History Form. Psychotropic
medication use (past and current) was queried within the
extensive SSC Medical History Interview (MHI). Treat-
ment use was dichotomized across the following categories
to indicate whether the child had ever (i.e., either currently
or in the past) used that treatment category: private speech/
language therapy; school-based speech/language therapy;
private occupational therapy; school-based occupational
therapy; intensive behavioral therapy (ABA, verbal behav-
ior [VB] therapy, Pivotal Response Treatment [PRT], Dis-
crete Trial Teaching [DTT], etc.); other intensive therapy
(TEACCH, Floortime, etc.); biomedical treatment (special
diet, chelation, etc.); any other treatment/therapy; and psy-
chotropic medication (any type).
Statistical Analysis
To investigate variables potentially predicting use of certain
treatment categories, a series of binary logistic regression
analyses were conducted for each treatment category. The
outcome variable was defined as ever having used a cate-
gory of treatment (yes or no). Potential predictor variables
included child’s current age, age of problem onset, severity
of ASD as measured by the CSS, child verbal cognitive
ability, household income, parent education, and parent per-
ceptions of ASD per the IPQ-RA subscales.
Preliminary analyses included (a) a series of logistic
regressions conducted on the BCM sample of SSC partici-
pants (n = 199), from which the subsample for the current
study was drawn, and (b) a series of logistic regressions
conducted with the IPQ-RA subscales. These preliminary
analyses helped identify the most salient potential predic-
tors to include in main logistic regression analyses for each
treatment category. Table A2 in Appendix A shows odds
ratios for all predictor variables (child and family variables,
and IPQ-RA subscales) entered for each treatment category,
per results of the preliminary analyses. Forward stepwise
regression was used to reduce the likelihood of overfitting
the models with a relatively small sample size (n = 68) com-
pared to the number of potential predictors. For all stepwise
logistic regression models described, correlational analyses
did not reveal any bivariate correlations above .60, indicat-
ing no redundancy or multicollinearity concerns (Tabachnick
& Fidell, 2007). Table A3 in Appendix A includes informa-
tion about the steps of these statistical analyses.
Likelihood of a Type I error increases as a function of
performing multiple analyses, but reducing this type of
error increases the likelihood of missing true effects (i.e.,
Type II error; Feise, 2002). Little research regarding the
contribution of multiple factors to ASD treatment selection
is available, so no adjustment for multiple comparisons
was made, considering that our primary goal was to iden-
tify potential factors that contribute to use of specific treat-
ment categories (Rothman, 1990). All analyses were run in
PAWS 22.
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Mire et al. 5
Results
Sample Characteristics and Treatment Category
Use
Descriptive characteristics for child- and family-specific
variables are presented in Tables B1 and B2 in Appendix B
for the current/recontacted sample (n = 68), as well as for
the total BCM site sample (n = 199) and larger SSC sample
(n = 2,115). Importantly, dependent sample t tests and
Pearson chi-square analyses indicated only one significant
difference between the complete SSC sample, the full BCM
sample, and our recontacted study sample: race/ethnicity,
χ2(4, 2382) = 47.23, p < .001. Most SSC families identified
as “Caucasian,” but at the BCM site and in our recontacted
subsample, there were higher percentages of Hispanic/
Latino participants. Most families reported that their child
had received some type of treatment during their life span,
with only a low percentage of families endorsing no treat-
ment categories at any time for their child (SSC: 4.4%;
BCM: 8.5%; current/recontacted: 2.9%; descriptive infor-
mation regarding the frequencies and proportions of chil-
dren in each sample who reported ever having each
treatment category is presented in Table B3 in Appendix B.)
Parent Perceptions as a Predictor for Choice of
Treatment Category
The primary focus of this study was to investigate the rela-
tive contribution of parent perceptions about their children’s
ASD compared with child- and family-specific characteris-
tics to predict whether they had ever used or not used vari-
ous treatment categories. For eight of the treatment
categories, separate tests of the full models with predictors
against the constant-only models were statistically signifi-
cant (p < .001), indicating that the set of predictors retained
for these treatment categories were statistically better than
intercept-only models. For “psychotropic medications” and
“no treatment,” tests of the full models against a constant-
only model were not statistically significant (i.e., the model
did not reliably distinguish between families with respect to
these two outcomes).
