FOI23.24 1400 Documents 7 and 8 ASD Diagnoses Disclosure log.pdf

FOI 23/24- 1400
DOCUMENT 8
ASD and DLD diagnoses after 6 years old
The content of this document is OFFICIAL.
Please note:
The research and literature reviews collated by our TAB Research Team are not to be
shared external to the Branch. These are for internal TAB use only and are intended to
assist our advisors with their reasonable and necessary decision-making.
Delegates have access to a wide variety of comprehensive guidance material. If
Delegates require further information on access or planning matters they are to call the
TAPS line for advice. The Research Team are unable to ensure that the information
listed below provides an accurate & up to date snapshot of these matters
Research questions:
1. What are the types of later life developmental disorder diagnoses?
2. What is the incidence of diagnoses for Autism Spectrum Disorder (ASD) and
Developmental Language Disorder (DLD) that occur after the age of 6?
3. What is the incidence of diagnosis for ASD for age groups:
• 0-6
• 7-15
• 16 and above?
4. What is the impact of a later diagnosis on the functional capacity and severity of
symptoms of people diagnosed with ASD or developmental delay?
5. Are there triggers or acute events that precipitate diagnoses?
6. What is the impact of the resolution of an acute event on functional capacity regardless
of diagnosis?
7. Are there therapies / treatments / protocols designed for people with later in life
diagnoses?
8. What is the impact on prevalence of changes to ASD criteria between DSM-IV and
DSM-5?
Date: 15/02/2022
Requestor: Jane Searle
Endorsed by (EL1 or above): n/a
Cleared by: Stephanie Pritchard
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1. Contents
ASD and DLD diagnoses after 6 years old................................................................................. 1
1.
Contents ....................................................................................................................... 2
2.
Summary ...................................................................................................................... 2
3.
Diagnosing developmental disabilities later in life ......................................................... 3
4.
Developmental Language Disorder............................................................................... 4
5.
Later diagnoses and autism .......................................................................................... 6
5.1  Age of first diagnosis ................................................................................................. 6
5.2  Reasons for later diagnosis ....................................................................................... 7
5.3  Outcomes for people with later diagnoses ................................................................. 8
5.4  Supporting people with later life diagnoses ............................................................... 9
5.5  Effect of DSM-5 on ASD prevalence........................................................................ 10
6.
References ................................................................................................................. 10
7.
Version control ............................................................................................................ 15
2. Summary
Researchers continue to improve early identification methods targeting developmental
disorders. This can reduce the waiting time for children to be diagnosed and for intervention to
begin. In some cases, children do not receive an accurate diagnosis until later childhood or
adolescence. Some are not diagnosed until adulthood. This paper focusses on the incidence
and impact of diagnosing Autism Spectrum Disorder (ASD) and Developmental Language
Disorder (DLD) after the age of 6.
There is limited information directly answering the research questions for an Australian
context. I have gathered information relevant to the research questions which may
approximate answers.
Issues related to overal  prevalence of ASD have been investigated in another TAB research
paper, RES 222 ASD diagnoses.
Types of later life developmental disorder diagnoses
Neurodevelopmental disorders (NDD) are a subset of developmental disorders defined by the
Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-5). They are one of the
most common classifications of childhood diagnoses and clinicians aim to diagnose the child
as early as possible. Personal, clinical, social and environmental factors can delay diagnosis.
Incidence of later life diagnoses
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No reliable and comprehensive estimates were found for incidence of later diagnoses of ASD
and DLD nationally or across all age groups. There is evidence that later diagnoses of DLD
are common in people with a history of involvement with youth justice and child protection.
Internationally, the latest systematic review finds the mean age for first diagnosis of ASD is
60.48 months (5 years). However, the best data for Australia suggests mean age of first
diagnosis is 6 years for children diagnosed before the age of 13. This is likely to be
considerably higher if older age groups are incorporated into the estimate.
Events leading to later life diagnoses
There is evidence suggesting personal, social and environmental factors can predict whether
someone will receive a later diagnosis of ASD. However, there is very little evidence
describing events that precipitate a diagnosis. One study suggests adults choose to begin the
assessment process due to encouragement by parents or spouses, difficulties with social
interaction or mental health issues.
Outcomes for people with later life diagnoses
There is evidence establishing the effectiveness of early intervention for people with ASD.
There is less evidence establishing the adverse outcomes for people with later diagnoses
though existing evidence does support the correlation of reduced functional capacity and
increased comorbid conditions in people with later diagnoses. People with missed diagnoses
of DLD are overrepresented in the youth justice and child protection systems.
Supports for people with later life diagnoses
All interventions should be age-appropriate and targeted at the person’s developmental stage.
For older people this may mean interventions targeted at achieving life-stage outcomes such
as employment and independent living. For people with ASD this may also mean accounting
for the likelihood of comorbid conditions.
Prevalence of ASD after DSM-5
Refer to RES 222 ASD diagnoses for further information. The restriction of DSM-5 diagnostic
criteria for ASD has contributed to a reduction in the number of ASD diagnoses even as the
prevalence of ASD continues to rise. The rise in prevalence should be attributed to factors
other than the change in diagnostic criteria in the DSM-5.
3. Diagnosing developmental disabilities later in life
The DSM-5 defines a group of NDDs which begin to manifest early in life and usually before
the child enters school. NDDs can be global (affecting general intelligence or social skills) or
specific (affecting specific aspects of learning or control of executive function) (DSM-5, 2013,
p.31). They include intellectual disabilities, communication disorders, ASD, attention deficit
hyperactivity disorder, specific learning disorder and motor disorders (including movement and
coordination disorders and tic disorders) (p.xiv-xv).
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Diagnosis of NDD is one of the most common types of diagnoses among children. Among
NDDs the most common are learning disorders (8%), DLD (7%), ASD (2%) and ADHD (2%)
(Micai et al, 2020, p.183). Behavioural signs are often observable within the first year of a
child’s life and some indications are known prior to the child’s birth. For example, if the child
has a sibling or other family member with a diagnosis of NDD it increases the risk that they will
also have an NDD (Micai, 2020).
There are several factors that may delay a diagnosis. For example, it may be hard to
determine if a child has social communication deficits until they are in situations which demand
more sophisticated social behaviour. NDDs are often co-occurring, which introduces the risk of
one diagnosis overshadowing other potential conditions and leading to later or missed
diagnoses. Diagnoses can be a lengthy and costly process, which may delay the diagnosis
itself or discourage some parents from beginning the process at all (Valentine et al, 2020;
Micai et al, 2020).
4. Developmental Language Disorder
Many children show significantly slowed language development before the age of three.
However, most of these children catch up to their peers after the age of three allowing them to
perform within normal limits on linguistic tasks. Children who do not catch up may be
diagnosed with DLD (Sansavini et al, 2021, p.2). DLD is the recommended label for language
disorders that are not associated with a specific cause (e.g. autism, down syndrome). The
term Specific language impairment (SLI) has also been used but the applications differ slightly
(McGregor, 2020, pp.39-40). Prevalence of DLD among children is roughly 7% (Walker &
Haddock, 2020, p.2; Ebbels et al, 2016, p.2). A report from the Deeble Institute of Health
Policy states that prevalence among children in Australia may be as high as 17%, with higher
rates in children from disadvantaged backgrounds (Walker & Haddock, 2020, p.2). This
estimate is unreliable. The authors admit the estimate is based on minimal data and they do
not offer an age range for the estimate.
