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Abstract

Autistic adults frequently show increased physical or mental health conditions and impairments in social, occupational, and other life domains. Accordingly, they often report reduced health-related quality of life. Yet, numerous barriers prevent effective healthcare for autistic adults. To date, the impact of healthcare barriers on health-related quality of life in autism has not been investigated. We, thus, sought to investigate the health-related quality of life of autistic adults without intellectual disability in Germany and analyze previously reported and novel predictors of health-related quality of life including healthcare barriers using multiple linear regression. Within a nationwide online survey, 311 autistic adults completed the Short-Form Health Survey and the Barriers to Healthcare Checklist. Demographic, clinical, and healthcare-related predictors were also assessed. Participants showed below-average mental and significantly reduced physical health-related quality of life compared to a German normative sample. Several independent variables differentially predicted mental and physical health-related quality of life. Healthcare barriers resulted as the only predictor, which explained variations in both domains: the more barriers autistic adults experienced, the lower their mental and physical health-related quality of life. Barriers emerged as the strongest predictor for mental health-related quality of life. Our results raise awareness for the barriers autistic adults experience in getting appropriate healthcare and the possible consequences for the individual. Improved healthcare access might contribute to better health-related quality of life in autism.

Lay abstract

Health-related quality of life reflects a person’s perspective on their well-being in physical, mental, social, work-related, and other aspects of health or life. Autistic adults typically report difficulties in many or all of these domains and, thus, often experience their health-related quality of life being reduced. Nonetheless, they do not obtain the professional support they need and report barriers to accessing or receiving appropriate healthcare. We know little about the impact of barriers to healthcare on health-related quality of life in autistic adults. In the present study, 311 autistic adults without intellectual disability in Germany completed an online survey on their current health-related quality of life and the number of barriers to healthcare they experience. In addition, they were asked about their personal and clinical background as well as about the amount of healthcare and support they recently received. We investigated how this information and, particularly, barriers to healthcare explained variations in individual levels of health-related quality of life. We found that barriers to healthcare, compared to most other variables, were a strong predictor of health-related quality of life: The more barriers autistic adults reported, the lower their experienced psychological and physical well-being. To our knowledge, this is the first paper to examine the relationship between barriers to healthcare and health-related quality of life in autism. Our results suggest that healthcare providers need to become aware of the barriers individuals with autism have in seeking and getting healthcare. Improved access to services might contribute to better health-related quality of life in autistic adults.

Keywords

adults autism spectrum disorders health services quality of life

Introduction

Health-related quality of life (HRQOL) is a multi-faceted construct that refers to the subjectively perceived well-being in physical, mental, social, work-related, and other domains of life (de Wit & Hajos, 2013). Considering the patient’s perspective and including these dimensions in the notion of health represented a paradigm shift in healthcare, particularly medicine (Bullinger & Quitmann, 2014), and was based on the World Health Organization’s definition of health as “a state of complete physical, mental, and social well-being and not merely the absence of disease.” Meanwhile, HRQOL has become increasingly important as a so-called “patient-reported outcome” for various health conditions (Megari, 2013). As such, current guidelines also highlighted HRQOL as an important outcome in autism spectrum disorder (ASD) (National Institute for Health and Care Excellence [NICE], 2012), as autistic adults are often affected in social and work-related domains of life (Espeloer et al., 2023; Howlin, 2013). Regardless of considerable heterogeneity in the spectrum—with some autistic adults living independently, being married, and socially integrated—there is a ubiquitous need for psychosocial support (David et al., 2022; Lord et al., 2022). Compromised health, as indicated by increased rates of somatic and mental illness, has been reported (Croen et al., 2015; Kohane et al., 2012; Vohra et al., 2017; Williams & Gotham, 2022). Thus, HRQOL represents a relevant outcome in autistic adults, which—as a patient-reported outcome that captures the first-person perspective and strengthens the patient’s involvement—also addresses the increased need of autistic adults for participation during treatment (Duckert et al., 2023).

Although research in autistic adults—as opposed to children—has only recently increased, there already is a body of evidence on HRQOL in autistic adults (Ayres et al., 2018; Kim & Bottema-Beutel, 2019; Mason et al., 2018; Mason, Mackintosh, et al., 2019; van Heijst & Geurts, 2015). Overall, most international evidence suggests poorer HRQOL in autistic adults (cf. Oakley et al., 2021). Current evidence on HRQOL in autistic adults in Germany is limited to one study, which also reported reduced HRQOL in a small sample of adolescents or young autistic adults (Kamp-Becker et al., 2010). Studies on HRQOL in autistic adults often differ in terms of sample size but also in the outcome measure (Ayres et al., 2018; Evers et al., 2022). The World Health Organization Quality of Life-BREF (WHOQOL-Group, 1998) and Short-Form Health Survey (SF-36/12/8; Ware & Sherbourne, 1992) are among the most widely used generic HRQOL measures. Studies using these measures in autistic adults showed particularly lower outcomes in the domains of psychological health/ mental HRQOL, physical health/ HRQOL, and social relationships (e.g., Jennes-Coussens et al., 2006; Kamio et al., 2013; Kamp-Becker et al., 2010; Mason et al., 2018; van Heijst & Geurts, 2015). Existing meta-analyses, however, strongly suggest that HRQOL can be considered generally reduced in autistic adults regardless of such methodological differences (van Heijst & Geurts, 2015).

In an attempt to understand this reduced HRQOL in autistic adults, potential predictors have been investigated. Demographic and autism-related variables are among the most investigated predictors of HRQOL in autistic adults. For example, (female) gender has often been associated with reduced HRQOL (Braden et al., 2022; Kamio et al., 2013; Khanna et al., 2014; Knüppel et al., 2018; Mason et al., 2018), while age did not result as significant predictor of HRQOL in recent meta-regression analyses (Kim & Bottema-Beutel, 2019; van Heijst & Geurts, 2015). It has been suggested that age at diagnosis is more critical to HRQOL compared to chronological age, with a later diagnosis being associated with reduced HRQOL and more negative outcomes possibly due to, for example, delayed therapeutic effects or delayed access to specific healthcare/ support services (Atherton et al., 2022; cf. Mason et al., 2018). Conversely, evidence on the predictive value of autism severity on HRQOL has also been mixed or weak (Kim & Bottema-Beutel, 2019; Leader et al., 2021; Renty & Roeyers, 2006; van Heijst & Geurts, 2015). Oakley and colleagues (2021) suggested that poorer HRQOL is not linked to autism per se but rather comorbidity burden (i.e., rates of depression). This is in accordance with the clinical observation that autistic adults often seek treatment not due to their “autism,” but because of somatic or mental health issues they experience. In fact, mental comorbidity, in particular, has now almost consistently been found as a significant negative predictor of HRQOL in autistic adults (Deserno et al., 2019; Knüppel et al., 2018; Mason, Mackintosh, et al., 2019; Park et al., 2019).

Despite increased comorbidity burden (Croen et al., 2015; Kohane et al., 2012; Lai et al., 2019; Vohra et al., 2017; Williams & Gotham, 2022) and the predictive value of comorbidity for HRQOL, numerous barriers to healthcare for autistic adults have been claimed, which hinder effective care for autistic adults. These exist at individual (e.g., communicative impairments in making appointments or describing symptoms), professional (e.g., lack of knowledge or reluctance to treat autism), and system-related levels (e.g., lack of services or inadequate services, limited access to existing services; Brede et al., 2022; David et al., 2022; Dern & Sappok, 2016; Duckert et al., 2023; Mason, Ingham, et al., 2019). Vogan and colleagues (2017) revealed that autistic adults with increased health concerns often show high general service use and use of emergency or inpatient care while reporting increased barriers to healthcare and lower satisfaction with services (also Gilmore et al., 2022). This suggests a healthcare system that does not meet the needs of autistic adults. In fact, barriers to accessing primary care in autism were significantly associated with not attending specialist referral when needed, requiring more intense treatment (e.g., surgery), or with serious health conditions due to delayed utilization of services (Doherty et al., 2022). Similarly, Brede and colleagues (2022) suggested that regular mental healthcare services do not adequately support or may even harm autistic adults.

