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• Published: 05 July 2022

Efficacy of psychosocial interventions for Autism spectrum disorder: an umbrella review

• Corentin J. Gosling   ORCID: orcid.org/0000-0003-1133-9344 1 , 2 , 3 ,
• Ariane Cartigny 2 , 4 ,
• Baptiste C. Mellier 1 ,
• Aleix Solanes 5 ,
• Joaquim Radua 5 , 6 , 7 &
• Richard Delorme 4 , 8  

Molecular Psychiatry volume  27 ,  pages 3647–3656 ( 2022 ) Cite this article

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• Autism spectrum disorders

A Correction to this article was published on 04 October 2022

This article has been updated

Introduction

The wide range of psychosocial interventions designed to assist people with Autism Spectrum Disorder (ASD) makes it challenging to compile and hierarchize the scientific evidence that supports the efficacy of these interventions. Thus, we performed an umbrella review of published meta-analyses of controlled clinical trials that investigated the efficacy of psychosocial interventions on both core and related ASD symptoms.

Each meta-analysis that was identified was re-estimated using a random-effects model with a restricted maximum likelihood estimator. The methodological quality of included meta-analyses was critically appraised and the credibility of the evidence was assessed algorithmically according to criteria adapted for the purpose of this study.

We identified a total of 128 meta-analyses derived from 44 reports. More than half of the non-overlapping meta-analyses were nominally statistically significant and/or displayed a moderate-to-large pooled effect size that favored the psychosocial interventions. The assessment of the credibility of evidence pointed out that the efficacy of early intensive behavioral interventions, developmental interventions, naturalistic developmental behavioral interventions, and parent-mediated interventions was supported by suggestive evidence on at least one outcome in preschool children. Possible outcomes included social communication deficits, global cognitive abilities, and adaptive behaviors. Results also revealed highly suggestive indications that parent-mediated interventions improved disruptive behaviors in early school-aged children. The efficacy of social skills groups was supported by suggestive evidence for improving social communication deficits and overall ASD symptoms in school-aged children and adolescents. Only four meta-analyses had a statistically significant pooled effect size in a sensitivity analysis restricted to randomized controlled trials at low risk of detection bias.

This umbrella review confirmed that several psychosocial interventions show promise for improving symptoms related to ASD at different stages of life. However, additional well-designed randomized controlled trials are still required to produce a clearer picture of the efficacy of these interventions. To facilitate the dissemination of scientific knowledge about psychosocial interventions for individuals with ASD, we built an open-access and interactive website that shares the information collected and the results generated during this umbrella review.

Pre-registration

PROSPERO ID CRD42020212630.

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Autism spectrum disorder

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that is characterized by social communication deficits which are associated with restricted and repetitive patterns of behaviors and interests that interfere with quality of life [ 1 ]. Numerous interventions have been designed to assist people with ASD and their families [ 2 , 3 , 4 ]. However, the wide range of psychosocial interventions makes it difficult to compile and hierarchize the scientific evidence supporting the efficacy of these interventions, resulting in insufficient dissemination of the scientific evidence which supports their efficacy [ 5 ]. Individuals with ASD are sometimes engaged in treatments for which there is weak empirical evidence or, more dramatically, that can result in harmful consequences [ 6 , 7 ]. The present umbrella review aims to provide a clearer picture of the efficacy of the psychosocial interventions on both core and related ASD symptoms.

A large variety of psychosocial interventions have been developed to improve ASD symptoms across the lifespan, and numerous clinical trials have been conducted to explore their efficacy [ 8 , 9 ]. The first interventional approach to be investigated in clinical studies were behavioral interventions, which are based on operant learning theories [ 10 ]. Promising results from the initial clinical studies that delivered these behavioral techniques at a very high intensity (referred to as early intensive behavioral interventions; EIBI), led to their widespread adoption in clinical practice [ 11 , 12 , 13 ]. In contrast to behavioral interventions, developmental interventions (DEV) are based on constructivist models [ 14 ]. DEV focus on supporting children’s social interactions with others during daily life activities, e.g., play [ 15 ]. Recently, the new category of naturalistic behavioral developmental interventions (NDBI) has emerged to describe practices that are rooted in both behavioral and developmental theories [ 16 ]. NDBI employ a diversity of behavioral techniques that promote the emergence of developmentally appropriate skills in a natural setting [ 17 ]. Social skills groups (SSG) are another psychosocial intervention that have received substantial experimental support [ 18 ]. Delivered in a group setting, SSG seek to improve social skills by combining structured learning of prosocial behaviors with drill and practice exercises during and between sessions. Besides, cognitive-behavioral therapy (CBT), which has shown robust efficacy on symptoms of various mental health conditions [ 19 ], has also been applied to individuals with ASD [ 20 ]. Based on a combination of principles from both behavioral and cognitive sciences, CBT target coping skills to enable individuals to modify their maladaptive thoughts, emotions, and behaviors. Unlike the approaches mentioned above, the Treatment and Education of Autistic and Related Communication-Handicapped Children (TEACCH) focuses on structuring the environment of individuals with ASD [ 21 , 22 ]. This program was developed to create a highly structured learning environment which capitalizes on the relative strengths of individuals with ASD, e.g., their visual skills. Finally, rather than the intervention content, the mode of delivery has also been the subject of clinical assessment for individuals with ASD. In particular, many studies explored the efficacy of parent-mediated interventions (PMI) [ 23 ] and technology-mediated interventions (TECH) [ 24 ].

When it comes to assessing the efficacy of a drug or a non-drug intervention, systematic reviews and meta-analyses are robust tools to improve clinical decision-making [ 25 ]. However, as with any experimental study, these studies are also prone to inconsistency and methodological biases, which may produce divergent conclusions by several reviews or meta-analyses on the same topic [ 26 ]. For example, two meta-analyses in the field of ASD published in 2009, which explored the efficacy of EIBI on intelligence quotient, showed either a large and statistically significant pooled effect size in favor of EIBI [ 27 ] or a small and non-significant pooled effect size [ 28 ]. These discrepancies strongly reinforce the uncertainty for health providers and patients in selecting the optimal therapeutic strategy. Umbrella reviews are additional tools of evidence synthesis that have emerged in recent years to overcome the methodological limitations of meta-analyses [ 29 , 30 ]. Umbrella reviews compile evidence from a large body of information by assessing the credibility of multiple meta-analytic results in a consistent, transparent, and reproducible framework [ 31 ]. In particular, umbrella reviews aim to direct clinical decision-makers to current best evidence relevant to a specific decision [ 32 ].

The objective of the present study was to perform an umbrella review that provided additional evidence on the efficacy of psychosocial interventions on both core and related ASD symptoms. We performed an algorithmic assessment of the credibility of the evidence that supported the efficacy of different psychosocial interventions using objective, transparent and reproducible criteria. We also critically assessed the methodology used in the meta-analyses to direct readers to best evidence. In parallel, we built an open-access online interactive resource that contains all of the information that we collected and all results that we generated for this umbrella review (Evidence-Based Interventions for Autism: Clinical Trials [EBIA-CT] database: https://www.ebiact-database.com ). In summary, the present work aimed to provide a more reliable and accessible source of evidence-based information about the efficacy of psychosocial interventions in individuals with ASD [ 33 , 34 ].

Search strategy and eligibility criteria

Reporting of this umbrella review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [ 35 ] and its completion followed the most recent guidelines for umbrella reviews [ 36 , 37 ]. This umbrella review was pre-registered on PROSPERO (ID CRD42020212630). The complete PRISMA checklist and the deviations from protocol are available online (Supplementary Tables  S1 and S2 , respectively).

Two authors (CJG and AC) searched five databases (Medline, EMBASE, CENTRAL, CINAHL, and PsycINFO) until October 1, 2021 using search terms related to two constructs: ASD and systematic review. Adaptations of previously validated search filters developed by the Hedges teams to retrieve systematic reviews and meta-analysis were used (see full strategies in Supplementary Text S 3 ) [ 38 , 39 , 40 , 41 ]. No restrictions were made based on language or date of publication. Titles and abstracts were screened independently and, for articles that were deemed to be eligible, full texts were downloaded and assessed independently for inclusion in final analyses. A senior author (RD) resolved conflicts between CJG and AC. References in included articles were also searched.

We included systematic reviews coupled with a meta-analysis of at least two controlled clinical trials (CCTs) that assessed the efficacy of psychosocial interventions on ASD symptoms in participants with ASD. A review was considered as systematic if labeled as such or if searches of scientific databases were performed in combination with explicit inclusion/exclusion criteria. The definition of ASD followed those used by primary authors and was in line with Diagnostic and Statistical Manual of Mental Disorders (DSM)-III, DSM-IV, DSM-IV-TR, DSM-5, International Classification of Diseases (ICD)−9, or ICD-10. There were no exclusion criteria regarding ages of participants. Meta-analyses which focused on the same type of intervention and on the same outcome but in distinct age groups (pre-school children: a mean age ranging from 0 to 5 years; school-aged children: a mean age ranging from 6 to 12 years; adolescents: a mean age ranging from 13 to 19 years; adults: a mean age > = 20 years) were reported separately. We included meta-analyses of both randomized and non-randomized controlled trials (RCTs and NRCTs, respectively) since we anticipated that many interventions were not assessed by RCTs. We were concerned that synthesizing evidence only from RCTs may lead to an incomplete picture of the efficacy of psychosocial interventions in people with ASD. However, a sensitivity analysis restricted to RCTs was conducted (see Supplementary Results S 2.A ).

Based on several seminal textbooks [ 3 , 4 , 42 , 43 ], we classified an intervention as psychosocial if assessing the efficacy of: (i) EIBI, (ii) NDBI, (iii) DEV, (iv) SSG, (v) PMI, (vi) CBT, (vii) TECH, and (viii) TEACCH. A description of each of these interventions can be retrieved from https://ebiact-database.com/interventions.html . Meta-analyses that pooled together trials which assessed the efficacy of at least two intervention types were excluded. An exception was made for PMI and TECH, since the focus of these intervention types was more on the delivery modalities than on content, and because readers may be interested specifically in the method used to deliver the intervention irrespective of the specific type of approach used. However, readers should be aware that there is a substantial overlap between PMI and NDBI/DEV, as parents are typically highly involved in the delivery of the intervention in NDBI and DEV. Meta-analyses of pharmacological interventions, occupational therapies, complementary and alternative medicine, or lifestyle interventions were not considered in this study.

Three of the eight pre-specified outcomes directly concerned core ASD symptoms: (i) overall ASD symptom severity, (ii) social communication deficit, and (iii) restrictive/repetitive behaviors or interests. We also considered five additional outcomes related to the main characteristics strikingly associated with ASD symptoms: (iv) cognitive global abilities (intelligence quotient, IQ), (v) adaptative behaviors, (vi) expressive language skills, (vii) receptive language skills, and (viii) the disruptive behaviors associated with ASD. We comprehensively extracted the data of all meta-analyses when specific articles reported several independent meta-analyses assessing the efficacy of the same intervention on the same clinical outcome. We resumed these meta-analyses into a unique pooled effect size using standard aggregating procedures (that are described further in the data analysis section below). The results of each individual meta-analysis are presented in Supplementary Results S 2.C .

To handle overlapping meta-analyses, i.e., two independent reports that assessed the efficacy of similar interventions on equivalent samples and outcomes, we first selected all the overlapping meta-analyses that were published after January 1st, 2016 and then selected the meta-analysis with the highest methodological quality (see data extraction below). The concordance between all overlapping meta-analyses was assessed in a sensitivity analysis (Supplementary Results S 2.B ). Moreover, we located several meta-analyses of a brand-name or specific intervention, e.g., a meta-analysis of Early Start Denver Model, while more comprehensive meta-analyses on the same intervention type were also available, e.g., a meta-analysis pooling together several NDBI. In these cases, we favored the more comprehensive meta-analysis, but we reported the results of the brand-name or specific intervention in Supplementary Results S 2.B .

Data extraction

For each trial reported in a meta-analysis, two author pairs (CJG and AC or CJG and BCM) independently extracted information regarding participants (the number of participants, their mean age or age range, their mean total IQ [verbal and non-verbal IQ or developmental quotient were used as proxies when total IQ was not available], total IQ range, and the sex ratio); interventions (study design, subtype of intervention, use of assistance/mediation during the intervention, setting of the intervention [e.g., clinic], type of practitioner delivering the intervention [e.g., educator], mean hours per week of intervention, and mean duration of the trials in months); outcomes (category of the outcome [e.g., improvement of the total IQ], the method used to assess the outcome [e.g., questionnaire], and the tool name); design of studies (NRCT vs. RCT); type of control group (treatment as usual, eclectic, waiting list/delayed, or active control treatment); risk of bias; and effect size (effect size metrics, value, and 95% confidence interval, standard error, or variance).

The AMSTAR-2 tool was used to assess the methodological quality of each meta-analysis retained in primary analyses [ 44 ]. Scoring was made independently by two authors pairs (CJG and AC or CJG and BCM). Five core criteria of AMSTAR-2 were used to select the meta-analysis with the highest methodological quality in the presence of overlap: the presence of a priori research design, the quality of search characteristics, the independence in study selection and data extraction, and the assessment of the risk of bias in the individual trials.

Data analysis

All analyses were performed in R environment (version 4.1.1) using the ‘metaumbrella’ package [ 45 ]. We re-analyzed each meta-analysis using a random-effects model with a restricted maximum likelihood estimator which was consistent with methods in previous umbrella reviews [ 46 , 47 ]. Original studies reported either the mean difference (MD), raw standardized mean difference, or bias-corrected standardized mean difference (SMD). We systematically converted MD and raw standardized mean difference to SMD so that all results were reported in a similar metric to facilitate interpretation. Moreover, the direction of the effect was reversed when needed so that a positive SMD systematically reflected an improvement, i.e., a symptom reduction or a competence improvement. When CCTs included in meta-analyses reported multiple outcomes or multiple independent subgroups, we used the standard aggregating approaches [ 48 ]. When a unique group was compared to two different groups (e.g., two experimental groups compared to one control group), the resulting effect sizes were conservatively assumed to come from the same participants.

Inconsistency was assessed using I² statistics. The 95% prediction interval was computed to inform the plausible range in which the effect sizes of future studies were expected to fall. Small study effects, i.e., the tendency of the smallest studies to report significantly higher effect size estimates compared to the largest studies, were explored using the Egger’s regression asymmetry test [ 49 , 50 ].

Assessment of the credibility of the evidence

Consistent with previous umbrella reviews [ 51 , 52 , 53 ], we assessed the credibility of the evidence concerning the efficacy of each intervention on each outcome into five ordinal classes using an algorithmic approach: convincing (Class I), highly suggestive (Class II), suggestive (Class III), weak (Class IV), and not significant (Class ns; Table  1 ). Note that this analysis did not seek to generate a hierarchy leading to treatment recommendations. Instead, the aim was to summarize many statistical results into a single composite score that captured the key findings of each meta-analysis. The criteria were derived from two classification systems that usually are used for umbrella reviews and which were adapted for the specific purposes of this study (Table  1 ) [ 31 , 54 ]. The presence of ‘small study effects’ was indicated if the p -value of the Egger regression test was ≤0.05. A study was considered as having a low risk of bias if the design used was a RCT and the risk of the outcome detection was low. We re-ran the assessment of the credibility of the evidence retaining only studies at low risk of bias in a sensitivity analysis to assess the robustness of the classes attributed in the primary analysis.

Data and code availability

Additional information on the results, R code supporting data analysis and raw data are publicly shared ( https://corentinjgosling.github.io/MP_2022_EBIACT_PSYCHOSOCIAL ).

Creation of the EBIA-CT database

We built an open-access and interactive database that displays information on the results of the meta-analyses and information collected on the CCTs to disseminate the data that we generated during this umbrella review. The information we collected about the effect sizes, participants, interventions, and risk of bias for CCTs is available in the database. If the age range and/or the IQ range were reported but the mean age and/or the mean IQ were unknown, we imputed the mean to be equal to the median of the reported range. Certain CCTs were described in several meta-analyses due to overlapping meta-analyses. We systematically favored the information collected by the meta-analysis retained in the primary analysis in this situation. If some information was absent from the main meta-analysis but was present in another meta-analysis, this information was used as a substitute. All the results of the statistical analyses conducted for this study were included in the database. Moreover, all the information collected on the CCTs concerning the participants, intervention, and risk of bias was averaged at the meta-analysis level and displayed in the database (a weighted average by the number of participants per CCT was performed). This database will be updated at least once per year over the next five years using the same methodology as described in this manuscript.

A total of 7493 reports were identified initially by our systematic review (Fig.  1 ). Among these reports, 96 were downloaded for full-text examination and 44 were deemed eligible for this study (see Supplementary Tables  S4 and S5 for the list of included and excluded studies, along with the reasons for exclusion). These 44 reports reported 128 meta-analyses (1488 effect size estimates) that were based upon more than 190 independent CCTs. The meta-analyses were categorized systematically into eight intervention subtypes: EIBI ( n  = 36), NDBI ( n  = 28), PMI ( n  = 20), TECH ( n  = 16), SSG ( n  = 15), TEACCH ( n  = 7), DEV ( n  = 4), and CBT ( n  = 2). We considered eight outcomes related to improvements in social communication deficit ( n  = 36), language deficit ( n  = 33), global cognitive abilities ( n  = 18), overall ASD symptom severity ( n  = 16), adaptive behaviors ( n  = 15), disruptive behaviors ( n  = 7), and restricted and repetitive behaviors ( n  = 3). We retained only the most recent and rigorous meta-analyses as described in the Methods section. Thus, 46 meta-analyses, derived from 18 reports, were included in the final analysis after discarding overlapping meta-analyses.

This figure visually summarise the screening process. More detailed information on the reasons for exclusion are available in Supplementary Materials.

Description of the meta-analyses included in the primary analysis

Of the 46 non-overlapping meta-analyses that were selected in the primary analysis, eight were derived from reports that met a ‘high’ methodological quality level according to the AMSTAR-2 tool while the remaining meta-analyses were derived from reports with a ‘critically low’ quality level (Supplementary Results S 1.B ). The main factors that lead to the downgrading of the quality ratings were the absence of either a list of excluded studies along with the reasons for exclusion, pre-registering, or a comprehensive literature search (in particular due to a restriction to articles written in English). The median number of CCTs per meta-analysis was six (interquartile range, IQR = [2, 10]), the median number of participants per meta-analysis was 238 (IQR = [68, 409]), and the median number of outcomes assessed for each intervention type was five, ranging from one to eight. The decrease of social communication symptoms was the most studied outcome. The reduction of restricted and repetitive behaviors was the least studied outcome and was targeted only in meta-analyses that explored the efficacy of NDBI and SSG.

More than half of the meta-analyses (24/46) demonstrated that participants in the experimental groups had significantly better outcomes compared to those in the control groups (i.e., a p -value of the pooled effect size < 0.05). Six of these 24 statistically significant meta-analyses had a pooled effect size associated with a p -value inferior to 1e-06, 13 were supported by a significant effect of the study with the lowest variance, and nine had a 95% prediction interval that excluded the null value. Regarding effect size magnitude, 19 meta-analyses (41%) had a moderate to large pooled effect size (SMD >= 0.50). However, a total of 12 meta-analyses we considered ( 26% ) showed a moderate or large inconsistency (I² statistics superior to 50%) and we also observed small-study effects for six meta-analyses (13%). Finally, 20% of participants per meta-analysis were included in studies at ‘low risk of bias’, on average. Therefore, most of the participants included in the meta-analyses that we considered were not randomly assigned to the groups or were not assessed blindly.

Credibility of the evidence

Regarding core ASD symptoms, as shown in Fig.  2 , EIBI, NDBI, DEV and PMI all displayed an efficacy on social communication in preschool children that was supported by suggestive evidence (Class III; Supplementary Results S 1.C ). The efficacy of SSG on social communication and overall ASD symptoms in school-aged children and adolescents was also supported by suggestive evidence. The efficacy of all other interventions on the overall ASD symptoms, social communication deficits or restricted/repetitive behaviors was supported by either weak or non-significant evidence (Class IV or Class ns). Regarding language skills, despite the relatively high number of meta-analyses that assessed the efficacy of psychosocial interventions on these outcomes (receptive skills: n  = 6, and expressive skills: n  = 6), only two interventions were supported by weak evidence: EIBI showed a significant pooled effect size on both expressive and receptive language, and NDBI on expressive language. All other interventions on language skills were supported by non-significant evidence. Regarding functional status (IQ and adaptive behaviors), these two outcomes were almost exclusively studied in preschool children, in whom EIBI showed suggestive evidence on both adaptive behaviors and IQ. All other interventions displayed either weak or non-significant evidence on these outcomes. Finally, only PMI showed a statistically significant pooled effect size on disruptive behaviors, with highly suggestive evidence in early school-aged children. Meta-analyses assessing the efficacy of EIBI, NDBI, TECH and SSG did not reach statistical significance on disruptive behaviors.

Some characteristics of the meta-analyses are displayed: mean age (yo = years old), class, sample size (N), and risk of bias (RoB; Low = more than 75% of the participants were in studies at low risk of bias; Med. = 50% to 75% of participants were in studies a low risk of bias; High = less than 50% of participants were in studies at low risk of bias).

Sensitivity analyses

A first sensitivity analysis restricted to studies at low risk of bias revealed that, only a third ( n  = 15) of the 46 meta-analyses included in our primary analysis included at least two RCTs at low risk of detection bias. Of these 15 meta-analyses, two remained with suggestive evidence (PMI on disruptive behaviors and social communication deficit in preschool children), and two remained with weak evidence (NDBI on cognition and DEV on overall ASD symptoms in preschool children). The efficacy of all other interventions was supported by non-significant evidence, even if two of these non-significant meta-analyses had a marginally significant p -value (Supplementary Results S 2.A ). Regarding the magnitude of the pooled effect sizes, whereas 41% of the meta-analyses showed a moderate-to-large pooled effect size in our primary analysis (SMD ≥ 0.50), only 20% of the meta-analyses had an effect size of this magnitude in this sensitivity analysis.

We then performed a second sensitivity analysis which gathered the meta-analyses that were excluded due to overlap. We observed that these overlapping meta-analyses reached similar conclusions in most cases, except for EIBI and NDBI in which more prominent differences occurred (Supplementary Results  2.B ). For the EIBI, a first reason for the disparity in the results relates to the inclusion (or exclusion) of an RCT that compared a professional-delivered EIBI group to a parent-delivered EIBI group. In contrast to the other trials included in the meta-analyses of EIBI, this RCT provides information on the implementation modalities of EIBI rather than on the efficacy of this intervention per se [ 55 ]. Because this RCT reported very small effect sizes (if not in disfavor of the ‘intervention’ group), the meta-analyses including this trial tended to report lower pooled effect sizes compared to others. Other reasons that may explain the disparity in results include the use of different effect size measures to quantify the effects of EIBI, e.g., some meta-analyses used the differences between groups at post-test while others quantified the difference between groups for pre-post changes, different inclusion criteria for intervention intensity , e.g., some meta-analyses require a minimum of 10 h per week while others require more than 20 h per week, or different approaches on the timeframe of the outcome assessment , e.g., some meta-analyses prioritize assessments performed immediately after the intervention, while others prioritize follow-up assessments. Notably, when we replicated our primary analysis but including all the CCTs identified by all meta-analyses on EIBI, the results remained unchanged. Furthermore, divergences regarding NDBI occurred between the two main meta-analytic reports that assessed the efficacy of this intervention on overall ASD symptoms [ 56 , 57 ]. The first meta-analysis found a modest but significant effect of NDBI on overall ASD symptoms (SMD = 0.38, p -value = 0.03) whereas the second found a very small, non-significant effect (SMD = 0.05, p -value = 0.75). The trials included in these two meta-analyses did not overlap fully and none of the papers provided a list of studies that were excluded from the final analysis. Interestingly, several meta-analyses of specific programs of NDBI, such as pivotal response treatment or early start Denver model, identified some trials that were not selected, or located, by the two main meta-analyses of NDBI. Thus, we repeated the calculations while including all the CCTs identified by all meta-analyses on NDBI. A total of 21 trials that assessed the efficacy of NDBI on overall ASD symptoms in preschool children were ultimately included in this reanalysis. The random-effects meta-analysis revealed a statistically significant but small pooled effect size (SMD = 0.22, p -value = 0.02), an efficacy that was supported by weak evidence.

Our umbrella review described here examined the results that were generated by a total of 128 meta-analyses. These meta-analyses synthesized the evidence provided by more than 190 unique CCTs which explored the efficacy of psychosocial interventions on core and related ASD symptoms. We observed that a substantial proportion of the meta-analyses displayed a moderate to large pooled effect size (i.e., SMD ≥ 0.50; 41% of the meta-analyses) and/or statistically significant results (53% of the meta-analyses) in favor of the psychosocial interventions. According to the algorithmic criteria developed for this umbrella review, we found that the efficacy of many of these psychosocial interventions was supported by highly suggestive (Class II) or suggestive (Class III) evidence depending on the age of the participants and the outcome under consideration. In preschool children EIBI, NDBI, DEV and PMI were supported by suggestive evidence: on social communication impairment, adaptive behaviors and IQ for EIBI, and on social communication for NDBI, PMI and DEV. In early school-aged children, highly suggestive evidence was found for the efficacy of PMI on disruptive behaviors. In late school-aged children and in adolescents, suggestive evidence was found for SSG on social communication and overall ASD symptoms. Regardless of the age of the participants, no intervention displayed an efficacy ranked as suggestive regarding either expressive or receptive language skills (EIBI and NDBI showed a significant pooled effect size but were supported by weak evidence), or repetitive and restricted behaviors (SSG had a significant pooled effect size that was supported by weak evidence). Thus, our results highlight the diversity of psychosocial approaches that are available for individuals with ASD, as well as the scientific evidence that supports the efficacy of these interventions on various outcomes at each stage of life.