I could not locate information on incidence of diagnoses by age. However, a 2021 review of
systematic reviews suggests that the optimal screening time for DLD is between 2 and 3
years, with a diagnosis expected around 4 years (Sansavini et al, 2021, p.2). However,
evidence is mixed with earlier screening increasing risk of false positives and later screening
increasing risk of negative consequences for the child (p.20).
There is some evidence that DLD is often missed entirely or misdiagnosed in childhood
(McGregor et al, 2020, p.40). A 2013 Australian study of 1607 children found only 45% of
children with communication problems received any help before the age of 5 and only 33%
received speech therapy (Skeat et al, 2014, p.219; Walker & Haddock, 2020, p.4). This does
not differentiate communication problems from DLD specifically and so we can’t
straightforwardly conclude that most children with DLD are undiagnosed. It should also be
noted that this study was prior to NDIS making early intervention available to more families.
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A more recent Australian study of young people leaving ‘out of home’ care found that mean
language scores were 2 standard deviations below the average. This level of deficit is often
used as an indication of DLD. Despite this only a single participant in the study had a
diagnosis relating to language difficulties (Snow et al, 2020, p.155). In another study of 44
young people leaving ‘out of home’ care between 16 and 26 years of age, Clegg et al found
that over 60% met criteria for DLD and yet none had a diagnosis (Clegg et al, 2021, p.2).
Results are similar in a youth justice setting (Snow et al, 2020, p.153; Clegg et al, 2021, p.2)
with Winstanley et al finding 60% of their sample of youth offenders having met criteria for DLD
despite no previous diagnoses (2021, p.399). These findings point to a high rate of unidentified
DLD in young people with involvement of child protection or justice and is consistent with
findings of previous studies that children from disadvantaged backgrounds are more likely to
experience language difficulties (Walker & Haddock, 2020). Because the sample sizes of the
studies were smal and the populations unrepresentative, it is not possible to use them to
reliably estimate prevalence of undiagnosed adolescents or young adults.
According to Walker and Haddock, research into the long-term effects of language impairment
in an Australian context is limited. However:
International longitudinal studies have found that children with language disorders who
do not receive intervention achieve lower levels of education and are subsequently at
higher risk of lower wages and reliance on welfare and of higher levels of redundancy,
under-employment and workplace conflict (2020, p.3).
As of 2016, most studies into the effectiveness of generalised intervention for symptoms of
DLD in school-age children (i.e. children over early intervention age) found no significant
effect. Results were more positive if the treatment group did not have receptive language
difficulties, which are more likely to persist and more difficult to treat. Some positive results for
receptive language skills were found if the interventions were targeted at specific areas, e.g.
receptive vocabulary, word finding, comprehension of specific grammatical structures, etc.
(Ebbels et al, 2016 pp.2-3). Ebbels et al found significant improvement on receptive and
expressive language skills in primary and secondary aged students with DLD receiving 1:1
speech and language therapy (2016, pp.8-9).
Sansavini et al note the consensus in the literature on the importance of early intervention and
diagnosis of DLD (Sansavini et al, 2021, p.14.). A 2021 systematic review of treatment studies
found some evidence for the effectiveness of early intervention on some areas of language
development. Early intervention effects last in the medium term for developing phonological
skills but results of intervention targeting general language skills is mixed (Rinaldi, 2021,
pp.18-19). For example, an Australian study by Wake et al compared the effect of home-based
therapy sessions on children with language disorders with typically developing children. After 2
years they found language abilities for children in the treatment group normalised though they
could not discern a significant effect of therapy sessions on most aspects of language
development, including receptive and expressive language. Some effect was discerned for
phonological awareness with a possible effect on reading ability (Wake et al, 2015, p.843).
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5. Later diagnoses and autism
5.1 Age of first diagnosis
I was able to find only one study using Australian data that tracks age of first diagnosis for
cohorts over 12 years old (Atherton et al, 2021) but this study was based on only 200 people. I
am unable to give a good account of incidence of first diagnosis for the age groups requested.
Two systematic reviews have tracked age at first autism diagnosis between 1990 and 2019.
Daniels and Mandell (2014) reviewed 42 papers published between 1990 and 2012. They
provide a wide mean age range for first diagnosis at between 38 and 120 months. Van t’ Hof et
al (2021) analysed data from 56 studies and found a mean age for first diagnosis of 60.48
months (5 years) with a mean age range of between 30.9 months and 234.57 months (2021,
p.862). The ranges provided by these reviews are significantly affected by age of participants
in the studies reviewed. Many studies included only children, some studies included only older
people. Daniels and Mandell use data from 12 countries. Van t’ Hof et al use data from 40
countries. Both reviews include a single study from Australia (Daniels & Mandell, 2014, p.14-
17; Van t’ Hof, 2021, p.867).
International data indicates that age of diagnosis is decreasing (Daniels & Mandell, 2014, p.6;
Sheldrick et al, 2017; Hanley et al, 2021). This contrasts with a recent UK-based study that
found mean age of diagnosis rose from 9.6 years in 1998 to 14.5 years in 2018 (Russell et al,
2021, p.3). This might be explained by the fact that the Russell et al considered the entire UK
population with an ASD diagnosis whereas the 2015 and 2021 systematic reviews included
mostly studies of children. It may also be explained by regional differences in early intervention
(Daniels & Mandell, 2014, p.10).
I have located 4 studies based on Australian data which discuss age of first diagnosis for ASD.
Nassar et al was included in the Daniels and Mandell systematic review and focused on West
Australian children between 2 and 8 years old. They found the mean age of first diagnosis
decreasing from 4 years to 3 years throughout the 1990s (Nassar et al, 2009, p.1245). A study
from Bent et al was included in the Van t’ Hof systematic review and focused on children under
7 years. They found a mean age of first diagnosis of 49 months (Bent et al, 2015, p.318). May
and Williams (2018) was not included in any of the reviews and looked at children under 13
years. Atherton et al was not included in any of the reviews and looked at 200 adults with ASD
between 18 and 57 years.
According to May and Williams, the average age of diagnosis of children aged 0-12 years old
is 6 years. The average is slightly higher in female children at 6.22 years. This estimate is
based on Medicare data tracking first diagnosis item numbers from 2008 until 2016 and
considers 73,463 children. The most frequent age of diagnosis is 5 until the year 2015/2016
when it lowers to 4 (May & Williams, 2018, p.5). In line with Russell et al (2021), May and
Williams find that the rate of increase of older children being diagnosed is higher than the rate
of increase for children under 5 (2018, pp.4-5). While this study underestimates total
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prevalence due to limitations in the data, it likely captures most diagnoses occurring in this age
range (p.2).
Based on a rough estimate obtained from a study by May and Williams (2018, p.4), for
Australians diagnosed with ASD under the age of 12, 49% were diagnosed under the age of 6
and 51% were diagnosed between 6 and 12 years old. However, these shares will be
significantly different when considering all those diagnosed with ASD in adolescence and
adulthood. The average age of first diagnosis is bound to be higher than 6 when considering
the entire population of Australians with ASD. This indicates that the average age of first
diagnosis is above the early intervention age (>6) (Goodwin et al, 2018, p.2). This would be
consistent with studies of other national populations. Atherton et al found the average age of
diagnosis for their adult cohort was 15 for males and 21 for females (Atherton et al, 2021, p.4).
