Sage Journals: Discover world-class research

Abstract

It is widely believed that early diagnosis and treatment of autism spectrum disorder is essential for better outcome. This is demonstrated by the American Academy of Pediatrics recommendation to screen all 1.5–2.5-year-old toddlers for autism spectrum disorder. However, multiple longitudinal studies of children diagnosed with autism spectrum disorder at 1.5–6 years of age and treated in community settings have not reported any associations between earlier diagnosis and improved outcome in core symptoms. Here, we quantified Longitudinal changes in core autism spectrum disorder symptoms of 131 children diagnosed at 1.2–5 years of age using the Autism Diagnostic Observation Schedule–Second Edition Calibrated Severity Scores over a 1-2 year period. We examined the prevalence and magnitude of Calibrated Severity Scores changes across children who were diagnosed at different ages. The results revealed that age of diagnosis was significantly correlated with poorer outcome (r(129) = 0.41, p < 0.001). Approximately 65% of the children diagnosed before 2.5 years of age exhibited improvements in Autism Diagnostic Observation Schedule–Second Edition Calibrated Severity Scores (⩾2 points) in contrast to only 23% of the children diagnosed after this age. Changes in younger children were driven by improvements in social symptoms despite deterioration in restricted and repetitive behaviors. These findings reveal that autism spectrum disorder diagnosis before the age of 2.5 is associated with considerable improvement in social symptoms. We suggest that greater brain plasticity and behavioral flexibility enable younger children to benefit more from autism spectrum disorder interventions even in community settings with heterogeneous services. This motivates further prioritization of early autism spectrum disorder screening as recommended by American Academy of Pediatrics guidelines.

Lay abstract

Keywords

autism spectrum disorder autism early diagnosis outcome severity

Introduction

Earlier diagnosis of autism spectrum disorder (ASD) enables earlier access to ASD-interventions, which are thought to improve outcome (Hyman et al., 2020; Zwaigenbaum, Bauman, Choueiri, et al., 2015; Zwaigenbaum, Bauman, Stone, et al., 2015). A variety of studies have demonstrated that children with ASD younger than 3 years of age exhibit improvements, mostly in adaptive behaviors and cognitive abilities, following targeted ASD-interventions including applied behavioral analysis (ABA) (Reichow et al., 2018; Remington et al., 2007; Zachor et al., 2007) and the Early Start Denver Model (ESDM) (Dawson et al., 2010; Estes et al., 2015; Fuller et al., 2020). However, unlike results from highly controlled ASD-intervention studies, mostly carried out with small groups in academic settings (Reichow, 2012), intervention services in most communities (i.e. “treatment as usual”) have been associated with considerably weaker gains. In fact, it is unclear whether children with ASD in the community benefit at all from earlier diagnosis and intervention (Nahmias et al., 2019).

Indeed, several longitudinal studies of large community samples, with children diagnosed at 1.5–6 years of age, have not reported any association between the age of diagnosis and outcome in core ASD symptoms (Georgiades et al., 2021; Gotham et al., 2012; Kim et al., 2016, 2018; Szatmari et al., 2015; Venker et al., 2014; Waizbard-Bartov et al., 2021). These studies utilized Autism Diagnostic Observation Scale–Second edition (ADOS-2) (Lord et al., 2012) Calibrated Severity Scores (CSS), which enable assessment of changes in core ASD symptoms over time, regardless of the child’s age or language abilities (Esler et al., 2015; Gotham et al., 2009; Hus et al., 2014). They demonstrated that ADOS-2 CSS of children can change over a 1–4-year period such that 8%–33% of the children improve, 8%–36% deteriorate, and 31%–88% remain stable (Gotham et al., 2012; Kim et al., 2016, 2018; Szatmari et al., 2015; Venker et al., 2014; Waizbard-Bartov et al., 2021). This heterogeneity in outcomes, however, was not associated with the age of ASD diagnosis in any of the studies. Note that most of these studies focused on identifying distinct developmental trajectories of ASD symptoms rather than specifically comparing changes in core symptom severity across children diagnosed at different ages.

The potential benefits of early ASD diagnosis and treatment are at the heart of an ongoing debate regarding the necessity of universal screening for ASD in 1.5–2.5-year-old children (Coury, 2015; Mandell & Mandy, 2015; Powell, 2016). While the American Academy of Pediatrics (AAP) recommends screening for ASD at 18 and 24 months of age (Hyman et al., 2020), a US preventive services task force statement concluded that there was insufficient evidence to assess the benefits or harms of screening for ASD at 18–30 months of age (Siu, 2016). Demonstrating significant benefits in the outcome of children with ASD who were diagnosed and treated before 2.5 years of age would provide further motivation for early screening. To determine whether children with ASD in a community setting benefit from earlier diagnosis, we followed the development of 131 children diagnosed at 1.2–5 years of age and examined whether children diagnosed earlier exhibited larger improvements in ADOS-2 CSS.

Methods

Setting and procedure

Families were recruited at the National Autism Research Center of Israel (NARCI) between 2017 and 2019. NARCI is located inside Soroka University Medical Center (SUMC) where approximately 150 children are diagnosed with ASD annually. SUMC is the only clinical center where children insured by the Clalit HMO (who cover 70% of the population in southern Israel) can receive an ASD diagnosis, thereby yielding a representative community sample of this geographical area (Dinstein et al., 2020; Meiri et al., 2017). ASD symptoms were assessed with the ADOS-2 (Lord et al., 2012) and cognitive abilities were assessed with the Bayley Scales of Infant Development–Third Edition (Viezel et al., 2014) or the Wechsler Preschool and Primary Scale of Intelligence (WPPSI) (Luiselli et al., 2013). All ADOS-2 assessments were administered according to guidelines, by the same trained clinician who has research reliability. Follow-up assessments were recommended to all families, 1–2 years after diagnosis, and were completed by approximately 40% of the children. This study included all children who completed a follow-up ADOS-2 assessment within the specified period. There was no community involvement in the design or interpretation of this study.

