research on the developmental course of autism has revealed that

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• Published: 03 November 2023

Autism through midlife: trajectories of symptoms, behavioral functioning, and health

• Jinkuk Hong   ORCID: orcid.org/0000-0002-6789-2571 1 ,
• Leann Smith DaWalt 1 ,
• Julie Lounds Taylor 2 ,
• Aasma Haider 3 &
• Marsha Mailick 1  

Journal of Neurodevelopmental Disorders volume  15 , Article number:  36 ( 2023 ) Cite this article

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This study describes change in autism symptoms, behavioral functioning, and health measured prospectively over 22 years. Most studies tracking developmental trajectories have focused on autism during childhood, although adulthood is the longest stage of the life course. A robust understanding of how autistic people change through midlife and into older age has yet to be obtained.

Using an accelerated longitudinal design with 9 waves of data, developmental trajectories were estimated from adolescence through midlife and into early old age in a community-based cohort ( n  = 406). The overall aim was to determine whether there were age-related increases or decreases, whether the change was linear or curvilinear, and whether these trajectories differed between those who have ID and those who have average or above-average intellectual functioning. Subsequently, the slopes of the trajectories were evaluated to determine if they differed depending on age when the study began, with the goal of identifying possible cohort effects.

There were significant trajectories of age-related change for all but one of the measures, although different measures manifested different patterns. Most autism symptoms improved through adulthood, while health worsened. An inverted U-shaped curve best described change for repetitive behavior symptoms, activities of daily living, maladaptive behaviors, and social interaction. For these measures, improved functioning was evident from adolescence until midlife. Then change leveled off, with worsening functioning from later midlife into early older age. Additionally, differences between autistic individuals with and without ID were evident. Although those who have ID had poorer levels of functioning, there were some indications that those without ID had accelerating challenges in their aging years that were not evident in those with ID – increases in medications for physical health problems and worsening repetitive behaviors.

Conclusions

Meeting the needs of the increasingly large population of autistic adults in midlife and old age requires a nuanced understanding of life course trajectories across the long stretch of adulthood and across multiple domains. Given the heterogeneity of autism, it will be important not to generalize across sub-groups, for example those who are minimally verbal and those who have above-average intellectual functioning.

Autism is a neurodevelopmental disorder and as such the investigation of developmental trajectories of autism symptoms and other characteristics has long been a critical approach to empirical research. Studies using repeated measures over a period of months (e.g., [ 1 , 2 , 3 , 4 ]) or years [ 5 , 6 ] are seen as the “gold standard” method for tracing these trajectories, and such studies have offered significant insights into brain and behavioral development in autism. Although most studies tracking developmental trajectories in those diagnosed with autism have focused on childhood, there are now a number of studies that extend the trajectories into the transition years and young adulthood [ 7 , 8 , 9 , 10 , 11 , 12 ]. Adulthood is the longest stage of the life course, yet until recently it has not been the focus of much longitudinal research. Thus, a robust understanding of how the features of autism change through adulthood and into older age has yet to be obtained.

To address this gap in the literature, the current study investigated trajectories of autism symptoms, behavioral functioning, and health from adolescence through midlife and into the early years of old age. We focused on autism symptoms and behavioral functioning as these are key features of autism and because their trajectories through childhood and into early adulthood are relatively well-established. We focused on physical health given its importance for quality of life in adulthood [ 13 ]. Physical health during adulthood also has been identified as a research priority by autistic adults and their families (e.g., [ 14 , 15 ]).

One focus of past research on autism in adulthood has been on childhood predictors of adult outcomes such as employment, independent living, and social integration, revealing the importance of cognitive ability and language development as predictors of some but not all adult outcomes [ 10 , 14 , 16 , 17 ]. Data from these studies also have been used to track change from childhood primarily up to the early adult years, highlighting how adaptive skills develop and maladaptive behaviors diminish when compared with these domains during childhood [ 7 ].

Virtually absent from the autism research literature is concomitant tracking of change from early adulthood into midlife and beyond, which is the focus of the present research.

Notably, most past research focused on the adolescence-to-adult transition suggests that improvements in skills and behaviors do not progress in a linear fashion. Autism symptoms and maladaptive behaviors have generally been found to improve across childhood and adolescence and into early adulthood ([ 18 , 19 ], but for an exception see [ 10 ]). However, these improvements in symptoms and behaviors slow after youth with autism exit school, with improvement even stopping for some young adults [ 20 ]. Daily living skills tend to improve throughout adolescence and early adulthood, but plateau when adults with autism are in their mid-to-late 20 s and then begin to decline [ 8 , 21 ]. However, little is known about the trajectories of these domains in the later decades of life.

Health is another domain that is important to consider when examining trajectories into older age. Studies examining changes in health over time for autistic adults have found that, for most individuals, body mass index and prescription medication use increase throughout early adulthood [ 22 , 23 ]. Tracing changes in health and behavioral functioning among autistic adults as they move into midlife and early old age may improve understanding of earlier mortality [ 24 , 25 , 26 ] and could point toward strategies for reducing this disparity.

Notably, beyond longitudinal studies that extend from childhood into adulthood, most studies of autistic adults have included primarily or exclusively individuals who do not have intellectual disability (ID) (e.g., [ 27 , 28 , 29 ]). Although recent epidemiological evidence estimates that about 38% of autistic children are classified as having ID [ 30 ], the current population of autistic adults who have ID is even larger. Therefore, understanding midlife and old age in both those with ID and those with average or above average intellectual functioning is of vital importance. Although there are other factors that contribute to the heterogeneity of autism, ID status stands out as particularly significant in shaping the emergence of symptoms during childhood and that continues to be prominent in adulthood.

Present study

The present study describes changes in autism symptoms, behavioral functioning, and health measured prospectively over 22 years. Using an accelerated longitudinal design approach to statistical analysis, we estimate developmental trajectories of change extending from adolescence through midlife and into the early years of old age in a community-based cohort that includes both autistic individuals with ID and those who have average or above-average intellectual functioning. We aim to identify points during the life course, starting in adolescence, when vulnerabilities may be increasing or decreasing, with a particular focus on midlife, a life stage that has received minimal attention in the autism research literature.

Midlife is a period of the life course that is not precisely defined, although it is often characterized in the general population as the period between 40 and 60 years of age, plus or minus 10 years [ 31 ]. It is seen as a stage of life that is a “pivotal period in the life course in terms of balancing growth and decline, linking earlier and later periods of life” [ 32 ]. Here we present repeated measures of autism symptoms, behavioral functioning, and health to characterize this pivotal period. Our overall aim is to characterize the patterns of age-related change. Accordingly, we investigated whether each domain showed age-related increases or decreases (or did not change), and whether the change was linear or curvilinear.

Given the heterogeneity of autism, we also evaluated whether these trajectories differed between those who have ID and those who have average or above-average intellectual functioning. Subsequently, we explored whether the slope of the trajectories differed depending on the age of the participant when the study began, with the goal of identifying possible cohort effects.

Data and sample

This report is based on an analysis of data from an ongoing longitudinal study of families of autistic adolescents and adults [ 33 ]. The study began in 1998 with families of 406 adolescents and adults diagnosed with autism living in Massachusetts and Wisconsin, and to date it has extended over 22 years. All participating families initially met three inclusion criteria: (1) the family had a son or daughter with an autism diagnosis given by an educational or health professional, (2) the proband was age 10 or older, (3) a researcher-administered Autism Diagnostic Interview-Revised (ADI-R) [ 34 ] profile was consistent with the diagnosis. When the study began, almost all (94.6%) met criteria for a diagnosis of autistic disorder. The remaining 22 cases (5.4%) were determined to have ADI-R profiles consistent with a diagnosis of Asperger’s disorder or Pervasive Developmental Disorder-Not Otherwise Specified (PDD-NOS) (see [ 35 ]), diagnoses in use at that time. The present analysis used repeated measures that were collected over nine study waves from the primary caregivers, mostly (96%) mothers, via in-home interviews as well as self-administered questionnaires. Although some autistic individuals directly provided data at three of the waves of the study, in the interest of including data from those of all cognitive and communicative ability levels, we use parent-report data for this analysis.

The nine waves of data (here referred to as Time 1 through Time 9) spanned 22 years. Previous reports of analyses from this study reported on shorter spans of time, and were based on fewer rounds of data collection gathered earlier in the life course (e.g., [ 8 , 19 , 36 ]). Although all of the measures reported here were used in our previous reports, here we nearly double the duration of the study period and add three additional repeated measures (Times 6 through Time 9), extending most measures through 2022. Table 1 shows the timing of each wave of data collection along with average ages and age ranges of the probands at each wave. As shown in Table 1 , on average, most waves of data collection were approximately 18 months apart except for Time 4 and 5 (about 44 months) and Time 8 and 9 (about 8 years). The average number of study waves for which participants contributed data was six, and more than a half (52.2%) participated in seven or more study waves. Although attrition remains a limitation of this study, those who were lost to attrition did not differ from those who remained in the study through Time 9 with respect to sex, ID status, and the outcome variables. Those who were lost to attrition were an older age at Time 1 (23.1 vs. 19.2 years, p  < 0.001) than those who remained.

The analytic sample consisted of all 406 autistic individuals. Their average age at Time 1 was 21.9 (SD = 9.4), ranging from 10 to 52. The majority were males (73.2%) and over two-thirds (69.7%) had a co-occurring diagnosis of ID. Nearly two-thirds (65.0%) lived with their mothers (and often other family members) at Time 1. During the study, 28 autistic individuals died. The data describing these individuals prior to death are included in the analysis. A larger number of mothers died during the study period ( n  = 73), resulting in change of reporters from the mother to another family member or in a few cases to a family friend. We conducted a sensitivity analysis including only those cases where the mother was the source of data. It revealed that all of the results presented below were fully replicated (see Supplemental Material ), reflecting the selection of measures that were well-validated and more objective than subjective reports of life course patterns.

Although the great majority of the families of the autistic individuals were White non-Hispanic (92.6%), there was significant socioeconomic heterogeneity. Median annual household income at Time 1 was between $50,000 and $60,000. Notably, 11.5% earned less than $20,000 per year, when the US poverty line was $17,050 for a family of four (Federal Register, Vol. 65, No. 31, Tuesday, February 15, 2000). Fewer than half of the mothers had achieved a bachelor’s degree (45.1%), and fully one-quarter (26.8%) had no education beyond high school.

Bivariate correlations among the measures used in the present research, as well as their associations with age, are presented in Table 2 . Although there were significant associations among the measures within a given domain, the associations across domains were generally non-significant to moderate.

Measures of autism symptoms included impairments in social reciprocity, impairments in verbal and non-verbal communication, and repetitive behaviors. Measures of behavioral functioning included independence in activities of daily living, maladaptive behaviors, and social participation. Measures of health included ratings of health, number of psychotropic medications prescribed for mental health symptoms, and number of non-psychotropic medications prescribed for physical health symptoms. All measures have been shown to be sensitive to change in prior research [ 19 , 23 , 36 , 37 , 38 ]. Not all measures were obtained at all nine timepoints, but all were collected at least six times over the study period. The specific times of data collection for each measure are indicated below.

Measures of autism symptoms

Autism symptoms were assessed using the Autism Diagnostic Interview-Revised (ADI-R) [ 34 ] at Time 1 through Time 6. The ADI-R is a standardized diagnostic interview administered to a parent or primary caregiver and used to diagnose autism based on a specified subset of 37 items that constitute a validated algorithm. Our study administered these 37 items at Time 1 to confirm diagnostic status. At each subsequent point of data collection, we administered the 33 items from the core diagnostic algorithm that are applicable to adolescents and adults (4 of the 37 items are specific to childhood). Ratings of current functioning were made at each point of data collection by interviewers who had participated in an approved ADI-R training program. We created four ADI-R sub-scales using 32 of the 33 items based on consultation with one of the instrument’s designers (C. Lord). This grouping of items is based on the clustering of items established by the ADI-R scoring protocol [ 34 ], our prior work using this instrument [ 39 ], and analysis of the factor structure of the instrument [ 40 ]. The sub-scales were impairments in social reciprocity, impairments in verbal communication, impairments in non-verbal communication, and repetitive behavior. A code of 0 signifies the absence of a given symptom, while codes of 1 and 2 indicate impairments characteristic of autism. Some items also used codes of 3, but these are routinely recoded as 2 s [ 34 ]. Algorithm items were summed to create the four domain scores. ADI-R items reflecting current levels of impairments in verbal communication were assessed for those individuals who were able to communicate verbally using at least three-word phrases on a daily basis (ADI-R item 30, n  = 318), 19 of whom shifted from being classified as non-verbal to using three-word phrases on a daily basis during the study period. The ADI-R has demonstrated good test–retest reliability and validity in past research [ 34 , 41 ]. In this sample, the internal consistency coefficients (Cronbach’s α) at Time 1 were 0.84, 0.71 and 0.53 for social reciprocity, impairments in non-verbal communication, and repetitive behavior, respectively.

Measures of behavioral functioning

Independence in activities of daily living (adl).

Independence in activities of daily living was measured longitudinally at seven times of data collection (Time 1, Time 4—Time 9) using the Waisman Activities of Daily Living Scale (W-ADL) [ 37 ]. Mothers rated the level of independence of their son or daughter with regards to 17 activities of daily living, measuring performance of personal hygiene (e.g., washing/bathing, grooming, toileting), housekeeping (e.g., home repairs, laundry), meal preparation (e.g., preparing simple food, drinking from a cup, washing dishes), and financial management (banking and managing daily finances) on a scale of 0 to 2 (0 = does not do at all, 1 = does it with help, 2 = does independently). Item scores were summed into a total score with higher scores signifying greater independence in daily living skills. For the present sample, scores ranged from 2 through 34. Past research has shown that the W-ADL is strongly correlated ( r  = 0.82) with the Daily Living scale within the Vineland Screener [ 8 , 37 ]. The internal consistency (Cronbach’s α) of the W-ADL at Time 1 was 0.903. Criterion validity of the W-ADL for adults with ASD was previously established [ 37 ]. The items in the W-ADL span skills generally acquired in early childhood (e.g., drinking from a cup) through those acquired in adulthood (e.g., banking), suggesting good representation of independence in daily living skills across the life course.

Maladaptive Behavior

Maladaptive behavior was measured longitudinally at all nine times of data collection using the Behavior Problems subscale of the Scales of Independent Behaviors-Revised (SIB-R) [ 42 ]. The SIB-R measures behavior problems, grouped in three domains: internalized behaviors (hurtful to self, unusual or repetitive habits, withdrawal or inattentive behavior), externalized behaviors (hurtful to others, destructive to property, disruptive behavior), and asocial behaviors (socially offensive behavior, uncooperative behavior). If a given behavior problem was manifested during the past 6 months, then frequency (1 =  less than once a month  to 5 =  1 or more times/hour ) and severity (1 =  not serious  to 5 =  extremely serious ) of the behavior were rated by mothers. Standardized algorithms [ 42 ] translate the frequency and severity ratings into a General Maladaptive Behavior Index, with higher scores indicating more severe behavior challenges. Reliability and validity have been established by Bruininks et al. [ 42 ]. The present analysis uses the General Maladaptive Behavior Index.

Social participation

Social participation was assessed longitudinally at all nine times of data collection. At each time point, mothers reported on the frequency with which their son or daughter spent time with friends or neighbors (0 = once a year or never, 1 = several times a year, 2 = once or twice a month, 3 = once a week, 4 = several times a week), an item drawn from the National Survey of Families and Households ( www.ssc.wisc.edu/nsfh/ ).

Measures of health

Health ratings.

Health ratings were obtained at all nine times of data collection. Mothers rated the health of their son or daughter (1 = poor, 2 = fair, 3 = good, 4 = excellent). Considerable previous research has provided evidence of the validity of such health ratings in predicting mortality [ 43 , 44 ]. In prior analyses of data from the present study, this measure of health was found to significantly predict mortality over the course of two decades [ 45 ].

Number of Prescription Medications

As a separate and objective indicator of physical health, at each time point, mothers listed names of all prescription medications currently taken by their son or daughter along with dosage and reason for taking each medication [ 23 ]. Medications were separated into psychotropic and non-psychotropic categories. As shown in Table 2 , self-rated health was not significantly associated with the number of psychotropic medications, whereas there was a significant negative association between self-rated health and the number of non-psychotropic medications ( r  = -0.115, p  < 0.05).

Psychotropic medications were prescribed for mental health problems and included antipsychotics, antidepressants, anxiolytics and sedative-hypnotics, central nervous system (CNS) stimulants, antimanic medication, anticonvulsant medications that were prescribed to an individual with no comorbid diagnosis of epilepsy or seizures (usually for bipolar symptoms), and hypotensive medications that were prescribed to an individual with no comorbid diagnosis of hypertension.

Non-psychotropic medications were prescribed for physical health problems and included anticonvulsants (for seizures), antiparkinson medications prescribed for side effects of antipsychotic medications (i.e., not prescribed to a person diagnosed with Parkinson's disease), antiemetics, and medications for hypertension, thyroid, diabetes, respiration, hormones, ocular, gastrointestinal (GI), and other miscellaneous purposes. Excluded from our analysis were over-the-counter medications, such as analgesics, laxatives, vitamins, antifungal medication, antacids, and topicals.

Medications were coded and classified based on Physician’s Desk Reference Drug Guide for Mental Health Professionals [ 46 ]. These classifications were reviewed and verified by a university-based pharmacist with over 20 years of experience. Separate counts of the number of psychotropic and non-psychotropic prescription medications were used in this report.

Predictor variables

The main predictor variable in the present study was age at each study point. Age for each individual was calculated from date of birth to the date of that individual’s data collection at each time point. In addition, we included three other predictors: age at Time 1, sex, and ID status. Age at Time 1 was controlled to evaluate whether the trajectories differed depending on the age of the participant when the study began. This variable was included to provide insight into possible cohort effects in the age-related trajectories (for example, whether individuals who were adolescents at the start of the study showed a different pattern of change as compared to those who were in adulthood when the study began). Sex was coded as 0 = male, 1 = female. ID status (0 = no intellectual disability, 1 = intellectual disability) was determined using a variety of sources. Individuals with standard scores of 70 or below on the Wide Range Intelligence Test (WRIT) [ 47 ] and the Vineland Screener [ 48 ] were classified as having intellectual disability, consistent with diagnostic guidelines [ 49 ]. For individuals with scores above 70 on either measure or when either of the measures for the person was missing, clinical consensus among three psychologists was reached to determine their ID status based on a review of medical and psychological records.

Data analysis

We used an accelerated longitudinal design (ALD; also referred to as a cohort-sequential design or cross-sequential design) to estimate trajectories in autism symptoms, behavioral functioning, and health of autistic adolescents and adults. The ALD estimates a single long-term longitudinal trajectory by combining multiple short-term longitudinal trajectories of each individual covering different periods. This way, the ALD makes it possible to estimate a growth trajectory spanning wider age ranges than the duration of time covered by a longitudinal study [ 50 ]. The present study spanned 22 years, including individuals as young as 10 and as old as 52 when the study began. We are thus able to estimate age-related trajectories spanning approximately 60 years.

The trajectories were estimated for each variable separately in order to evaluate their potentially unique age-related functions. As the present study analyzed data spanning the longest available period during adolescence and adulthood, our goal was to determine for each indicator whether there were age-related increases or decreases, whether the change was linear or curvilinear, and whether the trajectories differed between those who have ID and those who have average or above-average intellectual functioning. Subsequently, the slopes of the trajectories were evaluated to determine if they differed depending on age when the study began, with the goal of identifying possible cohort effects.

To assess linear and quadratic trajectories of each dependent variable, we estimated the mixed-effects growth curve models with polynomial functions of age. The Level-1 equation of the quadratic growth model is:

where Y it is a dependent variable for person i at time t , t  = 1,…, T i , where T i is the number of observations for person i ; a it represents age of person i at time t ; π 0 i represents the level of a measure at age 10 (intercept) for person i ; π 1 i represents the instantaneous linear change at age 10 for person i ; π 2 i represents the acceleration in each growth trajectory for person i ; e it is the random within-person error for person i at time t . For linear growth models, π 2 i is set as “0” and π 1 i represents the linear growth rate for person i at time t . For Level-2 equations, the person-level covariates – age at Time 1, sex, and ID status – were added to predict the baseline (intercept) differences of measures, with age variables (linear and quadratic terms) of person i specified as random. Then, to evaluate effects of ID status on these trajectories, cross-level interaction terms between ID status and age were added to the models.

For each measure, we estimated four models, each building on the previous one. Model 1 estimated the linear age effect and also included the age of the autistic individual when the study began, sex, and co-occurring ID status. For Model 2, to assess whether age was best estimated as a linear or curvilinear function, we added a term of age-squared to the variables in Model 1. In Models 3 and 4, to assess whether the age-related linear or curvilinear trajectories differed by ID status, we additionally included interaction terms (age X ID in Model 3 and age-squared X ID in Model 4). Following the approach of Joiner, Bergeman, and Wang [ 51 ], the best model was selected based on log-likelihood tests for nested models (Model 1 versus Models 2, 3, and 4; Model 2 versus Model 4; Model 3 versus Model 4), and Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values for non-nested models (Models 2 versus Model 3). We report the results of the best model. Once the best model was selected, we tested the significance of random effects components – random intercept variance and random slope variance – using likelihood-ratio tests. Note that the random effects components of each best model showed that there were significant variations in initial levels of all outcomes among individuals with ASD even after the baseline age and the ID status were controlled. Similarly, there were significant between-individual differences in the age-related trajectories for all outcomes except for impairments in non-verbal communication, as we report below. Following these main analyses, we also conducted an exploratory analysis with each best fitting model to probe for potential cohort effects.

All analyses were conducted using Stata version 17.0 [ 52 ]. The level of significance was set at equal to or less than 0.05. The examination of distributions of the variables yielded no evidence of skewed distribution, with skewness ranging from -0.61 to 1.26. For the two count variables (the number of prescription psychometric and non-psychometric medications), additional analyses using random intercept Poisson regressions were also performed. Since the results were similar to those from the growth curve models treating the outcomes as continuous variables, we reported the results based on the continuous variable analyses.

We included all individuals in the study sample in the analysis, regardless of the number of data points they contributed. Those with just one data point contributed to the estimation of the intercepts and the effect of age at the start of the study on the intercept, but they did not contribute to estimates of the trajectories. With Maximum Likelihood (ML) estimation, the greater the number of data points, the more influential the case was to the estimation of the age-related trajectories. By fitting our models in a multilevel framework (as opposed to repeated measures ANOVA, for example), we were able to retain all observed data and accommodate missing observations under the same missing at random (MAR) assumptions as are adopted when using full information maximum likelihood (FIML) estimation in other modeling contexts. For descriptive purposes, the individual trajectories of each participant are portrayed in figures that were generated by the equation in the best model for each indicator of autism symptoms, behavioral functioning, and health.

