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Abstract

The course of typical and atypical child development likely varies depending on families’ understanding and support for their child's developmental needs. Analyzing longitudinal data (N = 823 children with autism) from 37 cohorts, we explored the relation between family familiarity with autism (defined by the presence of an autistic sibling) and children's cognitive and social development and emotional/behavioral outcomes. Greater autism familiarity was associated with earlier age of diagnosis, but also with neurodevelopmental outcomes, over and above age at diagnosis. In early childhood, children in the autism familiar group performed better on the Mullen Scales of Early Learning compared to those in the unfamiliar group (mean T-score differences of 3–5). The later a child was diagnosed, the stronger the relation between autism familiarity and higher scores on receptive language and visual reception. Age at diagnosis was associated with more parent-rated problems on a range of Child Behavior Checklist scores in middle childhood and adolescence. Findings indicate that greater autism familiarity is associated with more positive cognitive developmental outcomes for children on the spectrum. Although correlational, these findings are consistent with the possibility that autistic children's development can be viewed as adaptation within a context that can be more (or less) supportive of children's developmental needs.

Keywords

autism family context adaptation developmental mismatches developmental systems

Introduction

Autism is a neurodevelopmental condition resulting in unique modes of sensory processing, social communication difficulties, and restricted interests and/or repetitive behaviors (American Psychiatric Association, 2013). The presence of cognitive and socioemotional difficulties varies widely among the estimated 1%–2% of the general population on the autism spectrum (Maenner et al., 2021; Zeidan et al., 2022). For example, about one-third of autistic children are classified as having an intellectual disability (Maenner et al., 2021). Additionally, up to 80% of autistic¹ people develop mental health conditions as they get older (Bakken et al., 2016; Lever & Geurts, 2016). Anxiety, depression, obsessive compulsive disorder (OCD), and posttraumatic stress disorder (PTSD) are particularly common (Haruvi-Lamdan et al., 2020; Lai et al., 2019).

Autism results from a combination of genetic and environmental factors. While etiology is unknown in most cases, autism is associated with known genetic mutations and syndromes (e.g., Rett syndrome, Down syndrome, fragile X syndrome; Ziats et al., 2021). Autism is also heritable, and the recurrence rate within families is about 10%–20% (Wood et al., 2015). Furthermore, parents of autistic children sometimes demonstrate autistic traits, known as the broader autism phenotype (BAP); approximately 18% of parents of autistic children report some autism-related characteristics of their own (Wood et al., 2015). Given historical changes to the diagnostic criteria and awareness of autism, it is also possible that some of the parents identified as exhibiting the BAP are themselves autistic but undiagnosed. This means that some, but not all, autistic individuals develop in a family context that is familiar with autism, whether from a first-person (e.g., parents are autistic) or third-person (e.g., parents have experience raising an autistic child) perspective. From a developmental systems perspective, the effect of any particular influence on development will depend on the context in which it occurs (e.g., Bronfenbrenner, 1979; Lerner, 1991; Zelazo, 2013). It is important, therefore, to explore the full range of developmental variation across contexts.

Consider the d/Deaf community, which is similar to the autistic community in some respects; for example, deafness is often conceptualized as a disability in a society built around the needs and abilities of hearing people (a full account of the parallels and difference between the d/Deaf and a/Autistic communities, including the history of advocacy and use of capitalization to indicate identity, is beyond the scope of this paper). Since more than 90% of deaf children are born to hearing parents (Mitchell & Karchmer, 2004), most research on deaf children is, by default, research on deaf children of hearing adults (DoH). When Schick and colleagues (2007) compared social cognition in deaf-of-deaf (DoD) and DoH children, they found that DoD children did not display the same delays as their DoH peers. The social cognition difficulties seen in DoH children cannot, therefore, be attributed to deafness per se; they depend in part on the family context. A similar mismatch between children's needs and parents’ preparedness to meet those needs might occur when autistic children are raised by non-autistic parents (Wood et al., 2015). For example, although high parental warmth is associated with positive developmental outcomes for nonautistic children (e.g., Gurdal et al., 2016), parental warmth in the form of physical affection might be perceived as intrusive by an autistic child. This is not to say that autistic children necessarily dislike physical affection, simply that non-autistic parents may fail to consider the possibility that sensory sensitivities might affect how autistic children perceive physical affection.

Several studies have examined outcomes for children in families with a single autistic child (simplex) versus multiple autistic children (multiplex) to identify phenotypic differences and explore potential differences in genetic etiology. The results of this research are mixed. Whereas Cuccaro et al. (2003) and Oerlemans et al. (2016) found no differences in symptom severity and cognitive performance, Banach et al. (2009) found higher non-verbal IQ scores in multiplex versus simplex school-age girls, although no differences were observed for boys. Dissanayake et al. (2019) found that multiplex versus simplex toddlers had higher scores on the Mullen Scales of Early Learning, despite similar autism severity and similar relations between cognition and symptom severity. Overall, there is preliminary partial support for the hypothesis that parents with more experience raising autistic children are either better prepared to seek services and/or to support their children's developmental needs in other ways. It should be noted, however, that a relatively well-powered study (N = 429) showing a multiplex advantage in cognition failed to find differences in multiplex families as a function of birth order, leading the authors to suggest that parents’ prior experience with autism is unlikely to contribute to the differences in outcomes sometimes observed between children in multiplex and simplex families (Berends et al., 2019). Results of research on birth order and developmental outcomes in autism are also mixed; however, Martin and Horriat (2012) found that later-born children with autism have increased symptom severity.