A sensitivity analysis limited to RCTs with outcome assessors blinded to experimental status was conducted to complement our main results and to shed light upon the evidence generated by low risk of bias studies. This analysis revealed that a substantial proportion of the meta-analyses that showed statistically significant results in our primary analysis included less than two low risk of bias studies. Of the meta-analyses that included at least two of them, only PMI, NDBI and DEV still retained statistically significant (or marginally significant) results in preschoolers in this sensitivity analysis. The efficacy of PMI on both disruptive behaviors and social communication was ranked as suggestive (Class III), and the efficacies of both NDBI on cognition and DEV on overall ASD symptoms were ranked as weak (Class IV). No intervention was assessed by at least two low risk of bias studies in school-aged children, adolescents, or adults.

Interestingly, parallel with the decrease in statistical significance in our sensitivity analysis that was confined to RCTs with blinded outcome assessors, the proportion of effect sizes that can be considered as moderate-to-large was also smaller when only RCTs at low risk of detection bias were considered. Whereas 41% of the meta-analyses had a moderate-to-large pooled effect size in the main analysis, only 20% had a pooled effect size of this magnitude in this sensitivity analysis. This result can suggest that restricting the analyses to RCTs in which outcomes were measured by blind assessors may have discarded large pooled effect sizes derived from biased trials. However, it is important to note that by restricting to blinded outcomes, the type of outcomes included in the meta-analyses also changed. In particular, the percentage of reported outcomes decreased, while the number of outcomes assessed by standardized tests increased. Therefore, it is possible that we observed a reduction in the magnitude of the pooled effect sizes in this sensitivity analysis because psychosocial interventions produce subtle effects that can be detected in patients’ daily lives (and are therefore captured in the reports of informants seeing the patient on a day-to-day basis) but fail to be detected in more structured, less ecological assessments. Future studies may benefit from the developments of outcomes that are designed specifically to capture changes in clinical trials and longitudinal studies [ 58 ], as well as the implementation of new methodologies such as ecological momentary assessment (EMA) [ 59 ].

An additional objective of our study was to build an open-access online interactive resource ( https://www.ebiact-database.com ) which contains all of the information and results that we collected and generated during this umbrella review. We aimed to facilitate the dissemination of knowledge about psychosocial interventions in people with ASD by providing an open-access source of evidence-based information. This database was directly inspired by a similar project in the field of depression, i.e., the METAPSY database [ 60 ]. However, compared to this meta-analytic database, our umbrella review approach allows to provide key information not only on individual trials, but also on meta-analyses that are already published in the literature. This feature makes it possible to provide reliable information based on a meaningful pool of trials, and thus saves users from having to undertake this pooling independently (that may lead to meaningless pooling, which is a well-known problem in meta-analyses [ 61 ]).

By providing privileged access to the results of the scientific literature, we anticipate that our database will assist clinicians in keeping abreast of the most recent scientific information. We also believe that this database will be useful to researchers. This resource may be used as an interface to easily perform scoping reviews and thereby to identify gaps in knowledge about psychosocial interventions in people with ASD. It may also increase the consistency of the CCTs included in future meta-analyses because researchers will have convenient access to the list of all CCTs included in previous meta-analyses. Finally, the database will be useful to trialists. When planning new trials, few teams consider existing meta-analyses on their topic to inform the choice of materials or the power analysis [ 62 , 63 , 64 , 65 ]. In this regard, it has been shown that the large variability in the tools used to measure similar outcomes in the field of ASD can make direct comparisons between trials difficult [ 66 ]. The EBIA-CT database also allows the identification of the main outcome measures used in previous CCTs in a matter of minutes. Because this database contains critical information that can be used to conduct a power analysis (such as the pooled effect size estimate of a meta-analysis, its 95% prediction interval, or the effect size of the largest study), it will be useful for estimating an appropriate sample size in new trials.

A major methodological choice that we made when designing this umbrella review was assessing the credibility of evidence using algorithmic criteria rather than assessing the quality of this body of evidence using more subjective approach such as the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach [ 67 , 68 ]. In line with umbrella review guidelines [ 32 ], the main goal of the current umbrella review was to identify and appraise all of the evidence produced by meta-analyses on the efficacy of psychosocial interventions in people with ASD to guide readers to current best evidence. Our goal was not to design new recommendations for treatment or to make conclusions about the comparative efficacy of different psychosocial interventions. Instead, our context-independent algorithmic assessment of the credibility of evidence only aimed to synthesize, in a unique indicator, a large amount of information about the results of each meta-analysis, including the presence of methodological bias in primary studies, presence of small study effects, and inconsistency, using a consistent and robust method, regardless of the interventions or outcomes assessed. Such an algorithmic approach is concordant with many similar umbrella reviews of meta-analyses of clinical trials in both mental and physical health [ 53 , 54 , 69 , 70 , 71 ].

A first limitation of the present work lies in the lack of evaluation on the efficacy of specific intervention techniques (such as prompting, modelling or reinforcement) used in the different psychosocial interventions assessed in the review. In other words, we did not provide information on the efficacy of each “active ingredient” used by each psychosocial intervention type. Inclusion criteria of our systematic review allowed identification of meta-analyses that assessed the efficacy of such specific techniques, but only meta-analyses of single case experimental studies were found on this topic, e.g., the meta-analysis performed by Wang and colleagues [ 72 ]. Thus, despite the strength of the CCT design, an important direction for future research involves expanding this umbrella review to other clinical trial designs to afford a larger picture of the efficacy of psychosocial interventions in individuals with ASD. A second limitation of our study is that the information collected at the CCT-level was drawn from the meta-analyses that were included and not directly from reports describing the CCTs. This limitation, which is inherent to the umbrella review method for feasibility reasons, sometimes leads to incomplete information at the CCT-level. Future updates of the database will address the missing information by routinely providing data from new meta-analyses on the topic as they become available.

In conclusion, this umbrella review reinforced previous findings that highlighted the promising role of psychosocial interventions in individuals with ASD. However, additional well-designed RCTs are required to draw a consistent picture of the efficacy of psychosocial interventions in ASD with a higher level of evidence. The companion open-access database designed in this study will facilitate the dissemination of evidence-based knowledge about psychosocial interventions in ASD and will contribute to strengthening the methodological design of future clinical trials. This database will be regularly updated to ensure that accurate information is conveyed over time.

Data availability

The datasets generated during and/or analyzed during the current study are publicly available ( https://github.com/CorentinJGosling/MP_2022_EBIACT_PSYCHOSOCIAL ).

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04 october 2022.

A Correction to this paper has been published: https://doi.org/10.1038/s41380-022-01807-0

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CJG conceptualized and designed the study, performed study selection and data extraction, drafted data analysis and drafted the manuscript. AC conceptualized and designed the study, performed study selection and data extraction, and critically reviewed data analysis and the manuscript for important intellectual content. BCM performed study selection and data extraction, and critically reviewed data analysis and the manuscript for important intellectual content. RD conceptualized and designed the study, resolved conflicts during study selection, and critically reviewed data analysis and the manuscript for important intellectual content. JR and AS conceptualized and designed the study, and critically reviewed data analysis and the manuscript for important intellectual content. All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.

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Gosling, C.J., Cartigny, A., Mellier, B.C. et al. Efficacy of psychosocial interventions for Autism spectrum disorder: an umbrella review. Mol Psychiatry 27 , 3647–3656 (2022). https://doi.org/10.1038/s41380-022-01670-z

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Wiggins LD, Levy SE, Daniels J, Schieve L, Croen LA, DiGuiseppi C, Blaskey L, Giarelli E, Lee LC, Pinto-Martin J, Reynolds A, Rice C, Rosenberg CR, Thompson P, Yeargin-Allsopp M, Young L, Schendel D.

Journal of Autism and Developmental Disorders, 2015

This is one of two reports that describe the characteristics of children enrolled in SEED. This report focuses on developmental characteristics. Children enrolled in SEED are divided into four groups: three with children who have varying types of developmental delays and disabilities, including ASD, and one with children from the general population. The report describes how various facets of children’s development vary across these four groups and highlights the many needs of children with ASD and other developmental disabilities.

Using standardized diagnostic instruments to classify children with autism in the Study to Explore Early Development.

Wiggins LD, Reynolds A, Rice CE, Moody EJ, Bernal P, Blaskey L, Rosenberg SA, Lee LC, Levy SE.

This report describes the SEED process for determining whether a child enrolled in the study will be classified as an ASD case. This classification is based on an in-person assessment given by trained SEED clinicians. Children enrolled in the study are screened for autism symptoms by asking their mothers to respond to a brief questionnaire.  Children with an indication of possible autism symptoms are assessed further during an in-person visit.  Clinicians give these children a more in-depth developmental evaluation known as Autism Diagnostic Observation Schedule and ask their mothers or other caregivers to participate in an interview known as the Autism Diagnostic Interview – Revised. Besides providing clinicians with information to determine a child’s ASD classification, these assessments provide valuable information on ASD-specific behaviors and traits, allowing researchers to better understand the different characteristics among children with ASD.

The Study to Explore Early Development (SEED): a multisite epidemiologic study of autism by the Centers for Autism and Developmental Disabilities Research and Epidemiology (CADDRE) network.

Schendel DE, Diguiseppi C, Croen LA, Fallin MD, Reed PL, Schieve LA, Wiggins LD, Daniels J, Grether J, Levy SE, Miller L, Newschaffer C, Pinto-Martin J, Robinson C, Windham GC, Alexander A, Aylsworth AS, Bernal P, Bonner JD, Blaskey L, Bradley C, Collins J, Ferretti CJ, Farzadegan H, Giarelli E, Harvey M, Hepburn S, Herr M, Kaparich K, Landa R, Lee LC, Levenseller B, Meyerer S, Rahbar MH, Ratchford A, Reynolds A, Rosenberg S, Rusyniak J, Shapira SK, Smith K, Souders M, Thompson PA, Young L, Yeargin-Allsopp M.

Journal of Autism and Developmental Disorders, 2012

This report describes SEED methods. SEED is one of the largest studies investigating genetic and environmental risk factors for autism spectrum disorder (ASD) and child health and behavioral traits associated with ASD. SEED enrolls preschool-aged children with ASD and other developmental disabilities and children from the general population in six sites across the United States. SEED methods focus on enrolling families from diverse populations in each area. A key strength of SEED includes the collection of in-depth information on child development, which allows researchers to more rigorously classify children into various study groups (ASD, other developmental disabilities, or population controls) than what is done in many other ASD research studies.  In SEED, researchers use standardized assessment tools to determine a children’s final study group and to assess specific behavioral traits among children with ASD. Another key strength is the collection of comprehensive data on child health and potential risk factors for ASD. SEED’s large and diverse sample of study participants allows researchers to analyze data in greater detail than most other ASD studies and answer many important questions about ASD.   Top of Page

Maternal Psychiatric Conditions, Treatment with SSRIs, and Neurodevelopmental Disorders

Ames JL, Ladd-Acosta C, Fallin MD, Qian Y, Schieve LA, DiGuiseppi, C, Lee LC, Kasten EP, Zhou G, MPH, MD, PhD, Pinto-Martin J, Howerton E, Eaton, CL, Croen LA, PhD

Biological Psychiatry, 2021

A study published online in Biological Psychiatry looked at whether psychiatric conditions during pregnancy, like depression, and the use of selective serotonin reuptake inhibitors (SSRIs) were associated with autism spectrum disorder (ASD) among the children of mothers who were treated. The study found ASD was more common among children of mothers who had psychiatric conditions during pregnancy. However, among the subset of children whose mothers had psychiatric conditions, ASD was not more common among those treated with SSRIs. The authors conclude that this study provides evidence that maternal psychiatric conditions during pregnancy, but not the use of SSRIs, are associated with increased risk of ASD. These findings have implications for clinical decision-making regarding the continuation of SSRI treatment during pregnancy.

Maternal Pre-Pregnancy Weight and Gestational Weight Gain in Association with Autism and Developmental Disorders in Offspring

Susana L. Matias, Michelle Pearl, Kristen Lyall, Lisa A. Croen, Tanja V. E. Kral, Daniele Fallin, Li-Ching Lee, Chyrise B. Bradley, Laura A. Schieve, Gayle C. Windham

Obesity, 2021

A study published online explored whether obesity in mothers prior to pregnancy or weight gain during pregnancy was associated with autism spectrum disorder (ASD) or other developmental disorders in their children.  Mothers classified as having severe obesity (body mass index ≥35 kg/m) prior to pregnancy had a significantly higher risk of having children with ASD and other developmental disorders. The largest amounts of weight gain during pregnancy were associated with ASD, particularly among male children. Since pre-pregnancy weight and weight gain during pregnancy are regularly measured and potentially modifiable, these findings could assist targeting high-risk mothers for early interventions.

Infection and Fever in Pregnancy and Autism Spectrum Disorders: Findings from the Study to Explore Early Development

Croen LA, Qian Y, Ashwood P, Ousseny Z, Schendel D, Pinto-Martin J, Fallin D, Levy S, Schieve LA, Yeargin-Allsopp M, Sabourin KR

Autism Research, 2019

This study evaluated the associations between a child having autism spectrum disorder (ASD) or other developmental disabilities (DD), and whether the child’s mother had an infection during her pregnancy. The Study to Explore Early Development’s (SEED’s) detailed data on type and timing of a mother’s infection and whether the mother also had a fever allowed researchers to conduct a more in-depth analysis on this topic than had been done previously. Study findings showed that overall maternal infections during pregnancy were common, occurring in approximately 60% of women in this study, and were not associated with having a child with ASD or DD. Certain infections – those that occurred in the second trimester and were accompanied by fever (7% of mothers) – were associated with ASD in children. These study findings add to other studies of risk factors that highlight the potential association between maternal health during pregnancy and ASD.

Neonatal jaundice in association with autism spectrum disorder and developmental disorder

Cordero C, Schieve LA, Croen LA, Engel SM, Siega-Riz AM, Herring AH, Vladutiu CJ, Seashore CJ, Daniels JL

Journal of Perinatology, 2019

This study examines the association between a child having jaundice just after birth and autism spectrum disorder (ASD) and other developmental disorders (DDs). Jaundice is a yellow discoloration of the skin and eyes that occurs in some newborns because of a build-up of bilirubin, a substance that forms when blood cells are broken down. While most jaundice lasts only a short time, high levels of bilirubin can affect the developing brain. The Study to Explore Early Development’s (SEED’s) detailed data on the health of mothers and their children allowed researchers to conduct a more in-depth analysis on this topic than had been done previously. Study findings showed that among children who had been born too early (or preterm), newborn jaundice was associated with both ASD and other DDs. However, among children born on time, newborn jaundice was not associated with either ASD or other DDs. This study highlights the association between newborn health and ASD and other DDs.

Early Life Exposure to Air Pollution and Autism Spectrum Disorder: Findings from a Multisite Case-Control Study

McGuinn LA, Windham GC, Messer LC, Di Q, Schwartz J, Croen LA, Moody EJ, Rappold AG, Richardson DB, Neas LM, Gammon MD, Schieve LA, Daniels JL

Epidemiology, 2020

This study used Study to Explore Early Development (SEED) data to examine the association between autism spectrum disorder (ASD) and exposure to air pollutants during key periods of brain development. Particulate matter (PM), or tiny particles of air pollution, and ozone are common air pollutants. Previous studies have found an association between ASD and exposure to these air pollutants; however, previous studies have been unable to look at exposure to these air pollutants in relation to key periods of brain development or account for potential differences in pollutants in regions of the United States. This study looked at air pollutant exposure among participants living in six different areas of the United States (located in California, Colorado, Georgia, Maryland, North Carolina, and Pennsylvania) during several critical periods: 3 months before pregnancy, each trimester of pregnancy, the entire pregnancy, and the first year of life. Study findings showed an association between air pollution and ASD by period of exposure; ASD was associated with ozone exposure during the third trimester and with PM exposure during the first year of life. These findings support previous studies of a positive association between ASD and potential exposure to air pollution during the late prenatal period and early postnatal period. Further investigation into these findings may be helpful in increasing our understanding of these association

Air pollution, neighborhood deprivation, and autism spectrum disorder in the Study to Explore Early Development

McGuinn LA, Windham GC, Messer LC, Di Q; Schwartz J, Croen LA, Moody EJ, Rappold AG, Richardson DB, Neas LM, Gammon MD, Schieve LA, Daniels JL

Environmental Epidemiology, 2019

This study used Study to Explore Early Development (SEED) data to examine whether the association between autism spectrum disorder (ASD) and early exposure to air pollution is modified by neighborhood deprivation.  Previous research, including studies using SEED data, have found an association between ASD and exposure to particulate matter (PM), or tiny particles of air pollution, during the first year of life; however, these studies did not look at different measures of neighborhood deprivation, which may also be associated with ASD and are often geographically correlated with air pollution. This study went beyond prior studies by combining data on pollution, roadway proximity, and neighborhood deprivation at the census tract level in six different areas of the United States. Study findings showed that the association between ASD and PM exposure during the first year of life was stronger for children living in neighborhoods of high deprivation, as compared to neighborhoods of moderate or low deprivation. Confirmation of these preliminary findings may be useful in future studies.

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Association Between Breastfeeding Initiation and Duration and Autism Spectrum Disorder in Preschool Children Enrolled in the Study to Explore Early Development

Soke GN, Maenner M, Windham G, Moody E, Kaczaniuk J, DiGuiseppi C, Schieve LA

Autism Res, 2019

This study compared breastfeeding initiation and duration among children with autism spectrum disorder (ASD) to children from the general population without ASD. SEED’s large sample size and diverse sample allowed researchers to conduct a more thorough assessment than previous studies. Study findings showed mothers of children with ASD were just as likely as mothers of children from the general population to initiate breastfeeding. However, among mothers who initiated breastfeeding, those who had children with ASD were less likely than those who had children without ASD to continue breastfeeding for longer than 6 months. The reasons for shorter breastfeeding duration among children with ASD are unclear. A longer duration of breastfeeding might protect a child from developing ASD, but it is also possible that early discontinuation of breastfeeding is related to underlying developmental conditions in children with ASD, such as child irritability, sensory, or health issues. To better understand why the duration of breastfeeding might be shorter among mothers of children with ASD compared to those without ASD, future studies should consider evaluating the reasons women discontinue breastfeeding.

Maternal diabetes and hypertensive disorders in association with autism spectrum disorder

Cordero C, Windham GC, Schieve LA, Fallin MD, Croen LA, Siega-Rizf AM, Engel SM, Herring AH, Stuebe AM, Vladutiu CJ, Daniels JL

This study examined associations between a child having autism spectrum disorder (ASD) or other developmental disabilities (DDs) and whether the child’s mother had diabetes or hypertension during pregnancy. Diabetes and hypertension are among the most common complications experienced by women during pregnancy. SEED’s large sample size and detailed data about the health of mothers and their children allowed researchers to conduct a more in-depth analysis on this topic than previous studies. Study findings showed that having hypertension during pregnancy was associated with both ASD and other DDs in children compared with not having hypertension during pregnancy. Diabetes during pregnancy was not associated with ASD, but was associated with other DDs. This study highlights the relationship between maternal health during pregnancy and children with ASD and other DDs.

Maternal Pre-pregnancy Body Mass Index (BMI) and Gestational Weight Gain in Relation to Autism Spectrum Disorder (ASD) and Other Developmental Disorders in Offspring

Windham GC, Anderson M, Lyall K, Daniels JL, Kral TV, Croen LA, Levy SE, Bradley CB, Cordero C, Young L, Schieve LA

This study examined the relationship between mother’s body mass index (BMI) before pregnancy, mother’s weight gain during pregnancy, and associations with ASD and other developmental disabilities (DDs). Although previous studies have reported an association between higher maternal BMI and ASD, having this information, along with weight gain during pregnancy in SEED, allowed researchers to conduct a more in-depth analysis on this topic than previous studies. Study findings show an association between higher pregnancy weight gain and having a child with ASD, and this association was even stronger when the mother was overweight or obese before becoming pregnant. On the other hand, while maternal BMI before pregnancy was associated with having a child with a DD, mother’s weight gain during pregnancy was not. This study highlights the possible effects of maternal weight on child having ASD or DDs and the importance of maintaining a heathy weight before and during pregnancy.

Brief Report: Maternal Opioid Prescription from Preconception through Pregnancy and the Odds of Autism Spectrum Disorder and Autism Features in Children

Rubenstein E, Young JC, Croen LA, DiGuiseppi C, Dowling NF, Lee LC, Schieve L, Wiggins LD, Daniels J

Journal of Autism and Developmental Disorders, 2018

This study examined possible associations between prescription of opioid medications just before and during pregnancy and ASD and other developmental disabilities (DDs). Currently, the information available on this topic is very limited. SEED collects detailed information about mothers’ health histories, including prescribed medication, which allowed researchers to conduct this exploratory analysis. Illicit opioid use was not included in this analysis. The study findings show that approximately 8% of mothers reported receiving an opioid prescription just before or during pregnancy; of these mothers, the majority (76%) received only one prescription. The most common reasons for opioid prescriptions were migraine headaches, injury, and back pain. Mothers who were prescribed opioids just before becoming pregnant were more likely to have a child with ASD or a child with DDs and some autism symptoms. Researchers were limited by small sample sizes; thus, they were not able to conduct a detailed assessment of whether the associations found were related to the medication itself, the reason the mother took the medication, or some other unknown factors that may be associated with opioid use. This study is among the first to assess possible associations between prescription of opioids just before or during pregnancy and ASD and other DDs. More research is needed to understand how opioid use before and during early pregnancy may impact a child’s development.

Family History of Immune Conditions and Autism Spectrum and Developmental Disorders: Findings from the Study to Explore Early Development

Croen, LA, Qian Y, Ashwood, P, Daniels JL, Fallin D, Schendel D, Schieve LA, Singer AB, Zerbo O

Autism Research, 2018

This study examined the relationship between autism spectrum disorder (ASD) and other developmental disorders (DDs) and having a family history of conditions related to immune system functioning. Such conditions include asthma, allergies, and autoimmune disorders such as eczema or psoriasis. Previous studies have suggested some association, but the results about specific conditions varied. SEED’s large sample size and detailed data on specific types of immune disorders allowed researchers to conduct an in-depth analysis on this topic and examine the associations with ASD alongside associations with other DDs. The study findings show that maternal history of eczema or psoriasis and asthma are associated with both ASD and other DDs in children. Researchers also found that children with ASD are more likely to have eczema or psoriasis and allergies than children without ASD. Autoimmune disorders were not notably increased among children with other DDs. This study highlights the relationship between maternal health before and during pregnancy and ASD and other DDs, and provides researchers more information about the health of children with ASD.

Case-control meta-analysis of blood DNA methylation and autism spectrum disorder

Andrews SV, Sheppard B, Windham GC, Schieve LA, Schendel DE, Croen LA, Chopra P, Alisch RS, Newschaffer CJ, Warren ST, Feinberg AP, Fallin MD, Ladd-Acosta C

Molecular Autism, 2018

In this study, researchers used SEED data and data from another study of children and adolescents with and without ASD to learn more about how genes are regulated in children with ASD. Many genes are turned on or off by a process called “methylation.”  Although methylation does not change a person’s actual genes (or genetic code), methylation helps different types of cells do their specific jobs by affecting which genes are turned on and which genes are not. The researchers examined children’s DNA to look for differences in the methylation of genes between children with and without ASD. Previous studies of methylation in relation to ASD were limited by small sample sizes. This study is one of the largest so far to look broadly at methylation patterns in children with and without ASD. The study showed several potential differences in methylation between children in the two groups. Some of the differences suggest links to brain function, and they were consistent with results from previous studies. These findings provide clues as to how genes might be related to ASD in children.

Associations Between the 2nd to 4th Digit Ratio and Autism Spectrum Disorder in Population-Based Samples of Boys and Girls: Findings from the Study to Explore Early Development.

Schieve LA, Tian L, Dowling N, Croen L, Hoover-Fong J, Alexander A, Shapira SK.

This study examined associations between ASD and the ratio of children’s index (2nd) finger length to their ring (4th) finger length. The ratio of finger lengths (or digit ratio) has been linked to the level of sex hormones a child was previously exposed to during pregnancy. Researchers study digit ratios because they rarely have direct measurements of fetal exposure to hormones.  Study findings in boys showed that digit ratio was associated with ASD, but only in certain subgroups, such as children who had ASD and also a birth defect or genetic syndrome. This suggests the association might not have been related to hormone levels, but might instead be explained by genetics.  Study findings in girls showed that digit ratio was associated with ASD and that the association was not limited to certain subgroups of children.  There has been little past study of the association between digit ratio and ASD, particularly in girls.  The findings in this report suggest that hormone exposures during pregnancy might be related to ASD in girls, but many gaps remain in our understanding of the underlying reasons for this association and further research is needed.

Autism Spectrum Disorder and Birth Spacing: Findings from the Study to Explore Early Development (SEED).

Schieve LA, Tian LH, Drews-Botsch C, Windham GC, Newschaffer C, Daniels JL, Lee LC, Croen LA, Danielle Fallin M.

