Time is of the essence: Age at autism diagnosis,sex assigned at birth,and psychopathology

Abstract

Age at autism diagnosis is associated with sex assigned at birth (hereafter, “sex”), such that girls/women are more likely to be delayed or “missed” entirely in the diagnostic process compared to boys/men. Later diagnosed individuals, especially girls/women, demonstrate increased anxious/depressive symptoms. Data on autistic youth from clinic-based (n = 1035; 22.9% assigned female) and sex-balanced research-based (n = 128; 43% assigned female) samples were probed via regression-based mediation models to understand relationships between diagnostic age, sex, and symptoms of anxiety/depression. We hypothesized diagnostic age would mediate the relationship between sex and anxious/depressive symptoms. In both samples, later diagnostic age predicted greater anxious and depressive symptoms, and sex did not directly predict anxious symptoms. In the clinic-based but not the research-based sample, individuals assigned female at birth were later diagnosed than those assigned male, and there was a significant indirect effect of sex on anxious and depressive symptoms through diagnostic age, such that those assigned female and later diagnosed experienced greater symptoms. Within the research-based sample only, sex predicted depressive symptoms. The present study provides an important impetus for further evaluating the implications of diagnostic timing, enhancing tools for recognizing autism in individuals assigned female at birth, and grounding research with real-world ascertainment strategies.

Lay Abstract

Previous research has shown that girls/women are diagnosed later than boys/men with autism. Individuals who are diagnosed later in life, especially girls/women, have greater anxious and depressive symptoms. Previous research has been limited due to narrow inclusionary criteria for enrollment in studies. The present study uses two samples—one clinic-based, large “real-world” sample and another research-based sample with strict criteria for autism diagnosis—to understand the relationships between diagnostic age, sex assigned at birth, and symptoms of anxiety/depression. In both samples, those who were diagnosed later had greater anxious/depressive symptoms, and anxiety was not predicted by sex. In the clinic-based but not research-based sample, those assigned female at birth were diagnosed later than those assigned male at birth. In the clinic-based sample only, individuals assigned female at birth and who were later diagnosed experienced greater symptoms of anxiety/depression compared to those assigned male who benefited from earlier diagnostic timing. Within the research-based sample, those assigned female at birth had greater depressive symptoms than those assigned male. These findings highlight the importance of timely identification of autism, especially for girls/women who are often diagnosed later. Community-based samples are needed to better understand real-world sex-based and diagnostic age-based disparities in mental health.

Keywords

autism diagnostic delay diagnostic timing mental health outcomes

Autism spectrum disorder (hereafter, “autism”) is a neurodevelopmental condition affecting approximately 1 in 36 youth in the United States (Maenner et al., 2023) with hallmark features including challenges with social communication and repetitive behaviors (American Psychiatric Association (APA), 2022). Although autism diagnoses can be made beginning around 18 months of age (Hyman et al., 2020), a recent meta-analysis found the mean diagnostic age to be 60.48 months, or about 5 years of age (mean diagnostic age range: 30.90–234.57 months; van’t Hof et al., 2021).

Autism diagnosis and sex/gender

There are heightened concerns about the under-recognition of autism in girls and women.¹ People referred to as “females” in the literature (PRF)² are diagnosed with autism later than people referred to as “males” (PRM; Fusar-Poli et al., 2022; Hosozawa et al., 2021; Kavanaugh et al., 2021; Mandy et al., 2022) or “missed” altogether, even when autistic features equivalent to PRM are present (Loomes et al., 2017). One explanation for why PRF may be missed during the diagnostic process is that the presentation of autism in some PRF may feature characteristics of what has been described as a “female autism phenotype,” which differs from traditional phenotypic profiles characterized based on PRM (Beggiato et al., 2017; Kirkovski et al., 2013; Lai & Baron-Cohen, 2015; Lockwood Estrin et al., 2021). The observable expression of autistic features by many individuals assigned female at birth may be more nuanced (Rea et al., 2022), and thus difficult for clinicians to assess and diagnose using existing standardized diagnostic measures (Beggiato et al., 2017; Kirkovski et al., 2013; Lai & Baron-Cohen, 2015; Lockwood Estrin et al., 2021). Ninety percent of autism diagnosticians participating in an Australia-based study viewed autism assessment as more challenging with PRF, citing a “mismatch” between the female expression of autism and current diagnostic tools and conceptualization (Tsirgiotis et al., 2022).

Autistic PRF and psychopathology

In addition to sex differences in diagnostic timing, autistic PRF and PRM differ in relation to degree of co-occurring psychopathology across the lifespan, namely anxiety and depression. Literature related to sex differences in anxiety is inconsistent (Magiati et al., 2016; Sukhodolsky et al., 2020). However broadly, sex differences in psychopathology may be present as early as toddlerhood (Hartley & Sikora, 2009; Prosperi et al., 2021) and continue to widen in adolescence and adulthood (Gotham et al., 2015; Oswald et al., 2016; Solomon et al., 2012). In a longitudinal study of children in the United Kingdom, PRF with greater parent-reported autistic traits exhibited significantly higher anxious and depressive symptoms compared to their PRM counterparts (Hallett et al., 2010, but see Nasca et al., 2020). Autistic women (both those assigned female and those whose gender identity is female) experience greater rates of co-occurring psychiatric conditions compared to their counterpart rates in the general population (Rødgaard et al., 2021; Sedgewick et al., 2021), and higher rates of psychiatric hospitalizations (Martini et al., 2022).

Age at autism diagnosis and psychopathology

Co-occurring anxiety and depression are associated with the age at autism diagnosis, such that those diagnosed later experience greater psychopathology (Hosozawa et al., 2021), even after controlling for both sex/gender and birth year (Rødgaard et al., 2021). Mandy and colleagues (2022) identified a differential experience between “early”-diagnosed (<7 years old) and “late”-diagnosed (8–14 years old) cohorts, with the late-diagnosed cohort experiencing greater emotional/behavioral challenges compared to their early-diagnosed peers. It has been argued that PRF being “missed” or later diagnosed are related to a negative cycle, in which PRF have higher rates of psychopathology (namely anxiety and depression), which may overshadow autistic traits (Bargiela et al., 2016; Suckle, 2021). In addition, Bargiela and colleagues (2016) suggest that greater psychopathology may be due to later diagnostic timing and inadequate support. Understanding the diagnostic timing and factors contributing to this timing is vital because later age at autism diagnosis is associated with reduced quality of life, including anxiety and depression (Atherton et al., 2022); therefore, the relationships among sex, diagnostic age, and psychopathology merit increased scrutiny.