Participants

We examined data from 131 children with ASD who were 2.6 years old, on average, at diagnosis and 4.1 years old, on average, at follow-up (Table 1). We compared children who were diagnosed <2.5 years old (younger group) and those diagnosed ⩾2.5 years old (older group). This cutoff was selected because it corresponds to the upper age limit of early screening recommendations (Siu, 2016). All children fulfilled ADOS-2 and the Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5) criteria for ASD as determined by both a developmental psychologist and either a child psychiatrist or pediatric neurologist. The diagnostic procedure included four visits to SUMC including an initial intake meeting with a developmental psychologist or social worker, an ADOS-2 assessment performed by a speech therapist with research reliability, a cognitive assessment performed by a developmental psychologist, and a final diagnosis meeting with a physician. Parents of all children provided informed consent, and the study was approved by the SUMC Helsinki Committee.

Table 1.

Characteristics of the children who participated in this study.

	Diagnosed <2.5 years		Diagnosed ⩾2.5 years		Entire sample
Number of children	57		74		131
Age at diagnosis (years)	1.9 ± 0.3		3.1 ± 0.5		2.6 ± 0.7
Age at follow-up (years)	3.5 ± 0.8		4.5 ± 0.6		4.1 ± 0.9
Sex (male/female)	40/17		55/19		95/36
Maternal age at birth (years)	31.8 ± 6 (N = 51)		31.5 ± 5 (N = 68)		31.7 ± 5.4 (N = 119)
Maternal education (years)	13.3 ± 2.3 (N = 49)		13.4 ± 2.2 (N = 58)		13.3 ± 2.3 (N = 107)
Paternal age at birth (years)	36 ± 8.7 (N = 49)		35.4 ± 6.8 (N = 64)		35.6 ± 7.6 (N = 113)
Paternal education (years)	13 ± 2.1 (N = 45)		13.3 ± 2.8 (N = 56)		13.2 ± 2.2 (N = 101)
ADOS-2 SA CSS at diagnosis*	9.1 ± 1.1		7 ± 2		7.9 ± 2
ADOS-2 RRB CSS at diagnosis *	7.1 ± 1.3		7.8 ± 1.5		7.5 ± 1.5
ADOS-2 total CSS at diagnosis *	8.9 ± 1.2		7.1 ± 1.8		7.9 ± 1.8
Cognitive score at diagnosis	75.3 ± 13.6 (N = 48)		76.2 ± 14.5 (N = 52)		76 ± 14 (N = 100)
ADOS-2 modules (number)*	Diagnosis	Follow-up	Diagnosis	Follow-up	Diagnosis	Follow-up
Toddler Module	56	3	9	0	65	3
Module 1	1	37	50	36	51	73
Module 2	0	15	14	30	14	45
Module 3	0	2	1	8	1	10

ADOS-2: Autism Diagnostic Observation Schedule–Second Edition; SA: social affect; CSS: Calibrated Severity Scores; RRB: restricted and repetitive behaviors.

Left column: children <2.5 years old at diagnosis; middle column: children >2.5 years old at diagnosis; right column: entire group.

Asterisks mark variables where there was a statistically significant difference across age groups (two-tailed t-test for continuous variables and chi-squared tests for categorical variables, p < 0.05). The distribution of ADOS modules differed significantly across age groups both at diagnosis and at follow-up.

Longitudinal change in ADOS scores

ADOS-2 CSS enable comparison of ASD severity across time-points as children grow older and are evaluated with different ADOS modules (Esler et al., 2015; Gotham et al., 2009; Hus et al., 2014). We calculated the difference between the ADOS CSS at follow-up and diagnosis (i.e. Change = follow-up − diagnosis), such that negative values indicated improvement (i.e. decrease in symptom severity). Following a similar recent study (Waizbard-Bartov et al., 2021), we classified children with changes greater than −/+2 ADOS-2 CSS points into groups that improved/deteriorated in symptom severity, respectively. This change threshold was selected because it corresponds to approximately one standard deviation of the ADOS-2 CSS in our entire sample at diagnosis (Table 1) and in samples reported by other studies (Szatmari et al., 2015; Venker et al., 2014; Waizbard-Bartov et al., 2021). We also performed analogous calculations separately for the social affect (SA) and the restricted and repetitive behaviors (RRB) domains using their respective CSS scales (Esler et al., 2015; Hus et al., 2014).

Baseline measures, intensity of intervention, and type of educational framework

Scores from cognitive assessments at diagnosis were available for 100 of the children (Table 1). In the remaining cases, children did not complete cognitive testing due to low cooperation with the clinician or missed appointments. Information regarding parental education and age was available for more than 100 of the children (Table 1). Parents of 78 children (34 children in the younger group and 44 children in the older group) completed a follow-up questionnaire within 3 months of the follow-up ADOS-2 assessment. Parents reported the average number of weekly treatment hours that the child received since their diagnosis including speech therapy, psychological treatments, occupational therapy, and developmental or behavioral interventions. In addition, parents indicated whether their child was in a special or mainstream/inclusive education setting. There were no significant differences in the ADOS-2 CSS longitudinal changes of children for whom data were missing and those for whom the data was complete (Table S1). Data regarding socioeconomic status and ethnic origin were not recorded.

Statistical analyses

Statistical analyses were performed using SPSS (Version 26.0) and Rstudio (Version 1.1.463). Comparisons of continuous variables across groups were performed with two-sample, two-tailed, t-tests, assuming equal variance across groups. We used Levene’s test to identify groups with unequal variance and in these cases performed t-tests assuming unequal variance. We calculated Cohen’s d to assess effect sizes and Pearson’s correlation coefficients to assess the relationships between continuous variables. Chi-square analyses were used to compare proportions across groups. We performed an analysis of covariance (ANCOVA) analysis to assess whether ADOS-2 SA CSS changes differed significantly across children diagnosed early and late, while controlling for their sex, initial cognitive scores, initial ADOS-2 SA CSS, and length of time between diagnosis and follow-up. This analysis was performed with 100 children for whom all data were available. Statistical significance was set to a p-value of 0.05 for all tests.