Autism Research, 2017

This study examined whether the amount of time between pregnancies was associated with ASD or other developmental disabilities in children. SEED’s detailed data on ASD subgroups and other developmental disabilities allowed researchers to conduct a more in depth analysis on this topic than those that have been done previously. The study findings show that both shorter and longer time periods between births are associated with having a child with ASD. Children conceived less than 18 months after their mother’s previous birth and children conceived 60 or more months after their mother’s previous birth were more likely to have ASD than children conceived between 18 to 59 months after their mother’s previous birth. The relationship was stronger in children with more severe ASD symptoms. Also, the association between birth spacing and ASD appeared to be unique to ASD, as there was no association found between birth spacing and having children with other developmental disabilities. The association between birth spacing and ASD was not explained by unplanned pregnancy, an underlying fertility disorder in the mother, or high blood pressure or diabetes during pregnancy. The findings from this study can help healthcare providers counsel their patients on pregnancy spacing.

Prenatal Alcohol Exposure in Relation to Autism Spectrum Disorder: Findings from the Study to Explore Early Development (SEED).

Singer AB, Aylsworth AS, Cordero C, Croen LA, DiGuiseppi C, Fallin MD, Herring AH, Hooper SR, Pretzel RE, Schieve LA, Windham GC, Daniels JL.

Paediatric and Perinatal Epidemiology, 2017

This study examined associations between alcohol use just before and during pregnancy and ASD or other developmental disabilities (DDs). Previous studies have shown that high levels of alcohol use in pregnancy are associated with child developmental effects, such as decreased intellectual ability, hyperactivity, learning difficulties, and autism-like traits. This study investigated whether lower levels of alcohol use before and during pregnancy were associated with developmental outcomes. Most mothers of children in SEED reported no or low levels of alcohol use before or during their pregnancies.  In fact, nearly all mothers reported no alcohol use in the second month of pregnancy or later (93-98% depending on month). Therefore, a main focus of the study was on alcohol use in the three months prior to pregnancy or the first month of pregnancy. The study findings show that modest alcohol use during these four months was not associated with increased risk for either ASD or other DDs.  Although this study did not find an association between ASD or other DDs and modest alcohol use before or during pregnancy, women who are pregnant or planning to become pregnant should continue to follow recommendations to avoid alcohol use because of other known effects on infant and child health.

Maternal and Paternal Infertility Disorders and Treatments and Autism Spectrum Disorder: Findings from the Study to Explore Early Development.

Schieve LA, Drews-Botsch C, Harris S, Newschaffer C, Daniels J, DiGuiseppi C, Croen LA, Windham GC.

Journal of Autism and Developmental Disorders, 2017

This study examined associations between ASD and whether, prior to becoming pregnant, a child’s mother had a condition that might have affected her ability to get pregnant (i.e., infertility). The study also looked at whether the mother had received any medical treatments to help her become pregnant or to prevent miscarriage during early pregnancy. SEED’s detailed data on specific types of infertility disorders and treatments allowed researchers to conduct a much more in depth analysis on this topic than those that have been done previously. The study findings show that several infertility disorders in the mother — including blocked tubes, uterine conditions such as fibroids, endometriosis, and polycystic ovarian syndrome — are associated with ASD in children. However, treatments for infertility or to prevent miscarriage were not associated with ASD.  While the reasons for the associations with infertility conditions could not be studied, possible explanations include increased inflammation during pregnancy or problems with the mother’s immune system. The findings from this study add to studies of other risk factors highlighting the relationship between maternal health before and during pregnancy and ASD.

Pleiotropic Mechanisms Indicated for Sex Differences in Autism.

Mitra I, Tsang K, Ladd-Acosta C, Croen LA, Aldinger KA, Hendren RL, Traglia M, Lavillaureix A, Zaitlen N, Oldham MC, Levitt P, Nelson S, Amaral DG, Hertz-Picciotto I, Fallin MD, Weiss LA.

PLOS Genetics, 2016

In this study, researchers used SEED data and data from other studies to investigate sex-specific genetic effects for ASD. The findings indicate involvement of genes on the X chromosome. These findings help us better understand how ASD might differ in girls and boys.

Presence of an Epigenetic Signature of Prenatal Cigarette Smoke Exposure in Childhood.

Ladd-Acosta C, Shu C, Lee BK, Gidaya N, Singer A, Schieve LA, Schendel DE, Jones N, Daniels JL, Windham GC, Newschaffer CJ, Croen LA, Feinberg AP, Daniele Fallin M.

Environmental Research, 2016

This study examined how environmental exposures, such as smoking during pregnancy, may impact gene regulation in children. Gene regulation is the process by which genes in a cell are turned on or off, and it is important for child development. Like other studies, researchers found that smoking during pregnancy affected gene regulation in children. However, while other studies have assessed these effects in children at the time of birth, the SEED sample provided an opportunity to look at gene regulation in older children. This study showed that the same pattern of gene effects was present in older children whose mothers had smoked in pregnancy as had been previously observed in newborns. These findings suggest that smoking during pregnancy may have lasting effects on child health and development.

Maternal Exposure to Occupational Asthmagens During Pregnancy and Autism Spectrum Disorder in the Study to Explore Early Development.

Singer AB, Windham GC, Croen LA, Daniels JL, Lee BK, Qian Y, Schendel DE, Fallin MD, Burstyn I.

Journal of Autism and Developmental Disorders, 2016

This study examined whether ASD was associated with the mother’s workplace exposure to certain chemicals or other substances during pregnancy. Because previous studies have shown associations between maternal asthma and allergy and ASD, researchers were particularly interested in exposure to substances that are known to trigger asthma symptoms, called asthmagens.  Examples of asthmagens include latex, certain drugs and chemicals such as dyes, and some cleaning products. The findings show that mothers of children with ASD had been exposed to slightly higher levels of workplace asthmagens than mothers of children in the general population. However, the difference was small and could have been due to chance. Many gaps remain in our understanding of how environmental exposures might impact the risk for ASD, and further research is needed.   Top of Page

Many Young Children with Autism Who Use Psychotropic Medication Do Not Receive Behavior Therapy: A Multisite Case-Control Study

Lisa D. Wiggins, PhD, Cy Nadler, PhD, Steven Rosenberg, PhD, Eric Moody, PhD, Nuri Reyes, PhD, Ann Reynolds, MD, Aimee Alexander, MS, Julie Daniels, PhD, Kathleen Thomas, PhD, Ellen Giarelli, PhD, and Susan E. Levy, MD, MPH

Pediatrics, 2021

A study published online in The Journal of Pediatrics explored the rates of psychotropic medication use among preschool-aged children (ages 2-5 years) with autism spectrum disorder (ASD).  While there are no medications to treat core symptoms of ASD, some medications may treat co-occurring symptoms such as attention problems, anxiety, aggression, and self-injurious behaviors.  However, The American Academy of Pediatrics recommends behavior therapy before medication is tried. In the study sample, 37 of 62 (59.7%) children with ASD who used psychotropic medications did not receive the behavior therapy prior to receiving medications.  Pediatricians are an important resource for children and families and can help facilitate behavioral treatment for children with ASD and other behavioral and developmental disorders.

Gastrointestinal Symptoms in 2- to 5-Year-Old Children in the Study to Explore Early Development

Reynolds AM, Soke GN, Sabourin KR, Croen LA, Daniels JL, Fallin MD, Kral TVE, Lee LC, Newschaffer CJ, Pinto-Martin JA, Schieve LA, Sims A, Wiggins LD, Levy SE

Journal of Autism and Developmental Disorders, 2021

This study compared gastrointestinal (GI) symptoms in 2,461 preschool children aged 30–68 months with autism spectrum disorder (ASD) to children with other developmental disabilities (DDs) and children from the general population (POP). Previous studies have shown that GI symptoms are common among children with ASD, but those studies have been limited by small sample sizes and lack of standard measures and comparison groups. Researchers used information from the Study to Explore Early Development (SEED)—including detailed information on GI symptoms, developmental level, and other problems such as anxiety (worry), aggression, and problems related to sleep and attention—to fill these gaps. Parents were asked to complete a detailed questionnaire on GI symptoms and a stool diary for their child. Based on these two instruments, 50.4% of children with ASD had GI symptoms, compared to 42.6% of children with other DDs and 30.6% of POP children. Among children with ASD, researchers also compared children who had lost skills they had previously developed (developmental regression) with those who had not lost previously developed skills and found that more children with developmental regression had GI symptoms (42.9%) than those without regression (31.8%).  Across all three study groups, GI symptoms were related to problems with sleep, attention, anxiety, and aggression.  These findings suggest that GI issues may be more common among children with ASD and are an important healthcare need to address.

Pica, Autism, and Other Disabilities

Fields VL, Soke GN, Reynolds A, Tian LH, Wiggins L, Maenner M, DiGuiseppi C, Kral TVE, Hightshoe K, Schieve LA

This study examined pica in preschool-aged children with autism spectrum disorder (ASD), other developmental disabilities (DDs), and children from the general population (POP). Pica is when a person eats non-food items with no nutritional value—such as paper, hair, paint, or dirt—which can result in medical problems. Previous research on pica in children with ASD and other DDs has been limited by small, non-representative samples, and has lacked a general POP comparison group. Researchers from the Study to Explore Early Development (SEED) examined pica in 4,739 preschool children aged 30–68 months with ASD, other DDs, and from the general population (POP).  Children with ASD and other DDs were further classified according to whether they had co-occurring intellectual disability (ID), and among children in the DD group, whether they had some ASD characteristics, for a total of 6 subgroups (ASD without ID, ASD with ID, DD with ASD characteristics, DD with ASD characteristics and ID, DD without ASD characteristics and with ID, and DD without ASD characteristics and without ID). Study results found that 23.2%, 8.4%, and 3.5% of children in the ASD, DD, and POP groups, respectively, had pica. Within the ASD group, pica was reported in 28.1% of children with ID and 14.0% of children without ID. Within the DD group, pica was reported in 26.3% of children with both ID and some ASD characteristics, 12.0% with some ASD characteristics but without ID, 9.7% with ID but without ASD characteristics, and 3.2% with neither ID nor ASD characteristics. These results show that pica may be common in young children with ASD, ASD characteristics, and/or ID, and suggest that young children in these groups can benefit from careful monitoring and safety precautions to prevent pica.  Parent prevention measures can include closely monitoring children, keeping items out of reach, using childproof locks, finding activities that occupy children’s attention, and informing other caregivers of concerns.

Mapping the Relationship Between Dysmorphology and Cognitive, Behavioral, and Developmental Outcomes in Children with Autism Spectrum Disorder

Tian LH, Wiggins LD, Schieve LA, 1, Yeargin-Allsopp M, Dietz P, Aylsworth AS, Elias ER, Julie E. Hoover‑Fong JE, Meeks NJL, Souders MC, Tsai ACH, Zackai EH, Alexander AA, Dowling NF, Shapira SK

Autism Research, 2020

This study looked at whether having more unusual physical traits (dysmorphic features (DFs)) was related to developmental problems and focused on children with autism spectrum disorder (ASD) compared to children from the general population (POP). Previous studies only looked at whether children with ASD and developmental problems had DFs; these studies did not always include a group of children without ASD. In this study, researchers used information from 881 preschool-aged children 2–5 years old enrolled in the Study to Explore Early Development (SEED). The study included an in-person physical examination where photographs, measurements, and hand scans were taken; these items were reviewed by clinical geneticists to determine the number of DFs in each child. This enabled researchers to ask whether a greater number of DFs was related to more developmental problems. The study found that children with ASD and ID had more language, movement, and learning issues as the number of DFs increased. Children with ASD but without ID had more movement and learning issues as the number of DFs increased. These relationships were not observed in the POP group. These findings suggest that DFs may be linked to the cognitive (learning and memory) problems of children with ASD. Additional studies on groups of children with ASD who do or do not have ID could help explain the findings.

Expressive Dominant Versus Receptive Dominant Language Patterns in Young Children: Findings from the Study to Explore Early Development

Reinhartsen DB, Tapia AL, Watson L, Crais E, Bradley C, Fairchild J, Herring AH, Daniels J

Journal of Autism and Developmental Disorders, 2019

This study examined language skills in children with autism spectrum disorder (ASD), children with other developmental disabilities (DD), and typically developing children from the general population (POP). Previous research has shown that children typically understand more vocabulary and complex language than they can express. However, some studies on the language patterns of children with ASD suggest they may be better at expressing than understanding language. Researchers used information from the Study to Explore Early Development (SEED) to categorize 2,571 children aged 30–68 months according to whether they understood or expressed language better or had similar language skills in both areas.  Study findings showed that all three groups of children were better able to understand than express language.  However, 23.6% of children in the ASD group were better at expressing language, as compared to 11.5% of children in the DD group and 10.8% of children in the POP group. Children in the ASD group who were better at expressing than understanding language typically had noticeable problems understanding language and were younger, had lower nonverbal cognitive skills, and had more serious social symptoms of ASD. These findings highlight the need to consider the type of language deficits when designing clinical interventions or treatment programs for children with ASD.

Wandering Among Preschool Children With and Without Autism Spectrum Disorder

Wiggins LD, DiGuiseppi C, Schieve L, Moody E, Gnakub Soke, Giarelli E, Levy S

Journal of Developmental and Behavioral Pediatrics, 2020

This study describes wandering in children ages 4–5 years with a confirmed autism spectrum disorder (ASD) diagnosis, children with a previous but unconfirmed ASD diagnosis (DDprevASD), children with other developmental disabilities (DD), and children from the general population (POP). Wandering, or leaving a supervised space or care of a responsible person, is common among toddlers who are exploring their environment and learning to be independent. Wandering typically becomes much less common after 4 years of age; however, some studies suggest that wandering may be more common among children with ASD than children with other DD and could compromise child safety and increase parental stress. In this study, researchers described 3,896 parent reports of wandering among their 4–5-year-old children enrolled in the Study to Explore Early Development (SEED) between 2007 and 2016. The researchers also examined the relationship between a child’s likelihood to wander and certain behavioral, developmental, and other factors. Study findings showed that wandering in children aged 4–5 years was reported in 60.4% of children with ASD, compared with 41.3% of children with DDprevASD, 22.3% of children with DD, and 12.4% of children in the POP group. Findings also showed that mood, anxiety, attention, and oppositional problems were all associated with wandering behavior, independent of ASD status. These results provide important information for parents and providers on the occurrence of wandering among children with and without ASD and associated conditions (such as anxiety and attention problems) that may place children at increased risk for wandering from safe environments. Moreover, these results may facilitate discussions between parents and providers about safety, prevention, and interventions that may improve the lives of children who wander and their families.

Injury-related treatments and outcomes in preschool children with autism spectrum disorder: Study to Explore Early Development (SEED)

DiGuiseppi C, Sabourin KR, Levy SE, Soke GN, Lee LC, Wiggins L, Schieve LA

This study examines the parent-reported treatments and outcomes of medically attended injuries among children with autism spectrum disorder (ASD) living in six different areas (located in California, Colorado, Georgia, Maryland, North Carolina, and Pennsylvania) in the United States in 2003–2006, compared to children with other developmental disabilities (DDs) and children from the general population (POP). The Study to Explore Early Development’s (SEED’s) in-depth data on the health of preschool children aged 2–5 years provided researchers with key information on these injuries. For each reported injury, parents were asked whether the injury resulted in loss of consciousness, an emergency department (ED) visit, hospitalization, surgery, or long-term behavior change. Parents were also asked if their child received any medication or injections for each medically attended injury reported. Study results showed that 30% of children in SEED had at least one medically attended injury. Of those children, 83% had at least one injury-related ED visit or hospitalization. Children with ASD were more likely than children from the POP group to have had a surgical procedure for an injury. Children with ASD were also less likely than those with DDs to receive medication or injections to treat injuries. These differences may be a result of characteristics of the child or injury or may reflect the clinicians’ ability to provide certain treatments or judge the severity of the child’s pain due to challenging behaviors associated with ASD. Further research may aid in understanding the differences in treatments prescribed to children with ASD compared to those prescribed to children with DDs or from the general population.

Early life influences on child weight outcomes in the Study to Explore Early Development

Kral TV, Chittams J, Bradley CB, Daniels JL, DiGuiseppi CG, Johnson SL, Pandey J, Pinto-Martin JA, Rahai N, Ramirez A, Schieve LA, Thompson A, Windham G, York W, Young L, Levy SE

Autism, 2019

This study examined overweight and obesity at age 2–5 years in children with and without autism spectrum disorder (ASD) or other developmental disorders (DDs). Obesity rates among U.S. children have increased markedly in recent decades and children with ASD have previously been shown to be at particularly high risk for obesity. SEED’s large sample and detailed data on children with ASD and other DDs allowed researchers to conduct a more in depth analysis on this topic than done previously. Study findings show that children born to mothers who were overweight/obese before becoming pregnant, or gained more weight than recommended during their pregnancies, were more likely to be overweight or obese between the ages of 2–5 years compared with children born to mothers who were underweight or normal weight prior to pregnancy and gained the recommended amount of weight during their pregnancies. These findings were similar for children with ASD, children with other DDs, and children without DDs. However, children with ASD were more likely than children in the other groups to have rapid weight gain in infancy; rapid weight gain was also associated with increased risk for being overweight or obese between ages 2–5 years. This study highlights the importance of maintaining a heathy weight before and during pregnancy and fostering healthy growth during infancy, among all children, including those with and without ASD.

Sleep Problems in 2- to 5-Year-Olds with Autism and Other Developmental Delays

This study assessed sleep problems, such as difficulties going to sleep or staying asleep through the night, in preschool-aged children with ASD, in comparison to children with other developmental disabilities (DDs) and children in the general population. SEED’s large sample and detailed data on preschoolers allowed researchers to conduct a more in-depth analysis on this topic than in previous studies. Study findings show that children with ASD and children with other DDs who have some ASD symptoms have more sleep problems than children with DDs without ASD symptoms and children in the general population. Even when researchers used a conservative definition to classify children as having sleep problems, 47% of children with ASD and 57% of children with other DDs who had some ASD symptoms were reported to have sleep problems, compared to 29% of children with DDs but no ASD symptoms and 25% of children in the general population. Sleep is important for development in young children. Addressing sleep problems among children with ASD and children with other DDs who have ASD symptoms is an important component of healthcare needs in this population.

A Novel Approach to Dysmorphology to Enhance the Phenotypic Classification of Autism Spectrum Disorder in the Study to Explore Early Development

This study used data from SEED to develop a new method to systematically classify certain physical features in children. The purpose of this system is to evaluate dysmorphology, which is the assessment of physical features that do not follow the typical pattern of growth and development. Children with multiple dysmorphic features often have an underlying genetic condition or had early pregnancy exposures that affected their development during the pregnancy.  The SEED dysmorphology classification method is more comprehensive than that used in previous studies. The findings from this study indicate that approximately 17% of children with ASD have a high number of dysmorphic features, and hence, meet the criteria for classification as dysmorphic. In contrast, approximately 5% of children from the general population control group met the criteria for classification as dysmorphic. Some, but not all, of the dysmorphology differences between children with and without ASD were explained by previously recognized and diagnosed genetic conditions and birth defects, which both occur more commonly in children with ASD. This is the first report of dysmorphology among children with ASD in a diverse U.S. population.

Relationship of Weight Outcomes, Co-occurring Conditions, and Severity of Autism Spectrum Disorder in the Study to Explore Early Development

Levy SE, Pinto-Martin JA, Bradley CB, Chittams J, Johnson SL, Pandey J, Alison Pomykacz A, Ramirez A, Reynolds A, Rubenstein E, Schieve LA, Shapira SK, Thompson A, Young L, Kral TV

Journal of Pediatrics, 2018

This study examined overweight and obesity among children with ASD, other developmental disabilities (DDs), and children from the general population. Study findings show that children with ASD or DDs were more likely to be overweight or obese than children from the general population. The proportion of children who were either overweight or obese was 28% in those with ASD, 25% in children with another DD, and 20% in children in the general population. Children with ASD or DDs were also more likely to have birth defects, medical disorders, seizure disorders, attention-deficit/hyperactivity disorder (ADHD), and psychiatric disorders than children from the general population. After controlling for these co-occurring conditions, the association between ASD and overweight or obesity was not changed, but the association between overweight and obesity and other DDs was reduced. In addition, among children with ASD, those with moderate or severe symptoms of ASD were more likely to be overweight or obese than children with mild ASD symptoms. Addressing overweight and obesity among children with ASD and other DDs is an important component of healthcare needs in this population.

Infections in Children with Autism Spectrum Disorder: Study to Explore Early Development (SEED)

Sabourin KR, Reynolds A,  Schendel D, Rosenberg S, Croen L, Pinto-Martin JA, Schieve LA, Newschaffer C, Lee LC, DiGuiseppi C

This study evaluated the association between early childhood infections and ASD and other developmental disabilities (DDs). SEED’s large sample size allowed researchers to conduct a more in-depth analysis on this topic than previous studies. The study findings show that children with ASD were more likely than children with other DDs and children from the general population to have had an infection in the first 28 days of life (early infection). Overall, 4.9% of children with ASD, 4.2% of children with other DDs, and 2.2% of children in the general population had an early infection recorded in their medical records. Children with ASD were also more likely to have an infection in the first 3 years of life than children in the general population, but children with ASD had similar rates of infection during their first 3 years as children with other DDs. This study highlights that ASD is associated with infections very early in the child’s life.

Brief Report: Self-Injurious Behaviors in Preschool Children with Autism Spectrum Disorder Compared to Other Developmental Delays and Disorders.

Soke GN, Rosenberg SA, Rosenberg CR, Vasa RA, Lee LC, DiGuiseppi C.

This study assessed self-injurious behavior, or SIB, among preschool-aged children with ASD in comparison to children with other developmental disabilities (DDs). The study showed that SIB is common in two groups of preschool-aged children – those with ASD and those for whom some autism-related symptoms are reported by their mother or other caregiver, even though they didn’t meet the criteria to be classified as an ASD case.  SIB was much less common in children with other DDs whose mother or caregiver did not report autism-related symptoms. These findings suggest that clinicians working with young children with DDs consider screening for SIB, even in children who do not have an ASD diagnosis.

Associations between Parental Broader Autism Phenotype and Child Autism Spectrum Disorder Phenotype in the Study to Explore Early Development.

Rubenstein E, Wiggins LD, Schieve LA, Bradley C, DiGuiseppi C, Moody E, Pandey J, Pretzel RE, Howard AG, Olshan AF, Pence BW, Daniels J.

Autism, 2018

This study assessed how the variation in developmental features among children with ASD was related to their parents’ own autism-related traits.  The presence of autism traits in family members of children with ASD is commonly referred to as the “broader autism phenotype” or BAP. The study findings show that if one or both parents have traits consistent with BAP, the child’s ASD is more likely to fall within a certain clinical presentation than if neither parent has traits consistent with BAP.  This clinical presentation in the child is characterized by average nonverbal abilities, mild language and motor delays, and increased frequency of other co-occurring developmental difficulties such as anxiety, depression, aggression, and attention difficulties.  The findings reported in this study could help better our understanding of the genetics of ASD.

The Prevalence of Gluten Free Diet Use among Preschool Children with Autism Spectrum Disorder.

Rubenstein E, Schieve L, Bradley C, DiGuiseppi C, Moody E, Thomas K, Daniels J.

This study estimated the proportion of children with ASD who had been on a gluten free diet. Altogether, 20% of preschool-aged children with ASD were currently or previously using a gluten free diet. In contrast, only 1% of children in the general population control group were using a gluten free diet. Children with ASD who also had gastrointestinal problems or had previously had a developmental regression were more likely to use a gluten free diet. This study demonstrates that gluten free diets are commonly used among children with ASD. More research is needed on the effectiveness of a gluten free diet in managing both gastrointestinal and behavioral symptoms related to ASD.

Injuries in Children with Autism Spectrum Disorder: Study to Explore Early Development (SEED).

DiGuiseppi C, Levy SE, Sabourin KR, Soke GN, Rosenberg S, Lee LC, Moody E, Schieve LA.

This study evaluated injuries in preschool-aged children with and without ASD and other developmental disabilities (DDs). Parents of children were asked whether their child had ever had an injury that required medical attention, and what types of injuries had occurred. The study findings showed that injuries were common in all groups of children and there was little difference between groups. Parents reported injuries for 32% of children with ASD, 28% of children with other DDs, and 30% of children in the general population. The most common injuries were open wounds and fractures and the most common reason for injuries was falls. While there was a slight difference in injuries between children with ASD and other DDs, further study found that this was largely explained by a higher level of attention problems in the children with ASD.

Homogeneous Subgroups of Young Children with Autism Improve Phenotypic Characterization in the Study to Explore Early Development.

Wiggins LD, Tian LH, Levy SE, Rice C, Lee LC, Schieve L, Pandey J, Daniels J, Blaskey L, Hepburn S, Landa R, Edmondson-Pretzel R, Thompson W.

This study used a complex computer program to assess the wide range of developmental characteristics among children with ASD.  Researchers identified four subgroups of children within the ASD group: 1) children with mild language delay and average cognitive functioning, but increased cognitive rigidity (or difficulty changing behaviors); 2) children with significant developmental delay, below average cognitive functioning, and repetitive motor behaviors; 3) children with general developmental delay, below average cognitive functioning, and moderate to highly severe autism symptoms; and 4) children with mild language and motor delays, but increased cognitive rigidity and high rates of problem behaviors. This study shows how information on developmental characteristics can be studied using advanced statistical methods to better understand ASD.  This information might also be useful in understanding children’s future health and development.

Self-injurious Behaviors in Children with Autism Spectrum Disorder Enrolled in the Study to Explore Early Development.