Descriptive findings

Table 3 presents descriptive data on the study sample, comparing autistic individuals who had co-occurring ID ( n  = 283) and those who had average or above-average intellectual functioning ( n  = 123) at baseline. Those who had ID were significantly older (by about five years) than those who did not have ID, but the two groups did not differ in sex. At the start of the study, there were significant differences between those who had co-occurring ID and those who had average or above-average intellectual functioning on most study variables. Those who had ID had significantly greater impairments in social reciprocity and communication (both verbal and non-verbal); were less independent in daily living skills; had greater levels of behavior problems; spent less social time with friends and neighbors; and were prescribed a greater number of non-psychotropic medications. The groups did not differ in repetitive behavior symptoms, ratings of health, or number of prescribed psychotropic medications.

Trajectories of age-related change

Age-related trajectories in autism symptoms, adi-r impairments in social reciprocity.

Model 3 was the best fitting model in the prediction of age-related trajectories of impairments in social reciprocity (see Table 4 A).

The associations of age and impairments in social reciprocity were linear and differed for those with and without ID (age X ID coefficient = 0.18, p  < 0.001). As descriptively illustrated in Fig.  1 A, those who did not have ID showed decreasing impairments in social reciprocity, whereas for those who had ID the level of impairments did not change from adolescence into adulthood and midlife.

Individual and average trajectories of change in autism symptom measures by ID status. A ADI-R Social reciprocity impairment. B ADI-R communication (non-Verbal) impairment. C ADI-R communication (Verbal) impairment ( n  = 299). D ADI-R repetitive behavior impairment. * p  < .05; ** p  < .01; *** p  < .001. note: ID Intellectual Disability, ADI-R Autism Diagnostic Interview-Revised

ADI-R impairments in non-verbal communication

Model 1 was the best fitting model for impairments in non-verbal communication (see Table 4 B). There was no significant age-related change in the level of impairment in non-verbal communication, nor were there differences by ID status (Fig.  1 B). The random effects components of the growth curve model showed that, while there were significant differences between individuals with ASD for the initial level of impairment in non-verbal communication, there were no inter-individual differences in the age-related trajectory, which was virtually flat.

ADI-R impairments in verbal communication

Similar to the patterns observed for impairments in social reciprocity, Model 3 was the best fitting model for impairments in verbal communication (see Table 4 C). The age-related trajectories of impairments in verbal communication were linear, with different slopes for those who had ID and those who did not (age X ID coefficient = 0.09, p  < 0.01). Based on visual description of Fig.  1 C, these impairments were more substantial for those with ID at all ages. Importantly, although impairments in verbal communication significantly decreased for both groups from their teenage years through midlife and beyond, for those who did not have ID the rate of decrease in impairments was much greater than for those who had ID.

ADI-R repetitive behavior

Model 4 was the best fitting model in the prediction of age-related trajectories of repetitive behaviors (see Table 4 D). Autistic individuals with and without ID differed in the slopes of their trajectories, as indicated by the age-squared X ID coefficient (-0.004, p  < 0.001). As described in Fig.  1 D, for individuals with ID, on average there was a linear age-related decrease in repetitive behaviors. In contrast, for those who did not have ID, the association between repetitive behaviors and age was curvilinear. The figure illustrates that, among those who did not have ID, repetitive behaviors decreased in severity during adolescence and the early years of adulthood, and increased in midlife and beyond. Additionally, visual inspection of the figure suggests that although the severity of repetitive behaviors was greater among those with ID for most of the adult years, later in the life course the symptoms of those who did not have ID ultimately exceeded the level of symptoms for those with ID.

Age-related trajectories of behavioral functioning

Activities of daily living.

Model 4 was the best fitting model in the prediction of age-related trajectories of activities of daily living (see Table 5 A).

The age-related trajectories in ADL independence for both those with ID and those who did not have ID were curvilinear. For both ID groups, on average, ADL independence increased during the adolescent and early adult years, but decreased in midlife and beyond. However, the slopes of the two groups differed, as indicated by the significant age-squared X ID coefficient (0.007, p  < 0.01). Visual description of Fig.  2 A indicated a steeper increase in ADL skills during adolescence and a more marked decrease during midlife and beyond for those who did not have ID than for those with ID.

Individual and average trajectories of change in behavioral functioning measures by ID status. A Activities of daily living (W-ADL). B Maladaptive behavior (SIB-R total score). C Time spent with friends/neighbors. * p  < .05; *** p  < .001. note: ID Intellectual Disability, W-ADL Waisman Activities of Daily Living Scale, SIB-R Scales of Independent Behaviors-Revised

Maladaptive behavior

Model 2 was the best fitting model in the prediction of age-related trajectories of maladaptive behavior (see Table 5 B). A curvilinear age-related trajectory was indicated by the significant age-squared coefficient (0.009, p  < 0.001), and the slope of the trajectory did not differ for those with ID and those who did not have ID. Visual inspection of Fig.  2 B suggests that, for both ID groups, the severity of maladaptive behavior decreased during the adolescent and early adult years, but increased during midlife and beyond.

Model 2 was the best fitting model in the prediction of age-related trajectories of socializing with friends and neighbors (see Table 5 C). The trajectories for both those with ID and those who did not have ID were curvilinear, as indicated by the significant age-squared coefficient (-0.001, p  < 0.05). For both ID groups, the frequency of socializing with friends and neighbors increased during the adolescent and early adult years and decreased in midlife and beyond (see Fig.  2 C). Descriptively, at around age 40, the frequency of spending time with friends and neighbors was approximately once or twice a month for those who did not have ID, but only around several times a year for those who had ID.

Age-related trajectories of health

Health ratings.

Model 1 was the best fitting model in the prediction of the age-related trajectory in health ratings (see Table 6 A).

Unlike most other indicators, the age-related trajectories did not differ between those who had co-occurring ID and those who did not have ID, either in level or slope. The association between age and health ratings was linear and negative (-0.02, p  < 0.001), as illustrated in Fig.  3 A. Descriptively, during adolescence, autistic individuals averaged between good and excellent health, whereas by the late 30 s and thereafter ratings averaged between fair and good health. Few were rated as having poor health at any point of the study.

Individual and average trajectories of change in health measures by ID Status. A Health rating. B The number of psychotropic medications. C The number of non-psychotropic medications. * p  < .05; *** p  < .001. note: ID Intellectual Disability

Number of psychotropic medications

Model 3 was the best fitting model in the prediction of the age-related trajectory of psychotropic medications that were prescribed for mental health symptoms (Table 6 B). For both those who had ID and those who did not, the age-related trajectories of the number of psychotropic medications increased linearly, but the rate of increase was greater for those who had ID, as indicated by the significant of the age x ID interaction coefficient (0.03, p  < 0.01). Visual inspection of Fig.  3 B suggests that during their teens, the two groups did not differ in the number of psychotropic medications they were prescribed, but by late midlife individuals who had ID averaged approximately three psychotropic medications whereas those who did not have ID averaged nearly two.

Number of non-psychotropic medications

Model 4 was the best fitting model in the prediction of the age-related trajectory of non-psychotropic medications that were prescribed for physical health symptoms (see Table 6 C). Autistic individuals with and without ID differed in the slopes of their trajectories, as indicated by the age-squared X ID interaction coefficient in Model 4 (-0.0015, p  < 0.01). As descriptively illustrated in Fig.  3 C, for individuals with ID, on average there was a linear age-related increase in the number of non-psychotropic medications they were prescribed. In contrast, for those who did not have ID, the association between age and the number of non-psychotropic medications was curvilinear, increasing more rapidly starting around midlife. Although during their early teens, the two groups did not differ in the number of non-psychotropic medications they were prescribed, by late midlife, those who had ID were prescribed an average of approximately four non-psychotropic medications, whereas those who did not have ID were prescribed an average of approximately three.

Exploration of cohort differences in age-related trajectories

As an exploratory follow-up analysis, we evaluated whether the age-related trajectories estimated in the best fitting models differed depending on the age of the autistic person when the study began. The interaction effect between Time 1 age and the age trajectory term (either age main effect or age X ID interaction effect) was tested in the best model for each dependent variable (see Supplemental Materials for the results). A significant interaction would suggest differences in trajectories between those who were younger versus older when the study began.

The only significant interaction effect was for independence in ADL skills (Time 1 age X age-squared X ID status = -0.0004, p  < 0.05). The coefficient suggests that for those who were adolescents at Time 1, independence was increasing during the study period, whereas those who entered the study in midlife had already peaked in their ADL independence, especially among those who did not have ID.

This study described age-related trajectories using an accelerated longitudinal design that estimated trends over a 60-year period. It drew from data collected prospectively over 22 years on a large community-based sample of autistic individuals and captured the heterogeneity of diagnosed autism at the time the study began. The analysis provided preliminary insights about patterns of change through adulthood, midlife, and into the early years of older age.

We investigated the extent of age-related change during the decades between adolescence and midlife and beyond, whether there were improvements or worsening in these domains, whether the change was linear or curvilinear, and how those with and without an ID diagnosis differed. To summarize the findings, there were significant trajectories of age-related change for all measures for both those with and without ID except impairments in non-verbal communication and impairments in social reciprocity; for these measures, there was no change for individuals who had ID. This dominant pattern of age-related change underscores the importance of longitudinal research over the full adult period for autistic individuals, a stage of life that has been beyond the upper age limit of most prior studies of autism. This is one of the notable contributions of the present research – that adulthood is not a static period for autistic individuals, although the patterns reported here need replication and extension further into old age.

However, this is not an uncomplicated story. In this two-decade prospective study, three patterns of change were observed, reflecting both the direction and slope of change. The first pattern was significant improvement over the adolescent and adult years, evident for two of the measures that comprise the diagnostic algorithm of autism (impairments in social reciprocity for those who do not have ID, and impairments in verbal communication regardless of ID status), both of which became linearly less severe as individuals aged. This pattern of reduction in autism symptom severity associated with advancing age is consistent with much past research (e.g., [ 18 , 53 ]), although there is some evidence that symptoms may increase in certain subgroups between early and middle childhood [ 54 ]. By extending the longitudinal pattern into the midlife period and beyond, the present study confirms that trajectories established earlier in the life course can be extended out to accurately reflect change during these later periods. This confirmation of earlier trajectories can only be reached in retrospect, and future research is needed to determine whether the linear reduction in symptoms continues into old age.

Second, a prominent pattern of significant worsening over the adolescent and adult years was observed for all three indicators of health – ratings of health worsened and the numbers of prescribed medications for both mental health and physical health symptoms increased with advancing age. Although these patterns are characteristic of the general population and although past cross-sectional research has documented the poorer health of autistic individuals than their age peers [ 55 , 56 ], whether worsening health begins earlier for those diagnosed with autism as compared with the general population cannot be determined from this study. This is a critical area for future research, especially since there have been population-level studies that suggest a shorter lifespan for individuals diagnosed with autism [ 24 , 26 ].

These first two patterns (improvement, worsening) reflect continuity across the study period, when patterns that are evident in adolescence and early adulthood continue into midlife and older age. In contrast, for the other measures we evaluated, there was a pattern of discontinuity during the study period, reflecting improvement during adolescence into adulthood, followed by a levelling off, and then worsening in midlife and beyond . This pattern of discontinuity was characteristic of activities of daily living, maladaptive behaviors, repetitive behavior symptoms, and socializing with friends and relatives. At midlife, independence in daily living skills peaked, behavior problems and repetitive behavior were at their lowest level, and interaction with friends and neighbors most frequent. These indicators of improved functioning in early adulthood have been reported by other studies of autism [ 57 , 58 ]. However, there have been few if any prior longitudinal reports of increased difficulties after their mid-thirties to mid-forties, trending back toward levels manifested during adolescence. These non-linear patterns of change reflect the observation made by Lachman et al. [ 32 ] regarding midlife as a pivotal time, linking periods of growth and decline. Furthermore, this pattern of gain and loss underscores the importance of taking the long-view on questions of adulthood for autistic individuals, as patterns of gain that are evident during early adulthood may not be sustained after midlife. Even when there is continuity, the slope of change may signal increasing difficulties associated with advancing age, as was observed for medications prescribed for physical health symptoms for autistic adults who did not have ID. Studies that base inferences on short periods of the adult years have the potential of misspecification of the long-term trajectory and direction of change, and may also underestimate the support needs of older autistic adults.

There are important implications of the present study for clinical practice, suggesting how support needs of autistic adults likely change with advancing age. For example, the observed gains in daily living skills and time socializing with friends, along with reductions in maladaptive behaviors, suggest that the years before midlife could be considered a time of thriving for many autistic adults; there may be a relatively good match of services to needs, on average, during this period of the life course. However, the observed declines in functioning at midlife and beyond may signal a need for a higher level of, or different constellation of, services for autistic adults during this period. Service plans that were originally designed and implemented during the transition into adulthood likely need to be reassessed and adapted to keep pace with the changing needs of autistic individuals and their families as they move into old age. Examining the interplay of formal services, natural supports, and outcomes for aging adults, along with the efficacy of targeted interventions for this period of the life course, will be critical areas of future research.

An additional important clinical implication of this research is to make it possible for autistic adults, their families, and society to plan for the future. The population diagnosed as having autism has increased exponentially since around the year 1990 [ 59 ], and the members of this “diagnostic boom” generation are now approaching or are well-into midlife. Developmental trajectories over the life course have the potential of revealing turning points, such as exiting high school, moving away from the parental home, or loss of a parent, when vulnerabilities in key domains may increase or decrease, signaling differential need for services and supports [ 60 , 61 ].

The results of the present study provide confirmatory evidence supporting much past research indicating that autistic adults who have ID have significantly poorer functioning than those without ID. Our results provide novel data indicating that the rate of age-related change differed for the two groups (i.e., a different slope). Those who did not have ID had sharper increases in independence in activities of daily living during adolescence and early adulthood and then more rapid decreases than those who had ID. Additionally, for those who had average or above average intellectual functioning, after midlife their repetitive behaviors increased with advancing age and became worse than those who had co-occurring ID. The two ID groups differed also in the rate of increase in the number of medications they were prescribed; surprisingly, those who did not have ID had a more rapid increase in medications prescribed for physical health problems after midlife. Stepping back from the details, these patterns suggest that those who have ID have a very different adult life course than those who have average or above-average intellectual functioning. Notably, in the present sample, although those with ID had greater impairments, for those who had average or above average intellectual functioning, worsening in some measures accelerated in their later years.

What are the implications of these divergent patterns of the adult life course? At the most basic level, those who have ID, as well as those who are minimally verbal, generally require a different constellation of supports and services throughout adulthood than those who can live independently, and this may also be true for their families, who are key to the provision of support across the life course. However, there are signs that autistic adults with average or above average intellectual functioning may have accelerating difficulties in their aging years, and thus their needs in specific areas may also increase as they age. Thus, it behooves service providers, policy makers, and researchers not to focus on only one group within the overall population of autistic individuals, as their differences in key aspects of the life course are consequential. Although the present study includes more of those who have ID than those who do not, our examination of differences between these groups revealed where in specific the groups followed divergent life course patterns (e.g., impairments in social reciprocity) and where they are similar (e.g., overall health ratings). Given these different life course patterns and needs, generalizing from those who do not have ID to those who do is not warranted, and has the potential to lead to conclusions or recommendations that do not reflect the needs of these individuals. The reverse pattern of generalization is also not appropriate.

The present findings also underscore the value of examining multiple measures longitudinally, rather than inferring change from a few dependent variables, as we found that the pattern of change across the life course tends to differ for different variables. It has been well-established in the field of autism that the population with this diagnosis is heterogeneous, even from the earliest years of childhood [ 62 ]. Similarly, it has been well-established in the field of gerontology that populations become more heterogeneous as they age due to the cumulative impact of both intrinsic and extrinsic factors [ 63 ]. This heterogeneity includes diversity among individuals in the rate of age-related change as well as diversity of patterns of change across indicators of aging. A recent large-scale longitudinal study of aging in the general population, based on the 30,000-member Canadian Longitudinal Study of Aging, highlighted that although overall heterogeneity increases with age, it does not do so uniformly across all domains [ 64 ], similar to the findings of the present study. Future research is needed to elucidate the factors that can account for heterogeneity in patterns of aging among autistic individuals.

Cohort effects could explain this difference in heterogeneity in trajectories, although in the present study, there was little evidence of cohort effects on the trajectories other than the significant Time 1 age effects that indicated that those who were younger at the start of the study had fewer impairments. This is not to say that cohort effects do not exist for individuals diagnosed with autism, but rather that they were not evident with respect to these age-related trajectories during the time period of the present study. Notably, all of the autistic individuals in this study were diagnosed before the change in the autism definition in the Diagnostic and Statistical Manual of Mental Disorders , fifth edition (DSM-5), which has led to substantial cohort differences, and thus generalization of our study findings to subsequent generations should be cautious.

Future research could approach the study of age-related change in autistic adults via the inclusion of trajectories in one domain as covariates or predictors of trajectories of other domains. For example, covarying trajectories of “autism” related symptoms (e.g., impairments in social reciprocity) in the estimation of trajectories of other characteristics (e.g., activities of daily living) could possibly yield a more nuanced understanding of how these might co-occur or how one might explain or account for the other. However, caution is needed prior to implementing such an approach, as the shape of the age-related trajectories of different outcomes are not identical. For example, the shape of the trajectory of autism symptoms in the present analysis, differed from the shape of the trajectory of activities of daily living, with the former being continuous age-related improvement and the latter being improvement until midlife followed by worsening. Thus, covarying characteristics that have different trajectories than outcomes might skew understanding of how these characteristics change over the adult years.

Limitations and strengths

This study is not without its methodological limitations. Like other long-term longitudinal studies, attrition is an important limitation, although we retained nearly half of the sample over the full two-decade study. Notably, the accelerated longitudinal design approach to statistical analysis made it possible to include the entire sample of 406 autistic individuals, although the length of each person’s trajectory was affected by the duration of their retention in the study. Some long-term longitudinal studies of aging such as the Midlife in the United States (MIDUS) study ( www.midus.wisc.edu ) have incorporated “refresher” cohorts to replace attrition cases and to evaluate cohort effects directly. This strategy would be valuable in studies of midlife and aging in autism.

Generalization of the present research results must be tempered by several factors. One is the lower representation of the full spectrum of autism as currently defined in the DSM-5. In addition, there were fewer data points reflecting autistic individuals in midlife and the early years of older age than adolescence through midlife. Although the data points reflecting early old age are sparser than earlier in the life course, these data constitute the unique contribution of the present study. Nevertheless, the age-related patterns reported here warrant replication. The lack of ethnic and racial diversity is an additional significant limitation, although the diversity of participants in socioeconomic status, particularly the inclusion of families living below the poverty line and the number of families where the mother does not have a college degree, is an important other source of diversity.

The type of data included in this report relied on parent (mother) reports and warrants replication with other data sources. Some measures selected for the present analysis were designed to be rated by others (e.g., ADI-R), while other measures were objective (e.g., number of prescription medications). Nevertheless, collecting data from other reporters, including directly from autistic individuals whenever possible, is an important next step in this line of research, as is the collection of biological indicators of aging.

Juxtaposed against these limitations are several strengths of the present study, including its long longitudinal study period extending over two decades, the prospective repeated measures approach, the inclusion of a community-based sample that was heterogeneous in age, ID status, and socioeconomic status, and the focus that spanned adolescence and adulthood and extended well into midlife and beyond. These limitations and strengths point the way toward future research.

Future research directions

Among the most important directions for future research are the need for replication and extension of the data collection period further into old age. Deeper investigation of the differences in the life course of those with and without ID, those who are minimally verbal, and those with other constellations of challenges is also needed to understand the distinct biopsychosocial characteristics of autism subgroups and their divergent needs as they age. Relatedly, future research on ethnically and racially diverse samples should be a priority.

Benchmarking with general population data is needed to determine if patterns observed here differ from the general population. Review of published data is a possible first step. For example, as the measure of time spent with friends and neighbors in the present sample was taken from a nationally-representative sample of the general population (the National Survey of Families and Households, www.ssc.wisc.edu/nsfh/ ), we can compare the frequency in the present sample to published national patterns (i.e., once or twice per month for the autistic adults in the present sample at around age 40 compared to five times per month at a mean age of 42 for the national sample, respectively [ 65 ]). Future studies with well-matched comparison groups would represent an important next step in benchmarking, although few studies of autism in adulthood employ this important research design approach (an exception is the Australian Longitudinal Study of Autistic Adults [ 66 ]).

The present study demonstrates notable age-related changes in the trajectories of outcomes. However, the random effect components of growth curve models revealed considerable variability between individuals in the initial levels and rates of change for all outcomes. Notably, ID status accounted for some of this variability, as evidenced by significant interactions between ID status and age (or the age quadratic term) for autism symptoms (impairments in social reciprocity, verbal communication, and repetitive behaviors), daily living skills, and number of non-psychotropic medications. Future research endeavors should aim to explore additional individual characteristics and contextual factors that could account for such interindividual variability.

Additionally, investigating the associations between the trajectories described here with adult outcomes (e.g., employment/retirement, residential independence) is an important line of future research. Latent class approaches may be helpful in this line of inquiry. Extending the accelerated longitudinal approach to other measures that potentially reflect change as autistic individuals age (e.g., cognitive, biomarkers, brain imaging indicators of aging) will be potentially important for future planning. Evaluating how the trajectories predict longevity and mortality would be a critical direction in life course research on autism. In future research, it will be valuable to explore associations among the trajectories to discover whether changes in one trajectory are correlated with changes in another, and furthermore whether some prior trajectories predict subsequent changes. Although this is beyond the scope of the present study, this is an important next step.

Lastly, it will be important to further conceptualize the definition of midlife for autistic people. As noted, it is referred to in general literature as a period that is roughly defined as spanning the decades between 40 and 60 plus or minus 10 years . As autistic adults may have poorer health and a shorter lifespan than their age peers, perhaps the beginning of midlife should be shifted downward for them. The slopes in the figures presented in this study suggest this possibility.

The present investigation is a preliminary step in the study of midlife and aging in those diagnosed with autism. Both the complexity and heterogeneity of autism during the period of the life course extending from adolescence into midlife and the earliest years of old age are highlighted by the present findings. Although aging in autism is a new research focus, this subgroup of the population is not new; it has always existed. Now is the time to prioritize research on this life stage, giving it the same careful attention that has revealed developmental processes during early childhood.

Availability of data and materials

The dataset analyzed in this study is not publicly available per IRB. When they enrolled in the study and at all subsequent rounds of data collection, participants were assured that raw data would not be shared, due to the sensitive nature of the study.

Abbreviations

Intellectual disability

Autism Diagnostic Interview-Revised

Pervasive Developmental Disorder-Not Otherwise Specified

Activities of Daily Living

Waisman Activities of Daily Living Scale

Scales of Independent Behaviors-Revised

Central Nervous System

Gastrointestinal

Wide Range Intelligence Test

Accelerated Longitudinal Design

Akaike Information Criterion

Bayesian Information Criterion

Diagnostic and Statistical Manual of Mental Disorders, fifth edition

Midlife in the United States

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Acknowledgements

We are extremely grateful to the families who participated in this study.