Importance of ascertainment methodology

Case ascertainment may be an important factor in disentangling these relationships. Prior research has found evidence that the PRM:PRF autism ratio (most commonly documented in the literature to be 4:1; APA, 2022) differs based on case ascertainment strategy. Studies of autistic individuals using active case ascertainment, or a population-based approach, achieved a more sex-balanced ratio (relatively more PRF and fewer PRM) than studies using a traditional academic research method of seeking out only those with previous diagnoses or meeting “gold-standard” diagnostic thresholds (D’Mello et al., 2022; Loomes et al., 2017). Traditional recruitment methods may also influence the sample’s age at autism diagnosis; Rødgaard and colleagues (2022) highlighted that the use of social media to recruit community-based participants yielded a later average age at diagnosis. To better account for the influence that case ascertainment may have on findings, the current study utilizes two samples: one identified through a real-world clinical intake process (“clinic-based”) and the other through research-based recruitment with “gold-standard” research inclusion/exclusion criteria (“research-based”).

To date, studies have examined age at autism diagnosis, sex, and psychopathology in a largely piecemeal fashion. The current study focuses on better understanding the complex relationships among these variables using differently ascertained samples. We, therefore, use regression-based mediation models to examine (1) associations of sex with age at autism diagnosis, (2) associations of sex with anxious and depressive symptoms, and (3) associations of age at autism diagnosis with anxious and depressive symptoms.

For both samples, we hypothesize the following:

Hypothesis 1: Sex is associated with later age at autism diagnosis, such that individuals assigned female at birth are diagnosed later than those assigned male at birth.

Hypothesis 2: Individuals assigned female at birth experience greater anxious and depressive symptoms compared to those assigned male at birth.

Hypothesis 3: Later age at autism diagnosis is associated with greater anxious and depressive symptoms.

Hypothesis 4: Age at autism diagnosis mediates the relationship between sex and anxious/depressive symptoms, such that there is an indirect effect of diagnostic timing on the relationship between sex and anxious/depressive symptoms.

Methods

Participants

Autistic participants were drawn from two US-based samples with different ascertainment strategies and inclusion criteria: a clinic-based sample from an outpatient academic medical center specializing in neurodevelopmental disorders (n = 1035) and a research-based sample recruited through a multi-modal, multi-site project using traditional academic research inclusion/exclusion criteria (n = 128). In the clinic-based sample, youth typically arrived at a diagnostic assessment through referral from primary care or specialty providers, or self-referral due to parent concerns. Participants in the research-based sample were recruited to participate through practices established at each site, including contacting those who had previously expressed interest in research participation or those diagnosed at the university clinic, or through typical academic recruitment practices (e.g. word of mouth, flyers).

Youth from these two Institutional Review Board-approved studies were included in the present study. In both samples, caregivers and adult participants provided informed consent, and youth in the research-based sample also provided written assent. Participants with missing data on variables of interest were not included in the present study. See Table 1 for demographics.

Table 1.

Sample characterization and group comparisons.

	Clinic-based sample (N = 1035)	Research-based sample (N = 128)	Group comparisons
Assigned female at birth, n (%)	237 (22.9)	55 (43.0)	X² (1) = 24.40, p < 0.001
Age at diagnosis (years), M (SD)	6.75 (4.20) range: 1.3–19.7	6.12 (3.75) range: 1.3–17	t(169.06) = –1.77, p = 0.08; Cohen’s d = 0.16
Age at participation (years), M (SD)	6.75 (4.20) range: 1.3–19.7	12.39 (2.87) range: 8–17.83	t(201.45) = 19.74, p < 0.001; Cohen’s d = 1.57
FSIQ standard score (FSIQ/GCA), M (SD)	95.10 (19.12) range: 34–141^a	100.93 (19.34) range: 70–162	t(667) = 3.10, p < 0.01; Cohen’s d = 0.30
ASEBA DSM-oriented affective problems age-normed t-score, M (SD)^b	62.13 (9.52) range: 50–95 39% clinically significant	62.97 (9.22) range: 50–82.5 41% clinically significant	t(1161) = 0.94, p > 0.1; Cohen’s d = 0.09
ASEBA DSM-oriented anxiety problems age-normed t-score, M (SD)^b	60.82 (9.70) range: 50–97 36% clinically significant	61.80 (8.20) range: 50–77 42% clinically significant	t(174.07) = 1.24, p > 0.1; Cohen’s d = 0.11
Ethnoracial identity, n (%)^c
American Indian	0 (0.0)	1 (0.8)
Asian	84 (8.1)	2 (1.6)
Black	185 (17.8)	6 (4.7)
White	381 (36.8)	96 (75.0)
Mixed	79 (7.6)	22 (17.2)
Hawaiian or Pacific Islander	0 (0.0)	1 (0.8)
Unknown	185 (17.9)	0 (0.0)
Hispanic or Latino/a/x	121 (11.7)	22 (17.19)
Not Hispanic or Latino/a/x	—	106 (82.8)

IQ = intelligence quotient; FSIQ = full-scale intelligence quotient; GCA = General Conceptual Ability; ASEBA = Achenbach System of Empirically Based Assessment.

n = 541.

t-score ⩾65 is considered clinically significant.

Ethnoracial identity was queried differently in the two samples. In the clinic-based sample, Hispanic/Latinx identity was classified as an ethnoracial category, rather than as an ethnicity distinct from race. Given differences in the ways in which ethno-racial identity was queried, we provide descriptive statistics but no statistical comparisons of the samples.

Clinic-based sample

Participants in the clinic-based sample received a first-time clinical diagnosis of autism using Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-V) criteria from an experienced clinical psychologist with specialized training in autism diagnostics. The Autism Diagnostic Observation Schedule-2 (ADOS-2; Lord et al., 2012) and/or Autism Diagnostic Interview-Revised (ADI-R; Lord et al., 1994) were administered in the majority of cases to aid in diagnosis (90.6% administered an ADOS, 9.2% administered an ADI). Still, clinicians made diagnostic decisions based on comprehensive review of all the relevant information related to developmental history and current support needs. All data were collected at the time of the initial diagnostic evaluation. For the present study, participants who received a first-time clinical autism diagnosis and had raw scores from the measure of interest (Achenbach System of Empirically Based Assessment (ASEBA) caregiver-report DSM-oriented anxious and affective/depressive symptoms) were included in analyses. No participants were excluded based on cognitive ability (intelligence quotient (IQ)). Data were collected between 2014 and 2021.

Research-based sample

Participants with an established autism diagnosis were recruited for entry into the first wave of GENDAAR (National Institute of Mental Health Data Archive Data Collection #2021) across multiple sites. In an effort to achieve a sex-balanced sample, GENDAAR recruitment included oversampling of individuals assigned female at birth, relative to diagnostic ratios. For the present study, GENDAAR participants met the following criteria: full-scale IQ (FSIQ) ⩾70 as estimated via the Differential Ability Scales-Second Edition (Elliott et al., 1990), and NICHD/NIDCD Collaborative Program of Excellence in Autism criteria for “Broad ASD,” based on ADOS and ADI-R thresholds (Lainhart et al., 2006; see Supplemental Material 1 for full criteria). For the present study, participants with a reported age at autism diagnosis and raw scores from the measures of interest (ASEBA caregiver-report DSM-oriented anxious and affective/depressive symptoms) were included in analyses. Unlike the clinic-based sample, participants entered the study with an established autism diagnosis, and the age at diagnosis was collected through caregiver report as part of a Medical History Interview. Consequently, the age at participation and age at diagnosis were not concurrent (the average age at diagnosis was 6.55 years prior to research participation). Data were collected between 2012 and 2017. Some of the participants reported on here are reported on elsewhere (Harrop et al., 2021).