Autism, 2017

This study assessed self-injurious behavior, or SIB, among children with ASD. SIB includes head-banging, hair-pulling, arm-biting, scratching, and hitting oneself. SIB is usually mild, but can be severe in some children and may result in injuries requiring medical care. Children with severe SIB may miss out on educational and social activities. This study showed that in the SEED sample, about 28% of preschool-aged children with ASD displayed SIB currently, and 47% had previously displayed SIB. Researchers found SIB was more common in children with low adaptive behavior scores and gastrointestinal, sleep, and behavioral problems. While its causes are not completely understood, identifying SIB early is helpful because it may reduce the likelihood of more severe SIB later.   Top of Page

Temperament Similarities and Differences: A Comparison of Factor Structures from the Behavioral Style Questionnaire in Children with and Without Autism Spectrum Disorder

Barger B, Moody EJ, Ledbetter C, D’Abreu L, Hepburn S, Rosenberg SA

Journal of Autism and Developmental, 2019

This study assessed the performance of the Behavioral Style Questionnaire (BSQ), a commonly used measure of temperament, in children aged 2–5 years with and without autism spectrum disorder (ASD). The BSQ contains 100 questions designed to measure nine different behavioral tendencies, or temperaments, that affect how well children respond to their environment. Previous research has suggested that the BSQ may function differently for children with ASD compared with typically developing children. As such, researchers used Study to Explore Early Development (SEED) data to compare the behavioral tendencies the BSQ identified among children diagnosed with ASD and among children from the general population. Study findings showed that the BSQ did not identify the behavioral tendencies that it was originally designed to measure. Moreover, while the BSQ measured certain behavioral tendencies similarly among children with ASD and children from the general population, for other behavioral tendencies it did not. One behavioral tendency, “Negative Social Interactions”, was unique among children with ASD, and was not found among children from the general population. These findings suggest that more research may help us better understand how the BSQ performs in different groups of children, including children with ASD.

ASD Screening with the Child Behavior Checklist/1.5-5 in the Study to Explore Early Development

Levy SE, Rescorla LA, Chittams JL, Kral TJ, Moody EJ, Pandey J, Pinto-Martin JA, Pomykacz A, Ramirez A, Reyes N, Rosenberg CR, Schieve LA, Thompson A, Young L, Zhang J, Wiggins L

J Autism Dev Disord., 2019

This study assessed the performance of a general developmental assessment tool, known as the Child Behavior Checklist (CBCL), as a screening tool for autism spectrum disorder (ASD) symptoms in preschool-aged children. The CBCL is a broad-spectrum checklist that includes 99 items completed by a parent or a caregiver. Researchers in this study were interested in a subset of 13 items related to pervasive developmental problems. Previous research on this topic produced inconsistent results. SEED’s large sample of children with and without ASD and other developmental disabilities (DDs) allowed for a more thorough assessment. The study results showed that scores from the 13-item subscale were significantly different for children in the ASD group and the DD with ASD features group, compared to children in the DD without ASD features group and the population control group. These findings suggest that this CBCL subscale was effective at identifying children with ASD features needing further evaluation and supports its use as an ASD screening tool. The findings are particularly noteworthy because the CBCL is already widely used by schools and health professionals to screen for other developmental issues such as attention, anxiety, and depression.

DSM-5 criteria for autism spectrum disorder maximizes diagnostic sensitivity and specificity in preschool children

Wiggins LD, Rice CE, Barger B, Soke GN, Lee LC, Moody E, Edmondson-Pretzel R, Levy SE

Soc Psychiatry Psychiatr Epidemiol, 2019

The Diagnostic and Statistical Manual of Mental Disorders (DSM) specifies standardized criteria for diagnosing individuals with autism spectrum disorder (ASD) and other conditions. Criteria for diagnosing ASD were revised between the fourth (DSM-IV-TR) and the fifth edition of the manual (DSM-5). The purpose of this study was to compare DSM-IV-TR and DSM-5 definitions of ASD using information from comprehensive developmental evaluations completed with preschool children enrolled in the Study to Explore Early Development (SEED). This study was important because it compared DSM-IV-TR and DSM-5 definitions of ASD by evaluating children at a time when they often are first diagnosed, using both criteria in a single clinic visit. Study findings showed that DSM-5 criteria had the best balance between identifying true ASD cases, while ruling out children with other developmental disorders, when compared to DSM-IV-TR criteria. Researchers also found good agreement between DSM-5 and DSM-IV-TR for autistic disorder and moderate agreement for a less stringent definition of ASD known as pervasive developmental disorder not otherwise specified (PDD-NOS). These findings support the DSM-5 criteria for ASD in preschool-aged children.

Bayesian Correction for Exposure Misclassification and Evolution of Evidence in Two Studies of the Association between Maternal Occupational Exposure to Asthmagens and Risk of Autism Spectrum Disorder

Singer AB, Fallin MD, Burstyn I

Current Environmental Health Reports, 2018

In this study, researchers used SEED data and data from another study of children with and without ASD to assess how potential errors in coding the data for certain risk factors might influence the findings of epidemiologic studies. Researchers often want to study the effects of certain exposures during pregnancy but may not have the exact data they need. It is rare to have biologic measurements of the chemicals women were exposed to during pregnancy.  Therefore, studies often rely on related information to classify study participants as “likely exposed” or “not exposed” to certain chemicals. For example, studies often use information on a person’s job — such as type of job and industry where the person worked — to estimate possible chemical exposures from their workplace. In this study, researchers used a statistical method to address the possibility that certain job coding schemes could result in errors when evaluating associations between workplace exposures and ASD. They propose a way researchers might use this method in future studies to assess, and possibly correct, exposure classification errors.

Influence of Family Demographic Factors on Social Communication Questionnaire Scores.

Rosenberg SA, Moody EJ, Lee LC, DiGuiseppi C, Windham GC, Wiggins LD, Schieve LA, Ledbetter CM, Levy SE, Blaskey L, Young L, Bernal P, Rosenberg CR, Fallin MD.

This study assessed how the responses to a standardized questionnaire to screen for autism symptoms varied by family demographic characteristics. The study findings indicate that test performance was different in families with an indication of low versus higher socioeconomic status. These findings are important for both researchers and clinicians using autism screening questionnaires; they should be mindful that these tools might perform differently in various sociodemographic groups of children and their parents.

The Broader Autism Phenotype in Mothers is Associated with Increased Discordance Between Maternal-Reported and Clinician-Observed Instruments that Measure Child Autism Spectrum Disorder.

Rubenstein E, Edmondson Pretzel R, Windham GC, Schieve LA, Wiggins LD, DiGuiseppi C, Olshan AF, Howard AG, Pence BW, Young L, Daniels J.

This study assessed whether parents who have autism traits reported their children’s potential autism symptoms in a similar way as parents without an indication of autism traits. The findings indicate that parents with autism traits report more autism traits in their children compared to parents without autism traits, but parent reports do not always match clinician assessments based on observed behaviors in the child. It is possible that parents with some autism traits are more adept at identifying subtle characteristics of autism in their child. Another possible explanation for the study findings is that questions on various child behaviors could be interpreted differently by parents with and without autism traits. Further study is needed. The findings reported in this study could help better our understanding of developmental assessment results in young children.

Screening for Autism with the SRS and SCQ: Variations across Demographic, Developmental and Behavioral Factors in Preschool Children.

Moody EJ, Reyes N, Ledbetter C, Wiggins L, DiGuiseppi C, Alexander A, Jackson S, Lee LC, Levy SE, Rosenberg SA.

This study assessed and compared the performance of two standardized questionnaires to screen for autism symptoms. The accuracy of each questionnaire varied depending on the child’s level of developmental functioning and family sociodemographic traits. For example, the instruments were less accurate when children had high levels of challenging behaviors or lower levels of developmental functioning. Test performance also varied in families with indication of lower versus higher socioeconomic status. These findings are important for both researchers and clinicians using autism screening questionnaires; they should be mindful that these tools perform differently in various sociodemographic groups of children and their parents.

Brief Report: The ADOS Calibrated Severity Score Best Measures Autism Diagnostic Symptom Severity in Pre-School Children.

Wiggins LD, Barger B, Moody E, Soke GN, Pandey J, Levy S.

This report describes SEED methodology for assessing autism symptom severity among children with ASD. Measuring a child’s autism symptoms is often challenging because many children with ASD also have other developmental conditions. This can make it difficult to separate a child’s social and communication challenges from the child’s other developmental delays or conditions. Researchers evaluated several measures of autism severity and found that the Autism Diagnostic Observation Schedule (ADOS) calibrated severity score best measured the severity of core autism symptoms in a way that did not include symptoms of other developmental conditions. Because of findings from this study, the ADOS calibrated severity score will be used in other SEED research to help scientists better understand how the severity of autism symptoms relates to ASD risk factors and health outcomes.

Cross-tissue Integration of Genetic and Epigenetic Data Offers Insight into Autism Spectrum Disorder.

Andrews SV, Ellis SE, Bakulski KM, Sheppard B, Croen LA, Hertz-Picciotto I, Newschaffer CJ, Feinberg AP, Arking DE, Ladd-Acosta C, Fallin MD.

Nature Communications, 2017

In this study, researchers used SEED data and data from other studies to learn more about genetics and genetic regulation in children with ASD. While it is well-understood that genetics are related to ASD, many unanswered questions remain, such as how certain genes are turned on or off. The information from this study provides insights about how certain genes might be related to ASD.

“Gap Hunting” to Characterize Clustered Probe Signals in Illumina Methylation Array Data.

Andrews SV, Ladd-Acosta C, Feinberg AP, Hansen KD, Fallin MD.

Epigenetics & Chromatin, 2016

This study assessed new laboratory approaches to analyzing information on genetics collected through SEED. The findings contribute to the growing literature on how genes and environmental factors might interact in a way that increases the risk for ASD. While this study does not directly study these interactions, researchers describe and demonstrate how new laboratory approaches could help identify genetic associations.   Top of Page

Feature Articles

Autism Research and Resources from CDC April is Autism Acceptance Month. The recognition raises awareness about autism acceptance and promotes inclusion and connectedness for people with autism.

Higher Autism Prevalence and COVID-19 Disruptions Autism spectrum disorder (ASD) continues to affect many children and families. The COVID-19 pandemic brought disruptions to early ASD identification among young children. These disruptions may have long-lasting effects as a result of delays in identification and initiation of services.

Past, Present, and Future Impact of SEED Since the launch of SEED in 2003, CDC has worked with its partners to learn more about the needs of children with autism spectrum disorder (ASD) and other developmental disabilities so that families, communities, and healthcare providers can deliver the supports and services needed to thrive.

Why Act Early if You’re Concerned about Development? Act early on developmental concerns to make a real difference for your child and you! If you’re concerned about your child’s development, don’t wait. You know your child best.

Early Identification and Prevalence of Autism Among 4-year-old and 8-year-old Children: An Easy Read Summary This is an Easy-Read Summary of two reports. The first report is about identifying autism early among 4-year-old children. The second report is on the number of 8-year-old children with autism. (Published December 2, 2021)

Health Status and Health Care Use Among Adolescents Identified With and Without Autism in Early Childhood: An Easy-Read Summary The is an Easy-Read Summary (Published April 30, 2021)

Identifying Autism Among Children: An Easy-Read Summary This is an Easy-Read Summary of two reports. The first report is about the number of 8-year-old children with autism. The second report is about identifying autism early among 4-year-old children. (Published March 27, 2020)

Articles by Year

Statewide county-level autism spectrum disorder prevalence estimates—seven U.S. states, 2018. Annals of Epidemiology, 2023. Shaw KA, Williams S, Hughes MM, et al. [ Read article ]

The Prevalence and Characteristics of Children With Profound Autism, 15 Sites, United States, 2000-2016. Public Health Reports, 2023. Hughes MM, Shaw KA, DiRienzo M, et al. [ Read article ]

Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 8 Years—Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2020. MMWR Surveillance Summaries, 2023. 72 (2): p. 1. Maenner MJ, Warren Z, Williams AR, et al. [ Read article ] [ Easy Read Summary ]

Early Identification of Autism Spectrum Disorder Among Children Aged 4 Years—Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2020. MMWR Surveillance Summaries, 2023. 72 (1): p. 1. Shaw KA, Bilder DA, McArthur D, et al. [ Read article ] [ Easy Read Summary ]

Social Vulnerability and Prevalence of Autism, Metropolitan Atlanta Developmental Disabilities Surveillance Program (MADDSP). Annals of Epidemiology, 2023. Patrick ME, Hughes MM, Ali A, et al. [ Read article ]

Individualized Education Programs and Transition Planning for Adolescents With Autism. Pediatrics, 2023. Hughes MM, Kirby AV, Davis J, et al. [ Read article ]

Defining in Detail and Evaluating Reliability of DSM-5 Criteria for Autism Spectrum Disorder (ASD) Among Children Journal of Autism and Developmental Disorders, 2022: p. 1-13. Rice CE, Carpenter LA, Morrier MJ, et al. [ Read article ]

Reasons for participation in a child development study: Are cases with developmental diagnoses different from controls? Paediatric and Perinatal Epidemiology, 2022. Bradley CB, Tapia AL, DiGuiseppi CG, et al. [ Read article ]

Early identification of autism spectrum disorder among children aged 4 years—Autism and Developmental Disabilities Monitoring Network, 11 sites, United States, 2018. MMWR Surveillance Summaries, 2021. 70 (10): p. 1. Shaw KA, Maenner MJ, Baikan AV, et al. [ Read article ] [Easy Read Summary]

Prevalence and characteristics of autism spectrum disorder among children aged 8 years—autism and developmental disabilities monitoring network, 11 sites, United States, 2018. MMWR Surveillance Summaries, 2021. 70 (11): p. 1. Maenner MJ, Shaw KA, Bakian AV, et al. [ Read article ] [Easy Read Summary]

Progress and Disparities in Early Identification of Autism Spectrum Disorder: Autism and Developmental Disabilities Monitoring Network, 2002-2016. Journal of the American Academy of Child & Adolescent Psychiatry, 2021. Shaw KA, McArthur D, Hughes MM, et al. [ Read article ]

Peri-Pregnancy Cannabis Use and Autism Spectrum Disorder in the Offspring: Findings from the Study to Explore Early Development. Journal of Autism and Developmental Disorders, 2021: p. 1-8. DiGuiseppi C, Crume T, Van Dyke J, et al. [ Read article ]

Comparison of 2 Case Definitions for Ascertaining the Prevalence of Autism Spectrum Disorder Among 8-Year-Old Children. Am J Epidemiol, 2021. 190 (10): p. 2198-2207. Maenner MJ, Graves SJ, Peacock G, et al. [ Read article ]

Heterogeneity in Autism Spectrum Disorder Case-Finding Algorithms in United States Health Administrative Database Analyses. Journal of Autism and Developmental Disorders, 2021: p. 1-14. Grosse SD, Nichols P, Nyarko K, et al. [ Read article ]

Maternal prepregnancy weight and gestational weight gain in association with autism and developmental disorders in offspring. Obesity, 2021. 29 (9): p. 1554-1564. Matias SL., Pearl M, Lyall K, et al. [ Read article ]

Maternal psychiatric conditions, treatment with selective serotonin reuptake inhibitors, and neurodevelopmental disorders. Biological psychiatry, 2021. 90 (4): p. 253-262. Ames JL, Ladd-Acosta C, Fallin MD,  et al. [ Read article ]

A preliminary epidemiologic study of social (pragmatic) communication disorder relative to autism spectrum disorder and developmental disability without social communication deficits. Journal of autism and developmental disorders, 2021. 51 (8): p. 2686-2696. Ellis Weismer S, Tomblin JB, Durkin MS, et al. [ Read article ]

Healthcare costs of pediatric autism spectrum disorder in the United States, 2003–2015. Journal of autism and developmental disorders, 2021. 51 (8): p. 2950-2958. Zuvekas SH, Grosse SD, Lavelle TA, et al. [ Read article ]

Association between pica and gastrointestinal symptoms in preschoolers with and without autism spectrum disorder: Study to Explore Early Development. Disability and Health Journal, 2021. 14 (3): p. 101052. Fields VL., Soke GN, Reynolds A, et al. [ Read article ]

Health Status and Health Care Use Among Adolescents Identified With and Without Autism in Early Childhood—Four US Sites, 2018–2020. Morbidity and Mortality Weekly Report, 2021. 70 (17): p. 605. Powell PS, Pazol K, Wiggins LD, et al. [ Read article ] [Easy Read Summary]

Evaluation of sex differences in preschool children with and without autism spectrum disorder enrolled in the study to explore early development. Res Dev Disabil, 2021. 112 : p. 103897. Wiggins L.D, Rubenstein E, Windham G, et al. [ Read article ]

Pica, Autism, and Other Disabilities. Pediatrics, 2021. 147 (2). Fields VL., Soke GN, Reynolds A, et al. [ Read article ]

Many Young Children with Autism Who Use Psychotropic Medication Do Not Receive Behavior Therapy: A Multisite Case-Control Study. J Pediatr, 2021. 232 : p. 264-271. Wiggins LD, Nadler C, Rosenberg S, et al. [ Read article ]

Gastrointestinal Symptoms in 2-to 5-Year-Old Children in the Study to Explore Early Development. Journal of Autism and Developmental Disorders, 2021. 51 (11): p. 3806-3817. Reynolds AM, Soke GN, Sabourin KR, et al. [ Read article ]

A Distinct Three-Factor Structure of Restricted and Repetitive Behaviors in an Epidemiologically Sound Sample of Preschool-Age Children with Autism Spectrum Disorder. J Autism Dev Disord, 2021. 51 (10): p. 3456-3468. Hiruma L, Edmondson Pretzel R, Tapia AL, et al. [ Read article ]

Spending on young children with autism spectrum disorder in employer-sponsored plans, 2011–2017. Psychiatric Services, 2021. 72 (1): p. 16-22. Grosse SD, Ji X, Nichols P, et al. [ Read article ]

CE: From the CDC: Understanding Autism Spectrum Disorder. AJN The American Journal of Nursing, 2020. 120 (10):p. 30-37. Christensen D, Zubler J [ Read article ]

Wandering Among Preschool Children with and Without Autism Spectrum Disorder. J Dev Behav Pediatr, 2020. 41( 4): p. 251-257. Wiggins L, DiGuiseppi C, Schieve L, et al. [ Read article ]

Early identification of autism spectrum disorder among children aged 4 years—Early Autism and Developmental Disability Monitoring Network, six sites, United States, 2016. MMWR Surveillance Summaries, 2020. 69( 3): p. 1. Shaw KA, Maenner MJ, Baio J, et al. [ Read article ] [Easy Read Summary]

Prevalance of autism spectrum disorder among children aged 8 years—autism and developmental disabilities monitoring network, 11 sites, United States, 2016. MMWR Surveillance Summaries, 2020. 69 (4): p. 1. Maenner MJ, Shaw KA, Baio J, et al. [ Read article ] [Easy Read Summary]

Disparities in documented diagnoses of autism spectrum disorder based on demographic, individual, and service factors. Autism Research, 2020. 13 (3): p. 464-473. Wiggins LD, Durkin M, Esler A, et al. [ Read article ]

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Autism Spectrum Disorder and Social Story Research: a Scoping Study of Published, Peer-Reviewed Literature Reviews

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• Published: 24 February 2021
• Volume 9 , pages 21–38, ( 2022 )

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• Louis John Camilleri   ORCID: orcid.org/0000-0001-7747-1108 1 ,
• Katie Maras 1 &
• Mark Brosnan 1  

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Social Stories (SS) is a widely used intervention for children on the autism spectrum. A search of databases (CINAH EBSCO, A+Eductaion, ERIC, Education Source, PsyINFO, PubMed, Science Direct, Scopus, Web of Science, and ABI Inform Global) identified that, since its development over 25 years ago, the research exploring SS has been reviewed 17 times. These reviews include synthesis of literature; systematic reviews, meta-analyses; comparative reviews; and descriptive reviews. A scoping review of these 17 literature reviews identified 5 major themes: (1) research design of SS studies, (2) effectiveness of SS, (3) factors influencing outcomes of SSs, (4) social validity of SS interventions, and (5) maintenance and generalisation of SS outcomes. Future recommendations related to SS research were also identified.

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Introduction

Autism spectrum disorder (ASD) is a set of neurodevelopmental disorders characterised by a deficit in social communication, social behaviours and restrictive and repetitive behaviours, activities, or interests (American Psychiatric Association 2013 ). Based on epidemiological studies conducted over the past 40 years, the prevalence of ASD appears to be increasing globally (WHO 2020 ). Recent studies indicate that ASD has an estimated prevalence rate of 1 in 54, with 4.3 of these being male for every one female diagnosed, among children aged 8 years in the USA (Maenner et al. 2020 ).

Autism has a significant and persistent impact on the lives of those receiving a diagnosis as well as their families (Begum and Mamin 2019 ). Also, in financial terms, the cost of supporting children with ASDs is estimated to be £2.7 billion each year in the UK alone (Knapp et al. 2009 ). The financial demands, as well as the persistent and the pervasive impact of ASD on a person’s current and future well-being, highlight the importance of providing support, and Social Stories™ is a widely used intervention that is liked by professionals and acceptable to children on the autism spectrum and their families.

Social Stories

The Social Story™ (SS) intervention was developed for, and is used frequently to assist, children with autism spectrum disorder (Kokina and Kern 2010 ; Pane et al. 2015 ). SS are narratives consisting of personalised text and illustrations. The intervention was introduced by Gray and Garand ( 1993 ) to provide individuals with ASD with the information they may need to learn new information and to understand and function appropriately in different social situations. Gray and Garand ( 1993 ) originally recommended Social Stories™ to be used only with higher functioning verbal pupils and that the entire story should be presented on a single sheet of paper without other visual distractions. Over time, some of these recommendations, particularly related to story style and format, have changed (Gray 1998 , 2004 , 2010 , 2015 ; Gray and Garand 1993 ). Social Stories have become a widely used intervention due to their low cost and accessibility, as well as their capacity to address parents’ support needs, such as managing challenging behaviour (Derguy et al. 2015 ; Wahman et al. 2019 ). Given the widely used nature of the intervention, as well as the variability in the recommendations for how the intervention should be delivered over the 25 years since it was developed, professionals must follow evidence-based practice and recommendations. This should ensure that the intervention is sound, and delivered appropriately (Suhrheinrich et al. 2014 ; Will et al. 2018 ). However, despite decades of research, there is still a question as to whether or not SS interventions should be considered an evidenced-based practice (e.g. Test et al. 2011 ).

Evidence-Based Practice

Intervention literature moves quickly, and so does evidence-based practice, which is an active and dynamic concept (Wong et al. 2015 ). The Canadian Psychological Association Task Force on Evidence-Based Practice of Psychological Treatments (Dozois et al. 2012 ) defines evidence-based practice (EBP) as the conscientious, explicit and judicious application of the best available research evidence to inform clinical practice and service delivery. Contrariwise, the use of treatments that are based on poor-quality research tend to waste time and money, and “prey upon the emotional vulnerability of parents and caregivers” (Zane et al. 2008 , p. 44). Reports by the National Clearinghouse on Autism Evidence and Practice Review Team (Odom et al. 2010 ; Steinbrenner et al. 2020 ), which aimed to document possible new EBPs whilst continuing to validate existing EBPs, failed to define the SS intervention as an EBP. However, they placed Social Narratives within the EPB category. In this case, Social Narratives were defined as “interventions that describe social situations in order to highlight relevant features of a target behaviour” (p. 29). Here Social Narratives were not considered “tantamount” to Carol Gray’s Social Stories™ ( 1993 ); rather, they were defined as a distinct type of narrative. Nevertheless, they were deemed to “fit” within the Social Narratives category. Nonetheless, these conclusions were challenged by Zimmerman and Ledford ( 2017 ) who report variable outcomes and absence of a sufficient number of rigorous studies on Social Narratives. They also advocate for professionals to be cautious with the use of social narratives in isolation for children.

Purpose of the Current Review

Clinical observation, qualitative research, single-subject research (SSR), and randomised control trials (RCTs) are amongst the research designs that can contribute towards an “evidence base” (APA 2006 ). However, most of the published research on SSs that has been undertaken has been within “the constraints of a wholly positivist or quantitative paradigm” (Styles 2011 , p.424). Very few descriptive and qualitative research designs have been published. Studies such as Sandt ( 2008 )—who presents a descriptive report that explains how the author used a SS to help students with autism participate in physical education (PE) lessons—or Smith ( 2001 )—who examined the impact on children’s social behaviour of a two-session workshop for groups of parents and teacher—provide descriptive evidence about the effectiveness of SSs. Nevertheless, although useful, such evidence could be considered anecdotal, particularly when considering the effectiveness—i.e. the degree of beneficial effect in real world clinical practice (Godwin et al. 2003 )—of the intervention.

Several reviews of literature, in the form of systematic reviews, meta-analysis, and narrative reviews, have been published, all of which contribute towards the current knowledge base on SSs. An increase in the quality of the experimental research over time has been noted by a number of these reviews, such as Test et al. ( 2011 ). However, the extent, range, and nature of research activity remains unclear, whist the question of efficacy remains unanswered, especially because of the high variability in research quality that these reviews have identified. Thus, the purpose of the current review was to provide up-to-date information about the current state of SS research, with a specific focus on the effectiveness of SS interventions and factors which influence outcomes. In turn, outcomes of these findings could contribute further to the debate of whether or not SSs should be considered an EBP.