This study was supported by grants from the National Institute on Aging (R01 AG08768, Mailick, PI), the National Institute of Mental Health (R01 MH121438, DaWalt & Taylor, MPIs) and Autism Speaks (#7724, Mailick, PI). Support was also provided by the Waisman Center’s IDDRC core grant (P50HD105353, Chang, PI).

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Hong, J., DaWalt, L.S., Taylor, J.L. et al. Autism through midlife: trajectories of symptoms, behavioral functioning, and health. J Neurodevelop Disord 15 , 36 (2023). https://doi.org/10.1186/s11689-023-09505-w

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ORIGINAL RESEARCH article

Children with autism spectrum disorder and neurodevelopmental regression present a severe pattern after a follow-up at 24 months.

• 1 Unit of Psychology and Paediatric Psychiatry, Reina Sofia University Hospital, Cordoba, Spain
• 2 Department of Paediatrics, Infanta Margarita Hospital, Cabra, Córdoba, Spain
• 3 Department of Paediatrics, Reina Sofia University Hospital, Córdoba University, Maimonides Biomedical Research Institute of Córdoba (IMIBIC), Córdoba, Spain
• 4 Centre for Biomedical Research on Rare Diseases (CIBERER), ISCIII, Madrid, Spain
• 5 Paediatric Research Unit, Reina Sofia University Hospital, Maimonides Biomedical Research Institute of Córdoba (IMIBIC), CIBERObn, Córdoba, Spain

This study examined the presence of neurodevelopmental regression and its effects on the clinical manifestations and the severity of autism spectrum disorder (ASD) in a group of children with autism compared with those without neurodevelopmental regression at the time of initial classification and subsequently.

Methods and Subjects: ASD patients were classified into two subgroups, neurodevelopmental regressive (AMR) and non-regressive (ANMR), using a questionnaire based on the Autism Diagnostic Interview-Revised test. The severity of ASD and neurodevelopment were assessed with the Childhood Autism Rating Scale Test-2, Strengths and Difficulties Questionnaire , and Pervasive Developmental Disorders Behavior Inventory Parent Ratings (PDDBI) and with the Battelle Developmental Inventory tests at the beginning of the study and after 24 months of follow-up. Fifty-two patients aged 2–6 years with ASD were included. Nineteen were classified with AMR, and 33 were classified with ANMR.

Results: The AMR subgroup presented greater severity of autistic symptoms and higher autism scores. Additionally, they showed lower overall neurodevelopment. The AMR subgroup at 24 months had poorer scores on the Battelle Developmental Inventory test in the following areas: Total personal/social ( p < 0.03), Total Motor ( p < 0.04), Expressive ( p < 0.01), and Battelle Total ( p < 0.04). On the PDDBI test, the AMR subgroup had scores indicating significantly more severe ASD symptoms in the variables: ritual score ( p < 0.038), social approach behaviors ( p < 0.048), expressive language ( p < 0.002), and autism score ( p < 0.003).

Conclusions: ASD patients exhibited a set of different neurological phenotypes. The AMR and ANMR subgroups presented different clinical manifestations and prognoses in terms of the severity of autistic symptoms and neurodevelopment.

Introduction

Autism and childhood disintegrative disorder (CDD) were classified as separate entities within pervasive developmental disorders ( 1 ) prior to the publication of the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM) ( 2 ). CDD was characterized by a period of at least 2 years of apparently normal development, followed by a clinically significant loss of previously acquired skills (before 10 years of age) in at least two of the following areas: expressive and receptive language, social skills and adaptive behavior, bowel and bladder control, and play and motor skills. However, the fact that up to a third of patients with autism spectrum disorder (ASD) present neurodevelopmental regression ( 3 ), combined with the difficulty of identifying completely normal development during the first 2 years of life, has led to the disappearance of CDD as a diagnostic category, and it is now included within ASD.

The children with ASD exhibit a set of phenotypes including neurological alteration. Genetic components including gene mutation, copy-number variations, and epigenetic modifications play important and diverse roles in ASDs ( 4 ). One of the main problems where there is a notable lack of consensus is the establishment of criteria for determining regression. The most widely used criteria are those of the Autism Diagnostic Interview-Revised (ADI-R) test, while aspects related to the loss of language and social skills are the most widely studied and reviewed ( 5 ). The loss of language is undoubtedly the phenomenon that parents most frequently refer to when reporting the onset of symptoms (although at the time of regression, patients already tend to show a highly restricted linguistic repertoire), whereas the loss of motor or adaptive behaviors is much less common ( 5 , 6 ). In terms of age, regression normally begins in the 2nd year of life, although it can emerge as late as 81 months ( 7 ). A review of 28 studies ( 4 ) reported that the mean age of the onset of regression was 20 months.

The significance of regression in clinical, pathogenic, and prognostic terms is insufficiently clear. Although regression has been previously linked to worse prognostic outcomes ( 8 ), such as impaired cognitive performance ( 9 ) and higher scores on the ADI-R ( 10 ), other studies have reported different findings ( 11 ). In a meta-analytic review ( 12 ), children with ASD and neurodevelopmental regression (AMR) were found to develop language skills before children with ASD and non-neurodevelopmental regression (ANMR) ( 3 , 13 ). Studies based on homemade videos suggest that children with AMR exhibit higher levels of social and linguistic development than those with ANMR at the age of 1 year; but not at the age of 2 years ( 14 ), children with AMR apparently present a typical initial level of social development, unlike children with ANMR ( 15 ).

These results are not conclusive, and other authors suggest that typical development is rare in children with AMR ( 12 , 14 ). The loss of previously acquired skills is not currently a diagnostic criterion, although some authors argue that it could be used as an early indicator of ASD ( 5 ), which could help to establish therapeutic strategies at an earlier stage. It is well-known that the ASD diagnosis is relatively stable over time, with up to 80% of adolescent and adult patients retaining an ASD diagnosis originally made during childhood ( 16 ).

Results of recent studies demonstrate that the onset of ASD involves alterations in the rates of key social and communication behaviors during the 1st years of life for most children. These and follow-up studies that extend past 36 months and continue evaluation of any child who presents with atypical early development and/or high-risk status suggest that regressive onset patterns occur much more frequently than previously recognized ( 17 ), with the consequence that the age of ASD diagnosis is often older than 4 years ( 18 , 19 ).

The hypothesis of the present study is that children with ASD who undergo neurodevelopmental regression have a poorer prognosis than those who do not undergo regression, and this could help to establish earlier treatment strategies and care, thereby improving the long-term developmental outcomes. The main goal of this study was to classify a sample of children with ASD into AMR and ANMR subgroups, determine the severity of the ASD symptoms and the degree to which general development is affected, and assess the development of both subgroups 24 months after the start of the study.

A longitudinal study was carried out on a cohort of children with ASD with broadly similar geographical and domestic conditions. The patients were diagnosed after being evaluated by two clinical psychologists specializing in ASD, who applied the DSM-5 criteria and various semi-structured clinical developmental interviews and psychological and behavioral tests that have been internationally recognized as reliable and valid for this purpose, primarily the Autism Diagnostic Observation Schedule-2 (ADOS-2) ( 13 , 20 ). Before the patients were included in the study, two pediatricians specializing in developmental alterations and ASD conducted a systematic inspection and reviewed the medical history to rule out any other pathology associated with ASD.

This study was approved by the Hospital Biomedical Ethics Committee, and it conformed to the fundamental principles established in the 1964 Declaration of Helsinki. The patients with ASD were included in the study once they had been diagnosed and showed to fulfill the criteria for inclusion and exclusion, and after their parents or legal guardians had provided informed written consent.

The inclusion criteria were patients with ASD aged 2–6 years who had a positive score on the ADOS-2 test and fulfilled the DSM-5 criteria. Moreover, the parents and legal guardians agreed not to make any substantial changes to the psycho-educational treatment that the patients received during the monitoring period. The exclusion criteria were presence of another pathology associated with ASD, undergoing pharmacological treatment for any pathology or any comorbidity of ASD, taking food supplements or any alternative treatment, or the parents' or legal guardians' intention to modify the child's psycho-educational treatment in the following 24 months.

The group of ASD children was further divided into two subgroups based on presence or absence of neurodevelopmental regression during the first 2 years of life, which was assessed using a six-item questionnaire following the guidelines used by the ADI-R for the evaluation of this process ( Table 1 ) ( 21 ). The ASD children who obtained a score equal to or >3 were included in the neurodevelopmental regression ASD subgroup (AMR), and those with a score <3 comprised the non-neurodevelopmental regression ASD subgroup (ANMR). The score 3 or >3 was considered a conservative cutoff point that would include in the AMR subgroup only the clearest cases of neurodevelopmental regression. The AMR subgroup presented neurodevelopmental delay, reaching a score lower than 70 on the cognitive quotient of the Battelle Developmental Inventory (BDI) test. All the children received developmental behavioral interventions.

Table 1 . Questionnaire to classify autism spectrum disorder patients according to neurodevelopmental regression and non-neurodevelopmental regression.

Standardized Diagnostic Measurements and Assessments of Autism Spectrum Disorder Severity

All the children with ASD underwent an initial developmental clinical interview, which identified the core symptoms of ASD, according to the DSM-5 clinical diagnostic criteria. Additionally, the following tests were administered in all the cases:

a) Autism Diagnostic Observation Schedule-2 ( 13 , 20 )

Children were given an ADOS module consistent with their language development and age. It was administered by two clinical psychologists with official training in the administration and quantitative interpretation of this test for research purposes. All the children with ASD in the study exceeded the cutoff point for the diagnosis of ASD.

b) Pervasive Developmental Disorders Behavior Inventory Parent Ratings ( 22 , 23 )

The standardized version of this test for the Spanish-speaking European population was used. This test was used to evaluate the symptomatic severity of pervasive developmental disorders in ASD patients. The Pervasive Developmental Disorders Behavior Inventory Parent Ratings (PDDBI), which was completed by all the parents of the children with ASD, evaluates the characteristic ASD core behavioral deficits (deficits in social interaction, language, and pragmatic communication and stereotyped behaviors), additional behavioral difficulties (fears and aggressive behaviors), and adaptive behaviors (social, linguistic, and learning skills). All the children with ASD in the study obtained a score ≥ 30.

c) Childhood Autism Rating Scale Test-2 (CARS-2) ( 24 )

This scale was designed to classify the severity of the autism pathology as mild, moderate, or severe.

d) Battelle Developmental Inventory, Second Edition ( 25 )

This test assesses the child's current level of development and functioning in five areas (personal/social, adaptive, motor movement, communication, and cognitive areas).

e) Strengths and Difficulties Questionnaire ( 26 )

This questionnaire is used to assess the presence of behavioral difficulties and adaptive behaviors.

Statistical Analysis

With regard to the size of the ASD sample used in this study, given that the prevalence of ASD in Spain is calculated at 1% ( 27 ), data are expressed as mean ± SD (95% confidence intervals), median (IQR), or absolute (relative frequencies). Accepting an alpha risk 0.05 and a beta risk 0.2 in a two-sided test, 18 subjects in each subgroup were necessary to recognize a minimum difference in the variables selected as statistically significant. Common standard deviation was assumed to be 1.5. A 20% dropout rate was expected. The Shapiro–Wilk test was used for normally distributed data. Homogeneity of variances was estimated using Levene's test. The mean values for normally distributed continuous variables among subgroups were compared using the unpaired Student's t -test. The Mann–Whitney U test was applied for asymmetrically distributed data. Categorical variables were assessed using the chi-squared test or Fisher's exact test. Mixed-design ANOVA with Sidak correction was used to compare the BDI results of the AMR and ANMR subgroups at baseline and at the second administration (18–24 months later). Data were analyzed with the Statistical Package for the Social Sciences 22 (SPSS). All the tests were two-tailed, and a p -value < 0.05 was regarded as statistically significant.

Fifty-three patients with ASD were selected, and one was excluded on the grounds that neurodevelopmental regression could not be determined because the patient was an adoptee from another country and the family did not have the required information. No significant difference was found when comparing the average ages of the control group and the ASD group. When all the children were tested for regression, 19 fulfilled the criteria for AMR and 33 fulfilled the criteria for ANMR. The average age of the AMR subgroup (43.74 ± 11.91 months) was similar to the age of the ANMR subgroup (43.64 ± 10.747; p = 0.89). The sex ratio was similar for both subgroups (the AMR subgroup comprised 85% males, and the ANMR subgroup was made up of 81.8%; p = 0.55). Table 2 shows the percentage for each subgroup in relation to a questionnaire based on the characteristics for regression suggested in the ADI-R, the number and percentages of answers in the test.

Table 2 . Number and percentages of answers on the test to classify children with autism spectrum disorder according to neurodevelopmental and non-neurodevelopmental regression.

All the children had a score above the cutoff point for the diagnosis of ASD. No differences were observed in the scores for communication, interaction, play, and stereotypes on the ADOS test (results not shown). Cases scoring ADI-R ≥ 3 were classified as neurodevelopmental regression. ADI-R ≥ 3 was significantly related to the Childhood Autism Rating Scale Test (CARS), PDDBI, and BDI tests in the ASD subgroup that presented regression. The patients with ASD in the AMR subgroup had significantly poorer scores on the CARS test (AMR: 35.9 ± 8.12 vs. ANMR: 30.6 ± 6.11, p = 0.009), and significantly poorer autism scores on the PDDBI test (AMR: 53.53 ± 10.69 vs. ANMR: 46.22 ± 9.45, p = 0.022). On the BDI, the AMR subgroup had a lower overall score, indicating poorer neurodevelopment (AMR: 46.22 ± 9.45 vs. ANMR: 53.53 ± 10.69, p = 0.022).

In the detailed analysis of the various areas covered by the BDI (B. Total personal/social, B. Total Motor, B. Expressive and Battelle Total), significantly higher scores were observed in the ANMR subgroup compared with the AMR subgroup, which indicates greater global neurological development ( Table 3 ). The PDDBI test results suggested that the ANMR subgroup was less severely affected in terms of social approach behaviors, expressive language, and autism score ( Table 4 ).

Table 3 . Comparisons of the Battelle Inventory results in the autism spectrum disorder subgroups with and without neurodevelopmental regression at baseline and at a follow-up of 24 months.

Table 4 . Analysis of the Pervasive Developmental Disorders Behavior Inventory test in the ASD subgroups with and without neurodevelopmental regression at baseline and at a follow-up at 24 months.

The Strengths and Difficulties Questionnaire (SDQ), CARS, BDI, and PDDBI tests were re-administered 24 months after the first assessment to evaluate how both subgroups had changed regarding general development. The SDQ test did not reveal any differences between the two dates. Scores on the CARS test declined similarly for both subgroups between baseline and at 12–18 months ( Figure 1 ). The BDI exhibited improvements in the receptive and communication areas in the AMR subgroup, and the differences in these areas compared with the ANMR subgroup were eradicated as a result. However, the total motor movement skills of the AMR subgroup declined compared with the ANMR subgroup ( Table 3 ). On the PDDBI test, the ANMR subgroup showed significant reductions in the sensory and autism scores ( Table 4 ).

Figure 1 . Comparison of the Childhood Autism Rating Scale Test (CARS-2) scores between the neurodevelopmental regression (AMR) and non-neurodevelopmental regression (ANMR) groups in two study times of follow-up declined similarly for both groups. * p < 0.05, ** p < 0.01.

The results of the present study provide evidence of an ASD phenotype of neurodevelopmental regression during infancy, and how it differs from that of other groups without this symptomatology and severity, even after 24 months of follow-up. Thirty-five percent of all the children with ASD were classified as having neurodevelopmental regression using a especially designed questionnaire based on the regression characteristics suggested by the ADI-R. This result does not greatly differ from the figures reported by other authors ( 3 ) with other tools. All the ASD patients met the inclusion criteria. The data obtained from the tests, which were administered to the entire series, seem to indicate that the sample was correctly classified into two subgroups presenting symptoms of varying severity. Moreover, the questionnaire that was used to make this classification was designed following the criteria of the ADI-R clinical interview for regression ( Table 1 ) and has been used by the current authors' research group with notable results ( 28 , 29 ).

Initially, the AMR subgroup scored more poorly on all the tests assessing the presence or severity of ASD symptoms. On the BDI, which does not assess the presence of ASD symptoms but rather the child's overall development in such important areas as cognition, language, and adaptive behavior, the AMR subgroup also obtained worse results than the ANMR subgroup, with statistically significant differences in all the areas except gross motor movement. Therefore, the AMR subgroup seemed to exhibit not only more severe ASD symptoms but also general global development that was markedly poor, than did the ANMR subgroup. This result is consistent with the findings published by other authors ( 8 – 10 ).

The clinical evolution of both subgroups is significant. The data obtained after 24 months from the start of the study using the CARS, PDDBI, and BDI questionnaires show how the differences between the two subgroups persisted. The AMR and ANMR subgroups exhibited improvements in some areas of the Battelle, chiefly those related to language. In other areas, both subgroups presented changes, but they were not statistically significant. These findings suggest that the differences between the two subgroups in terms of global development were relatively stable after 24 months. As has already been noted, approximately 80% of patients who receive an ASD diagnosis in the 1st years of life continue to meet the diagnostic criteria in adolescence and adulthood ( 10 ). The data obtained from the present study, despite limitations stemming from the sample size and the fact that it only involved 24 months of monitoring, point to the possibility that the majority of the 20% of patients who cease to meet the ASD criteria in later stages of development may not belong to the AMR subgroup.

Ozonoff et al. ( 17 – 19 ) examined different ways of measuring the onset of symptoms of ASD. Their findings suggest that declining developmental skills, consistent with a regressive onset pattern, are common in children with ASD. Their results bring into question the accuracy of the methods used for measuring the onset of ASD symptoms. Thurm et al. ( 30 ) reviewed the evolution of autism diagnosis and ASD diagnostic tools. They considered the criteria for making the clinical distinction between intellectual disability (ID) with and without ASD. They assessed diagnostic boundaries between ID and secondary vs. idiopathic ASD. The diagnosis of ID in the context of an ASD may be one of the strongest indicators of an associated condition of secondary autism. In the fifth edition of the DSM, regression was not included as a diagnostic criterion ( 2 ). However, some authors have suggested the possibility of using neurodevelopmental regression as a precursor of the disorder ( 5 ), which would enable the diagnostic process to be streamlined and appropriate treatment to be started earlier. Prospective studies demonstrate that the onset of ASD involves deterioration in the rates of key social and communication behaviors during the 1st years of life for most children. The regressive onset patterns occur much more frequently than previously acknowledged ( 17 ).

In the present authors' context, the diagnosis of ASD involves a process that can often last months. Following the initial suspicions of some developmental alterations, a pediatrician working in primary health care is the first professional to assess the patient. Often, a lack of information and specific training in ASD can cause the pediatrician to adopt a conservative stance: cases are monitored for a few months to see how the patients develop before they are referred to a specialist clinic. Once they are referred to the appropriate clinic for assessment, not only is an initial clinical interview required but also specific tests for the diagnosis of ASD (generally the ADOS-2, in the case of the authors' hospital) must be administered.

Moreover, in the case of patients with high general ability, good language, and relatively intact non-verbal communication skills, it is only when they enter preschool, where they face increased behavioral expectations, particularly in terms of their responses to peers and unfamiliar adults, that parents, caregivers, and other professionals become concerned about possible atypical development ( 6 ). Consequently, diagnosis is delayed further still. As has been already noted, up to 20% of the children initially diagnosed with ASD cease to fulfill the diagnostic criteria in later years, which further complicate the establishment of a definite diagnosis in very young patients.

At present, early intervention programs are the only programs that have been found to improve behavioral alterations, aid neurodevelopment, and reduce the severity of ASD symptoms ( 31 ). In the present study, all the patients who were selected fulfilled the DSM-5 criteria for the diagnosis of ASD and scored above the cutoff threshold on the ADOS-2, with the AMR subgroup obtaining the highest scores. Therefore, although regression has not still been established as a diagnostic criterion, the authors agree with the view that has been expressed elsewhere that it could be used as a red flag to initiate early intervention immediately after regression, without needing to wait for a definitive diagnosis. Given the neural plasticity of young children, early intervention programs would be inducive of a better prognosis.

One of the most important benefits of determining a prognostic factor capable of identifying those patients who, at the time of diagnosis are at risk of developing unfavorably, is the ability to plan more aggressive treatment strategies. Currently, there are no curative treatments for ASD ( 32 ). Treatments that have exhibited greater efficacy in attenuating the most severe symptoms of the disorder have been intensive psycho-educational treatments. The presence of neurodevelopmental regression may indicate the need to begin this type of therapy in a timely way to maximize the developmental potential of the child. In addition to the use of neurodevelopmental regression as an isolated prognostic factor, it would be valuable to be able to link it to other well-established prognostic factors in ASD. Various authors have suggested a low cognitive level as an indicator of a poor prognosis of ASD.

In the present sample, the cognitive level, as determined by the BDI, was statistically significantly worse in the AMR subgroup than in the ANMR subgroup (AMR 61.72 ± 20.71 vs. ANMR 79.33 ± 18.89) ( p = 0.021). On average, the AMR subgroup scored below 70, the point that indicates low functioning in ASD, and these differences were maintained in the second assessment 24 months later.

One interesting aspect for establishing the degree of ID is adaptive functioning ( 33 – 35 ). Indeed, in the DSM-5, adaptive functioning has replaced the intelligence quotient (IQ) as the means of classifying the severity of ID because what determines the degree of support that a person with ID needs is his or her degree of adaptation to the environment and not the IQ score determined with a psychometric test. In the present study, the BDI results show that the AMR subgroup had significantly worse adaptive functioning scores, a finding that persisted at the follow-up 24 months later. This finding suggests that, in principle, the AMR subgroup will require a greater degree of support to negotiate the demands of daily life than the ANMR subgroup, and that these differences may be maintained throughout childhood and probably into adolescence and adulthood. The children with ASD exhibited a degree of disability that fundamentally stems from the severity of their symptoms, although other factors, such as family support and the age of the patient, may also play a role.

The limitations of the present study include the difficulty of collecting a reasonably large sample of children with ASD drawn from similar geographical and family backgrounds. However, the number of children included was consistent with the estimated sample size and had sufficient power to detect differences and associations.

Conclusions

The AMR and ANMR forms of ASD should be considered as different neurological phenotypes within ASD, with symptoms that are quantitatively different in their degrees of severity. Regressive onset patterns in ASD occur frequently; therefore, early detection of regression in neurodevelopment in these patients is a priority for establishing specific early strategies and individualized psycho-educational and pharmacological treatments.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics Statement

The studies involving human participants were reviewed and approved by Ethics committee of Investigation of Cordoba. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.

Author Contributions

PM-B, AG-F, and MD were involved in the experimental work. KF-R processed the experimental data and performed the biochemical analysis. MD realized the statistical analysis. PM-B, MG-C, MD, and JP-N drafted the manuscript. All authors participated in the discussion and final manuscript version and involved in the study design and plan work.