For the main analyses, Table B4 in Appendix B shows
results for the predictors (including IPQ-RA subscales) that
were predictive of having used each treatment category.
Odds ratios (OR; Exp[B]) indicate the change in odds that
result from a unit change in the predictor. An odds ratio
greater than 1 suggests that as the predictor increases, the
odds of having ever had a particular treatment increase; an
odds ratio less than 1 indicates that as the predictor increases,
the odds of having ever had that treatment decreases.
Predictors are more influential as the odds ratio is farther
from 1. For example, for biomedical treatment, the OR for
the Child Age is .789, which suggests that for every year
increase in the child’s age, the odds of having ever used
biomedical treatment decreased by approximately 21%.
The following predictors reliably predicted having ever
used each of the treatment categories: private speech therapy:
Timeline–Acute/Chronic IPQ-RA subscale; school-based
speech therapy: verbal cognitive score; private occupational
therapy: age of onset, verbal cognitive score, and IPQ-RA
Treatment Control subscale; school-based occupational ther-
apy: IPQ-RA Identity subscale; intensive behavioral treat-
ment: verbal cognitive score and IPQ-RA Identity subscale;
other intensive treatment: IPQ-RA Treatment Control sub-
scale; biomedical treatment: child current age and age of
onset; and psychotropic medications (any): IPQ-RA Identity
subscale and IPQ-RA Treatment Control subscale.
Discussion
Our study offers information about how parent perceptions
influence the treatments they choose for their children with
ASD, with the primary aim being to investigate whether
parents’ perceptions of their child’s ASD contribute more to
treatment selection than family and child-specific charac-
teristics. Data were collected on a local subsample of fami-
lies who participated in the larger SSC project. Recontacted
participants were asked to complete the IPQ-RA, yielding
data about their perceptions of the course and nature of their
child’s ASD diagnosis. Though the overwhelmingly White,
upper-middle-class sample was not representative of the
U.S. or general ASD population, the study results yielded
initial information about parents’ treatment decisions for
youth with ASD which were previously unavailable.
Moreover, the stringent data collection procedures for the
SSC ensured that clinical diagnoses of ASD were based on
gold-standard diagnostic instruments and that data were
obtained using multiple methods and from multiple infor-
mants— an improvement over prior studies investigating
treatments in ASD that relied solely on parental report of
diagnosis (Al Anbar et al., 2010; Bowker, D’Angelo, Hicks,
& Wells, 2011; Dardennes et al., 2011; Goin-Kochel et al.,
2007; Green et al., 2006).
Higher-than-average socioeconomic status has been
identified as a potential bias in other studies related to ASD
treatment (Green et al., 2006; Patten et al., 2013; Smith &
Antolovich, 2000), including in selecting, implementing,
and following-through on treatment. Overall, 76.4% of
families in our sample exceeded the national median house-
hold income level ($51,914; U.S. Census Bureau, 2010)
and also in terms of parent educational attainment (27.9%
of the U.S. adult population hold a bachelor’s degree or
higher; U.S. Census Bureau, 2010). A higher-than-average
socioeconomic status (SES), combined with SSC families’
commitment to the rigorous study requirements, may be
related to their knowledge of, accessibility to, and active
pursuit of specific treatments for their children.
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6 Focus on Autism and Other Developmental Disabilities
Treatment Category Use
Families of children with ASD often use many treatments
simultaneously rather than choosing a single treatment type
(Bowker et al., 2011; Goin-Kochel et al., 2007; Green et al.,
2006), and this was true in our study, as almost all of the
children/adolescents in our sample reportedly received at
least one category of treatment at some point in their lives
(97.1%). Akins et al. (2014) found a similar percentage of
families in their study used some type of service for their
child with ASD (97.8%), while others have found a some-
what lower percentage of families (e.g., Bowker et al.,
2011, 76.7%). While the Bowker study used web-based
data collection only (like many treatment-focused studies;
for example, Goin-Kochel et al., 2007; Green et al., 2006),
the Akins study involved more intensive and time-consum-
ing data collection, as was the case in the SSC study. This
may have influenced the higher frequency of reported treat-
ment use.
School-based services were among the most frequently
used, and parents’ reliance on schools to provide treatment
for their children with ASD has been highlighted previously
(Thomas et al., 2007). Private speech therapy was the second
most frequently used treatment category, and the high rates of
using speech therapy (school or private) found in this study
were consistent with previous reports (Bowker et al., 2011;
Green et al., 2006; Hanson et al., 2007; Patten et al., 2013).
