A Cross-Neurotype Analysis of Camouflaging and Illness Perceptions in People with Central Sensitivity Syndromes

Abstract

Background:

Central sensitivity syndromes (CSS) are a group of conditions, including fibromyalgia, migraine, and others, that are thought to share a common mechanism of central sensitization—that is, pain and hypersensitivity that originate in the central nervous system. Research suggests that autistic adults may be more likely to have a CSS, and that autistic traits, sensory sensitivity and anxiety, all contribute to an association. This study aimed to explore whether autistic camouflaging could also be related to CSS symptoms in autistic and nonautistic adults. In addition, we completed an analysis of illness perceptions to determine whether autistic and nonautistic people may experience chronic illness differently.

Methods:

The sample comprised 504 adults (88 men, 416 women) with clinical diagnoses of autism, CSS, both diagnoses or neither (i.e., a comparison group), who completed online self-report validated questionnaires, including the Autism Spectrum Quotient, Central Sensitization Inventory, Sensory Perception Quotient, the Camouflaging Autistic Traits Questionnaire, the Brief Illness Perceptions Questionnaire, the Patient Health Questionaire-9, and Generalized Anxiety Disorder-7.

Results:

Camouflaging significantly predicted CSS symptoms in this sample. Autistic people with a CSS had higher camouflaging scores (mean: 130.28) than the other diagnostic groups, with a significant difference between the comparison and the CSS-only group (p < 0.001). The autism-only and CSS-only groups had significantly higher camouflaging scores than the comparison group (p < 001) but not from each other (119.35 vs. 107.94). Autistic people reported a significantly more negative effect of chronic illness on their life (f (1333) = 5.289 p = 0.022); there were few other differences in illness perceptions between autistic and nonautistic people with a CSS.

Conclusion:

Autistic camouflaging is associated with CSS symptoms. Autistic people who receive a CSS diagnosis are particularly at risk for greater illness-related disability, including poorer quality of life and mental health.

Community Brief

Why is this an important issue?

Lots of research has demonstrated that autistic adults are more vulnerable to poor physical health and chronic illness. Autistic people seem to be more likely to have central sensitivity syndromes (CSS). Conditions listed under the CSS umbrella include fibromyalgia, irritable bowel syndrome, and migraine. These conditions are considered to share a common mechanism of “central sensitization,” in which the neural signals in a person’s brain and spinal cord become amplified, causing several symptoms, including pain and sensory hypersensitivity, fatigue, and brain fog. We do not have a good understanding of why CSSs are common in autistic people, or what impact CSSs have on autistic people’s lives.

What was the purpose of this study?

We wanted to explore how some psychological or social factors in people’s lives might differ between autistic and nonautistic people with and without a CSS. We were particularly interested in whether there was a relationship between camouflaging, which is when people use social strategies and techniques to hide their autistic traits, and CSS symptoms. We also wanted to explore whether autistic and nonautistic people think about and perceive chronic illness differently.

What did the researchers do?

We used data from 504 online survey participants to compare social camouflaging scores between autistic and nonautistic people with and without a CSS. We also explored group differences in autistic and nonautistic illness beliefs between a subset of people who all had a CSS diagnosis.

What were the results of the study?

While autistic people camouflaged the most, nonautistic people with a CSS had higher camouflaging scores than the comparison group (nonautistic people without a CSS). This could suggest that the CAT-Q, the measure used to explore camouflaging, may also be capturing techniques that nonautistic people use in everyday life to manage others’ impressions of them, such as through concealing symptoms and effects of chronic illness. Alternatively, it could suggest that a significant number of autistic people receive a CSS diagnosis before autism is recognized, and that diagnostic overshadowing could be happening—that is, traits and experiences related to autism could be missed or misattributed by clinicians to an already diagnosed CSS. Autistic people with a CSS also reported a greater impact of illness on their life, even though the number of symptoms they experienced did not differ significantly from nonautistic people with a CSS. This could mean that, in addition to autistic people being more likely to experience chronic illness, the impact of chronic illness may be greater on autistic people than for nonautistic people.

What do these findings add to what was already known?

While it is known that autistic people are more prone to physical ill health, these findings add to our understanding of the association between autism and CSS. They also suggest that underdiagnosis of autism and/or diagnostic overshadowing could occur in people with both CSS symptoms and autistic traits, and emphasize the heavy impact of chronic illness on autistic quality of life.

What are potential weaknesses in the study?

The participants were recruited online with a large proportion of women responding, and more nonautistic people with a CSS than autistic people.

How will these findings help autistic adults now or in the future?

These findings draw attention to the experience of autistic people with a chronic illness and suggest future directions for research into autism and co-occurring conditions.

Background

An increased research interest in physical health and autism in recent years has highlighted the vulnerability of autistic individuals to both chronic disease^1,2 and premature mortality.^3,4 Autistic adults, particularly autistic women, appear to be more prone to chronic illness, and while health issues have been demonstrated across all organ systems,⁵ some associations with specific conditions are well established, such as epilepsy^6,7 and gastrointestinal disorders,^8,9 and some have been more recently explored, such as joint hypermobility and the Ehlers–Danlos syndromes (EDS).^5,10,11

Research has also demonstrated that autistic adults appear to be more vulnerable to experiencing symptoms of central sensitization, and health conditions related to these symptoms.^5,12 Central sensitization is a phenomenon in which the central nervous system becomes “hyperexcitable” and sensory input, particularly pain, is amplified.^13–17 Conditions linked to central sensitization include fibromyalgia syndrome (FMS), irritable bowel syndrome (IBS), myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), and migraine, and these are therefore often collectively referred to as “central sensitivity syndromes” (CSS)^18,19 or sometimes “chronic overlapping pain conditions.”²⁰

Some of the associations between autism and CSS can be explained by shared etiological factors, including a demonstrated link between autism, joint hypermobility and the EDS.^10,11 Symptomatic joint hypermobility often underlies CSS and is underrecognised.^21–23 Autism also appears to be more prevalent in families with a history of autoimmune disorders,^24,25 with immune differences, inflammation, and autoimmunity also identified in CSS.^26–29 Genetics are likely to play a role in a relationship between autism and CSS; autism is highly heritable,³⁰ and some CSSs, particularly migraine and FMS, have been demonstrated to have clear genetic predictors.^31–33 Lastly, research focused on autism and pain has highlighted that autistic people may be more sensitive to pain than the general population, more vulnerable to chronic pain, and that this vulnerability could be related to general sensory sensitivity.^12,34–39

For complex conditions such as CSS, a focus on genetic and biological mechanisms can be helpful, but chronic illness is a complicated interplay of biological, psychological, social, and behavioral factors.^40–43 CSSs have repeatedly been linked to psychological factors, particularly chronic stress,^42,44,45 anxiety and depression,^46–48 domestic abuse,^49–51 and trauma.⁵² People with CSS also often experience social stigma and discrimination, including from health professionals,^53–56 which may contribute to poor mental health and exacerbate physical symptoms.

