Exclusion of females in autism research: Empirical evidence for a "leaky" recruitment-to-research pipeline

Abstract

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

Keywords: ABIDE; ADOS; Autism Physical Health Survey; Channel 4; IMAGES; LifeLines; Musicial Universe; SPARK; autism spectrum disorder; diagnosis; exclusion criteria; females; inclusion criteria; recruitment; sex differences.

FIGURE 1

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

FIGURE 2

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

FIGURE 3

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