As reviews of SS already exist, the aim of the present study was not to conduct yet another literature review of one form or another, but rather to conduct a scoping review of the existing reviews. A scoping review is an exercise in mapping the existing literature (Ehrich et al. 2002 ). An adapted version of the scoping process outlined by Arksey and O’Malley ( 2005 ) was utilised for this review. This entailed (1) the identification of the research aims; (2) the searching for relevant studies; (3) the systematic selection of studies; (4) the charting of the data; and (5) the presentation of the results.

Identifying Relevant Studies

The electronic databases searched were CINAH EBSCO, A+Eductaion (Informit), ERIC, Education Source, PsyINFO, PubMed, Science Direct, Scopus, Web of Science, and ABI Inform Global. The search was limited to English language publications. Search terms used were “Social Story” and “Social Stories”. The terms were combined using the Boolean operand “OR” and across strings using the Boolean operand “AND”. The publication date was not restricted. The search results were managed and analysed using EndNote™ X9 (Endnote 2013 ).

Study Eligibility and Selection

The objective of the initial search was to investigate the current state of research on SS. The search that was informed by this goal yielded 459 citations following the removal of duplicates and the exclusion of citations that were not in English, were not peer-reviewed, were not related to ASD, and that were not about SS research. Titles and keywords of the remaining articles were further screened in more detail. Autism was not included in the original search terms as there are a wide range of potential variations and reading the titles and keywords ensured that the relevant studies were not erroneously excluded. This resulted in 119 full-text articles. The reading of these articles highlighted that they were already included in several reviews of literature that ranged from synthesis of literature; systematic reviews; meta-analyses; comparative reviews; and descriptive reviews. The publication dates of these articles ranged from 2004 to 2019. Thus, this scoping review included exclusively peer-reviewed reviews of literature. The final number of articles that met the inclusion criteria, and that were included in the current scoping review, was 17. The study search and selection process are presented in Fig.  1 .

PRISMA flow diagram for scoping review

The search results were analysed in terms of the aims identified for this scoping review. An inductive, data-driven, analysis was carried out to identify themes that could appraise the current state of SS research. A deductive analysis, which aimed to map the elements reported in the reviews of literature that specifically focused on outcomes (i.e. effectiveness of SS interventions) and factors which influence outcomes, was also carried out. NVIVO-12 software (QRS International 1999 ) was employed for this stage of the review, whilst a semantic content analysis of the data, as described by Braun and Clarke ( 2006 ), led to the coding of data according to the pre-defined research aims. Descriptive characteristics including title, author(s), year of publication, type of review, inclusion criteria, and the number of studies included in the review were extracted and organised. Also, key findings and conclusions, as well as recommendations for future research, were added to descriptive information to create detailed extraction tables (Tables  1 , 2 , 3 , and 4 ).

The 17 reviews included in this scoping study reported on a total of 120 individual studies focusing on SSs, which were conducted from 1995 to 2018. Some of these articles are included in more than one of the research syntheses included in this scoping review. Two themes were identified, as a result of the deductive analysis, whilst a further 3 themes were identified as a result of the inductive analysis of the 17 articles. The themes are (1) research design of SS research, (2) effectiveness of SS, (3) factors influencing outcomes of SSs, (4) social validity of goals of SS interventions, and (5) maintenance and generalisation of SS intervention outcomes. The “factors influencing outcomes” category consist of 2 further subthemes, which are (3.1) environmental factors and (3.2) within-child factors. These themes and subthemes are organised in hierarchical order (see Fig.  2 ) which illustrates their relevance in relation to the research objectives of this scoping review.

Themes identified from inductive and deductive analysis of articles that were included in the scoping review

1) Research design

The research design most frequently encountered in SS research is single-subject research (Ali and Frederickson 2006 ; Bucholz 2012 ). The only exception to this is Karkhaneh et al.’s ( 2010 ) review where only randomised control trials (RCTs) and controlled clinical trials (CCT) are included. Table 2 presents an overview of the different research designs that have been included in the reviews. AB designs (where “A” describes a baseline phase and “B” an intervention phase) feature regularly in reviews conducted before 2012. After 2012, the reviews highlight the recurrent use of studies that, unlike AB designs, could see threats to internal validity, such as variations of ABAB designs, alternating treatment designs, and multiple-baseline designs (which involves the concurrent measurement of two or more behaviours in a baseline condition, followed by the application of the treatment variable to one of the behaviours).

The single-case research design quality utilised in SS research has been a persistent concern (McGill et al. 2015 ). The quality of individual studies included in any review will determine the quality of the outcomes of a review, and will consequently impact the strength and validity of the claims made by that review (Schlosser et al. 2007 ). The quality of single-case research should be rigorously analysed for conclusions on causal relationships among interventions and outcomes to be determined. Tools for quality appraisal of single-case research guides are relatively novel (Lobo et al. 2017 ), but are available in the works of Horner et al. ( 2005 ) and What Words Clearinghouse Standards (Kratochwill et al. 2013 ).

From the 17 reviews selected, only 8 used quality appraisal criteria of studies they reviewed. Reynhout and Carter ( 2011 ), Test et al. ( 2011 ), and Mayton et al. ( 2013 ) utilised Horner et al. ( 2005 ) criteria. McGill et al. ( 2015 ) and Qi et al. ( 2018 ) utilised What Works Clearinghouse Standards (WWC). Karal and Wolfe ( 2018 ) and Aldabas ( 2019 ) used National Autism Centre’s (NAC 2015 ) Scientific Merit Rating Scale (SMRS) as a means of objectively assessing if the methods used in each study were sufficiently rigorous to determine whether or not SS intervention was effective for participants on the autism spectrum. Karkhaneh et al. ( 2010 ) only reviewed RCT and CCTs, and thus utilised the Jadad Scale (Jadad et al. 1996 ) which is a validated scale that is used for assessing the quality of reports of RCTs.

Nine studies failed to evaluate the methodological quality of the studies that were included in the review. These included Sansosti et al. ( 2004 ); Kokina and Kern ( 2010 ); Reynhout and Carter ( 2006 ); Styles ( 2011 ); Bucholz ( 2012 ); Rhodes ( 2014 ); Saad ( 2016 ); and Rodríguez et al. ( 2019 ).

With the introduction of standards in 2005 (Horner et al. 2005 ) as well as WWC standards (Kratochwill et al. 2013 ), an increase in study quality has been reported. In McGill et al.’s ( 2015 ) review, the authors explain how only one study, published from 1995 to 2004 included in their review, met all seven of WWC’s design standards. In contrast, eight studies published from 2005 to 2012 met all seven of the design standards. Overall, the 15% increase in average indicators met as well as the nominal increase in the number of studies meeting all of the standards across the periods provides some evidence of systematic improvements in single-case research quality over time.

2) Effectiveness of SS

The main focus of the reviews of literature that were included in this scoping study was to investigate the effectiveness of SS intervention. Sansosti et al. ( 2004 ) concluded that the empirical foundation regarding the effectiveness of Social Stories is limited (Sansosti et al. 2004 ). Kokina and Kern ( 2010 ) argue that their findings are indicative of questionable effectiveness of Social Story interventions for students with ASD (Kokina and Kern 2010 ). Reynhout and Carter ( 2006 ); Sansosti et al. ( 2004 ); and Bucholz ( 2012 ) argue that the effects of SS are highly variable. More recent reviews, such as Karal and Wolfe ( 2018 ); Qi et al. ( 2018 ); and Aldabas ( 2019 ), indicate the SS research published since 2013 has increased in quality and has also reported relatively higher effectiveness ratings.

Mean difference effect size statistic was used to interpret outcome of intervention using group designs (0.80 = large effect size, 0.50 = moderate, and 0.20 = small). Visual analysis ratings (VARs) (+ 2 significant decrease in target behaviours from baseline, + 1 moderate decrease in target behaviours from baseline, 0 little to no decrease in target behaviours from baseline, − 1 moderate increase in target behaviours from baseline, − 2 significant increases in target behaviours from baseline), percentage of non-overlapping data (PND) statistic (PNDs > 80 are indicative of a strong effect, 60 to 79 is a moderate effect, and PNDs < 60 indicate negligible effect), improvement rate difference (IRD) (range 0 to 1.00, > 0.75 indicate very large effect sizes, scores between 0.70 and 0.75 indicate large, scores between 0.51 and 0.70 indicate moderate, and scores less than 0.50 indicate small effect sizes), and points exceeding the median (PEM) (range 0 to 1. < 0.7 reflects an intervention that is not effective, PEM of 0.7 to 0.9 reflects moderate effectiveness, PEM of 0.9 to 1 reflects a highly effective treatment) were used to interpret the efficacy of SS intervention in single-case experimental designs. Whilst it is claimed that the IRD metric is the strongest validated metric, when compared to PND and PEM (Parker et al. 2007 ), nevertheless, IRD seems to have been employed to a limited extent whilst PND is the most used metric in the reviews.

Reynhout and Carter ( 2006 ) obtained a Mean PND of 43 (range 16–95) which indicates that SS intervention is ineffective according to PND evaluative criteria (refer to Scruggs and Mastropieri 1998 , 2001 ; Scruggs et al. 1987 ). Kokina and Kern ( 2010 ) report a mean PND score of 60% (range, 11–100%) for SS interventions. This score places SS in the low/questionable effectiveness category according to Scruggs and Mastropieri ( 1998 ). Reynhout and Carter ( 2011 ) report on PND (mean 51, range 0–100, SD = 30) and IRD (mean 0.57, range 0–1, SD = 0.26) metrics and suggest that the SS intervention is only mildly effective. On the other hand, the PEM metric resulted in a mean score of 72% (range 0–100, SD 26), which is indicative of moderate effectiveness.

In Test et al.’s ( 2011 ) review, PNDs could be calculated only for 10 of the 28 studies reviewed because the research design utilised meant that a functional relation could not be determined. However, the PND scores for 6 of the studies indicated “very effective” or “effective” outcomes, whilst the findings of the remaining studies were indicative of questionable or ineffective outcomes.

McGill et al. ( 2015 ) obtained an overall mean VAR of 0.68 (range 0 to + 2) and a mean PND of 51% (range 0–100%). Such scores are indicative of small-to-negligible effects. However, the weighted effect size estimator of 0.79 indicated moderate-to-large treatment effects. Karal and Wolfe ( 2018 ) obtained an average IRD score of 0.61 which is indicative of moderate effectiveness of SS interventions.

Qi et al. ( 2018 ) found a median PND value of 70%, which, contrary to findings from previous reviews, indicate that overall SS interventions are deemed effective for individuals with ASD. Similarly, Aldabas ( 2019 ) reported a mean effect size of 0.70, which is a high effect size and suggests that SS interventions are effective for individuals with ASD.

3) Factors Influencing Outcomes

Effectiveness of SS intervention may vary depending on the environment as well as within-child variables (Rust and Smith 2006 ). Overall effect sizes indicate that social stories are moderately effective, but specific intervention characteristics are associated with stronger outcomes (Karal and Wolfe 2018 ). The factors that have been identified to influence the outcomes of a SS intervention could be grouped into two broad categories: environmental factors and within-child factors (refer to Fig. 2 ). Within-child factors refer to characteristics of the individual participants. Environmental factors refer to a set of variables related to the research context and setting which are not participant related.

Description of participant characteristics is highly variable across SS literature. The poor or limited descriptions of participants are reported to make it difficult to determine if any specific participant characteristics were associated with intervention effectiveness. However, based on the articles included in this scoping study, age and gender, reading ability, verbal comprehension, and intellectual ability were within-child variables that were hypothesised to potentially influence outcomes of SS research. Environmental factors refer to a set of variables related to the research context and setting which are not participant related. Rather such factors are deemed to be pertinent to the environment in which the research was carried out. These factors are intervention setting, delivery of the intervention, modality, Gray’s criteria, comprehension checks, treatment packages, treatment intensity, and treatment integrity.

Age and Gender

The analysis of the reviews synthesised in this scoping review indicated that each review included 22 to 227 participants (refer to Table 4 ). Most of these participants were males. The ages of these participants ranged from 2 to 57 years. However, the more common age range was that of 3 to 15 years. Karal and Wolfe’s ( 2018 ) findings support and highlight the positive effects of SSs for school-aged autistic children whose ages range from 8 to 11 years. Mayton et al.’s ( 2013 ) findings also support the view that the effect of SSs is lower in studies with participants older than age 9.

Reading Ability

Rhodes ( 2014 ) and Reynhout and Carter ( 2006 ) propose that a child’s reading ability is a characteristic that could be considered a confounding variable. Nevertheless, it seems that, from the few studies reviewed that have included standardised achievement scores (such as Bledsoe et al. 2003 ; Brownell 2002 ; Kuoch and Mirenda 2003 ; Staley 2002 ; Thiemann and Goldstein 2001 ), the reading ability does not have a significant impact on the outcome of the intervention (Rhodes 2014 ).

Verbal Comprehension

Karkhaneh et al. ( 2010 ), Rhodes ( 2014 ), and Styles ( 2011 ) include in their reviews an article by Quirmbach et al. ( 2009 ) which highlights how a verbal comprehension index of at least 68 or greater on the Weschler Intelligence Scales for Children 4th Edition (Wechsler 2003 ) was associated with better effectiveness outcomes.

Intellectual Ability

Gray and Garand’s ( 1993 ) original focus was for SS to be used with higher functioning individuals who possess basic language ability. However, Kokina and Kern ( 2010 ) report that the effects of SS intervention seem to be somewhat higher for participants with lower cognitive ability than for individuals with high or average intelligence. Nevertheless, this factor is one which is underresearched, as the intellectual ability of individuals participating in SS research is rarely included in the participants’ description (Reynhout and Carter 2006 ).

Intervention Setting

Most of the studies conducted were reported to have been carried out in school settings (Aldabas 2019 ; Qi et al. 2018 ). More specifically, most deployed SS in structured classroom or small group settings (Styles 2011 ). The setting where the SS intervention is carried out is reported to impact intervention outcome. Interventions in general education reportedly produce larger effects when compared to home settings (Kokina and Kern 2010 ; McGill et al. 2015 ).

Intervention Delivery

McGill et al. ( 2015 ) reported that SS interventions delivered by researchers produce larger effects than those delivered by teachers. Rodríguez et al. ( 2019 ) report that whilst the majority of studies included in their review report on SS intervention that is conducted in schools by teachers, the results show a promising and positive effect of the intervention if it is carried out by people such as family members and teachers.

Intervention Modality

Combining visual elements with verbal cues is a common practice in SS interventions. Visual elements which have been included in SS interventions are photographs of participants, peers, and the environment; computer-presented social stories; and video feedback. Texts, graphics, animations, images, videos, and sounds are also reported to have been used in SS interventions delivered through technological aids (Sani Bozkurt et al. 2017 ).

Gray’s Criteria

Criteria for SS interventions were officially introduced by Carol Gray in 2004. In 2010, and subsequently, in 2014, these 10 criteria were revised. Gray’s criteria are reported to have been developed with learning characteristics of people with ASD in mind (Gray 2004 ). However, it is unclear whether a SS intervention’s conformity with such criteria is less or more effective than interventions that do not. Reynhout and Carter’s ( 2006 ) analysis concluded that from the 16 studies before 2004, a number of these deviated considerably from the criteria prescribed by Gray ( 2003 ). However, outcome measures indicated that a deviation from Gray’s criteria did not negatively impact PND.

Test et al.’s ( 2011 ) review reported that 75% of the studies included in their review stated that they had used Gray’s Criteria for developing SS interventions. They report that of the five out of the six studies that yielded “very effective” or “effective” PND scores used Gray’s criteria. On the other hand, both studies with intervention PNDs of 0% (i.e. not effective) also reported using Gray’s criteria. In his review of literature, Aldabas ( 2019 ) recommends practitioners to construct sound SSs through the implementation of sound guidelines such as Gray’s. However, similar to Reynhout and Carter ( 2006 ), outcomes, in terms of effect size, indicate that adherence to Gray’s criteria alone may not necessarily guarantee effectiveness. This seems to indicate that the relation between Gray’s criteria and SS effectiveness is unclear.

Comprehension Checks

Comprehension checks may be an important part of the implementation of SS intervention. Indeed, early guidelines by Gray and Garand ( 1993 ) required comprehension check to be a fixed component of the intervention. This to prevent inaccurate interpretation of the situation. In Reynhout and Carter’s ( 2006 ) review, it is reported that stories where authors reported a comprehension component yielded a higher mean PND than those who did not. Similarly, in Kokina and Kern’s ( 2010 ) meta-analysis, lower PND scores were obtained for the studies that did not involve comprehension checks. Furthermore, Styles ( 2011 ) reports that in studies where SS were read regularly, as the participant’s comprehension of the SS improved, so did the reported effectiveness of the intervention.

Treatment Packages

Ali and Frederickson ( 2006 ) report that the evidence base in 2006 suggested that SS interventions can be used alone or can be supported by combining it with other approaches. The use of SS interventions along with other approaches, such as prompting or reinforcement strategies, is reported in 3 of the reviews. Test et al. ( 2011 ) report that in 17 out of 28 studies, SS treatment packages have been evaluated. That indicates that 60% of the studies were not evaluating SS outcomes, but SS in combination with other interventions. Test et al. ( 2011 ) also report that in six of the studies that were included in their review that had “very effective” or “effective” PNDs, only two investigated Social Stories only whilst four studies investigated treatment packages that included Social Stories. The need for clarity on what is exactly being investigated (i.e. SSs alone vs treatment package that include SSs), as well as the need for more research on the efficacy of SS as part of a comprehensive treatment package, has been highlighted in a number of reviews such as Kokina and Kern ( 2010 ), McGill et al. ( 2015 ), and Aldabas ( 2019 ).

Treatment Intensity

Treatment intensity refers to the number of social stories an individual is exposed to, and the number of times it is read every day. Karkhaneh et al. ( 2010 ) remark that some studies describe treatment frequency and duration, but do not explore treatment dose for short-term and long-term maintenance. Kokina and Kern’s ( 2010 ) review notes that in the few studies that used several Social Stories per child, a higher treatment effect was reported. This could indicate that higher treatment intensity is associated with improved outcomes.

Treatment Integrity

Treatment integrity is a term that refers to the degree to which interventions are implemented as intended (Gresham 1989 ). Sansosti et al. ( 2004 ) reports that few studies exist that have assessed treatment integrity or procedural reliability. Test et al. ( 2011 ) reports that 37.5% of studies published from 1995 to 2004 included procedural reliability, whilst 58.3% of studies published from 2005 to 2007 measured procedural reliability. Test et al.’s ( 2011 ) findings suggest that measures of treatment integrity may be associated with intervention effectiveness. Similarly, Bucholz ( 2012 ) reports that the ineffectiveness of SS intervention may be due to a lack of treatment integrity. Qi et al. ( 2018 ) report that from the studies that reported treatment fidelity, the median PND was 75%, and the means of PEM, PEM-T (i.e. Percentage of data exceeding the median trend line), and PDO 2 (i.e. Pairwise data overlap squared) were 93%, 100%, and 92%, respectively. For studies that did not report fidelity, the median PND was 50%. Nevertheless, the lack of consistent reporting of treatment fidelity makes it difficult to conclude with a degree of certainty that treatment fidelity could influence the effectiveness of the intervention. Interestingly, in Rhodes’s ( 2014 ) review, the two studies that had unsuccessful outcomes had treatment integrity of 100%.

4) Social Validity

Social validity refers to the observed social significance of the goals selected, the acceptability of procedures employed, and the effectiveness of the outcomes produced in interventions as perceived by service users (Snodgrass et al. 2018 ). Sansosti ( 2004 ) reports that most of the studies published at the time did not report on social validity. This made it difficult to determine whether caregivers and/or educators perceive such interventions to be acceptable for children with ASD. The lack of reporting on qualitative research is also highlighted in Reynhout and Carter’s ( 2006 ) review—where only three of the sixteen studies reviewed examined an aspect of social validity—and also by Test et al. ( 2011 ).

Reynhout and Carter ( 2011 ) report on formal measures of social validity made using Martens et al.’s ( 1985 ) Intervention Rating Profile—15, and other or other ad hoc scales aimed at measuring family members’ reported perceptions. Similar measures were reported in 53% of the studies in Reynhout and Carter’s ( 2011 ) review, and in 59% of the studies included in McGill et al.’s ( 2015 ) review.

In more recent reviews, such as those carried out by Aldabas ( 2019 ) and Rodríguez et al. ( 2019 ), the authors have concluded that the increased reporting on social validity by teachers found SS intervention to be one of the most effective methods to teach new behaviours and decrease inappropriate behaviours in schools. Furthermore, Rodríguez et al. ( 2019 ) report that SS intervention is a tool with great reported acceptability from professionals, family members, and people with ASD themselves.

5) Maintenance and Generalisation

It is clear, from the studies included in the reviews, that single-case research is focusing mostly on determining the functional relationship between SS intervention and behavioural change. However, this emphasis has shifted gradually to include response maintenance as well as generalisability of behaviour change. The term maintenance refers to the measurement of effectiveness when the intervention is withdrawn, whilst the term generalisation refers to the effect of the intervention outside the direct environment, or context, of the study.

Reviews by Sansosti et al. ( 2004 ) and Ali and Frederickson ( 2006 ) highlight the lack of programming for maintenance and generalisation in SS research that was published at the time. Reynhout and Carter ( 2006 ) also mention that maintenance and generalisation are inadequately addressed in the studies included in their review. This pattern of methodological shortcomings was again reported by Karkhaneh et al. ( 2010 ), Kokina and Kern ( 2010 ), Reynhout and Carter ( 2011 ), Test et al. ( 2011 ), and Bucholz ( 2012 ). These reviews emphasise the importance of including, within the research design, evaluation of response maintenance.

Styles’s ( 2011 ) descriptive review, however, argues that whilst the concept of maintenance of effects after the cessation of the intervention is important to research, the concept of generalisation is not an adequate measure of the effectiveness of SS. He argues that the goal of the SS intervention is not generalisation. Rather, SS interventions are context- and situation-specific. Thus, whilst it would be desirable to have learnt behaviours generalised beyond the specific context, the scope of SS, in the first place, is more context-specific. Nevertheless, Styles ( 2011 ) maintains that there is insufficient evidence to suggest that positive outcomes are routinely maintained after SS has been withdrawn. Such conclusions were also reached by Mayton et al. ( 2013 ) and Rhodes ( 2014 ), who argue that it is unclear whether maintenance of behaviour is dependent upon continuing the SS intervention or not.

Qi et al. ( 2018 ) also came to a similar conclusion with regard to maintenance and generalisation effects. From the studies included in their review, only 7 of the 22 studies provided generalisation data to other settings. However, from these limited studies, it is suggested that SS intervention was effective or very effective in the maintenance of target skills.

The scoping review produced a comprehensive synthesis of research on SS interventions as reported through the various literature reviews identified. The empirical research on SS interventions is relatively large and is mostly based on single-subject research (SSR) designs. The examination of the effectiveness of interventions is the area in which SSR studies are most well-represented (Morgan and Morgan 2001 ). SSR is experimental and aims to document causal, or functional, relationships between independent and dependent variables (Horner et al. 2005 ). Participants in a single-subject experiment provide their own control data for comparison in a within-subject rather than a between-subject design. Such controls are seen to be threats to internal validity (Krasny-Pacini and Evans 2018 ).

Sansosti et al. ( 2004 ) published the first review that focuses on SS. The objective of their research synthesis was to evaluate the effectiveness of SS intervention. According to Sansosti et al. ( 2004 ), AB designs presented limited control over threats to internal validity in SSR studies that were carried out. Furthermore, they argue that ABA or ABAB designs are also not adequate to ascertain the effectiveness of social stories. The reason for this is related to the withdrawal of the intervention, an aspect of the design that could be harmful to the participant since it is unsafe for participants to return to the baseline phase of the intervention (Reynolds 2008 ). Secondly, the “reversal” of the behaviour to baseline conditions once the SS intervention is withdrawn may not even be possible since the objective of a SS is to attain long-standing behavioural change. Thus, when the target of the SS intervention is the decrease of inappropriate behaviours, the return to baseline conditions of the targeted behaviour could also be interpreted as an ineffective intervention outcome. Nevertheless, the use of single-case experiments, particularly multiple-baseline design, seems to have presented researchers with the opportunity to see to the issue of heterogeneity in autism symptomology as well as to the issues of ethics and “irreversibility”.

The strength of a SS intervention is indeed its “customisability”. Thus, whilst every intervention is based on similar principals, no one social story is the same as another. Furthermore, great variability in the administration can lead to great variability in terms of outcomes. One of these “variables” is intervention setting. The majority of studies are carried out in schools and structured classroom setting. Furthermore, the number of SS interventions being carried out by researchers and professionals outnumber the interventions carried out by teachers. Parents and guardians are those who figure the least in literature. This could be a result of the poor treatment fidelity reported in studies that centre around parents. Nevertheless, the small sample of studies in which parents administered the intervention has reported promising results. Yet, outcomes of such studies could have been confounded by administration procedures that might have deviated from the standard, and in so doing delivering some sort of treatment packages that included verbal prompts, encouragement, and reinforcement.

Reports, such as McGill et al.’s ( 2015 ), of larger effect sizes being reported in interventions that were delivered by researchers should lead to questioning the accessibility of the intervention, i.e. whilst it is reported that social stories are largely popular as a result of their “ease of use”, McGill et al.’s ( 2015 ) report seems to indicate that outcomes of the administration are more positive in settings where the administrators are highly trained individuals. Thus, this finding could challenge the notion that SSs are easy to create and to use, as the outcomes are tied to professional preparation and knowledge of the intervention.