Results

The age of diagnosis was significantly correlated with longitudinal change in ADOS-2 CSS (r(129) = 0.41, p < 0.001, Figure 1) such that children diagnosed earlier exhibited larger improvements over time (i.e. more negative ADOS-2 CSS changes). Separating the ADOS-2 CSS into its SA and RRB components revealed a clear dissociation such that children diagnosed earlier exhibited significantly larger improvements in ADOS-2 SA CSS (r(129) = 0.44, p < 0.001), and also exhibited a trend for larger deterioration in ADOS-2 RRB CSS (r(129) = −0.12, p = 0.18).

Figure 1.

Longitudinal changes in ADOS-2 CSS versus age of diagnosis. Scatter plots presenting longitudinal change in ADOS-2 CSS versus age of diagnosis. Negative changes indicate improvement in ASD severity over time. (a) Change in total ADOS-2 CSS. (b) Change in ADOS-2 SA CSS. (c) Change in ADOS-2 RRB CSS. Each point represents a single child. Black: children diagnosed <2.5 years of age. Gray: children diagnosed >2.5 years old. Line: least squares fit. Asterisk: significant Pearson’s correlation (p < 0.05).

To further assess the importance of early diagnosis, we separated children into those diagnosed before 2.5 years-old (younger group) and after (older group). Most baseline measures were similar across the two groups (Table 1). There were no significant between-group differences in parental age at birth (t < 0.46, p > 0.73), parental education levels (t > −0.62, p > 0.53), or sex (χ²(1, N = 131) = 0.11, p = 0.74). There were also no significant differences in cognitive scores (t₍₉₈₎ = −0.3, p = 0.77) or in the amount of children who completed cognitive testing (χ²(1, N = 131) = 2.74, p = 0.09). Total ADOS-2 CSS, however, were significantly higher in the younger group (t_(126.4) = 6.68, p < 0.001, d = 1.12), as were the ADOS-2 SA CSS (t₍₁₁₄₎ = 7.79, p < 0.001, d = 1.27). In contrast, ADOS-2 RRB CSS were significantly lower in the younger group (t₍₁₂₉₎ = −2.66, p = 0.01, d = −0.47). Hence, younger children exhibited more severe SA symptoms and less severe RRB symptoms at diagnosis.

Changes in ADOS-2 CSS over time

We quantified the prevalence and magnitude of longitudinal ADOS-2 CSS changes in each of the groups (Figures 2 and 3). The percentage of children who exhibited improvements (i.e. ADOS-2 CSS decreased by ⩾2 points) was significantly higher in the younger group (65%) compared to the older group (23%, χ²(1, N = 131) = 21.7, p < 0.001). ADOS-2 CSS improvement was significantly larger in the younger group (M = −2.3, standard deviation (SD) = 2.2) compared to the older group (M = −0.07, SD = 1.9, t₍₁₂₉₎ = −6.26, p < 0.001, d = −1.1). The younger group improved in SA CSS (M = −2.95, SD = 2.1) significantly more than the older group (M = −0.19, SD = 1.8; t₍₁₂₉₎ = −8.01, p < 0.001, d = −1.4), but deteriorated in RRB CSS (M = 0.63, SD = 1.93) significantly more than the older group (M = −0.16, SD = 2; t₍₁₂₉₎ = 2.2, p = 0.03, d = 0.4).

Figure 2.

Individual changes in ADOS CSS between diagnosis and follow-up. Scatter plots demonstrate the change in ADOS-2 CSS of individual children between diagnosis and follow-up assessments. Top row: ADOS-2 SA CSS. Middle row: ADOS-2 RRB CSS. Bottom row: total ADOS-2 CSS. Left column: children diagnosed <2.5 years old. Right column: children diagnosed >2.5 years old. Diagonal lines: upper and lower boundaries of considerable change across assessments (+/− 2 points on the ADOS-2 CSS). Children located between the lines remained static (i.e. changed <2 points) while children above the top line deteriorated considerably and children below the bottom line improved considerably. Each point represents a single child.

Figure 3.

Magnitude of changes in ADOS CSS. Comparison of the magnitude of ADOS-2 CSS changes across age groups. (a) Results from the entire sample. (b) Results from subgroups of children matched on sex and initial ADOS-2 severity. (c) Females only. (d) Males only. Each panel contain the comparisons of the total ADOS CSS (left), RRB CSS (middle), and SA CSS (right). Black: children diagnosed <2.5 years old; gray: children diagnosed >2.5 years old; asterisk: significant difference across groups (two-tailed t-test, p < 0.05).

Comparison of matched subgroups

To ensure that our results were not due to differences in ASD severity at diagnosis, we analyzed a subset of 36 children from each group who were matched on sex and had identical ADOS-2 CSS at diagnosis. This revealed equivalent results to those reported above (Figure 3). Improvement in total ADOS-2 CSS was significantly larger in the younger group (t₍₇₀₎ = −2.82, p = 0.006, d = −0.66) and was driven by a significantly larger improvement in SA CSS (t₍₇₀₎ = −4.8, p < 0.001, d = −1.13), despite a significantly larger deterioration in RRB CSS (t₍₇₀₎ = 2.58, p = 0.01, d = 0.6).