Group differences between samples

Results of group comparisons to detect sample differences are included in Table 1 (within-group comparisons by sex available in Supplemental Material 2). The clinic-based sample was diagnosed later than the research-based sample; however, this difference was not statistically significant, after accounting for unequal variances (t(169.06) = –1.77, p = 0.08; Cohen’s d = 0.16). Compared to the clinic-based sample, the research-based sample had a statistically greater number of individuals assigned female at birth, higher overall IQ scores, and an older age at completion of caregiver-report measures of interest. Despite the inadequate ability to characterize sample differences in ethnoracial identity due to different querying between the samples, visual inspection of the ethnoracial breakdown within each sample suggests the clinic-based sample had more representation of diverse ethnoracial identities compared to the research-based sample, which was 75% White (see Table 1).

Measures

Characterization

In the clinic-based sample, an FSIQ score (or General Conceptual Ability score) was used to assess cognitive ability through a variety of measures: the Wechsler Intelligence Scale for Children-Fifth Edition (Wechsler, 2014), Wechsler Intelligence Scale for Children-Fourth Edition (Wechsler, 2003), the Wechsler Adult Intelligence Scale-Third Edition (Wechsler, 1997), Wechsler Abbreviated Scale of Intelligence-Second Edition (Wechsler, 2011), Wechsler Abbreviated Scale of Intelligence (Wechsler, 1999) or the Differential Ability Scales-Second Edition (Elliott et al., 1990). FSIQ scores were obtained for only 52.27% of the clinic-based sample (n = 541). Cognitive assessments of IQ were largely not administered to children under the age of six in this clinic, as clinical focus was on providing rapid diagnostic autism assessment. In the full research-based sample (n = 128), cognitive ability was captured solely through the Differential Ability Scales-Second Edition General Conceptual Ability Standard Score (Elliott et al., 1990).

In both samples, participant sex refers to sex assigned at birth and was queried through a standard caregiver-report demographics survey. In the clinic-based sample, information regarding gender identity was collected only when proactively reported by the child/family (1.1% noted gender diversity in the sample); no information concerning gender identity or gender diversity status was collected from participants in the research-based sample.

Anxious and depressive symptomatology

Anxious and depressive symptoms were probed using the caregiver-report ASEBA measures: Child Behavior Checklist/1.5–5 (n_clinic-based = 611), Child Behavior Checklist/6–18 (n_clinic-based = 418; n_{research-based} = 128), and Adult Behavior Checklist (n_clinic-based = 6). The ASEBA instruments are some of the most widely used and well-researched behavior rating scales, have been validated in autistic samples (Pandolfi et al., 2012), and are commonly used in autism research related to youth psychopathology (e.g. Baribeau et al., 2023; Kanne et al., 2009; Vasa et al., 2020). The DSM-oriented “affective/depressive” and “anxious” problems subscales have been validated for use in autistic populations (Magyar & Pandolfi, 2017) and were employed for these analyses reported here. These scales contain items selected by experts to reflect the developmental profile of psychopathology within the different age groups (see Achenbach, 2013). The use of traditional sex-normed t-scores in models including sex could impact the ability to detect sex’s role in psychopathology. To evaluate anxious and depressive symptoms without utilizing sex-normed t-scores, we used a novel approach previously described by Strang and colleagues (2022), which creates an average normed score between male and female norms. To use this approach, both the male- and female-normed t-scores for each participant were calculated on the appropriate Achenbach measure and then averaged (hereafter, “age-normed” t-scores). These age-normed t-scores were used in all analyses.

Data analytic plan

All analyses were conducted using SPSS, v27. After checking for outliers and confirming normality of distributions for variables of interest (|skewness values| < 1), we compared demographic and variables of interest between the two samples to characterize the samples (see Table 1). We conducted mediation analyses using PROCESS (Hayes, 2022), which is an ordinary least squares regression-based approach to evaluate path-based analyses. PROCESS is a statistical tool that generates both direct and indirect effects within a given mediation model; for the present study, we conceptualized our hypothesized pathways as a simple mediation model (PROCESS model 4, see Figure 1). In total, four simple mediation models were tested, including one per sample in which anxious symptoms was the dependent variable, and one per sample in which depressive symptoms was the dependent variable. Consistent with Hayes’ (2022) recommendations, we reported the unstandardized beta coefficients of the individual pathways (e.g. a, b, and c′). Statistical significance for indirect effects was determined by bootstrap confidence intervals (CIs) based on 10,000 bootstrap samples that do not include zero. A major difference between the two samples relates to the timing of diagnosis and caregiver-report of anxiety and depression, as these were concurrent in the clinic-based but not research-based sample. To address this difference, a covariate of age at research participation (i.e. age at caregiver-report of psychopathology) was included in models using the research-based sample.

Figure 1

Schematic diagram of the simple mediation models.

Community involvement statement

The present study is exclusively secondary data analysis, including one sample from whom data were collected for clinical purposes. While no community members were directly involved in the design of this study, the questions this study seeks to address have consistently been elevated by the autistic community as fundamental research priorities (e.g. Bargiela et al., 2016; Roche et al., 2021).

Results

A full summary of all model coefficients for the following analyses is available in Supplemental Material 3 (Tables S2–S5). Distributions of psychopathology across diagnostic age are included in Supplemental Material 4.

Sex and age at autism diagnosis

Sex predicted diagnostic age in the clinic-based sample (a = 0.70, p < 0.05), such that assigned female at birth individuals were diagnosed later than their assigned male at birth peers. Sex did not predict diagnostic age in the research-based sample (a = 0.95).

Sex and psychopathology

Inconsistent with our hypothesis, sex did not predict anxious symptoms in either sample (c′_clinic-based = 0.48, c′_{research-based} = 2.46). While sex did not predict depressive symptoms in the clinic-based sample (c′= 0.61), sex was predictive of depressive symptoms in the research-based sample (c′= 4.39, p < 0.01), such that individuals assigned female at birth showed higher caregiver-endorsed depressive symptoms than their peers assigned male at birth.

Age at autism diagnosis and psychopathology

In both samples, later diagnostic age predicted higher rates of both anxious (b_clinic-based = 1.01, p < 0.001; b_{research-based} = 0.56, p < 0.01) and depressive symptoms (b_clinic-based = 0.77, p < 0.001; b_{research-based} = 0.67, p < 0.01).

Mediating role of age at autism diagnosis

Results from the clinic-based sample indicated age at autism diagnosis mediated the relationship between sex and anxious symptoms, as a bootstrap CI for the indirect effect based on 10,000 bootstrap samples did not cross zero (95% CIs [0.08, 1.36]). Age at autism diagnosis also mediated the relationship between sex and depressive symptoms in the clinic-based sample (95% CIs [0.06, 1.06]). In both analyses, the association of sex assigned female with depression and anxiety was mediated by later age at diagnosis. In the research-based sample, age at autism diagnosis did not mediate the relationship between sex and either anxious (95% CIs [–0.02, 0.19]) or depressive (95% CIs [−0.18, 1.66]) symptoms. These results are summarized in Figures 2 and 3.