Almost half of our sample endorsed having ever used psy-
chotropic medications, which is consistent with findings of
other studies (Aman, Lam, & Van Bourgondien, 2005,
45.2%; Mandell et al., 2008, 56%). Our sample’s reports of
ever using ABA-based intensive behavioral treatments
(25.0%) were also within the range of other studies’ out-
comes (Bowker et al., 2011, 37.0%; Green et al., 2006,
22.7%–36.4%). Ascertaining our findings about biomedical
treatment use (23.5%) is difficult to compare with previous
studies because many treatments were included on the SSC-
created form (i.e., vitamins/supplements, special diets, chela-
tion, etc.). However, Akins et al. (2014) also surveyed a
variety of CAM treatments, many of which overlap with the
categories of treatments captured in our study, and found a
comparatively higher rate of CAM-use reports from parents
of children with ASD (39.3%).
Contributions of Parent Perceptions
Several authors have pointed out the importance of profes-
sionals’ understanding of parent treatment selection for
ASD (Christon et al., 2010; Green et al., 2006; Patten et al.,
2013; Smith & Antolovich, 2000). Our findings suggested
that several factors, including child- and family-specific
characteristics, as well as parent perceptions about the
nature, course, and impact of their child’s ASD diagnosis,
contribute meaningfully to treatment categories selected by
parents. This supports findings from previous studies that
there are likely many factors influencing the decisions par-
ents make about what kind of treatments they choose for
their children with ASD (Aman, 2005; Green et al., 2006).
In our study, parent perceptions about their child’s ASD
contributed to types of treatments used by parents, consistent
with the work of Al Anbar et al. (2010) and Dardennes et al.
(2011). In particular, parents’ perceptions about the number
of symptoms they believed to be directly related to their
child’s ASD diagnosis (i.e., Identity), the extent to which they
could control their child’s treatment (i.e., Treatment Control),
and how chronic they viewed the ASD (i.e., Timeline–Acute/
Chronic) had some bearing on which treatment categories
they had used. Though the influence of most predictors had
relatively small effects on whether or not families had ever
used the various treatments (i.e., as indicated by odds ratios
close to 1), when perceptions were influential for certain
treatment categories, we often found a relatively greater
effect of the perceptions than of other factors (i.e., child and
family characteristics). This supports that parent perception
of their child’s ASD is a salient factor in treatment selection
(Avdi et al., 2000; Hebert & Koulouglioti, 2010).
Differences between our findings and those of Al Anbar
et al. (2010; that is, higher Consequences = more educative
treatments; higher Timeline–Cyclical = more psychotropic
treatment; higher Personal Control = lower biomedical and
psychotropic treatments) could be related to (a) differences
in defining treatment categories; (b) cultural factors related
to treatment choices (Al Anbar et al., 2010, study was con-
ducted in France); or (c) statistical approach (Al Anbar et al.
study used only IPQ-RA subscales in their analyses,
whereas the current study included child- and family-spe-
cific factors, as well as the IPQ-RA Identity subscale).
Identity. We included the IPQ-RA Identity subscale in our
study because we were interested in whether the number of
symptoms parents ascribed to their children’s ASD predicted
treatment choices. We found that for every additional symp-
tom that parents attributed to ASD (i.e., Identity subscale
score), likelihood of school-based occupational therapy
increased by 29% and likelihood of intensive behavioral
treatment increased by 32%. It may be that the IPQ-RA Iden-
tity subscale reflects readily observable symptomatology
(i.e., “has repetitive movements”) and influences perceptions
not only of parents but of school-based decision-making
teams or other professionals that a child could benefit from
school-based or community services. However, only parent
perceptions were measured in this study so we could not test
this possibility. Alternatively, perhaps parents’ perceptions
about greater number of symptoms being associated with
ASD prompted these parents to advocate for more school-
based and behavioral services.
Attributing more symptoms to ASD (Identity) decreased
the likelihood of psychotropic medication use by 18% for
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Mire et al. 7
every additional symptom endorsed. It may be that parents’
choose psychotropic medications to manage co-occuring (but
not core ASD) symptoms, or perhaps parents ascribed the
symptoms they observed to something other than their child’s
ASD diagnosis. Persons with ASD have high rates of comor-
bid diagnoses (Deprey & Ozonoff, 2008), and many prescrip-
tions given to children with ASD target associated symptoms,
such as aggression, irritability, hyperactivity, and/or mood
(Gerhard, Chavez, Olfson, & Crystal, 2009; Myers, Johnson,
and the Council on Children with Disabilities, 2007; Witwer
& Lecavalier, 2005).