The need to adhere, or not, to Gray’s ( 2004 ) criteria for writing social stories is also a confounding variable. The evidence seems to indicate that whilst adherence to Gray’s criteria could yield sound SSs, adherence to these criteria alone may not guarantee effectiveness. Furthermore, the lack of reporting on the use or adherence to Gray’s Criteria has been highlighted in most of the reviews. This limits the conclusions that can be made on this issue.

The issue of the modality of delivery also seems to be central to the question of SS intervention effectiveness. Modality, in this case, refers to the mode of administration (using electronic devices or print) and also refers to the inclusion of photos, text, graphics, animations, and sounds. The literature identified does not attempt to answer the question of which modality produces more positive outcomes. However, Kokina and Kern ( 2010 ) conclude that Functional Behavioural Analysis (FBA) could guide Social Story interventions. Thus, it could be conceivable to argue that knowledge of the individual’s distinctive characteristics—which include intellectual and verbal abilities, language (expressive and receptive) skills, behaviours, needs, preferences, strengths and weaknesses—could be important when administering SS interventions.

Overall, it seems that the question of “is a SS intervention effective for children with autism” is still not adequately answered in literature. The finding that the large majority of studies consist of male participants also puts into questions the effectiveness of SS interventions with female participants. Early reviews, e.g. Sansosti ( 2004 ) indicate that the evidence of the effectiveness of SS is limited. The PND scores reported by various reviews that are synthesised in this study are variable. These scores range from a mean PND of 43 (Reynhout and Carter 2006 ) to median PND scores of 70 (Qi et al. 2018 ). These scores are indicative of effects which range from negligible to moderate, respectively. These scores could be a result of the poor-quality SS research. There has been a reported improvement in the quality of the research since 1995, especially since the introduction of Horner et al.’s ( 2005 ) evaluative criteria for single-case research, National Autism Center’s ( 2009 ) Scientific Merit Rating Scale (SMRS), and Kratochwill et al.’s ( 2013 ) WWC standards. However, the variability in such quality suggests that notwithstanding the 120 studies included in the reviews, the evidence to support the effectiveness, or not, of the intervention is weak.

Furthermore, PND scores appear to be the most frequently used metric to evaluate the effect of the SS intervention. However, the PND metric is not necessarily the best, or even the only, way to measure outcomes of single-case research. Whilst it is claimed that the IRD metric is the strongest validated metric when compared to PND and PEM (Parker et al. 2009 ), nevertheless, IRD seems to have been employed to a limited extent to measure outcomes, whilst PND is the most used metric in the reviews. It could be argued that PND lacks sensitivity or discrimination ability (Parker et al. 2007 ). Thus, the procedure used to “measure” the effectiveness of SS intervention could be rethought.

Finally, the question of whether or not SS interventions could be considered an EPB is not answered in the literature. Whilst promising, the most frequent reply to this question is that “further research is needed on the effectiveness of social stories”.

Recommendations for Future Research

Outcomes of this scoping review indicate that notwithstanding the relatively large body of research, the great variability in reported outcomes of SS interventions is substantial. The improvement in the quality of research has been noted in the reviews following 2012. This could be attributed to the introduction of guidelines such as the NAC and WWC research guidelines for SSR. Together with this reported improvement, more positive outcomes have also been recorded. However, the implications of this scoping review go beyond the mere cataloguing of reported outcomes. Rather, the synthesis of literature has implications on aspects of research that should be seen by future researchers to continue to improve quality as well as contribute towards answering the question of whether or not SS interventions could be considered an EBP.

As recommended by both NAC and WWC guidelines, the variables that are reported to possibly confound/effect intervention outcomes should be included and more thoroughly described in research reports. These variables include (1) SS adherence to Gray’s criteria, (2) modality of social story delivery, (3) data on maintenance and generalisation of behaviours/skills, (4) number of sessions carried out, (5) goal of intervention, (6) intervention setting, (7) information regarding treatment fidelity, and (8) information on who carries out the intervention.

Several considerations should also be made when reporting on participants’ characteristics. Details, such as age, gender, intellectual ability, reading ability, and severity of difficulties should be adequately reported. Furthermore, Functional Behaviour Analysis (LaBelle and Charlop-Christy 2002 ) should also be carried out to ascertain the frequency and intensity of the behaviours that are going to be targeted. A better understanding of the target behaviour could also yield more apposite stories. Such information could also inform the social validity of the intervention, i.e. the degree to which the goal is important. To inform further this aspect of research, qualitative research strategies could also be employed, as well as quantitative measures of peers’ behaviours aimed to compare changes in behaviour to neurotypical children’s behaviour. Such measures should be put in place to see threats to the internal validity of the research design.

The issue of gender in SS research should also be taken into consideration, since most of the literature available focuses on males, as most of those diagnosed with autism are male. Future research should be sensitive to potential gender differences. Finally, the issue of which measure should be used to summarise and evaluate SSR outcomes is still relevant. Thus, whilst it is argued, in this paper, that PND is not necessarily the most adequate standard to evaluate SSR, the use of other outcome metrics which include, but are not limited to, VAR, IRD, PEM, PAND (see Parker et al. 2011 ) should be considered. Thus, researchers should present original data of baseline and intervention observations, both graphically and numerically, in the published report to ensure the accurate calculations of the various metrics.

Limitations

Several limitations associated with this review must be recognised. Specifically, findings are limited to databases included in the scoping process, which means that not all available research could have been identified. Also, this study did not include an evaluation of the quality of reviews that were identified and included. Furthermore, two of the reviews, namely Reynhout and Carter ( 2006 ) and Reynhout and Carter ( 2011 ), also included studies with children that were not autistic. These reviews were included because more than 80% of the studies that they included were with participants with autism. Finally, since the scoping review only included synthesis and other reviews of literature, it could have excluded other published research that was published in 2019 and 2020 that had not been included in the identified papers.

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Camilleri, L.J., Maras, K. & Brosnan, M. Autism Spectrum Disorder and Social Story Research: a Scoping Study of Published, Peer-Reviewed Literature Reviews. Rev J Autism Dev Disord 9 , 21–38 (2022). https://doi.org/10.1007/s40489-020-00235-6

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CONCEPTUAL ANALYSIS article

Research, clinical, and sociological aspects of autism.

• ESPA Research, Unit 133i Business Innovation Centre, The Robert Luff Laboratory, Education & Services for People With Autism Research, Sunderland, United Kingdom

The concept of autism continues to evolve. Not only have the central diagnostic criteria that define autism evolved but understanding of the label and how autism is viewed in research, clinical and sociological terms has also changed. Several key issues have emerged in relation to research, clinical and sociological aspects of autism. Shifts in research focus to encompass the massive heterogeneity covered under the label and appreciation that autism rarely exists in a diagnostic vacuum have brought about new questions and challenges. Diagnostic changes, increasing moves towards early diagnosis and intervention, and a greater appreciation of autism in girls and women and into adulthood and old age have similarly impacted on autism in the clinic. Discussions about autism in socio-political terms have also increased, as exemplified by the rise of ideas such as neurodiversity and an increasingly vocal dialogue with those diagnosed on the autism spectrum. Such changes are to be welcomed, but at the same time bring with them new challenges. Those changes also offer an insight into what might be further to come for the label of autism.

Introduction

Although there is still debate in some quarters about who first formally defined autism ( 1 ), most people accept that Kanner ( 2 ) should be credited as offering the first recognised description of the condition in the peer-reviewed scientific literature. The core diagnostic features covering issues in areas of social and communicative interaction alongside the presence of restricted and/or repetitive patterns of behaviour ( 3 ) described in his small caseload still remain central parts of the diagnosis today. The core issue of alterations in social cognition affecting emotion recognition and social attention ( 4 ) remain integral to the diagnosis of autism. The additional requirement for such behaviours to significantly impact on various areas of day-to-day functioning completes the diagnostic criteria.

From defining a relatively small group of people, the evolution of the diagnostic criteria for autism has gone hand-in-hand with a corresponding increase in the numbers of people being diagnosed. Prevalence figures that referred to 4.5 per 10,000 ( 5 ) in the 1960s have been replaced by newer estimates suggesting that 1 in 59 children (16 per 1,000) present with an autism spectrum disorder (ASD) in 2014 ( 6 ). The widening of the definition of autism has undoubtedly contributed to the significant increase in the numbers of people being diagnosed. It would be unacceptably speculative however, to define diagnostic changes as being the sole cause of the perceived prevalence increases.

Alongside the growth in numbers of people being diagnosed with autism so there have been changes in other areas related to autism; specifically those related to the research, clinical practice and sociological aspects of autism. Many of the changes have centred on key issues around the acceptance that autism is an extremely heterogeneous condition both in terms of presentation and also in relation to the genetic and biological complexity underlying its existence. That autism rarely exists in some sort of diagnostic vacuum is another part of the changes witnessed over the decades following the description of autism.

In this paper we highlight some of the more widely discussed changes in areas of research, clinical practice and sociological terms in relation to autism. We speculate on how such changes might also further develop the concept of autism in years to come.

Autism Research

As the definition of autism has subtly changed over the years, so ideas and trends in autism research have waxed and waned. The focus on psychology and behaviour as core descriptive features of autism has, in many respects, guided research and clinical views and opinions about the condition. Social cognition, including areas as diverse as social motivation, emotion recognition, social attention and social learning ( 4 ), remains a mainstay of research in this area. The rise of psychoanalysis and related ideas such as attachment theory in the early 20th century for example, played a huge role in the now discredited ideas that maternal bonding or cold parenting were a cause of autism. The seemingly implicit need for psychology to formulate theories has also no doubt played a role in perpetuating all-manner of different grand and unifying reasons on why autism comes about and the core nature of the condition.

As time moved on and science witnessed the rise of psychiatric genetics, where subtle changes to the genetic code were correlated with specific behavioural and psychiatric labels, so autism science also moved in the same direction. Scientific progress allowing the genetic code to be more easily and more cost-effectively read opened up a whole new scientific world in relation to autism and various other labels. It was within this area of genetic science that some particularly important discoveries were made: (a) for the vast majority of people, autism is not a single gene “disorder,” and (b) genetic polymorphisms whilst important, are not the only mechanism that can affect gene expression. Mirroring the role of genetics in other behavioural and psychiatric conditions ( 7 ), the picture that is emerging suggests that yes, there are genetic underpinnings to autism, but identifying such label-specific genetic issues is complicated and indeed, wide-ranging.

What such genetic studies also served to prove is that autism is heterogeneous. They complemented the wide-ranging behavioural profiles that are included under the diagnostic heading of autism. Profiles that ranged from those who are profoundly autistic and who require almost constant attention to meet their daily needs, to those who have jobs, families and are able to navigate the world [seemingly] with little or minimal support for much of the time.

It is this heterogeneity that is perhaps at the core of where autism is now from several different perspectives. A heterogeneity that not only relates to the presentation of the core traits of autism but also to how autism rarely manifests in a diagnostic vacuum ( 8 ). Several authors have talked about autism as part of a wider clinical picture ( 9 , 10 ) and how various behavioural/psychiatric/somatic issues seem to follow the diagnosis. Again, such a shift mirrors what is happening in other areas of science, such as the establishment of the Research Domain Criteria (RDoC) project ( 11 ). RDoC recognised that defining behavioural and psychiatric conditions on the basis of presented signs and symptoms does not necessarily “reflect” the relevant underlying processes and systems that might be important. It recognised that in order to deliver important clinical information about how and why a condition manifests, or the best strategies to intervene, research cannot just singularly start with the label. Science and clinical practice need more information rather than just a blanket descriptive label such as autism.

To talk about autism as a condition that also manifests various over-represented comorbid labels also asks a fundamental question: is the word “comorbidity” entirely accurate when referring to such labels? ( 12 ). Does such comorbidity instead represent something more fundamental to at least some presentations of autism or is it something that should be seen more transiently? Numerous conditions have been detailed to co-occur alongside autism. These include various behavioural and psychiatric diagnoses such as depression, anxiety and attention-deficit hyperactivity disorder (ADHD) ( 13 ). Other more somatic based conditions such as epilepsy ( 14 ), sleep ( 15 ) and various facets of gastrointestinal (GI) functioning ( 16 ) have also been discussed in the peer-reviewed science literature. Some of these co-occurring conditions have been described in the context of specific genetic conditions manifesting autism. Issues with the BCKDK (Branched Chain Ketoacid Dehydrogenase Kinase) gene for example, have been discussed in the context of autism, intellectual (learning) disability and epilepsy appearing together ( 17 ). Such a diagnostic combination is not unusual; autism often being described as the primary diagnosis with epilepsy and learning disability seen as “add-ons.” But should this be the case? Other evidence pointing to the possibility that epilepsy might under some circumstances beget autism ( 18 ) suggests that under some circumstances, such co-occurring conditions are so much more than just co-occurring or comorbid.

Other evidence for questioning the label “comorbid” comes from various animal models of autism. Accepting that one has to be particularly careful about extrapolating from animal models of autism to the more complex presentation of autism in humans ( 19 ), various models have suggested that autism may for some, fundamentally coexist with GI or bowel issues ( 20 , 21 ). Such observations have been noted across different animal models and cover important issues such as gut motility for example, that have been talked about in the context of autism ( 22 ).

Similarly, when one talks about the behavioural and psychiatric comorbidity in the context of autism, an analogous question arises about whether comorbidity is the right term. Anxiety and depression represent important research topics in the context of autism. Both issues have long been talked about in the context of autism ( 1 , 13 , 23 ) but only in recent years have their respective “links” to autism been more closely scrutinised.

Depression covers various different types of clinical presentations. Some research has suggested that in the context of autism, depressive illnesses such as bipolar disorder can present atypically ( 24 ). Combined with other study ( 25 ) suggesting that interventions targeting depressive symptoms might also impact on core autistic features, the possibility that autism and depression or depressive symptoms might be more closely linked than hitherto appreciated arises. Likewise with anxiety in mind, similar conclusions could be drawn from the existing research literature that anxiety may be a more central feature of autism. This on the basis of connections observed between traits of the two conditions ( 26 ) alongside shared features such as an intolerance of uncertainty ( 27 ) exerting an important effect.

A greater appreciation of the heterogeneity of autism and consideration of the myriad of other conditions that seem to be over-represented alongside autism pose serious problems to autism research. The use of “autism pure” where research participants are only included into studies on the basis of not having epilepsy or not possessing a diagnosis of ADHD or related condition pose a serious problem when it comes to the generalisation of research results to the wider population. Indeed, with the vast heterogeneity that encompasses autism, one has to question how, in the context of the current blanket diagnosis of autism or ASD, one could ever provide any universal answers about autism.

Autism in the Clinic

As mentioned previously, various subtle shifts in the criteria governing the diagnosis of autism have been witnessed down the years. Such changes have led to increased challenges for clinicians diagnosing autism from several different perspectives. One of the key challenges has come about as a function of the various expansions and contractions of what constitutes autism from a diagnostic point of view. This includes the adoption of autism as a spectrum disorder in more recent diagnostic texts.

The inclusion of Asperger syndrome in the DSM-IV and ICD-10 diagnostic schedules represented an expansion of the diagnostic criteria covering autism. Asperger syndrome defined by Hans Asperger ( 28 ) as autistic features without significant language impairment and with intelligence in the typical range, was included in the text for various different reasons. Allen Frances, one of the architects of the DSM-IV schedule, mentioned the importance of having a “ specific category to cover the substantial group of patients who failed to meet the stringent criteria for autistic disorder, but nonetheless had substantial distress or impairment from their stereotyped interests, eccentric behaviors, and interpersonal problems ” ( 29 ). It is now widely accepted that the inclusion of Asperger syndrome in diagnostic texts led to an increase in the number of autism diagnoses being given.

More recent revisions to the DSM criteria covering autism—DSM-5—included the removal of Asperger syndrome as a discrete diagnosis on the autism spectrum ( 30 ). Instead, a broader categorisation of autism spectrum disorder (ASD) was adopted. The reasons for the removal of Asperger syndrome from DSM-5 are complex. The removal has however generally been positively greeted as a function of on-going debates about whether there are/were important differences between autism and Asperger syndrome to require a distinction ( 31 ) alongside more recent revelations about the actions of Asperger during World War II ( 32 ). Studies comparing DSM-IV (and its smaller revisions) with DSM-5 have also hinted that the diagnostic differences between the schedules may well-impact on the numbers of people in receipt of a diagnosis ( 33 ).

Shifts in the diagnostic text covering autism represent only one challenge to autism in the clinical sense. Other important factors continue to complicate the practice of diagnosing autism. Another important issue is a greater realisation that although the presence of observable autistic features are a necessary requirement for a diagnosis of autism, such features are also apparent in various other clinical labels. Autistic features have been noted in a range of other conditions including schizophrenia ( 34 ), personality disorders ( 35 ) and eating disorders ( 36 ) for examples. Coupled with the increasingly important observation that autism rarely exists in a diagnostic vacuum, the clinical challenges to accurately diagnosing autism multiply as a result.

The additional suggestion of “behavioural profiles” within the autism spectrum adds to the complexity. Terms such as pathological demand avoidance (PDA) coined by Newson and colleagues ( 37 ) have started to enter some diagnostic processes, despite not yet being formally recognised in diagnostic texts. Including various autistic traits alongside features such as “resisting and avoiding the ordinary demands of life” and the “active use of various strategies to resist demands via social manipulation,” debate continues about the nature of PDA and its diagnostic value ( 38 ).

Early diagnosis and intervention for autism have also witnessed some important clinical changes over the years. Driven by an acceptance of the idea that earlier diagnosis means that early intervention can be put in place to “ameliorate” some of the more life-changing effects of autism, there has been a sharp focus on the ways and means of identifying autism early and/or highlighting those most at risk of a diagnosis. It's long been known that there is a heritable aspect to autism, whether in terms of traits or diagnosis ( 39 ). In this respect, preferential screening for autism in younger siblings when an older child has been diagnosed is not an uncommon clinical sentiment ( 40 ). Other work looking at possible “red flags” for autism, whether in behaviour ( 41 ) or in more physiological terms still continue to find popularity in both research and clinical terms.

But still however, autism continues to confound. As of yet, there are only limited reliable red flags to determine or preclude the future presence of autism ( 42 ). Early behavioural interventions for autism have not yet fulfilled the promise they are said to hold ( 43 ) and autism is not seemingly present in the earliest days of development for all ( 44 , 45 ). There is still a way to go.

Autism in a modern clinical sense is also witnessing change in several other quarters. The traditional focus of autism on children, particularly boys, is being replaced by a wider acceptance that (a) autism can and does manifest in girls and women, and (b) children with autism age and mature to become adults with autism. Even the psychological mainstay of autism—issues with social cognition—is undergoing discussion and revision.

On the issue of autism presentation in females, several important themes are becoming more evident. Discussions about whether there may be subtle differences in the presentation of autism in females compared to males are being voiced, pertinent to the idea that there may be one or more specific female phenotypes of autism ( 46 ). Further characterisation has hinted that sex differences in the core domain of repetitive stereotyped behaviours ( 47 ) for example, may be something important when it comes to assessing autism in females.

Allied to the idea of sex differences in autism presentation, is an increasing emphasis on the notion of camouflaging or masking ( 48 ). This masking assumes that there may active or adaptive processes on-going that allow females to hide some of their core autistic features and which potentially contributes to the under-identification of autism. Although some authors have talked about the potentially negative aspects of masking in terms of the use of cognitive resources to “maintain the mask,” one could also view such as adaptation in a more positive light relating to the learning of such a strategy as a coping mechanism. Both the themes of possible sex differences in presentation and masking add to the clinical complexity of reliably assessing for autism.

Insofar as the growing interest in the presentation of autism in adulthood, there are various other clinical considerations. Alongside the idea that the presentation of autism in childhood might not be the same as autism in adulthood ( 49 ), the increasing number of people receiving a diagnosis in adulthood is a worthy reminder that autism is very much a lifelong condition for many, but not necessarily all ( 50 ). The available research literature also highlights how autism in older adults carries some unique issues ( 51 ) some of which will require clinical attention.

Insofar as the issue of social cognition and autism, previous sweeping generalisations about a deficit in empathy for example, embodying all autism are also being questioned. Discussions are beginning debating issues such as how empathy is measured and whether such measurements in the context of autism are as accurate as once believed ( 52 ). Whether too, the concept of social cognition and all the aspects it encompasses is too generalised in its portrayal of autism, including the notion of the “double empathy problem” ( 53 ) where reciprocity and mutual understanding during interaction are not solely down to the person with autism. Rather, they come about because experiences and understanding differ from an autistic and non-autistic point of view. Such discussions are beginning to have a real impact on the way that autism is perceived.

Autism in Sociological Terms

To talk about autism purely through a research or clinical practice lens does not do justice to the existing peer-reviewed literature in its entirety. Where once autism was the sole domain of medical or academic professionals, so now there is a growing appreciation of autism in socio-political terms too, with numerous voices from the autism spectrum being heard in the scientific literature and beyond.

There are various factors that have contributed to the increased visibility of those diagnosed with autism contributing to the narrative about autism. As mentioned, the fact that children with autism become autistic adults is starting to become more widely appreciated in various circles. The expansion of the diagnostic criteria has also played a strong role too, as the diagnostic boundaries of the autism spectrum were widened to include those with sometimes good vocal communicative abilities. The growth in social media and related communication forms likewise provided a platform for many people to voice their own opinions about what autism means to them and further influence discussions about autism. The idea that autistic people are experts on autism continues to grow ( 54 ).

For some people with autism, the existing narrative about autism based on a deficit model (deficits in socio-communicative abilities for example) is seemingly over-emphasised. The existing medical model of autism focusing such deficits as being centred on the person does not offer a completely satisfying explanation for autism and how its features can disable a person. Autism does not solely exist in a sociological as well as diagnostic vacuum. In this context, the rise and rise of the concept of neurodiversity offered an important alternative to the existing viewpoint.

Although still the topic of some discussion, neurodiversity applied to autism is based on several key tenets: (a) all minds are different, and (b) “ neurodiversity is the idea that neurological differences like autism and ADHD are the result of normal, natural variation in the human genome ” ( 55 ). The adoption of the social model of disability by neurodiversity proponents moves the emphasis on the person as the epicentre of disability to that where societal structures and functions tend to be “ physically, socially and emotionally inhospitable towards autistic people ” ( 56 ). The message is that subtle changes to the social environment could make quite a lot of difference to the disabling features of autism.

Although a popular idea in many quarters, the concept of neurodiversity is not without its critics both from a scientific and sociological point of view ( 57 ). Certain key terms often mentioned alongside neurodiversity (e.g., neurotypical) are not well-defined or are incompatible with the existing research literature ( 58 ). The idea that societal organisation is a primary cause of the disability experienced by those with the most profound types of autism is also problematic in the context of current scientific knowledge and understanding. Other issues such as the increasing use of self-diagnosis ( 59 ) and the seeming under-representation of those with the most profound forms of autism in relation to neurodiversity further complicate the movement and its aims.

The challenges that face the evolving concept of neurodiversity when applied to autism should not however detract from the important effects that it has had and continues to have. Moving away from the idea that autistic people are broken or somehow incomplete as a function of their disability is an important part of the evolution of autism. The idea that autism is something to be researched as stand-alone issue separate from the person is something else that is being slowly being eroded by such a theory.

The concept of autism continues to evolve in relation to research, clinical practice and sociological domains. Such changes offer clues as to the future directions that autism may take and the challenges that lie ahead.

The continuing focus on the huge heterogeneity and comorbidity clusters that define autism are ripe for the introduction of a new taxonomy for describing the condition. A more plural definition—the autisms—could represent one starting position ( 60 ) encompassing a greater appreciation that (a) there is variety in the presentation of the core features of autism, (b) there are seemingly several different genetic and biological pathways that bring someone to a diagnosis of autism, (c) different developmental trajectories are an important facet of the autism spectrum, and (d) the various “comorbidities” that variably present alongside autism may offer important clues about the classification of autism. Some authors have stressed that a multi-dimensional conceptualisation may be more appropriate than a categorical concept ( 61 ) but further investigations are required.

In relation to the proposed pluralisation of the label, several long held “beliefs” about autism are also ripe for further investigation. The idea that autism is innate and presents in the earliest days in all does not universally hold ( 45 ). The finding that some children experience a period of typical development and then regress into autism ( 62 ) is becoming more readily discussed in research and clinical circles, albeit not universally so. Similarly, the belief that autism is a lifelong condition for all is also not borne out by the peer-reviewed literature ( 63 ). Terms such as optimal outcome ( 64 ) might not be wholly appropriate, but do nonetheless, shed light on an important phenomenon noted in at least some cases of autism where diagnostic cut-off points are reached at one point but not another. These and other important areas provide initial support for the adoption of the idea of the plural autisms.

Allied to the notion of “the autisms” is the requirement to overhaul the terminology around the use of the “level of functioning” phrase ( 65 ). “High functioning” is typically used to describe those people on the spectrum who present with some degree of communicative language, possess typical or above-average intelligence and who can seemingly traverse the world with only minimal levels of support. “Low functioning”, conversely, is used to describe those with significant support needs who may also be non-communicative. Aside from the societal implications of labelling someone “low functioning” and the possible connotations stemming from such a label, such functioning categorisation do not seemingly offer as accurate a representation as many people might think. The high-functioning autistic child who for example, has been excluded from school on the basis of their behaviour, cannot be readily labelled “high-functioning” if the presentation of their autistic behaviours has led to such a serious outcome. This on the basis that part of the diagnostic decision to diagnose autism is taken by appreciation of whether or not presented behaviours significantly interfere with day-to-day living ( 3 ). What might replace functioning labels is still a matter for debate. The use of “levels of support requirement” utilised in current diagnostic criteria offer a template for further discussions. Such discussions may also need to recognise that the traits of autism are not static over a lifetime ( 51 ) and support levels may vary as a result.

