Unveiling the female autistic profile

Kalliopi Demetriou and Ian Fairholm argue that a self-perpetuating cycle keeps us from getting women the support they need.

We are becoming increasingly aware that females with autism are being mis- or under-diagnosed. Autism continues to be diagnosed at considerably lower rates in females than in males, with studies reporting a striking 1:4 ratio (Fombonne, 2009). This has often been attributed to a biologically increased risk for autism in males – for example, see Simon Baron-Cohen on empathising, systemising and the ‘extreme male brain’ theory. However, such theories have been highly criticised as lacking robust empirical support. Most importantly, they fail to explain why females who do receive an autism diagnosis, receive it on average five years later than males (Rynkiewicz & Łucka, 2015). Accounts from females who self-identify with autism, but have not received a clinical diagnosis, have also brought to the fore the idea that we are missing females with autism (e.g. Gould, 2017; Loomes et al., 2017). 

Perhaps this should not come as a surprise, considering that diagnostic procedures have been developed mainly from male samples. In fact, Thompson and colleagues (2003) conducted a large-scale review of 392 empirical articles on autism and estimated that 80 per cent of the collective sample was comprised of males. In addition, only 5 per cent of the studies including both sexes analysed data separately for males and females. It seems reasonable to assume that our current knowledge and capacity to diagnose autism is biased towards the male presentation, meaning that females are falling under the radar.

Many researchers have devoted their work to unveiling the female autistic profile, in order to improve our understanding and capacity to provide timely diagnosis. Here, we argue that progress in this will remain slow while challenges in the field keep us locked in a self-perpetuating cycle. 

Samples

Given that relatively few women have an autism diagnosis, studies which compare males and females with autism, or simply focusing on females with the diagnosis, struggle to recruit high enough numbers (Mandy et al., 2012). This results in low powered analysis, hindering our capacity to identify differences and patterns in the female presentation of autism.

Possibly due to the difficulties in recruiting large numbers of autistic females, many studies in the field include samples with broad age ranges, spanning childhood and adolescence (e.g. Backer van Ommeren et al., 2017; Mandy et al., 2012),. Considering that autism is characterised by changes in symptomatic expression with age (McGovern & Sigman, 2005), samples with such wide age ranges include a lot of variability in symptoms, thus creating a blurry picture which might be confounding existing sex differences. In fact, the studies that use wide age-ranges tend not to identify sex differences, whereas the few studies with restricted age ranges report sex differences in core autistic symptoms (Hartley & Sikora, 2009). 

Measures 

The assessment tools that are being used for diagnosis and in research in this field have often been developed based predominantly on male samples. These include questionnaires such as the Social Responsiveness Scale (SRS; Constantino & Gruber, 2012), semi-structured clinical interviews such as the Autism Diagnostic Interview-Revised (ADI-R; Lord et al., 1994), and clinically administered observational schedules such as the Autism Diagnostic Observation Schedule (ADOS-2; Lord et al., 2000; Oosterling et al., 2010). Considering that these instruments were designed to assess the stereotypical presentation of autism in males, they might omit symptoms relevant to the female profile (Pilowsky et al., 1998). For example, studies have shown that within the domain of repetitive restrictive behaviour, autistic girls show repetitive interests that are more in line with neurotypical girls, such as fixations on boybands, ponies and stickers, whereas autistic boys show striking and eccentric interests, including fixations on mechanical objects and small parts (Hiller et al., 2016). In the domain of reciprocal social interaction, females with autism tend to misbehave in rather passive ways, such as failing to do what they are told, which contrasts with autistic males who are more likely to act-out and show more aggressive behaviours (Werling & Geswchwind, 2013). 

Studies which rely on reports from parents (e.g. Frazier & Hardan, 2017) can also be problematic. This is because parent-reported autistic symptoms can be biased by sex-stereotypes and expectations. For example, Holtmann et al. (2007) examined sex differences in autism based on both experts and parents’ ratings and found that parents reported more social problems in their daughters than sons, which was not reflected in the experts’ ratings. It is likely that parents’ ratings are driven more by their sex-related social expectations rather than the actual behaviours of their children. 

There’s also a significant issue that crosses samples and measures. Research which focuses on females with an existing autism diagnosis (as evident in systematic reviews such as Hull et al., 2017) isn’t reaching those it most needs. Females with an autism diagnosis are likely those who have not gone under the radar, precisely because they resemble the stereotypical male profile. We’re locked in a self-perpetuating cycle, where our biased knowledge on autism – accumulated through decades of research focusing on males – is obstructing our capacity to look beyond the stereotypical presentation. It’s only by breaking this cycle that we’ll fully formulate the female profile, to aid the timely diagnosis of females who are being missed.

