637503
research-article2016
AUT0010.1177/1362361316637503AutismEditorial
Editorial
The importance of characterizing
intervention for individuals with autism
The estimated annual cost of caring for individuals with
autism is US$268 billion (Leigh and Du, 2015). What is
this buying? Beyond a general knowledge of setting and
type of service provided (e.g. educational, mental health,
speech therapy, and occupational therapy), we know very
little about the type and quality of interventions being
delivered. For several reasons, it is imperative that we be
able to accurately and efficiently characterize the treatment
that children with autism receive in their communities.
One reason for this urgency links treatment to our basic
understanding of mechanism and subtype in autism. Most
efforts to identify different kinds of autism, using either
behavioral or biological measures, have been frustratingly
disappointing. Yet there is still growing consensus that
autism really comprises a set of phenotypically linked disorders, and that if we could distinguish among them, we
could improve our understanding of their basic biology,
leading to more targeted treatments and supports. An
important unexplored strategy for subtyping autism may
be to examine response to treatment. Thus far, we have
examined predictors of treatment success; however, these
have primarily been relatively gross measures of intelligence and language use (e.g. Ben-Itzchak and Zachor,
2011). A few studies have examined specific behaviors
relating to response to specific, well-characterized treatments (e.g. Schreibman et al., 2009; Sherer and
Schreibman, 2005; Yoder and Stone, 2006). However,
these studies have not led to phenotypic descriptions that
provide a better understanding of either the biology that
may subtype the disorder or methods to prescribe treatment a priori (Stahmer et al., 2010; Vivanti et al., 2014). If
we wish to examine this question, we will either have to
standardize treatment for a very large and heterogeneous
sample of individuals with autism—a very costly
endeavor—or we will have to be able to measure the active
ingredients of treatment in the schools and clinics in which
treatment is delivered.
The second reason for characterizing intervention is the
need to identify active mechanisms, with the goal of refining treatments (e.g. Kasari and Lawton, 2010; Schreibman
et al., 2015). Due to the complexity of treatment of autism,
many evidence-based interventions are packages composed of several components. Each of these components
has a cost associated with implementing it, including
the time it takes to train providers to accurately use each
Autism
2016, Vol. 20(4) 386­–387
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component. Evidence-based interventions with varying
brand names may utilize similar components even when
differing in overall theoretical orientation (Schreibman
et al., 2015) and may lead to similar outcomes when implemented with high fidelity (Boyd et al., 2014). However,
these interventions may also differ in important ways.
Therefore, it is important to examine the relative contribution, mechanism of action, and necessity of each component of an intervention package to achieve optimal child
outcomes (Lerner et al., 2012; Sanetti et al., 2009; Schulte
et al., 2009). We know that community providers often
pick and choose specific components of an intervention to
fit their clients and setting rather than use the package as a
whole (Stahmer et al., 2005). Examining the impact of
individual components of an intervention will help determine whether their methods of modifying and combining
treatments are likely to be both effective and efficient. If
we could measure the delivery of each component accurately, we could dismantle components that are mostly
associated with positive outcome.
The third reason to characterize interventions is that
we can measure the quality of care individuals with
autism receive. The service delivery infrastructure for
individuals with autism has not kept pace with the dramatically increasing number of children diagnosed.
Service providers rush to hang out shingles saying that
they treat autism, but there are few licensure and credentialing practices in place. Although this has increased
access to care, there continues to be extreme variability
in service intensity and quality. Even if we measure the
number of service hours, and perhaps the “type” of service, which may often include educational services,
speech and language therapy, occupational therapy, parent-implemented intervention, and social skills groups,
there is a huge range in the quality of intervention within
these services. Families and payers have few ways of
assessing the quality of the care individuals with autism
receive. Similar to quality assurance and improvement
procedures that have been put in place to assess the care
provided for other health conditions, we need accurate
measures that can be implemented with relatively little
time burden to assess quality of care.
The measures currently available are narrow in scope,
either focused on a specific intervention or a specific service
setting. Typically, intervention developers will create a
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387
Editorial
protocol to guide implementation or a tool for assessing
fidelity of implementation. Developing accurate, low-cost
fidelity measurement is in many ways more challenging.
The process of measuring fidelity involves several elements: (1) identifying important treatment components, or
“key ingredients”; (2) developing an instrument that allows
for valid and reliable measurement of these components;
and (3) identifying a system for assessing the components,
including methods for providing feedback to providers
(Schoenwald et al., 2011). The fidelity process must measure the key ingredients of an intervention and be both psychometrically sound (effective) and usable in clinical care
(efficient). Researchers in children’s mental health have
validated fidelity measures for clinicians and supervisors in
community settings for individual evidence-based practices
(e.g. Multi-Systemic Therapy; Schoenwald and Henggeler,
2003); however, most methods of rating fidelity in autism
have primarily been used for research.
There have been some important, recent efforts to characterize program quality without focus on a specific intervention. For example, the Autism Program Environment
Rating Scale (APERS), developed by the National
Professional Development Center for Autism, is a tool to
guide professional development and coaching efforts in
educational settings. However, measures developed so far
are typically setting specific, time consuming, and require
significant clinical expertise to ensure accuracy (e.g. Hume
et al., 2011; Mandell et al., 2013). Developing methods for
measuring quality indicators in usual care for ASD across
service systems (i.e. education, mental health, and affiliated care) and interventions (e.g. behavioral, developmental, and environmental) that are efficient and accurate will
require a targeted and coordinated effort among intervention researchers and community collaborators.
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Aubyn C Stahmer1, Jessica Suhrheinrich2
and David S Mandell3
1MIND Institute and Department of Psychiatry
and Behavioral Sciences, School of Medicine,
University of California, Davis, USA
2Child and Adolescent Services Research Center and
Department of Psychiatry, School of Medicine, University
of California, San Diego, USA
3Center for Mental Health Policy and Services Research
& Department of Psychiatry and Pediatrics, Perlman
School of Medicine, University of Pennsylvania, USA
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