5.2  Reasons for later diagnosis
The rate of older people being diagnosed with autism is increasing. This appears to be true for
adults (Russell et al, 2021, p.6) and older children (May & Williams, 2018, pp.4-5). Avlund et
al. (2021) identify reasons that children may not receive an ASD diagnosis until later childhood
or adolescence including:
•  symptoms of other developmental disorder overshadow social impairments
•  diagnostic threshold may not be met until it is clearer that the social
demands on the child exceed their abilities
•  the autistic symptoms may be expressed dif erently in early and later
childhood
•  socio-economic factors may influence the support a child receives (Avlund
et al., 2021; Parikh et al, 2018).
A Melbourne based study also identifies limitations on resources as a primary reason that
people do not receive a diagnosis until adolescence. They also note that symptoms being
missed by the school system or primary care physician may result in missed diagnosis
(Aggarwal & Angus, 2015, p.4).
International trends confirm that children are more likely to be diagnosed earlier if they have
more severe autistic symptoms and more likely to be diagnosed later if they have milder
autistic symptoms (Daniels & Mandell, 2014, p.7; Sheldrick et al, 2017 p.8; May & Williams,
2018, p.1; Parikh et al. 2018, p.6; Hanley et al, 2021, p.5; Avlund et al, 2021, pp.3849-3850).
There is also some evidence to suggest that more severe symptoms can delay a diagnosis of
autism if they are interpreted as symptoms of intellectual disability (Avlund et al, 2021, p.3851).
A 2021 study by Atherton et al contrasts with the prevailing opinion, suggesting that people
diagnosed later do not present differently but diagnoses may be missed due to environmental
factors (Atherton et al, 2021, p.6). However, their results are also compatible with a worsening
of symptoms over time in adults lacking proper diagnosis.
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Barriers to adult autism diagnosis may include the following:
•  there are few adult diagnostic screening tools
•  difficulty remembering or recovering early developmental history
•  limited understanding of adult autism in health professionals
•  specialist multi-disciplinary team is often needed
•  it requires significant time and effort from the patient
•  symptoms of other conditions may mask autistic symptoms
•  misdiagnosis or camouflaging of symptoms (Rødgaard et al, 2021, p.5;
Scattoni et al, 2021, p.4130; Adamou et al, 2021, pp.1-2; Lai & Baron-
Cohen, 2015; Legg et al, 2022, p.1).
There is mixed evidence to support these ideas. Rødgaard et al find that misdiagnosis or
overshadowing of other childhood diagnoses may account for some of the reason autism
diagnoses are missed. However, only 31% of males and 39% of females had childhood
diagnoses at all, meaning that misdiagnosis or overshadowing cannot explain why diagnoses
was not given in childhood for most later diagnosed people (2021, p.2).
A 2020 scoping review notes that factors prompting adult diagnosis include encouragement by
parents or spouse, difficulties with social interaction or mental health issues (Huang et al,
2020).
5.3  Outcomes for people with later diagnoses
I could find only a single study that investigates quality of life for people diagnosed with autism
later in life. Atherton et al found that people diagnosed earlier scored better on quality-of-life
measures than people diagnosed later. Increasing age of diagnosis was correlated with
increased social anxiety, social avoidance, and a lack of social support (2021, p.6).
Strong evidence suggests early intervention supports for children with ASD are ef ective in
improving outcomes (Avlund et al, 2021, p.3843; Whitehouse et al, 2020; Productivity
Commission, 2017; Clark et al, 2017, p.2; Zwaigenbaum et al, 2015, p.6; Estes et al. 2015).
When children are diagnosed earlier, they have more access to services and interventions
when their brains are most malleable. This means they can acquire skills from a younger age
and build on these skills through their school years (Clark et al, 2017, pp.1-2).
Clark et al (2017) compared two groups of 7–9-year-olds with earlier or later diagnosis. The
first group received diagnoses at 24 months. The second group received diagnoses between 3
and 5 years old. Those children diagnosed later received interventions later, received
significantly less overall intervention, were slightly less likely to attend mainstream schooling,
received more support at school age, had lower cognitive and language ability and were more
likely to have an intellectual disability. These findings support the idea of improved outcomes
for people diagnosed earlier and reduced functional capacity for people diagnosed later.
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However, considering the ages of the comparison groups, this study may not reflect the
outcomes for people diagnosed after early intervention age (<6 years). On the other hand, the
underlying theory behind the effect is that earlier intervention works by making use of younger
children’s more malleable brains (Clark et al, 2017, pp.1-2; Anderson et al, 2014, p.8). If this is
true then we may predict a similar trend for people diagnosed after early intervention age. As
we found in 4.1 Later diagnosis of autism, this prediction is complicated by confounding factors
such as multiple diagnoses for people with ASD, which may mean that they receive
interventions targeting autistic symptoms even without a diagnosis of ASD.
Adults with autism typically have multiple diagnoses (Pelicano et al, 2020; Keller et al, 2020;
Lai & Baron-Cohen, 2014). Adults with autism have an increased risk of depressive disorders,
anxiety disorders, obsessive-compulsive disorder, attention deficit hyperactivity disorder, and
personality disorders:
•  more than 50% show increased depressive symptoms or depressive
disorder
•  as many as 66% report suicidal thoughts
•  more than 50% may be diagnosed with anxiety disorders
•  up to 40% may be diagnosed with attention deficit hyperactivity disorder
•  up to 30% may be diagnosed with obsessive-compulsive disorder (Lai &
Baron-Cohen, 2014, pp.1018-1019).
This information does not specify age of first diagnosis. However, there is evidence to suggest
that later diagnosed people are more likely to have additional diagnoses (Daniels & Mandell,
2014; Goodwin et al, 2018; Pelicano et al, 2020; Rødgaard et al, 2021). A study of school age
children by Goodwin et al notes that of people diagnosed between 5 and 18 years old, 58%
had a psychiatric diagnosis. Of people diagnosed before 5 years old, only 29% had an
additional diagnosis (Goodwin et al, 2018, p.4). In a smal  qualitative study of late diagnosed
adults with autism, Pelicano et al note that of 28 participants in the study, 16 had at least one
other psychiatric diagnosis and only 4 did not have any other medical condition (Pelicano et al,
2020, pp.21-23).
5.4  Supporting people with later life diagnoses
There is little research of the post-diagnostic needs of adults with ASD (Scatoni, 2021, p.2).
Adults diagnosed with autism later in life have complex reactions and family, friends and
clinicians supporting them should be aware of the potentially life-changing consequences of an
adult diagnosis. In particular, later diagnosed adults and their caregivers report frustration with
lack of post-diagnostic support (Legg et al, 2022, p.2; Scatoni et al, 2021, p.4142).
The UK’s National Institute for Health and Care Excel ence (NICE) has developed a series of
clinical guidelines for people with autism. They recommend supports should be tailored to the
person’s age and developmental level (NICE, 2021a, para. 1.3.1). However the
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recommendations for support do not differ substantially for adults and young people except
regarding their relative levels of autonomy and stages of life. For example, supported
employment programmes (NICE, 2021b, paras. 1.4.11-12) or residential care programmes
(paras. 1.8.11-14) could be considered for adults with autism.
Considering the increased risk of co-morbid diagnoses as described in 5.1 Outcomes for
people with autism, an increased focus on physical and mental health may be warranted. A
2020 systematic review by Benvenides et al found both cognitive behavioural therapy and
mindfulness techniques had an emerging body of evidence as strategies for improving the
health outcomes of older adults with autism. However, there is evidence that both strategies
are also useful for children with autism (Benvenides et al, 2020, p.1351).