Whatever terminology is put forward to replace functioning labels, there is a need to address some very apparent differences in the way that parts of the autism spectrum are viewed, represented and included in research. Described as the “understudied populations” by some authors ( 66 ) those with limited verbal communicative language and learning disability have long been disadvantaged in research terms and also in more general depictions of autism. In more recent times, there has been a subtle shift to acknowledge the bias that exists against those with a more profound presentation of autism ( 67 ). Further developments are however required to ensure that such groups are not excluded; not least also to guarantee the generalisability of autism research to the entire spectrum and not just one portion of it.

On the topic of generalisability to the entire autism spectrum, the moves to further involve those diagnosed with autism in research, clinical and sociological discussions presents opportunities and obstacles in equal measure. The application of the International Classification of Functioning, Disability and Health (ICF) to autism ( 68 ) to measure “health-related functioning” represented a key moment in autism participatory research. Taking on board various views and opinions about autism, the development of the ICF core autism sets has allowed those with autism and their significant others to voice their opinions about autism ( 69 ).

Such joint initiatives are to be welcomed on the basis of the multiple perspectives they offer including lived experience of autism. But with such participation, so questions are also raised about how representative such opinions are to the entire autism spectrum ( 70 ). Questions on whether those who are able to participate in such initiatives “can ever truly speak for the entire autism spectrum?” are bound to follow. Questions also about whether such first-hand reports are more important than parental or caregiver input when it comes to individuals on the autism spectrum are likewise important to ask. This bearing in mind that those with autism participating in such initiatives bring with them the same potential biases as researchers and clinicians carry with them about the nature of autism, albeit not necessarily in total agreement.

The translation of research findings into clinical practice represents another important issue that has yet to be suitably addressed. Although covering a sizeable area, several important stumbling blocks have prohibited the move from “bench to bedside” when it comes to autism research. The focus for example, on the overt behavioural presentation of autism, has in some senses continued to hinder the translational progress of more biological-based findings into autism practice. Nowhere is this seemingly more evident than when it comes to the over-representation of gastrointestinal (GI) issues in relation to autism and their management or treatment. Despite multiple findings of such issues being present ( 16 ), very little is seemingly offered despite autism-specific screening and management guidance being in place for nearly a decade at the time of writing ( 71 ). Other quite consistently reported research findings in relation to low functional levels of vitamin D ( 72 ) for example, have similarly not sparked massive shifts in clinical practices. Ignoring such potentially important clinical features contributes to a state of relative health inequality that is experienced by many on the autism spectrum.

Without trying to prioritise some areas over others, there are some important topics in relation to autism that are becoming important to autism research and clinical practice. Many of these topics are more “real life” focused; taking into account the impact of autism or autistic traits on daily living skills and functioning. These include issues such as the truly shocking early mortality statistics around autism ( 73 ) and the need for more detailed inquiry into the factors around such risks such as suicide ( 74 ) and self-injury ( 75 ) and wandering/elopement ( 76 ) alongside the considerable influence of conditions such as epilepsy.

Although already previously hinted at in this paper, the nature of the relationship between autism and various “comorbid” conditions observed to be over-represented alongside is starting to become more widely discussed in scientific circles. Whether for example, moves to intervene to mitigate issues such as depression in relation to autism might also have knock-on effects on the presentation of core autistic features is something being considered. Interest in other topics such as employment, ageing, parenting and the worrying issue of contact with law enforcement or criminal justice systems ( 77 ) are also in the ascendancy.

Conclusions

Autism as a diagnostic label continues to evolve in research, clinical practice and sociological terms. Although the core features described by Kanner and others have weathered such evolution, important shifts in knowledge, views and opinions have influenced many important issues around those core behaviours. As well as increasing understanding of autism, many of the changes, past and present, have brought about challenges too.

Author Contributions

All authors contributed equally to the writing and review of this manuscript.

This paper was fully funded by ESPA Research using part of a donation from the Robert Luff Foundation (charity number: 273810). The Foundation played no role in the content, formulation or conclusions reached in this manuscript.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Keywords: autism, research, clinical, sociological, knowledge, future

Citation: Whiteley P, Carr K and Shattock P (2021) Research, Clinical, and Sociological Aspects of Autism. Front. Psychiatry 12:481546. doi: 10.3389/fpsyt.2021.481546

Received: 28 June 2019; Accepted: 30 March 2021; Published: 29 April 2021.

Reviewed by:

Copyright © 2021 Whiteley, Carr and Shattock. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Paul Whiteley, paul.whiteley@espa-research.org.uk

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Study identifies new metric for diagnosing autism

Autism spectrum disorder has yet to be linked to a single cause, due to the wide range of its symptoms and severity. However, a study by University of Virginia researchers suggests a promising new approach to finding answers, one that could lead to advances in the study of other neurological conditions.

Current approaches to autism research involve observing and understanding the disorder through the study of its behavioral consequences, using techniques like functional magnetic resonance imaging that map the brain's responses to input and activity, but little work has been done to understand what's causing those responses.

However, researchers with UVA's College and Graduate School of Arts & Sciences have been able to better understand the physiological differences between the brain structures of autistic and non-autistic individuals through the use of Diffusion MRI, a technique that measures molecular diffusion in biological tissue, to observe how water moves throughout the brain and interacts with cellular membranes. The approach has helped the UVA team develop mathematical models of brain microstructures that have helped identify structural differences in the brains of those with autism and those without.

"It hasn't been well understood what those differences might be," said Benjamin Newman, a postdoctoral researcher with UVA's Department of Psychology, recent graduate of UVA School of Medicine's neuroscience graduate program and lead author of a paper published this month in PLOS: One . "This new approach looks at the neuronal differences contributing to the etiology of autism spectrum disorder."

Building on the work of Alan Hodgkin and Andrew Huxley, who won the 1963 Nobel Prize in Medicine for describing the electrochemical conductivity characteristics of neurons, Newman and his co-authors applied those concepts to understand how that conductivity differs in those with autism and those without, using the latest neuroimaging data and computational methodologies. The result is a first-of-its-kind approach to calculating the conductivity of neural axons and their capacity to carry information through the brain. The study also offers evidence that those microstructural differences are directly related to participants' scores on the Social Communication Questionnaire, a common clinical tool for diagnosing autism.

"What we're seeing is that there's a difference in the diameter of the microstructural components in the brains of autistic people that can cause them to conduct electricity slower," Newman said. "It's the structure that constrains how the function of the brain works."

One of Newman's co-authors, John Darrell Van Horn, a professor of psychology and data science at UVA, said, that so often we try to understand autism through a collection of behavioral patterns which might be unusual or seem different.

"But understanding those behaviors can be a bit subjective, depending on who's doing the observing," Van Horn said. "We need greater fidelity in terms of the physiological metrics that we have so that we can better understand where those behaviors coming from. This is the first time this kind of metric has been applied in a clinical population, and it sheds some interesting light on the origins of ASD."

Van Horn said there's been a lot of work done with functional magnetic resonance imaging, looking at blood oxygen related signal changes in autistic individuals, but this research, he said "Goes a little bit deeper."

"It's asking not if there's a particular cognitive functional activation difference; it's asking how the brain actually conducts information around itself through these dynamic networks," Van Horn said. "And I think that we've been successful showing that there's something that's uniquely different about autistic-spectrum-disorder-diagnosed individuals relative to otherwise typically developing control subjects."

Newman and Van Horn, along with co-authors Jason Druzgal and Kevin Pelphrey from the UVA School of Medicine, are affiliated with the National Institute of Health's Autism Center of Excellence (ACE), an initiative that supports large-scale multidisciplinary and multi-institutional studies on ASD with the aim of determining the disorder's causes and potential treatments.

According to Pelphrey, a neuroscientist and expert on brain development and the study's principal investigator, the overarching aim of the ACE project is to lead the way in developing a precision medicine approach to autism.

"This study provides the foundation for a biological target to measure treatment response and allows us to identify avenues for future treatments to be developed," he said.

Van Horn added that study may also have implications for the examination, diagnosis, and treatment of other neurological disorders like Parkinson's and Alzheimer's.

"This is a new tool for measuring the properties of neurons which we are particularly excited about. We are still exploring what we might be able to detect with it," Van Horn said.

• Birth Defects
• Medical Devices
• Nervous System
• Brain Tumor
• Learning Disorders
• Disorders and Syndromes
• Autistic spectrum
• Brain damage
• Social cognition
• Rett syndrome
• Attention-deficit hyperactivity disorder
• Learning disability

Story Source:

Materials provided by University of Virginia College and Graduate School of Arts & Sciences . Original written by Russ Bahorsky. Note: Content may be edited for style and length.

Journal Reference :

• Benjamin T. Newman, Zachary Jacokes, Siva Venkadesh, Sara J. Webb, Natalia M. Kleinhans, James C. McPartland, T. Jason Druzgal, Kevin A. Pelphrey, John Darrell Van Horn. Conduction velocity, G-ratio, and extracellular water as microstructural characteristics of autism spectrum disorder . PLOS ONE , 2024; 19 (4): e0301964 DOI: 10.1371/journal.pone.0301964

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Investigating the stereotypes pre-service teachers associate with pupils with special educational needs

by Philip Stirm, Leibniz-Institut für Bildungsforschung und Bildungsinformation

In the course of inclusion, teachers are increasingly instructing pupils with special educational needs. Stereotypes regarding these children and adolescents can influence how the teachers deal with them. The DIPF | Leibniz Institute for Research and Information in Education has now investigated how pre-service teachers imagine autistic pupils and those with Down syndrome and dyslexia.

The study , published in Teaching and Teacher Education , revealed pronounced stereotypes among the pre-service teachers—including how competent and warm the individual pupil groups are. The DIPF wants to develop educational programs to counteract such generalizations.

"Autistic pupils are perceived as particularly competent and less warm, pupils with Down syndrome as particularly warm and less competent, and pupils with dyslexia as less competent and also relatively less warm," says Charlotte Schell, lead author of the article.

In comparison, autistic pupils were perceived as the most competent and least warm, while children with Down syndrome were perceived as the most warm and least competent. Children and adolescents with dyslexia were in the middle of the comparison in each case.

Numerous individual stereotypes mentioned by pre-service teachers were systematically incorporated into the overarching categories of "competent" and "warm." Some of these diverse attributions were particularly widespread. "For example, there was a strong tendency among pre-service teachers to see autistic pupils as highly gifted and introverted, pupils with Down syndrome as good-natured and awkward and pupils with dyslexia as lazy and low achieving," explains Schell.

Even if such stereotypes may apply to individuals, they are too generalized and ignore individual differences between pupils. "It falls short to put all pupils in the same box. They have specific behaviors and abilities that differ greatly from one another. They therefore need individual support," says Schell.

For example, if teachers consider a child to be very intelligent or even highly gifted based on an autism diagnosis, they may overlook their needs and not provide them with enough support. After all, many autistic pupils are not gifted. In turn, if a child with dyslexia is seen as lazy based on stereotypes, teachers may ask them to work harder instead of providing targeted support according to their needs.

The scientific investigation

For their investigation, the DIPF team worked with pre-service teachers who were at different stages of their studies, had taken different subjects and were studying for different school types. In a preliminary study, the researchers first conducted interviews with 13 of these students in which they were asked to name stereotypes that they associate with the groups mentioned. This revealed a broad spectrum of attributions—such as impulsive, unintelligent, but also open or savant.

The researchers incorporated the results of the first study into a standardized questionnaire in order to record the empirical characteristics of the stereotypes in connection with the three groups of pupils . A total of 213 pre-service teachers completed this questionnaire in a larger second study. The strength of the individual attributions was then statistically processed and assigned to overarching categories using factor analysis.

Implications and further research

The studies were carried out as part of the research project "Stereo-Disk—Stereotypes as obstacles for professional diagnostics in an inclusive school context." As part of the project, the DIPF is developing educational programs for teachers to reduce the impact of stereotypes on their assessments of children with special educational needs —for example, seminars that deepen knowledge about the educational needs of individual groups and diagnostic skills. The current studies highlight the need for such programs.

For future studies, the researchers have developed a model on how individual stereotypical attributions can be structured even better. Based on their investigations, they recommend classifying them into the categories "academic competence," "warmth," "social skills" and "behavioral problems."

Schell emphasizes that further research on the topic would be useful. "We looked at the stereotypes only in pre-service teachers and only for three of the pupil groups in particular need of support," says the DIPF researcher. The project team is also currently investigating the effects of stereotypes on behavior in more detail.

Provided by Leibniz-Institut für Bildungsforschung und Bildungsinformation

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Exclusion of females in autism research: Empirical evidence for a “leaky” recruitment‐to‐research pipeline

Anila m. d'mello.

1 McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge Massachusetts, USA

5 Present address: Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX; Peter J. O'Donnell Brain Institute, University of Texas Southwestern Medical Center, Dallas, TX; Department of Psychology, University of Texas at Dallas, Cambridge Richardson, USA

Isabelle R. Frosch

2 Department of Psychology, Northwestern University, Evanston Illinois, USA

Cindy E. Li

3 Hock E. Tan and K. Lisa Yang Center for Autism Research at Massachusetts Institute of Technology, Cambridge Massachusetts, USA

Annie L. Cardinaux

4 Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge Massachusetts, USA

John D.E. Gabrieli

Associated data.

Supplementary Figure S1 Effect of age on male:female ratios in the SPARK sample. As older individuals are included in the sample, male:female sex ratios become more equal.

De‐identified data from the Autism Research Participant Database at MIT are available upon request. Data from comparison databases are publicly available, or available via request (e.g., SPARK database access requires application).

Autism spectrum disorder (ASD) is characterized by challenges in social communication and the presence of repetitive behaviors or restricted interests. Notably, males are four times as likely as females to be diagnosed with autism. Despite efforts to increase representation and characterization of autistic females, research studies consistently enroll small samples of females, or exclude females altogether. Importantly, researchers often rely on standardized measures to confirm diagnosis prior to enrollment in research studies. We retrospectively analyzed the effects of one such measure (Autism Diagnostic Observation Schedule, ADOS) on research inclusion/exclusion rates by sex in autistic adults, all of whom had a preexisting community diagnosis of autism ( n  = 145, 95 male, 50 female). Using the ADOS as a confirmatory diagnostic measure resulted in the exclusion of autistic females at a rate over 2.5 times higher than that of autistic males. We compared sex ratios in our sample to those in other large, publically available datasets that rely either on community diagnosis (6 datasets, total n  = 42,209) or standardized assessments (2 datasets, total n  = 214) to determine eligibility of participants for research. Reliance on community diagnosis rather than confirmatory diagnostic assessments resulted in significantly more equal sex ratios. These results provide evidence for a “leaky” recruitment‐to‐research pipeline for females in autism research.

Lay Summary

Despite efforts to increase the representation of autistic females in research, studies consistently enroll small samples of females or exclude females altogether. We find that despite making up almost 50% of the initially recruited sample based upon self‐report of community diagnosis, autistic females are disproportonately excluded from research participation as a result of commonly used autism diagnostic measures. In our sample, and several other publically available datasets, reliance on community diagnosis resulted in significantly more equal sex ratios.

INTRODUCTION

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by challenges in social communication and the presence of repetitive behaviors or restricted interests. Historically, autism has been viewed as a predominantly male disorder with male to female sex ratios typically reported as 4:1 (Lai, Lombardo, et al.,  2015 ) and ranging as high as 7:1 (Rutherford et al.,  2016 ). Subsequent findings, however, suggest that the sex ratio discrepancy may be smaller than originally thought (Barnard‐Brak et al.,  2019 ), and even equal in some samples (see Burrows et al.,  2022 ). In fact, ratios as low as 3:1 have been reported in children (Loomes et al.,  2017 ) and may be even lower among adults (Posserud et al.,  2021 ). In light of these findings, there have been calls to improve characterization of females on the autism spectrum and to increase their representation in research (Barnard‐Brak et al.,  2019 ; Jack et al.,  2021 ; Watkins et al.,  2014 ). Underrepresentation of women in research is not unique to autism. Across both basic research studies and clinical trials, males are often disproportionately overrepresented. This can lead to failures in the diagnosis and treatment of women, including reduced efficacy of and unforeseen negative side effects from pharmacological treatment (Shansky & Murphy,  2021 ), increased service needs and barriers to treatment relative to males, and other unmet treatment needs (Koffer Miller et al.,  2022 ). Despite increased awareness of the underrepresentation of females in prevalence estimates and research studies, calls from the scientific and autism community to include more females in research, requirements of federal funding agencies, and the best efforts of researchers to recruit more females, empirical autism research studies continue to report small sample sizes of females or male‐only studies. This poses a problem for both basic science and the clinical relevance of this research for females on the autism spectrum.

Of note, other neurodevelopmental conditions, such as attention‐deficit/hyperactivity disorder (ADHD), provide strong evidence that females may be overlooked rather than absent. For example, ADHD girls are underdiagnosed relative to boys, and sex differences in prevalence can be attributed to clinician tendency to overlook less overt, yet clinically significant, symptoms in ADHD girls. This may be due in part to sex differences in symptom presentation (e.g., ADHD females are more likely to be inattentive than hyperactive) and to the perception that ADHD is rare in females (Hinshaw et al.,  2022 ). Similarly, autism is perceived to be a primarily male disorder and is diagnosed less often in females despite equal or higher symptom severity (Cola et al.,  2022 ; Lockwood Estrin et al.,  2020 ; Mandy et al.,  2012 ; Rea et al.,  2022 ). In addition, autistic females may mask or camouflage symptoms (Lai et al.,  2017 ), are sometimes misdiagnosed due to concurrent diagnoses (Lai, Baron‐Cohen, & Buxbaum,  2015 ; Rutherford et al.,  2016 ), have lower probabilities of referral for diagnosis (Cumin et al.,  2021 ), and as a result tend to be diagnosed later in life (Fusar‐Poli et al.,  2020 ; Hiller et al.,  2016 ). (A note on terminology: 1) We use identity first‐language [“autistic”] in concordance with the expressed preference of many individuals on the autism spectrum; Bottema‐Beutel et al.,  2021 . 2) We use binary sex‐based terms “male” and “female” rather than gender‐based terms due to the nature of our data set.) These findings of sex‐based factors in diagnostic outcomes provide a compelling framework for understanding underrepresentation of females in autism research.

Clinician and societal‐level perceptual biases about autism coupled with actual sex‐based phenotypic differences also have downstream effects on the development, scoring, and interpretation of standardized measures used to diagnose autism in the clinic and to confirm diagnosis in research (Rea et al.,  2022 ). In research, gold‐standard diagnostic measures (most commonly the Autism Diagnostic Observation Schedule, ADOS or the autism diagnostic interview, ADI) are often used to confirm diagnosis and to exclude participants who do not meet measure‐specified cut‐offs for autism. These tools are used even when a participant has received a prior community diagnosis (a diagnosis made by general medical practitioners, neuropsychologists, and mental health providers). Further, these measures are often used as the sole metric of inclusion with strict adherence to cut‐offs and without the addition of clinical judgment. This not only diverges from what is recommended by the tools' creators (Lord et al.,  2000 ), but is also quite different from community diagnostic practices. Community diagnostic tools are typically varied and can include behavioral assessment, clinical interviewing, parent report, medical and symptom history, and self‐reports. In addition, community diagnosis largely relies on standard, empirically‐derived criteria for ASD as outlined in the DSM‐5 (and its international analogue: International Classification of Diseases, 11th Review; World Health Organization,  1992 ) rather than measure‐specific cut‐off scores. These practical and conceptual differences allow for potential discrepancies between community diagnosis and diagnosis as confirmed by the ADOS. In research, these differences coupled with evidence that females often score lower on the ADOS (and other confirmatory diagnostic measures) (e.g., Bastiaansen et al.,  2011 ; Lai et al.,  2011 ; Ratto et al.,  2018 ) may result in increased exclusion of females from research participation.

In the current study, we analyzed data from individuals with an existing community diagnosis of ASD who were recruited to participate in autism research. We retrospectively examined sex‐based differences in symptom severity as measured by the ADOS, and then investigated how using the ADOS as a confirmatory diagnostic assessment differentially affected inclusion rates of males and females in autism research. We then compared our site‐specific findings to patterns of inclusion/exclusion by sex in several publically available databases that used either community diagnosis or confirmatory diagnostic assessments to determine inclusion in research.

Participants

We conducted a retrospective review of an internal research database of autistic participants to determine how application of confirmatory diagnostic assessments affected inclusion of males and females. The Autism Research Participant Database at the Massachusetts Institute of Technology (MIT) is a shared resource, established in 2007, and supported by the Simons Center for the Social Brain and the Hock E. Tan and K. Lisa Yang Center for Autism Research at MIT. The database consists of a total of 376 adults and children (291 males; 85 females) recruited from the community with an existing clinical diagnosis of autism, autism spectrum disorder, Asperger's syndrome, or PDD‐NOS (prior to new criteria in the DSM‐V) as reported by the participant. Participants were recruited to the database in an ongoing manner between 2007 and 2020, and matched with specific research studies based on participant interest and individual study criteria. To target the present analyses to verbally fluent adolescents and adults, we included only participants who were administered a Module 4 ADOS or ADOS‐2 (best suited for verbally fluent adolescents and adults) and for whom all item‐level ADOS scores were available. Of this group of participants, we further included only participants 16 years or older to reflect the ADOS manual recommendations regarding appropriate age ranges for Module 4 administration. This resulted in a final sample size of n  = 145 individuals (95 males, 50 females; mean age = 29.55 ± 10.80, ranging in age from 16 to 65 years old). Participant‐reported information was used to categorize participants into one of two groups (male or female) that we refer to as “sex” in this manuscript. Given the retrospective nature of this study, we were limited by binary sex or gender options, depending on how questionnaires were worded. All participants provided informed consent to be included in the database (or caregiver consent and assent for minors).

Confirmatory diagnostic assessment process

Prior to participation and matching with available studies within MIT, research‐reliable examiners (all female) administered the ADOS or ADOS‐2: a standardized, semi‐structured measure widely used in the assessment of autism (Lord,  2012 ; Lord et al.,  2000 ). All participants in the current sample were administered the ADOS Module‐4. ADOS scores and cut‐offs are determined by an algorithm that combines scores across symptom domains. Higher ADOS scores are associated with greater frequency or degree of autism symptoms (Lord et al.,  2000 ). Individuals who scored below the ADOS cut‐off for “autism spectrum disorder” or “autism” (Lord et al.,  2000 ) were characterized as not meeting the criteria for a research‐reliable autism diagnosis (i.e., have a research diagnosis that does not confirm their reported community diagnosis).

To align more closely with changes to ASD criteria in the DSM‐5, especially with regard to identifying sensory issues, the authors of the ADOS devised a new diagnostic algorithm for Module 4 (Hus & Lord,  2014 ). This new algorithm now includes restricted and repetitive behaviors (RRBs) as part of the total score. To determine whether exclusion rates by sex were driven by the absence of RRBs in the total cut‐off scores, we rescored all ADOSes for which we had complete item‐level data in accordance with the new 2014 algorithm (Hus & Lord,  2014 ) and re‐ran our analyses (total n  = 142, 93 males, 49 females). New algorithm scores were not able to be calculated for three participants (one female and two males) whose itemized ADOS scores were unavailable. After rescoring using the new algorithm, n  = 1 female and n  = 5 males who previously met criteria no longer met criteria, and n  = 6 females and n  = 6 males who previously did not meet criteria now met criteria.

Statistical analysis

Statistical analyses were conducted with STATA software (Statacorp LLC, 2019). Two‐sample t tests were conducted to determine whether ADOS scores differed by sex (two‐tailed p  < 0.05). We also conducted a two‐sample t‐test to determine whether there were sex differences by age (defined in years at the time at which the ADOS was administered). Chi‐squared tests were conducted to assess whether rates of inclusion in research differed by sex. Lastly, logistic regression analyses were used to determine the relationship between ADOS severity and sex (standardizing the ADOS Communication and Reciprocal Social Interaction Total score, and controlling for age).

Comparison databases

We compared sex ratios of participants in our database with participant sex ratios in eight large publically‐available datasets commonly used in autism research (Table  1 ). The process used to confirm autism diagnosis differed across the datasets, with the SPARK and Warrier et al. (2020) databases relying on community diagnosis and the ABIDE I and II databases using ADOS to confirm diagnosis (details below). Datasets that used self‐report as the primary measure of autism status often included additional questions to solicit more information about the diagnosis (e.g., “What year was the diagnosis made?”, “Who made the diagnosis?”, “Name the type of provider”, etc.).

Summary of comparison datasets and diagnostic confirmation methods

Note : Methods to confirm autism diagnostic status for research purposes varied across the comparison datasets. Datasets that used self‐report as the primary measure of autism status often included additional questions to solicit more information about the diagnosis (e.g., What year was the diagnosis made? Who made the diagnosis? Name the type of provider, etc.). Several data sets also included neurotypical participant groups, which were not considered in our analyses. For the purposes of the current analyses, “Total N” and N's listed for males and females reported in this table refer to the samples sizes of only autistic participants from that dataset.

Abbreviations: ABIDE, autism brain imaging data exchange; ADOS, autism diagnostic observation schedule; ASD, autism spectrum disorder; SR, self‐report.