Thus, there is reason to believe that barriers to healthcare have a direct impact on the subjective well-being of autistic adults. Unfortunately, statistical evidence on the direct relationship between barriers to healthcare and mental or physical HRQOL in autistic adults is lacking. A few studies now suggested a positive correlation between received family or social and other support—which often compensates for healthcare disparities—and HRQOL in autistic adults (Bishop-Fitzpatrick & Rubenstein, 2019; Charlton et al., 2023; Kamio et al., 2013; Khanna et al., 2014; Leader et al., 2021; Mason et al., 2018; Renty & Roeyers, 2006). Yet, there is only little evidence on the relationship between HRQOL and utilization of other specific healthcare services in autism (i.e., outpatient, inpatient, and emergency visits).

Thus, the present study aimed to (1) investigate the current mental and physical HRQOL of autistic adults in Germany and (2) analyze a comprehensive set of previously studied and novel predictors of both HRQOL dimensions including barriers to healthcare using multiple linear regression analyses. To our knowledge, this is the first study to investigate the statistical relationship between experienced barriers to healthcare and individual levels of mental or physical HRQOL. Identifying the most relevant predictors of HRQOL in autistic adults including potential healthcare barriers will not only help to identify vulnerabilities in autistic individuals but also vulnerabilities within the current healthcare system in order to inform healthcare providers and policymakers to improve healthcare and outcomes for autistic adults.

Methods

Participants

Participants were recruited across Germany using purposive, quota, and snowball sampling methods through the study’s collaborative network, publicly available contacts from autism-related and healthcare associations, local and countrywide outpatient clinics, local social media, and personal contacts. Participants were included in the online survey if they (1) were adults, (2) reported a professionally confirmed diagnosis of ASD with (3) no co-occurring intellectual disability (IQ > 70) as well as (4) sufficient language skills for the survey completion. Asking participants about a formerly confirmed clinical diagnosis of ASD, the diagnostic category, and diagnostic age has been shown to be highly predictive of the actual existence of the diagnosis (Baron-Cohen et al., 2015; Daniels et al., 2012). Participation was voluntary and anonymous. Informed consent was obtained prior to participation. No incentives were given for survey participation.

Data collection and measures

The study was conducted in accordance with the WMA Declaration of Helsinki and was approved by the Local Psychological Ethics Committee of the Center for Psychosocial Medicine of the University Medical Center Hamburg-Eppendorf (ref. LPEK-0227). Data were collected, processed, and stored in accordance with the EU General Data Protection Regulation. Data were collected as part of a larger nationwide online survey (David et al., 2022), programmed in LimeSurvey GmbH (n.d.). Demographic, clinical, and utilization-related data were assessed as potential predictors of HRQOL (Table 1). In addition, participants completed the Short-Form Health Survey as operationalization of physical and mental HRQOL (SF-8; Beierlein et al., 2012; Ware et al., 2001) and the Barriers to Healthcare Checklist–Short Form (BHC-SF; Raymaker et al., 2017).

Table 1.

Predictor variables and sample description.

	M (SD)	n (%)
Demographic
Age (years)	38.9 (11.5)
Gender		Female: 172 (55.3%)Male: 113 (36.3%)Diverse: 26 (8.4%)
Relationship status		In a relationship (married/in a relationship): 79 (25.4%)No relationship (single/divorced/ widowed): 232 (74.6%)
Education		High school or equivalent (e.g., technical diploma): 218 (70.1%)Lower or no degree (secondary school/elementary school/special school/no degree): 93 (29.9%)
Employment status		Employed (employed full-time/part-time/in training): 138 (44.4%)Not employed (unemployed/marginal employment/not applicable): 173 (55.6%)
Clinical
Age at autism diagnosis (years)	32.5 (13.0)
Required support due to social difficulties (DSM-5 interaction)	3.5 (1.1)	Note: rated on a 5-point Likert scale (1 = none, 2 = little, 3 = moderate, 4 = quite, 5 = very)
Required support due to restrictive behavior and interests (DSM-5 behavior)	3.3 (1.1)	Note: rated on a 5-point Likert scale (1 = none, 2 = little, 3 = moderate, 4 = quite, 5 = very)
Number of somatic comorbidities (last 6 months)	1.4 (1.1)
Number of mental comorbidities (last 6 months)	1.5 (1.4)
Healthcare-related
Outpatient treatment (number of visits last 6 months)	21.0 (26.5)
Inpatient treatment (number of days last 6 months)	4.1 (15.5)
Emergency treatment (number of contacts last 6 months)	0.3 (0.9)
Informal support (number of days last 6 months)	30.0 (54.2)
Professional/ formal support (number of contacts last 6 months)	9.0 (25.2)
Barriers to Healthcare
BHC-SF sum score	8.1 (4.1)

M: mean, SD: standard deviation. DSM-5: Diagnostic and Statistical Manual of Mental Disorders, 5th edition; BHC-SF: Barriers to Healthcare Checklist–Short Form.

HRQOL

HRQOL was assessed using the German version of the SF-8, for which German normative data were available (n = 2552; Beierlein et al., 2012). The SF-8 provides results equivalent to the SF-36. It contains eight items corresponding to eight subscales (Physical Functioning, Role Physical, Bodily Pain, General Health, Vitality, Social Functioning, Role Emotional, and Mental Health) rated on a 5-/6-point Likert-type scale. Rating subscales were used to calculate two summary scores indicative of mental and physical HRQOL (mental component summary score, MCS; physical component summary score, PCS). The MCS and PCS were then calculated as the weighted sum of the subscale scores. By using a norm-based scoring method, scores can be presented as standardized T-scores (with M = 50, SD = 10). The SF-8 has good to adequate psychometric properties in terms of overall reliability and validity (Beierlein et al., 2012; Yiengprugsawan et al., 2014). There is also evidence of psychometrically sound applications of the SF-8, SF-12, and SF-36 in autistic adults (Hayakawa et al., 2015; Khanna et al., 2015; van Heijst & Geurts, 2015).

Demographic variables

Participants provided information about age (years), gender (female, male, and diverse), relationship status (single, married/ in a relationship, divorced, widowed), highest educational degree/ attainment (high school diploma, secondary school, elementary school, special school, no degree) and employment status (full-time, part-time, student, unemployed, and other).

Clinical variables

Clinical variables included type of confirmed ASD diagnosis (autistic disorder, atypical autism, Asperger syndrome, other), diagnostic age (years), and presence of somatic and/or mental comorbidities (summed number). Self-rated autism symptom severity was assessed with two items analogous to the “Diagnostic and Statistical Manual of Mental Disorders” (4th ed.; DSM-5; American Psychiatric Association, 2013). Participants rated the following two items: “How much support do you need because of difficulties with interpersonal communication and social interactions?” (DSM-5 interaction) and “How much support do you need because you persist in behavioral habits, routines, or interests that are important to you (e.g., difficulties in self-organization or dealing with change)?” (DSM-5 behavior). Responses were given on a 5-point Likert-type scale (“none (1)—little (2)—moderate (3)—quite (4)—very (5)”).