5.5 Effect of DSM-5 on ASD prevalence
For more information please refer to RES 222 ASD diagnoses.
A 2019 systematic review investigated the effect of the changes to ASD diagnostic criteria
between the DSM-IV-TR and the DSM-5. They found that approximately 1 in 5 people who
would have received a diagnosis in DSM-IV-TR would not have received a diagnosis in the
DSM-5. Further, only 28.8% of those who no longer meet ASD criteria would go on to meet
diagnostic criteria for Social Communication Disorder (SCD) (Kulage et al, 2019, p.19). This
means roughly 14% of people who met diagnostic criteria under DSM-IV no longer meet
criteria for ASD or SCD. It is unclear what proportion of those people would go on to meet
diagnostic criteria for other conditions and what proportion would remain below threshold for
any DSM-5 diagnosis. According to this review, DSM-5 is contributing to a reduction in ASD
diagnoses while the overall prevalence estimates continue to rise.
6. References
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autism spectrum disorder using the autism diagnostic observation schedule, second
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Atherton, G., Edisbury, E., Piovesan, A., & Cross, L. (2021). Autism through the ages: A mixed
methods approach to understanding how age and age of diagnosis affect quality of life.
Journal of Autism and Developmental Disorders. doi/10.1007/s10803-021-05235-x
Aggarwal, S., & Angus, B. (2015). Misdiagnosis versus missed diagnosis: diagnosing autism
spectrum disorder in adolescents. Australasian Psychiatry: Bulletin of Royal Australian
and New Zealand College of Psychiatrists, 23(2), 120–123.
doi/10.1177/1039856214568214
American Psychiatric Association. (2013). Diagnostic and statistical manual of mental
disorders (DSM-5) (5th ed.). American Psychiatric Association Publishing.
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Avlund, S. H., Thomsen, P. H., Schendel, D., Jørgensen, M., Carlsen, A. H., & Clausen, L.
(2021). Factors associated with a delayed autism spectrum disorder diagnosis in
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7. Version control
Version Amended
Brief Description of Change
Status
Date
by
1.0
AHR908 Research paper on later life diagnoses for
Final
15/02/2022
ASD and DLD

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DOCUMENT 9
Diagnoses of Autism Spectrum Disorder using
the DSM-5
The content of this document is OFFICIAL.
Please note:
The research and literature reviews collated by our TAB Research Team are not to be
shared external to the Branch. These are for internal TAB use only and are intended to
assist our advisors with their reasonable and necessary decision-making.
Delegates have access to a wide variety of comprehensive guidance material. If
Delegates require further information on access or planning matters they are to call the
TAPS line for advice.
The Research Team are unable to ensure that the information listed below provides an
accurate & up-to-date snapshot of these matters
Research questions:
1. What is the accuracy of Autism Spectrum Disorder diagnoses using the DSM 5,
particulary for ASD levels 2 and 3 and particularly focussing on the interrater reliability of
single discipline assessments?
2. What is the incidence of ASD diagnosis among family members? How likely is it that
multiple siblings in a family will all have Autism Spectrum Disorder?
3. How has the rate of diagnosis of ASD changed since the publication of the DSM 5
diagnostic criteria?
Date: 10/12/2021
Requestor: Shannon Atkins
Endorsed by (EL1 or above): Shannon Atkins
Cleared by: Felicity Fallon
1. Contents
Diagnoses of Autism Spectrum Disorder using the DSM-5 ........................................................ 1
1.
Contents ....................................................................................................................... 1
2.
Summary ...................................................................................................................... 2
3.
Frequency of ASD diagnoses in families ...................................................................... 2
4.
Accuracy and inter-rater reliability of ASD diagnoses using DSM-5.............................. 3
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5.
Influence of DSM-5 ASD criteria on the prevalence of ASD ......................................... 4
6.
Literature Summary ...................................................................................................... 6
7.
References ................................................................................................................. 19
8.
Version control ............................................................................................................ 20

2.  Summary
This literature review addresses questions relating to the prevalence of Autism Spectrum
Disorder (ASD). Findings include:
•  ASD is strongly genetic. If someone has a family with ASD it is more likely that they
will be diagnosed with ASD and it is more likely they will display autistic traits even if
they don’t meet the threshold for a diagnosis.
•  DSM-5 diagnoses of ASD are overall more accurate than DSM-IV diagnoses. A true
positive diagnosis is more likely if multiple assessment tools are used in the context
of a multi-disciplinary team.
•  The changes to DSM-5 ASD criteria likely reduced the frequency of ASD diagnoses,
although prevalence continues to rise as a result of other factors.
These findings are provisional and may be altered with further research. Evidence supporting
the high heritability of ASD is strong. Evidence is less reliable for prevalence estimates and
accuracy of diagnoses. There is significant effort to understand the prevalence of ASD
worldwide and to understand the effect of changes to the DSM-5 criteria. However, current
studies are often marred by bias, lack of controls and smal  or unrepresentative samples. That
being said, there is wide-spread consensus in the literature around the above findings.
3.  Frequency of ASD diagnoses in families
Estimations of heritability of ASD range from 0.64 – 0.91, with some consensus emerging in
the range 0.80 – 0.87 (Bai et al 2020; Sandin et al 2017; Tick et al 2016). High heritability
means that for any two people, the more genes they share with each other, the more likely it is
that they will share the highly heritable trait (Downes & Matthews, 2020). The closer the
genetic relationship between a person with ASD and their relative, the more likely the relative
will also have ASD. The literature notes recurrence rates of 80% for identical twins and 20%
for non-identical siblings (Bai et al 2020; Girault et al 2020).
This is supported by population-based studies showing the likelihood of a person having ASD
is increased if they have a family member with ASD (Girault et al 2020; Bai et al, 2020;
Hansen et al 2019). One study predicts a 2-fold increase in likelihood of ASD diagnosis if you
have a cousin with ASD and an 8-fold increase in likelihood of ASD diagnosis if you have an
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older sibling with ASD (Hansen et al 2019). Girault et al (2020) also note that a sibling is even
more likely to get a diagnosis of ASD if there are multiple people in the family with ASD.
Family members are also more likely to have more autistic traits (short of an ASD diagnosis) if
someone in the family is diagnosed with ASD (Girault et al 2020; Page et al 2016). Girault et al
also notes that a person with ASD getting a higher score on the Social Communication
Questionnaire results in an increased chance of their sibling getting a diagnosis of ASD
(Girault et al 2020).
4. Accuracy and inter-rater reliability of ASD diagnoses
using DSM-5
While I was able to locate information establishing inter-rater reliability of DSM-5 ASD
diagnoses, this should be treated with caution. The results do not come from studies that
explicitly set out to study the accuracy of DSM-5 diagnoses. Studies examining other features
of ASD or ASD diagnostic practices will often use inter-rater reliability to ensure study quality.
In their study of ASD prevalence, Baio et al found 92.3% inter-rater agreement on presence or
absence of ASD using DSM-5 criteria (2018, p.7). Taheri et al secured 100% inter-rater
agreement for overall diagnosis and between 70% and 100% agreement on individual criteria
(2014, p.118). In their study of gender differences in ASD diagnosis, Hiller, Young and Weber
found substantial inter-rater agreement with Cohen’s kappa scores of between 0.75 and 0.93
(2014, pp.4-5). Young and Rodi also secured strong inter-rater agreement for overall diagnosis
with Cohen’s kappa score of 0.91 (2014, p.761). These results demonstrate potential for high
inter-rater agreement with DSM-5 ASD diagnoses, with somewhat lower agreement in
individual criteria. They do not speak to accuracy of severity ratings (i.e. requiring support,
requiring substantial support, requiring very substantial support).