Comparison across boys and girls

Separating male and female children yielded equivalent results (Figure 3). Improvement in total ADOS-2 CSS was significantly larger in the younger group in males (t₍₉₃₎ = −4.48, p < 0.001, d = −0.93) and females (t₍₃₄₎ = −4.57, p < 0.001, d = −1.53). This was driven by significantly larger improvements in SA CSS of males (t₍₉₃₎ = −5.99, p < 0.001, d = −1.24) and females (t₍₃₄₎ = −5.47, p < 0.001, d = −1.82), despite a non-significant trend for larger deterioration in RRB CSS of males (t₍₉₃₎ = 1.91, p = 0.06) and females (t₍₃₄₎ = 1.11, p = 0.27).

Initial symptom severity

Significant negative correlations were apparent between the ADOS-2 CSS at diagnosis and their change over time (Figure 4) in both the younger (r(55) = −0.38, p = 0.003) and older (r(72) = −0.44, p < 0.001) groups. This indicated that children with more severe initial symptoms improved to a larger extent in both groups. In contrast, initial cognitive scores were not significantly correlated with changes in ADOS-2 CSS in the younger (r(46) = −0.03, p = 0.82) or older (r(50) = 0.01, p = 0.95) groups.

Figure 4.

Predictors of change in ADOS CSS. (a) Scatter plots demonstrating the relationship between initial ADOS CSS and change in ADOS CSS for the younger (left) and older (right) groups. (b) Scatter plots demonstrating the relationship between initial cognitive scores and change in ADOS CSS for the younger (left) and older (right) groups. Pearson’s correlation coefficients are noted in each panel. Line: least squares fit; asterisk: significant correlation (p < 0.05).

Parental characteristics and time across assessments

We did not find any significant correlations between the magnitudes of change in ADOS-2 CSS and parental characteristics in either group, including maternal (r < 0.25, p > 0.07) and paternal (r > −0.16, p > 0.26) age at birth or maternal (r < 0.14, p > 0.35) and paternal (r < −0.09, p > 0.5) education. Magnitudes of change in ADOS-2 CSS were also not correlated with the amount of time between ADOS-2 assessments in either group (r < 0.12, p > 0.3).

Treatment intensity and type of educational setting

There was no significant difference between the number of parent-reported weekly treatment hours (i.e. treatment intensity) in the younger (M = 3.5, SD = 3.5) and older group (M = 2.78, SD = 2.16, t_(52.71) = 1.08, p = 0.28). Furthermore, the percentage of younger children who were placed in special versus inclusive education settings (76% vs 24%, respectively) was nearly identical to that of the older children (77% vs 23%, χ²(1, N = 78) = 0, p = 1).

Combined analysis

In a final ANCOVA analysis, we tested whether longitudinal changes in ADOS-2 SA CSS differed across the two age groups when accounting for the children’s sex, cognitive score at diagnosis, ADOS-2 SA CSS at diagnosis, and the length of time between diagnosis and follow-up. This analysis demonstrated that significant differences between children diagnosed early and late (F_(1,94) = 18.52, p < 0.001, $η_{p}^{2} = 0.165$ ) were apparent even when accounting for these covariates. The magnitude of longitudinal ADOS-2 SA CSS change, as estimated by this statistical model, was almost four times larger in the younger group (M = −2.6, standard error (SE) = 0.3) than the older group (M = −0.7, SE = 0.3), demonstrating the benefit of early diagnosis and treatment regardless of the children’s initial ADOS-2 or cognitive scores.

Discussion

Our results demonstrate that children with ASD who were diagnosed earlier exhibited a larger reduction in the severity of social ASD symptoms within 1–2 years. Specifically, children diagnosed before 2.5 years of age were nearly three times more likely to exhibit considerable reductions in the severity of social symptoms as compared with children diagnosed at older ages. Equivalent results were evident when examining boys and girls, suggesting that boys and girls benefit similarly from early diagnosis. Furthermore, equivalent results were evident when accounting for initial ASD severity and cognitive abilities using an ANCOVA analysis and when comparing subgroups of children who were strictly matched on initial ASD severity and sex.

These findings are particularly encouraging given that participating children received heterogeneous clinical and educational services (i.e. “treatment as usual”) that were available in their community. To the best of our knowledge, children diagnosed at younger ages did not receive different services from those diagnosed at older ages. Both age groups exhibited equivalent placement ratios in special versus inclusive educational settings, and there was no significant difference in the number of treatment hours per week reported by parents. Taken together, these results reveal that early ASD diagnosis is associated with better outcome, even in a community setting where children receive heterogeneous services. Hence, these findings strengthen the motivation to screen for ASD at 18–30 months of age in order to reduce the mean age of ASD diagnosis (e.g. Guthrie et al., 2019), which often occurs after 4 years of age (Constantino et al., 2020; Kerub et al., 2020; Sheldrick et al., 2017).

Early diagnosis, early intervention, and improved outcome

Previous studies, typically performed in university settings, have reported that early intervention with structured programs such as ABA (Remington et al., 2007; Zachor et al., 2007) or ESDM (Dawson et al., 2010; Estes et al., 2015) are effective in improving social functioning, cognitive abilities, and adaptive behaviors of children with ASD. Clinical and educational care in the community, however, tends to be less structured and more variable, yielding poorer improvements over time (Nahmias et al., 2019).

Several large longitudinal studies have reported that between the ages of 1.5 and 6 years, only 8%–33% of children with ASD exhibit improvements in ADOS-2 CSS while the vast majority remain stable or deteriorate over time (Gotham et al., 2012; Kim et al., 2016, 2018; Szatmari et al., 2015; Venker et al., 2014; Waizbard-Bartov et al., 2021). Most importantly, these studies did not report any significant relationship between the magnitude of improvement in core ASD symptoms and the age of diagnosis. Our results were in line with some of these studies in demonstrating relatively modest rates of improvement in ADOS-2 CSS scores of children diagnosed after 2.5 years of age (i.e. 23%). However, in our sample, improvement in ADOS-2 CSS of children diagnosed before 2.5 years of age was almost three times as prevalent (i.e. 65%).