We leveraged data from the Environmental influence on Child Health Outcomes (ECHO) program, funded by the National Institutes of Health, to determine the robustness of any cognitive and behavioral differences as a function of autism familiarity regardless of birth order, on the assumption that even experience with a second-born autistic child will change the extent to which parents can understand and meet their children's developmental needs. The ECHO program is a collaboration of existing pediatric longitudinal studies that includes data from 69 existing cohorts, and includes approximately 50,000 children and their primary caregivers from 44 U.S. states and Puerto Rico, with participants of diverse race, ethnicity, and socioeconomic status (Gillman & Blaisdell, 2018). Data for the current study were drawn from a total of 37 cohorts (see Table S9 in the online supplemental materials).

We hypothesized that greater family familiarity with autism (autism familiarity), defined as having another child with an autism spectrum disorder (ASD) diagnosis, would be associated with more positive developmental outcomes for autistic children in cognitive, social, and/or emotional and behavioral domains. Alternatively, if having more than one autistic child increases parent and/or family stress, or is indicative of a higher genetic load for autism-related traits, one might expect family familiarity to be associated with more negative developmental outcomes. Evidence consistent with the hypothesis that autism familiarity co-occurred with more positive outcomes would encourage further strengths-based research on the context in which autistic children develop and underscore the need in subsequent research to disentangle family familiarity with autism from potential genetic differences between multiplex and simplex children, and to identify mechanisms whereby family familiarity can support autistic children's development. From the available measures, we examined cognitive development using the Mullen Scales of Early Learning (MSEL), a standardized measure of cognitive function in young children, normed for infancy to 68 months of age (Mullen, 1995). A number of parent-reported scales of behavior were also available. The Social Responsiveness Scale (SRS) was used to assess social development. The SRS measures multiple facets of social ability, including social awareness, social information processing, reciprocity, social anxiety, and avoidance (Constantino et al., 2003). The Child Behavior Checklist (CBCL) provides a measure of emotional and behavioral problems (Achenbach & Rescorla, 2000). Finally, although autistic parents might be particularly well-equipped to raise autistic children given their own lived experience, information about whether parents have an autism diagnosis was not available within the ECHO cohorts.

Methods

Participants

All individual participant data were drawn from the central ECHO database. We restricted the full ECHO sample to individuals with autism about whom there was information regarding the autism status of their siblings, determined via a questionnaire to caregivers (N = 823). Two ECHO cohorts—namely, Revisiting the CHildhood Autism Risk from Genetics and the Environment (ReCHARGE; Hertz-Picciotto et al., 2006) and Extremely Low Gestational Age Newborn (ELGAN; O'Shea et al., 2009)—contain many children diagnosed with autism. ReCHARGE (a population-based case–control study) specifically oversampled children with autism, while ELGAN consists of children born before 28 weeks gestational age, relatively many of whom were later diagnosed with ASD. An additional 35 cohorts also include individuals with autism for whom relations between familial familiarity with autism and neurodevelopmental outcomes could be examined. Of the full sample, 402 children were from ReCHARGE, 122 were from ELGAN, and the remaining 365 were from other ECHO cohorts.

This sample was divided into two groups based on autism familiarity, defined by the presence or absence of a sibling with autism (familiar, n = 136; unfamiliar, n = 687). The sample was further subdivided based on data availability for the neurodevelopmental outcomes listed above (MSEL, SRS, and CBCL). Subsamples were identified that maximized joint data coverage across multiple outcomes to maximize the comparability of analytical results. To this end, three subsamples were identified: (a) a subsample from early childhood with data on the MSEL (n = 430; age = 1–5 years; M_age = 3.86); (b) a subsample from early childhood with data on the preschool CBCL (n = 264; age = 1.5–6 years: M_age = 2.93); and (c) a subsample from middle childhood and adolescence with data on the SRS and school-age CBCL (n = 487; age = 5–19 years; M_age = 11.68). In this SRS/CBCL subsample, 410 children had the SRS, 357 had the CBCL, and 312 had both. Table S1 in the online supplemental materials contains a cross-tabulation of membership in these subsamples. Within each of these subsamples, measurements for children with multiple measurements were averaged, yielding a single score per child.

Measures

Most children in our sample whose parents had information collected from the Social Responsiveness Scale (SRS) via a parent report were administered the SRS-2, but some families were administered the SRS or short form SRS-2 (Sturm et al., 2017). The SRS-2 showed good internal consistency (.95) in a clinical sample of school-aged children, and the test-retest reliability of the original SRS ranged from .88 to .95 (Constantino & Gruber, 2012). Given the similarity across SRS versions and forms, all data were harmonized and are reported as SRS in the results. Additional information about the SRS and other measures is included in the Supplemental Material.