This study was supported by the Research Grant INVEST from the Spanish Society of Pediatrics (AEPED) and a Research Grant from the SPAOYEX. The funding bodies did not partake in the design, collection, analyses, and interpretation of the data, or in writing the 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, children, neurodevelopmental regression, diagnostic measurements, autism severity

Citation: Martin-Borreguero P, Gómez-Fernández AR, De La Torre-Aguilar MJ, Gil-Campos M, Flores-Rojas K and Perez-Navero JL (2021) Children With Autism Spectrum Disorder and Neurodevelopmental Regression Present a Severe Pattern After a Follow-Up at 24 Months. Front. Psychiatry 12:644324. doi: 10.3389/fpsyt.2021.644324

Received: 03 January 2021; Accepted: 16 February 2021; Published: 26 March 2021.

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Copyright © 2021 Martin-Borreguero, Gómez-Fernández, De La Torre-Aguilar, Gil-Campos, Flores-Rojas and Perez-Navero. 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: Mercedes Gil-Campos, mercedes_gil_campos@yahoo.es

† These authors have contributed equally to this work

‡ ORCID: Maria Jose De La Torre-Aguilar orcid.org/0000-0002-2473-0660 Mercedes Gil-Campos orcid.org/0000-0002-9007-0242 Juan Luis Perez-Navero orcid.org/0000-0002-2026-5909

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• Open access
• Published: 25 April 2024

Comparison of the efficacy of parent-mediated NDBIs on developmental skills in children with ASD and fidelity in parents: a systematic review and network meta-analysis

• Yuling Ouyang 1 , 2 ,
• Junyan Feng 1 ,
• Tiantian Wang 1 ,
• Yang Xue 1 ,
• Zakaria Ahmed Mohamed 1 &
• Feiyong Jia 1  

BMC Pediatrics volume  24 , Article number:  270 ( 2024 ) Cite this article

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Recently, studies on behavioral interventions for autism have gained popularity. Naturalistic Developmental Behavior Interventions (NDBIs) are among the most effective, evidence-based, and widely used behavior interventions for autism. However, no research has been conducted on which of the several NDBI methods is most effective for parents and children with autism spectrum disorders. Therefore, we conducted a network meta-analysis to compare the specific effects of each type of parental-mediated NDBI on children’s developmental skills and parent fidelity.

PubMed, Embase, Cochrane Library, Medline, Web of Science, China National Knowledge Infrastructure (CNKI), CINAHL, and Wanfang databases were searched from inception to August 30, 2023. A total of 32 randomized controlled trial studies that examined the efficacy of different NDBIs were included.

Parents of children with ASD who received Pivotal Response Treatment (PRT) reported significant improvements in their children’s social skills (SUCRA, 74.1%), language skills (SUCRA, 88.3%), and parenting fidelity (SUCRA, 99.5%). Moreover, parents who received Early Start Denver Model (ESDM) reported significant improvements in their children’s language (SMD = 0.41, 95% CI: 0.04, 0.79) and motor skills (SMD = 0.44, 95% CI: 0.09, 0.79). In terms of the efficacy of improving parent fidelity, the results showed that the Improving Parents as Communication Teachers (ImPACT) intervention significantly improved parent fidelity when compared with the treatment-as-usual group (TAU) (SMD = 0.90, 95% CI: 0.39, 1.42) and the parental education intervention (PEI) (SMD = 1.10, 95% CI:0.28, 1.91).There was a difference in parent fidelity among parents who received PRT(SMD = 3.53, 95% CI: 2.26, 4.79) or ESDM(SMD = 1.42, 95% CI: 0.76, 2.09) training compared with PEI.

In conclusion, this study revealed that parents can achieve high fidelity with the ImPACT intervention, and it can serve as an early first step for children newly diagnosed with ASD. It also showed that parent-mediated ESDM is effective in improving language and motor skills for children with ASD and can be used as part of the second stage of parent training. Parent-mediated PRT can also be used as a third stage of parent training with sufficient training intensity to further improve language, social, and motor skills.

Peer Review reports

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by social communication impairments and restricted, repetitive behaviors [ 1 ]. Early intervention is often strongly recommended for young children with autism to facilitate developmental skills in key areas to promote positive long-term outcomes [ 2 ]. There are many types of early childhood interventions recommended for this population and NDBIs(naturalistic developmental behavioral intervention)are among the most effective, evidence-based, and widely used early childhood interventions for autism [ 3 ].

NDBIs are behavioral interventions that combine developmental psychology principles with those of applied behavior analysis (ABA). This method involves sharing control between the child and the therapist, utilizing natural contingencies, and utilizing various behavioral strategies to teach skills that are developmentally appropriate and prerequisite [ 3 ]. There are several types of NDBIs [ 4 , 5 , 6 , 7 ] including PRT (Pivotal Response Treatment), ESDM (Early Start Denver Model), ImPACT (Improving Parents as Communication Teachers), JASPER (Joint Attention, Symbolic Play, Engagement, and Regulation), ESI (Early Social Interaction), RIT (Reciprocal Imitation Training), Social ABCs, CPMT (Cooperative Parent-Mediated Therapy), which not only follow NDBI principles, but have their own characteristics in different functional domains as well.

Grounded in Bronfenbrenner’s [ 8 ] ecological systems theory, parents play a crucial role in the early interventions provided to young children with disabilities, helping foster the child’s growth and development [ 9 ]. Empowering families by coaching parents can allow families to play a greater role in promoting children’s skill development [ 10 ]. Through the parent-mediated NDBI approach, parents have more opportunities to intervene with their children, which increases the intensity of intervention and can help children maintain skills [ 11 ]. At the same time, parents can help their children generalize skills in more new scenarios [ 12 ]. Most NDBIs include a parent intervention component. In JASPER, PRT, EMT and ImPACT, parents are the main agents of intervention, and in ESDM, family intervention is to enhance the intervention effect of the therapist [ 13 ]. Therefore, parent-mediated NDBI is a very promising intervention model.

Many studies have demonstrated that parent-mediated NDBI is effective [ 5 , 14 ], however, parent-mediated NDBIs do not have significant effects in all developmental skills [ 15 , 16 , 17 ]. Several reasons may explain this. (1) Autistic children have different developmental characteristics. Since the developmental level, family environment, and severity of symptoms of each autistic child are different, there is also extreme heterogeneity in different developmental skills among children with ASD [ 18 ]. (2) Various NDBI have different focuses. PRT emphasizes that interventionists master intervention skills in “pivotal” areas which are designed to target motivation and maintain strong treatment fidelity; ESDM is typically used in children with ASD around the ages of 2 to 5 years old, and is a comprehensive intervention that targets developmental milestones [ 11 , 19 ]; JASPER is a low intensity intervention for very young children with ASD and older prelinguistic individuals with ASD, focusing particularly on the foundations of social-communication, especially joint attention and play [ 20 ]; ImPACT is a short-term parent education program focused on teaching social communication to children with ASD or developmental language delay [ 21 ]; ESI is a comprehensive and family-centered model for toddlers with ASD and their families [ 22 , 23 ]; RIT emphasizes the social role of imitation [ 24 ]; Social ABCs is an on-site parental intervention training model, the core content includes functional language and positive emotion sharing [ 25 ]; CPMT is a parent-mediated intervention method that emphasizes cooperative interaction [ 7 ]. Many studies have discussed the commonalities of NDBIs [ 4 , 26 ], but no studies have examined the differences of NDBIs using quantitative method. (3) Parents receive training of varying intensity. Studies have shown that the intensity of direct intervention given to autistic children by therapists is not related to the child’s later outcomes [ 27 , 28 ]. However, no studies have examined whether increasing the intensity of parent training will indirectly affect the efficacy of interventions for children.

Since parents have the opportunity to intervene in natural settings with their autistic children, family intervention needs to be recognized as an important component of early intervention. Therefore, it is imperative that clinicians determine which NDBI is most appropriate for the families of children with ASD. However, no research has been conducted on which of the several NDBI methods are most effective for parents and children with ASD, thus significantly limiting the effectiveness of the NDBI. We, therefore conducted a systematic review and network meta-analysis of randomized controlled trials (RCTs) to compare the effects of different types of parent-mediated NDBI on different developmental domains (language, social and motor skills) of children as well as parenting fidelity. We hoped this meta-analysis would help clinicians determine which NDBIs is the most appropriate for families of children with ASD.

Search strategy

As of August 9, 2023, a total of nine databases were searched to identify studies eligible for the Project AIM meta-analysis, including PubMed, Cochrane Library, Embase, Medline, China National Knowledge Infrastructure (CNKI), CINAHL, Web of Science and Wanfang databases. The search strategy was “autistic”, “autism”, “Asperger” and “parent”, “caregiver”, “mother” and “RCT”, “randomized clinical trial”, “randomized controlled trial”. The details of the search strategy were provided in Appendix S 1 .

Selection criteria

The selection criteria were based on PICOS principle, specific criteria were given in Table  1 . In our study, the control group was divided into 2 groups, the treat as usual (TAU) group, and the parent education intervention (PEI) group. The PEI group and the experimental group used the same intervention method, but the time therapist guided for parents did not exceed 50% of the experimental group [ 29 ]. NDBI methods with a total number of studies more than 2 in this meta-analysis are classified as Common NDBI, and NDBI methods with a total number of studies less than or equal to 2 are classified as Uncommon NDBI. Referring to previous similar studies, the outcome of any measure of ASD children and parents was incorporated, including the skills of language, social, motor and parent fidelity.

Data extraction and quality assessment

Relevant data were extracted independently by two researchers by using standardized extraction forms. Across all rated items for included studies, agreement on calculations was 90%. Disagreements were attributable to (1) miscalculations, (2) unidentified outcome.

Risk of bias was rated using the Risk of Bias 2.0 (RoB 2.0), which were divided into five domains, including randomization process, deviations from the intended interventions, missing outcome data, measurement of the outcome, and selection of the reported results. Each domain could be ranked as three levels of risk levels, like “low risk”, “high risk” or “some concerns”. The evaluation was conducted independently by two researchers.

All disagreements were solved by consensus, and where consensus was not achieved, assistance was sought from the statistical consultation clinic of the First Hospital of Jilin University.

Statistical analyses

We carried out network meta-analysis using Stata statistical software 17.0 with Stata packages network, mvmeta, metareg, metan, metafunnel and metaninf. Publication bias was examined using funnel plot analysis [ 30 ]. Effect size heterogeneity was examined using I 2 as a measure of the proportion of true heterogeneity to total effect size variance. We used the random-effects model rather than the fixed-effects model to calculate mean difference effect size of parent training on the outcome of child and parent because random-effects models are more conservative [ 31 ]. Sensitivity analysis was used to assess the stability of the meta-analysis results on the P value of the forest plot and the ranking of the SUCRA plot. During sensitivity analysis we excluded studies with a sample size of less than 20 and Uncommon NDBIs whose total number of studies is less than 3.

Study identification and selection

The initial search yielded 7744 records. No additional records were identified from other sources of the 7744 identified studies, 1604 references were duplicates. After screening the title/abstracts and full-text information, 4672 and 1429 studies were excluded, respectively. Finally, 32 studies that met the inclusion criteria were included. The study selection flow chart is shown in Fig.  1 .

Flow diagram of the search and selection of the included studies

Characteristics of the included studies

These studies were published between 2006 and 2023. They had a combined sample size of 1743 participants, ranging in age from 6 months to 60 months old with a median age of 39.06 months (SD = 14.08). Of the 32 studies included, 21 were conducted in the USA, 6 in China, 1 in Ireland, 1 in Canada, 1 in India, and 2 in Italy. The major characteristics of the included studies is presented in Table  2 .

The NDBI subgroups included in this study were PRT ( n  = 5), ImPACT ( n  = 5), RIT ( n  = 1), JASPER ( n  = 6), ESDM ( n  = 9), ESI ( n  = 2), Social ABCs ( n  = 1) and CPMT ( n  = 2) and other NDBI( n  = 1). Different parent training methods included individual courses ( n  = 23), group( n  = 4), Individual courses plus group( n  = 5). The mean (SD) duration of intervention was 21.34 ± 23.00 weeks. Children’s outcomes were mainly assessed using developmental scales (Mullen, GDS, VB-MAPP、VABS); and scales assessing autistic traits (ADOS-II, CARS, SRS, SCQ). The parents’ fidelity scales were based on the EMDS, ImPACT, RIT, or self-made scales. The majority of the selected assessment tools were administered by professional evaluators or individuals actively participating in the research, who are collectively referred to as “researchers” in this study. Assessment tools that include parental reports were used in only five studies. The overall network map of different NDBIs is shown in Fig.  2 .

The overall network map of different NDBIs

Risk of bias in the included studies

During the randomization process, three studies were deemed high-risk due to the absence of randomization [ 32 , 37 ,  36 ]. In terms of outcome measurement, three studies were considered high-risk: one due to parental completion of assessments [ 35 ], and the other for the use of self-made scale [ 34 ] and reliance on a single scale throughout the research [ 23 ]. The remaining studies were categorized as low risk or presented some concerns. The overall risk of bias assessment for the included studies is depicted in Fig. 3 . Specific results of risk of bias and publication bias can be seen in Appendix S 2 and S 3 .

The overall risk of bias of included studies

• Parent fidelity

This meta-analysis revealed that most interventions exhibited a significant difference in parent fidelity between trained and untrained parents (SMD = 1.67, 95% CI: 0.74 to 2.61). However, the intensity of training, whether high or low, did not yield significant differences in parent fidelity (SMD = 0.97, 95% CI: -0.01 to 1.95). For instance, the ImPACT intervention significantly improved parent fidelity compared to TAU (SMD = 0.90, 95% CI: 0.39 to 1.42) and PEI (SMD = 1.10, 95% CI: 0.28 to 1.91). Similarly, the RIT intervention showed positive outcomes in parent fidelity (SMD = -3.32, 95% CI: 1.60 to 5.03). Additionally, the Social ABCs group exhibited significantly higher parent fidelity than the TAU group (SMD = 4.02, 95% CI: 3.14 to 4.91). Conversely, no significant difference in parental fidelity was observed with the ESDM interventions (SMD = 0.91, 95% CI: -0.03 to 1.85); however, the level of fidelity varied significantly with the training’s intensity high or low (SMD = 1.42, 95% CI: 0.76 to 2.09). In the PRT intervention, a notable difference in parent fidelity was observed between lower and higher intensity PEI (SMD = 3.53, 95% CI: 2.26 to 4.79). However, In the case of the JASPER intervention, increasing training intensity did not improve parent fidelity (SMD = -0.26, 95% CI: -0.76 to 0.25). A detailed description of parent fidelity is provided in Table  3 .

Language skills of children with ASD

Overall, there is a difference in the development of children’s language skills between parents who receive training and those who do not (SMD = 0.40, 95% CI: 0.15, 0.65). However, the intensity of the training—whether more or less intensive—does not affect the development of these skills (SMD =-0.02, 95% CI: -0.33, 0.29). Notably, a significant difference is observed only when the ESDM is employed, distinguishing between trained and untrained parents (SMD = 0.41, 95% CI: 0.04 to 0.79).

Social skills of children with ASD

Regarding children’s social skills, we found a statically significance difference on whether parents have received training (SMD = 0.49, 95% CI: 0.18, 0.80) and the level of training intensity, whether high or low (SMD = 0.41, 95% CI: 0.07, 0.74). However, in the context of common NDBI, no statistically significant differences were observed. Conversely, within the realm of uncommon NDBIs, ESI (SMD = 0.70, 95% CI: 0.01, 1.38) and RIT (SMD = 0.49, 95% CI: 0.18, 0.80) have demonstrated notable efficacy.

Motor skills of children with ASD

In terms of motor skills development, there is a notable overall difference between children of parents who received training and those who did not (SMD = 0.48, 95% CI: 0.21, 0.74). In this study, a significant difference in children’s motor skills was observed in the context of the ESDM training (SMD = 0.44, 95% CI: 0.09 to 0.79). However, when PRT (SMD = 0.46, 95% CI: -0.09 to 1.01) or ESI (SMD = 0.63, 95% CI: -0.05 to 1.31) were implemented, the differences in motor skills development were not statistically significant.

Ranking the parent-mediated NDBIs in different developmental domains

PRT emerged as the top-ranked intervention across several domains: it achieved the highest scores in social skills (SUCRA, 74.1%), language skills (SUCRA, 88.3%), and parent fidelity (SUCRA, 99.5%). ESDM ranked second in these domains, with scores of (SUCRA, 67.3%) in social skills, (SUCRA, 67.5%) in language skills, and (SUCRA, 63.8%) in parent fidelity. ImPACT and JASPER were closely matched as the third highest-ranking interventions, with scores in social skills (SUCRA, 60.7%; 54.0%, ), language skills (SUCRA, 35.4% and 36.5%), and parent fidelity (SUCRA, 43.5% and 48.4%).

Subsequent to a sensitivity analysis, the overall forest maps remained relatively stable, though there were some shifts in the rankings across domains. Due to the limited number of studies in some Naturalistic Developmental Behavioral Interventions (NDBI) subgroups, and because not all outcomes covered every domain, we excluded the SUCRA values of less common NDBIs from the main domains in our post-analysis refinement. This step was taken to minimize potential errors in the study. Detailed forest maps, SUCRA maps, and the results of the sensitivity analysis are presented in Appendix S 4 and Appendix S 5 . For a comprehensive breakdown of these findings, please refer to Table  4 .

The purpose of the current study was to evaluate the efficacy of various NDBIs across multiple domains: children’s language, social and motor skills, and parental fidelity. Initially, the effectiveness of different NDBIs was compared against the TAU group to ascertain their relative impact across these domains. Subsequently, a comparison with the PEI group was conducted to assess variations in intervention intensity for parents. Finally, the study aimed to rank the different intervention methods based on their effectiveness in each respective domain.

The analysis revealed that ImPACT is more readily operationalized by parents in terms of achieving fidelity. In contrast, PRT and the ESDM necessitate a heightened intensity of parent training to attain comparable levels of fidelity. Specifically, parent-mediated ESDM demonstrates notable improvements in language and motor skills among children with ASD. Furthermore, when administered with sufficient training intensity, parent-mediated PRT shows promising potential in enhancing children’s language abilities, social interactions, and motor skills.

Recent research suggests that high-fidelity parent implementation of intervention combined with frequent opportunities for results in the greatest child gains [ 26 , 38 ]. A study of parent fidelity in P-ESDM showed that only about half of the studies met the criteria for fidelity [ 39 ], in this study, there was no significant difference between parents who received ESDM training and the TAU group. This may be because the ESDM system emphasizes that parents only assist in enhancing the effects of the therapist’s intervention [ 13 ], so the intensity of parent training may not be enough, however, significant differences can be seen in ESDM training for parents under large or small intervention intensity, which once again proves that the original parent training intensity of ESDM is not enough. In ImPACT, good parent fidelity is shown, which may be related to its flexible online course model and complete teaching manual [ 21 ]. After indirect comparison, this study concluded that PRT is a more effective method to improve parent fidelity than ImPACT. The pace and difficulty level of teaching of PRT are constantly individualized based on a child’s skills and motivation, and the instructional cues and materials are varied to help children broaden their attention and generalize learning from the outset [ 40 ], so PRT is difficult to understand immediately. Our research shows that parents who receive higher-intensity PRT training show better fidelity, which is contrary to the study of Svetlana [ 41 ]. In their study, PRT was used to train parents in specific language skills, which cannot fully convince researchers that PRT’s short-term parent training can achieve good fidelity among parents in all domains of children. We believe that more intensive PRT training is needed for parents to achieve fidelity standards. According to current research, JASPER is not the best choice for improving parent fidelity. RIT and Social ABCs have shown the potential to improve parent fidelity. This may be because RIT only emphasizes imitation [ 24 ], which is easy for parents to understand, while Social ABCs emphasizes step-by-step real-time teaching [ 25 ], making it easier for parents to combine theory and practice.

With growing globalization, interconnectedness, and complexity of our societies, social skills have become increasingly important which not only promotes good cooperation, but also helps us achieve good mental health [ 42 ]. However, social impairment is the core defect of ASD, and it is difficult to fundamentally improve it [ 1 ]. In this study, overall parent-mediated NDBI can enhance the social skills of children with ASD, which is consistent with the meta-analysis results of Micheal Sandbank [ 27 ]. In the meta-analysis of each NDBI methods, significant effects cannot be directly seen, which may still be related to the risk of bias in studies. Through indirect comparison, the best way to improve the social skills of children with ASD through parent training is PRT.

The World Health Organization has identified language as 1 of the domains of development that is associated with not only early learning and academic success but also economic participation and health across the lifespan [ 29 ]. Among children with ASD, many, except Asperger children, have language delays [ 43 ]. In terms of children’s language skills, this study shows that parent-mediated ESDM has a good effect. This result is consistent with Elizabeth’s review study [ 15 ]. A meta-analysis showed that PRT can significantly improve the language skill of children with ASD [ 17 ], in our research, when compared with various NDBIs, parent-mediated PRT was the best method to improve language function in children with ASD, while in direct comparison of control group, it cannot directly reflect its superiority in improving language in children with ASD, which may have something to do with parents’ accumulation of professional knowledge, and further research is needed.

Motor coordination deficits are commonly found in people with ASD [ 44 ]. The most critical one is the integration disorder of motor and social information [ 45 ]. ESDM has detailed gross and fine motor development milestone targets, and emphasizes the coordination of eyes and movements [ 46 ]. In our research, ESDM showed good efficacy in improving the motor skills of children with autism. In indirect comparison, PRT showed better efficacy than ESDM, and further direct demonstration is needed in follow-up studies.

In general, the quality of most of the included studies was relatively high, while heterogeneity was low. Readers should, however, be aware of the following limitations when interpreting the results of this study: There are few studies on uncommon NDBIs, and a large number of studies are needed to demonstrate their effects in various domains; Another issue that requires attention is the diversity of the measures used to evaluate intervention outcomes. For autistic children, proximity and boundedness of outcome cannot be ignored. Outcomes that were coded as proximal to the intervention tends to have significantly larger effects than those that were coded as distal. Compared to context-bound outcomes, the effect sizes were usually smaller for outcomes coded as generalized or potentially context-bound [ 5 ]. Moreover, the evaluation results reported by some parents may lack objectivity due to parents’ insufficient understanding for children’s normal development and behavior [ 4 ]. For parents, a unified standard is needed to put into practice for the evaluation of the parent fidelity of NDBI, so as to compare the efficacy between different parent-mediated NDBI [ 6 ].

Conclusions

In conclusion, this study demonstrated that parent-mediated ImPACT interventions are effective in achieving high fidelity among parents, positioning them as a suitable initial intervention for children recently diagnosed with ASD. In the subsequent phase of parent training, parent-mediated ESDM has been shown to enhance language and motor skills in children with ASD. Finally, with adequate training intensity, parent-mediated PRT shows potential for further enhancements in language, social, and motor skills. This positions it as an integral third stage in a structured and comprehensive parent training program for children with ASD.

Availability of data and materials

The data used to support the findings of this study are included within the article.