We believe that this considerable difference in the outcome of children diagnosed early was revealed in our study for two main reasons. First, we examined children who were diagnosed at earlier ages than those included in most previous studies. Only two previous studies have assessed changes in ADOS CSS of children diagnosed before the age of 2 years old (Kim et al., 2016, 2018). Second, this study specifically focused on the relationship between age of diagnosis and changes in symptom severity while all previous studies focused on the identification of distinct sub-types of ASD developmental trajectories. All of the studies noted above performed clustering analyses to identify sub-groups children with distinct initial ASD characteristics (e.g. Kim et al., 2016), or distinct changes in symptoms over time (e.g. Szatmari et al., 2015). In contrast, in our study, we specifically compared outcome across children diagnosed before and after 2.5 years of age.

Furthermore, our analyses revealed that the substantial improvement in ADOS-2 CSS of younger children was driven by even larger improvements in their SA CSS, despite deterioration in their RRB CSS (Figure 3). It has been reported that SA symptoms are easier to identify at early ages in contrast to RRB symptoms (MacDonald et al., 2007; Mishaal et al., 2014; Mooney et al., 2006). In addition, most early intervention protocols focus on improving SA impairments rather than RRB symptoms (Brian et al., 2017; Dawson et al., 2010; Harrop et al., 2017). We suggest that easier identification of SA symptoms and a stronger intervention focus on these symptoms may explain the substantial improvement in their severity despite a deterioration in RRB symptoms in younger children. These findings are in line with studies demonstrating that SA and RRB symptoms change independently over time (Fountain et al., 2012; Harrop et al., 2014) and motivate the development of early interventions that address RRB symptoms and effectively decrease their severity.

Treatment as usual in Israel

In Israel, parents can place children with ASD either in special education classes, which include eight ASD children who are cared for by a relatively large professional staff, or in inclusive education, where the child is accompanied by an aid to classes with up to 30 typically developing children. Eclectic combinations of ABA (Reichow, 2012; Zachor et al., 2007), Floortime/DIR (Mercer, 2017), NDBA (Schreibman et al., 2015), and TEACCH (Virues-Ortega et al., 2013) techniques are implemented in the majority of special education classes and in a minority of inclusive education classes. In addition, all children with ASD are eligible for 3 weekly hours of government-funded therapy from a speech therapist, occupational therapist, psychologist, and/or physical therapist. This demonstrates the large heterogeneity of clinical and educational services that children with ASD receive in most Western countries (Nahmias et al., 2019). The main contribution of this study is in quantifying the benefit of early diagnosis apparent despite this heterogeneity.

Brain plasticity and maturation

In line with previous proposals (Dawson, 2008), we believe that the benefit of early ASD diagnosis is due to the application of targeted ASD-interventions during early periods of development where brain plasticity and behavioral flexibility are at their peak. Early developmental periods with extensive exploration, behavioral flexibility, and remarkable capacity for learning are followed by maturation, which reduces plasticity and flexibility in order to solidify knowledge (e.g. language in humans (Gervain, 2015) or singing in birds (London, 2019)). Our results suggest that intervening before 2.5 years of age is important for taking advantage of early flexibility in the development of social capabilities.

An interesting alternative explanation is that better outcome in younger children may be due to natural maturation (i.e. spontaneous improvements) rather than early intervention. We cannot rule out this explanation because we did not measure longitudinal changes in children who were diagnosed early and did not receive any intervention. Nevertheless, we believe that this alternative explanation is unlikely for two reasons. First, numerous studies have shown that early interventions, administered at 18–30 months of age, are effective in improving social symptoms, adaptive behaviors, and cognitive abilities of children with ASD (Zwaigenbaum, Bauman, Choueiri, et al., 2015). This has motivated governments to establish early ASD-intervention services at considerable cost (Manning et al., 2011; Piccininni et al., 2017). Second, studies of infants at high risk of developing ASD have demonstrated that most children who develop ASD exhibit continuous decline relative to typically developing peers during their second year of life (Landa & Garrett-Mayer, 2006; Ozonoff et al., 2016). Hence, spontaneous improvements without intervention are unlikely.

Limitations

This study had several limitations. First, we assessed changes in outcome using only the ADOS-2 CSS, which involves a limited clinical observation. Corroborating the clinical impression with parent and teacher reports may further strengthen the reported findings. Second, in addition to the ADOS-2 assessments, a more comprehensive evaluation of outcome should include additional measures of cognitive abilities, language abilities, and adaptive behaviors, which are known to improve significantly following early intervention (Dawson et al., 2010; Estes et al., 2015; Zachor et al., 2007) and were not included in this study. Third, we estimated the intensity of early intervention services with parent reports that did not address the type or quality of intervention, which are likely to explain additional variability in longitudinal ADOS-2 CSS changes. While parents of younger children did not report that their children received more services, we acknowledge that community services may be more readily allocated to children with more severe symptoms as reported by some studies (Kim et al., 2016). Finally, since the selection of appropriate ADOS-2 module is dependent on the age and language abilities of the child, we acknowledge that children diagnosed before 2.5 years of age were assessed with distinct ADOS-2 modules than those used with the children who were diagnosed at later ages. Hence, the conclusions of this study are reliant on the accuracy of the calibrated severity scores that have been developed to enable comparisons across modules (Esler et al., 2015; Gotham et al., 2009; Hus et al., 2014).

Conclusion

This study demonstrates that diagnosis of ASD before 2.5 years of age is associated with considerable benefits for children diagnosed within a community setting. While reliable ASD diagnoses can be performed during the second year of life (Steiner et al., 2012), most diagnoses are currently performed after the age of four (Constantino et al., 2020; Kerub et al., 2020; Sheldrick et al., 2017). This study offers strong evidence in support of calls to reduce the age of ASD diagnosis and intervention in the community as an imperative initial step for improving clinical outcomes in children with ASD (Klin et al., 2020).