Healthcare-related variables

Utilization history of professional healthcare, professional/ formal support services and informal support for the last 6 months were collected using modified versions of the German Questionnaire for the Assessment of Health Services in Old Age (FIMA; Seidl et al., 2015) and the Questionnaire on the Utilization of Medical and Nonmedical Care Services in Mental Disorders (FIMPsy; Grupp et al., 2018). These included outpatient treatment/care (summed visits), inpatient care (summed days), and emergency care (summed contacts). Informal support was assessed as the summed number of days that family members or friends provided help. Formal support included household help, social worker, nursing service/home care or legal custodians (summed number of contacts).

Barriers to healthcare

We used a translated version of the BHC-SF (Raymaker et al., 2017; Peth et al., submitted). The checklist was developed specifically for autistic people to assess barriers they often experience in clinical settings. It shows good content and construct validity (Raymaker et al., 2017). Barriers on the checklist relate to emotional difficulties, executive and sensory dysfunction, problems with healthcare navigation, patient-provider communication, provider attitudes, socioeconomic and social support limitations, as well as environmental factors (e.g., “I don’t understand the healthcare system” or “I find it hard to handle the waiting room”). The Short Form includes 17 items rated “yes” (=1) or “no” (=0), which are summed to a total score (minimum score: 0 = no barriers at all, maximum score: 17 = barriers in every aspect asked about).

Data analysis and selection of predictors

Data were analyzed using Microsoft Excel and IBM SPSS Statistics (Version 27). Missing values in predictors were imputed using the Expectation-Maximization method implemented in SPSS. For somatic comorbidities 6.4%, for mental comorbidities 5.5%, for inpatient care 0.3%, for informal support 4.8%, and for formal support 4.8% of data were missing and replaced.

We performed three kinds of analyses. First, two-tailed independent t-tests were performed (unequal variances assumed, p < 0.05 considered significant) to determine whether the mean HRQOL for the MCS (i.e., mental HRQOL) and PCS (i.e., physical HRQOL) of the SF-8 in the autism group differed from the German normative sample (Beierlein et al., 2012). Second, bivariate correlational analyses (Pearson correlations between metric variables, point-biserial correlations between categorical and metric variables, Phi coefficients for correlations between categorical variables) were performed in order to (1) examine relationships between HRQOL and demographic, clinical, and healthcare-related variables and (2) to detect multicollinearity between predictors (as indicated by high correlations between two variables). Multicollinearity between predictors in our regression model was further assessed using the variance inflation factor (VIF). If two variables were highly correlated and had a critical VIF, as indicated by a VIF value > 5 (Menard, 2001), they were excluded from the multiple regression model. Interpretation of effect sizes/correlations was based on the conventions of Cohen (1988) (small/weak: d = 0.2/r = 0.10, medium/moderate: d = 0.5/r = 0.30, large/high: d = 0.8/r = 0.50).

Third, multiple regression analyses with either the MCS or PCS as dependent variable were conducted in order to identify significant predictors of mental and physical HRQOL that significantly explained variance in each of the SF-8 domains. The standardized beta coefficients (i.e. absolute value) were inspected in order to evaluate the strength of each predictor for explaining variance in the dependent variable. Categorical variables with more than two categories were recoded into dichotomous format, that is, gender (female = 1, male/diverse = 0), education (high school = 1, lower education/other = 0), marital status (1 = married/in a relationship, no relationship/other = 0), and employment status (1 = employed or in training, unemployed/other = 0). Predictors were entered into regression analyses in one block. Prior to multiple linear regression analyses, a power analysis was conducted online (using an online tool by Hemmerich, 2019) in order to determine the necessary sample size for our regression model to become significant, given the number of predictor variables planned. Previous evidence from regression analyses on HRQOL showed R²s between 0.03 and 0.60 (Lawson et al., 2020; Mason et al., 2018; Renty & Roeyers, 2006), which increased from small to large effect sizes when including disability- or support-related in addition to demographic predictors into the model. A conservatively calculated power analysis indicated that with an estimated R² = .08 (between small and medium effect; Cohen, 1988), a statistical power of 0.80, and a significance level of α = 0.05, a sample size of n = 236 would be sufficient with 16 predictors.

Community involvement

Our neurodivergent research team engaged peer researchers as well as clinical psychologists specialized in ASD diagnosis and treatment. The online survey was designed in a participatory manner in consultation of autistic adults. Members of the autism community piloted and critically reviewed earlier versions of the survey. Research was generally conducted in close collaboration with relevant stakeholders.

Results

Sample description

Data from 311 autistic participants were included in this study. Of all participants, 269 (86.5%) reported a diagnosis of “Asperger syndrome,” 20 (6.4%) “atypical autism,” 11 (3.5%) “autistic disorder,” and 11 (3.5%) “other” (for further sample description, see Table 1).

Mental and physical HRQOL compared to the German normative sample

Participants reported reduced mental HRQOL (M = 32.8, SD = 12.2) compared to the German normative sample (M = 53.3, SD = 7.8; Beierlein et al., 2012). As these mean scores represent normalized T-scores, this result also indicates that mental HRQOL in autistic adults was below average (more than 1.5 standard deviations below T = 50). This group difference was significant and corresponded to a large effect size (t(342) = 29.0, p < 0.001; Cohen’s d = 2.0; Figure 1).

Figure 1.

Mean scores of the mental and physical components of the SF-8 in our sample compared to the German normative sample (Beierlein et al., 2012). Legend: MCS as indicator of mental HRQOL, PCS as indicator of physical HRQOL. MCS and PCS represent standardized norm-based scores. Autistic adults (n = 311) in Germany showed significantly reduced HRQOL on both components of the SF-8 compared to the German normative sample (n = 2552; Beierlein et al., 2012). In particular, mental HRQOL was considerably below average in autistic participants.

Physical HRQOL was also reduced (M = 45.9, SD = 9.9, range = 20.5–65.2) compared to the German normative sample (M = 50.3, SD = 8.4; Beierlein et al., 2012). This group difference was also significant and corresponded to an almost moderate effect size (t(367) = 7.5, p < 0.001; Cohen’s d = 0.48; Figure 1).

Correlation analyses

The full correlation matrix is shown in Table 2. Mental HRQOL showed moderate negative correlations with mental comorbidities (r = −0.404, p < 0.001) and the BHC-SF sum score (r = −0.416, p < 0.001). That is, mental HRQOL decreased with increasing number of mental comorbidities and the amount of self-reported barriers to healthcare. Similarly, physical HRQOL showed moderate negative correlations with somatic comorbidities (r = −0.462, p < 0.001) and the BHC-SF sum score (r = −0.420, p < 0.001). All other correlations between HRQOL components and the other variables were small or non-significant (Table 2).

Table 2.

Correlation analyses.