Abbreviations

• Autism spectrum disorder

Naturalistic developmental behavioral intervention

randomized controlled trials

Surface under the cumulative ranking curves

Project Autism Intervention Meta-analysis

Network meta-analysis

China National Knowledge Infrastructure

The treat as usual group

The parent education intervention group

The risk of bias

Pivotal response treatment

Early start denver model

Applied behavior analysis

Improving parents as communication teachers

Joint attention, symbolic play, engagement, and regulation

Early social interaction

Cooperative Parent-Mediated Therapy

Reciprocal Imitation Training

Mullen Scales of Early Learning

Social Responsiveness Scale

Social Communication Questionnaire

Verbal Behavior Milestones Assessment and Placement Program

Autism Diagnostic Observation Schedule

Preschool Language Scales

Behavioral Intervention Rating Scale

Vineland Adaptive Behavior Scales

Griffiths Developmental Scales – Chinese

Childhood Autism Rating Scale

Functional-Emotional Assessment Scale

Early Social Communication Scales

Communication and Symbolic Behavior Scales

MacArthur-Bates Communicative Development

Social communication checklist

Project ImPACT for Toddlers–Parent Intervention Fidelity

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Acknowledgements

The authors thank all the participants in the study.

This research is supported by the National Natural Science Foundation of China (81973054).

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Yuling Ouyang, Junyan Feng, Tiantian Wang, Yang Xue, Zakaria Ahmed Mohamed & Feiyong Jia

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Ouyang, Y., Feng, J., Wang, T. et al. Comparison of the efficacy of parent-mediated NDBIs on developmental skills in children with ASD and fidelity in parents: a systematic review and network meta-analysis. BMC Pediatr 24 , 270 (2024). https://doi.org/10.1186/s12887-024-04752-9

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Accelerating Science to Improve Early Autism Screening

April 23, 2024 • Feature Story • 75th Anniversary

At a Glance

• Autism is a neurodevelopmental disorder that affects how people interact, communicate, and learn.
• Making early autism screening part of routine health care helps connect families to support and services as early as possible.
• Despite American Academy of Pediatrics guidelines, only a small fraction of pediatricians reported screening for autism at well-child visits.
• NIMH-supported efforts to close the gap between science and practice have yielded key insights into effective strategies for expanding early autism screening.
• Researchers are identifying new tools for detection, new models for delivering services, and new strategies for embedding early autism screening and rapid referral into routine health care.

As many parents of young children know all too well, visits to the pediatrician typically involve answering a series of questions. Health care providers may ask about the child’s eating and sleeping habits or about their progress toward walking, talking, and many other developmental milestones. Increasingly, they’re also asking questions that could help identify early signs of autism.

Autism is a neurodevelopmental disorder that affects how people interact, communicate, behave, and learn. It is known as a “spectrum” disorder because there is wide variation in the type and severity of symptoms people experience.

Today, thanks to research focused on embedding routine screening in well-baby checkups, the early signs of autism can be identified in children as young as 12–14 months. These efforts, many supported by the National Institute of Mental Health (NIMH), show that making early autism screening part of routine health care can have a significant impact on children and families, helping connect them to support and services as early as possible.

“This progress wasn’t inevitable or linear,” explains Lisa Gilotty, Ph.D., Chief of the Research Program on Autism Spectrum Disorders in the Division of Translational Research at NIMH. “Rather, it’s part of an evolving story that reflects the persistent, collective efforts of researchers and clinicians working to translate science into practice.”

Identifying the disconnect

The modern concept of autism as a neurodevelopmental disorder first emerged in the 1940s and coalesced into a diagnostic label by the 1980s. Diagnostic criteria evolved over time and, by the early 2000s, clinicians had evidence-based tools they could use to identify children with autism as early as 36 months. At the same time, evidence suggested that parents may notice signs even earlier, in the child’s second year of life.

“Reducing this gap—between observable signs and later identification and diagnosis—became an urgent target for researchers in the field,” said Dr. Gilotty. “The research clearly showed that kids who were identified early also had earlier access to supports and services, leading to better health and well-being over the long term.”

Researcher Diana Robins, Ph.D.   , then a doctoral student, wondered whether an evidence-based early screening tool might help close the gap. With support from NIMH  , Robins and colleagues developed the Modified Autism Checklist for Toddlers (M-CHAT)   , which they introduced in 2001. They aimed to provide pediatricians with a simple screening measure that could identify children showing signs of autism as early as 24 months.

The science behind early screening continued to build and gain momentum over the next few years. By the mid-2000s, researchers were exploring the possibility of using various developmental screening tools—such as the Communication and Symbolic Behavior Scales, First Year Inventory, and Ages & Stages Questionnaires—to identify early signs of autism.

The growing body of evidence did not go unnoticed. In 2006, the American Academy of Pediatrics (AAP) issued evidence-based guidelines recommending autism-specific screening   for all children at the 18-month visit. In a later update, they recommended adding another autism-specific screening at the 24-month visit, recognizing that some children may start showing signs a bit later in development.

To the research community, these new guidelines signified a huge step forward for science-based practice. But this sense of progress was soon dashed by reality.

When researchers actually surveyed health care providers, they found that very few knew about or followed the AAP guidelines. For example, in a 2006 study   , 82% of pediatricians reported screening for general developmental delays, but only 8% reported screening for autism. Most of the pediatricians said they weren’t familiar with autism-specific screening tools, and many also cited a lack of time as a significant barrier to screening.

The disconnect between science and practice prompted concern in the research community. A series of conversations in scientific meetings and workshops led to a crystallizing moment for the staff at NIMH.

“There was a period of several years in which researchers would go off and do unfunded work and then bring it back to these meetings and say, ‘This is what I've been working on,’” said Dr. Gilotty. “It was an impetus for those of us at NIMH to say, ‘We’re going to do something about this.’”

Bridging the gap

Gilotty worked with colleagues Beverly Pringle, Ph.D., and Denise Juliano-Bult, M.S.W., who were part of NIMH’s Division of Services and Intervention Research (DSIR) at the time, to synthesize several file drawers’ worth of different measures, meeting notes, and research papers and distill them into an NIMH funding announcement.

The announcement, issued in 2013, focused on funding for autism services research in three critical age groups: toddlers  , transition-age youth  , and adults  . NIMH ultimately funded five 5-year research projects that specifically examined screening and services in toddlers. The projects focused on interventions that emphasized early screening and connected children to further evaluation and services within the first two years of life.

In 2014, Denise Pintello, Ph.D., M.S.W., assumed the role of Chief of the Child and Adolescent Research Program in DSIR. She directed the research portfolio that included these projects, which sparked an idea:

“It was such an exciting opportunity to connect these researchers because the projects were all funded together as a cluster,” she said. “I thought, ‘Let’s encourage these exceptional researchers to work closely together.’”

At NIMH’s invitation, the researchers on the projects united to form the ASD Pediatric, Early Detection, Engagement, and Services (ASD PEDS) Research Network. Although the ASD PEDS researchers were using different research approaches in a range of settings, coming together as a network allowed them to share knowledge and resources, analyze data across research sites, and publish their findings together   . The researchers also worked together to identify ways that their data could help address noticeable gaps in the evidence base.

Building on the evidence

Together, the ASD PEDS studies have screened more than 109,000 children, yielding critical insights into the most effective strategies for expanding early autism screening.

For example, an ASD PEDS study   led by Karen Pierce, Ph.D.   , showed the effectiveness of integrating screening, evaluation, and treatment (SET) in an approach called the Get SET Early model.

Working with 203 pediatricians in San Diego County, California, Pierce and colleagues devised a standardized process that the providers could use to screen toddlers for autism at their 12-, 18-, and 24-month well-child visits. The researchers also developed a digital screening platform that scored the results automatically and gave clear guidelines for deciding when to refer a child for further evaluation.

These improvements boosted the rate at which providers referred children for additional evaluation and sped up the transition from screening to evaluation and services. The study also showed that autism can be identified in children as young as 12–14 months old, several years earlier than the nationwide average of 4 years.

This and other studies showed that incorporating universal early screening for autism into regular health care visits was not only feasible but effective. Working closely with health care providers allowed researchers to build trust with the providers and address their concerns.

“There is this sense that if you sit down and really talk with pediatricians, you can bring them into the fold,” said Dr. Gilotty. “Once you get some key people, you get a few more and a few more, and then it becomes something that ‘everybody’ is doing.”

Meeting the need

At the same time, the ASD PEDS studies have also explored ways to reach families with young children outside of primary care settings. Numerous studies have shown that some families are much less likely to have access to early screening and evaluation, including non-English-speaking families, families with low household incomes, and families from certain racial and ethnic minority groups.

“Screening is most effective when everyone who needs it has access to it,” said Dr. Pintello. “Addressing these disparities is a critical issue in the field and NIMH’s efforts have prioritized focusing on underserved families.”

One way to accomplish this is to integrate standardized universal screening into systems that are already serving these families. For example, in one study , ASD PEDS investigators Alice Carter, Ph.D.   , and Radley Christopher Sheldrick, Ph.D.   , worked with the Massachusetts Department of Public Health to implement an evidence-based screening procedure at three federally funded early intervention sites.

The researchers developed a multi-part screening and diagnosis process that included both clinicians and caregivers as key decision-makers. They hypothesized that this standardized process would minimize procedural variations across the early intervention sites and help to reduce existing disparities in ASD screening and diagnosis.

The results suggested their hunch was correct. All three study sites showed an increase in the rate of autism diagnosis with the new procedure in place, compared with other intervention sites that served similar communities. Importantly, the standardized procedure seemed to address existing disparities in screening and diagnosis. The increased rate of diagnosis observed among Spanish-speaking families was more than double the increase observed among non-Spanish-speaking families.

Looking to the future

Researchers are continuing to explore the best ways to put existing evidence-based screening methods into practice. At the same time, NIMH is also focused on research that seeks to develop new and improved screening tools. Evidence from neuroimaging and eye tracking studies suggests that, although the age at which observable features of autism emerge does vary, subtle signs can be detected in the first year of life. NIMH is supporting a suite of projects that aim to validate screening tools that can be used to identify signs of autism before a child’s first birthday.

“In other words, are there measures we can use to identify signs even before parents and clinicians begin to notice them?” explained Dr. Gilotty. “This is the critical question because the earlier kids are identified, the earlier they can be connected with support.”

These projects leverage sophisticated digital tools to detect subtle patterns in infant behavior. For example, researchers are using technology to identify patterns in what infants look at, the vocalizations they make, and how they move. They’re using technology to examine synchrony in infant–caregiver interactions. And they’re developing digital screening tools that can be administered via telehealth platforms.

The hope is that new tools identified and validated in this first stage will go on to be tested in large-scale, real-world contexts, reflecting a continuous pipeline of research that goes from science to practice.

“As a result of targeted research funded by NIMH over the last 10 years, we are seeing new tools for detection, new models for delivering services, and new strategies for embedding early screening and rapid referral into routine health care,” said Dr. Pintello.

“I feel like it’s just the beginning of the story—we are just now seeing the impact of bringing science-based tools and practices into the hands of health care providers. Over the next few years, we hope that ongoing efforts to bridge science and practice will help us meet the unique needs of children at the exact time that they need services.”

Publications

Broder Fingert, S., Carter, A., Pierce, K., Stone, W. L., Wetherby, A., Scheldrick, C., Smith, C., Bacon, E., James, S. N., Ibañez, L., & Feinberg, E. (2019). Implementing systems-based innovations to improve access to early screening, diagnosis, and treatment services for children with autism spectrum disorder: An Autism Spectrum Disorder Pediatric, Early Detection, Engagement, and Services network study. Autism , 23 (3), 653–664. https://doi.org/10.1177/1362361318766238  

DosReis, S., Weiner, C., Johnson, L., & Newschaffer, C. (2006). Autism spectrum disorder screening and management practices among general pediatric providers. Journal of Developmental and Behavioral Pediatrics , 27 (2), S88–S94. https://doi.org/10.1097/00004703-200604002-00006  

Eisenhower, A., Martinez Pedraza, F., Sheldrick, R. C., Frenette, E., Hoch, N., Brunt, S., & Carter, A. S. (2021). Multi-stage screening in early intervention: A critical strategy for improving ASD identification and addressing disparities. Journal of Autism and Developmental Disorders, 51 , 868–883. https://doi.org/10.1007/s10803-020-04429-z  

Feinberg, E., Augustyn, M., Broder-Fingert, S., Bennett, A., Weitzman, C., Kuhn, J., Hickey, E., Chu, A., Levinson, J., Sandler Eilenberg, J., Silverstein, M., Cabral, H. J., Patts, G., Diaz-Linhart, Y., Fernandez-Pastrana, I., Rosenberg, J., Miller, J. S., Guevara, J. P., Fenick, A. M., & Blum, N. J. (2021). Effect of family navigation on diagnostic ascertainment among children at risk for autism: A randomized clinical trial from DBPNet. JAMA Pediatrics , 175 (3), 243–250. https://doi.org/10.1001/jamapediatrics.2020.5218  

Pierce, K., Gazestani, V., Bacon, E., Courchesne, E., Cheng, A., Barnes, C. C., Nalabolu, S., Cha, D., Arias, S., Lopez, L., Pham, C., Gaines, K., Gyurjyan, G., Cook-Clark, T., & Karins, K. (2021). Get SET Early to identify and treatment refer autism spectrum disorder at 1 year and discover factors that influence early diagnosis. The Journal of Pediatrics, 236 , 179–188. https://doi.org/10.1016/j.jpeds.2021.04.041  

Robins, D. L., Fein, D., Barton, M. L., & Green, J. A. (2001). The Modified Checklist for Autism in Toddlers: An initial study investigating the early detection of autism and pervasive developmental disorders. Journal of Autism and Developmental Disorders , 31 , 131–144. https://doi.org/10.1023/A:1010738829569  

Sheldrick, R. C., Carter, A. S., Eisenhower, A., Mackie, T. I., Cole, M. B., Hoch, N., Brunt, S., & Pedraza, F. M. (2022). Effectiveness of screening in early intervention settings to improve diagnosis of autism and reduce health disparities.  JAMA Pediatrics , 176 (3) ,  262–269. https://doi.org/10.1001/jamapediatrics.2021.5380  

• NIMH Health Information Page: Autism Spectrum Disorder
• NIMH Brochure: Autism Spectrum Disorder
• NIMH Statistics Information: Autism Spectrum Disorder (ASD)
• NLM MedlinePlus: Autism Spectrum Disorder 
• HHS Interagency Autism Coordinating Committee 

Developmental approaches to understanding and treating autism

Affiliation.

• 1 Centre for Research in Autism and Education, Department of Psychology and Human Development, Institute of Education, London, UK. t.charman @ ioe.ac.uk
• PMID: 20460929
• DOI: 10.1159/000314032

Over the past decade our understanding of early social communication development in young children with autism has undergone a remarkable change. We now know something about how young children with autism process the social world in a very different way from typical children. This has led to truly developmental models of autism. In turn, these have had profound impacts on research and practice. Several screening instruments to prospectively identify autism have been developed. In some cases autism can be diagnosed in children as young as 2 years of age. The study of 'high-risk' siblings has allowed prospective study of infants from as young as 6 months of age. There is increasing evidence that intervention approaches that focus on social and communication development can ameliorate symptoms and change the developmental course of the disorder. This article will highlight some of the key theoretical and clinical lessons learned from this decade of research.

(c) 2010 S. Karger AG, Basel.

• Autistic Disorder / diagnosis
• Autistic Disorder / psychology*
• Autistic Disorder / therapy*
• Biomedical Research / methods
• Child Development
• Child, Preschool
• Communication
• Psychiatric Status Rating Scales
• Social Behavior
• Social Perception

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A broadening of diagnostic criteria and increased awareness of autism has led to a large number of individuals whose difficulties remained undetected until adulthood. This systematic review aimed to synthesise empirical evidence of the experience of individuals who received their diagnosis of autism in adulthood. Eight studies met the inclusion criteria. A meta-ethnographic approach was used to synthesise the findings. Analysis revealed two meta-themes: feeling “ like an alien ” and the “ not guilty ” verdict, each with three associated subthemes. The findings indicate the need for timely diagnosis and provision of post-diagnostic support to alleviate the mental health implications of not having a framework to understand one’s experiences and to support the process of adjustment to the diagnosis.

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Evidence-Based Practices for Children, Youth, and Young Adults with Autism: Third Generation Review

What is autism?

The Diagnosis of Autism: From Kanner to DSM-III to DSM-5 and Beyond

Avoid common mistakes on your manuscript.

Autism is a neurodevelopmental disorder characterised by impairments in social communication and restricted, repetitive patterns of behaviour, interests or activities (American Psychiatric Association (APA), 2013 ). Autism is typically diagnosed in childhood, with the average age of autism diagnosis globally being 60.48 months (van’t Hof et al., 2021 ). More recently there is a trend of seeking a diagnosis in adulthood. This can possibly be explained by increasing public awareness (Hansen et al., 2015 ; Huang et al., 2020 ) and impairments becoming more apparent when faced with the challenges of adulthood (Young et al., 2011 ).

Detection and diagnosis of autism can be delayed due to a number of factors. Some individuals learn to hide autism-related characteristics and use compensatory strategies (known as masking or camouflaging) to make up for social communication difficulties (Hull et al., 2017 ; Lai et al., 2017 ), whilst others may have received another related diagnosis with the underlying autism remaining undetected (Mazzone et al., 2012 ). Research suggests autism is under-diagnosed in females (Loomes et al., 2017 ) as a result of greater instances of masking and diagnostic procedures being biased against females (Kirkovski et al., 2013 ; Lai et al., 2017 ). Furthermore, it is important to note that there have been substantial changes to diagnostic criteria over the past 40 years (Huang et al., 2020 ). Advances in brain imaging, genetic and behavioural investigations have influenced the identification and assessment of autism (Murphy et al., 2016 ).

A broadening of diagnostic criteria and increased awareness of autism led to a large number of individuals whose difficulties remained undetected until adulthood (Huang et al., 2020 ; Lai & Baron-Cohen, 2015 ). Assessment of autism in adulthood presents unique challenges as it relies on knowledge of the individual’s developmental history (Huang et al., 2020 ). However, adults and their families often lack access to childhood medical records or there is inaccurate recall of developmental milestones (Huang et al., 2020 ; Lai & Baron-Cohen, 2015 ; Rutherford et al., 2016 ). Some adults find it challenging to provide self-reports of their difficulties (Bishop & Seltzer, 2012 ) and autism can present as part of a complex presentation of co-occurring mental health conditions (Howlin et al., 2014 ; Moss et al., 2015 ). Additional challenges for adults include a lack of valid and reliable assessment measures, service availability (Wigham et al., 2019 ) and social barriers such as anxiety, mistrust of healthcare professionals and stigma (Lewis, 2017 ).

Autistic people 1 are at risk of emotional, behavioural, social, occupational and economic difficulties (Howlin & Moss, 2012 ). The timely detection of autism can reduce these risks and lead to improvements in quality of life due to the identification of needs, provision of support, access to services and reduction of stigma and self-criticism (Calzada et al., 2012 ; Hurlbutt & Chalmers, 2002 ; Portway & Johnson, 2005 ; Wong et al., 2015 ). Diagnosis may also help reduce masking as individuals feel more comfortable not conforming to non-autistic expectations leading to improved quality of life (Bradley et al., 2021 ).

Research into undiagnosed adults is essential as many individuals who receive a diagnosis of autism in adulthood are being treated for social difficulties, anxiety and mood disorders (Bishop-Fitzpatrick et al., 2018 ; Geurts & Jansen, 2012 ) without their core difficulty being recognised. Prior to diagnosis, autistic adults’ difficulties are often misunderstood and poorly addressed negatively impacting their wellbeing and functioning (Bargiela et al., 2016 ; Portway & Johnson, 2005 ). Therefore, the lack of appropriate diagnosis can compound autism-related difficulties.

Research has largely focused on autism in childhood with research in adulthood comparatively limited; a 2017 review estimated that only 3.5% of published research involved adults (Howlin & Magiati, 2017 ). Previous reviews focused on the characteristics of autistic adults following childhood diagnosis (Kirby et al., 2016 ; Magiati et al., 2014 ); however, have not considered the challenges of receiving a diagnosis in adulthood. Individuals who receive their diagnosis in childhood may differ in their experiences, psychosocial outcomes and autism beliefs from individuals who receive their diagnosis in adulthood as they had not had a framework in which to make sense of their difficult life experiences (Brugha et al., 2011 ).

Furthermore, reviews also synthesised the literature on the suitability of assessment and diagnostic tools for adults and identified best practise for the assessment (Baghdadli et al., 2017 ; Falkmer et al., 2013 ; Hayes et al., 2018 ). They have not, however, provided an understanding of the individual experiences of assessment and diagnosis. The diagnostic process can often be experienced as challenging and arduous (Crane et al., 2018 ). Levels of satisfaction with the diagnostic process are mixed for adults who receive a diagnosis of autism in the UK; 40% of respondents were “very/quite” dissatisfied, whilst 47% were “very/quite” satisfied (Jones et al., 2014 ).

Moreover, Huang et al.’s ( 2020 ) scoping review provided an overview of research on the diagnosis of autism in adulthood. Their findings suggested that accessibility of services and processes are inconsistent, formal support services are inadequate and receiving a diagnosis of autism has a significant emotional impact for adults. However, due to the broad nature of the review question, the identified focus of the empirical research was wide-ranging and did not allow for a detailed discussion of each theme.

Given the ageing population, increasing diagnosis rates (Lyall et al., 2017 ), limited access to diagnostic services for adults (National Institute for Health and Care Excellence [NICE] 2012 ) and high costs associated with autism (Horlin et al., 2014 ), investigating the experience of diagnosis in adulthood is of considerable importance. To empower and advocate for an individual following a diagnosis, clinicians must understand those individuals’ unique experiences. This could inform the development of specialised support programmes and thus access to appropriate support.

Therefore, the present review aims to systematically synthesise empirical evidence to address the question: “ What are the experiences of autistic individuals who receive their diagnosis in adulthood? ”.

This systematic review was created under the guidance of the “Preferred Reporting Items for Systematic Reviews and Meta-analyses” (PRISMA 2020; Page et al., 2021 ). The PRISMA checklist was used to facilitate preparation, reporting and gauge completeness and transparency of the review (Page et al., 2021 ; Online Resource 1 ). This review was registered on the International Prospective Register of Systematic Reviews (PROSPERO) and a published protocol is available under the registration number CRD42021279148. Ethical approval was granted by Coventry University Ethics Committee.

Systematic Literature Search

Database search.

The systematic literature search was carried out in October 2021 for papers that explored the experience of receiving a diagnosis of autism in adulthood. Studies were identified using electronic databases: Medline, Embase, Web of Science and APA PsycInfo. Additional studies were found using Google Scholar and manual review of reference lists of extracted articles. A database auto-alert was also set up to gather any relevant articles which were published between the time of the search and the end of March 2022.

Search Strategy

A structured search strategy was created using relevant key words determined with the assistance of a specialist librarian. Table 1 presents an overview of the key search terms used. The search strategy followed Boolean logic when searching for key words. The Boolean operators “AND” and “OR” were used to construct a combination of keywords. The truncation * was used to retrieve different variations of search terms. The detailed search strategy used across the four databases for searches are presented in Online Resource 2 .