Supplemental Material

sj-docx-1-aut-10.1177_13623613211049011 – Supplemental material for Early diagnosis of autism in the community is associated with marked improvement in social symptoms within 1–2 years

Supplemental material, sj-docx-1-aut-10.1177_13623613211049011 for Early diagnosis of autism in the community is associated with marked improvement in social symptoms within 1–2 years by Nitzan Gabbay-Dizdar, Michal Ilan, Gal Meiri, Michal Faroy, Analya Michaelovski, Hagit Flusser, Idan Menashe, Judah Koller, Ditza A Zachor and Ilan Dinstein in Autism

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was made possible by a National Autism Knowledge Center grant from the Israeli Ministry of Science and Technology awarded to I.D., I.M., and G.M.

ORCID iDs

Nitzan Gabbay-Dizdar

Gal Meiri

Idan Menashe

Judah Koller

Ditza A Zachor

Supplemental material

Supplemental material for this article is available online.

References

Brian

J. A.

Smith

I. M.

Zwaigenbaum

Bryson

S. E.

(2017). Cross-site randomized control trial of the Social ABCs caregiver-mediated intervention for toddlers with autism spectrum disorder. Autism Research, 10(10), 1700–1711. https://doi.org/10.1002/aur.1818

Constantino

J. N.

Abbacchi

A. M.

Saulnier

Klaiman

Mandell

D. S.

Zhang

Hawks

Bates

Klin

Shattuck

Molholm

Fitzgerald

Roux

Lowe

J. K.

Geschwind

D. H.

(2020). Timing of the diagnosis of autism in African American children. Pediatrics, 146(3), e20193629. https://doi.org/10.1542/PEDS.2019-3629

Coury

D. L.

(2015). Babies, bathwater, and screening for autism spectrum disorder. Journal of Developmental & Behavioral Pediatrics, 36(9), 661–663. https://doi.org/10.1097/DBP.0000000000000227

Dawson

(2008). Early behavioral intervention, brain plasticity, and the prevention of autism spectrum disorder. Development and Psychopathology, 20(3), 775–803. https://doi.org/10.1017/S0954579408000370

Dawson

Rogers

Munson

Smith

Winter

Greenson

Donaldson

Varley

(2010). Randomized, controlled trial of an intervention for toddlers with autism: The early start Denver model. Pediatrics, 125(1), e17–e23. https://doi.org/10.1542/peds.2009-0958

Dinstein

Arazi

Golan

H. M.

Koller

Elliott

Gozes

Shulman

Shifman

Raz

Davidovitch

Gev

Aran

Stolar

Ben-Itzchak

Snir

I. M.

Israel-Yaacov

Bauminger-Zviely

Bonneh

Y. S.

Gal

, et al. (2020). The national autism database of Israel: A resource for studying autism risk factors, biomarkers, outcome measures, and treatment efficacy. Journal of Molecular Neuroscience, 70(9), 1303–1312. https://doi.org/10.1007/s12031-020-01671-z

Esler

A. N.

Bal

V. H.

Guthrie

Wetherby

Weismer

S. E.

Lord

(2015). The autism diagnostic observation schedule, toddler module: Standardized severity scores. Journal of Autism and Developmental Disorders, 45(9), 2704–2720. https://doi.org/10.1007/s10803-015-2432-7

Estes

Munson

Rogers

S. J.

Greenson

Winter

Dawson

(2015). Long-term outcomes of early intervention in 6-year-old children with autism spectrum disorder. Journal of the American Academy of Child and Adolescent Psychiatry, 54(7), 580–587. https://doi.org/10.1016/j.jaac.2015.04.005

Fountain

Winter

A. S.

Bearman

P. S.

(2012). Six developmental trajectories characterize children with autism. Pediatrics, 129(5), e1112–e1120. https://doi.org/10.1542/peds.2011-1601

10.

Fuller

E. A.

Oliver

Vejnoska

S. F.

Rogers

S. J.

(2020). The effects of the early start Denver model for children with autism spectrum disorder: A meta-analysis. Brain Sciences, 10(6), 368. https://doi.org/10.3390/brainsci10060368

11.

Georgiades

Tait

P. A.

McNicholas

P. D.

Duku

Zwaigenbaum

Smith

I. M.

Bennett

Elsabbagh

Kerns

C. M.

Mirenda

Ungar

W. J.

Vaillancourt

Volden

Waddell

Zaidman-Zait

Gentles

Szatmari

(2021). Trajectories of symptom severity in children with autism: Variability and turning points through the transition to school. Journal of Autism and Developmental Disorders. Advance online publication. https://doi.org/10.1007/s10803-021-04949-2

12.

Gervain

(2015). Plasticity in early language acquisition: The effects of prenatal and early childhood experience. Current Opinion in Neurobiology, 35, 13–20. https://doi.org/10.1016/j.conb.2015.05.004

13.

Gotham

Pickles

Lord

(2009). Standardizing ADOS scores for a measure of severity in autism spectrum disorders. Journal of Autism and Developmental Disorders, 39(5), 693–705. https://doi.org/10.1007/s10803-008-0674-3

14.

Gotham

Pickles

Lord

(2012). Trajectories of autism severity in children using standardized ADOS scores. Pediatrics, 130, e1278–e1284. https://doi.org/10.1542/peds.2011-3668

15.

Guthrie

Wallis

Bennett

Brooks

Dudley

Gerdes

Pandey

Levy

S. E.

Schultz

R. T.

Miller

J. S.

(2019). Accuracy of autism screening in a large pediatric network. Pediatrics, 144(4), e20183963. https://doi.org/10.1542/peds.2018-3963

16.

Harrop

Gulsrud

Shih

Hovsepyan

Kasari

(2017). The impact of caregiver-mediated JASPER on child restricted and repetitive behaviors and caregiver responses. Autism Research, 10, 983–992. https://doi.org/10.1002/aur.1732

17.