	1.	2.	3.	4.	5.	6.	7.	8.	9.	10.	11.	12.	13.	14.	15.	16.	17.	18.
1. Mental HRQOL (MCS)	-	0.068	0.008	−0.040	0.016	−0.052	0.180 ^**	−0.066	−0.142 ^*	−0.207 ^***	−0.135 ^*	−0.404 ^***	−0.132 ^*	−0.213 ^***	−0.126 ^*	−0.069	−0.150 ^**	−0.416 ^***
2. Physical HRQOL (PCS)		-	−0.221 ^***	−0.111	−0.087	0.100	0.126 ^*	−0.231 ^***	−0.149 ^**	−0.203 ^***	−0.462 ^***	−0.263 ^***	−0.193 ^**	−0.128 ^*	−0.089	−0.231 ^***	−0.119 ^*	−0.420 ^***
3. Age			-	0.039	0.207 ^**	0.085	0.138 ^*	0.830 ^***	0.066	0.003	0.194 ^**	−0.067	0.052	−0.040	−0.117 ^*	0.013	−0.102	0.078
4. Gender				-	0.064	0.105	−0.017	0.099	−0.097	−0.086	0.102	−0.043	0.026	−0.054	−0.095	0.039	0.101	0.111
5. Relationship status					-	0.058	0.088	0.274 ^***	−0.079	−0.011	0.031	−0.059	−0.089	−0.066	−0.005	0.180 ^**	−0.106	−0.008
6. Education						-	0.103	0.091	−0.041	−0.115 ^*	0.046	0.025	0.038	0.057	−0.016	−0.094	0.000	−0.013
7. Employment status							-	0.150 ^**	−0.031	−0.191**	−0.097	−0.137 ^*	−0.111	−0.027	−0.053	−0.021	−0.111 ^*	−0.146 ^**
8. Diagn. age								-	0.057	0.009	0.177 ^**	0.005	0.065	−0.022	−0.104	−0.092	−0.025	−0.074
9. DSM-5 interaction									-	0.438 ^***	−0.031	0.155 ^**	0.200 ^***	0.105	−0.009	0.022	0.087	0.168 ^**
10. DSM-5 behavior										-	0.081	0.261 ^***	0.220 ^***	0.023	0.076	0.122 ^*	0.193 ^**	0.296 ^***
11. Somatic comorbidities											-	0.293 ^***	0.227 ^***	0.153 ^**	0.101	−0.051	0.179 ^**	0.218 ^***
12. Mental comorbidities												-	0.185 ^**	0.154 ^**	0.189 ^**	0.136 ^*	0.126 ^*	0.344 ^***
13. Outpatient treatment													-	0.068	0.136 ^*	0.057	0.211 ^***	0.160 ^**
14. Inpatient treatment														-	0.154 ^**	−0.046	−0.019	0.044
15. Emergency treatment															-	0.069	0.044	0.112 ^*
16. Informal support																-	0.067	0.206 ^***
17. Formal support																	-	0.211 ^***
18. BHC-SF																		-

SF-8: Short-Form Health Survey; MCS: mental component summary score of the SF-8; PCS: physical component summary score of the SF-8; DSM-5: Diagnostic and Statistical Manual of Mental Disorders, 5^th edition; BHC-SF: Barriers to Healthcare Checklist–Short Form.

p < 0.05; ^**p < 0.01; ^***p < 0.001.

Analyses between predictor variables, and with respect to multicollinearity, revealed that age was significantly and highly correlated with age at diagnosis (r = 0.830, p < 0.001). Variables were kept for the multiple regression analysis after inspection of the VIF in the regression model (VIF < 5; Table 3). Furthermore, moderate positive correlations were detected between both DSM-5 scales (r = 0.438, p < 0.001) and between the number of mental comorbidities and the BHC-SF sum score (r = 0.344, p < 0.001). All other correlations between predictor variables were small or non-significant (Table 2).

Table 3.

Multiple regression analyses (N = 311).

	Mental HRQOL (MCS)					Physical HRQOL (PCS)
	b	SE	95% CI	ß	p	b	SE	95% CI	ß	p	VIF
Demographic predictors
Age	0.14	0.09	[−0.05, 0.33]	0.13	0.145	−0.04	0.07	[−0.19, 0.10]	−0.05	0.552	3.42
Gender	−0.44	1.25	[−2.89, 2.01]	−0.02	0.724	−0.91	0.95	[−2.77, 0.96]	−0.05	0.339	1.10
Relationship status	0.51	1.48	[−2.40, 3.43]	0.02	0.728	−1.35	1.13	[−3.56, 0.87]	−0.06	0.232	1.19
Education	−1.56	1.33	[−4.17, 1.05]	−0.06	0.240	2.70	1.01	[0.71, 4.68]	0.13	0.008	1.06
Employment status	2.73	1.25	[0.27, 5.19]	0.11	0.030	0.95	0.95	[−0.92, 2.82]	0.05	0.318	1.11
Clinical predictors
Diagnostic age	−0.17	0.08	[−0.34, 0.00]	−0.18	0.044	−0.09	0.06	[−0.21, 0.04]	−0.12	0.176	3.49
DSM-5 interaction	−0.26	0.62	[−1.49, 0.96]	−0.02	0.673	−0.75	0.47	[−1.69, 0.18]	−0.08	0.113	1.33
DSM-5 behavior	−0.30	0.62	[−1.52, 0.93]	−0.03	0.636	−0.03	0.47	[−0.96, 0.91]	0.00	0.957	1.45
Somatic comorbidities	0.45	0.60	[−0.74, 1.63]	0.04	0.459	−2.95	0.46	[−3.85, −2.05]	−0.33	<0.001	1.30
Mental comorbidities	−2.06	0.48	[−3.00, −1.12]	−0.24	<0.001	−0.22	0.36	[−0.93, 0.50]	−0.03	0.552	1.36
Healthcare-related predictors
Outpatient treatment	0.00	0.02	[−0.05, 0.04]	−0.01	0.897	−0.01	0.02	[−0.04, 0.03]	−0.02	0.749	1.19
Inpatient treatment	−0.12	0.04	[−0.20, −0.04]	−0.16	0.002	−0.04	0.03	[−0.10, 0.01]	−0.07	0.142	1.09
Emergency treatment	−0.31	0.72	[−1.73, 1.11]	−0.02	0.666	−0.20	0.55	[−1.28, 0.87]	−0.02	0.712	1.10
Informal support	−0.02	0.01	[−0.04, 0.01]	−0.07	0.199	−0.01	0.01	[−0.03, 0.01]	−0.06	0.205	1.16
Formal support	0.02	0.03	[−0.03, 0.07]	0.04	0.479	−0.04	0.02	[−0.08, 0.00]	−0.10	0.045	1.15
Barriers to healthcare
BHC-SF	−0.87	0.17	[−1.19, −0.54]	−0.29	<0.001	−0.62	0.13	[−0.86, −0.37]	−0.25	<0.001	1.32

MCS: mental component summary score of the SF-8; PCS: physical component summary score of the SF-8; b: unstandardized coefficient, SE: standard error, CI: confidence interval for b, ß: standardized coefficient (beta), VIF: variance inflation factor. DSM-5: Diagnostic and Statistical Manual of Mental Disorders, 5^th edition; BHC-SF: Barriers to Healthcare Checklist–Short Form.

Multiple linear regression analyses

Predictors of mental HRQOL

The overall regression model with all 16 predictors was statistically significant (F(16, 294) = 8.08, p < 0.001) and explained 26.8% of the variance in the mental HRQOL component (R² = 0.305; adj. R² = 0.268), indicating a high goodness of fit (Cohen, 1988). Number of Barriers to healthcare as assessed by the BHC-SF, were awere significant and strongest negative predictors of mental HRQOL (ß = −0.29, p < 0.001; Table 3), followed by the number of mental comorbidities (ß = −0.24, p < 0.001), age at diagnosis (ß = −0.18, p = 0.044) and days of inpatient treatment (ß = −0.16, p = 0.002). Fourth, employment status was a significant positive predictor of mental HRQOL (i.e., being employed was associated with higher mental HRQOL; ß = 0.11, p = 0.03).

Predictors of physical HRQOL

The overall regression model with all 16 predictors was statistically significant (F(16, 294) = 11.85, p < 0.001) and explained 35.9% of the variance in the physical HRQOL component (R² = 0.392; adj. R² = 0.359), indicating a high goodness of fit (Cohen, 1988). The number of somatic comorbidities was a significant and strongest negative predictor of physical HRQOL (ß = −0.33, p < 0.001; Table 3), followed by the number of self-reported barriers to healthcare (ß = −0.25, p < 0.001) and the amount of professional/ formal support (ß = −0.10, p = 0.045). Educational attainment was a significant positive predictor of physical HRQOL (i.e., having a high school diploma or equivalent was associated with higher HRQOL; ß = 0.13, p = 0.008).