Identification of Studies

After duplicates were removed, titles and abstracts were screened for relevance by the first and second author independently. Inter-rater reliability analysis using the Kappa statistic was performed, the overall Kappa coefficient was κ = 0.521, indicating moderate agreement (Viera & Garrett, 2005 ). In some instances, there was a disagreement over which articles to retain; however, following a discussion, there was complete agreement across all article titles and abstracts reviewed.

Eligibility

Following the initial screening of the title and abstract, the full-text remaining articles were screened for eligibility using specified inclusion and exclusion criteria (Table 2 ). The first and second author independently reviewed the full texts. Inter-rater reliability analysis was conducted, the overall Kappa coefficient was κ = 0.754, indicating substantial agreement (Viera & Garrett, 2005 ). There was disagreement over three articles; however, following discussion, there was complete agreement on all full-text articles.

Studies were only included if they used a qualitative design or a mixed-method design where the qualitative findings were reported separately. Only research exploring individuals’ experiences of assessment and diagnostic process in adulthood were included. NICE guidance for diagnosing and managing autism considers adults to be individuals aged 18 and over (NICE, 2012 ). Consequently, studies were only included where participants received their diagnosis aged 18 or over given the process and experience of diagnosis in adulthood is likely to be experienced differently to children. Studies were only included if participants received a diagnosis of autism following assessment, as the experiences of adults who have undergone assessment and not received a diagnosis are likely to differ significantly. Samples were included if participants had a confirmed diagnosis using any classification system. Studies were excluded if the participants self-diagnosed their autism.

Only studies published in English were included to enable interpretation by the authors. Additionally, only articles published between 2008 and 2022 were included as the diagnosis of autism in adulthood is a relatively recent phenomenon. Furthermore, this ensured studies indicated in the scoping review conducted by Huang et al., ( 2020 ) were reviewed in addition to any further studies published since their final search in November 2018.

Data Extraction

The first and second author independently extracted the data from each paper to ensure accuracy. The following data points were extracted from each study: author, year, country, research aims, research design, sampling method, sample characteristics, method of data collection, method of data analysis and key findings.

Quality Assessment

To assess the quality of the studies identified, the Critical Appraisal and Skills Programme (CASP) checklist was used (CASP 2018 ). The CASP does not produce a quality score; however, in line with other researchers (Boeije et al., 2011 ; Lachal et al., 2017 ) a three-point scale was applied to each criterion (0 = criterion not met, 1 = criterion partially met, 2 = criterion fully met). The total score for each article was calculated by summing the scores such that articles could receive a total score between zero and 20. The articles were individually assessed against the CASP criteria by the first and second author independently and the mean CASP score was used for the final rating. Inter-rater reliability analysis was performed; the overall Kappa coefficient was κ = 0.749, indicating substantial agreement (Viera & Garrett, 2005 ).

Data Analysis

The meta-ethnographic approach outlined by Noblit & Hare, ( 1988 ) guided the review process. Meta-ethnography is one of the most widely used and influential methodologies for synthesising qualitative studies in health and social care research (Dixon-Woods et al., 2007 ; France et al., 2014 ; Hannes & Macaitis, 2012 ) and can produce a new interpretation, model or theory which goes beyond the findings of individual studies that are synthesised (Noblit & Hare, 1988 ). Therefore, meta-ethnography has the potential to generate new evidence on how patients experience their health condition or treatment, thus how this may influence treatment adherence (Campbell et al., 2011 ) and can help to understand why interventions or services work in certain settings but not in others (Noyes et al., 2018 ).

To reduce the impact of subjectivity and ensure the trustworthiness of the data, the second author was involved in all phases of the meta-ethnography. Both authors determined the research question, focus of the synthesis, conducted the literature search and made decisions on inclusion criteria and quality assessment. The first author completed the remaining phases independently; however, the second author coded a section of a transcript by extracting metaphors and themes. The authors then compared transcripts to consolidate the meaning of the codes used. The final phases of data analysis were regularly presented at research meetings to the second author to ensure adherence to the procedure.

In total, 1637 articles were initially identified, of which 378 were duplicates, resulting in 1259 studies considered suitable for further screening. Following the screening of titles and abstracts, 1236 records were excluded. Full texts for the remaining 23 eligible articles were reviewed and a further 17 articles were excluded from the review at this stage. Additionally, studies were identified via other methods including Google Scholar, citation searching and database auto-alerts. A further 12 articles were identified and assessed for eligibility, of which 10 were duplicates, resulting in an additional two studies retained for inclusion. A further screening of studies who have cited Huang et al., ( 2020 ) paper and database auto-alerts was completed prior to publication which resulted in an additional study retained for inclusion. This resulted in nine articles being retained for quality assessment (QA) (Fig.  1 ).

PRISMA 2020 flow diagram for new systematic reviews which included searches of databases, registers and other sources. *Consider, if feasible to do so, reporting the number of records identified from each database or register searched (rather than the total number across all databases/registers). **If automation tools were used, indicate how many records were excluded by a human and how many were excluded by automation tools. From: page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372: n71. https://doi.org/10.1136/bmj.n71 . For more information, visit: http://www.prisma-statement.org/

Study Design and Quality

Included studies were from the UK ( n  = 6), USA ( n  = 1), Australia ( n  = 1) and South Africa ( n  = 1). All studies employed a qualitative methodology except for Powell & Acker, ( 2016 ) who used a mixed-method approach. Across the studies, the sample size ranged from eight (Atherton et al., 2022 ) to 77 participants (Lewis, 2016 ). All studies used a mixed-gender sample except for Leedham et al., ( 2020 ) who only included females and Lupindo et al., ( 2022 ) who only included males. The mean age of participants ranged from 29.8 years (Atherton et al., 2022 ) to 60.38 years (Hickey et al., 2018 ) and the mean age at diagnosis ranged from 34.75 years (Lupindo et al., 2022 ) to 49.19 years (Lilley et al., 2021 ).

Studies used a number of different methods to collect data; semi-structured interview ( n  = 5), free-associative narrative technique ( n  = 1), open-ended text questions ( n  = 1), an oral history approach ( n  = 1) and an online open-ended survey ( n  = 1). There was also a range of methods used to analyse the data including thematic analysis ( n  = 5), interpretative phenomenological analysis ( n  = 3) and Collaizzi’s descriptive phenomenology method ( n  = 1). Characteristics of the nine studies included in this review are presented in Table 3 .

Based on their appraisal using the CASP tool, the studies included in the review were of good quality with all papers scoring between 80 and 95%. QA scores for individual papers are presented in Table 3 . Lower-scoring papers often lacked sufficient detail with regard to the relationship between the researcher and participants and ethical issues. Not describing how researchers’ subjectivity was managed, reduces the credibility of results. Only the papers by Lupindo et al., ( 2022 ) and Stagg & Belcher, ( 2019 ) provided a reflexive comment on their positionality. However, other papers did describe some reflexive techniques (Leedham et al., 2020 ; Lewis, 2016 ; Lilley et al., 2021 ; Punshon et al., 2009 ). Furthermore, all but one paper (Powell & Acker, 2016 ) evidenced that approval from an ethics committee was granted; however, the level of detail about ethical procedures varied across articles.

Other limitations highlighted from the CASP related to the data analysis process and the contribution of the study to existing knowledge. The studies by Leedham et al., ( 2020 ) and Lewis, ( 2016 ) did not include a second researcher as part of the analysis process, therefore, reducing the trustworthiness of the results. Furthermore, the suitability of the data analysis procedure may be questioned for the study by Stagg & Belcher, ( 2019 ) which used thematic analysis (TA) when interpretative phenomenological analysis (IPA) may have been more appropriate, therefore, limiting the conclusions drawn from the findings. IPA has a dual focus on the unique characteristics of individual participants making sense of their experience and patterning of meaning across participants whereas TA focuses solely on the patterning of meaning across participants. Therefore, IPA appears to align better with (1) the aim of the study to explore the lived experience of individuals receiving the diagnosis and (2) the free-associate narrative interview technique used which allows the interview to change dependent on the interviewee’s experience.

Additionally, the paper by Powell & Acker, ( 2016 ) only included two qualitative questions and asked participants to describe their experiences in a few sentences. This reduced the quantity of qualitative information available, therefore limiting the extent to which its findings contribute to current understanding. Moreover, the samples used in the three studies were predominantly White, well-educated and middle-class (Atherton et al., 2022 ; Hickey et al., 2018 ; Lilley et al., 2021 ). No additional demographic data was provided in four studies (Leedham et al., 2020 ; Powell & Acker, 2016 ; Punshon et al., 2009 ; Stagg & Belcher, 2019 ). Cultural values have been recognised to shape beliefs about autism, behavioural expectations and experiences of stigma and discrimination (Grinker & Cho, 2013 ; Lilley et al., 2020 ; Norbury & Sparks, 2013 ). Therefore, it is possible the results are not transferable to other populations with different cultural and socioeconomic backgrounds.

Data Synthesis

Across the studies, two meta-themes were identified: feeling “ like an alien ” and the “ not guilty ” verdict. Meta-themes with respective subthemes are depicted in Table 4 . The individual paper’s contribution to the meta-themes and subthemes is shown in Table 5 . Each meta-theme and respective subthemes are discussed in detail below. For further quotations evidencing each subtheme see Online Resource 3 .

Feeling “Like an Alien”

This meta-theme conceptualises the experiences of difference shared by participants. Awareness of difference typically occurred in childhood and adolescence and continued into adulthood. Not all participants acknowledged their own difference; however, they were aware that others perceived them as different. Some participants viewed their differences positively, “ we are one rung up on the evolutionary ladder ” (Punshon et al., 2009 , p.278), whereas others felt “ mental ” , “ wrong ” or “ defective ” (Leedham et al., 2020 , p.139) because of them. This awareness of difference, generally associated with a range of distressing emotions, was often what prompted individuals to seek a diagnosis (Jones et al., 2014 ). Three subthemes were identified: “ Different—yes, special—yes, unique—yes, able—very much so, superior—yes, … but defective—never! ” , “ I just was a square peg in a round hole ” and “ I felt so lacking as a person ” .

“Different—Yes, Special—Yes, Unique—Yes, Able—Very Much so, Superior—Yes, … but Defective—Never!”

This subtheme highlights the positive ways in which participants viewed themselves: “ I knew I didn’t think like others, but knew I was in many ways, superior to them ” (Lewis, 2016 , p.348). Academic success, creativity and curiosity were commonly reported as a source of positive difference: “ a number described themselves as ‘nerds’ [able to] ‘outthink’ most of [their] peers at high school…, always curious to start with; [someone] who read seven science fiction books in one day ” (Lilley et al., 2021 , p.6). For some participants “ the solitary pursuit of interests allowed for more immersion in particular activities and experiences of mastery and achievement ” (Hickey et al., 2018 , p.362). For others, being disconnected from people allowed for embracing the “ richness of their interior lives ” (Lilley et al., 2021 , p.5), especially in childhood: “ as a child I was in my own world—carefree, happy …. I was pretty much my own person ” (Lilley et al., 2021 , p.5).

“I Just Was a Square Peg in a Round Hole”

This subtheme identifies how despite the positive aspects of difference highlighted in some of the papers, many participants perceived it negatively. This was often exacerbated by receiving messages from others that they were “ odd, different and hard to understand ” (Atherton et al., 2022 , p.9). Participants frequently reported that they felt marginalised, excluded and that they did not fit in with their peers: “ I had difficulties making friends and as a result I was severely bullied. I’ve been cut on my back with a razor, I’ve had my head pushed into a toilet, I’ve been physically beaten ” (Lupindo et al., 2022 , p.9). Social misunderstandings were common across their life with participants reporting feeling both misunderstood and also misunderstanding others:

The biggest memories I have is starting primary school and literally being on the playground and feeling like I was on an alien planet. I couldn’t understand these children and I didn’t understand how to interact with them, and everything they did was like a foreign language. (Atherton et al., 2022 , p.9)

Participants were aware of being different from their peers: “ I feel like I’m a different type of human to non-autistic humans ” (Stagg & Belcher, 2019 , p.353) and commented on challenges they experienced including sensory sensitivity, insomnia, meltdowns, difficulties with education or employment. Many did not understand or were unable to explain their difference. In retrospect, this was likely due to the fact that they did not have a framework of an autism diagnosis to explain their characteristics: “ I always felt like I was just different enough to be able to recognise it, but not smart enough to figure out what my differences were so that I could fix it ” (Lewis, 2016 , p.348). Most individuals felt the need to mask their autistic traits. Although masking could be advantageous for social participation, the consequences were complex. Individuals still felt disconnected from others and the process of masking was stressful and exhausting. Masking negatively impacted the individual’s identity and increased the risk of self-harm behaviours, addiction and mental health conditions:

I was trying to cover it up and pretend I was ‘normal’ and pretend that everything was okay when inside I was dying of pain because it was all going wrong, and it was all difficult and nothing made sense. (Punshon et al., 2009 , p.276)

“I Felt so Lacking as a Person”

This subtheme explores the distressing emotions that were experienced as a result of participants feeling different from others and internalised beliefs that they were “ wrong ” , “ broken ” or “ bad ” (Leedham et al., 2020 , p.138). Many shared feelings of loneliness as a result of limited social networks despite efforts to socialise and develop friendships which were experienced as distressing: “ the most excruciating loneliness … inside just complete and utter turmoil ” (Lilley et al., 2021 , p.7). In the absence of any other framework to explain their difference many individuals blamed themselves and developed negative self-concepts:

I thought of myself as weird and strange … just odd … I used to beat myself up a lot about the things that I failed to do. Because obviously it’s my fault because I should know … I am clever enough … obviously it’s because I’m not making an effort … I am too lazy … I am not interested in things other people are interested in, it’s obviously because I am weird and strange and you know, different to everyone. (Punshon et al., 2009 , p.276)

Mental health difficulties were common including anxiety, depression, eating disorders, suicidal ideation and suicide attempts; “ I had some suicidal tendencies, and I has some just general frustrations because I couldn’t quite understand what was happening inside. I felt isolated and anxious and felt like I couldn’t take it anymore ” (Lupindo et al., 2022 , p. 10). These difficulties were associated with trauma, academic struggles, bullying and both their own and others’ lack of understanding of their experiences: “ I was exhausted trying to figure it out … why things were so different for me … by the time I had got to that diagnosis, I was already half dead, I was already in a functioning depressed state ” (Leedham et al., 2020 , p.139).

The “Not Guilty” Verdict

This meta-theme illustrates the experience of receiving a diagnosis of autism in adulthood shared by participants. Gaining a diagnosis resulted in a complex mix of feelings, “ relieved but a bit daunted and overwhelmed ” (Powell & Acker, 2016 , p.75), and significantly influenced their relationships with others. Three subthemes were identified: “ One moment of him and his team … saying yes, I’m insane or I’m not” , “After years of feeling defective I finally had the answers” and “I belong somewhere with other people who are like me ” .

“One Moment of Him and His Team … Saying, Yes, I’m Insane or I’m Not”

This subtheme highlights participants’ experiences of mental health services and the diagnostic process. Participants often accessed a variety of different services and received numerous medical opinions. Many found their experiences were not understood by professionals therefore they initially received alternative diagnoses which did not explain their difficulties. Misdiagnoses often led to failed support and reinforced participants’ feelings of being different:

[Clinician] would say, ‘oh, you’ve got borderline personality disorder (BPD)’ … I explained to him exactly why I wasn’t BPD … I wasn’t getting any answers, I just stopped going. I just stopped asking for help, I just stopped you know, looking for answers. (Leedham et al., 2020 , p.139)

Strengths such as academic ability or social skills and stereotyped views were commonly reported as barriers for recognition and identification of autism: “ I speak neurotypical fluently. I don’t look autistic. They’ll say, ‘Well, you went to a mainstream school, you got two degrees. You’re in employment, in fact, successfully self-employed. Come on. Why waste time on you ” (Atherton et al., 2022 , p.8) . For others, their family perceiving their behaviour as unique and not problematic led to not seeking professional support; “ I don’t think there was much concern about it … my family’s really fairly accepting ” (Lupindo et al., 2022 , p.9).

Lengthy assessment processes including multiple professionals created further barriers to appropriate assessment and identification. Moreover, clinicians’ lack of knowledge in the area further compounded the barriers to diagnosis. The approach of healthcare professionals often increased experiences of feeling misunderstood and blamed for their difficulties: “ It appeared to my clinical psychologist that I’d got Asperger syndrome although my clinical psychologist wasn’t trained to give that diagnosis … [my psychiatrist] said, ‘How can I help you if you don’t tell me what’s wrong?’ ” (Punshon et al., 2009 , p.274). Some participants struggled to find a professional to diagnose them due to being adults and others were refused a formal assessment: “ The psychologist said that there would be no point in doing this formally as I was already an adult and there were no services in place for my situation ” (Lewis, 2016 , p.351). This resulted in some participants opting for a private assessment. Others reported only receiving a diagnosis following seeking help for their children’s difficulties:

My daughter … she didn’t cope in the normal … then somebody told us that her brother was diagnosed with Asperger’s and that [therapist name] was leader in the field … he called us back and he said … he wants to see me next … so then I waited for my session and then it was discovered (Lupindo et al., p.9).

For some participants, a diagnosis of autism afforded access to various support such as counselling, benefits and workplace adaptations which enabled an understanding of how autism affected their life: “ [it has had a] positive effect on my working conditions … it helped me to get help from social services … I have contacted local support groups … done research ” (Powell & Acker, 2016 , p.77). However, the support available for adults was frequently criticised as it was often limited: “ I am appalled and lament for the thousands of adult aspies who have struggled—and continue to struggle—to live in a world that is alien to them … what of the adults? Why are we the forgotten ones? ” (Lewis, 2016 , p.351). Additionally, after a long battle to gain a diagnosis, some participants expressed feeling anxious that their diagnosis and access to services would be taken away:

I am always dead paranoid that someone is going to say, ‘Oh we have made a mistake and you haven’t got Asperger syndrome … you are just depressed and psychotic [laughs]. So, you can’t have any access to any of the services. Go away. (Punshon et al., 2009 , p.274)

“After Years of Feeling Defective, I Finally Had the Answers”

This subtheme explores participants’ individual experiences post-diagnosis. Participants expressed feelings of relief as they were able to make sense of their experiences after many years of living without a diagnosis. Difficulties such as making and maintaining friendships or romantic relationships, academic and employment difficulties and mental health challenges could be understood in the context of the diagnosis: “ after 50 years of not understanding the ‘why’ of myself, finding out I was an Aspie was a light in the darkness, best thing that happened to me ” (Lewis, 2016 , p.350) . When the autism diagnosis replaced a previous diagnosis participants were particularly pleased: “ not being labelled BPD or just stroppy by the Community Mental Health Team (CMHT) means a lot to me ” (Powell & Acker, 2016 , p.75). Despite the relief highlighted by participants, many felt “ devastated ” and saw the diagnosis as “ another nail in the coffin ” (Lewis, 2016 , p.349). For some the process of re-evaluating their lives in the context of the diagnosis was painful and frustrating:

It was looking back and then suddenly realising there was this genetic connection and great feelings of inadequacy; that I must’ve been a really bad carer for my parents when they were terminally ill. And just having to, psychologically, on your own, reassess your whole life. And at the age of 53 it’s going back a long way. (Hickey et al., 2018 , p.361)

Some participants shared that they did not feel comfortable with their new autistic identity given the potential to be seen only through a negative lens: “ I thought ‘am I just anything other than these symptoms?’ Um, that really upset me … I sort of started doubting my ability to do my job ” (Leedham et al., 2020 , p.139). Others reported worries due to the lifelong nature of the diagnosis: “ I am never going to be like one of these ‘normal’ people … I thought ‘I am stuck being like this now’ ” . (Punshon et al., 2009 , p.278) A period of grief following diagnosis was common among participants; this was associated with sadness at how their life may have been different had their needs been appropriately understood:

Disappointment, deeply felt, that I had to wait until I was 45 years old to get a diagnosis. Saddened, too, for all the lost opportunities that would likely have come about had I known and received intervention and loving understanding as a child. (Lewis, 2016 , p.351)

Feeling angry about being failed by professionals previously was commonly reported: “ there’s these glaring issues and you see how multiple times you were failed by various professionals that should have and could have seen issues ” (Atherton et al., 2022 , p.10). However, a minority expressed feeling pleased that they had not received their diagnosis in childhood as they felt it could have limited their ability to reach their full potential and it enabled them to find ways to adapt: “ living without a diagnosis was a hard teacher, but a good one ” (Lewis, 2016 , p.350). Increased understanding of their difficulties helped them “ to plan and prepare for situations, knowing how [they] may react, and how to avoid difficult situations … so [they] can keep to places and activities [they are] comfortable with ” (Stagg & Belcher, 2019 , p.354). It also enabled individuals to attribute negative experiences to autism leading to increased compassion and self-acceptance: “ having that knowledge was such a powerful thing because I could understand and forgive myself ” (Lilley et al., 2021 , p.8).

Following their diagnosis, some participants shared that they felt able to show their true self after years of masking autistic traits: “ There’s more accepting of who I am … you don’t try and copy other people and try and fit in. You’re trying to be yourself ” (Lupindo et al., 2022 , p.12). However, for others, autistic stereotypes provided further justification for continuing to mask: “ the feeling of looking out at people and knowing I had to hide aspects of myself and invent others never left ” (Lewis, 2016 , p.350).

“I Belong Somewhere with Other People Who Are Like Me”

This subtheme explores participants’ interpersonal experiences post-diagnosis. The diagnosis provided opportunities to meet others with autism which enabled them to share experiences, enhance their understanding of autism and feel accepted by others: “ you’re accepted. You don’t have to sort of hide anything … the people, some of them are on my wavelength” (Hickey et al., 2018 , p.363). Participant’s self-acceptance was largely influenced by how family and friends responded to their diagnosis:

“I’m fortunate enough that a lot of my friends tend to and my family as well they’re very accepting and they don’t perceive it as anything wrong. And because of that acceptance, it kind of made it easier for me to accept myself” (Lupindo et al., 2022 , p.12-13).