The Child Behavior Checklist (CBCL; Achenbach & Rescorla, 2000) is an empirically validated assessment of children's emotional and behavioral presentation, as reported by parents. The preschool version is administered to parents of children aged 1½–5 years, and the school age form is used with children aged 6–18 years. Test–retest reliability over an 8-day period was high for the CBCL/1½–5, with a Total Problems r of .90. Correlations for individual scales ranged from .68 to .92, though most were above .80. The mean test-retest reliability for all scales was .85 (Achenbach & Rescorla, 2000).

The Mullen Scales of Early Learning (MSEL; Mullen, 1995) is a measure of early childhood cognition for children up to age 68 months. The scoring range for each item is variable, ranging from 0 to 5 points, though a correct response on most items receives a “1” (Shank, 2011). Five scales make up the MSEL: gross motor, fine motor, visual reception, receptive language, and expressive language. The raw scores for each domain are converted to T-scores (M = 50, SD = 10), age equivalent scores, and percentile ranks (Shank, 2011). The gross motor scale is only designed for children up to 33 months (Farmer et al., 2016) and very few children in our sample received it, so we did not include it in our analysis. The Early Learning Composite (ELC) score is derived by summing scores on the MSEL (excluding gross motor) and converting to a standard score (Shank, 2011).

The MSEL demonstrates convergent validity with other commonly used measures of early development. For example, the MSEL's Early Learning Composite score is positively correlated (r = .70) with the Bayley Scales of Infant Development (Bayley, 2006) Mental Development Index (Shank, 2011). Further, in a sample of both typically developing children and those with autism or developmental delay, the MSEL ratio IQ was found (Farmer et al., 2016) to be highly correlated (r ∼.90) with IQ scores on the Differential Abilities Scales, second edition (DAS-II; Elliott & Brook, 2007). Estimates of the ELC's internal consistency ranged from .83 to .95, with variability in the internal consistency for individual scales (.75–.83), and test–retest reliability was somewhat higher for children aged 1–24 months (.82–.85) than for children 25–56 months (<.80) at an average retest interval of 11 days (Shank, 2011).

Covariates

Child age at ASD diagnosis, sex, race, ethnicity (Hispanic vs. non-Hispanic), gestational and maternal age at delivery, singleton versus multiple birth, caregiver education level and income assessed at or before the outcome measurement, and caregiver marital status were selected to describe the sample and serve as covariates in statistical analysis because of their potential relevance to the autism phenotype and/or developmental outcomes (e.g., Lord et al., 2020).

Statistical analysis

First, to assess unadjusted differences between the familiar and unfamiliar groups, we conducted univariate two-group tests of significance, using Wilcoxon rank sum tests for continuous variables, Pearson's chi-squared tests for categorical variables with all cell counts $\geq$ 5, and Fisher's exact tests for count data with simulated p-values based on 2000 replicates for count data with any cell count < 5. In adjusted analyses, we sought to account for missing data on covariates and, when multiple outcomes were available for a subsample, missing data on outcomes. To this end, we used multiple imputation (Rubin, 1987), a simulation-based technique that simulates multiple plausible completed data sets based on a data set that contains missing data. Here, we used the smcfcs package (Bartlett et al., 2022) to perform multiple imputation in a way consistent with our estimated linear models (see below), including proper imputation to test interaction terms (Bartlett et al., 2015).

Using 100 imputed data sets, we estimated linear models for each domain and composite score for the outcome measures used in each subsample from the binary indicator of autism familiarity (familiar vs. unfamiliar) and relevant covariates. In a baseline model, we estimated the group difference between children from familiar and unfamiliar families adjusting for age at ASD diagnosis, gestational and maternal age at birth, a binary indicator of multiple versus singleton birth for the assessed child, caregiver education and income level, and a binary indicator of whether the child's caregiver was married or cohabitating with their partner. Due to very little income data for the MSEL subsample (N < 5 with nonmissing values), income was excluded as a covariate in this subsample only. In our next modeling step, we also adjusted for child sex for comparison with the baseline model. In additional models, we added either: (a) an interaction term between child sex and familiarity; (b) an interaction term between age at ASD diagnosis and familiarity; or (c) both. Estimates, standard errors, p values, and fractions of missing information (FMI), which estimates the added amount of uncertainty in each estimate due to missing data, were calculated using Rubin’s (Rubin, 1987) pooling rules.

In all analyses, the unfamiliar group was the baseline group for the indicator of autism familiarity. Age at diagnosis was centered at the sample mean within each subsample (Tables S3–S5 in the online supplemental materials). For the early childhood period, there were two subsamples, with children either receiving the preschool CBCL or the MSEL. When interpreting regression coefficients, we refer to the model without moderation (Tables S3–S5 in the online supplemental materials, “No Moderation”) if neither interaction was significant, and otherwise to the model with moderation.