Figure 2.

Results of the statistical simple mediation models predicting anxious symptoms.

Figure 3.

Results of the statistical simple mediation models predicting depressive symptoms.

Post hoc power analyses

To better understand the differential mediation findings by sample, post hoc statistical power analyses were calculated using the Monte Carlo power analysis for indirect effects (Schoemann et al., 2017) at the p < 0.05 level with 20,000 Monte Carlo replications. Results indicated statistical power to detect indirect effects of 62% for the clinic-based sample (n = 1035) and 18%–23% for the research-based sample (n = 128).

Discussion

To our knowledge, this is the first study to comprehensively investigate the relationships among (assigned) sex, diagnostic timing, and psychopathology across clinic- and research-ascertained samples of autistic youth. Findings from previous literature have been limited by narrow sample inclusionary criteria, thus the present study utilized two samples to enhance generalizability. Across both samples, the most robust finding emphasizes that those diagnosed later with autism experienced greater anxious and depressive symptoms. In addition, there was no direct effect of sex on anxious symptoms in either sample. We found a divergence between the “real-world” clinic- and traditional academic research-based samples, such that those assigned female were diagnosed later than those assigned male at birth, and the pathway of sex to psychopathology was mediated by diagnostic timing, in the clinical setting but not the research setting. Solely in the research-based sample, individuals assigned female experienced greater depressive symptoms. Notably, interpretation of divergent findings is limited without the ability to disentangle case ascertainment from statistical power. However, the presence of convergent findings across samples of differing sample size, statistical power, and case ascertainment underscores the generalizability and ecological validity of the findings.

The present findings provide a window into developmental psychopathology in autistic youth, as studies considering diagnostic timing and sex in relation to psychopathology have been overwhelmingly focused on adults. In both samples, rates of symptoms of anxiety and depression were much higher than those in other similarly aged autistic samples (Valicenti-McDermott et al., 2023), including rates established from the same measure (Kanne et al., 2009). The prevalence of psychopathology in the present samples also exceeds that of pooled estimates of co-occurring psychopathology in autistic samples across the lifespan (20% anxiety disorders, 11% depressive disorders; Lai et al., 2019). The present study’s high prevalence rates add to the wide variability in the prevalence of anxiety and depression in autistic individuals, which has been noted in recent meta-analyses of pooled and current estimates (Hollocks et al., 2019; Lai et al., 2019).

In accordance with prior literature (e.g. Hosozawa et al., 2021), later diagnosis was linked to greater anxious and depressive symptoms. The strength of this finding across two samples with notable differences underscores the importance of early identification of autism. In fact, despite higher rates of psychopathology overall, youth in the present sample diagnosed prior to age six had nearly identical rates of anxiety and depression, when compared to a similarly aged sample (Baribeau et al., 2023), which further supports the link between early diagnostic timing and positive mental health outcomes. Diagnosis is a primary vehicle driving access to services that can increase quality of life and lead to better developmental and mental health outcomes (Dawson, 2008; Elder et al., 2017; Fernell et al., 2013). For example, early intervention services often target social communication (Fuller & Kaiser, 2020), which can improve peer relationships and lead to positive downstream mental health outcomes (Dall et al., 2022).

Although stigma can accompany an autism diagnosis and related disclosure (Botha et al., 2020), for many, the receipt of an autism diagnosis is essential to identity formation (Humphrey & Lewis, 2008; Kenny et al., 2016), which is a key predictor of mental health (Côté, 2018; Erikson, 1968). In fact, a meta-synthesis of qualitative research into the diagnostic process underscores newly later-diagnosed individuals’ re-evaluation of their own identity, often becoming more self-compassionate and self-understanding (Wilson et al., 2023). In re-evaluating their identity, autistic people explicitly cite the impact that lacking a diagnosis had on their mental health, feeling “a lot of suffering might have been avoided” with an earlier diagnosis (Punshon et al., 2009).

In addition to convergent findings between samples related to diagnostic age and psychopathology, sex was not predictive of anxious symptoms in either sample. Despite some reported links between sex/gender and psychopathology for autistic individuals (Martini et al., 2022; Rødgaard et al., 2021; Sedgewick et al., 2021), the body of literature on autistic youth’s sex/gender differences in anxiety is discordant. The present findings converge with others of preadolescent youth (Ambrose et al., 2020; Sukhodolsky et al., 2020) and may be attributable to sex-based trajectories of psychopathology that become apparent only with maturation (Gotham et al., 2015; van Steensel et al., 2011).

In contrast to previously documented sex differences in age at diagnosis (Hosozawa et al., 2021; Kavanaugh et al., 2021; Mandy et al., 2022), there were no differences in age at diagnosis by sex in the research-based sample. However, in the clinic-based sample, those assigned female were diagnosed later than their peers assigned male at birth, as has been reported in other community-based samples (D’Mello et al., 2022). Participants were required to meet diagnostic thresholds on “gold-standard” diagnostic measures to be included in the research-based sample. Thus, entry into the research-based sample required successful navigation of a diagnostic system often biased toward those assigned male. In contrast, the clinic-based sample was ascertained through typical clinical intake processes with fewer prerequisites for inclusion; diagnosis in the clinic-based sample prioritized expert clinical judgment and relied less rigidly on thresholds of “gold-standard” diagnostic assessments, such as in cases presenting with complex co-occurring conditions or low tolerance for behavioral assessments. It may be that inclusionary/exclusionary methods and recruitment pathways used in traditional academic research favor autistic individuals who exhibit the canonical autism phenotype typically identified earlier and in those assigned male at birth (D’Mello et al., 2022), whereas those identified through clinical referral are more resemblant of the heterogeneity commonly associated with autism.

Sex predicted depressive symptoms in the research-based, but not in the clinic-based sample. Notably, the research-based sample participants are about six years older than the clinic-based sample, and their average age hovers around puberty (M = 12.39). Consistent with findings outside of autism (Jalnapurkar et al., 2018; Salk et al., 2017), findings suggest that sex differences in depressive symptoms in autistic individuals emerge with age (Solomon et al., 2012). Findings from one study suggest that sex-related differences in trajectories of depressive symptoms in autistic youth may be particularly heightened in early adolescence (Oswald et al., 2016) and thus depressive symptoms may not be as pronounced in the younger clinic-based sample.

Age at autism diagnosis statistically mediated the relationship between sex and psychopathology in the clinic-based, but not in the research-based sample. These differential findings are likely, in part, a reflection of the disparate statistical power between the two samples, specifically the low statistical power of the research-based sample to detect an indirect effect. In addition, younger age at diagnosis in the research-based sample may have impacted the strength of the mediation. The mediating role of diagnostic timing may not be as pronounced in the research-based sample, which benefits from not only early diagnosis but access to services that bolster developmental and mental health outcomes. In comparison to the clinic-based sample, individuals assigned female in the research-based sample had a shorter window of time pre-diagnosis—a time marked by challenges for many undiagnosed individuals, especially those assigned female at birth.