Harrop

McConachie

Emsley

Leadbitter

Green

(2014). Restricted and repetitive behaviors in autism spectrum disorders and typical development: Cross-sectional and longitudinal comparisons. Journal of Autism and Developmental Disorders, 44(5), 1207–1219. https://doi.org/10.1007/s10803-013-1986-5

18.

Hus

Gotham

Lord

(2014). Standardizing ADOS domain scores: Separating severity of social affect and restricted and repetitive behaviors. Journal of Autism and Developmental Disorders, 44(10), 2400–2412. https://doi.org/10.1007/s10803-012-1719-1

19.

Hyman

S. L.

Levy

S. E.

Myers

S. M.

(2020). Identification, evaluation, and management of children with autism spectrum disorder. Pediatrics, 145(1), e20193447. https://doi.org/10.1542/peds.2019-3447

20.

Kerub

Haas

E. J.

Meiri

Bilenko

Flusser

Michaelovski

Dinstein

Davidovitch

Menashe

(2020). Ethnic disparities in the diagnosis of autism in Southern Israel. Autism Research, 14, 193–201. https://doi.org/10.1002/aur.2421

21.

Kim

S. H.

Bal

V. H.

Benrey

Choi

Y. B.

Guthrie

Colombi

Lord

(2018). Variability in autism symptom trajectories using repeated observations from 14 to 36 months of age. Journal of the American Academy of Child and Adolescent Psychiatry, 57(11), 837–848.e2. https://doi.org/10.1016/j.jaac.2018.05.026

22.

Kim

S. H.

Macari

Koller

Chawarska

(2016). Examining the phenotypic heterogeneity of early autism spectrum disorder: Subtypes and short-term outcomes. Journal of Child Psychology and Psychiatry and Allied Disciplines, 57(1), 93–102. https://doi.org/10.1111/jcpp.12448

23.

Klin

Micheletti

Klaiman

Shultz

Constantino

J. N.

Jones

(2020). Affording autism an early brain development re-definition. Development and Psychopathology, 32, 1175–1189. https://doi.org/10.1017/S0954579420000802

24.

Landa

Garrett-Mayer

(2006). Development in infants with autism spectrum disorders: A prospective study. Journal of Child Psychology and Psychiatry and Allied Disciplines, 47(6), 629–638. https://doi.org/10.1111/j.1469-7610.2006.01531.x

25.

London

S. E.

(2019). Developmental song learning as a model to understand neural mechanisms that limit and promote the ability to learn. Behavioural Processes, 163, 13–23. https://doi.org/10.1016/j.beproc.2017.11.008

26.

Lord

Rutter

Di Lavore

Risi

Gotham

Bishop

(2012). Autism Diagnostic Observation Schedule, Second Edition (ADOS-2) manual (part I): Modules 1-4. Western Psychological Services.

27.

Luiselli

Happé

Hurst

Freeman

Goldstein

Mazefsky

Carter

A. S.

Kaufman

A. S.

Simmons

E. S.

Eernisse

E. R.

Tsatsanis

Fluit

Klein-Tasman

Feinstein

Kahl

Poyau

Jones

C. R. G.

Volkmar

F. R.

Wolf

J. M.

, et al. (2013). Wechsler preschool and primary scale of intelligence. In Encyclopedia of autism spectrum disorders. https://doi.org/10.1007/978-1-4419-1698-3_866

28.

MacDonald

Green

Mansfield

Geckeler

Gardenier

Anderson

Holcomb

Sanchez

(2007). Stereotypy in young children with autism and typically developing children. Research in Developmental Disabilities, 28(3), 266–277. https://doi.org/10.1016/j.ridd.2006.01.004

29.

Mandell

Mandy

(2015). Should all young children be screened for autism spectrum disorder? Autism, 19(8), 895–896. https://doi.org/10.1177/1362361315608323

30.

Manning

S. E.

Davin

C. A.

Barfield

W. D.

Kotelchuck

Clements

Diop

Osbahr

Smith

L. A.

(2011). Early diagnoses of autism spectrum disorders in Massachusetts birth cohorts, 2001-2005. Pediatrics, 127(6), 1043–1051. https://doi.org/10.1542/peds.2010-2943

31.

Meiri

Dinstein

Michaelowski

Flusser

Ilan

Faroy

Bar-Sinai

Manelis

Stolowicz

Yosef

L. L.

Davidovitch

Golan

Arbelle

Menashe

(2017). The Negev Hospital-University-Based (HUB) autism database. Journal of Autism and Developmental Disorders, 47, 2918–2926. https://doi.org/10.1007/s10803-017-3207-0

32.

Mercer

(2017). Examining DIR/FloortimeTM as a treatment for children with autism spectrum disorders. Research on Social Work Practice, 27(5), 625–635. https://doi.org/10.1177/1049731515583062

33.

Mishaal

R. A.

Ben-Itzchak

Zachor

D. A.

(2014). Age of autism spectrum disorder diagnosis is associated with child’s variables and parental experience. Research in Autism Spectrum Disorders, 8(7), 873–880. https://doi.org/10.1016/j.rasd.2014.04.001

34.

Mooney

E. L.

Gray

K. M.

Tonge

B. J.

(2006). Early features of autism: Repetitive behaviours in young children. European Child & Adolescent Psychiatry, 15(1), 12–18. https://doi.org/10.1007/s00787-006-0499-6

35.

Nahmias

A. S.

Pellecchia

Stahmer

A. C.

Mandell

D. S.

(2019). Effectiveness of community-based early intervention for children with autism spectrum disorder: A meta-analysis. Journal of Child Psychology and Psychiatry, 60(11), 1200–1209. https://doi.org/10.1111/jcpp.13073

36.