For outcomes where autism familiarity had a significant interaction with child sex, we additionally calculated the simple association between autism familiarity and the outcome for males using the coefficient estimates (Est_MALE = Est_FEMALE + Est_INTERACTION) and standard errors [SE_MALE = √(SE_FEMALE² + SE_INTERACTION²)]. We then compared the resulting ad-hoc test statistic (Est_MALE/ SE_MALE) to a z distribution to determine significance. Similarly, when there was a significant interaction between autism familiarity and age at diagnosis, we additionally calculated the simple association between age at diagnosis and the outcome for the familiar group using the coefficient estimates (Est_FAMILIAR= Est_FAMILIAR+ Est_INTERACTION) and standard errors [SE_FAMILIAR= √(SE_FAMILIAR²+ SE_INTERACTION²)], comparing the resulting ad-hoc test statistic (Est_FAMILIAR/ SE_FAMILIAR) to a z distribution to determine significance.

Results

Tests of unadjusted autism familiarity differences (familiar vs. unfamiliar) are reported alongside the descriptive statistics in Table S2 in the online supplemental materials. Regression results for outcome scores with any significant estimate for autism familiarity, age at diagnosis, sex, or associated interactions for each analysis subsample are described below. Beta-hats represent the estimated values of associations between variables. The full set of regression results can be found in Tables S3–S5 in the online supplemental materials. Results are reported with respect to the T score metric of both predictor and outcome, and therefore all estimates are directly interpretable on a norm-referenced, standardized metric. In particular, estimates representing group differences by categorical predictors (e.g., sex) of autism familiarity are in T score units, and estimates representing associations between continuous predictors and outcome can be interpreted on the metric of Pearson's r.

Early childhood

Mullen Scales of Early Learning. In the MSEL subsample, the familiar group contained 48 children; there were 372 children in the unfamiliar group. There were no differences in race and ethnicity, gestational age, or age at autism diagnosis, between children in the familiar and unfamiliar groups (Table S6 in the online supplemental materials). Furthermore, there were no differences in child sex, parent education or marital status, or singleton/multiple birth between the groups. Information on family income was not available for this subsample.

Unadjusted analysis showed children in the autism familiar group outperformed those in the unfamiliar group on all four of the MSEL scales included in the analysis: expressive language (p = .01), receptive language (p = .03), visual reception (p = .03), and fine motor (p = <.01) scales (Table S2 in the online supplemental materials). Mean T-score differences were between 3 and 5, corresponding to differences between one-third and one-half of a standard deviation. After controlling for covariates in the multiple regression model, the association between autism familiarity and fine motor skills remained significant [ $\hat{b}$ = 5.55 (SE = 2.01), p = .02; Table S3 in the online supplemental materials, “No Moderation”]. Although there was no association between familiarity and performance on any of the other scales at the average age of diagnosis (2.57 years), we did observe an interaction between autism familiarity and age at diagnosis for two of the MSEL scales: receptive language [ $\hat{b}$ = 1.71 (.87), p = .05; Table S3 in the online supplemental materials, “Moderation”] and visual reception [ $\hat{b}$ = 2.24 (1.07), p = .04; Table S3 in the online supplemental materials, “Moderation”]. The later a child was diagnosed, the stronger the relation between familiarity and scores on these scales (Figure 1(a) and (b)). Specifically, each one-year delay in diagnosis relative to the average age at diagnosis in the sample (2.57 years) was associated with an additional benefit of autism familiarity for performance on receptive language (1.71 T-score points) and visual reception (2.24 T-score points). In each case, the simple association of age at diagnosis on the scale score was not significantly different from zero for either the familiar or unfamiliar groups.

Figure 1.

ASD familiarity × age at diagnosis interactions.

Child sex was associated with performance on expressive language [ $\hat{b}$ = −3.99 (1.42), p < .01; Table S3 in the online supplemental materials, “No Moderation”], fine motor [ $\hat{b}$ = −5.61 (1.64), p < .01; Table S3 in the online supplemental materials, “No Moderation”], receptive language [ $\hat{b}$ = −5.78 (1.71), p = <.01; Table S3 in the online supplemental materials, “Moderation”], and visual reception [ $\hat{b}$ = −5.42 (2.09), p = .01; Table S3 in the online supplemental materials, “Moderation”] scales, with females outperforming males on each scale. No significant moderation of the autism familiarity x MSEL associations by child sex was observed.

Child Behavior Checklist, Preschool. As in the MSEL subsample, most children in the early-childhood CBCL sample were in the autism unfamiliar (n = 210) rather than the familiar (n = 54) group. Unlike the MSEL subsample, however, comparison of demographic data revealed the familiar group to be more White (75% vs. 52%; p < .001) and more Hispanic (31% vs. 18%; p = .031; Table S7 in the online supplemental materials) than the unfamiliar group. Children in the unfamiliar group were born an average of 4.30 weeks earlier than those in the familiar group (30.3 vs. 34.6 weeks, p < .001). Children in the unfamiliar group received an autism diagnosis an average of two years later than children in the familiar group (p < .001). There were no group differences in child sex, parent education, marital status or income, maternal age at delivery, or singleton/multiple birth.