Sex differences in psychopathology may contribute to diagnostic bias or the “overshadowing” of core autism features by anxious or depressive symptoms. Thus, the mediating role of diagnostic timing found in the clinic-based sample provides a quantitative illustration of prior qualitative work (Bargiela et al., 2016; Milner et al., 2019). Living with undiagnosed autism is a more common experience for assigned female relative to assigned male at birth individuals, making them more vulnerable to reduced quality of life, inadequate treatment or support, and identity challenges. Despite negative experiences with the diagnostic assessment process, many parents of autistic PRF and autistic PRF themselves cite finally receiving a diagnosis as a relief (Freeman & Paradis, 2023; Milner et al., 2019), especially after extended time lost due to misdiagnoses and missed opportunities for appropriate intervention.

Parents of autistic PRF diagnosed later in childhood report that finally receiving the diagnosis helped them to better understand and support their daughters (Rabbitte et al., 2017). This notion extends to autistic PRF diagnosed in adulthood, many of whom characterize the diagnostic process as a “sense-making” experience (Kelly et al., 2022), in which many can understand their experiences through a neurodiversity-affirming framework after experiences of marginalization and stigma (Milner et al., 2019). However, life pre-diagnosis is often marked with challenges. Later diagnosed PRF describe their experience of life pre-diagnosis as exhausting, and one woman recounted that by the time she received a diagnosis in adulthood, she was “already in a functional depressed state” (Leedham et al., 2020). Previous qualitative work such as this, paired with the present quantitative findings linking age at diagnosis and psychopathology, positions autism diagnostics as one pivotal target for improving quality of life, as our findings suggest the link between sex and psychopathology is through the age at diagnosis for autistic youth.

Limitations

When interpreting the differences in results by sample, differences in case ascertainment and sample size likely played a role in the detection of meaningful differences. Although results of the post hoc power analyses improve our understanding of the differences between the samples, the present study remains limited in its ability to detect the key driver in the differential findings between the two samples. While statistical power reasonably played an important role, differences in case ascertainment likely also played a role in the meaningful differences between the samples; however, this hypothesis could not be directly tested due to incomparability between the statistical models tested by sample (e.g. age at diagnosis and caregiver-report of psychopathology being concurrent in the clinic-based, but not research-based sample). Future research must continue to evaluate the ways in which case ascertainment methodology may impact sex-related findings related to diagnostic timing.

An additional limitation is that the present samples are both cross-sectional, limiting the ability of the present study to detect temporal outcomes of late diagnosis. While this limits the ability to draw causal conclusions, the present study provides foundational evidence to support the mediating role of diagnostic age that should be further scrutinized in longitudinal studies. In addition to being cross-sectional, individuals were diagnosed either before or at the time of completion of psychopathology measures. Given this timing, the present study could not investigate alternative hypotheses, such as later age at diagnosis being an outcome of greater anxious and depressive symptoms rather than a predictor or a bidirectional relationship existing between age at diagnosis and psychopathology.

Psychopathology was measured via a broadband caregiver-report measure that was developed for non-autistic populations, which may impact the interpretation of the findings. Although the ASEBA scales used in the present study have been validated for use in autistic samples, self-report measures and those developed for autistic populations may provide added insight into the presentation of mental health challenges in autistic youth. In addition, multi-informant approaches to understanding developmental psychopathology are preferred, as informants often differ in their reports of psychopathology (De Los Reyes & Kazdin, 2005).

Moreover, measures of psychopathology in the clinic-based sample were captured at the time of the autism diagnostic assessment, a moment of overall distress that may impact ratings of anxious and depressive symptoms. However, convergent findings between the research-based and clinic-based samples mitigate this concern, as the research-based sample has the benefit of separate timepoints between time of diagnosis and research participation.

Related to timing, data collection for the present study took place over several years, in which the United States experienced many changes that could not be controlled for in the present analyses. Over the last 10 years, youth mental health challenges in the United States have drastically increased (Bommersbach et al., 2023), due in part due to the emergence of social media (O’Reilly, 2020). In addition, the American Academy of Pediatrics’ recommendation for routine screening and surveillance (Myers et al., 2007) and the COVID-19 pandemic (Zwaigenbaum et al., 2021) transformed diagnostic practices and resultant timing of autism diagnosis (see Supplemental Material 5 for detailed discussion in relation to the present study).

The present sample was also limited based on the availability of demographic information for both samples. Lack of parity between the samples in the querying of ethnoracial identity prevented the statistical examination of sample differences. Recent guidance from both the American Psychological Association and American Medical Association urges researchers to avoid collapsing ethnoracial identities (American Psychological Association, 2019; Flanagin et al., 2021). The present study is limited by the narrow representation of diverse ethnoracial identities, particularly in the predominantly White research-based sample, which prevents analysis of specific identities.

Finally, the present samples lacked systematic data regarding gender identity, thus only allowing for analyses of sex assigned at birth. Gender diversity has known linkages to later autism diagnosis, and autistic gender-diverse youth experience compounded mental health challenges (Warrier et al., 2020). Future research should investigate mechanisms and outcomes of late diagnosis in gender-diverse individuals.

Conclusions

Age at autism diagnosis is a significant predictor of psychopathology in both research-based and clinic-based samples. The link between later age at diagnosis and greater anxious and depressive symptoms suggests that early identification may be a pathway to better mental health outcomes. When it comes to identification of autism, time is of the essence; those who are diagnosed later, especially girls/women, have greater mental health challenges, perhaps as a product of referral and diagnostic system failures in timely identification and services. The present study provides an important impetus for further evaluating sex-based disparities in diagnostic timing and mental health and enhancing tools for recognizing autism in girls/women. Future research is needed to disentangle the role that case ascertainment plays in findings related to sex, age at diagnosis, and mental health outcomes.

Supplemental Material

sj-docx-1-aut-10.1177_13623613241249878 – Supplemental material for Time is of the essence: Age at autism diagnosis, sex assigned at birth, and psychopathology

Supplemental material, sj-docx-1-aut-10.1177_13623613241249878 for Time is of the essence: Age at autism diagnosis, sex assigned at birth, and psychopathology by Jessica V Smith, Goldie A McQuaid, Gregory L Wallace, Emily Neuhaus, Andrea Lopez, Allison B Ratto, Allison Jack, Alexis Khuu, Sara J Webb, Alyssa Verbalis, Kevin A Pelphrey and Lauren Kenworthy in Autism

Footnotes

Author’s note

The present results were presented at the International Society for Autism Research (INSAR) 2023 Annual Meeting.

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 work was supported by the District of Columbia Intellectual and Developmental Disabilities Research Center (DC-IDDRC; grant no. U54HD090257) by NICHD (PI: T. Haydar, W. Gaillard), Clinical and Translational Science Institute at Children’s National Hospital (grant no. UL1TR001876), and National Institute of Mental Health (NIMH) Autism Center of Excellence Network (grant no. R01 MH100028; PI(s): K.A.P., L.K., A.J.).