It follows that the autistic community, who have poorer physical health^1,5 and are more vulnerable to psychosocial factors linked with CSS, including domestic violence,⁵⁷ poor mental health,^58–60 post-traumatic stress disorder (PTSD),^61–63 stigma, and discrimination,^64,65 may be more likely to experience CSS.^5,12 Although there is little research directly exploring how psychological and social aspects may interact with or contribute to physical health difficulties in autism, a relationship between somatic symptoms and mental health has been demonstrated in a number of studies.^12,36,37,66 Common CSS symptoms such as chronic fatigue, reduced tolerance of sensory stimuli, and cognitive difficulties or “brain fog” also overlap substantially with descriptions of autistic burnout,^67–69 a phenomenon only recognized in the literature relatively recently. In addition, autistic people experience barriers to health care that could increase stress levels and contribute to missed or delayed health diagnoses.^65,70

A psychosocial factor that may contribute to poor physical health in autism is a phenomenon referred to as camouflaging, or masking, a coping strategy adopted by many autistic people through which they hide autistic traits and/or use techniques that help them appear more similar to other people.^71,72 Camouflaging is associated with delayed autism diagnosis and poor mental health^73–75 and has also been associated with poorer physical health outcomes,^76–78 with a recent study positing that camouflaging could be a chronic stressor that can lead to a pathophysiological state experienced as autistic burnout.⁷⁹ It is possible that camouflaging autistic traits might also be related to a higher incidence of CSS symptoms in autistic people. While camouflaging is predominantly referred to as an autistic phenomenon, it is important to note that there are also descriptions of “passing” or “concealment” within chronic illness,^80,81 through which people hide elements of their chronic illness to manage others’ impressions of them and avoid stigma. It therefore could be the case that camouflaging could be both a contributor to, and an outcome, of chronic illness in autistic people.

Since autism and chronic illness can both be considered stigmatized identities, some of the reasons behind masking or concealing aspects of themselves may be similar for both diagnostic groups.^72,82,83 Indeed, the Camouflaging Autistic Traits Questionnaire (CAT-Q), originally developed to measure levels of camouflaging in autistic people, has been demonstrated to capture impression management in the general population in recent studies.^84,85 However, there are also likely to be critical differences between autistic camouflaging and illness concealment, as autism is present from birth (even if identified much later), and many autistic people consider autism to be part of their core identity.^86,87 The relationship between camouflaging, autistic identity, and autism disclosure appears complex,⁸⁸ whereas these relationships have not been explored to the same extent in chronic illness, with more literature focusing on how people hide their symptoms after they become ill,^80,81,89 than on whether impression management or stigma may cause or contribute to those symptoms.^83,90

In this study, we aimed to explore whether camouflaging could be a contributory factor to the experience of CSS symptoms, with higher camouflaging scores expected to correlate with more CSS symptoms. We also looked at camouflaging scores and diagnostic order in people with both diagnoses, to investigate whether camouflaging scores were higher in those people diagnosed with a CSS before their autism was recognized.

Another factor strongly associated with physical health outcomes and chronic illness is a person’s perceptions and beliefs about illness.^91,92 Illness beliefs are widely measured using quantitative self-report questionnaires such as the revised Illness Perception Questionnaire (IPQ-R) and its brief counterpart (B-IPQ).^93–96 These measures aim to capture different aspects of illness perception and management, including the emotional and physical effects of illness on quality of life, beliefs about length of illness and treatment efficacy, and how much control and understanding a person feels that they have over and about their condition.

Illness perceptions have so far not been assessed in autistic people with chronic illness, but existing knowledge about autistic cognitive processing suggests that there are likely to be differences between autistic and nonautistic populations. One cognitive difference that could affect how autistic people perceive a chronic illness is an “intolerance of uncertainty”^97–99 that has been associated with somatic symptoms⁶⁶ in a recent study. Autistic people also tend to have difficulties with interoception, that is, they may sense internal bodily sensations differently^100,101 and are more likely to experience alexithymia, or difficulty identifying their emotions.^66,102 Alexithymia and interoception are both related to maladaptive illness perceptions and negative health outcomes in the research literature.^{101,103–106} Monotropism, combined with hyperfocus, may also mean that autistic people tend to research and understand their illness in greater detail than nonautistic people with a chronic illness.^107,108 Illness perceptions have been shown to be an important factor in health outcomes,⁹¹ and a better understanding of how autistic people experience chronic illness could therefore provide important directions on how best to support them. This study therefore aimed to bridge this research gap by comparing the illness perceptions of autistic and nonautistic people with a chronic illness.

The research study aimed to answer the following research questions:

Is there a relationship between camouflaging and symptoms of central sensitization?

Do autistic people with a chronic illness camouflage more than autistic people without a chronic illness, or nonautistic people with a chronic illness?

Are there differences in illness perceptions in autistic and non-autistic participants with a chronic illness?

For people diagnosed with both autism and a CSS, does the timing of their diagnoses (autism first or CSS first) have a relationship with the degree of camouflaging, or with CSS scores?

Preregistered Hypotheses

The analysis plan for this study was preregistered with OSF, osf.io/h5bvm, in accordance with the principles of Open Science.¹⁰⁹ The preregistration included a number of specified hypotheses, as follows: 1.

Camouflaging (CAT-Q) scores will be positively correlated with autistic traits in the whole sample.

CAT-Q scores will be a significant predictor of CSS symptoms in the whole sample.

Autistic participants with a CSS will score higher on the CAT-Q than nonautistic participants with a CSS.

Nonautistic participants with a CSS will score higher on the CAT-Q than the comparison group (who are neither autistic nor have a CSS).

Autistic people diagnosed with a CSS before their autism will have higher camouflaging scores than those diagnosed with autism first. This hypothesis is based on the theory that higher social camouflaging may hide autistic traits and therefore help-seeking may lead to a CSS diagnosis before autism is recognized.

Anxiety and depression scores will be correlated with negative illness perceptions.

Due to characteristic features of autism such as monotropism and hyperfocus, autistic people will score higher on the B-IPQ for understanding of their illness.

Due to characteristic features of autism such as intolerance of uncertainty, autistic participants will* have lower perceptions of control over their illness than nonautistic participants.