Mazurek et al (2019) looked at use of severity ratings among clinicians. They found that
assessment of severity levels of social communication and restrictive, repetitive behaviours
using DSM-5 criteria largely agrees with other assessment tools as well as parental
assessment of severity (p.7). However, they do point out a strong link between intelligence and
severity ratings, which may mean that clinicians are conflating ASD symptoms with difficulties
related to intellectual disability. Mazurek et al suggest that clinicians may be having difficulty:
“determining whether to assign ratings based on ASD symptom severity alone (more
consistent with text examples) or based largely on need for support (more consistent
with the level descriptors). If clinicians adhere to the latter interpretation, there may be
greater potential for conflation of intellectual and symptom-related impairment. This
poses problems for both inter-rater reliability and construct validity” (p.7).
Mazurek et al are also unaware of any studies looking at the inter-rater reliability of severity
level assessments (p.8).
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Hausman-Kedem et al looked at a group of 87 participants who had been diagnosed with ASD
from psychologists or physicians in the community. They had predominately single-disciplinary
diagnoses. Hausman-Kedem et al found the diagnoses did not hold up in 23% of cases when
compared with best practice clinical estimates (2018, p.6). They also find that results of Autism
Diagnostic Observation Schedule-2 (ADOS-2) substantially agrees with final best practice
clinical estimates (2018, p.7). While the support for ADOS-2 is backed up by other studies, the
discrepancy between community diagnoses and best practice clinical estimates is complicated
by the participants’ having DSM-IV diagnoses and the researchers using updated DSM-5
categories.
In their 2018 systematic review, Wigham et al found some support for diagnostic measures
such as ADOS for adults, though they note that accuracy increases when multiple
questionnaires and measures are used. They also observe that difficulties arise when
distinguishing between ASD and some mental health conditions such as schizophrenia (p.15).
While there is better evidence to support tools used to diagnose ASD in children (Whigham,
2018, p.1), Randall et al found reason to be cautious about results supporting accuracy of
diagnostic tools (2018, p.3). According to the evidence obtainable, ADOS scored highest for
sensitivity and all tools assessed had similar results for specificity (p.2).
Further investigation will be required to provide a fuller picture of the overall accuracy of DSM-
5 diagnoses and of tools based on DSM-5 diagnostic criteria. Despite some lack of confidence
in the evidence, there is agreement in the literature that use of a variety of tools from a multi-
disciplinary team gives the highest chance of correctly diagnosing a person with ASD.
5. Influence of DSM-5 ASD criteria on the prevalence of
ASD
Autism prevalence rates are increasing (Taylor et al, 2020; Chiarotti & Venerossi, 2020; CDC
2020). The Autism and Developmental Disabilities Monitoring network (ADDM) estimates
prevalence at 1 in 44 in sample United States communities (CDC 2020; Maener et al, 2021;
Baio et al, 2018). Autism Spectrum Australia estimates prevalence at 1 in 70 in Australia
(Autism Spectrum Australia, 2018). The reasons for the increase are likely to be complex and
the exact proportion of the increase that is attributable to different factors is still a matter for
debate. Kulage et al suggest:
“parental awareness and acceptance, less stigmatization, better trained clinicians, more
thorough data collection methods, and even increasing genetic tendencies could be
contributing factors. In addition, comorbid diagnoses are now allowable for ASD under
DSM-5, enabling clinicians to give multiple comorbid diagnoses of intellectual disability,
ASD, and ADHD, which could also explain why ASD rates have continued to rise since
publication of the DSM-5” (Kulage et al, 2019, p.19).
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Estimates of ASD prevalence are rising despite tightening diagnostic criteria in the current
addition of the DSM-5. Since before publication of the DSM-5 there was concern about what
the changes to ASD diagnostic criteria would do to ASD prevalence rates and especially
whether people who failed to meet the new criteria would no longer be eligible for support
(Kulage et al, 2019).
Kulage et al published a systematic review of the literature looking at the effect of the changes
to ASD diagnostic criteria between the DSM-IV-TR and the DSM-5. They found that
approximately 1 in 5 people who would have received a diagnosis in DSM-IV-TR would not
have received a diagnosis in the DSM-V. Further, only 28.8 percent of those who no longer
meet ASD criteria would go on to meet diagnostic criteria for Social Communication Disorder
(SCD) (Kulage et al, 2019, p.19). This means roughly 14% of people who met diagnostic
criteria under DSM-IV no longer meet criteria for ASD or SCD. It is unclear what proportion of
those people would go on to meet other diagnostic criteria and what proportion would remain
below threshold for any DSM-5 diagnosis.
According to this review, DSM-5 is contributing to a reduction in ASD diagnoses while the
overall prevalence estimates continue to rise.

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6.  Literature Summary

Author /
Title
Source
Aim / Objective
Methods
Results
Quality of
Date
Research
Inter-rater reliability of DSM-5 ASD Diagnoses
1
Mazurek et al
Factors associated  Autism, 23(2):468-
To evaluate the use of
Descriptive
Higher severity
The study is based
2019
with DSM-5 severity  476
these severity ratings
quantitative study of  ratings in both
on a large sample
level ratings for
for social
248 children and
domains were
that appears
autism spectrum
communication and
adolescents with
associated with
representative in
disorder
repetitive behaviour
DSM-5 diagnoses.
younger age, lower  terms of gender and
domains and to
All participants
intelligence
functioning. No
examine their relation
received a non-
quotient, and
significant bias was
to other measures of
standardized
greater Autism
detected however
severity and clinical
diagnostic clinical
Diagnostic
the clinicians
features.
interview,
Observation
undertaking the
standardized
Schedule–Second
assessments are
observation using
Edition domain-
specialists in ASD
the Autism
specific symptom
diagnosis and may
Diagnostic
severity. Greater
not be
Observation
restricted and
representative of
Schedule–Second
repetitive behavior
clinicians in the
Edition (ADOS-2),
severity was
community.
cognitive
associated with
assessment, and
higher parent-
assessment of
reported
behavioral
stereotyped
functioning.
behaviours.
Participants were
Severity ratings
assessed by a
were not associated
psychologist,
with emotional or
physician or multi-
behavioural
disciplinary team.
problems. Strong
associations
between
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Author /
Title
Source
Aim / Objective
Methods
Results
Quality of
Date
Research
intelligence quotient
and DSM-5 severity
ratings in both
domains suggest
that clinicians may
be including
cognitive
functioning in their
overall
determination of
severity.
2
Hausman-
Accuracy of
Journal of
To compare community  87 participants
23% of participants  The sample size is
Kedem et al
Reported
Psychopathology
diagnoses of Autism
(85% male, average  with a reported
small and males are
2018
Community
and Behaviour
Spectrum Disorder
age 7.4 years), with  community
over-represented.
Diagnosis of Autism  Assessment. 40(3):  (ASD) reported by
reported community  diagnosis of ASD
Consensus
Spectrum Disorder
367–375.
parents to consensus
diagnosis of ASD
were classified as
diagnoses made
diagnoses made using
were evaluated
non-spectrum
using DSM-5
standardized tools plus  using the Autism
based on our
criteria were
clinical observation.