Treatment Control. Parents’ perceptions of having control
over the treatment were associated with a higher likelihood
of ever having used private occupational therapy (33%
increase), other intensive treatments (61% increase), and
psychotropic medications (49% increase). Each of these are
treatments parents may be especially likely to initiate on
their own (e.g., school-based and intensive behavioral treat-
ments, in particular, may be more likely to be recommended
by other professionals, as these are often considered to be
“traditional” treatments, and implemented by professionals
across settings). It may be that parents feel more confident
about making decisions related to these particular types of
treatments (i.e., influencing prescriptions from their child’s
physician, a professional they see regularly).
Timeline–Acute/Chronic. We also found that the more parents
believed that their child’s ASD is chronic, the less likely
they were to pursue private speech therapy (i.e., 21% lower
likelihood of private speech therapy for every one-point
increase on the Timeline–Acute/Chronic scale). Use of
speech therapy tends to decrease as children age (Cidav,
Lawer, Marcus, & Mandell, 2013; Mire et al., 2015), and it
is possible that as parents note chronic symptomatology,
they are less likely to pursue this type of treatment. Alterna-
tively, it may be that as parents observe progress in speech
therapy as their children get older, they decide that private
services (i.e., those outside of what their child’s school
offers) are unnecessary.
No influence of parent perception on treatment. Parent per-
ceptions did not influence choices about all treatment cate-
gories in this sample. School-based speech therapy was
predicted only by a child’s verbal cognitive score; the likeli-
hood of receiving school-based speech therapy decreased
by 4% for every point increase on verbal cognitive score.
Children with higher verbal functioning may not meet eligi-
bility criteria for school-based speech therapy, and parents
do not have complete control over whether or not their child
receives this treatment (i.e., school-based teams, including
parents, decide school-based services), as they do with
other treatments. Alternately, because the majority of par-
ticipants had received school-based speech therapy, this fact
may have affected this statistical analysis. Similarly, the use
of biomedical treatment was predicted only by a child’s cur-
rent age and their age of ASD symptom onset. Biomedical
treatment was 21% less likely for every year increase in
age, and it was 6% less likely for every month later that
ASD symptoms were noted. Parents may perceive biomedi-
cal treatments as more natural or less invasive than other
types (Hanson et al., 2007), which may be the reason why
parents opt for biomedical interventions over others—like
psychotropic medications—when children are younger. In
addition, factors related to ASD symptom onset influence
parents’ perceptions about the cause of a child’s ASD
(Goin-Kochel, Mire, & Dempsey, 2014), and it is plausible
that treatment selection also may be related to onset-related
variables.
Limitations
Treatment was not the primary focus of the SSC data collec-
tion efforts. Consequently, and as noted previously, indi-
vidual treatments were not queried, and treatment data were
collected via retrospective parent report only. However, that
we focused only on whether families had ever used or not
used different categories of treatments likely lessened the
possibility of recall errors. The “other treatment/therapy”
category allowed parents to endorse treatments they did not
identify as belonging somewhere else, and further examina-
tion of what treatments comprised this category may be
enlightening (casual review indicated that “social skills”
was often captured here).
With regard to the estimate of ASD severity, there is
some question about whether the CSS is the best measure-
ment for ASD symptom severity across time (de Bildt et al.,
2011); alternative methods of comparing ADOS scores (i.e.,
conversion to z-scores; Black, Wallace, Sokoloff, &
Kenworthy, 2009) could be considered in future studies.
With regard to the IPQ-RA, though widely used with
chronic illness research, it has only recently been applied to
ASD (Al Anbar et al., 2010), and a different measure of par-
ent perceptions may yield different results regarding treat-
ment choices for children with ASD. In addition, the
IPQ-RA captures parent perceptions at a single time point
so it is unknown how variations in perceptions over time
may impact treatment selection. Finally, the impact of race
and ethnicity on treatment choices was not explored in this
study because of disproportional representation of the
Caucasian race compared with other categories. Persons
from different racial/ethnic backgrounds may approach
treatment for ASD differently (Akins et al., 2014; Mandell
& Novak, 2005)—though studies suggest that the racial/
ethnic identity confounds other critical variables, particu-
larly SES (Kaufman & Cooper, 2001)—and so future inves-
tigations of differences among families of more diverse
backgrounds is warranted.