There will be greater variability in treatment efficacy illness beliefs in autistic participants compared with nonautistic participants.

10.

Autistic participants will* have higher scores related to how much they experience symptoms and the degree to which their life is affected by their illness compared with nonautistic participants.

11.

There will* be differences in the reported emotional effect of illness between autistic and nonautistic participants, but the association between alexithymia and chronic pain makes a direction difficult to predict.

Methods

Participants

We recruited participants through the King’s College London internal recruitment bulletins, support charities, including Fibromyalgia Action U.K. and the National Autistic Society, the Autism Research Centre participant database at the University of Cambridge, and through social media such as Facebook groups associated with autism or FMS. The information sheet and recruitment materials made it clear that the survey was exploring a possible association between autism and CSS. Potential participants received a link to an information sheet and online questionnaire hosted on Qualtrics. All participants were required to be 18 years or older and provided written informed consent before starting survey completion. Participation was incentivized through prize draw entry for one of five £50 Amazon vouchers, and through provision of sum scores and an explanation of each questionnaire at the end of the survey. Data collection was online, in English, and based on self-report. The study was approved by the Psychiatry, Nursing and Midwifery Research Ethics Subcommittee at King’s College London (HR-18/19-8634 and RESCM-22/23-8634).

Measures

Demographic and clinical information

We asked all participants questions regarding their demographic background (e.g., age, gender, level of education, and employment status) as well as clinical information about formal diagnoses relating to autism and CSS, including year of diagnosis. For autistic participants with a CSS, we created an additional variable to indicate which diagnosis came first, based on the year of each diagnosis provided.

Central sensitization

We measured symptoms of central sensitization using the Central Sensitization Inventory (CSI).¹⁹ This is a valid and reliable self-report questionnaire^12,110,111 consisting of two parts. Part A is measured using 25 items that assess symptoms of central sensitization on a five-point Likert scale ranging from 0 “never” to 4 “always.” A score of 40 on Part A is considered the clinical cutoff point that best distinguishes between CSS and non-CSS patients.¹¹⁰ In the current sample, the Cronbach’s α = of Part A is 0.95 indicating excellent internal consistency. Part B contains a list of CSS diagnoses and related disorders. Participants could select the condition as either a formal clinical diagnosis or as a suspected/self-diagnosis. All CSS conditions listed in this section—restless legs syndrome, FMS, ME/CFS, IBS, migraine/tension headaches, multiple chemical sensitivity, and temporomandibular joint disorder—were included in the online survey. In addition, interstitial cystitis was included in the survey, separately from Part B, as this has been recognized as a CSS in a later article by the authors of the CSI.¹¹⁰ Participants who indicated in our survey to have one or more formal CSS diagnoses were included in the CSS group.

Sensory sensitivity

The 35-item Sensory Perception Quotient (SPQ) was developed to assess sensory sensitivity in adults with and without autism. The SPQ shows good internal consistency and validity^112–114 and has a Cronbach’s α = 0.94 in this sample. The measure is assessed across five sensory modalities on a Likert scale ranging from 0 “strongly agree” to 3 “strongly disagree.” A low SPQ score therefore indicates higher sensitivity than a high score. It is important to note that, although we have used the SPQ as a measure of sensory sensitivity in this study, this language does not acknowledge the difference between perceptual sensitivity, affective reactivity, and neural excitability.¹¹⁵ The SPQ is thought to most closely measure perceptual sensitivity, that is the ability to detect and discriminate between sensory stimuli, although further research is needed to explore whether it captures pure perceptual sensitivity, as can be measured through psychophysical testing, or rather the ability to attend to differences in stimuli in a real-world environment.

Autistic traits

In this study, we used the 50-item Autism Spectrum Quotient, or AQ, to measure autistic traits.¹¹⁶ Items in the AQ are scored on a four-point Likert scale ranging from 1 “definitely agree” to 4 “definitely disagree.” Twenty-four of the 50 items are reverse scored where “agree” responses are characteristic of autism. The AQ scores for this study were calculated using the full Likert values to capture the full range of autistic traits. This scoring method produces a minimum AQ score of 50 and a maximum of 200.¹¹⁷ The AQ is widely used and has been extensively evaluated, however, there is disagreement about the reliability, sensitivity, and specificity of the measure, which varies from “adequate” to “good.”^118–122 A Cronbach’s α of 0.96 in this sample indicated excellent internal consistency.

Depression

The Patient Health Questionaire-9 (PHQ-9) is a widely used 9-item measure designed to capture depression symptoms and severity in the general population.¹²³ The nine Likert scale items range from 0 “Not at all” to 3 “Nearly every day,” with a separate question also asking how much these problems affect daily life. A higher score on the PHQ-9 indicates greater symptom severity. The PHQ-9 has been demonstrated to be a reliable diagnostic tool and has been tested and used in many different countries and patient populations,^124–127 including autistic people,¹²⁸ with a Cronbach’s α of 0.91 in this sample.

Anxiety

The Generalized Anxiety Disorder-7 (GAD-7) is a brief measure that assesses generalized anxiety symptoms.¹²⁹ Each of the seven Likert-scale items range from 0 “Not at all” to 3 “Nearly every day.” A separate question scored from 0 “Not difficult at all” to 3 “Extremely difficult” assesses how much anxiety symptoms are affecting daily life. A higher score on the GAD-7 indicates greater symptom severity. As for the PHQ-9, the GAD-7 has been globally utilized and tested and has been found to have good validity and reliability,^129,130 with a Cronbach’s α of 0.93 in this sample.

Camouflaging

The CAT-Q is a 25-item self-report measure of autistic social camouflaging behaviors.¹³¹ Items in the CAT-Q are scored on a seven-point Likert scale ranging from 1 “Strongly disagree” to 7 “Strongly agree.” Five of the items are reverse scored where “disagree” responses are characteristic for autistic camouflaging. This scoring method produces a range of possible scores from 25 to 175. The CAT-Q can be used as a total score, but also has three subscales designed to capture the domains of Compensation, Masking, and Assimilation. Compensation is conceptualized as attempts to find ways around social and communication difficulties, Masking refers to hiding aspects of one’s persona, and Assimilation refers to attempts to “blend in” and to hide discomfort. The CAT-Q has demonstrated good reliability and validity,^84,131,132 with a Cronbach’s α of 0.86 in this sample indicating good internal consistency.