Diagnostic
consensus
compared to
Observation
diagnosis.
community
Schedule) (ADOS-
Participants
diagnoses using
2), Differential
enrolled with
DSM-IV criteria.
Ability Scale (DAS-
community
Results may reflect
II), and Vineland
diagnosis of PDD-
changes in criteria
Adaptive Behaviour  NOS were
as well as
Scales (VABS-II).
significantly more
differences between
Detailed
likely to be
community
developmental and  classified as non-
diagnosis and
medical history was  spectrum on the
consensus
obtained from all
study consensus
diagnosis.
participants.
diagnosis than
Diagnosis was
Participants with
based on clinical
Autism or Asperger.
consensus of at
This study shows
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Author /
Title
Source
Aim / Objective
Methods
Results
Quality of
Date
Research
least two expert
suboptimal
clinicians, using test  agreement between
results, clinical
community
observations, and
diagnoses of ASD
parent report.
and consensus
diagnosis using
standardized
instruments.
3
Wigham et al
Psychometric
Autism, 23(2): 287-
Systematic review of
Systematic review
Limited evidence for  Design of included
2019
properties of
305
research evidence on
accuracy of
studies were case–
questionnaires and
structured
structured
control, cross
diagnostic
questionnaires and
questionnaires.
sectional or
measures for
diagnostic measures for
Sensitivity and
retrospective,
autism spectrum
adults with Autism
specificity of
making comparison
disorders in adults:
published since 2014.
structured
of results difficult.
A systematic review
questionnaires were  Case-control
best for individuals
studies are at risk of
with previously
bias which limits to
confirmed ASD and  relevance of the
reduced in
reviews results.
participants referred  However, the
for diagnostic
authors point out
assessments, with
that both stronger
discrimination of
and weaker studies
ASD from mental
agreed on the poor
health conditions
psychometric
especially limited.
properties of the
For adults with
tools investigated.
intel ectual
disability, diagnostic
accuracy increased
when a combination
of structured
questionnaires were
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Author /
Title
Source
Aim / Objective
Methods
Results
Quality of
Date
Research
used. In mental
health settings, the
use of a single
structured
questionnaire is
unlikely to
accurately identify
adults without
autism spectrum
disorder or
differentiate autism
spectrum disorder
from mental health
conditions.
4
Randall et al
Diagnostic tests for  Cochrane Database  To identify which
Systematic review
ADOS scored a
Studies reviewed
autism spectrum
of Systematic
diagnostic tools,
summary sensitivity  showed some risk
disorder (ASD) in
Reviews
including updated
of 0.94 and a
of bias though
preschool children
versions, most
summary specificity  studies at high risk
accurately diagnose
of 0.80. When
of bias were
ASD in preschool
compared with
excluded. Overall,
children when
other assessed
authors advice to
compared with multi-
tools, ADOS scored  interpret results with
disciplinary team
highest for
caution due to
clinical judgement. To
sensitivity and all
sample sizes of
identify how the best of
tools had similar
included studies
the interview tools
results for
and potential
compare with CARS,
specificity.
conflicts of interest.
then how CARS
compares with ADOS:
which ASD diagnostic
tool - among ADOS,
ADI-R, CARS, DISCO,
GARS, and 3di - has
the best diagnostic test
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Author /
Title
Source
Aim / Objective
Methods
Results
Quality of
Date
Research
accuracy?; is the
diagnostic test
accuracy of any one
test sufficient for that
test to be suitable as a
sole assessment tool
for preschool children?;
is there any
combination of tests
that, if offered in
sequence, would
provide suitable
diagnostic test
accuracy and enhance
test efficiency?; if data
are available, does the
combination of an
interview tool with a
structured observation
test have better
diagnostic test
accuracy (i.e. fewer
false-positives and
fewer false-negatives)
than either test alone?
Frequency of ASD diagnoses in families
1
Bai et al
Inherited Risk for
Biological
Review data on
Quantitative
1.55% of children in  The sample is large
Sept 2020
Autism Through
Psychiatry; 88:480– frequency of ASD
correlational study
the cohort were
(847,732 children in
Maternal and
487
among family members  using data from the  diagnosed with
total and 13,103
Paternal Lineage
Swedish National
ASD. Among their
diagnosed with
Patient Register
maternal /paternal
ASD) and so results
and the Multi-
aunts and uncles
are robust.
Generation Register  0.24% and 0.18%
However the
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Author /
Title
Source
Aim / Objective
Methods
Results
Quality of
Date
Research
for a cohort of
were diagnosed
sample is drawn
children born
with ASD,
entirely from
between 2003 and
respectively.
Swedish national
2012. Researchers  Offspring of
registers and so
compared
mothers with a
may not be wholly
frequency of ASD
sibling(s) diagnosed  applicable to other
diagnosis with
with ASD had
national contexts
family relations and  higher rates of ASD  (depending on
sex in a group of
than the general
variation in
847,732 children.
population (relative  diagnostic habits).
risk, 3.05; 95%
Also, diagnoses are
confidence interval,  made using ICD
2.52–3.64). These
8,9, and 10. Results
findings establish a  may be different
robust general
using DSM-5
estimate of ASD
diagnoses.
transmission risk for
siblings of
individuals affected
by ASD, the first
ever reported. Our
findings do not
suggest female
protective factors as
the principal
mechanism
underlying the male
sex bias in ASD.
2
Girault et al
Quantitative trait
Journal of
To investigate how
Compared 385
Older siblings’
The study uses a
2020
variation in ASD
Neurodevelopment
quantitative variation
pairs of toddlers
scores on the
substantial sample
probands
al Disorder 12:5
in ASD traits and
and their older
Social
of 385 sibling pairs.
and toddler sibling
broader developmental  siblings using data
Communication
However, the study
outcomes at 24
domains in older
from the Infant
Questionnaire
uses DSM-IV to
months
siblings with ASD
Brain Imagining
predicts whether
diagnose toddlers
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Title
Source
Aim / Objective
Methods
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Quality of
Date
Research
(probands) may inform
Study. Toddlers and  younger siblings will  as the IBIS data
outcomes in
older siblings were
receive an ASD
was gathered
their younger siblings.
each assessed
diagnosis. There is
before the DSM-5 a
using age
large variation in
was released. The
appropriate
autistic traits
study did not
diagnostic and
exhibited by
consider if this
adaptive behaviour  siblings. However,
would have an
assessment tools to  the severity of
impact on results.
determine presence  autistic traits in the
Also, while age-
of ASD and autistic  older sibling
appropriate clinical
traits.
predicts severity in
tools were used in
the younger sibling.  the assessment of
the subjects, the
different tools casts
some doubts on the
comparison
between older and
younger siblings.
3
Hansen et al
Recurrence risk of
Journal of the
To estimate ASD
International
Research found an  Very large sample
2019
autism in siblings
American Academy  recurrence risk among
population-based
8.4-fold increase in  of almost 9 million
and cousins: a
of Child and
siblings and cousins by  cohort study of
the risk of ASD
children (with
multi-national
Adolescent
varying degree of
children born 1998– following an older
29,998 cases of
population-based
Psychiatry, 58(9):
relatedness and by
2007. Follow up
sibling with ASD
ASD and 33,769
study
866–875
sex
2011–2015.
and a 17.4-fold
cases of childhood
Subjects were
increase in the risk
autism). Measures
monitored for an
of Childhood Autism  both shared genetic
ASD diagnosis in
(CA) following an
and non-genetic
their older siblings
older sibling with
factors. There was
or cousins
CA. A 2-fold
missing parental
(exposure) and for
increase in the risk
information in only a
their own ASD
for cousin
small proportion of
diagnosis
recurrence was
the sample. Results
(outcome). The
observed for both
are robust.
relative recurrence
disorders.