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8 Focus on Autism and Other Developmental Disabilities
Future Directions
Although not investigated in the current work, parents’
decisions about which treatments to pursue for their chil-
dren with ASD diagnoses may be, in part, related to their
attributions for the “cause” of their child’s ASD. For exam-
ple, Al Anbar et al. (2010) found that parents who attributed
the cause of ASD to environmental factors were much more
likely to use nutritional and detoxification treatments, while
beliefs about a genetic cause were related to a higher use of
vitamin supplements. The influence of parents’ perceptions
about cause was not examined in the current study, but the
IPQ-RA included a section that measured the degree to
which parents agreed with a number of potential causes for
their child’s ASD. A brief review of these data indicated that
almost half agreed or strongly agreed that toxins in immuni-
zations may have caused their child’s ASD, even though the
link between immunizations and ASD is unsupported and
indeed developed from fraudulent studies (Flaherty, 2011).
The examination of treatment pursuit related to perceived
cause potentially has important implications, and future
studies should focus on the role of parent perceptions about
causes and the link to treatment, particularly with regard to
beliefs about immunizations.
Summary and Conclusions
Overall, our results suggest that several factors contribute to
families’ selection of treatment choices, which begins to shed
light on why families choose the treatments that they do—an
area where literature is just emerging. These findings also
underscore that understanding parents’ treatment selection
varies depending on the type of treatment in question. Parent
perceptions, in addition to child- and family-specific factors,
were investigated as potentially contributory to what treat-
ment categories are pursued by families of children with
ASD. When parent perceptions predicted use of particular
treatment categories, the relative contribution of these factors
was somewhat stronger than that of the child- and family-
specific factors. Parents’ choices of several treatment catego-
ries were influenced by the degree to which parents felt they
had control over their child’s ASD treatment, viewed their
child’s demonstrated behaviors as related to ASD, and per-
ceived their child’s ASD as being chronic. These findings
have important implications for the development and design
of future studies focused on how treatment decisions are
made, particularly with regard to the importance of parent
perceptions.
Increased awareness of and rapidly rising prevalence of
ASD diagnoses are reasons that ASD is increasingly rele-
vant to professionals across settings. Treatment for chil-
dren with ASD is critical, and research about effectiveness
of certain treatments is continuously emerging. Though
various professionals often are involved in ASD treatment
planning and delivery, ultimately parents assume primary
responsibility for choosing, consenting for, and following
through with treatments. Professionals can provide accu-
rate information about treatment options, disseminate up-
to-date research about evidence-based therapies and news
that may affect treatment recommendations, and advocate
for whole-family needs. Understanding factors influencing
parents’ treatment decisions will enhance professionals’
abilities to support families experiencing ASD. As practi-
tioners strive to collaborate with and meet the needs of
affected children and their families, it is our hope that the
current study will contribute meaningfully to knowledge
about the individualized aspects of ASD treatment choices
made by parents.
Appendix A
Preliminary Analysis Tables
Table A1. Descriptive Characteristics of the Revised Illness Perception Questionnaire for Autism (IPQ-RA) Subscales.
IPQ-R subscale M SE SD Range
Identity 8.93 0.387 3.164 2–14
Timeline–Acute/Chronic 23.88 0.572 4.717 6–30
Consequences 22.72 0.612 5.043 9–30
Personal Control 25.1 0.375 3.096 19–30
Treatment Control 19.68 0.366 2.998 10–25
Illness Coherence 16.41 0.478 3.941 6–25
Timeline–Cyclical 11.63 0.41 3.381 4–20
Emotional Representations 20.4 0.597 4.927 9–30
Note. Higher scores on Identity, Timeline–Acute/Chronic, Consequences, Timeline–Cyclical, and Emotional Representations dimensions indicate
strongly-held beliefs, respectively, that symptoms observed are attributable to ASD, that ASD is chronic, that there are negative consequences
associated with ASD, that ASD is cyclical in nature, and that there are negative feelings associated with ASD. High scores on Personal Control,
Treatment Control, and Illness Coherence represent positive beliefs about controllability of ASD and personal understanding of ASD (Using and
Scoring the IPQ-R, n.d.). ASD = autism spectrum disorder; IPQ-R = Revised Illness Perception Questionnaire.