Discussion

The present study provides a current picture of mental and physical HRQOL in autistic adults without co-occurring intellectual disability across Germany. Autistic adults reported significantly reduced mental (which was far below average) and physical HRQOL. Several independent variables had a significant effect for mental or physical HRQOL such as mental and somatic comorbidity rates. Barriers to healthcare emerged as the independent and only predictor that significantly explained variation in both HRQOL domains: The more barriers autistic adults experienced, the lower their mental and physical HRQOL. Particularly for mental HRQOL, healthcare barriers emerged as the strongest predictor. This is the first study to shed light on the impact of healthcare barriers on HRQOL in autistic adults.

Reduced HRQOL in autistic adults in Germany

Here, autistic adults without intellectual disability in Germany reported significantly poorer physical and mental HRQOL compared to a German normative sample. Particularly, mental HRQOL was far below average (Figure 1). Our finding is in accordance with the notion of increased psychiatric comorbidity burden in autistic adults (Croen et al., 2015; Kohane et al., 2012; Lai et al., 2019; Vohra et al., 2017) and with previous evidence on particularly compromised (mental) HRQOL in autistic adults using other versions of the SF (Braden et al., 2022; Helles et al., 2017; Khanna et al., 2014). For example, Khanna and colleagues (2014) reported that autistic adults in the United States showed significantly lower mental HRQOL on the SF-12 compared to the general U.S. population. This effect was found across age and gender, whereas physical HRQOL was significantly reduced as a function of age. Unfortunately, we also did not include a matched comparison group, but compared HRQOL in our sample of autistic adults with German normative data. Our finding is in line with Braden and colleagues (2022), who—using the SF-36—found HRQOL to be significantly reduced in both autistic men and women compared to a matched sample of neurotypical participants.

Differential modulations of mental versus physical HRQOL may suggest different factors associated with either mental or physical well-being in autism, also implying possibly different target groups (e.g., older vs. younger individuals and women vs. men) or actions when aiming at improving mental or physical HRQOL in autistic adults. Multiple linear regression analysis helps to identify healthcare priorities in autistic adults (“understanding what works for whom and when, and what are some of the predictable needs and variations that need to be considered to support autistic individuals,” Lord et al., 2022).

Unique predictors of mental or physical HRQOL in autistic adults: demographic, clinical, and healthcare-related variables

Here multiple regression analyses revealed non-overlapping sets of predictors of either mental or physical HRQOL in our autistic participants. Autistic adults who were particularly vulnerable to lower mental HRQOL more often reported unemployment, later diagnosis of ASD, more mental health comorbidities, and greater use of inpatient treatment. Those who were particularly vulnerable to reduced physical HRQOL reported lower educational attainment, more somatic comorbidities, and increased use of professional support services.

With respect to demographic predictors, employment status has previously been associated with HRQOL in autistic adults (e.g., Khanna et al., 2014; Knüppel et al., 2018; Mason et al., 2018). Here, being employed positively predicted mental HRQOL. Mental illness, particularly depression, has been related to unemployment in autistic adults (Park et al., 2019). This notion was supported by our data, which showed weak negative correlations between being employed and the number of mental comorbidities as well as autism-related behavioral difficulties (Table 2). Discrepancies with other studies, which have not linked employment status to (mental) HRQOL (Kamio et al., 2013; for an overview, van Heijst & Geurts, 2015) might be explained by differences in sample constitutions. Unemployment rates were increased in our sample (Table 1). For physical HRQOL, higher educational attainment emerged as a significant positive demographic predictor in autistic adults (see also Mason et al., 2018). Educational attainment has previously been associated with healthcare utilization and self-care behavior in other physical health conditions (e.g., Alguwaihes & Shah, 2009). Educational attainment in our sample was high (Table 1), potentially compensating and weakening the effect of reduced physical HRQOL in contrast to the mental component (Figure 1). Our bivariate data analyses further showed weak negative correlations between educational attainment and autism-related behavioral difficulties. These might also affect individual health management (incl. the use of preventive services) or imply increased barriers to accessing healthcare, making external informal or formal support for healthcare necessary (Table 2).

With respect to clinical predictors, diagnostic age negatively predicted mental HRQOL, that is, participants who received an ASD diagnosis later in life reported lower mental HRQOL (Atherton et al., 2022). Although our sample also included participants who were diagnosed at <18 years of age (15% of participants), on average, participants were diagnosed in their thirties (Table 1). Early diagnosis may be an indicator of greater severity of autism, but it also represents a door opener to specific interventions and support services critically important to promoting health and other outcomes (Burke & Taylor, 2023; Skaletski et al., 2021). In fact, late diagnosis of ASD has been associated with increased or more severe mental health conditions, which—if left untreated—lead to alarmingly poor outcomes (Croen et al., 2015; Lai et al., 2019; Maddox & Gaus, 2019; Mandy et al., 2022). It is therefore not surprising that, here, the number of self-reported mental comorbidities (in line with Helles et al., 2017; Lawson et al., 2020; Mason et al., 2018) negatively predicted mental HRQOL in our participants (while the number of somatic comorbidities negatively predicted physical HRQOL; compare Charlton et al., 2023).

With respect to healthcare-related predictors, only the duration of inpatient treatment emerged as a significant, negative predictor of mental HRQOL, while contacts to professional support services emerged as a significant, negative predictor of physical HRQOL. Although inpatient care has previously also been associated with improved outcomes for autistic individuals (Melvin et al., 2022), psychiatric inpatient admission has typically been linked to increased autism severity and mental health comorbidity burden (Righi et al., 2018). Use of inpatient treatment, thus, might indicate increased mental healthcare needs in autism. Indeed, there was a weak positive correlation between duration of inpatient care and comorbidity rates (Table 2), supporting the notion that inpatient treatment reflects more severe or complex mental health problems, and is therefore associated with poorer mental HRQOL. Conversely, adverse health effects of mental healthcare services themselves in autistic adults have been discussed (Brede et al., 2022) as well as the possibility that utilization of inpatient treatment and associated severe health problems may arise from barriers at the primary care level (Doherty et al., 2022). Unfortunately, the cross-sectional nature of our data does not allow us to pin down the exact nature of the relationship between inpatient treatment and mental HRQOL, that is, as mediated by psychiatric comorbidities or ineffective healthcare provision in autistic adults.

For physical HRQOL, formal support contacts emerged as a specific negative predictor. These included nursing service or home care, often granted when a person needs care due to physical illness and being dependent on the help of others. As such, the use of formal support services might also be specifically indicative of more severe physical health problems, hence showing an association with worse physical HRQOL (see Table 2 for positive correlations between formal support utilization and autism severity, comorbidities, and outpatient treatment). On the other hand, increased support service utilization—while physical health issues persist or worsen—has also been interpreted as evidence for barriers to accessing more basic care services and for unmet healthcare needs in autism (e.g., Gilmore et al., 2022; Renty & Roeyers, 2006).

Our findings suggest that facilitation of early diagnosis, supported employment, and effective, early treatment should be key priorities in order to improve HRQOL in autistic adults—particularly mental HRQOL—which was seriously affected in our sample. These may include education of practitioners and employers for adult autism, increased availability of autism-specific diagnostic and therapeutic structures for adults, and autism-specific supported employment (see also Duckert et al., 2023; Mason, Ingham, et al., 2019).