The interaction between autism familiarity and child sex was significant for the Somatic Complaints [ $\hat{b}$ = 4.38 (2.16), p = .04] subscale (Table S4 in the online supplemental materials, “Moderation”). For Somatic Complaints, the simple association with familiarity was −3.48 (1.73) for females (familiarity was associated with fewer somatic complaints) and .90 (2.77) for males; here, the association between familiarity and Somatic Complaints for females was significantly different from zero, and the association between familiarity and Somatic Complaints was significantly different between males and females, but the association for males was not significantly different than zero.

Age at diagnosis was associated with degree of problems on the Aggressive Behavior [ $\hat{b}$ = −.28 (.14), p = .04; Table S4 in the online supplemental materials, “No Moderation”], Anxious/Depressed [ $\hat{b}$ = −.26 (.13), p = .05; Table S4 in the online supplemental materials, “No Moderation”], Autism Spectrum DSM [ $\hat{b}$ = −.71 (.23), p < .01; Table S4 in the online supplemental materials, “No Moderation”], Depressive [ $\hat{b}$ = −.39 (.19), p = .04; Table S4 in the online supplemental materials, “No Moderation”], Dysregulation Profile [ $\hat{b}$ = −.84 (.38), p = .03; Table S4 in the online supplemental materials, “No Moderation”], Internalizing [ $\hat{b}$ = −0.68 (.27), p = .01; Table S4 in the online supplemental materials, “No Moderation”], and Withdrawn [ $\hat{b}$ = −.75 (.22), p < .01; Table S4 in the online supplemental materials, “No Moderation”] subscales as well as the total T-score ( $\hat{b}$ = −.70 (.29), p < .02; Table S4 in the online supplemental materials, “No Moderation”). Other subscales were not correlated with age at diagnosis after adjusting for covariates.

Unadjusted comparisons between children in the autism familiar and unfamiliar groups showed that those in the familiar group had more difficulty with attention (difference of 3.0 T-score points; p = .03), aggressive behavior (difference of 2.9 T-score points; p = .009), externalizing problems (difference of 4.0 T-score points; p = .01), dysregulation (difference of 5.0 T-score points; p = .01), as well as higher scores on the oppositional defiant DSM scale (difference of 2.0 T-score points; p = .02) and syndrome scale total (difference of 4.0 T-score points; p = .03; Table S2 in the online supplemental materials). However, after adding covariates to the model, only the relation between autism familiarity and aggressive behavior [2.09 (1.00), p = .04; Table S4 in the online supplemental materials, “No Moderation”] remained significant.

Middle childhood and adolescence

In middle childhood and adolescence, the outcomes of interest were social ability and emotional/behavioral problems, assessed via parent ratings on the SRS and CBCL (school age), respectively. Children in the autism unfamiliar group (n = 476) received their autism diagnosis an average of 13 months later than children in the familiar group (n = 119; p < .001; Table S8 in the online supplemental materials). No other covariates were significantly different between the two groups. There were no significant associations between any of the independent variables (autism familiarity, sex, and age at diagnosis) and performance on the SRS, either total or subscales, nor were there any significant interactions. Additionally, performance on the SRS did not differ between the autism familiar and unfamiliar groups in the unadjusted analysis (Table S2 in the online supplemental materials).

Analysis of the CBCL revealed a significant interaction between autism familiarity and child sex for the Anxiety DSM5 [ $\hat{b}$ = 6.29 (2.84), p = .03], Anxious/Depressed [ $\hat{b}$ = 5.48 (2.44), p = .03], Dysregulation Profile [ $\hat{b}$ = 13.44 (6.21), p = .03], and Withdrawn/Depressed [ $\hat{b}$ = 6.15 (2.58), p = .02] CBCL subscales (Table S5 in the online supplemental materials, “Moderation”). The simple association of familiarity for females was not significant for any subscale, and despite the significant interaction term indicating the relation with familiarity was different between males and females, the simple association of familiarity for males was not significant for any subscale. Additionally, there was an interaction between autism familiarity and age at diagnosis for the Social Problems DSM5 subscale [ $\hat{b}$ = .65 (.31), p = .04; Table S5 in the online supplemental materials, “Moderation”; Figure 1(c)]. The simple association of age at diagnosis was not significant for either the autism familiar or unfamiliar group.

There was an association between autism familiarity and the Somatic DSM5 subscale [ $\hat{b}$ = 2.21 (1.00), p = .03] such that parents in the familiar group reported less concern regarding somatic problems. Later age at diagnosis was associated with more difficulty on the Withdrawn/Depressed subscale [ $\hat{b}$ = .41 (.21), p = .05] and Depressive DSM5 subscale [ $\hat{b}$ = 0.35 (0.16), p = .03]. Specifically, each one-year delay in receiving a diagnosis was associated with a .41 T-score point increase in withdrawn/depressed symptomatology and .35 T-score point increase in depressive symptomatology. There was an association between child sex and scores on the Somatic Complaints [ $\hat{b}$ = −1.81 (0.88), p = .04] subscale, with males exhibiting fewer symptoms.