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Mire et al. 9
Table A2. Odds Ratios for Child and Family Variables Retained (p < .05; n = 199) and IPQ-RA Subscales Retained (p < .15; n = 68)
From Preliminary Analyses for Entry Into Main Analyses, by Treatment Category.
Predictors
Treatment category
P-ST S-ST P-OT S-OT Int Bx Oth Int Bio Med Psy Med Any Oth No Tx
Child
Age — — — — — 0.791 0.852 b—b
Age onset 0.956 — 0.965 — — — 0.963 b—b
CSSa— — — — — — — b—b
Verbal cognitive 0.986 0.975 0.988 0.988 0.975 0.972 0.983 b—b
Family
Parent education — — 1.567 — 1.532 — 1.727 b—b
Family income 1.217 — — — — 1.415 — b1.253 b
RA scales
Identity — 1.25 — 1.288 1.373 1.488 — 0.885 — —
Timeline–Acute/Chronic 0.877 — — — — 0.881 — — — 1.884
Consequences — — — — 1.157 — — — — —
Personal Control — — — — — — — 1.227 — —
Treatment Control 1.17 — 1.24 — 1.166 1.57 — 1.411 — —
Illness Coherence — 1.28 — 1.106 — — — — — 0.745
Timeline–Cyclical — 0.844 — — — — — — — —
Emotional Representations — — — — 1.141 — — — — —
Note. IPQ-RA = Revised Illness Perception Questionnaire for Autism; P-ST = Private Speech Therapy; S-ST = School-Based Speech Therapy; P-OT =
Private Occupational Therapy; S-OT = School-Based Occupational Therapy; Int Bx = Intensive Behavioral (e.g., ABA, etc.); Oth Int = Other Intensive
Therapies (e.g., TEACCH, etc.); Biomed = Biomedical (e.g., chelation; vitamins/supplements); Psy Med = Psychotropic Medication; Any Oth = Any Other
Treatment Not Listed (e.g., social skills training, etc.); No Tx = No treatment endorsed; CSS = calibrated severity score; ABA = applied behavior analysis.
aCalibrated Severity Score, calculated from the Autism Diagnostic Observation Schedule (ADOS). bPreliminary analyses indicated test of the full models
with child/family predictors against the constant-only model was not statistically significant (p < .001).
Table A3. Summary of Steps in the Statistical Analysis Plan for Deriving Potential Predictors for Use of Various Treatment Types.
Analysis Purpose Independent variables/predictors
Dependent
variable/
outcome Sample used
How results informed
study
Preliminarya1. Identifying
demographic
and clinical
predictors for
main analyses
Demographic and clinical characteristics:
child age, age of onset, CSSb, verbal
cognitive estimate, household income,
parent education
Did or did
not receive
treatment
type
Full local
sample
(BCM);
n = 199
Variables found to be
predictors of use in each
treatment category
(p < .05) were entered as
IV(s) in main analyses
2. Identifying
IPQ-RA
subscales
predictors for
main analyses
Each IPQ-RA subscale (Timeline–
Acute/Chronic; Timeline–Cyclical;
Consequences; Personal Control;
Treatment Control; Illness Coherence;
and Emotional Representations; Identity)
Did or did
not receive
treatment
type
Recontacted
local sample;
n = 68
Subscales found to be
predictors of use in each
treatment category
(p < .15)c were entered
as IV(s) in main analyses
Main Identifying
predictors of
use of various
treatment types
For respective treatment types, the
demographic and clinical predictor(s)
+ IPQ-RA subscales retained as
predictive, per preliminary analyses
Did or did
not receive
treatment
type
Recontacted
local sample;
n = 68
Demographic, clinical, and/
or parent perception
variables predictive of
using various types of
treatment
Note. CSS = calibrated severity score; IPQ-RA = Revised Illness Perception Questionnaire for Autism; ADOS = Autism Diagnostic Observation
Schedule; IVs = independent variables.
aConducted to identify the most salient potential predictors to include in main analyses for each treatment category. bCalibrated Severity Score from
the ADOS. cIPQ-RA subscales were retained for entry into subsequent logistic regressions main analyses for each treatment category when the odds
ratio (Exp[B]) p < .15, in order to increase the likelihood of retaining subscales that would be important predictors of treatment category use in the
main analyses (Hosmer & Lemeshow, 1989, as cited in Tabachnick & Fidell, 2007).