Illness perceptions

The B-IPQ⁹⁴ was based on the longer IPQ⁹³ and designed to be a fast and simple measure of illness perceptions. Unlike other measures that form scales based on multiple items, the B-IPQ is designed to assess one illness perception per question across areas such as consequences, timeline, self-efficacy, treatment efficacy, illness concern, and emotional impact. The last item also asks patients to list the three things they consider to be the most likely causes of their illness and was therefore excluded from the quantitative analyses in this study. The B-IPQ has been shown to demonstrate good validity and test–retest reliability and is a widely used measure.^94,96 Because this questionnaire asks about illness, it was only administered to those who indicated having a formal CSS diagnosis (i.e., comparison participants and autism-only participants were not asked to complete this measure).

Statistical analyses

First, we used Pearson’s correlations on the full sample of participants to establish whether autistic traits were correlated with camouflaging as measured on the CAT-Q. We then conducted a four-stage hierarchical regression analysis to explore whether autistic traits, sensory (perceptual) sensitivity, anxiety, and camouflaging might significantly predict CSS symptoms. We included age and gender in stage one as controls, with each construct added in a separate stage to explore their effect on the variance in CSI scores. Due to the inclusion of gender as a predictor and control, we excluded the small group of participants who had indicated “other” gender before analysis began.

To investigate group differences, we divided the participants into four groups—“autism only” for those participants with a formal diagnosis of autism and without a CSS diagnosis, “CSS only” for participants with a formal CSS diagnosis only and no autism, “autism and CSS” for participants with a formal diagnosis of autism and one or more CSS, and “comparison,” who did not have a diagnosis of autism or CSS. We excluded participants who suspected autism or a CSS from the comparison group, and participants who indicated “other” gender were also excluded from the analysis due to insufficient power. We placed participants with a formal autism diagnosis, who indicated they suspected they had a CSS but did not have a formal CSS diagnosis, in the autism-only group. Likewise, those with a formal CSS diagnosis and suspected autism were placed in the CSS-only group. We evaluated group differences using analysis of covariance (ANCOVA), including gender and age as covariates. We corrected pairwise comparisons and post hoc tests of the camouflaging subscales for 12 tests using Bonferroni correction, with a resultant p value of 0.004. To investigate the possibility of undiagnosed autism or CSS affecting the results, we conducted additional exploratory ANCOVAs not specified in our preregistered analysis plan, through which group differences were examined based on suspected diagnoses rather than clinical diagnoses. For these analyses, all 504 participants were included, as those participants with no clinical diagnosis that suspected autism, CSS, or both were placed in the corresponding diagnostic group. The comparison group remained the same, that is, those people with neither an autism nor a CSS diagnosis. The autism-only group consisted of those people who were suspected or diagnosed autistic but did not have a CSS. The CSS-only group included those with a formal or suspected CSS diagnosis but no autism. The autism and CSS group included participants who had both diagnoses, had one diagnosis and suspected the other, or who had neither diagnosis but suspected both.

We conducted the remaining analyses, focusing on illness perceptions, only for those participants who had a formal CSS diagnosis. First, we used Spearman’s correlations to ascertain whether anxiety and depression scores were correlated with negative illness perceptions. We then evaluated group differences in individual illness perceptions using ANCOVA, including gender and age as covariates. We corrected pairwise comparisons for multiple testing using Bonferroni correction, as previously. Levene’s test for equality of variances was utilized to explore whether there was greater variability in treatment efficacy illness beliefs or the emotional effect of illness in autistic participants. Lastly, we used ANCOVA on the subgroup of participants with both autism and CSS, controlling for age and gender, to explore whether diagnostic order was related to any differences in camouflaging scores, and exploratory ANCOVAs to analyze diagnostic order in relation to CSS and AQ scores. All analyses were conducted using SPSS 29.0 [49].

Results

Descriptive statistics

There were 15 participants who indicated their gender as “other” (including nonbinary, gender-fluid, and agender participants), and we had to exclude these participants before analysis due to insufficient sample size. This left a sample of 504 adults (88 men, 416 women). Key demographic information and analysis groups are shown in Table 1.

Table 1.

Demographic Information and Participant Groups

	Autism and CSS		Autism only		CSS only		Comparison		Excluded
	M	F	M	F	M	F	M	F	M	F
Number (%) of male and female participants per group	18 (21%)	68 (79%)	13 (43%)	17 (57%)	19 (8%)	232 (92%)	24 (26%)	67 (74%)	14 (30%)	32 (70%)
Age
Mean	46.17	39.63	52.92	37.35	46	40.58	41.17	29.88	43.36	34.88
SD	16.58	10.62	13.38	10.07	12.61	12.03	20.08	13.39	16.298	12.11
Minimum age	18	19	34	20	19	18	19	18	23	18
Maximum age	68	68	71	56	65	73	90	72	73	66
Education
No education or primary school only	0	1	0	1	0	4	0	0	0	0
Education up to age 16	3	5	2	0	1	21	1	0	3	2
Education up to age 18	3	15	5	3	7	61	8	12	2	5
Undergraduate degree	5	31	1	6	5	86	13	29	6	9
Postgraduate degree	7	16	5	7	6	60	2	26	3	16
Employment
Employed full-time (30 hpw+)	4	11	2	4	10	56	8	20	4	6
Employed part-time (<30 hpw)	0	12	2	6	0	42	0	8	1	5
Self employed	2	4	1	4	3	18	3	3	5	4
Government supported training	0	0	0	0	0	0	0	0	0	0
Full-time student	1	4	0	1	1	18	7	28	0	10
Part-time student	0	3	0	0	0	1	0	1	0	1
Unemployed and available to work	1	1	1	1	0	3	1	4	0	0
Permanently sick/disabled	6	27	2	1	4	68	0	0	0	2
Retired	3	2	4	0	1	7	5	3	3	0
Looking after the home or family	0	1	1	0	0	12	0	0	1	3
Doing something else	1	3	0	0	0	7	0	0	0	1
CSS diagnosis
No clinical diagnosis	0	0	0	0	0	0	0	0	0	0
One clinical CSS diagnosis	11	14	0	0	13	63	0	0	0	0
Multiple clinical CSS diagnoses	7	54	0	0	6	169	0	0	0	0

CSS, central sensitivity syndrome.

The mean age was 39.3 years, with men significantly older than women, t (502) = 4.597, p < 0.001. A total of 96% of participants with one or more CSS scored at or above the clinical cutoff of 40 on the CSI. A total of 116 participants (23%) had a formal diagnosis of autism, with 85% of those scoring at or above the suggested cutoff score of 145¹¹⁷ on the AQ.

Participants were also able to indicate if they did not have a clinical diagnosis but suspected they might be autistic or have a CSS. Eighty-three participants indicated suspected autism, and 53 indicated suspected CSS. Of these participants, 46 had no clinical diagnoses but suspected they were autistic (10), had CSS (25), or both (11). Those participants with no formal clinical diagnosis but a suspicion of autism and/or CSS were excluded from the comparison group for the clinical group differences analysis.