Many participants found that their friends, family and work colleagues were more understanding of their difficulties; “ … it’s more like they understand that OK, I have a diagnosis, and I have a disability. And that I’m not OK with work and stuff like that. So, I would say it’s less stressful ” (Atherton et al., 2022 , p.9). However, the disclosure of diagnosis was also at times met with unhelpful reactions from others. Some significant others did not appreciate its magnitude:

I just expected him [husband] to say something … or realise how massive this was for me and he didn’t for ages, and about two weeks later I just said, ‘look, this is huge for me … to you I’m no different, but to me I’m completely different’. (Leedham et al., 2020 , p.141)

Others, who held stereotyped or stigmatised views of autism or its genesis, either refuted any blame: “ there is a distinct reaction from my dad … that it can’t possibly be his fault that one of his children doesn’t work properly. Something he made doesn’t work properly ” (Punshon et al., 2009 , p.279), or they felt relief they were not responsible for the participant’s difficulties to date: “ I think my mum … you know, often she felt that she was getting the blame … not just from psychiatrists but also from other people ” (Punshon et al., 2009 , p.279) . Relationship changes were common post-diagnosis with some significant others being more supportive in light of new understanding of the experienced difficulties: “ … he’ll [husband] now take the lead in situations where he knows I’m not comfortable, whereas before he just thought I was being awkward ” (Leedham et al., 2020 , p.141). However, other participants experienced a negative change in their relationships and chose to end them: “ … the implication … I was automatically wrong because I had this Asperger’s thing … So that was unexpected, and I had to walk away ” (Leedham et al., 2020 , p.141). Finally, some reported others expected less of them or experienced discrimination, which fostered a sense of regret at seeking a diagnosis:

She started bullying me quite seriously from then on, and within about eighteen months I was out of a job, and I think if I hadn’t bothered finding out what Asperger’s was, I would have just been this lonely person who just carried on. I sometimes wonder whether I should have, is it a bad thing to have had the diagnosis. (Stagg & Belcher, 2019 , p.355)

This meta-ethnography extends the current literature by drawing attention to the experiences of receiving a diagnosis of autism in adulthood. The findings are consistent with Huang et al.’s ( 2020 ) scoping review; however, this review provides a more detailed exploration of the themes including the experience of difference pre-diagnosis, the diagnostic process and individual and interpersonal experiences post-diagnosis. The review also illustrates the impact of the delayed diagnosis on the individual’s mental health, access to support, relationships and life opportunities. Furthermore, four of the eight studies reviewed focus on middle to late adulthood (Hickey et al., 2018 ; Leedham et al., 2020 ; Lilley et al., 2021 ; Stagg & Belcher, 2019 ) which is a particularly underserved group in autism research and practise (Happé & Charlton, 2012 ).

Prior to diagnosis participants had an awareness of difference but a poor understanding of why they were different from others; this has been documented in previous research (Baldwin & Costley, 2016 ; Huws & Jones, 2015 ; Müller et al., 2008 ). For some, this perceived difference was viewed positively, which suggests it is important for clinicians to recognise and value autistic individuals’ strengths whilst providing support for areas of difficulty. For the majority, however, their differences were related to feeling excluded and social misunderstandings. Social interaction difficulties are the most commonly reported trait in this population (Hofvander et al., 2009 ; Kanai et al., 2011 ). As a strategy to facilitate fitting in, participants often engaged in masking. Similar to previous findings, masking was reported to be exhausting, stressful (Hull et al., 2017 ) and detrimental to mental health (Cage et al., 2018 ). Participants shared experiences of loneliness, negative self-perceptions and mental health problems as a consequence of not understanding their difficulties.

The diagnostic process was an intensely emotional time with various barriers impeding appropriate identification. Previous research also documented a range of barriers to diagnosis, in particular for females whose needs are often under recognised and misattributed to alternative diagnoses (Bargiela et al., 2016 ; Gould, 2017 ). Consistent with previous literature (Crane et al., 2016 , 2018 ; Evans et al., 2022 ) a significant lack of professional support available post-diagnosis was highlighted.

Adults diagnosed with autism experience complex reactions with feelings of relief, anger and sadness commonly reported. The diagnosis was often highly valued as it enabled them to make sense of their difficulties, increasing compassion and self-acceptance, a finding which has been previously documented (Jones et al., 2014 ; Rosqvist, 2012 ; Tan, 2018 ). Having a framework to understand oneself where needs and strengths can be identified and supported is critical. The diagnosis provides autistic adults with an opportunity to reclaim parts of their life history which were misunderstood and develop coping strategies to manage current difficulties (Kanfiszer et al., 2017 ; Tan, 2018 ).

A diagnosis of autism enabled opportunities for shared experiences which were viewed positively. Previous research suggests interactions with other autistic adults are validating, fulfilling and normalising (Bargiela et al., 2016 ; Hickey et al., 2018 ; Tan, 2018 ). Consistent with previous research, adults described both supportive and unhelpful reactions from others post-diagnosis; the latter were commonly influenced by stereotyped or stigmatised views of autism (Crane et al., 2018 ).

Clinical Implications and Recommendations

Understanding the assumptions that lead to misdiagnoses or delayed diagnosis would be an important initiative. Participants frequently reported not feeling understood by professionals, therefore it is important that clinicians have a broad, comprehensive and up-to-date knowledge of autism for effective identification and support. Clinicians should receive training highlighting the different presentations of autism across the lifespan and considering other barriers to identification such as gender differences, cultural differences, high intelligence or masking. Furthermore, training should extend beyond an understanding of autism characteristics and focus on promoting respectful and effective therapeutic support (Nicolaidis et al., 2015 ); this would increase knowledge and reduce the stigma associated with autism (Gillespie-Lynch et al., 2015 ). NICE guidance suggests autism strategy groups should ensure the provision of multi-agency training (NICE, 2012 ).

Mental health difficulties were commonly reported pre-diagnosis. As traits of autism and mental illness symptoms may present similarly, co-occurring mental health conditions may be a further barrier to the diagnosis given the complexity of differential diagnosis (Lai & Baron-Cohen, 2015 ; Lehnhardt et al., 2013 ). Therefore, clinicians should consider using screening tools for adults presenting at mental health services to recognise possible signs of undiagnosed autism and avoid misdiagnosis. NICE guidance suggests for adults who do not have a moderate or severe learning disability the Autism Spectrum Quotient-10 items should be used (NICE, 2012 ). This would provide an opportunity for clinicians to identify autism and refer individuals for assessment (Geurts & Jansen, 2012 ). A score of 6 or more suggests a comprehensive assessment should be offered (NICE, 2012 ).

Progression from feeling misunderstood to self-acceptance only occurred following diagnosis. This highlights the need for timely diagnosis to enable individuals to better understand their needs. Diagnostic processes for adults are poor in comparison to those for children and lack formal support services (Huang et al., 2020 ). Compared to services available for children, adult services receive very little funding, support and assistance (Camm-Crosbie et al., 2019 ). Ensuring equitable access to assessment and support services for adults is essential and is in line with the national strategy for autistic children, young people and adults: 2021 to 2026 on improving early identification, reducing waiting times and improving diagnostic pathways for adults (Department of Health and Social Care & Department for Education, 2021 ).

Considering the complex mental health needs of individuals receiving a diagnosis of autism in adulthood (Geurts & Jansen, 2012 ; Hofvander et al., 2009 ) and emotional responses post-diagnosis (Jones et al., 2014 ; Lewis, 2016 ), the lack of post-diagnostic support is a concern. Receiving a diagnosis is a complex and emotional life event which involves a reconceptualization of identity. Services should recognise adults will be coping with years of being misunderstood, excluded and criticised without understanding the cause. Access to mental health services is critical to support this process of reflection and adjustment as this is unlikely to be resolved within the assessment process. Diagnostic services often do not provide ongoing support (Crane et al., 2018 ; Evans et al., 2022 ); therefore, a collaborative multidisciplinary effort is required to develop support pathways post-diagnosis. Creating therapeutic services where individuals can be supported with disclosure, identity, masking, mental health difficulties and sensory challenges is crucial. It is important that services are tailored to the individual’s needs, in particular to their autism, to prevent withdrawal from services (Crane et al., 2019 ; NICE, 2012 ).

There is a lack of empirically supported mental health supports for autistic individuals (Benevides et al., 2020 ; NICE, 2012 ). Autism groups and meeting others with autism was viewed as beneficial, allowing for shared experiences and acceptance. Information on local support groups should be provided routinely as part of the diagnostic process. Preliminary research suggests autistic-led therapy for adults following diagnosis could be beneficial as a means of increasing understanding and knowledge by interacting with autistic peers (Crane et al., 2021 ). Therefore, services should explore the development of peer-led supports. Furthermore, for services providing support for coexisting mental health difficulties to autistic adults, interventions should be informed by existing NICE guidelines for the specific disorder (NICE, 2012 ). However, it is critical that staff have a core understanding of autism and the possible impact of treatment, and they should consider seeking advice from a specialist autism service regarding delivering and adapting interventions (NICE, 2012 ).

Limitations

This meta-ethnography has some limitations. Reviewed studies reported experiences of individuals who received their diagnosis across different healthcare systems around the world. It is recognised that there are different assessment processes in these respective settings. Furthermore, whilst some autism traits appear similar across cultures, other traits are specific to particular cultures and must be accounted for during assessment (Carruthers et al., 2018 ).

Time since diagnosis varied significantly across samples. Some participants received their diagnosis in the months prior to participation (Atherton et al., 2022 ; Powell & Acker, 2016 ), others had recently been diagnosed and/or received their diagnosis up to 20 years ago (Hickey et al., 2018 ; Leedham et al., 2020 ; Lilley et al., 2021 ; Punshon et al., 2009 ). Time since diagnosis is likely to influence the findings as individuals make sense of their experiences and assimilate their autistic identity over time. Previous research indicates that a greater number of years since diagnosis is associated with less dissatisfaction with being autistic (Corden et al., 2021 ).

It is acknowledged that papers not written in English were excluded from the current review introducing a language bias (Butler et al., 2016 ). Due to the time and cost implications of translation, this was unavoidable.

Future Research

Studies conducted in Western countries dominate research on the experiences of autism diagnosis in adulthood. Disparities in culture, awareness of autism and healthcare systems in different countries will have important implications for the prevalence of autism and experience of the diagnostic process. Further research in non-Western countries would allow for consideration of these factors and inform policy development.

To address the underrepresentation of marginalised groups, researchers should promote diversity, equity and cultural humility, and ensure their research addresses the specific needs and interests of these groups (Maye et al., 2021 ). In addition, researchers should aim to recruit large representative samples or samples in which marginalised groups are over-represented to replicate previously accepted findings and gain insight into health outcomes among these groups (Robertson et al., 2017 ; West et al., 2016 ). To achieve this, culturally competent researchers should collaborate with marginalised communities to identify and eliminate barriers to participation and co-produce research.

Individuals diagnosed with autism in adulthood often wished they were diagnosed earlier (Baldwin & Costley, 2016 ; Lewis, 2016 ; Powell & Acker, 2016 ). Some research has focused on the unique experiences of individuals diagnosed in middle-to-late adulthood (Hickey et al., 2018 ; Leedham et al., 2020 ; Lilley et al., 2021 ; Stagg & Belcher, 2019 ). Previous research, however, has not compared experiences of adults diagnosed at different ages. Understanding the distinct needs of adults receiving an autism diagnosis at each developmental stage would help to inform the development of tailored supports. Future research should consider exploring whether experiences of the diagnostic process vary across adulthood.

Given many individuals diagnosed with autism in adulthood are supported by their parents or partners, future research should explore the experiences of supporting their autistic child or partner to adapt to the diagnosis. A large body of research explores the experiences of parents of children who receive a diagnosis. Due to differences of parenting an adult and the longer time taken to receive the diagnosis, the impact is likely to be different. NICE guidance (2012) and the Think Autism strategy (Social Care, Local Government and Care Partnership Directorate, Department of Health, 2014 ) highlighted the impact on carers supporting individuals with a diagnosis of autism. This research could assist the development of appropriate therapeutic support for carers.

Broadening of diagnostic criteria and increased awareness of autism has resulted in a “ lost generation ” of individuals whose autism has remain undiagnosed until adulthood (Lai & Baron-Cohen, 2015 , p.1013). Diagnosis can prompt a process of sense-making which can be disrupted by a lack of post-diagnostic support. Existing services for adults are limited and underfunded (Huang et al., 2020 ), with few evidence-based supports for autistic adults (NICE, 2012 ). This meta-ethnography provides further clarity on the experiences of adults prior to receiving their diagnosis, during the diagnostic process and post-diagnosis which can be used to inform the development of adult diagnostic and support services. The current understanding provides a starting point for enabling positive experiences across diagnostic services for adults. It also highlights the importance of healthcare systems being equipped for the needs of the “lost generation” as more adults seek an explanation for their differences.

Data Availability

All papers included in this review are in the public domain and can be found using academic databases and/ or Google Scholar.

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The Quantitative Nature of Autistic Social Impairment

• John N Constantino 1   na1  

Pediatric Research volume  69 ,  pages 55–62 ( 2011 ) Cite this article

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• Autism spectrum disorders
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Autism, like intellectual disability, represents the severe end of a continuous distribution of developmental impairments that occur in nature, that are highly inherited, and that are orthogonally related to other parameters of development. A paradigm shift in understanding the core social abnormality of autism as a quantitative trait rather than as a categorically defined condition has key implications for diagnostic classification, the measurement of change over time, the search for underlying genetic and neurobiologic mechanisms, and public health efforts to identify and support affected children. Here, a recent body of research in genetics and epidemiology is presented to examine a dimensional reconceptualization of autistic social impairment—as manifested in clinical autistic syndromes, the broader autism phenotype, and normal variation in the general population. It illustrates how traditional categorical approaches to diagnosis may lead to misclassification of subjects (especially girls and mildly affected boys in multiple-incidence autism families), which can be particularly damaging to biological studies and proposes continued efforts to derive a standardized quantitative system by which to characterize this family of conditions.

Research in child development over the past several decades has revealed that clinical neuropsychiatric syndromes often represent the severe end of continuous distributions of core competencies and/or deficiencies that occur in nature. This concept was seminally applied to child psychopathology by Achenbach and colleagues ( 1 ) who developed and validated an empirically based dimensional system of measurement; the implementation of that system in studies involving hundreds of thousands of children has revolutionized the way in which disorders of behavior and development are understood. Although on the surface, the difference between categorical (“all-or-nothing”) and dimensional classification systems may appear trivial, a paradigm shift between the two can have profound implications for the exploration of neural mechanisms underlying behavior, the enhancement of statistical power in biological research, the monitoring of effects of intervention, and helping parents understand (and accept) the nature of a psychiatric condition in a child. In recent years, the conceptualization of autistic syndromes as part of a continuum of social variation in nature has garnered considerable attention in science.

The fact that a variety of rare single-gene disorders now account for some 10–15% of autistic syndromes (at the time of this writing) might seem to reinforce a categorical concept of autism; that the “autisms” actually represent a collection of such discrete disorders of social cognition ( 2 ). Just as is true for intellectual disability, however, the identification of such discrete syndromes of deficiency does not necessarily contradict the notion of a continuum, rather that the continuum as a whole (the “bell curve” for the example of intelligence) may be comprised, in part, of an array of clusters, each engendered by its own cause—independent, partially overlapping, or fully overlapping with the underlying causes of other clusters—and varying in range of severity. In addition, a discrete genetic cause may result in varying phenotypic expression (variable penetrance has been directly observed for specific structural chromosomal variations associated with autism) ( 3 ), presumably on the basis of the manner in which that discrete genetic cause interacts with other attributes of the genotype or life experience of the individual. Alternatively, quantitative variation in an inherited trait may arise from the additive or interactive effects of multiple genetic influences of modest effect—whether rare or common, inherited, or de novo —as well as environmental influences, the totality of which relate to severity in a stochastic fashion.

For the 85% of autism cases in which specific molecular or structural genetic variations are not yet appreciable, it remains unknown whether such multigenic influences are at play; however, it has become increasingly clear that in some 25% of families affected by autism, multiple family members are affected by clinical or subclinical autistic traits; and that within this subset of families, the distribution of autistic traits and symptoms appears highly quantitative, in a way that makes it extremely difficult to establish a clear demarcation between those affected versus unaffected ( 4 , 5 ).

Multigenic mechanisms of causation have been identified for other complex conditions (diabetes, obesity, hypertension) and specific diseases (discussed below), in which common susceptibility alleles of relatively minor effect exert joint or interactive effects on the condition of interest, and some of the features of these medical conditions bear striking similarities to what is observed in autism spectrum conditions. For example, the short-segment variety of Hirschsprung's Disease—a disorder of neuronal migration to the large intestine—affects boys five times more commonly than girls and exhibits both sporadic and familial patterns of intergenerational transmission. In the familial form of the disease, length of the gut that is affected (a straightforward quantitative index measured in centimeters) is associated with the number of recessive susceptibility alleles possessed by the patient and is predictive of sibling recurrence risk ( 6 , 7 ). This multifactorial genetic mechanism contrasts with the rarer and more severe long-segment Hirschsprung's Disease, which follows a dominant pattern of autosomal transmission of single gene mutations of major effect. In schizophrenia, a neurodevelopmental disorder in which the normal process of synaptic pruning in adolescence is believed to be dramatically accelerated (in contrast to the disruption in synapse formation that is believed to occur in autism), sporadic cases have been associated with an elevated occurrence of de novo copy number variations (CNVs), some of which are the same as those observed in excess among individuals affected by sporadic autism ( 8 ).

Quantitative Variation in Autistic Symptomatology in Affected Families

Numerous studies, using various methods of measurement, have documented the aggregation of autistic syndromes, symptoms, or traits in the close relatives of children with autism. Such observations of familial aggregation have ranged from a full diagnosis of autistic disorder [for which siblings of children with autism have a relative risk of 20 or higher ( 9 )], to milder autistic syndromes [Asperger syndrome, pervasive developmental disorder-not otherwise specified (PDD-NOS)] ( 10 ), to subclinical behavioral features of the autistic syndrome ( 4 , 5 , 11 – 13 ), to the “broader autism phenotype,” including personality traits that are akin to autistic symptoms ( 14 – 19 ), and to developmental impairments or delays that more specifically involve language ( 15 , 16 , 18 , 19 ).

Lauritsen et al . ( 9 ) additionally observed that the siblings of children with Asperger Syndrome in the Danish National Register had a 13 times higher than general population risk for the development of full-blown autism, which constitutes some of the strongest evidence to date that the two disorders share common underlying genetic susceptibility factors. Studies that have carefully differentiated simplex autism (single family member affected, also referred to as sporadic autism) from multiplex autism (two or more family members affected, also referred to as familial autism) have suggested that familial aggregation of subclinical autistic traits may occur only in multiplex autism ( 2 , 5 , 14 ) ( Fig. 1 ), providing further evidence for differentiation of mechanisms of genetic transmission for sporadic versus familial forms of the disorder.

In a recent large-scale study ( n = 1235 families) of the siblings of children affected by autism spectrum disorder (ASD) ( 4 ), 10% were clinically affected, but an additional 16% were nondiagnosed siblings who nevertheless exhibited substantial elevations in social and/or communicative impairment occurring in these family members with a frequency approximately one order of magnitude more commonly than observed in the general population. Furthermore, when “recurrence” in this sample was operationalized using quantitative indices standardized by gender rather than absolute categorical definitions of case status, the typically cited 3:1 sex ratio of males to females narrowed to 3:2. In essence, girls in families affected by autism were twice as likely to fall above a first percentile cutoff for females than they were to acquire a clinical diagnosis, and over 10 times more likely to exceed the first percentile for autistic social impairment than girls in the general population.

Thus what is observed among clinically and subclinically affected individuals in families affected by autism is a very wide, and in essence, continuous distribution of autistic symptoms and traits ( 4 , 5 ) comprising the so-called autism spectrum (as depicted for boys in multiple-incidence families in Fig. 1 ). The question of how quantitative variation among clinically affected individuals relates to the variation observed for autistic-like traits in the general population ( i.e. generally less severe variations in social communication that are measurable using the same methods as used in the studies above) remains another open and important question. Given the fact that even very subtle levels of autistic symptomatology have been shown to aggregate in some of the family members of children affected by ASD, it is possible that variation in the general population is influenced by the same sets of genetic and environmental factors that influence autism itself (this is discussed in further detail below). We have recently shown that when parents ( 4 ) and classroom teachers ( 5 ) rate the severity of ASD-diagnosed versus undiagnosed populations, the point of greatest differentiation of the respective distributions occurs at a point that is at approximately 1.5 standard deviations above the general population mean (thus not at an extreme tail), suggesting that there is substantial overlap between the general population distribution and the continuum of affectation that encompasses clinically diagnosed patients.

The Factor Structure of Quantitative Autistic Traits

A key aspect of quantitative variation in autistic symptomatology involves the questions of whether (or to what extent) the three components of the autistic syndrome (social deficits, communicative deficits, and stereotypic behavior/restricted interests) represent independent symptom clusters ( i.e. each with its own quantitative or qualitative architecture), or rather co-vary (“travel” together) in nature. In this review, we focus on autistic social impairment as a core feature of autistic syndromes but recognize that when language impairments and stereotypic behaviors occur independently from autism, they may exhibit distributions and patterns of aggregation that diverge from the pattern described here for autistic social impairment. Here, we summarize briefly what is understood about the associations between the three-symptom domains of the autism triad to best contextualize the implications of a quantitative architecture for autistic social impairment.

Although some large-scale general population studies have suggested that the inherited influences on the three domains of symptoms are substantially ( 20 ) or partially ( 21 ) independent of one another, a caveat is that such studies can be confounded by measurement methods that do not ascertain autistic symptoms and traits with enough specificity. For example, if children with common nonautism-related disorders [such as specific language impairment (SLI) or conduct disorder] are contributing to elevations in “autistic trait” scores ascertained in large populations, it can lead to overestimates of the extent to which separate lines of inheritance are responsible for the symptoms, because many of these syndromes are likely to have their own causal mechanisms independent from autism.

In contrast, factor, cluster, and latent class analyses of autistic symptoms in family studies have revealed substantial overlap in the three-criterion domains for autism delineated in the Diagnostic and Statistical Manual of Mental Disorders 4th edition (DSM-IV). For example, Spiker et al . ( 22 ) studied sibling pairs affected by autism and found that empirically derived clusters of symptoms within families differed not by specific symptom sets, but by the degree of impairment that existed (mild, moderate, or severe) across all three DSM-IV criterion domains for autism. Their findings were most consistent with a model of autistic symptomatology arising from a single, heritable, continuously distributed deficit, which might influence dysfunction in all three-symptom domains.

Similarly, Sung et al . ( 12 ) examined features of the broader autism phenotype in the relatives of autistic probands and found evidence for the primary aggregation of highly heritable social deficits that explained variation in symptomatology across other domains of the autistic syndrome. Constantino et al . ( 23 – 25 ) applied factor, cluster, and latent class analysis to diagnostic interview and quantitative trait data [using the Social Responsiveness Scale (SRS)] from children representing the entire range of autistic social impairment, from minimally affected to severe, and consistently observed a unitary factor structure across data sources and methods of analysis, reinforcing the syndromic nature of autistic impairment. A recent principal components factor analysis of an accumulated Washington University sample of 1799 boys from 1799 separate families, including over 300 clinically affected by autism, yields the factor structure depicted in the scree plot in Figure 3 , indicating that a primary factor accounts for greater than 30% of the variance, with all other possible factors contributing only minor components of variance. It is important to note that in this observation and in previous studies, the symptoms which load most strongly onto the principal factor represent all three of the DSM-IV criterion domains for autism.

Scree plot of principal components analysis of SRS sample of 1799 boys from 1799 separate families, representing the full range of variation in autistic severity from unaffected to severely affected by clinical autistic syndromes.