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10 Focus on Autism and Other Developmental Disabilities
Appendix B
Results Tables
Table B1. Frequency (%) of Demographic Characteristics by Data Set.
Characteristic Current sample (n = 68) BCM site (n = 199) SSC (n = 2,115)
Female 8 (11.9) 26 (13.1) 289 (13.7)
Child race/ethnicity
Asian American 3 (4.5) 9 (4.5) 83 (3.9)
Black/African American 4 (6.0) 9 (4.5) 82 (3.9)
Hispanic/Latino 16 (23.9) 52 (26.3) 230 (10.9)
More than one 3 (4.5)a5 (2.5)a113 (5.3)
Other 28 (1.3)
White 41 (61.2) 123 (62.1) 1,579 (74.7)
Household income ($)b
Less than 51k 13 (19.1) 34 (17.1) 326 (15.4)
51k–100k 22 (32.3) 78 (39.2) 833 (39.4)
>101k 30 (44.1) 80 (40.2) 827 (39.1)
Father education
Less than high school 2 (3.0) 9 (4.5) 46 (2.2)
High school diploma or equivalent 7 (10.5) 20 (10.1) 249 (11.9)
Some college 16 (23.9) 55 (27.8) 537 (25.8)
Bachelor’s/4-year degree 20 (29.9) 70 (35.4) 673 (32.3)
Graduate/professional degree 22 (32.8) 44 (22.2) 580 (27.8)
Mother education
Less than high school 0 (0) 3 (1.5) 23 (1.1)
High school diploma or equivalent 4 (6.0) 19 (9.6) 178 (8.4)
Some college 23 (34.3) 61 (30.8) 610 (29.0)
Bachelor’s/4-year degree 22 (32.8) 69 (34.8) 760 (36.2)
Graduate/professional degree 18 (26.9) 46 (23.2) 530 (25.2)
Note. SSC = Simons Simplex Collection.
aCombination of “More than one” and “Other” categories. bThis represents a summary of number of families’ incomes from these categories; analyses
were conducted using all nine categories of the SSC income variable: less than 20k, 21k to 35k, 36k to 50k, 51k to 65k, 66k to 80k, 81k to 100k, 101k
to 130k, 131k to 160k, and more than 161k.
Table B2. Child-Specific Characteristics by Data Set.
Characteristic
Sample
Current sample (n = 68) BCM site (n = 199) SSC (n = 2,115)
M (SD) Range M (SD) Range M (SD) Range
Child age (yrs) 8.74 (3.7) 4.0–17.11 8.29 (3.38) 4.0–17.11 8.49 (3.50) 4.0–17.11
Age of onset (months) 23.9 (16.92) 1–70 22.82 (14.98) 1–72 21.93 (13.92) 1–108
Verbal cognitive score 76.01 (33.29) 8–140 77.77 (31.09) 8–167 79.42 (30.48) 5–167
ADOS CSSa7.28 (1.66) 5–10 7.29 (1.61) 4–10 7.40 (1.70) 4–10
Note. SSC = Simons Simplex Collection; ADOS = Autism Diagnostic Observation Schedule; CSS = calibrated severity score.
aCalibrated Severity Score, calculated from the ADOS.
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Mire et al. 11
Table B3. Frequencies of Ever Using Different Treatment Categories Across Samples.
Treatment category
Current sample (n = 68) BCM site (n = 199) SSC (n = 2,115)
Frequency (%) Frequency (%) Frequency (%)
Speech therapy–private 35 (51.5) 107 (53.5) 1,113 (52.6)
Speech therapy–school 55 (80.9) 146 (73.0) 1,701 (80.4)
Occupational therapy–private 34 (50.0) 79 (41.8) 876 (41.4)
Occupational therapy–school 28 (41.2) 80 (42.3) 1,406 (66.4)
Intensive behavior 17 (25.0) 46 (24.3) 761 (36.1)
Other intensive 4 (5.9) 16 (8.5) 305 (14.4)
Biomedical 16 (23.5) 42 (21.0) 503 (23.8)
Psychotropics 32 (47.1) 95 (49.0) 883 (41.7)
Any other 20 (29.4) 57 (28.5) 1,098 (51.9)
No treatment 2 (2.9) 17 (8.5) 93 (4.4)
Note. SSC = Simons Simplex Collection.