Summary of results from all samples

In two of the three subsamples, children in the autism familiar group received an autism diagnosis on average earlier than children in the unfamiliar group. Moreover, older age at diagnosis was associated with fewer problems with aggression, dysregulation, withdrawal, and anxious/depressed, internalizing, depressive, and autism symptoms in early childhood. Total problems were also lower in those with an older age at diagnosis. In early childhood, children in the autism familiar group showed better performance on all four of the Mullen Scales of Early Learning (MSEL) for which we had data, consistent with some previous research (Berends et al., 2019; Dissanayake et al., 2019). After controlling for covariates, however, only the association between autism familiarity and fine motor skills remained significant. Importantly, however, age at diagnosis interacted with autism familiarity for the receptive language and visual reception scales. For the MSEL, administered between 1 and 5 years of age, the magnitude of the association between familiarity and MSEL scores increased with each additional year a diagnosis was delayed. The simple association of age at diagnosis was not significant for either group.

Results for socioemotional development and behavioral adjustment were mixed, and varied as a function of age, which was confounded with measures (CBCL or SRS) in these data; for the CBCL, we had data from ages 1.5 to 19 years, but for the SRS we had data from middle childhood through adolescence (5–19 years). There were no group differences in the Social Responsiveness Scale (SRS) scores, no relations between autism familiarity and SRS scores, and neither child sex nor age at diagnosis was associated with SRS scores. Furthermore, there were no significant interactions between either autism familiarity and child sex or autism familiarity and age at diagnosis when SRS scores were used as the outcome.

In contrast, the CBCL yielded several associations with autism familiarity. After accounting for covariates, autism familiarity was associated with more parent-reported aggressive behavior on the CBCL in early childhood, and there was also some suggestion that females might show a stronger buffering association between autism familiarity and depressive and somatic symptoms. In middle childhood and adolescence, parents in the familiar group reported less concern regarding somatic problems. There was also a significant interaction between familiarity and age at diagnosis for social problems such that familiar parents whose children received a later diagnosis reported more social problems.

Discussion

Greater autism familiarity was associated with earlier age of diagnosis, but also with neurodevelopmental outcomes, over and above age at diagnosis. In early childhood, children in the autism familiar group performed better on the MSEL compared to those in the unfamiliar group (mean T-score differences between one-third and one-half of a standard deviation). The later a child was diagnosed, the stronger the relation between autism familiarity and scores on receptive language and visual reception. Autism familiarity was also associated with more parent-reported aggressive behavior on the CBCL in early childhood; in middle childhood and adolescence, familiar parents reported fewer somatic problems, but those whose children received a later diagnosis reported more social problems. Age at diagnosis was associated with more parent-rated problems on a range of CBCL scores in middle childhood and adolescence. Overall, findings indicate that greater autism familiarity is associated with earlier diagnosis, more positive cognitive developmental outcomes for children on the spectrum, and also more parental sensitivity to behavioral problems. It should be noted, however, that children were not necessarily screened for ADHD or other co-occurring conditions that might influence parents’ reports of behavioral issues, so relevant data were not included in our analysis.

Autism is a neurodevelopmental variation arising in the context of many individual-level factors, including genetics, prematurity, and advanced paternal age. However, fully understanding and supporting development for children on the autism spectrum requires consideration of how these children develop in different social contexts. Although the current study was not designed to identify underlying mechanisms, the finding that autism familiarity was associated with cognitive developmental outcomes is consistent with the notion that parents who are more familiar with autism may be better equipped to support their child's development, particularly when diagnosis and early intervention services are delayed or unavailable (e.g., Berends et al., 2019; Kanne & Bishop, 2021). More generally, the synchrony or mismatch between the developmental needs of children with specific neurodevelopmental conditions and their parents’ preparedness to address those needs might be an important potential influence on developmental outcomes, worthy of further research.

The social cognitive development of deaf children born to hearing adults (DoH) provides one example of how development varies as a function of family familiarity (Mitchell & Karchmer, 2004; Schick et al., 2007), and in the current study, we found further evidence for another (Wood et al., 2015). In particular, we found evidence that parental familiarity confers benefits above and beyond, or possibly synergistically with, recognizing early signs of autism and facilitating an earlier diagnosis, which is consistent with the possibility that a family already familiar with autism is better equipped to understand their child and provide an environment to support their early neurocognitive development, particularly in the absence of early intervention services. In a complex developing system, even small perturbations can have cascading consequences and lead to meaningful differences in developmental outcomes (e.g., Masten & Cicchetti, 2010). Results for socioemotional development and behavioral adjustment were mixed, and there were no effects for the Social Responsiveness Scale (SRS). One potential explanation for these null findings on the SRS is that both autism groups in this sample scored relatively low when compared with others on the autism spectrum. The autism familiar and unfamiliar groups’ mean SRS total T-scores were 64 and 65, respectively, both of which are indicative of “mild” social difficulties. It is unknown whether the pattern of results observed in this study would hold for autistic children with more significant social challenges.

In contrast to the SRS, autism familiarity was associated with parent-rated aggressive behavior on the CBCL in early childhood. Autism familiarity might be expected to alter parents’ expectations regarding problem behaviors (e.g., making them more sensitive to aggressive behavior), and it might also help prepare parents to support their children’s adaptation (e.g., mitigating negative effects of stress on aggression or somatic problems). In middle childhood and adolescence, parents in the familiar group reported less concern regarding somatic problems.