Barriers to healthcare predict both lower mental and physical HRQOL in autistic adults. Whereas different independent positive and negative predictors were associated with either mental or physical HRQOL in autistic adults, healthcare barriers negatively predicted both. Autistic adults in Germany experience clinically relevant barriers to healthcare. Especially for reduced mental HRQOL, barriers emerged as the strongest predictor (in line with Brede et al., 2022, who emphasized several adverse effects of current mental health services for autistic adults’ mental well-being). Standardized beta coefficients were inspected in order to compare the strength of the effect of each independent variable to the dependent variable, with higher absolute coefficients indicating stronger effects on HRQOL. In doing so, barriers emerged as the strongest independent predictor for mental HRQOL (after mental comorbidities) and the second-strongest predictor for physical HRQOL (for which physical comorbidities showed the strongest effect).

Self-reported barriers to healthcare were correlated with increased somatic and, especially, mental comorbidity, with autism severity, unemployment, increased outpatient and emergency treatment as well as informal and formal support. While the negative relationship between barriers to healthcare and present health problems can quite intuitively be understood, the positive relationship between such barriers and healthcare utilization seems less intuitive. Yet, the present findings are consistent with previous evidence on high general service use or emergency/ hospital treatment in autism and concurrent experience of healthcare barriers as well as lower service satisfaction in autism (e.g., Brede et al., 2022; Doherty et al., 2022; Gilmore et al., 2022; Renty & Roeyers, 2006; Vogan et al., 2017). Formal and, particularly, informal support (by family members) thereby often compensates for healthcare barriers in autism by aiding navigation within the healthcare system, facilitating access to healthcare or assisting patient-provider communication. Our findings underscore the notion of unmet healthcare needs in autism (Brede et al., 2022; Platos & Pisula, 2019), which is further underlined by the recent dramatic finding that autistic adults in the United Kingdom receive poorer quality healthcare than those without autism (Weir et al., 2022).

Barriers to healthcare here were measured using the BHC-SF (Raymaker et al., 2017). Compared to a previous study using the BHC-SF in the United States, our autistic participants on average reported increased barriers to healthcare (with a mean sum score on the BHC-SF that was almost double the mean score reported by Nicolaidis et al., 2016). This quantifies and corroborates previous qualitative findings from Germany and other countries (Calleja et al., 2022; Dern & Sappok, 2016; Duckert et al., 2023) but also points to particular shortcomings of the healthcare system for autistic adults in Germany (possibly similar to the United Kingdom as cited above; Weir et al., 2022). Public and clinical awareness of autism in adulthood in Germany has increased relatively recently. As such, we know little about prevalence rates or other epidemiological data on adult autism in Germany for the last 20 years (Roy & Strate, 2023). Unfortunately, from the present data, it remains unclear where the increased rates of self-reported barriers in our German sample arise from. The cross-sectional correlational findings do not allow inferences about the direction of relationships. As such, comorbidity rates, autism severity, or delayed diagnosis of autism could have contributed to increased access barriers or vice versa.

The present ratings on the BHC-SF (Raymaker et al., 2017) provide information about specific ways in which healthcare for autistic adults in Germany—but not only here—could be improved. Our findings suggest that reducing barriers to healthcare could lead to improved HRQOL and reduced adverse outcomes in autistic adults. There is also good reason to believe that reducing barriers might lead to more effective and less costly healthcare in autism (Doherty et al., 2022; Höfer et al., 2022).

Limitations

There are several limitations to our study. First, the present cross-sectional observational study is limited in its ability to make causal inferences. That is, although here barriers to healthcare significantly predicted HRQOL in autistic adults, we cannot draw the conclusion that barriers cause lower HRQOL. Our results also do not address possible nested, mediated, or interaction effects between predictor variables (e.g., between barriers to healthcare and mental health conditions). As such, our findings may reflect different mechanisms— which are likely to be complex in autistic adults—associated with barriers to healthcare on the one hand and HRQOL on the other. Future research, possibly longitudinal or intervention studies, will be necessary to tease these apart to inform successful health care for autistic adults.

Second, we cannot rule out sampling bias as our data are based on self-report by autistic adults with online access and sufficient cognitive and language skills to complete the survey and reflect on their own health situation. By definition, HRQOL is a patient-reported outcome for which self-report is key. Our sample constitution and results are comparable to those of previously published HRQOL studies. Yet, our results do not represent the entire autism spectrum (e.g., early-diagnosed participants, autistic adults with intellectual disability). In Germany, but also internationally, there are specific services for autistic adults with but not without intellectual disability, who—being considered “high-functional” and with disabilities not being obvious—are often confronted with so-called invisible barriers in accessing or finding the much-needed healthcare or support services within regular healthcare structures (Beresford et al., 2020; Brede et al., 2022). Accordingly, there is now growing evidence that autistic adults without intellectual disabilities often show especially poor health or employment outcomes (Taylor & Seltzer, 2011). Unfortunately, our data did not allow comparisons between sub-groups of autistic adults with and without intellectual disability or other comparison groups (especially equally underserved patient populations with unmet healthcare needs).

Conclusions and implications

Autistic adults in Germany experience reduced mental and physical HRQOL and significant barriers to accessing the healthcare they need. The present study is the first to demonstrate a direct link between the barriers to healthcare autistic adult's experience and their mental and physical HRQOL. Reducing barriers to healthcare along with effective treatment and prevention of mental and somatic illness and facilitation of employment and education were identified as the highest priorities for actions in order to improve the health status of autistic adults. In light of the UN Convention on the Rights of Persons with Disabilities (2006), which calls for equal rights for people with disabilities, including equal access to and quality of healthcare, the present findings have alarming and urgent implications for the healthcare system, service providers, health insurers, and policymakers, who must finally become aware of the barriers and needs autistic adults in seeking healthcare.

Footnotes

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: The study was funded by the Innovation Fund of the German Federal Joint Committee of Health Insurance Companies (Gemeinsamer Bundesausschuss, G-BA, study title “BarrierefreiASS” (01VSF19011)). The funding body had no role in the design of the study or in writing the manuscript, nor did they have a role in data collection, analyses or interpretation of data.

ORCID iDs

Nicole David

Sophia Dückert

Judith Peth

References

Alguwaihes

Shah

(2009). Educational attainment is associated with health care utilization and self-care behavior by individuals with diabetes. The Open Diabetes Journal, 2. 24–28. 10.2174/1876524600902010024.

American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.).

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. https://doi.org/10.1007/s10803-021-05235-x

Ayres

Parr

J. R.

Rodgers

Mason

Avery

Flynn

(2018). A systematic review of quality of life of adults on the autism spectrum. Autism, 22(7), 774–783. https://doi.org/10.1177/1362361317714988

Baron-Cohen

Bowen

D. C.

Holt

R. J.

Allison

Auyeung

Lombardo

M. V.

Smith

Lai

M. C.

(2015). The “reading the mind in the eyes” test: Complete absence of typical sex difference in ~400 men and women with autism. PLOS ONE, 10(8), Article e0136521. https://doi.org/10.1371/journal.pone.0136521

Beierlein

Morfeld

Bergelt

Bullinger

Brähler

(2012). Messung der gesundheitsbezogenen Lebensqualität mit dem SF-8[Measuring health-related quality of life with the SF-8: German norms from a representative self-administered survey]. Diagnostica, 58(3), 145–153. https://doi.org/10.1026/0012-1924/a000068

Beresford

Mukherjee

Mayhew

Heavey

Park

A. L.

Stuttard

Allgar

Knapp

(2020). Evaluating specialist autism teams’ provision of care and support for autistic adults without learning disabilities: The shape mixed-methods study. NIHR Journals Library.