Ozonoff

Young

G. S.

Landa

R. J.

Brian

Bryson

Charman

Chawarska

Macari

S. L.

Messinger

Wendy

Zwaigenbaum

Iosif

(2016). Diagnostic stability in young children at risk for autism spectrum disorder: A Baby Siblings Research Consortium Study. Journal of Child Psychology and Psychiatry, 56(9), 988–998. https://doi.org/10.1111/jcpp.12421

37.

Piccininni

Bisnaire

Penner

(2017). Cost-effectiveness of wait time reduction for intensive behavioral intervention services in Ontario, Canada. JAMA Pediatrics, 171(1), 23–30. https://doi.org/10.1001/jamapediatrics.2016.2695

38.

Powell

C. M.

(2016). Autism screening or smoke screen and mirrors? JAMA Neurology, 73(4), 386–387. https://doi.org/10.1001/jamaneurol.2016.0126

39.

Reichow

(2012). Overview of meta-analyses on early intensive behavioral intervention for young children with autism spectrum disorders. Journal of Autism and Developmental Disorders, 42(4), 512–520. https://doi.org/10.1007/s10803-011-1218-9

40.

Reichow

Hume

Barton

E. E.

Boyd

B. A.

(2018). Early intensive behavioral intervention (EIBI) for young children with autism spectrum disorders (ASD). Cochrane Database of Systematic Reviews, 5(5), CD009260. https://doi.org/10.1002/14651858.CD009260.pub3

41.

Remington

Hastings

R. P.

Kovshoff

Espinosa

F. D.

Jahr

Brown

Alsford

Lemaic

Ward

(2007). Early intensive behavioral intervention: Outcomes for children with autism and their parents after two years. American Journal on Mental Retardation, 112(6), 418–438. https://doi.org/10.1352/0895-8017(2007)112[418:EIBIOF]2.0.CO;2

42.

Schreibman

Dawson

Stahmer

A. C.

Landa

Rogers

S. J.

McGee

G. G.

Kasari

Ingersoll

Kaiser

A. P.

Bruinsma

McNerney

Wetherby

Halladay

(2015). Naturalistic developmental behavioral interventions: Empirically validated treatments for autism spectrum disorder. Journal of Autism and Developmental Disorders, 45(8), 2411–2428. https://doi.org/10.1007/s10803-015-2407-8

43.

Sheldrick

R. C.

Maye

M. P.

Carter

A. S.

(2017). Age at first identification of autism spectrum disorder: An analysis of two U.S. surveys. Journal of the American Academy of Child and Adolescent Psychiatry, 56(4), 313–320. https://doi.org/10.1016/j.jaac.2017.01.012

44.

Siu

A. L.

(2016). Screening for autism spectrum disorder in young children U.S. preventive services task force recommendation statement. Journal of the American Medical Association, 315(7), 691–696. https://doi.org/10.1001/jama.2016.0018

45.

Steiner

A. M.

Goldsmith

T. R.

Snow

A. V.

Chawarska

(2012). Practitioner’s guide to assessment of autism spectrum disorders in infants and toddlers. Journal of Autism and Developmental Disorders, 42, 1183–1196. https://doi.org/10.1007/s10803-011-1376-9

46.

Szatmari

Georgiades

Duku

Bennett

T. A.

Bryson

Fombonne

Mirenda

Roberts

Smith

I. M.

Vaillancourt

Volden

Waddell

Zwaigenbaum

Elsabbagh

(2015). Developmental trajectories of symptom severity and adaptive functioning in an inception cohort of preschool children with autism spectrum disorder. JAMA Psychiatry, 72, 276–283.

47.

Venker

C. E.

Ray-Subramanian

C. E.

Bolt

D. M.

Weismer

S. E.

(2014). Trajectories of autism severity in early childhood. Journal of Autism and Developmental Disorders, 44, 546–563. https://doi.org/10.1007/s10803-013-1903-y

48.

Viezel

Zibulsky

Dumont

Willis

J. O.

(2014). Bayley scales of infant and toddler development, third edition. In Encyclopedia of special education. https://doi.org/10.1002/9781118660584.ese0278

49.

Virues-Ortega

Julio

F. M.

Pastor-Barriuso

(2013). The TEACCH program for children and adults with autism: A meta-analysis of intervention studies. Clinical Psychology Review, 33(8), 940–953. https://doi.org/10.1016/j.cpr.2013.07.005

50.

Waizbard-Bartov

Ferrer

Young

G. S.

Heath

Rogers

Wu Nordahl

Solomon

Amaral

D. G.

(2021). Trajectories of autism symptom severity change during early childhood. Journal of Autism and Developmental Disorders, 51, 227–242. https://doi.org/10.1007/s10803-020-04526-z

51.

Zachor

D. A.

Ben-Itzchak

Rabinovich

A. L.

Lahat

(2007). Change in autism core symptoms with intervention. Research in Autism Spectrum Disorders, 1(4), 304–317. https://doi.org/10.1016/j.rasd.2006.12.001

52.

Zwaigenbaum

Bauman

M. L.

Choueiri

Kasari

Carter

Granpeesheh

Mailloux

Smith Roley

Wagner

Fein

Pierce

Buie

Davis

P. A.

Newschaffer

Robins

Wetherby

Stone

W. L.

Yirmiya

Estes

, et al. (2015). Early intervention for children with autism spectrum disorder under 3 years of age: Recommendations for practice and research. Pediatrics, 136(Suppl. 1), S60–S81. https://doi.org/10.1542/peds.2014-3667E

53.

Zwaigenbaum

Bauman

M. L.

Stone

W. L.

Yirmiya

Estes

Hansen

R. L.

McPartland

J. C.

Natowicz

M. R.

Choueiri

Fein

Kasari

Pierce

Buie

Carter

Davis

P. A.

Granpeesheh

Mailloux

Newschaffer

Robins

, et al. (2015). Early identification of autism spectrum disorder: Recommendations for practice and research. Pediatrics, 136(Suppl. 1), S10–S40. https://doi.org/10.1542/peds.2014-3667C

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.02 MB