Correlations and hierarchical regression

As predicted in hypothesis 1, camouflaging scores on the CAT-Q were highly correlated with autistic traits as measured on the AQ, r = 0.695, p < 0.001 across all participants. For hypothesis 2, a four-stage hierarchical multiple regression with a CSI score as the dependent variable was utilized again across all participants. Stage one of the regression analysis included age and gender, stage two included camouflaging (CAT-Q), stage three added sensory sensitivity (SPQ) and anxiety (GAD) and stage four autistic traits (AQ) (see Table 2, analysis 1). While the first three stages all contributed significantly to the model, autistic traits were not shown to be a significant predictor of CSS symptoms (p = 0.078) at stage four, and camouflaging also became nonsignificant at this stage (p = 0.195). We theorized that this was likely because of multicollinearity between autistic traits and social camouflaging behaviors, and this was supported by a moderate variance inflation factor of 2.271. An alternative exploratory regression analysis was therefore undertaken (Table 2, analysis 2) in which autistic traits were removed from the model. In this model, higher camouflaging scores were a significant predictor of CSS symptoms (p = 0.005) as well as higher anxiety scores (p < 0.001) and greater perceptual sensitivity (p < 0.001).

Table 2.

Hierarchical Regression Analyses Exploring Predictors of Central Sensitivity Syndrome Symptoms

	Stage one			Stage two			Stage three			Stage four
	Beta	t	p	Beta	t	p	Beta	t	p	Beta	t	p
Analysis 1
(Constant)		9.479	<0.001^***		−1.309	0.191		5.677	<0.001^***		5.224	<0.001^***
Gender	0.278	6.397	<0.001^***	0.325	8.372	<0.001^***	0.221	6.955	<0.001^***	0.217	6.812	<0.001^***
Age	0.187	4.311	<0.001^***	0.149	3.845	<0.001^***	0.144	4.572	5.902	0.151	4.727	5.902
AQ				0.443	11.575	<0.001^***	0.113	3.092	0.002^**	0.079	1.766	0.078
SPQ							−0.297	−8.118	<0.001^***	−0.287	−7.668	<0.001^***
GAD-7							0.427	12.321	<0.001^***	0.419	11.875	<0.001^***
CAT-Q										0.059	1.297	0.195
Analysis 2
(Constant)		9.479	<0.001^***		0.244	0.807		7.347	<0.001^***		6.696	<0.001^***
Gender	0.278	6.397	<0.001^***	0.262	6.825	<0.001^***	0.218	6.163	<0.001^***	0.205	6.572	<0.001^***
Age	0.187	4.311	<0.001^***	0.207	5.392	<0.001^***	0.132	3.656	<0.001^***	0.158	4.981	<0.001^***
CAT-Q				0.450	11.935	<0.001^***	0.248	6.189	<0.001^***	0.106	2.847	0.005^**
SPQ							−0.402	−9.871	<0.001^***	−0.299	−8.119	<0.001^***
GAD-7										0.426	12.150	<0.001^***

Analysis 1: R2 = 0.091 for Stage 1: ΔR2 = 0.192 for Stage 2 (p < 0.001): ΔR2 = 0.262 for Stage 3 (p < 0.001): ΔR2 = 0.002 for Stage 4 (p < 0.001). Analysis 2: R2 = 0.091 for Stage 1: ΔR2 = 0.201 for Stage 2 (p < 0.001): ΔR2 = 0.116 for Stage 3 (p < 0.001): ΔR2 = 0.135 for Stage 4 (p < 0.001).

p < 0.05.

^**

p < 0.01.

^***

p < 0.001.

AQ, Autism Spectrum Quotient; SPQ, Sensory Perception Quotient; GAD-7, Generalized Anxiety Disorder-7; CAT-Q, Camouflaging Autistic Traits Questionnaire.

Group differences

For this analysis, we separated participants into four groups based on clinical diagnoses, a comparison group, autism only, CSS only, and autism and CSS together. Participants who did not have any formal clinical diagnoses but suspected they may be autistic or have a CSS were excluded from the comparison group to ensure accurate group comparisons. This left a sample size of n = 458 (n = 91 comparison, n = 30 autism only, n = 251 CSS only, n = 86 autism and CSS). A chi-square test of independence showed that there was a significant association between gender and participant group, X² (3, N = 458) = 38.48, p < 0.001.

Table 3 (clinical diagnoses) presents the group comparisons of camouflaging scores, controlling for age and gender. All three diagnostic groups had significantly higher camouflaging scores than the comparison group (CSS and autism = 130.28, autism only = 119.35, CSS only = 107.94, and comparison = 86.62, p < 0.001 for all group differences), confirming hypothesis 4. Autistic people who also had a CSS scored significantly higher on the CAT-Q than CSS-only participants and controls (130.28 vs. 107.94, p < 0.001), in line with hypothesis 3 but not the autism-only group (130.28 vs. 119.35, p = 0.388). The difference in CAT-Q scores between the autism-only (mean 119.35) group and the CSS-only group (mean 107.94) was not significant (p = 0.228).

Table 3.

Comparisons in Camouflaging Scores Between Different Clinical Groups with Gender and Age as Covariates

		Participant group
	Measure	Comparison	Autism only	CSS only	CSS and autism
	N	91	30	251	86
	CAT-Q
Clinical Diagnosis	Estimated marginal mean (SE)	86.62 (3.014)	119.35 (5.133)	107.94 (1.781)	130.28 (2.979)
	f Test	f (3,452) = 36.941 p < 0.001^***
	Covariates [gender, age]	[ f (1,452) = 0.093 p = 0.760, f (1,452) = 14.238 p ≤ 0.001***]
	Mean differences
	Comparison group	—	−32.725, p < 0.001^***	−21.319, p < 0.001^***	−43.656, p < 0.001^***
	Autism only	32.725, p < 0.001^***	—	11.406, p = 0.228	−10.931 p = 0.388
	CSS only	21.319, p < 0.001^***	−11.406, p = 0.228	—	−22.337, p < 0.001^***
	CSS and autism	43.656, p < 0.001^***	10.931, p = 0.388	22.337, p < 0.001	—
Suspected Diagnosis	N	91	23	214	176
	Mean differences
	Estimated marginal mean (SE)	87.98 (2.777)	123.91 (5.445)	98.72 (1.774)	128.78 (1.932)
	f Test	f (3,498) = 68.411 p < 0.001***
	Covariates [gender, age]
	Mean differences
	Comparison group	—	−35.923, p < 0.001^***	−10.731, p = 0.009^**	−40.799, p < 0.001^***
	Autism only	−35.923, p < 0.001^***	—	25.192, p < 0.001^***	4.876, p = 1.00
	CSS only	−10.731, p = 0.009^**	−25.192, p < 0.001^***	—	30.068, p < 0.001^***
	CSS and autism	−40.799, p < 0.001^***	4.876, p = 1.00	−30.068, p < 0.001^***	—

Group differences significant at 0.05 level with Bonferroni adjustment for multiple comparisons.