There are several possible interpretations for the specific pattern of results we observed. One factor to consider is the difference in measure format. The MSEL is a standardized, clinician administered measure. In contrast, the CBCL and SRS are parent report measures. It is possible, or even likely, that familiarity with autism changes how parents perceive their child relative to expectations. The limitations of parent-report for measures like the SRS are well known (e.g., Warren et al., 2012). Indeed, research indicates that non-autism factors, such as non-verbal IQ and externalizing problems, affect parent ratings on the SRS (Hus et al., 2013). Other research has also shown that parents rate their child's language ability differently as a function of diagnosis (autism vs. Down syndrome), even when there are no objectively measured differences in language development (Smith et al., 2014). Furthermore, parent expectations appear to influence important child outcomes even during the transition to adulthood. Carter et al. (2012) found that whether parents expected young adults with intellectual and developmental disabilities (I/DD) to have paid employment in the first few years after leaving school predicted whether these young adults did in fact end up gainfully employed. Similarly, a family's preference for paid work in the community is associated with a higher odds ratio of integrated employment and other paid community work versus sheltered or nonwork experiences for transition-age youth with I/DD (Simonsen & Neubert, 2013). While correlational, these findings are consistent with the suggestion that parent expectations influence children's developmental trajectory, whether assessed on a clinical measure (i.e., SRS) or on “real world” outcomes such as employment.

A second possibility is that the relation between autism familiarity and outcomes depends on the age of the child. The MSEL, which showed the most robust associations, was administered in early childhood, whereas the SRS (which yielded no significant associations) was only administered during the middle childhood and adolescence periods. While we cannot rule out that familiarity is differentially related to developmental outcomes at different ages, it is unlikely that this fully explains the observed pattern of results because the CBCL was administered during both time periods, and the CBCL yielded autism familiarity group differences during both periods.

Finally, it is possible that associations with autism familiarity depend on the specific area of development being considered. For example, autism familiarity may lead to more specific cognitive developmental expectations that are better attuned to autistic children, allowing for more autonomy supportive and developmentally appropriate parenting that support aspects of cognitive development (e.g., Bernier et al., 2010). Perhaps this was especially true for, or beneficial for, the kind of motor, linguistic, and cognitive milestones measured by the MSEL.

Limitations

We analyzed a relatively large sample of data from the ECHO program to examine autism familiarity in relation to developmental outcomes from early childhood to adolescence. However, because different cohorts collected overlapping information, we did not have access to the same measures for all of the children. For example, the 287 children in our sample who received the MSEL received no other relevant measure and were therefore only included in the early childhood MSEL analysis. Given the partial confound between measures and children's age, the possibility remains that the different results for different measures can be attributed to differences in children's age (or to sample differences). These differences in analytic samples should be viewed as potential impediments to statistical power, but the purpose of this study was to explore differences in developmental outcomes for autistic children with and without autistic family members, as opposed to differences in the specific measures or domains. Related to sample limitations, we were unable to explore developmental trajectories because cohorts often administered measures at only one time point. In some cases, this was because the measure itself was only appropriate for certain ages (e.g., MSEL). In other cases, such as with the SRS, the measure could have been administered at different ages, with different versions as appropriate, but was not available for enough children in early childhood (N ≈ 100) to warrant inclusion as a separate analysis within that age group. That said, the CBCL yielded autism familiarity group differences during both periods.

Another limitation is that, due to the secondary nature of the analyses, individual cohorts did not always include the same child and family characteristics in their study designs, yielding groups that were not completely equivalent regarding covariates in at least one subsample. For example, in the early childhood CBCL sample, gestational age was significantly different between children in the familiar and unfamiliar groups. The average gestational age for unfamiliar children in this sample was just 30.3 weeks, likely due to the fact that the ELGAN cohort (14% of the total sample) was specifically measuring outcomes for children born at extremely low gestational ages. Etiology may be important in determining developmental trajectories, and although much more work is still to be done in this area, it is possible that autism that is hereditary versus linked to extreme prematurity may represent distinct phenotypes. This presents a confounding variable that cannot be disentangled from that of a family's autism familiarity. Finally, autism familiarity was determined by caregivers reporting on whether another child had a diagnosis of autism but did not include information on the age of the affected family members relative to that of the target child. Thus, the amount of familiarity experienced during the target child's development likely varied from child to child, representing an uncontrolled source of variance in our indicator of autism familiarity. Future studies designed specifically to answer the research questions addressed in this paper could work to eliminate preexisting group differences. We also note that we did not attempt to statistically adjust or account for the ECHO cohort membership. This was done so that between-cohort differences in other variables of interest (e.g., caregiver education and autism familiarity) would not be masked by the cohort membership, as would occur if cohorts were included as a fixed (e.g., using dummy variables) or random factor in our analyses.