ORCID iDs

Jessica V Smith

Goldie A McQuaid

Gregory L Wallace

Emily Neuhaus

Allison B Ratto

Supplemental material

Supplemental material for this article is available online.

Notes

References

Achenbach

T. M.

(2013). DSM guide for the ASEBA. University of Vermont, Research Center for Children, Youth, & Families.

Ambrose

Adams

Simpson

Keen

(2020). Exploring profiles of anxiety symptoms in male and female children on the autism spectrum. Research in Autism Spectrum Disorders, 76, Article 101601.

American Psychiatric Association. (2022). Neurodevelopmental disorders. In Diagnostic and statistical manual of mental disorders (5th ed., text rev.). https://doi.org/10.1176/appi.books.9780890425787.x01_Neurodevelopmental_Disorders

American Psychological Association. (2019). APA task force on race and ethnicity guidelines in psychology: Promoting responsiveness and equity. https://www.apa.org/about/policy/guidelines-race-ethnicity.pdf

Atherton

Edisbury

Piovesan

Cross

(2022). Autism through the ages: A mixed methods approach to understanding how age and age of diagnosis affect quality of life. Journal of Autism and Developmental Disorders, 52(8), 3639–3654.

Bargiela

Steward

Mandy

(2016). The experiences of late-diagnosed women with autism spectrum conditions: An investigation of the female autism phenotype. Journal of Autism and Developmental Disorders, 46(10), 3281–3294.

Baribeau

D. A.

Vigod

S. N.

Pullenayegum

Kerns

C. M.

Vaillancourt

Duku

. . . Szatmari

(2023). Developmental cascades between insistence on sameness behaviour and anxiety symptoms in autism spectrum disorder. European Child & Adolescent Psychiatry, 32(11), 2109–2118.

Beggiato

Peyre

Maruani

Scheid

Rastam

Amsellem

. . .Delorme

(2017). Gender differences in autism spectrum disorders: Divergence among specific core symptoms. Autism Research, 10(4), 680–689.

Bommersbach

T. J.

McKean

A. J.

Olfson

Rhee

T. G.

(2023). National trends in mental health–related emergency department visits among youth, 2011–2020. JAMA, 329(17), 1469–1477. https://doi.org/10.1001/jama.2023.4809

10.

Botha

Dibb

Frost

D. M.

(2022). “Autism is me”: An investigation of how autistic individuals make sense of autism and stigma. Disability & Society, 37(3), 427–453.

11.

Côté

J. E.

(2018). Youth development in identity societies: Paradoxes of purpose (1st ed.). Taylor & Francis.

12.

Dall

Fellinger

Holzinger

(2022). The link between social communication and mental health from childhood to young adulthood: A systematic review. Frontiers in Psychiatry, 13, Article 944815.

13.

Dawson

(2008). Early behavioral intervention, brain plasticity, and the prevention of autism spectrum disorder. Development and Psychopathology, 20(3), 775–803.

14.

De Los Reyes

Kazdin

A. E

. (2005). Informant discrepancies in the assessment of childhood psychopathology: A critical review, theoretical framework, and recommendations for further study. Psychological Bulletin, 131(4), 483–509.

15.

D’Mello

A. M.

Frosch

I. R.

C. E.

Cardinaux

A. L.

Gabrieli

J. D. E.

(2022). Exclusion of females in autism research: Empirical evidence for a “leaky” recruitment-to-research pipeline. Autism Research, 15(10), 1929–1940.

16.

Elder

J. H.

Kreider

C. M.

Brasher

S. N.

Ansell

(2017). Clinical impact of early diagnosis of autism on the prognosis and parent–child relationships. Psychology Research and Behavior Management, 10, 283–292. https://doi.org/10.2147/PRBM.S117499

17.

Elliott

C. D.

Murray

G. J.

Pearson

L. S.

(1990). Differential Ability Scales. The Psychological Corporation.

18.

Epps

H. V.

Astudillo

Martin

Y. D. P.

Marsh

(2022). The Sex and Gender Equity in Research (SAGER) guidelines: Implementation and checklist development. European Science Editing, 48, Article e86910. https://doi.org/10.3897/ese.2022.e86910

19.

Erikson

E. H.

(1968). Identity, youth and crisis. W. W. Norton Company.

20.

Fernell

Eriksson

M. A.

Gillberg

(2013). Early diagnosis of autism and impact on prognosis: A narrative review. Clinical Epidemiology, 5, 33–43.

21.

Flanagin

Frey

Christiansen

S. L

., & AMA Manual of Style Committee. (2021). Updated guidance on the reporting of race and ethnicity in medical and science journals. JAMA, 326(7), 621–627. https://doi.org/10.1001/jama.2021.13304

22.

Freeman

N. C.

Paradis

(2023). Parent experiences of obtaining an autism diagnosis for their daughter: An interpretative phenomenological analysis. Autism, 27(4), 1068–1078.

23.

Fuller

E. A.

Kaiser

A. P.

(2020). The effects of early intervention on social communication outcomes for children with autism spectrum disorder: A meta-analysis. Journal of Autism and Developmental Disorders, 50(5), 1683–1700. https://doi.org/10.1007/s10803-019-03927-z

24.

Fusar-Poli

Brondino

Politi

Aguglia

(2022). Missed diagnoses and misdiagnoses of adults with autism spectrum disorder. European Archives of Psychiatry and Clinical Neuroscience, 272(2), 187–198.

25.

Gotham

Brunwasser

S. M.

Lord

(2015). Depressive and anxiety symptom trajectories from school age through young adulthood in samples with autism spectrum disorder and developmental delay. Journal of the American Academy of Child & Adolescent Psychiatry, 54(5), 369–376.

26.

Hallett

Ronald

Rijsdijk

Happé

(2010). Association of autistic-like and internalizing traits during childhood: A longitudinal twin study. American Journal of Psychiatry, 167(7), 809–817.

27.

Harrop

Libsack

Bernier

Dapretto

Jack

McPartland

J. C.

. . . & Pelphrey

(2021). Do biological sex and early developmental milestones predict the age of first concerns and eventual diagnosis in autism spectrum disorder? Autism Research, 14(1), 156–168.

28.

Hartley

S. L.

Sikora

D. M.

(2009). Sex differences in autism spectrum disorder: An examination of developmental functioning, autistic symptoms, and coexisting behavior problems in toddlers. Journal of Autism and Developmental Disorders, 39(12), 1715–1722. https://doi.org/10.1007/s10803-009-0810-8

29.

Hayes

A. F.

(2022). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. Guilford Publications.

30.

Hisle-Gorman

Landis

C. A.

Susi

Schvey

N. A.

Gorman

G. H.

Nylund

C. M.

Klein

D. A.

(2019). Gender dysphoria in children with autism spectrum disorder. LGBT Health, 6(3), 95–100.

31.

Hollocks

M. J.

Lerh

J. W.

Magiati

Meiser-Stedman

Brugha

T. S.

(2019). Anxiety and depression in adults with autism spectrum disorder: A systematic review and meta-analysis. Psychological Medicine, 49(4), 559–572. https://doi.org/10.1017/S0033291718002283

32.