^**

p < 0.01.

^***

p < 0.001.

We then completed an exploratory ANCOVA to investigate whether group differences in camouflaging scores were different if those participants who suspected they may have autism or a CSS, but were not formally diagnosed, were placed in the respective diagnostic grouping instead. Table 3 (suspected diagnoses) presents the group comparisons of camouflaging scores, controlling for age and gender in these new groupings. The pattern of the results was similar to the clinical groupings analyses (see Table 3), however, this time the difference in CAT-Q scores between the autism-only group (mean 123.91) and the CSS-only group (mean 98.72) was significant (p < 0.001).

We completed a post hoc analysis of the three camouflaging subdomains of Compensation, Masking, and Assimilation to explore whether the CAT-Q was capturing different aspects of camouflaging for different groups. First, this comparison was done for the clinical diagnostic groups (n = 458). For the compensation subdomain, mean scores followed the same pattern as for the full camouflaging scores, with the autism and CSS group having the highest score (45.54), then-autism only (39.42), then CSS-only (32.73), and then the comparison group (25.73) where there were significant differences between all scores and the comparison group (p < 0.001) but not between the autism and CSS group and autism-only group (p = 0.143) or between the CSS-only and autism-only group (p = 0.047). Assimilation scores for all diagnostic groups were again significantly higher than the comparison group (autism and CSS = 46.01, autism only = 42.36, CSS only = 36.97, comparison = 26.79, p < 0.001 for all) but again there were no significant differences between autism and CSS and autism-only assimilation scores (p = 0.682) or between CSS-only and autism-only scores (p = 0.072). Masking scores (autism and CSS = 38.73, CSS only = 38.24, autism only = 37.57, comparison = 34.1) were significantly different only between CSS-only and the comparison group (p = 0.004) and between the autism and CSS and the comparison group (p = 0.009); there was no significant difference between autistic-only participants and the comparison group, or any significant differences between the diagnostic groups.

When this analysis was run for the full participant sample (n = 504) taking into account suspected diagnoses as well as clinical diagnoses, the pattern for the CAT-Q subdomains was different. For compensation, there were only significant differences (p < 0.001) between the autism-only (40.33) and autism and CSS (43.89) groups and the comparison group (26.29) and CSS group (28.84), with the latter two groups having similar scores. For masking, only the autism and CSS (40.09) group had a significant difference from the comparison group (34.46) with the autism-only group (38.62) and CSS-only group (36.26) differences nonsignificant. The autism and CSS group also scored significantly higher than the CSS-only group (p < 0.001). For assimilation, however, there were significant differences between all three diagnostic groups and the comparison group (p < 0.001), with no significant difference between autism-only (44.63) and autism and CSS (44.81) scores, but significant differences between CSS-only (33.62) and the comparison group (27.23, p < 0.001), as well as between both autism groups and the CSS-only group (p < 0.001).

Diagnostic order—group differences

We undertook an analysis of camouflaging, autistic traits, and CSS symptoms within the autism and CSS group (n = 86) to establish whether the order of receipt of clinical diagnosis, that is, autism first or CSS first, made a difference to these scores. In contrast to our hypothesis (5) that autistic people diagnosed with a CSS before their autism would have higher camouflaging scores, no significant difference was found. Additional analyses were undertaken to explore whether diagnostic order affected CSS, AQ, or depression scores in this sample; no significant differences were found.

Illness perceptions

We only completed an analysis of illness perceptions for those participants with a diagnosed CSS who had completed the brief-IPQ. This included 248 participants with CSS only and 84 participants with dual autism and CSS diagnoses. In line with hypothesis 6, depression symptoms as measured on the PHQ-9 were significantly correlated with all negative illness perceptions, with the exception of illness understanding (Table 4). Anxiety symptoms were significantly related to the effect of illness on quality of life, treatment efficacy, illness symptoms, illness concern, and emotional effect, but not to perceptions of length of illness, control, or understanding.

Table 4.

Brief Illness Perception Questionnaire—Correlations and Group Differences

	Correlations		Mean (SD)		ANCOVA
IPQ wording	Depression	Anxiety	Autism and CSS	CSS only	f Test
IPQ1: How much does your illness affect your life?	0.426^a	0.254^a	8.67 (0.189)	8.16 (0.110)	f (1,333) = 5.289, p = 0.022^b
IPQ2: How long do you think your illness will continue?	0.164^a	0.096
IPQ3: How much control do you feel you have over your illness?	−0.159^a	−0.056	4.03 (0.297)	3.95 (0.177)	f (1,308) = 0.054, p = 0.817
IPQ4: How much do you think your treatment can help your illness?	−0.275^a	−0.165^b
IPQ5: How much do you experience symptoms from your illness?	0.444^a	0.320^a	8.21 (0.188)	7.86 (0.109)	f (1,332) = 2.671, p = 0.103
IPQ6: How concerned are you about your illness?	0.366^a	0.271^a
IPQ7: How well do you feel you understand your illness?	−0.078	−0.032	7.43 (0.282)	7.17 (0.163)	f (1,328) = 0.590, p = 0.443
IPQ8: How much does your illness affect your emotionally?	0.515^a	0.510^a
Depression (PHQ-9)	—	0.727^a

Significant at the 0.01 level (2-tailed).

Significant at the 0.05 level (2-tailed).

IPQ, Illness Perception Questionnaire; PHQ-9, Patient Health Questionaire-9.

We analyzed group differences on individual B-IPQ items comparing autistic and nonautistic participants with a CSS. Contrary to hypotheses 7 and 8, there was no significant difference in illness understanding (f (1,328) = 0.590, p = 0.443) or illness control (f (1,308) = 0.054, p = 0.817) with autistic and nonautistic participants scoring almost identically on the latter item. However, autistic people felt significantly more affected by their illness than nonautistic people (f (1,333) = 5.289, p = 0.022.), despite no significant difference in their reported experience of illness symptoms (f (1,332) = 2.671, p = 0.103). Levene’s test revealed that, contrary to hypothesis 11, there was no significant difference in variability of scores for question 8 regarding emotional affect (f (1,334) = 0.558, p = 0.455), but in line with our hypothesis that autistic people would demonstrate broader variability in their answers related to treatment efficacy, Levene’s test showed a significant difference in variability for question 4 (f (1,239) = 4.868, p = 0.028).