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Title
Source
Aim / Objective
Methods
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Quality of
Date
Research
risk was estimated
Researchers also
for different sibling-  found a significant
and cousin-pairs,
difference in sibling
for each site
ASD recurrence risk
separately and
by sex.
combined, and by
sex
4
Sandin et al
The Heritability of
Journal of the
To calculate the
Sample of
On one model
The sample is very
2017
Autism Spectrum
American Medical
heritability of ASD by
3,557,446 pairs of
comparing on
large but taken from
Disorder
Association;
reanalysing a previous
siblings was
heritability and non- only Swedish
318(12): 1182-1184   data set.
examined for
shared
sample and so may
presence of ASD.
environmental
not be wholly
Total of 14,516
factors, heritability
applicable to other
children were
was estimated at
national contexts.
diagnosed with
0.87. On a model
Frequency of ASD
ASD. Liability
with all 4 factors,
diagnoses in the
threshold models
heritability was
sample (<0.5%) is
were used to
0.69. Using only
far below incidence
identify additive and  twins in the sample,  in the general
non-additive genetic  heritability was
population (1-2%).
factors, shared and  0.87. The
This study focusses
non-shared
heritability of ASD is  on heritability and
environmental
high and the risk of  may not reflect
factors.
ASD increased with  other familial
increasing genetic
factors.
relatedness.
5
Page et al
Quantitative autistic  Molecular Autism
To fill a gap in the
Researchers
Measured
Small sample
2016
trait measurements  7:39
literature by
examined QAT
correlations
relative to these
index background
investigating the
scores in siblings
(between children
types of studies and
genetic risk for ASD
relationship of
and parents of 83
with ASD and i) first  while the study
in Hispanic families
quantitative autistic
Hispanic children
degree relative, ii)
depends on a less
traits (QAT) to liability
with ASD, and 64
unaffected first
heterogeneous
of ASD in an example
non-ASD controls,
degree relatives in
sample than other
using the Social
ASD affected
studies (Hispanics),
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Title
Source
Aim / Objective
Methods
Results
Quality of
Date
Research
non-Caucasian
Responsiveness
families and iii)
it also i) restricts its
population.
Scale-2.
spouses) supported  conclusions to this
previous studies of
population group;
non-Hispanic
and ii) shows how
populations.
the results for this
group coincide with
studies of other
population groups.
6
Tick et al
Heritability of
Journal of Child
To assess the evidence  Systematic review
ASD heritability
Results are robust.
2016
autism spectrum
Psychology and
of environment and
and meta-analysis
estimates were 64–
The review contains
disorders: a meta-
Psychiatry 57:5;
genetic factors in the
of all ASD twin
91%. Shared
a meta-analysis of
analysis of twin
585-595
aetiology of ASD
studies.
environmental
twin studies, which
studies
effects became
are the standard for
significant as the
heritability studies.
prevalence
Authors have also
rate decreased from  explained
5–1%: 07–35%.
discrepancy
between the results
of the meta-analysis
and previous
studies, namely, an
over-estimation of
the significance of
environmental
factors was due to
some previous
studies’
overinclusion of
non-identical twins
in the samples.
7
Frazier et al
Quantitative autism  Molecular Autism
To establish the
Researchers
Non-ASD children

2015
symptom patterns
extent to which family
analysed data from  manifested elevated
recapitulate
transmission pattern
5515 siblings (2657  ASD symptom
differential
non-ASD and 2858  burden when they
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Title
Source
Aim / Objective
Methods
Results
Quality of
Date
Research
mechanisms of
and sex modulate ASD  ASD). Autism
were members of
genetic
trait aggregation
symptom levels
multiple incidence
transmission in
were measured
families—this effect
single and multiple
using the Social
was accentuated for
incidence families
Responsiveness
male children in
Scale (SRS) and by  female ASD-
computing DSM-5
containing
symptom scores
families—or when
based on items
they had a history
from the SRS and
of language delay
Social
with autistic
Communication
qualities of speech.
Questionnaire.
Recurrence risk for
ASD was higher for
children from
female ASD-
containing families
than for children
from male-only
families
Influence of DSM-5 ASD criteria on the prevalence of ASD
1
Kulage et al
How has DSM-5
Journal of Autism
To 1) determine the
Systematic review
Using a random
The study is of high
2019
Affected Autism
and Developmental  change in frequency of  using PRISMA
effects model, the
quality as a
Diagnosis? A 5-
Disorders
ASD diagnosis in the
guidelines.
pooled proportion
systematic review
Year Follow-Up
first five years after
Qualitative and
suggests a 20.8%
and meta-analysis,
Systematic
publication of the
quantitative meta-
reduction in ASD
although the
Literature Review
revised DSM-5 ASD
analysis of 33
diagnoses. Pooled
underlying data has
and Meta-analysis
criteria; (2) identify the
published articles.
effects suggest
a moderate risk of
DSM-IV-TR autism
statistically
bias stemming from
subtypes most affected
significant
lack of masking of
by the new criteria; and
reductions in ASD
raters to results of
(3) assess the potential
diagnoses of 10.1%  the references
of an alternative
for those with AD
standard, DSM-IV-
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Title
Source
Aim / Objective
Methods
Results
Quality of
Date
Research
diagnosis of SCD for
and 23.3% for those  TR diagnosis, and
individuals who meet
with Asperger’s
failure to assess
DSM-IV-TR but
Disorder when
interrater
not DSM-5 ASD
DSM-5 criteria were  agreement in
diagnostic criteria
applied. The
classification of
reduction in
DSM-5 diagnoses.
diagnoses for PDD- Findings should be
NOS was not
interpreted with
statistically
caution however
significant. Less
this study does
than one-third
represent the most
[28.8%] of those
comprehensive
who met DSM-IV-
exploration of the
TR ASD diagnostic  data available.
criteria but not
DSM-5 would meet
SCD diagnostic
criteria.
2
Baio et al
Prevalence of
Centre for Disease
To determine ASD
The first phase
For 2014, the
Sample size is
2018
Autism Spectrum
Control and
prevalence in 11
involves review and  overall prevalence
adequate to draw
Disorder Among
Prevention –
communities in the
abstraction of
of ASD among the
conclusion about
Children Aged 8
Morbidity and
United States.
comprehensive
11 ADDM sites was  estimated
Years — Autism
Mortality Weekly
evaluations that
16.8 per 1,000 (one  prevalence in the
and Developmental  Report –
were completed by
in 59) children aged  age and
Disabilities
Surveil ance
professional service  8 years.
communities
Monitoring Network,  Summaries 67(6)
providers in the
studied. However,
11 Sites, United
community. In the
ADDM study is
States, 2014
second phase of
sometimes used as
the study, all
an estimate of
abstracted
prevalence for the
information is
entire United
reviewed
States. Samples
systematically by
chosen are not
experienced
representative of
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Title
Source
Aim / Objective
Methods
Results
Quality of
Date
Research
clinicians to
the entire US. Did
determine ASD
not specify whether
case status.
raters were aware
of the findings of
other raters.