Bishop-Fitzpatrick

Rubenstein

(2019). The physical and mental health of middle aged and older adults on the autism spectrum and the impact of intellectual disability. Research in Autism Spectrum Disorders, 63, 34–41. https://doi.org/10.1016/j.rasd.2019.01.001

Braden

B. B.

Pagni

B. A.

Monahan

Walsh

M. J. M.

Dixon

M. V.

Delaney

Ballard

Ware

J. E.

Jr. (2022). Quality of life in adults with autism spectrum disorder: Influence of age, sex, and a controlled, randomized mindfulness-based stress reduction pilot intervention. Quality of Life Research, 31(5), 1427–1440. https://doi.org/10.1007/s11136-021-03013-x

10.

Brede

Cage

Trott

Palmer

Smith

Serpell

Mandy

Russell

(2022). “We have to try to find a way, a clinical bridge”—autistic adults’ experience of accessing and receiving support for mental health difficulties: A systematic review and thematic meta-synthesis. Clinical Psychology Review, 93, 102131. https://doi.org/10.1016/j.cpr.2022.102131

11.

Bullinger

Quitmann

(2014). Quality of life as patient-reported outcomes: Principles of assessment. Dialogues in Clinical Neuroscience, 16(2), 137–145. https://doi.org/10.31887/DCNS.2014.16.2/mbullinger

12.

Burke

M. M.

Taylor

J. L.

(2023). To better meet the needs of autistic people, we need to rethink how we measure services. Autism, 27(4), 873–875. https://doi.org/10.1177/13623613231164495

13.

Calleja

Kingsley

Amirul Islam

F. M.

McDonald

(2022). Barriers to accessing healthcare: Perspectives from autistic adults and carers. Qualitative Health Research, 32(2), 267–278. https://doi.org/10.1177/10497323211050362

14.

Charlton

R. A.

McQuaid

G. A.

Wallace

G. L.

(2023). Social support and links to quality of life among middle-aged and older autistic adults. Autism, 27(1), 92–104. https://doi.org/10.1177/13623613221081917

15.

Cohen

(1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.

16.

Croen

L. A.

Zerbo

Qian

Massolo

M. L.

Rich

Sidney

Kripke

(2015). The health status of adults on the autism spectrum. Autism, 19(7), 814–823. https://doi.org/10.1177/1362361315577517

17.

Daniels

A. M.

Rosenberg

R. E.

Anderson

Law

J. K.

Marvin

A. R.

Law

P. A.

(2012). Verification of parent-report of child autism spectrum disorder diagnosis to a web-based autism registry. Journal of Autism and Developmental Disorders, 42(2), 257–265. https://doi.org/10.1007/s10803-011-1236-7

18.

David

Duckert

Gewohn

Konig

Rahlff

Erik

Vogeley

Schottle

Konnopka

Schulz

Peth

(2022). Mixed-methods investigation of barriers and needs in mental healthcare of adults with autism and recommendations for future care (BarrierfreeASD): Study protocol. BMJ Open, 12(8), Article e061773. https://doi.org/10.1136/bmjopen-2022-061773

19.

Dern

Sappok

(2016). Barriers to healthcare for people on the autism spectrum. Advances in Autism, 2(1), 2–11. https://doi.org/10.1108/Aia-10-2015-0020

20.

Deserno

M. K.

Borsboom

Begeer

Agelink van Rentergem

J. A.

Mataw

Geurts

H. M.

(2019). Sleep determines quality of life in autistic adults: A longitudinal study. Autism Research, 12(5), 794–801. https://doi.org/10.1002/aur.2103

21.

de Wit

Hajos

. (2013). Health-related quality of life. In Gellman

M. D.

Turner

J. R.

(Eds.), Encyclopedia of behavioral medicine (pp. 929–931). Springer. https://doi.org/10.1007/978-1-4419-1005-9_753

22.

Doherty

Neilson

O’Sullivan

Carravallah

Johnson

Cullen

Shaw

S. C. K.

(2022). Barriers to healthcare and self-reported adverse outcomes for autistic adults: A cross-sectional study. BMJ Open, 12(2), Artilce e056904. https://doi.org/10.1136/bmjopen-2021-056904

23.

Duckert

Gewohn

Konig

Schottle

Konnopka

Rahlff

Erik

Vogeley

Schulz

David

Peth

(2023). Barriers and needs in mental healthcare of adults with autism spectrum disorder in Germany: A qualitative study in autistic adults, relatives, and healthcare providers. BMC Psychiatry, 23(1), 528. https://doi.org/10.1186/s12888-023-05026-x

24.

Espeloer

Proft

Falter-Wagner

C. M.

Vogeley

(2023). Alarmingly large unemployment gap despite of above-average education in adults with ASD without intellectual disability in Germany: A cross-sectional study. European Archives of Psychiatry and Clinical Neuroscience, 273(3), 731–738. https://doi.org/10.1007/s00406-022-01424-6

25.

Evers

Maljaars

Schepens

Vanaken

G. J.

Noens

(2022). Conceptualization of quality of life in autistic individuals. Developmental Medicine & Child Neurology, 64(8), 950–956. https://doi.org/10.1111/dmcn.15205

26.

Gilmore

Krantz

Weaver

Hand

B. N.

(2022). Healthcare service use patterns among autistic adults: A systematic review with narrative synthesis. Autism, 26(2), 317–331. https://doi.org/10.1177/13623613211060906

27.

Grupp

Konig

H. H.

Riedel-Heller

Konnopka

(2018). FIMPsy—Questionnaire for the assessment of medical and non medical resource utilisation in mental disorders: Development and application (FIMPsy—Fragebogen zur Inanspruchnahme medizinischer und nicht medizinischer Versorgungsleistungen bei psychischen Erkrankungen: Entwicklung und Verwendung). Psychiatr Prax, 45(2), 87–94. https://doi.org/10.1055/s-0042-118033

28.

Hayakawa

Okada

Nomura

Tsukada

Nakamura

(2015). Is high job control a risk factor for poor quality of life in workers with high autism spectrum tendencies? A cross-sectional survey at a factory in Japan. Journal of Occupational Health, 57(5), 419–426. https://doi.org/10.1539/joh.14-0231-OA

29.

Helles

Gillberg

I. C.

Gillberg

Billstedt

(2017). Asperger syndrome in males over two decades: Quality of life in relation to diagnostic stability and psychiatric comorbidity. Autism, 21(4), 458–469. https://doi.org/10.1177/1362361316650090

30.

Hemmerich

(2019). StatistikGuru: Poweranalyse und Stichprobenberechnung für Regression[StatistikGuru: Power analysis and sample calculation for regression]. https://statistikguru.de/rechner/poweranalyse-regression.html

31.

Höfer

Hoffmann

Dörks

Kamp-Becker

Küpper

Poustka

Roepke

Roessner

Stroth

Wolff

Bachmann

C. J.

(2022). Health services use and costs in individuals with autism spectrum disorder in Germany: Results from a survey in ASD outpatient clinics. Journal of Autism and Developmental Disorders, 52, 540–552. https://doi.org/10.1007/s10803-021-04955-4

32.

Howlin

(2013). Social disadvantage and exclusion: Adults with autism lag far behind in employment prospects. Journal of the American Academy of Child and Adolescent Psychiatry, 52(9), 897–899. https://doi.org/10.1016/j.jaac.2013.06.010

33.

Jennes-Coussens

Magill-Evans

Koning

(2006). The quality of life of young men with Asperger syndrome: A brief report. Autism, 10(4), 403–414. https://doi.org/10.1177/1362361306064432

34.