Hosozawa

Sacker

Cable

(2021). Timing of diagnosis, depression and self-harm in adolescents with autism spectrum disorder. Autism, 25(1), 70–78.

33.

Humphrey

Lewis

(2008). “Make me normal”: The views and experiences of pupils on the autistic spectrum in mainstream secondary schools. Autism, 12(1), 23–46.

34.

Hyman, S. L., Levy, S. E., Myers, S. M., &

Council on Children with Disabilities Section on Developmental Behavioral. (2020). Identification, evaluation, and management of children with autism spectrum disorder. Pediatrics, 145(1), Article e20193447.

35.

Jalnapurkar

Allen

Pigott

(2018). Sex differences in anxiety disorders: A review. Journal of Psychiatry Depression and Anxiety, 4(12), 3–16.

36.

Kanne

S. M.

Abbacchi

A. M.

Constantino

J. N.

(2009). Multi-informant ratings of psychiatric symptom severity in children with autism spectrum disorders: The importance of environmental context. Journal of Autism and Developmental Disorders, 39(6), 856–864. https://doi.org/10.1007/s10803-009-0694-7

37.

Kavanaugh

B. C.

Schremp

C. A.

Jones

R. N.

Best

C. R.

Sheinkopf

S. J.

Morrow

E. M.

(2021). Moderators of age of diagnosis in > 20,000 females with autism in two large US studies. Journal of Autism and Developmental Disorders, 53(2), 864–869.

38.

Kelly

Sharma

Jieman

A. T.

Ramon

(2022). Sense-making narratives of autistic women diagnosed in adulthood: A systematic review of the qualitative research. Disability & Society, 39(3), 663–695.

39.

Kenny

Hattersley

Molins

Buckley

Povey

Pellicano

(2016). Which terms should be used to describe autism? Perspectives from the UK autism community. Autism, 20(4), 442–462.

40.

Kirkovski

Enticott

P. G.

Fitzgerald

P. B.

(2013). A review of the role of female gender in autism spectrum disorders. Journal of Autism and Developmental Disorders, 43(11), 2584–2603.

41.

Lai

M. C.

Baron-Cohen

(2015). Identifying the lost generation of adults with autism spectrum conditions. The Lancet Psychiatry, 2(11), 1013–1027.

42.

Lai

M. C.

Kassee

Besney

Bonato

Hull

Mandy

. . .Ameis

S. H.

(2019). Prevalence of co-occurring mental health diagnoses in the autism population: A systematic review and meta-analysis. The Lancet Psychiatry, 6(10), 819–829.

43.

Lai

M. C.

Lombardo

M. V.

Auyeung

Chakrabarti

Baron-Cohen

(2015). Sex/gender differences and autism: Setting the scene for future research. Journal of the American Academy of Child & Adolescent Psychiatry, 54(1), 11–24.

44.

Lainhart

J. E.

Bigler

E. D.

Bocian

Coon

Dinh

Dawson

. . .Volkmar

(2006). Head circumference and height in autism: A study by the collaborative program of excellence in autism. American Journal of Medical Genetics. Part A, 140(21), 2257–2274.

45.

Leedham

Thompson

A. R.

Smith

Freeth

(2020). “I was exhausted trying to figure it out”: The experiences of females receiving an autism diagnosis in middle to late adulthood. Autism, 24(1), 135–146.

46.

Lockwood Estrin

Milner

Spain

Happé

Colvert

. (2021). Barriers to autism spectrum disorder diagnosis for young women and girls: A systematic review. Review Journal of Autism and Developmental Disorders, 8(4), 454–470.

47.

Loomes

Hull

Mandy

W. P. L.

(2017). What is the male-to-female ratio in autism spectrum disorder? A systematic review and meta-analysis. Journal of the American Academy of Child & Adolescent Psychiatry, 56(6), 466–474.

48.

Lord

Rutter

DiLavore

Risi

Gotham

Bishop

(2012). Autism diagnostic observation schedule (ADOS-2) (2nd ed.).Western Psychological Corporation.

49.

Lord

Rutter

Le Couteur

(1994). Autism diagnostic interview-revised: A revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. Journal of Autism and Developmental Disorders, 24(5), 659–685.

50.

Maenner

M. J.

Warren

Williams

A. R.

Amoakohene

Bakian

A. V.

Bilder

D. A.

. . .Shaw

K. A.

(2023). Prevalence and characteristics of autism spectrum disorder among children aged 8 years–Autism and developmental disabilities monitoring network, 11 sites, United States, 2020. Morbidity and Mortality Weekly Report. Surveillance Summaries, 72(2), 1–14.

51.

Magiati

Ong

Lim

X. Y.

Tan

W.-L.

Ong

A. Y. L.

Patrycia

. . .Howlin

(2016). Anxiety symptoms in young people with autism spectrum disorder attending special schools: Associations with gender, adaptive functioning and autism symptomatology. Autism: The International Journal of Research and Practice, 20(3), 306–320. https://doi.org/10.1177/1362361315577519

52.

Magyar

C. I.

Pandolfi

(2017). Utility of the CBCL DSM-oriented scales in assessing emotional disorders in youth with autism. Research in Autism Spectrum Disorders, 37, 11–20. https://doi.org/10.1016/j.rasd.2017.01.009

53.

Mandy

Midouhas

Hosozawa

Cable

Sacker

Flouri

(2022). Mental health and social difficulties of late-diagnosed autistic children, across childhood and adolescence. Journal of Child Psychology and Psychiatry, 63(11), 1405–1414.

54.

Martini

M. I.

Kuja-Halkola

Butwicka

Du Rietz

D’Onofrio

B. M.

Happé

. . . Taylor

M. J.

(2022). Sex differences in mental health problems and psychiatric hospitalization in autistic young adults. JAMA Psychiatry, 79(12), 1188–1198.

55.

Milner

McIntosh

Colvert

Happé

(2019). A qualitative exploration of the female experience of autism spectrum disorder (ASD). Journal of Autism and Developmental Disorders, 49, 2389–2402.

56.

Myers

S. M.

Johnson

C. P

., & American Academy of Pediatrics Council on Children with Disabilities. (2007). Management of children with autism spectrum disorders. Pediatrics, 120(5), 1162–1182. https://doi.org/10.1542/peds.2007-2362

57.

Nasca

B. C.

Lopata

Donnelly

J. P.

Rodgers

J. D.

Thomeer

M. L.

(2020). Sex differences in externalizing and internalizing symptoms of children with ASD. Journal of Autism and Developmental Disorders, 50(9), 3245–3252.

58.

O’Reilly

(2020). Social media and adolescent mental health: The good, the bad and the ugly. Journal of Mental Health, 29(2), 200–206. https://doi.org/10.1080/09638237.2020.1714007

59.

Oswald

T. M.

Winter-Messiers

M. A.

Gibson

Schmidt

A. M.

Herr

C. M.

Solomon

(2016). Sex differences in internalizing problems during adolescence in autism spectrum disorder. Journal of Autism and Developmental Disorders, 46, 624–636.