Table B4. Main Analyses: Forward Stepwise Logistic Regression Results by Treatment Category (n = 68).
Treatment categorya/
predictors retained B SE Wald df p Exp(B)
95% CI for exp(B)
Lower Upper
Private ST
Timeline–acute/chronic −0.233 0.088 7.019 1 .008 0.792 0.667 0.941
School-based ST
Verbal cognitive −0.037 0.015 6.245 1 .012 0.963 0.936 0.992
Private OT
Age onset (months) −0.046 0.019 5.587 1 .018 0.955 0.920 0.992
Treatment control 0.283 0.108 6.928 1 .008 1.328 1.075 1.640
School-based OT
Identity 0.253 0.093 7.415 1 .006 1.288 1.073 1.545
Intensive behavioral
Verbal cognitive −0.023 0.009 6.198 1 .013 0.977 0.960 0.995
Identity 0.279 0.126 4.892 1 .027 1.321 1.032 1.691
Other intensive
Treatment control 0.476 0.234 4.126 1 .042 1.609 1.017 2.546
Biomedical
Child age −0.237 0.114 4.343 1 .037 0.789 0.631 0.986
Age onset (months) −0.066 0.029 5.106 1 .024 0.937 0.885 0.991
Psychotropic meds
Identity −0.198 0.095 4.328 1 .037 0.820 0.681 0.989
Treatment control 0.401 0.121 11.06 1 .001 1.494 1.179 1.893
Note. CI = confidence interval; ST = speech therapy; OT = occupational therapy.
aTest of the final models for Any Other Treatment and No Treatment with predictors against the constant-only models were not statistically significant
(p < .001).
Acknowledgments
We are grateful to all of the families at the participating Simons
Simplex Collection (SSC) sites, as well as the principal inves-
tigators (A. Beaudet, R. Bernier, J. Constantino, E. Cook, E.
Fombonne, D. Geschwind, R. Goin-Kochel, E. Hanson, D.
Grice, A. Klin, D. Ledbetter, C. Lord, C. Martin, D. Martin, R.
Maxim, J. Miles, O. Ousley, K. Pelphrey, B. Peterson, J. Piggot,
C. Saulnier, M. State, W. Stone, J. Sutcliffe, C. Walsh, Z.
Warren, E. Wijsman).
Authors’ Note
Approved researchers can obtain the SSC population data set
described in this study (https://ordering.base.sfari.org/~browse_
collection/archive[sfari_collection_v14_1]/ui:view())) by apply-
ing at https://base.sfari.org.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect
to the research, authorship, and/or publication of this article.
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12 Focus on Autism and Other Developmental Disabilities
Funding
The author(s) disclosed receipt of the following financial support
for the research, authorship, and/or publication of this article: This
work was supported by a grant from the Simons Foundation
(Simons Foundation Autism Research Initiative [SFARI] to
Simons Simplex Collection [SSC-15] to R. Goin-Kochel and A.
Beaudet).
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Author Biographies
Sarah S. Mire is Assistant Professor of Psychological, Health, &
Learning Sciences at the University of Houston. Currently, Sarah’s
primary research focuses on how family systems are impacted by
autism spectrum disorder, particularly as it relates to treatment
selection and implementation.
Whitney Gealy is a doctoral candidate in the School Psychology
PhD program at the University of Houston. Whitney is interested
in researching familial impacts associated with autism spectrum
disorder.
Tom Kubiszyn is Professor Emeritus in Psychological, Health, &
Learning Sciences at the University of Houston. Tom’s research
has focused on considerations related to both psychosocial and
psychotropic treatment for children and adolescents.
Andrea Backscheider Burridge is Clinical Professor and
Associate Dean of Undergraduate Studies in the College of
Education at the University of Houston. Andrea studies correlates
of change throughout the lifespan.
Robin P. Goin-Kochel is Assistant Professor in the Department
of Pediatrics at Baylor College of Medicine and the Associate
Director for Research for the Autism Center at Texas Children’s
Hospital. Robin has been researching various aspects of autism
spectrum disorder for the past 14 years, including parents’ use of
various treatments for their children.
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