3
Taheri, Perry,
A Further
Journal on
To determine whether
File review of 22
Only 55% of the
Reassessments
Factor
Examination of the
Developmental
children and
children and
sample met the
were completed
2014
DSM-5 Autism
Disability 20(1)
adolescents diagnosed  adolescents
DSM-5 criteria for
using DSM-5
Spectrum Disorder
with Autistic Disorder or  previously
ASD; this included
checklist rather than
Criteria in Practice
PDD-NOS on DSM-IV
diagnosed under
69% of those who
clinical diagnoses.
criteria would continue
DSM-IV criteria.
had an original
Children diagnosed
to meet DSM-5 ASD
Records were then
DSM-IV-TR
with Aspergers
criteria. To replicate
reassessed using
diagnosis of AD,
were excluded.
and extend the findings  DSM-5 criteria.
and only 17% (one
Small sample size
of an earlier paper in a
child) with an
although study
different sample of
original diagnosis of  intentional y worked
older individuals with
PDD-NOS.
as an extension of a
lower cognitive and
previous study with
adaptive skills
an adequate
sample size.
Although masking
of participants
occurred, the study
did not specify
whether raters were
aware of the
findings of other
raters.
4
Hiller, Young
Sex Differences in
Journal of Abnormal  To explore sex
Quantitative
While no sex
Adequate sample
and Weber
Autism Spectrum
Child Psychology
differences in the
descriptive study of  differences were
size and reported
2014
Disorder based on
behavioural
138 children with
found in the broad
inter-rater reliability
DSM-5 Criteria:
presentation of girls
ASD. Diagnoses
social criteria
between clinicians
Evidence from
and boys diagnosed
were provided by
presented in the
but study did not
two clinicians and
DSM-IV-TR or
specify whether
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Research
Clinician and
with high-functioning
then statistical
DSM-5, numerous
raters were aware
Teacher
ASD.
analyses were
differences were
of the findings of
Reporting
applied.
evident in how boys  other raters.
and girls came to
meet each criterion.
5
Young and Rodi  Redefining Autism
Journal of Autism
To compare overlap of
223 subjects who
Of the 210
Adequate sample
2014
Spectrum Disorder
and Developmental  DSM-IV pervasive
were either referred  participants in the
size and reported
Using DSM-5: The
Disorders 44:758–
development delay
for a DSM-IV
present study who
inter-rater reliability
Implications of the
765
diagnoses and DSM-5
diagnosis and did
met DSM-IV TR
between clinicians
Proposed DSM-5
autism diagnoses.
not receive one, or
criteria for a PDD
but study did not
Criteria for Autism
who received a
only 57.1 % met
specify whether
Spectrum Disorders
DSM-IV diagnoses
DSM-5 criteria for
raters were aware
were reassessed
autism spectrum
of the findings of
using DSM-5
disorder when
other raters. DSM-5
criteria.
criteria were applied  diagnoses were
concurrently during  completed by one
diagnostic
or two clinicians
assessment
and so did not meet
best practice
guidelines for
clinical
assessments.
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9, 2021).
Bai, D. et al. (2020) “Inherited risk for autism through maternal and paternal lineage,”
Biological psychiatry, 88(6), pp. 480–487.
Baio, J. et al. (2018) “Prevalence of autism spectrum disorder among children aged 8 years -
autism and Developmental Disabilities Monitoring Network, 11 sites, United States, 2014,”
MMWR Surveil ance Summaries, 67(6), pp. 1–23
Centre for Disease Control and Prevention (2020) Autism prevalence studies data table,
Cdc.gov. Available at: https://www.cdc.gov/ncbddd/autism/data/autism-data-table.html
(Accessed: December 9, 2021).
Chiarotti, F. and Venerosi, A. (2020) “Epidemiology of autism spectrum disorders: A review of
worldwide prevalence estimates since 2014,” Brain sciences, 10(5)
Downes, S. M. and Matthews, L. (2020) “Heritability,” The Stanford Encyclopedia of
Philosophy. Spring 2020. Edited by E. N. Zalta. Metaphysics Research Lab, Stanford
University.
Girault, J. B. et al. (2020) “Quantitative trait variation in ASD probands and toddler sibling
outcomes at 24 months,” Journal of neurodevelopmental disorders, 12(1), p. 5.
Hansen, S. N. et al. (2019) “Recurrence risk of autism in siblings and cousins: A multinational,
population-based study,” Journal of the American Academy of Child and Adolescent
Psychiatry, 58(9), pp. 866–875.
Hausman-Kedem, M. et al. (2018) “Accuracy of reported community diagnosis of Autism
Spectrum Disorder,” Journal of psychopathology and behavioural assessment, 40(3), pp. 367–
375. doi: 10.1007/s10862-018-9642-1.
Hiller, R. M., Young, R. L. and Weber, N. (2014) “Sex differences in autism spectrum disorder
based on DSM-5 criteria: evidence from clinician and teacher reporting,” Journal of abnormal
child psychology, 42(8), pp. 1381–1393. doi: 10.1007/s10802-014-9881-x.
Kulage, K. M. et al. (2020) “How has DSM-5 affected autism diagnosis? A 5-year follow-up
systematic literature review and meta-analysis,” Journal of autism and developmental
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Maenner, M. J. et al. (2021) “Prevalence and characteristics of Autism spectrum disorder
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Mazurek, M. O. et al. (2019) “Factors associated with DSM-5 severity level ratings for autism
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476. doi: 10.1177/1362361318755318.
Page, J. et al. (2016) “Quantitative autistic trait measurements index background genetic risk
for ASD in Hispanic families,” Molecular autism, 7(1). doi: 10.1186/s13229-016-0100-1.
Randall, M. et al. (2018) “Diagnostic tests for autism spectrum disorder (ASD) in preschool
children,” Cochrane database of systematic reviews, 7(7), p. CD009044. doi:
10.1002/14651858.CD009044.pub2
Sandin, S. et al. (2017) “The heritability of autism spectrum disorder,” JAMA: the journal of the
American Medical Association, 318(12), pp. 1182–1184.
Taheri, A, Perry, A, and Factor, D C. (2014) “A Further Examination of the DSM-5 Autism
Spectrum Disorder Criteria in Practice,” Journal of Developmental Disabilities, 20(1), pp.116-
121
Taylor, M. J. et al. (2020) “Etiology of autism spectrum disorders and autistic traits over time,”
JAMA psychiatry (Chicago, Ill.), 77(9), pp. 936–943
Tick, B. et al. (2016) “Heritability of autism spectrum disorders: a meta-analysis of twin
studies,” Journal of child psychology and psychiatry, and allied disciplines, 57(5), pp. 585–595.
Wigham, S. et al. (2019) “Psychometric properties of questionnaires and diagnostic measures
for autism spectrum disorders in adults: A systematic review,” Autism: the international journal
of research and practice, 23(2), pp. 287–305. doi: 10.1177/1362361317748245.
Young, R. L. and Rodi, M. L. (2014) “Redefining autism spectrum disorder using DSM-5: the
implications of the proposed DSM-5 criteria for autism spectrum disorders,” Journal of autism
and developmental disorders, 44(4), pp. 758–765. doi: 10.1007/s10803-013-1927-3.
8.  Version control
Version  Amended
Brief Description of Change
Status
Date
by
0.1
AHR908  Literature review on the incidence and
Draft
10-12-21
reliability of ASD diagnoses using DSM-5
criteria.
1.0
FFM634  Final
Completed
10-12-21

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