Subsequent to the original reports summarized above, Gotham et al . ( 26 ) and Lord et al . ( 27 ) factor analyzed data from thousands of structured diagnostic observations of children with autism and concluded that the symptoms encompassing social deficiency and communicative deficiency comprised a single empirically derived factor. Even at face value, a review of typical autistic traits reveals aspects of overlap across symptom domains that provide insight into key unifying constructs. The tendency for the social ability of individuals with ASD to be compromised by an over-focus on details “missing the forest for the trees” is highly reminiscent of the preoccupation with detail that characterizes young affected children's unusual (stereotypic) play with toys and older children's restriction in range of interests. It has been hypothesized that an underlying deficit in the assignment of salience ( 28 ) and the consequent absorption with details could underlie the social, stereotypic/behavioral, and even the communicative deficits ( e.g. sentence comprehension compromised in the context of preserved decoding of individual words) of the autistic syndrome.

These observations provide a new framework for understanding quantitative variation in autistic symptomatology and challenge the three-criterion taxonomy for differentiating specific PDDs in DSM-IV, by raising the possibility that each of the common disorders (Autistic Disorder, Asperger Syndrome, PDD-NOS) lie along a continuous distribution of impairment with a more parsimonious underlying factor structure. In such a reconceptualization, variation in autistic severity interacts with variation in other domains of development (general cognition, temperament, proneness to anxiety) to produce specific profiles of individual adaptation. For example, Asperger Syndrome, which is described in DSM-IV as a separate disorder (defined as autistic impairment without substantive language delay and generally characterized by average to above average intellectual functioning), might be viewed as an autistic syndrome that is compensated by a level of intellectual functioning that is adequate to sustain normal language development, even in the presence of a level of social impairment that is otherwise typically associated with language impairment. In that sense, it is the preservation of a relatively high level of general cognition—not the existence of a separate disorder—that uncouples the usual association between social and communicative impairment observed in autistic syndromes.

Molecular genetic studies are now just beginning to add to our knowledge of the factoral structure of autistic syndromes. Although many disparate mutations have been associated with the same triad of symptoms observed in autism, quantitative trail loci (QTL) analyses have indicated that common autism susceptibility alleles may preferentially confer risk among specific subsets of autistic patients, for example those with versus without severe language delays ( 29 ). It has also been observed that a given large chromosomal rearrangement or allelic variation associated with autism ( i.e. one that occurs more common in ASD than in the general population) might have highly variable phenotypic expression, as observed for 16p11 deletions which are found in patients with autism, patients with mental retardation without autism, and in relatively typically developing individuals within autism-affected families ( 3 ). Recognizing that a wide range of quantitative autistic traits may be present in the siblings of children with autism, it is possible to explore whether the association between phenotype and underlying genetic or neurobiologic mechanisms might be more appreciable when considering quantitative autistic trait information from all siblings in affected families rather than restricting analysis to the transmission of categorical disease states. Molecular genetic analyses using quantitative trait information from all children of participating families (not just the fully affected subjects) have indicated that specific linkage signals in two independent multiplex family registries were substantially enhanced when adopting this approach ( 30 , 31 ).

Finally, it is well recognized that some syndromes of SLI are inherited independently from autism, however, as discussed above, several studies have reported that language disorders ( 15 , 16 , 18 , 19 ), which can often be accompanied by distinct autistic qualities (pronoun reversal, socially inappropriate phrases), or by mild-to-moderate levels of social deficiency, also aggregate in the unaffected siblings of autistic probands ( 4 ). Thus, although the relationships between the symptom domains of the autism triad remain a subject of active investigation, there is accumulating evidence that they represent correlated behavioral manifestations of an underlying quantitative neurodevelopmental impairment. Ultimately, developmental, molecular genetic, and neuroimaging studies are poised to make major contributions to our understanding of how (and in what combinations) the various clinical manifestations of autism arise, and this in turn will inform strategies for intervention for specific subgroups. It is highly conceivable that the severity distribution that constitutes the ASDs is fully continuous with the distribution of subclinical autistic symptomatology that is appreciable in the general population (as reviewed below). It has recently been demonstrated for autism ( 32 ) and for other complex diseases such as hypertension ( 33 ) that variation within the normal phenotypic range can be caused by variation in the same genes that are responsible for clinical disease states. From an evolutionary standpoint, it is possible that inherited factors that are actually adaptive when phenotypic expression is mild (as may well be the case for autistic traits) may be highly preserved in the population and result in clinical disease states only when severely expressed or in interaction with other genetic or environmental factors.

Quantitative Variation in Autistic Symptomatology in the General Population

Several large general population studies involving symptom counts and/or quantitative ratings of the severity of autistic social impairment, using disparate measurement methods ( 34 – 36 ) have consistently shown that the distribution of autistic symptoms in epidemiologic samples is continuous; this is shown for social responsiveness scale (SRS) total scores in a general population twin sample in the histogram in Figure 2 . The SRS is a quantitative measure that was designed for use in public health settings and large research samples. The items of the SRS are focused on the ascertainment of core deficiencies that characterize autism spectrum conditions, and therefore it elicits severity ratings for impairment in reciprocal social behavior, social communication, and abnormalities in the domain of stereotypic behavior/restricted interests. Because the SRS exhibits a unitary factor structure, total scores are used as an index of autistic severity. A limitation of the scale by design is that impairments in language ascertained by the SRS are limited to social communication deficits to avoid confounds with specific language impairment (SLI) and deficits in general cognition.

Reciprocal social behavior in the general population: distribution of score for reciprocal social deficits, parent-report SRS: males (▪); females (□). Adapted from Constantino JN and Todd RD Arch Gen Psychiatry 60:524–530 Copyright© 2003 American Medical Association, with permission.

Using this measurement method quantitative variations measured throughout the course of childhood and adolescence have exhibited an extraordinary level of preservation of interindividual differences (intraclass coefficients on the order of 0.80) over years of time ( 37 ), demonstrating a level of stability that is similar to that observed in clinical autistic syndromes. On the basis of the distribution depicted in Figure 2 , it appears highly arbitrary where distinctions would be drawn between the designations of affected versus unaffected status, and ASD can be viewed as the extreme end of a normative distribution for reciprocal social behavior (and its associated traits) that occurs in nature. The average child with a current designation of PDD-NOS scores at about the 99th percentile of the severity distribution for males. As is depicted in Figure 3 , the female distribution is shifted toward the nonpathological end across the entire distribution; this results in sex ratios being exaggerated when extreme cutoffs using nonstandardized scores are implemented as criteria for diagnosis, which is the case for all traditional autism diagnostic methods. Females who score at the 99th percentile for their standardized distribution are only half as likely as males to acquire a clinical diagnosis of ASD using current community standards for diagnosis ( 4 ), and this may explain a substantial share of what is universally observed as a 3:1 sex ratio for categorical ASD diagnoses.

To ascertain whether gender-specific genetic effects account for the nonpathological shift in the distribution for females compared with males, opposite sex dizygotic twins have been studied ( 34 ). Opposite-sex twin pairs represent a gender-comparison condition within the classic twin design, in which common environmental influences can be modeled and controlled for. The results of structural equation modeling applied to the data in Figure 2 indicated that the genes influencing autistic traits appeared to be the same for males and females in the general population. Lower prevalence (and severity) of autistic traits in females was found not to be a function of sex-linked genetic influences, rather a possible result of increased sensitivity to environmental influences (in females), which operate to reduce the phenotypic expression of genetic susceptibility factors and promote social competency ( 34 ). This is highly consistent with findings on sibling recurrence in clinically affected families ( 4 ): given similar levels of genetic susceptibility, girls appear relatively protected (compared with boys) from severe phenotypic expression of this liability.

It is important to note that variation in the general population is highly heritable, at the same level of genetic influence that autism itself is believed to be inherited ( 34 , 35 , 38 ). This appears to be true throughout the distribution, i.e. both for social competency and social deficiency. This does not necessarily imply that social competency and social deficiency are controlled by the same genetic factors, but that possibility exists, and could conceivably be explored in very large epidemiologic twin samples ( n on the order of 10,000 pairs) in which the numbers of clinically affected subjects are large enough to estimate whether genetic influences on a) quantitative trait scores and b) categorical designations of affectation status (at a clinical level cutoff) are determined by the same set of genetic influences—as of this writing, no such analysis has ever been published.

What is known about genetic influences on “subthreshold” autistic traits is that they overlap at least partially with those that influence some forms of autism: they preferentially aggregate in the unaffected family members of many children with ASD ( 4 , 5 , 11 ), particularly those for whom other members of the family are also fully affected by ASD (so-called multiple-incidence or “multiplex” ASD). Recently, St. Pourcain et al . ( 32 ) showed that a common variation in genetic variant on 5p14.1 (rs4307059), a replicated susceptibility allele for ASD, is also associated with social communication spectrum phenotypes in the general population. Notably, the association in the general population was explained neither by single-trait associations nor by overall behavioral adjustment problems but by a joint effect of multiple subthreshold social, communicative, and cognitive impairments—this is highly consistent with findings supporting a unitary factor structure for the autistic syndrome, discussed above.

The question also arises whether inherited influences on quantitative autistic traits are the same as or different from those influences involved in other dimensional domains of psychopathology. Twin designs are capable of answering such questions about genetic overlap, as long as the various traits of interest are all measured in the subjects in a given genetically informative sample. A series of studies in the general population have indicated that a) scores for internalizing behavior and externalizing behavior explained only a minority of the variance in quantitative autistic trait scores ( 39 ); b) there is moderate phenotypic and/or genetic overlap between attention problem syndromes and quantitative autistic traits ( 34 ); c) elevation in quantitative autistic trait scores may exacerbate co-occurring psychopathologic syndromes ( 25 ); and d) youths with severe mood or anxiety disorders exhibit substantially higher autistic trait scores than healthy controls ( 40 ) and the level of impairment incurred by these and other variations in personality and behavior may be predicted in part by co-occurrence of subclinical autistic symptomatology ( 41 ). Recently, Lichtenstein et al. ( 42 ) analyzed data from one of the largest twin studies ever to ascertain data on symptoms of neurodevelopmental impairment in children ( n = 10,895 twin pairs). It revealed that although ∼ 80% of the variation in liability for ASDs was due to genetic effects, a large share of that genetic variance was shared, respectively, with Attention-Deficit Hyperactivity Disorder (ADHD), developmental coordination disorder, tic disorders and learning disorder, when respective bivariate analyses were applied to the data. Similarly, in a sample of 674 young adult Australian twins, Reiersen et al . ( 43 ) reported significant overlap of additive genetic influences for self-reported autistic traits and ADHD symptoms. Taken together these findings suggest that the inherited liabilities that predispose to ADHD and other neurodevelopmental conditions may exacerbate or contribute to the development of autistic syndromes.

The Clinical Ascertainment of Autistic Severity

A variety of established and emerging rating scales for social impairment in autism are capable of reliably quantifying its severity—these include checklists or questionnaires of current functioning completed by adult-informants (usually parents, teachers, or both) who have observed a child routinely in his/her naturalistic social contexts, assessments of developmental history as provided by parents, and direct observations of current social communicative behavior in response to structured or semistructured social prompts. Each domain of observation can provide a unique and valuable vantage point for assessing the severity of autistic social impairment (as well as its relationship to other domains of development and psychopathology) in a given child.

There are many ways in which multi-informant characterization of a developing child can help avoid misclassification that would otherwise arise from single-informant or single-context assessment ( 44 ). Young children with milder forms of ASD who might be rapidly identified by an experienced day care teacher could go unrecognized if observed exclusively at home, because young parents are often inexperienced with respect to normative trajectories of social development. Moreover, such children can be reasonably competent in one-on-one social interactions with caring adults who can support and scaffold their interpersonal behavior, but have a great deal of difficulty in less structured social contexts in the company of larger numbers of close-aged peers. This extends into childhood and adolescence as well—there are many children who respond reasonably appropriately to a clinician-examiner in the context of a structured diagnostic assessment but may be observed by teachers to display floridly inappropriate social behavior when in an unstructured context at school (lunch, gym, recess, and bathroom breaks between classes). For these reasons, it is important to acquire information from multiple sources [ideally direct clinical observation, parent-report, and teacher-report, refer Constantino et al . ( 24 )] in the clinical evaluation of social impairment in a child suspected of having an ASD.

Endophenotypes and Quantitative Mapping of Childhood Development

In addition, a quantitative conceptualization of autism motivates the search for endophenotypes, which are inherited, quantitative phenotypic components of a syndrome. Endophenotypes, if they exist for autism, should be appreciable in some or many of the unaffected family members of clinically affected individuals and might more closely map to specific genetic, neurobiologic, or psychologic factors that contribute to (but are not by themselves sufficient to cause) the phenotypic expression of the syndrome itself. Endophenotypes are not the same as associated symptoms of a condition ( e.g. self-injurious behavior in Lesch-Nyan syndrome), rather core components of the syndrome that appear in subclinical forms in unaffected relatives. Subclinical autistic traits exhibiting a unitary factor structure (as described earlier in this review) appear to constitute a social endophenotype for males in multiple-incidence (multiplex) autistic families, and the search for more precise endophenotypic components of the autistic syndrome ( e.g. electrophysiologic, neuroimaging, or developmental trajectory markers) is under way.

As an example of the way in which two candidate endophenotypes might interact to confer susceptibility to autistic social impairment, Reiersen et al . ( 45 ) analyzed data on inattention and motor coordination deficits (separately inherited domains of quantitative neurodevelopmental deficiency) in >800 children in the general population and showed that their co-occurrence is associated with the development of autistic social impairment, as shown in Figure 4 . These associations are reminiscent of a syndrome of “Deficits in Attention, Motor Coordination, and Perception” (DAMP) that has been observed and previously described in the child psychiatric literature ( 46 ).

Percentage of subjects with clinically significant autistic traits, stratified by presence or absence of DSM-IV ADHD and by endorsement of Child Behavior Checklist (CBCL) motor problem items. n = 851. Number of subjects in each group is shown above each bar. DAMP, deficits in attention, motor control, and perception. Reprinted from Reiersen AM et al. J Am Acad Child Adolesc Psychiatry 47:662–672; Copyright© 2008 Elsevier, Ltd., with permission.

The current list of proposed candidate endophenotypes for autism is long, but the series of investigations required to establish them as true endophenotypes (through studies confirming heritability, patterns of familial aggregation, trait-like stability, and specific association with autism) is long as well. Endophenotypic candidates being actively pursued in autism research at the time of this writing include head circumference trajectory in infancy ( 47 ), laterality (handedness), electrophysiologic and brain activation responses to socially relevant auditory and visual stimuli, sensory dysfunction, EEG and event-related potential abnormality, newly discovered neuroimaging phenotypes ( 48 , 49 ), an array of neuropsychologic deficits (theory of mind, self-other representation, social motivation, capacity for shared intentionality, abnormalities in visual social engagement), a variety of language endophenotypes (prosodic deficits, sentence comprehension abnormalities, timing of developmental milestones such as age of first word or first phrase), motor coordination problems, involuntary/repetitive motor movements, insistence on sameness, perseveration, and inattention. Each of these might be uniquely influenced by specific brain networks that could become targets of preventive or therapeutic interventions.

Finally, it is important to consider the implications of the interaction between a) quantitative distributions of deficiency in reciprocal social behavior (accompanied by relative deficiency in communication and the presence of stereotypic behaviors and/or restricted interests as occurs in the autistic syndrome); and b) quantitative variation in general cognition, which is an independent developmental construct that can be differentiated from autistic social impairment within the normal distribution of intelligent quotient (IQ) in the population, especially when using current measurement methods. Even if largely uncorrelated within the general population, clinical syndromes of deficiency in any developmental domain can exert expectable consequences on other developmental domains, and many cases of ASD are complicated by co-occurring intellectual deficiency. Autism can occur in the context of low IQ, high IQ, or anything in between, and it is often difficult to know whether syndromes of combined social and cognitive impairment represent a cognitive disorder with secondary social impairment, the reverse, or some combination of two independent conditions.

Related to this is the confusion that can arise in clinically disentangling such deficits in young children with developmental delay. An example is the extremely common clinical scenario of the toddler who presents with language delay; it is perplexing (yet potentially relevant to the choice of intervention strategy) to determine whether that delay is most attributable to a primary cognitive deficit, an autistic syndrome, an SLI, or some combination of dimensional variants of these conditions. A goal for the next decade is to map quantitative variation in social competency to quantitative variation in other fundamental (orthogonal) domains of childhood development.

If each neurodevelopmental domain is fundamentally quantitative in nature, it will ultimately require established maps of the expectable relations between the variables (analogous to the height versus weight norms used in pediatric practice) to accurately ascertain their relative contributions in a given case of developmental delay ( 50 ). We eagerly await a next generation of developmental-epidemiologic studies (some of which are currently under way) that will map the ways in which quantitative variations in fundamental domains of development (especially cognition, capacity for reciprocal social behavior, language, emotional regulation, sensorimotor function and interpersonal experience) interact with one another over the course of development from infancy to adulthood. It is possible that such studies will pave the way for a new system of characterizing all syndromes of developmental delay, along measurable quantitative axes, each of which might allow more precise associations with contributing biological mechanisms ( 37 ).

Abbreviations

attention-deficit hyperactivity disorder

autism spectrum disorder

Diagnostic and Statistical Manual of Mental Disorders 4th edition

pervasive developmental disorder-not otherwise specified

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John N Constantino: J.N.C. received royalties from Western Psychological Services for the commercial distribution of the Social Responsiveness Scale, which was originally developed to measure quantitative variation in autistic symptomatology in large research populations and for possible application in clinical, educational, and public health settings.

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Supported by a Grant from the National Institute of Child Health and Human Development (HD42541) [J.N.C.].

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Constantino, J. The Quantitative Nature of Autistic Social Impairment. Pediatr Res 69 , 55–62 (2011). https://doi.org/10.1203/PDR.0b013e318212ec6e

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DOI : https://doi.org/10.1203/PDR.0b013e318212ec6e

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Understanding autism: The path to diagnosis, awareness and support

Mayo Clinic Staff

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Diagnosing a person with autism spectrum disorder can be challenging. It's a medical condition that no blood test, brain scan or objective test can pinpoint. And because of each person's distinctive pattern of symptoms, it can be hard to determine its severity.

As people gain familiarity with autism, however, they are becoming more open to discussing the diagnosis and seeking treatment. Society is also becoming more motivated to learn about neurodivergent conditions, including autism.

What is autism spectrum disorder?

Autism spectrum disorder  is a condition related to brain development that affects how a person perceives and socializes with others, causing problems in social interaction and communication. It includes conditions that previously were considered separate, including autism, Asperger's syndrome, childhood disintegrative disorder and an unspecified form of pervasive developmental disorder.

Autism affects children and adults in three areas: communication, social interaction and behaviors.  Children with autism spectrum disorder  may struggle with recognizing their emotions and may feel them more intensely. Regulating their anger and frustration can be difficult and lead to intense bursts of emotions. Children with autism also have higher rates of anxiety and depression.

Each child with autism spectrum disorder is likely to have a distinctive pattern of behavior and level of severity. A healthcare professional will generally describe the severity of the condition based on the person's level of impairments and how those affect their ability to function.

A child or adult with autism spectrum disorder may have problems with social interaction and communication skills, including any of these signs:

• Can't start a conversation, keep one going or can only start one to make requests or label items.
• Doesn't appear to understand simple questions or directions.
• Doesn't express emotions or feelings and appears unaware of others' feelings.
• Doesn't speak or has delayed speech.
• Fails to respond to their name or appears not to hear you sometimes.
• Has difficulty recognizing nonverbal cues, such as interpreting other people's facial expressions, body postures or tone of voice.
• Has poor eye contact and lacks facial expression.
• Inappropriately approaches a social interaction by being passive, aggressive or disruptive.
• Prefers playing alone.
• Repeats words or phrases verbatim but doesn't understand how to use them.

Awareness of autism behaviors

According to the  Centers for Disease Control and Prevention (CDC) , the latest research from 2023 shows that 1 in 36 children was diagnosed with autism. This is an increase from 1 in 44 children just  two years ago .

Children tend to become more aware of their diagnosis around puberty. Kids recognize their differences from their peers and notice their struggle to fit in. They might notice they're not being invited to participate in certain activities or being accepted in the same way as many of their peers. Social interactions become more crucial for young people in middle and high school, which can be stressful for someone on the autism spectrum.

Parents may notice symptoms early on when they see how their child's behaviors, communication and social interactions differ from their peer group. It can be challenging for parents to accept that their child is different from other children. Parents may feel guilty and responsible, even though this developmental condition has no known cause.

Living with autism spectrum disorder

As the number of people living with autism spectrum disorder increases, it's critical to seek out educational opportunities that can help with understanding autism spectrum disorder. What are the strengths and disadvantages of the child? How can that knowledge be used to strengthen the skills of a child with autism? Answering these questions can help identify specific interventions to teach skills relevant to the child.

For example, if a child struggles with regulating emotions, this can be addressed through  treatment  to help them gain more control over their emotions and behaviors.

No cure exists for autism spectrum disorder, and there's no one-size-fits-all treatment. The goal of treatment is to maximize your child's ability to function by reducing their autism spectrum disorder symptoms and  supporting their development and learning . Early intervention during the preschool years is key.

Treatment options may include:

• Behavior and communication therapies
• Educational therapies
• Family therapies
• Medications

When you have a child or loved one with autism, the chance of them having anxiety or depression is increased. Evaluating and treating these symptoms can improve their level of functioning and their overall mental health.

One of the most critical things parents, friends or classmates of someone diagnosed with autism spectrum disorder can do is educate yourself about it while recognizing their strengths.

You can develop increased compassion for your loved ones, classmates, friends and colleagues by recognizing and understanding more about the condition. While you can't eliminate a child's autism or wait for them to outgrow it, you can minimize some of its symptoms and improve quality of life.

Janice Schreier  is a child and adolescent clinical therapist in  Psychiatry & Psychology  in  La Crosse , Wisconsin.

This article first appeared on the Mayo Clinic Health System blog .

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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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VUMC Seeking Clinicians who Currently Care for Adults with IDD for Project

Posted by stasikjs on Saturday, April 27, 2024 in Internships and Applications .

Are you a clinician caring for adults with intellectual or developmental disabilities (I/DD)? If yes, the Vanderbilt University Medical Center (VUMC) has an opportunity for you to enhance your skills and improve patient outcomes. 

Join experienced clinicians at VUMC who have developed an ECHO I/DD training program designed to foster an all-teach/all-learn environment. The program will consist of live video-conferencing sessions that meet twice a month for an hour for six months, starting in May 2024.

By participating in this program, you will not only enhance your skills but also  be compensated  for your valuable time. Moreover, the sessions will count for CME, adding to your professional development. The sessions will include managing co-occurring medical and psychiatric conditions, supporting adults with I/DD in the clinic, housing, and community support, supporting families/caregivers, post-secondary education and employment, and more. 

Wellpoint sponsors this project, and clinicians who accept Wellpoint insurance are eligible to participate. For more information, please  get in touch with  Dr. Beth Malow, the Vanderbilt site director, at  [email protected] ,  or see the flyer below. 

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