SPARK (Simons Foundation Powering Autism Research for Knowledge)

The SPARK database currently consists of almost 100,000 autistic individuals (>15,000 adults) and includes phenotypic as well as genetic data (The SPARK Consortium 2018). Autism diagnosis is ascertained via a survey question inquiring about diagnosis source (e.g., pediatrician, psychologist, etc.). As part of the available data, SPARK also asks questions meant to provide increased confidence in the diagnosis. SPARK then calculates an “ASD validity flag” that identifies participants for whom diagnostic status might be more uncertain (e.g., diagnosed prior to 15 months of age, never accessed services for autism, had a diagnosis that was rescinded). To match the age range used in our original MIT database analysis, we included all adults age 16 years and above with a reported community diagnosis of autism, autism spectrum disorder, Asperger's, or Pervasive Developmental Disorder. We excluded participants who had an “age validity flag,” an “ASD validity flag,” or an “ASD confound flag” as determined by SPARK, defined as confounding medical or psychiatric diagnoses (e.g., diagnosis of schizophrenia, other psychosis, or schizoaffective disorder (participant self‐report); prematurity (gestational age < 28 weeks); blindness; fetal alcohol syndrome/alcohol or drug exposure in mother's pregnancy; insufficient oxygen at birth with NICU stay; bleed into the brain; cognitive delays or impairment due to another medical condition or exposure (such as brain injury, stroke, lead poisoning, FAS, HIV, radiation, hydrocephalus, brain tumor, drug effects, etc.); brain infection such as bacterial meningitis or encephalitis). Additionally, we excluded participants with an unspecified diagnostic source (e.g., “not sure” who made the diagnosis) and those with a cognitive impairment at time of enrollment. This resulted in a total sample size of n  = 12,212 individuals (7708 males, 4504 females; mean age = 26.09 years, range = 16 to 86 years).

Sample reported by Warrior and colleagues

A manuscript by Warrier et al. ( 2020 ), summarizes several datasets originally intended to examine rates of autism in transgender and gender‐diverse individuals, collapsing across five separate UK‐based publically‐available datasets (Channel 4, Musicial Universe, LifeLines, IMAGE, Autism Physical Health Survey). Across each of these separate datasets, individuals were asked to self‐report their autism diagnosis. The datasets differed in the manner in which they ascertained autism diagnostic status (Table  1 ). The total sample size of autistic individuals across all datasets included in Warrier et al. was n  = 29,997 (14,799 males; 15,198 females, see Warrier et al.,  2020 for additional demographic details).

Autism brain imaging data exchange I

The autism brain imaging data exchange I (ABIDE I) database consists of 17 independent research sites that have publicly shared imaging and phenotypic data. A majority of these sites (14 out of 17) utilize the ADOS as part of their inclusion criteria to verify diagnostic status. We calculated sex ratios across ABIDE I to assess whether ADOS usage was associated with sex ratios comparable to those we obtained after ADOS administration in the MIT database. We excluded one site that intentionally recruited male‐only samples. From the remaining 13 sites, we included all individuals with a Module‐4 ADOS Total Score (item level scores were unavailable) who were 16 years and older. This resulted in a total sample size of n  = 123 individuals (109 males, 14 females; Mean age 25.26 ± 7.78; ranging in age from 16 to 55 years).

Autism brain imaging data exchange II

The autism brain imaging data exchange II (ABIDE II) database consists of 19 independent research sites all of which mention the use of the ADOS as part of their inclusion criteria and confirmatory diagnostic procedure. We excluded two sites that explicitly reported recruiting an all‐male sample, and we included only individuals with a Module‐4 ADOS Total Score who were 16 years and older. This resulted in a total sample of n  = 91 (80 males; 11 females; Mean age = 27.71 ± 12.42; ranging in age from 16 to 62 years).

Sex differences in assessment scores and exclusion rates for autism research

We first assessed whether sex differences existed among individuals recruited for participation in the MIT Autism Research Participant Database by comparing diagnostic scores on the ADOS Module 4 between males and females. Importantly, all individuals recruited had a community diagnosis of ASD. Females had lower scores on both ADOS Communication ( t [143] 2.93, p  = 0.004) and Reciprocal Social Interaction ( t [143] = 3.25, p  = 0.001) sub‐scales compared to males. There were no sex differences on the ADOS Stereotyped Behaviors and Restricted Interests sub‐scale ( t [143] = 0.65, p  = 0.514) (Table  2 ). To quantify the extent to which ADOS scores were predictive of sex, we conducted logistic regressions (controlling for age as community diagnosed females were older than community diagnosed males, t [143] = 2.53, p  = 0.012). Higher ADOS scores were predictive of sex, with each increase of one standard deviation in ADOS score resulting in an almost twofold increase in the probability of being male (odds ratio = 1.80; p  = 0.004; LCI = 1.20, UCI = 2.69).

Comparison of ADOS scores by sex

Note : p ‐values are based on two sample t tests between the total sample of males and females ( n  = 145). Hedges' g is an effect size measure typically used for unequal sample sizes.

Abbreviations: ADOS, autism diagnostic observation schedule; SD, standard deviation.

These sex differences in ADOS scores impacted the final sex ratio of eligible participants meeting inclusion criteria compared to the recruited sex ratio (Figures  1 and ​ and2), 2 ), as females were more likely to fall below the diagnostic cut‐off scores. After ADOS administration, a greater proportion of females were excluded from further research participation than males (50% of community diagnosed females [ n  = 25] were excluded from further research participation compared with only 19% of community diagnosed males [ n  = 18] [total n  = 145, X 2 (1) = 15.14, p  < 0.001]). In the final post‐ADOS sample ( n  = 102), only 50% of females ( n  = 25) met criteria on the ADOS for “autism” or “autism spectrum” classification, compared with 81% of males ( n  = 77). This shifted the sex ratio (males: females) from 1.9:1 in the recruited sample to 3.1:1 in the post‐ADOS sample.

Proportion of community‐diagnosed adults excluded from research following confirmatory diagnostic assessment. Relative rates of inclusion and exclusion in females versus males recruited for research with a community diagnosis. The “Excluded” percentage indicates the proportion of participants who did not meet cutoff scores for autism or autism spectrum disorder on the autism diagnostic observation schedule (ADOS) and were therefore excluded from research studies. Blue, male; Red, female

Percentage of sample by sex in databases using self‐report of community autism diagnosis in comparison to databases that used the autism diagnostic observation schedule (ADOS) to confirm autism diagnosis. The MIT community diagnosed sample ( n  = 50 female; n  = 95 male) and SPARK database sample ( n  = 4504 female; n  = 7708 male) consisted of autistic individuals who self‐reported a community diagnosis of autism. The MIT post‐ADOS sample ( n  = 25 female; n  = 77 male) consists of participants who self‐reported an autism diagnosis which was subsequently confirmed by the ADOS. The ABIDE I dataset ( n  = 14 female; n  = 109 male) and ABIDE II dataset ( n  = 11 female; n  = 80 male) consist of participants who self‐reported an autism diagnosis and had an ADOS Module 4 total score. The Warrier et al. study consisted of five separate datasets (Channel 4, Musicial Universe, LifeLines, IMAGES, Autism Physical Health Survey), all of which used community diagnosis to determine eligibility. Each dataset used slightly different methods for ascertaining autism status (see Table  1 for details). Blue, male; Red, female

To determine whether new algorithms meant to provide better alignment with DSM‐5 diagnostic criteria for ASD might result in greater inclusion of females, we calculated exclusion rates by sex using the revised ADOS‐2 algorithms (Hus & Lord,  2014 ). These algorithms include sensory symptoms and RRBs as part of the total score (Hus & Lord,  2014 ). The new algorithm resulted in a net percentage increase of both females and males meeting criteria on the ADOS ( n  = 29/49 females included [59%], n  = 76/93 males included [82%]), as well as a moderate decrease in the final male‐to‐female ratio (2.6:1 compared with 3.1:1 using the original algorithm). Exclusion rates decreased with the new algorithm for both males and females, revealing a greater decrease for females (18% males excluded vs. 19% when using the original algorithim; 41% females excluded as compared with 50% when using the original algorithm). Interestingly, rescoring with the new algorithm also resulted in changes in diagnostic status in a few individuals (i.e., participants who previously met criteria no longer met, n  = 1 female, n  = 5 males). However, even when using the revised algorithm, there were significant differences in exclusion rates by sex (total n  = 142, X 2 [1] = 8.46, p  = 0.004), with a greater proportion of females being excluded post‐ADOS than males (Figure  2 ).

Comparison to large, publically‐available autism datasets

We next examined whether our findings paralleled sex ratios in large, publically‐available autism datasets. We identified datasets that either used (a) community diagnosis (reported by the participant or family member of the participant) to characterize participants (The SPARK Consortium; five separate datasets as reported in Warrier et al.,  2020 ) or (b) relied at least in part on the ADOS to determine inclusion (ABIDE I; ABIDE II) (see Table  1 ). Sex ratios were calculated for each of these datasets. The proportion of females with an ASD designation was markedly smaller in datasets wherein the ADOS was used to determine research eligibility (male:female ratios, ABIDE I, 7.8:1; ABIDE II, 7.3:1) compared with datasets that employed self‐reported community diagnosis (SPARK, 1.7:1; C4, 0.95:1; MU, 1.8:1; IMAGE, 1.1:1; LifeLines, 1.3:1; APHS, 0.68:1) (Figure  2 ). Studies using samples based on community diagnosis often included additional questions about that diagnosis, but the specific questions varied across samples and it is thus unclear what role these questions had for inclusion/exclusion. Our findings indicate that the sex ratio of the MIT community diagnosed sample closely parallels the sex ratio of datasets that used self‐reported community diagnosis to determine inclusion. In contrast, the sex ratio of the MIT post‐ADOS sample was similar to that of the ABIDE samples, which also utilized the ADOS to confirm diagnosis of participants.

The current study is the first to empirically assess how differing research practices for confirming diagnosis and determining inclusion ultimately affect the number of ASD females deemed eligible to participate in research. Across our sample and several other large datasets, using community diagnosis to determine eligibility resulted in more equal sex ratios between females and males. The use of common diagnostic assessment measures such as the ADOS had a disproportionate effect on the exclusion of autistic females in research relative to autistic males. We suggest that these procedures may contribute to the underrepresentation of females in autism research, and provide evidence for a “leaky” recruitment‐to‐research pipeline for autistic females.

Confirmatory diagnostic assessments affect the male‐to‐female sex ratio in ASD research

In the current study, the MIT community‐diagnosed sample showed sex ratios of ~2:1 (male:female) prior to implementation of confirmatory diagnostic assessments. ADOS usage to confirm diagnosis resulted in a disproportionate exclusion of females as compared to males. This was driven by lower social‐communication ADOS scores in females, several of which fell below cut‐off scores for an autism designation. Sex differences in ADOS scores and exclusion rates persisted even when using updated scoring algorithms designed to reflect the most current understanding of ASD criteria in the DSM‐5, specifically the inclusion of RRBs in the ADOS total score (Hus & Lord,  2014 ). Similar results emerged across eight large publically‐available datasets that used different measures to determine inclusion (community diagnosis vs. ADOS), with some datasets that relied on community diagnosis even showing a greater number of females than males. Importantly, although ratios of 2:1 (male:female) in our original recruited sample may seem anomolous given the existing literature and consensus estimates commonly reporting sex ratios of around 4:1, we replicated these ratios in much larger international and publically available datasets (e.g., SPARK, C4, LifeLine, IMAGE, MU, and APHS) all of which relied on community diagnosis. Our findings highlight that relying on cut‐off scores on confirmatory diagnostic assessments without expert clinical consensus are one potential contributor to the small sample sizes of autistic females in research, and also potentially contribute to the variable sex ratios found in the ASD literature (Loomes et al.,  2017 ).

These results are consistent with several studies from the past decade suggesting that gold‐standard instruments such as the ADOS poorly identify autistic females (Lai et al.,  2011 ; Ratto et al.,  2018 ; Rynkiewicz & Łucka,  2018 ; Tillmann et al.,  2018 ). Why might these tools have these effects? Of most import, several ASD trait and diagnostic measures, including the ADOS, were standardized predominantly on males and do not offer sex‐specific norms (Baron‐Cohen et al.,  2001 ; Lord et al.,  2000 ). Studies examining the validity of the ADOS, and several other commonly used diagnostic assessments, use primarily male samples (Medda et al.,  2019 ) and in some cases, exclusively male samples. Therefore, females are more likely to be classified as autistic by the ADOS when they display behaviors more similar to their male peers, despite suggestions that the female ASD phenotype may be distinct from that of males (Medda et al.,  2019 ). In addition, autistic females are more likely than their male counterparts to mask autistic traits in social and clinical settings, also known as camouflaging (Lai et al.,  2017 ; Tubío‐Fungueiriño et al.,  2021 ). Further, females' self‐report of their own autistic traits is much higher than when measured by observational measures such as the ADOS (Cook et al.,  2021 ; Hull et al.,  2020 ; Lai et al.,  2017 ). Tools such as the ADOS are not designed to detect camouflaging, resulting in lower scores (fewer challenges detected) for females, potentially exacerbating sex‐based exclusions. These tools may also be vulnerable to gender‐based biases on the part of the clinician or administrator. For example, social communication skills are perceived as being better in autistic girls than boys (despite equivalent symptom severity in this domain), resulting in better first impressions (Cola et al.,  2022 ). One study reported that autistic females used more social words than males during clinical assessments, even when matched for symptom severity. Crucially, individuals who used more social words received lower ADOS scores by clinicians (Cola et al.,  2022 ). These diagnostic tools are also less accurate in capturing certain subgroups or phenotypes, such as autistic females with high IQs (Ratto et al.,  2018 ). Lastly, previous reports have found that repetitive behaviors and restricted interests (RRBs) may be more predictive of autism diagnostic status than social communication scores (Troyb et al.,  2016 ). However, in most current research practice, only social communication scores are used to determine cut‐offs (although see Hus & Lord,  2014 , as of 2014 the new ADOS algorithm includes RRBs in the total score). Here, we report robust sex differences in social communication symptoms, but no sex differences in RRBs as measured by the ADOS. Concordantly, use of the new algorithm (which incorporates RRBs into the total cut‐off score) did slightly increase the number of females meeting criteria for ASD, suggesting that including RRBs may contribute to decreasing the sex difference in ADOS scores driven largely by differences in social communication (but see Lai & Szatmari,  2020 for evidence of sex differences in RRBs). Despite this slight increase, however, use of the new algorithm did not significantly alter inclusion rates by sex.

Apart from the particularly disproportionate effect on females, our data provide evidence that the use of confirmatory diagnostic assessments narrows the research sample overall, excluding large proportions of both females and males with a community autism diagnosis (e.g., 19% of community diagnosed males in our recruited sample were excluded based on ADOS cutoffs). This suggests that the ADOS, whether used as an assessment or diagnostic confirmation tool, might only be capturing a certain part of the autistic population, which could be distinct from the broader community diagnosed sample. While some argue that a more homogenous autism sample might actually strengthen research findings (Mottron,  2021 ), a more homogeneous sample may not adequately capture characteristics representative of the full autism spectrum. It is unclear how accessing a certain proportion of the autism population affects research findings, and how results arising from this research generalize to autism in general.

Underepresentation of females in autism research begins before recruitment: Barriers to obtaining diagnosis

There are several factors that may limit participation of autistic females in research that occur prior to the research process. Females face greater barriers to obtaining an autism diagnosis which may bias the sex ratio and result in a smaller pool of females before the point of research (for a review, see Lockwood Estrin et al.,  2020 ). These barriers include differing phenotypic presentation that may not align with conceptualizations of autism (e.g., RRBs that more closely match societal norms), perception of autism as a male disorder, and social norms surrounding social communication abilities in females (Cola et al.,  2022 ; Hiller et al.,  2014 ). For example, diagnosticians report that they perceive diagnostic assessments of ASD to be more challenging when the client is female, and attribute this difficulty to their perceptions of incongruence between current ASD tools (and conceptualization) and female presentation (Tsirgiotis et al., 2021 ). Indeed, one study found that despite no difference in the duration of diagnostic assessments, females were still less likely to receive a diagnosis and parents of ASD girls had to exaggerate symptoms in order to get a diagnosis for their daughters (Rutherford et al.,  2016 ).

These issues may be influenced by development. Although not explicitly explored in this study, male:female ratios in community‐based samples changed markedly as a function of age (see Data  S1 for statistics, table, and a brief discussion). These age‐related discrepancies may speak to biases in diagnostic practices and gendered social norms. For instance, due to societal norms, repetitive or stereotyped play patterns in girls may, on the surface, seem more appropriate than those in boys (focus on repetitive play with dolls vs. wheels on a truck; [Giarelli et al.,  2010 ; Hiller et al.,  2016 ]). This, in turn, may lead to differences in perception, identification, and scoring of RRBs in standardized assessments, and ultimately later age of diagnosis in females—a commonly observed occurrence (Harrop et al.,  2021 ). Indeed, girls are less likely to meet diagnostic thresholds than boys, despite having equally high levels of autistic traits (Dworzynski et al.,  2012 ; Kalb et al.,  2022 ; Mo et al.,  2021 ), and teachers report significantly fewer concerns about social skills in girls than boys (Hiller et al.,  2014 ). This suggests that females may not be referred for diagnosis as often, or as early, as males. Consistent with this, we found that community‐diagnosed females in our sample tended to be older on average than males (Table  S1 , Figure  S1 ).

Lastly, the numbers of autistic females in research may be constrained by true prevalence of autism in females. However, given issues raised in this article and elsewhere, the true prevalence is difficult to determine. These diagnostic and conceptual difficulties, even prior to research, suggest that additional sex‐based norming and tool evaluation is needed to improve diagnosis in autistic females.

Implications and considerations for future research

The potential underrepresentation of autistic females in autism research has multiple implications for the study, characterization, and acceptance of autistic women in society, as well as access to services. Scientifically, consistently small sample sizes of autistic females make it difficult to fully understand autism in females. Underrepresentation in research not only perpetuates the perception that autism is a male disorder, but may create a cycle in which basic science questions and investigations of autism are constrained to specific phenotypes based on what has already been studied in the previous literature (Figure  3 ). Our research suggests that females who are ultimately included in research (i.e., whose diagnoses are confirmed using the ADOS) exhibit a specific phenotype that may or may not represent autism in females more broadly. Crucially, exclusion begets more exclusion. Because autism tends to be viewed as a characteristically male disorder, researchers are often excused from including adequate samples of females in research. As a striking example, in a sample of over 1400 studies focusing on the brain structure and function in autism, only 4 studies focused on female‐only samples compared to 434 using male‐only samples (Mo et al.,  2021 ). This may, in turn, inform the topic and focus of future autism research, constrain recruitment efforts, and even impede the development of diagnostic tools. Lastly, an important goal of research is to ultimately inform the delivery of services and support to individuals with ASD. Indeed, non‐male identifying autistic individuals (both females and individuals with other non‐male gender identities) report greater difficulty accessing services than males (Koffer Miller et al.,  2022 ), and autistic females tend to feel ignored or unseen (Bargiela et al.,  2016 ).

Diagram of the interactions between research, diagnostic, and recruitment practices. If females are excluded from any part of these processes it magnifies the degree of discrepant exclusion rates. The outcomes of research may directly or indirectly contribute to biased perceptions and diagnostic practices in autism (and vice versa). Reduced representation of females in autism research (due to focus on males, recruitment of primarily male samples, etc.) may increase the general perception that autism is primarily a male disorder, and strengthens the idea that the ASD male phenotype is the phenotypic template on which diagnostic definitions should be based. These perceptions have knock‐on effects on the construction of diagnostic tools and assessments: Because these tools are normed in primarily male samples, they act to further entrench biased perceptions about what may or may not reflect true autistic behavior. These issues have basic science and translational implications: Our understanding of autism is unlikely to be entirely accurate without adequate representation of females, and fewer females in research lessen the probability that any results will generalize broadly to autism or improve outcomes for autistic individuals. *Refers to the development of assessment tools, and their implementation in diagnosis and determination of research eligibility.

How might research adapt to address these issues? To begin, community diagnosis reported by the participant or family member, along with confirmatory details and procedures, can serve as a valuable consideration for researchers to increase representation of females in research. Crucially, researchers do not have to forgo all diagnostic confirmation when using self‐report of community diagnosis. Several large autism databases ask participants to provide further information about their diagnosis (e.g., copy of the diagnostic report, source of diagnosis). Further, there may be cases in which additional confirmatory procedures are beneficial (for instance when an extended period of time has elapsed since participant's last evaluation, in light of research showing that a subset of autistic children may “outgrow” their diagnoses, [Anderson et al.,  2014 ; Fein et al.,  2013 ]). In addition, researchers can report pre‐ and post‐confirmatory diagnostic assessment sample sizes (how many individuals were originally recruited, how many were excluded as a result of diagnostic confirmation, which criteria are particularly exclusionary, whether there was discrepancy between community diagnosis and research‐based diagnostic confirmation measures, and how these numbers differ by sex and gender). This reporting, coupled with a critical examination of exclusion criteria, will inform researchers about exclusion criteria that are sex‐related. Exclusion criteria may interact with sex and gender identity. Given higher rates of gender diversity in ASD than in non‐ASD individuals, it is especially important to assess the effects of diagnostic confirmation by both gender and sex (see Warrier et al.,  2020 for discussion of gender identity in autistic individuals, Beltz et al.,  2019 for recommendations on best practices for conducting research in sex differences, and Strang et al.,  2020 on the importance of considering both sex and gender in autism research). For researchers using the ADOS or other confirmatory assessments, it is important to understand how interactions between examiner biases and sex differences can play a role in the identification of autism traits that may qualitatively differ in females and are closer to societally accepted behavioral norms. Researchers can also consider using sex‐independent tasks and trait assessments that differ between autistic and non‐autistic populations, but do not show sex differences (e.g., the Reading the Mind in the Eyes Task shows no sex differences in ASD, Baron‐Cohen et al.,  2015 ). Additionally, measurements should be used in concert with clinical judgment and self‐report. Our research and that of others suggests that strict categorical cut‐offs exclude females to a greater degree, and confirmatory diagnostic tools are less accurate in identifying individuals near cut‐off borders (Charman & Gotham,  2013 ). Lastly, journals and autism research societies may also play a role by discouraging reviewers from penalizing manuscripts that rely on community diagnosis. Together, these considerations could contribute to increasing representation of females in autism research.

Limitations and conclusions

The current results should be interpreted within the context of a few limitations. For instance, although all individuals had a community diagnosis of ASD, we did not ascertain the source of, or age of, community diagnosis in all participants. Second, inclusion was determined using the ADOS and we cannot, therefore, speak to the applicability of other commonly used confirmatory diagnostic measures (e.g., the ADI). In addition, the current study only examined adolescents or adults who were verbal (administered the Module 4 of the ADOS) and it is unclear whether similar results would hold in samples that are non‐speaking, minimally verbal, or cognitively impaired. Further, the included comparison databases differed in the extent to which co‐occurring medical and psychiatric issues were characterized, as well as how strictly ADOS cut‐offs were applied or reconciled with clinical judgment. Moreover, here, we used binary sex definitions, which may not capture gender‐based differences, including those specific to nonbinary‐identifying and transgender individuals. Lastly, it is possible that basic science research relies more on strict ADOS cut‐offs than does clinically‐oriented research or research teams in which a clinician is present, and it is unclear what proportion of autism research studies adhere to rigid cut‐offs or use the ADOS to confirm diagnosis.

Despite these limitations, we find robust evidence that confirmatory diagnostic assessments commonly used in autism research may contribute to the small sample sizes of females in autism research. By examining both our sample and more than 42,000 autistic individuals from eight comparison datasets, we find that utilizing self‐report of community diagnosis can contribute to dramatically lower sex ratios in autism research. Our analyses reveal that even datasets that explicitly obtain diagnostic reports to confirm autism status had more balanced sex ratios than those that used only the ADOS. Strong reliance on such measures may play a role in perpetuating the disproportionate exclusion of females in autism research. Across females and males, future research should characterize both the brain and behavior in autistic individuals with community diagnoses, comparing those who are and who are not excluded by standardized measures. These considerations could play an important role in increasing representation of females in research (see Figure  3 ).

ETHICS STATEMENT

All procedures were approved by the MIT Institutional Review Board (Committee on the Use of Humans as Experimental Subjects).

Supporting information

Supplementary Table S1 Autistic females are older in community diagnosed samples

ACKNOWLEDGMENTS

The authors thank the individuals who participated in autism research at MIT and acknowledge support from the Hock E. Tan and K. Lisa Yang Center for Autism Research and the Simons Center for the Social Brain at MIT for the creation of the MIT Autism Research Participant Database, support of autism recruitment at MIT, and support of the autism research coordinator (Cindy E. Li). The authors would further like to acknowledge funding support from the Simons Center for the Social Brain at MIT (postdoctoral fellowship to Anila M. D'Mello and Targeted Project Grant: Predictive Processes in Autistic and Neurotypical Individuals: a behavioral, neural and developmental investigation to John D.E. Gabrieli) and National Institutes of Mental Health (F32 MH117933 to Anila M. D'Mello). The authors also thank Drs. Pawan Sinha and Robert Joseph for comments on the manuscript.

D'Mello, A. M. , Frosch, I. R. , Li, C. E. , Cardinaux, A. L. , & Gabrieli, J. D. E. (2022). Exclusion of females in autism research: Empirical evidence for a “leaky” recruitment‐to‐research pipeline . Autism Research , 15 ( 10 ), 1929–1940. 10.1002/aur.2795 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

Funding information Hock E. Tan and K. Lisa Yang Center for Autism Research; National Institute of Mental Health; Simons Center for the Social Brain at MIT; National Institutes of Mental Health, Grant/Award Number: F32 MH117933

DATA AVAILABILITY STATEMENT

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