So how might we break that cycle, in research and practice?

Go beyond clinically ascertained samples

Recent research has moved beyond the recruitment of samples solely from autism diagnostic clinics, for example by using social media and other online platforms in an effort to recruit larger sample sizes and reach females from the wider autism community (e.g. Bargiela et al., 2016). A remarkable initiative along these lines is Greenberg et al.’s (2018) collaboration with Channel 4. They developed a website where users completed a number of scientific measures relating to autistic symptoms, obtaining the biggest sample to date of 36,648 autistic individuals, including 18,460 females. However, similar to other online studies, they identified the autistic sample based on participants’ disclosure of a clinical autism diagnosis – the clinical-ascertainment bias is still likely to be present. Future studies could include screening questionnaires with low cut-off points and also invite females who self-identify with the autism diagnosis to complete the online measures. Those presenting with suspected autism could then be invited for a detailed assessment to identify possible undiagnosed cases. Findings from such population-wide studies could help reach those females who might never be referred for an autism assessment due to their unique presentation.

Involve experts by experience

Females who remain under the radar for many years and are finally diagnosed with autism in their adult life, such as in this account, should be at the forefront of research in this field. These are the females with the postulated unique female profile, which has led to them being missed for a substantial part of their lives. Capitalising on their expertise could move this field forwards.

Qualitative research methods would be best suited for such research, really tapping into lived experiences of autism. For example, some recent studies employing these methods found that social pressures motivate these females to camouflage their symptoms, meaning that they remain undiagnosed (Milner et al., 2019). Such research can provide a wealth of evidence from which we could develop more suitable assessment procedures to identify these females. Their experiences and insights on what has helped them could hugely benefit other women who receive a diagnosis late on in life.

We also need to incorporate and give more weight to self-report questionnaires or interviews as part of the diagnostic assessment, in order to gain insights from females themselves regarding their experiences and perceived difficulties. The Autism-spectrum Quotient (AQ; Baron-Cohen et al., 2001) is one example of a self-report questionnaire which has been shown to identify more autistic features in adult females with autism compared to the ADOS standardised autism assessment tool (Lai et al., 2011).

Be research aware, and trust clinical judgment 

It is important that clinicians are updated with research findings and best practice methods for the diagnosis of females with autism. Education and training could be achieved through programmes such as the ‘Women and Girls’ online training module on the National Autistic Society website, which is developed by experts in the field in conjunction with autistic females and gives an insight into the latest empirical evidence regarding females’ phenotype and diagnostic recommendations.

Considering the biased nature of standardised autism assessment tools, it is also important that clinicians rely more on their clinical judgment and expertise rather than basing their diagnostic decision solely on outcomes of standardised assessments (Kreiser & White, 2014). 

Looking forward

There’s no doubt that researchers have a challenge on their hands, in their efforts to accumulate robust evidence on the female presentation of autism. Our intention in setting out those challenges has not been to feed pessimism, but to raise awareness and encourage an open discussion about ways forward. We have made some recommendations which we hope will go some way towards more females receiving a timely diagnosis.

This is important. Although the females who fly under the radar might appear to be managing well, testimonials from those who receive a late diagnosis illustrate that they have been suffering throughout their lives, often exhausted, their needs being misunderstood and misattributed to different diagnoses coupled with ongoing mental health difficulties (Bargiela et al., 2016). Strikingly, research from Sarah Cassidy and Simon Baron-Cohen has suggested that camouflaging, together with unmet support needs, is uniquely associated with increased suicidality in this population (Cassidy et al., 2014; Richards et al., 2019).

We can help these women recognise why they've been finding things so tough, and enable more acceptance and understanding of themselves, and from those around them, which should in turn enable them to get appropriate support. Depending on their individual needs this could take the form of psychoeducation on autism, social skills training, emotional support and nurturing of their individual strengths. All of this understanding, acceptance and action based on the female autistic profile could mean that future generations of females will hopefully not have to suffer in the first place.

- Kalliopi Demetriou is a Research Master student in Psychopathology at Maastricht University [email protected]

“This article was inspired by my experience working as an Assistant Psychologist in Berkshire CAMHS Autism Assessment Team, and my interactions with females and their families within that context. I invite anyone with lived experiences to share their voice and views on this topic.”

- Ian Fairholm is a Senior Lecturer at the University of Bath [email protected]   

- Find much more on autism, including autistic people in psychological research, in our archive

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