Discussion

This study aimed to explore two psychosocial factors that could contribute to an association between autism and CSS, camouflaging, and illness beliefs. We compared social camouflaging scores in four participant groups: autistic people without a CSS, autistic people with a CSS, nonautistic people with a CSS, and a comparison group. We compared illness perceptions between autistic and nonautistic people with a CSS. We first explored group differences based on clinical diagnosis only, excluding those participants without any formal clinical diagnosis from the sample (n = 458) and then repeated these analyses with the groups instead based on suspected diagnosis (n = 504), to establish whether undiagnosed autism was affecting the results.

We found that, in this sample, camouflaging was a significant predictor for CSS symptoms, but only when autistic traits were not included in the regression model. For the clinical diagnostic groupings, autistic people with a CSS had the highest camouflaging score and the comparison group the lowest. While all clinical groups scored significantly higher than the comparison group, the difference between the CSS-only camouflaging score and the autism-only group was not significant.

As there were participants in the autism-only group and CSS-only group who had indicated they suspected an autism or CSS diagnosis, we conducted additional exploratory analyses based on suspected, rather than confirmed, diagnoses. This yielded different results; while there were still significant differences between all diagnostic groups and the comparison group, there was also a significant difference between the camouflaging scores of the CSS-only group and both the autism groups, although not between the two autism groups.

Considering the CAT-Q subdomains, the patterns were similar in the clinical diagnostic grouping, that is, there were significant differences between all clinical groups and the comparison group, and between the autism and CSS group and CSS-only group, but not between the autism-only and CSS-only groups for each subdomain except masking, where there were no significant differences between the groups. When we explored group differences based on suspected diagnoses, this pattern changed. For compensation, we found that autistic participants scored significantly higher than nonautistic participants, but there was no significant difference between the compensation scores of the CSS-only and comparison participants. Masking scores were only significantly different for the autism and CSS group and no other diagnostic groups. Assimilation scores, however, were significantly different from the comparison group for all diagnostic groups, with significant differences between both the autism groups and the CSS-only group but not between the two autism groups. These results could suggest that the questions asked in the “compensation” domain (e.g., “When I am interacting with someone, I deliberately copy their body language or facial expressions”) are capturing camouflaging behaviors specific to autism, questions in the masking domain (e.g., “I am always aware of the impression I make on other people”) may be more relevant to people with both autism and CSS, and the assimilation questions (e.g., “In social situations, I feel like I am pretending to be ‘normal’”) may capture more general impression management across different neurotypes. However, as the mean differences between subdomains were small, it is also possible that the significant effects were due to statistical thresholding.

Camouflaging was assessed in this study using the CAT-Q,¹³¹ a psychometric self-report measure developed to assess the use of social camouflaging strategies to minimize the visibility of autistic traits. Recent studies have suggested, however, that that the CAT-Q can be applied to the wider population and reflect a nearly identical dimensional structure to that in autism, suggesting that passing, concealment, and masking could all be conceptualized as forms of impression management.^82,84 This theory is supported by our finding that nonautistic chronically ill participants scored higher for camouflaging on the CAT-Q, even when those people who suspected they could be autistic were removed from this group and placed into one of the autistic participant groups. However, we did find higher CAT-Q scores in autistic participants, and different patterns within the subdomains of the CAT-Q, that suggest this measure is still capturing behaviors specific to autistic impression management.

The concept of autistic camouflaging, or masking, has received increased research interest in recent years,^{73,76,133–135} and many studies have demonstrated clear links between camouflaging and poorer mental health outcomes,^75,78,136 greater camouflaging in autistic women,^137–139 and a relationship between camouflaging scores and age at autism diagnosis.^71,74,133 However, there has been less discussion in the literature about the social reasons behind autistic camouflaging⁷² or how the concept relates to other reports of “passing” or “concealment” in minority groups,^82,140–142 including the chronically ill.^81,143–145 Passing is the practice of actively concealing or changing behavior or appearances to claim a less marginalized identity, for example, healthy, heterosexual, White,⁸¹ and has been linked with poor physical health outcomes in minority groups.^83,90 Our results suggest that autistic camouflaging, and impression management more generally, are related to physical health symptoms (CSS symptoms in this case) and may be a predictor of worse physical health outcomes for autistic and nonautistic people alike. CAT-Q scores were also strongly related to CSS symptoms in the regression analysis, suggesting that, as theorized in the introduction, camouflaging could be both a predictor and an outcome of CSS symptoms. We propose that high camouflaging in autistic people could create a negative feedback loop if they also develop a chronic illness, where further camouflaging of both illness symptoms and autistic traits exacerbates physical health symptoms. While we did not find significant differences with diagnostic order of autism and CSS, this may be because of the small sample size for the clinical autism and CSS group. Further studies could look further at the diagnostic order, age at autism diagnosis, and age at CSS diagnosis, to explore underdiagnosis, diagnostic overshadowing, and camouflaging in this underresearched intersection.

For illness perceptions, first, we predicted that autistic people would score higher for understanding of their illness, as they may be more likely to research their condition to minimize anxiety and uncertainty around symptoms.^66,98 We also hypothesized that intolerance of uncertainty in autistic adults may lead to more variability in beliefs around treatment efficacy in autistic people, as well as a reduced perception of control over symptoms. In this cohort, while autistic people did report more variability in their views of treatment efficacy, there were no significant differences in illness understanding or perception of control between autistic and nonautistic people with a CSS. While it is possible that no differences were found due to the sample size in this comparison, it could also be the case that a lower tolerance for uncertainty affected autistic participants’ perceptions of understanding and control, such that any increased research did not feel sufficient in terms of increased understanding. It is also possible that the wording of the IPQ is interpreted in a different way between autistic and nonautistic people. The only IPQ item that showed a significant difference between both groups was the item asking about the impact of their CSS on quality of life. This impact was reported as greater for autistic people, even though they did not report more symptoms than the nonautistic group. It may also be the case that these results demonstrate that autistic and nonautistic people perceive and experience chronic illness very similarly despite different cognitive styles.