Finally, results of the current study show that having more than one autistic child is associated with more positive cognitive developmental outcomes, and not more negative outcomes, as might be expected, for example, if having multiple autistic children elicited more parenting-related stress or were indicative of a higher genetic load for autism-related traits. Further research is needed, however, to identify the mechanisms underlying the link between autism familiarity and autistic children's cognitive development. Several possibilities remain, and these might interact in complex ways. For example, families who are familiar with autism due to the presence of another autistic child might be better positioned to meet an autistic child's developmental needs for a variety of reasons, including experience of parenting an autistic child or earlier (or better) access to various types of support. In the current study, autism familiarity was associated with earlier diagnosis (and perhaps therefore earlier intervention) but results also showed that the later a child was diagnosed, the stronger was the relation between autism familiarity and cognitive outcomes, indicating a role for familiarity beyond access to a diagnosis.

Implications

This study was correlational and exploratory in nature, but results point to the possibility that parents who are more familiar with autism might be better prepared to support their child's development, particularly when diagnosis and early intervention services are delayed or unavailable (Kanne & Bishop, 2021). More research is needed to interrogate the effects of within-family neurotype similarity on developmental outcomes, and to determine the mechanisms underlying this association. We encourage future studies to explore the concept of autism familiarity more directly and include parent neurotype in order to assess whether the autistic children of autistic parents benefit from matching neurotypes. This line of research recognizes the likely importance of considering the impact of relationships and community when seeking to understand the development trajectories of autistic individuals, and perhaps atypical development more generally. Research that only considers individual children, regardless of the family context, is likely to neglect ways in which family systems co-adapt over time (possibly over generations). In contrast, a developmental systems approach that adopts a dynamic person-context unit of analysis might offer important insights regarding how best to support diverse developmental trajectories.

Supplemental Material

sj-docx-1-ndy-10.1177_27546330241280701 - Supplemental material for The relation between family familiarity with autism and developmental outcomes

Supplemental material, sj-docx-1-ndy-10.1177_27546330241280701 for The relation between family familiarity with autism and developmental outcomes by Isabelle F Morris, Maxwell Mansolf, Phillip R Sherlock, Catrina Calub, Carlos A Camargo Jr., Rachel S Kelly, Kristen Lyall, Cindy T McEvoy, Jessie Northrup, Greta Wilkening, Rosalind J Wright, Philip David Zelazo and on behalf of program collaborators for Environmental influences on Child Health Outcomes in Neurodiversity

Footnotes

Acknowledgments

The authors wish to thank our ECHO colleagues, the medical, nursing and program staff, as well as the children and families who participated in the ECHO cohorts. The authors also acknowledge the contribution of the following ECHO program collaborators: ECHO Components—Coordinating Center: Duke Clinical Research Institute, Durham, North Carolina: Smith PB, Newby KL, and Benjamin DK; Data Analysis Center: Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD: Jacobson LP; Research Triangle Institute, Durham, NC: Parker CB; and Person-Reported Outcomes Core: Northwestern University, Chicago, IL: Gershon R and Cella D.

Authors’ contribution

Isabelle F. Morris served as lead for conceptualization, methodology, writing-original draft, and writing-review and editing. Maxwell Mansolf served as lead for formal analysis, writing-original draft, and writing-reviewing and editing. Phillip R. Sherlock served as lead for formal analysis and writing-reviewing and editing. Catrina Calub served as lead for reviewing and editing. Carlos A. Camargo, Jr.: reviewing and editing. Rachel S. Kelly served as lead for reviewing and editing. Cindy T. McEvoy served as lead for collected data and critically reviewed the manuscript. Jessie Northrup served as lead for reviewing and editing. Greta Wilkening: reviewing and editing. Rosalind J. Wright served as lead for reviewing and editing. Philip David Zelazo served as lead for conceptualization, methodology, writing-review and editing, and supervision. All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.

Declaration of conflicting interests

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

Funding

Research reported in this publication was supported by the Environmental influences on Child Health Outcomes (ECHO) program, Office of The Director, National Institutes of Health, under Award Numbers U2COD023375 (Coordinating Center), U24OD023382 (Data Analysis Center), U24OD023319 with co-funding from the Office of Behavioral and Social Sciences Research (OBSSR; Person Reported Outcomes Core, Gershon, Cella), UH3OD023253 (Camargo), UH3OD023342 (Newschaffer/Lyall/Volk), UH3OD023288 (Dr Cindy McEvoy), OD023244 (Hipwell/Keenan), UH3D023337 (Wright), UH3OD023365 (Hertz-Picciotto), and T32MH073124 (Rogers), and the National Institutes of Health (grant numbers: U24OD023319, U24OD023382, and U2COD023375). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Ethical Approval

Research involved analysis of existing data from human subjects on the ECHO de-identified database. Local Institutional Review Boards (IRBs) and/or the central ECHO IRB (Western IRB) reviewed all research methods and procedures.

Informed consent

All participants provided informed consent to include their data in this database and for those data to be used in secondary analyses.

Data,material,or code availability

The datasets for this manuscript are not publicly available because as per the NIH-approved ECHO Data Sharing Policy, ECHO-wide data have not yet been made available to the public for review/analysis. Requests to access the datasets should be directed to the ECHO Data Analysis Center, ECHO-DAC@rti.org.

Disclosure statement

The authors have no relevant financial or non-financial interests to disclose.

ORCID iD

Isabelle F Morris

Supplemental material

Supplemental material for this article is available online.

Notes

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