60.

Pandolfi

Magyar

C. I.

Dill

C. A.

(2012). An initial psychometric evaluation of the CBCL 6–18 in a sample of youth with autism spectrum disorders. Research in Autism Spectrum Disorders, 6(1), 96–108.

61.

Prosperi

Turi

Guerrera

Napoli

Tancredi

Igliozzi

. . .Vicari

(2021). Sex differences in autism spectrum disorder: An investigation on core symptoms and psychiatric comorbidity in preschoolers. Frontiers in Integrative Neuroscience, 14, Article 594082.

62.

Punshon

Skirrow

Murphy

(2009). The not guilty verdict: Psychological reactions to a diagnosis of Asperger syndrome in adulthood. Autism, 13(3), 265–283.

63.

Rødgaard

E. M.

Jensen

Miskowiak

K. W.

Mottron

. (2021). Autism comorbidities show elevated female-to-male odds ratios and are associated with the age of first autism diagnosis. Acta Psychiatrica Scandinavica, 144(5), 475–486.

64.

Rødgaard

E.-M.

Jensen

Miskowiak

K. W.

Mottron

(2022). Representativeness of autistic samples in studies recruiting through social media. Autism Research, 15(8), 1447–1456.

65.

Rabbitte

Prendeville

Kinsella

(2017). Parents’ experiences of the diagnostic process for girls with autism spectrum disorder in Ireland: An Interpretative Phenomenological Analysis. Educational and Child Psychology, 34(2), 54–66.

66.

Rea

H. M.

Øien

R. A.

Shic

Webb

S. J.

Ratto

A. B.

(2022). Sex differences on the ADOS-2. Journal of Autism and Developmental Disorders, 53(7), 2878–2890.

67.

Roche

Adams

Clark

(2021). Research priorities of the autism community: A systematic review of key stakeholder perspectives. Autism, 25(2), 336–348.

68.

Salk

R. H.

Hyde

J. S.

Abramson

L. Y.

(2017). Gender differences in depression in representative national samples: Meta-analyses of diagnoses and symptoms. Psychological Bulletin, 143(8), 783–822.

69.

Schoemann

A. M.

Boulton

A. J.

Short

S. D.

(2017). Determining power and sample size for simple and complex mediation models. Social Psychological and Personality Science, 8(4), 379–386.

70.

Sedgewick

Leppanen

Tchanturia

(2021). Gender differences in mental health prevalence in autism. Advances in Autism, 7(3), 208–224.

71.

Solomon

Miller

Taylor

S. L.

Hinshaw

S. P.

Carter

C. S.

(2012). Autism symptoms and internalizing psychopathology in girls and boys with autism spectrum disorders. Journal of Autism and Developmental Disorders, 42(1), 48–59.

72.

Strang

J. F.

Chen

Nelson

Leibowitz

S. F.

Nahata

Anthony

L. G.

. . .Kenworthy

(2022). Transgender youth executive functioning: Relationships with anxiety symptoms, autism spectrum disorder, and gender-affirming medical treatment status. Child Psychiatry and Human Development, 53(6), 1252–1265.

73.

Strang

J. F.

Janssen

Tishelman

Leibowitz

S. F.

Kenworthy

McGuire

J. K.

. . . Anthony

L. G.

(2018). Revisiting the link: Evidence of the rates of autism in studies of gender diverse individuals. Journal of the American Academy of Child and Adolescent Psychiatry, 57(11), 885–887. https://doi.org/10.1016/j.jaac.2018.04.023

74.

Suckle

E. K.

(2021). DSM-5 and challenges to female autism identification. Journal of Autism and Developmental Disorders, 51(2), 754–759.

75.

Sukhodolsky

D. G.

Lecavalier

Johnson

Smith

Swiezy

Bearss

. . .Scahill

(2020). Anxiety in 3- to 7-year-old children with autism spectrum disorder seeking treatment for disruptive behavior. Autism: The International Journal of Research and Practice, 24(2), 400–410. https://doi.org/10.1177/1362361319866561

76.

Tsirgiotis

J. M.

Young

R. L.

Weber

(2022). A mixed-methods investigation of diagnostician sex/gender-bias and challenges in assessing females for autism spectrum disorder. Journal of Autism and Developmental Disorders, 52(10), 4474–4489.

77.

Valicenti-McDermott

Schlussel

Hurley

Rivelis

Bernstein

Cardin

M.-J.

(2023). Comorbidities in school-age children and adolescents with autism in an ethnically diverse population: Brief report. Journal of Child and Adolescent Psychopharmacology, 33(5), 190–194. https://doi.org/10.1089/cap.2023.0006

78.

van Steensel

F. J.

Bögels

S. M.

Perrin

. (2011). Anxiety disorders in children and adolescents with autistic spectrum disorders: A meta-analysis. Clinical Child and Family Psychology Review, 14, 302–317.

79.

van’t Hof

Tisseur

Van Berckelear-Onnes

Van Nieuwenhuyzen

Daniels

A. M.

Deen

. . . Ester

W. A

. (2021). Age at autism spectrum disorder diagnosis: A systematic review and meta-analysis from 2012 to 2019. Autism, 25(4), 862–873.

80.

Vasa

R. A.

Keefer

McDonald

R. G.

Hunsche

M. C.

Kerns

C. M.

(2020). A scoping review of anxiety in young children with autism spectrum disorder. Autism Research: Official Journal of the International Society for Autism Research, 13(12), 2038–2057. https://doi.org/10.1002/aur.2395

81.

Warrier

Greenberg

D. M.

Weir

Buckingham

Smith

Lai

M.-C.

. . .Baron-Cohen

(2020). Elevated rates of autism, other neurodevelopmental and psychiatric diagnoses, and autistic traits in transgender and gender-diverse individuals. Nature Communications, 11(1), Article 3959.

82.

Wechsler

(1997). Wechsler Adult Intelligence Scale (3rd ed.). Harcourt Assessment.

83.

Wechsler

(1999). Wechsler Abbreviated Scale of Intelligence. Hartcourt Assessment.

84.

Wechsler

(2003). Wechsler Intelligence Scale for Children (4th ed.). Harcourt Assessment.

85.

Wechsler

(2011). Wechsler Abbreviated Scale of Intelligence (2nd ed.). NCS Pearson.

86.

Wechsler

(2014). Wechsler Intelligence Scale for Children (5th ed.). NCS Pearson.

87.

Wilson

R. B.

Thompson

A. R.

Rowse

Freeth

(2023). The experience of seeking, receiving, and reflecting upon a diagnosis of autism in the UK: A meta-synthesis of qualitative studies conducted with autistic individuals. Research in Autism Spectrum Disorders, 103, Article 102135.

88.

Zwaigenbaum

Bishop

Stone

W. L.

Ibanez

Halladay

Goldman

. . .Wetherby

(2021). Rethinking autism spectrum disorder assessment for children during COVID-19 and beyond. Autism Research: Official Journal of the International Society for Autism Research, 14(11), 2251–2259. https://doi.org/10.1002/aur.2615

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