There are many reasons why chronic illness, and CSS in particular, might be particularly challenging for autistic people. CSS are health conditions associated with chronic pain and fatigue, as well as increased sensory sensitivity across a number of domains.¹⁴⁶ Autism is also associated with sensory differences,¹⁴⁷ and the combination of autistic sensory processing and central sensitization may create an increased sensory burden that impacts on quality of life. In addition, although autistic people do not always consider their neurodivergence a disability,⁸⁷ many do, and being multiply disabled is known to have significant effects on mental health and quality of life.^91,148 CSSs are also associated with “brain fog,” or cognitive processing difficulties; for autistic people who may already struggle with working memory and executive function,¹⁴⁹ this additional difficulty can be overwhelming and contribute to anxiety and low mood. Autistic people also report multiple barriers to health care that may be particularly challenging in the context of chronic illness diagnosis and management, such as communication with health professionals, managing appointments, and accessing sensory-friendly health environments.^65,70 Research on autism and pain indicates that autistic people, contrary to previous widespread beliefs, seem to be more sensitive to pain^34,39 and this is likely to mean that their experience of CSS symptoms and quality of life are more negatively impacted. Lastly, there is evidence that autistic adults are more sensitive to life stressors¹⁵⁰ and more prone to heath anxiety,¹⁵¹ both of which may increase the impact of chronic illness.

Regardless of the reasons why autistic adults may experience a greater impact of chronic illness on their lives, this finding has important implications for the health and well-being of autistic people and highlights a need for further research, as well as person-centered health interventions and care.

Limitations

Although the participant sample for this study was large and diverse, the recruitment method for the study resulted in a greater uptake from people with CSS, particularly women, who are known to be more likely to participate in online surveys.^152,153 Some likely selection bias must therefore be acknowledged. The number of autistic people with CSS in this study was also much smaller than the CSS-only group, which may limit the power of group comparisons. Participation in this study was also limited to people who were able to complete an online survey. Therefore, these results may not generalize to every autistic person or CSS patient. Cross-sectional surveys are also limited when exploring cause and effect, and therefore any inferences drawn in this article should be approached with caution. Given these limitations, the results of this study should be considered exploratory and requiring confirmation in larger robust longitudinal studies.

The questions in the survey used in this study allowed participants to indicate if they suspected that they had a CSS, or autism, but were not diagnosed. We decided to delineate our primary groups based on clinical diagnosis only, as this allowed us to consider possible diagnostic overshadowing or underdiagnosis, but we recognize that this also made direct comparisons between clinical groups more difficult as there were likely to be undiagnosed autistic people in the CSS-only group and vice versa. We included an additional analysis looking at suspected diagnoses to mitigate this limitation. There may also be other aspects that could affect camouflaging scores, including an autism diagnosis in adulthood and differences in the times between receiving a CSS and an autism diagnosis, and these factors could be explored in future research.

Cross-sectional research using self-report questionnaires can also be limited both by difficulties with identifying and discussing bodily sensations in individual participants, and by psychometric limitations of the questionnaires themselves. Individual differences in interoceptive awareness, or the ability to perceive internal sensations, appear to be more pronounced in autistic people,^100,101 with mismatches found between self-reported interoceptive aptitude and actual interoceptive performance.^154,155 This could affect answers on several of the included measures, particularly those pertaining to physical symptoms, and may make it more difficult to disentangle symptom severity from perceptions about illness severity. The CSI itself also contains some items very specific to CSS and other broader items that may capture other elements of being autistic, for example, “I have difficulty remembering things.”¹¹⁰

Another study limitation is that the survey included a variable for “gender” but not for “sex assigned at birth.” Participants were able to select one of three options (male/female/prefer to self-describe), and then provide further details if necessary. The wording could therefore have been interpreted as either “sex assigned at birth” or as “gender of identification,” and there is a chance that some participants included in the analyses may have been transgender. As for a previous study,¹² a very small number of participants selected “self-describe,” with the majority being nonbinary or agender, and this group had to be excluded due to lack of power. Since there is research indicating that gender nonconforming and trans autistic people may be at increased risk of physical health problems,^156–158 future research should prioritize robust recruitment of trans and nonbinary autistic adults, to investigate whether this group experiences greater CSS symptoms than cisgender autistic men or women.

This study aims to understand whether camouflaging scores and illness perceptions differ between autistic and nonautistic people with and without a CSS. It should be noted that many of the participants in this study had multiple chronic health conditions and not just a CSS; some had multiple CSSs such as FMS, ME/CFS, and migraine, some had underlying connective tissue disorders and/or joint hypermobility, and others had other health problems such as arthritis or autoimmune conditions in addition to the CSS considered in this study. Participants were asked to answer the questionnaires based on their CSS, but for people with multiple conditions it is likely that the illness perceptions captured will be related more broadly to chronic illness, as it can often be difficult to pick apart which symptoms relate to which health condition. It is also possible that the experience of multiple chronic illnesses will have affected the CSI scores in this study, however, excluding all participants with other health issues would have drastically reduced the participant number and would not have been reflective of a general CSS group.

Conclusions

In conclusion, camouflaging of autistic traits may contribute to physical symptoms of CSS, and camouflaging scores indicate that autism may be underdiagnosed in CSS patients. The results also suggest that the CAT-Q may be capturing elements of impression management in CSS patients that do not relate directly to autistic traits, but possibly to elements of “passing” or “illness concealment,” and further research is needed to explore this. Clinicians need to be aware that autistic people experience a greater illness impact on their quality of life than nonautistic people and may therefore need additional support.

Footnotes

Acknowledgments

The authors thank the participants of the CENSSAS study for their contributions. For the purposes of open access, the author has applied a Creative Commons Attribution (CC BY) license to any Accepted Author Article version arising from this submission.

Authorship Confirmation Statement

S.G. proposed the research question and developed the initial research design. S.G. and R.A.H. prepared and administered the questionnaire, and S.G. managed the corresponding data. S.G. analyzed and interpreted the data with support from R.A.H. and S.N. and wrote the first draft of the article. All authors read, contributed to, and approved the final article.

Ethics Approval and Consent to Participate

The protocol of this study was approved by the PNM Research Ethics Subcommittee at King’s College London (approval number HR-18/19-8634) and all participants provided written informed consent.

Availability of Data and Materials

The datasets generated and/or analyzed during the current study will be available via the U.K. Data Service.

Author Disclosure Statement

The authors declare that they have no competing interests.

Funding Information

S.G. is supported by a studentship from the London Interdisciplinary Social Sciences Doctoral Training Partnership (ESRC grant no ST11325). R.A.H. is funded by a grant from the National Institute for Health and Care Research (NIHR200842) using U.K. aid from the U.K. Government. The views expressed in this publication are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.

*

The original preregistered wording of hypotheses 8, 10, and 11 used “may” instead of “will,” and so the wording has been changed in this article to reflect the intent.

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