Kamio

Inada

Koyama

(2013). A nationwide survey on quality of life and associated factors of adults with high-functioning autism spectrum disorders. Autism, 17(1), 15–26. https://doi.org/10.1177/1362361312436848

35.

Kamp-Becker

Schröder

Remschmidt

Bachmann

C. J.

(2010). Health-related quality of life in adolescents and young adults with high functioning autism-spectrum disorder. Psycho-social Medicine, 7, Doc03. https://doi.org/10.3205/psm000065

36.

Khanna

Jariwala

West-Strum

(2015). Validity and reliability of the medical outcomes study short-form health survey version 2 (SF-12v2) among adults with autism. Research in Developmental Disabilities, 43–44, 51–60. https://doi.org/10.1016/j.ridd.2015.06.006

37.

Khanna

Jariwala-Parikh

West-Strum

Mahabaleshwarkar

(2014). Health-related quality of life and its determinants among adults with autism. Research in Autism Spectrum Disorders, 8(3), 157–167. https://doi.org/10.1016/j.rasd.2013.11.003

38.

Kim

S. Y.

Bottema-Beutel

(2019). A meta regression analysis of quality of life correlates in adults with ASD. Research in Autism Spectrum Disorders, 63, 23–33. https://doi.org/10.1016/j.rasd.2018.11.004

39.

Knüppel

Telléus

G. K.

Jakobsen

Lauritsen

M. B.

(2018). Quality of life in adolescents and adults with autism spectrum disorder: Results from a nationwide Danish survey using self-reports and parental proxy-reports. Research in Developmental Disabilities, 83, 247–259. https://doi.org/10.1016/j.ridd.2018.09.004

40.

Kohane

I. S.

McMurry

Weber

MacFadden

Rappaport

Kunkel

Bickel

Wattanasin

Spence

Murphy

Churchill

(2012). The co-morbidity burden of children and young adults with autism spectrum disorders. PLOS ONE, 7(4), Article e33224. https://doi.org/10.1371/journal.pone.0033224

41.

Lai

M. C.

Kassee

Besney

Bonato

Hull

Mandy

Szatmari

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.

42.

Lawson

L. P.

Richdale

A. L.

Haschek

Flower

R. L.

Vartuli

Arnold

S. R.

Trollor

J. N.

(2020). Cross-sectional and longitudinal predictors of quality of life in autistic individuals from adolescence to adulthood: The role of mental health and sleep quality. Autism, 24(4), 954–967. https://doi.org/10.1177/1362361320908107

43.

Leader

Barrett

Ferrari

Casburn

Maher

Naughton

Arndt

Mannion

(2021). Quality of life, gastrointestinal symptoms, sleep problems, social support, and social functioning in adults with autism spectrum disorder. Research in Developmental Disabilities, 112, 103915. https://doi.org/10.1016/j.ridd.2021.103915

44.

LimeSurvey GmbH. (n.d.). LimeSurvey: An open source survey tool. http://www.limesurvey.org

45.

Lord

Charman

Havdahl

Carbone

Anagnostou

Boyd

Carr

de Vries

P. J.

Dissanayake

Divan

Freitag

C. M.

Gotelli

M. M.

Kasari

Knapp

Mundy

Plank

Scahill

Servili

Shattuck

. . .McCauley

J. B.

(2022). The lancet commission on the future of care and clinical research in autism. Lancet, 399(10321), 271–334. https://doi.org/10.1016/S0140-6736(21)01541-5

46.

Maddox

B. B.

Gaus

V. L.

(2019). Community mental health services for autistic adults: Good news and bad news. Autism Adulthood, 1(1), 15–19. https://doi.org/10.1089/aut.2018.0006

47.

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. https://doi.org/10.1111/jcpp.13587

48.

Mason

Ingham

Urbanowicz

Michael

Birtles

Woodbury-Smith

Brown

James

Scarlett

Nicolaidis

Parr

J. R.

(2019). A systematic review of what barriers and facilitators prevent and enable physical healthcare services access for autistic adults. Journal of Autism and Developmental Disorders, 49(8), 3387–3400. https://doi.org/10.1007/s10803-019-04049-2

49.

Mason

Mackintosh

McConachie

Rodgers

Finch

Parr

J. R.

(2019). Quality of life for older autistic people: The impact of mental health difficulties. Research in Autism Spectrum Disorders, 63, 13–22. https://doi.org/10.1016/j.rasd.2019.02.007

50.

Mason

McConachie

Garland

Petrou

Rodgers

Parr

J. R.

(2018). Predictors of quality of life for autistic adults. Autism Research, 11(8), 1138–1147. https://doi.org/10.1002/aur.1965

51.

Megari

(2013). Quality of life in chronic disease patients. Health Psychology Research, 1(3), Article e27. https://doi.org/10.4081/hpr.2013.e27

52.

Melvin

C. L.

Barnoux

Alexander

Roy

Devapriam

Blair

Tromans

Shepstone

Langdon

P. E.

(2022). A systematic review of in-patient psychiatric care for people with intellectual disabilities and/or autism: Effectiveness, patient safety and experience. BJPsych Open, 8(6), Article e187. https://doi.org/10.1192/bjo.2022.571

53.

Menard

(2001). Applied logistic regression analysis (2nd ed.). Sage.

54.

National Institute for Health and Care Excellence. (2012). Autism spectrum disorder in adults: Diagnosis and management. https://www.nice.org.uk/guidance/cg142

55.

Nicolaidis

Raymaker

McDonald

Kapp

Weiner

Ashkenazy

Gerrity

Kripke

Platt

Baggs

(2016). The development and evaluation of an online healthcare toolkit for autistic adults and their primary care providers. Journal of General Internal Medicine, 31(10), 1180–1189. https://doi.org/10.1007/s11606-016-3763-6

56.

Oakley

B. F.

Tillmann

Ahmad

Crawley

San Jose Caceres

Holt

Charman

Banaschewski

Buitelaar

Simonoff

Murphy

Loth

(2021). How do core autism traits and associated symptoms relate to quality of life? Findings from the longitudinal European autism project. Autism, 25(2), 389–404. https://doi.org/10.1177/1362361320959959

57.

Park

S. H.

Song

Y. J. C.

Demetriou

E. A.

Pepper

K. L.

Norton

Thomas

E. E.

Hickie

I. B.

Hermens

D. F.

Glozier

Guastella

A. J.

(2019). Disability, functioning, and quality of life among treatment-seeking young autistic adults and its relation to depression, anxiety, and stress. Autism, 23(7), 1675–1686. https://doi.org/10.1177/1362361318823925

58.

Platos

Pisula

(2019). Service use, unmet needs, and barriers to services among adolescents and young adults with autism spectrum disorder in Poland. Bmc Health Services Research, 19(1), 587. https://doi.org/ARTN587/10.1186/s12913-019-4432-3

59.

Raymaker

D. M.

McDonald

K. E.

Ashkenazy

Gerrity

Baggs

A. M.

Kripke

Hourston

Nicolaidis

(2017). Barriers to healthcare: Instrument development and comparison between autistic adults and adults with and without other disabilities. Autism, 21(8), 972–984. https://doi.org/10.1177/1362361316661261

60.

Renty

J. O.

Roeyers

(2006). Quality of life in high-functioning adults with autism spectrum disorder: The predictive value of disability and support characteristics. Autism, 10(5), 511–524. https://doi.org/10.1177/1362361306066604

61.

Righi

Benevides

Mazefsky

Siegel

Sheinkopf

S. J.

Morrow

E. M.

(2018). Autism and Developmental Disabilities Inpatient Research Collaborative (ADDIRC). Predictors of inpatient psychiatric hospitalization for children and adolescents with autism spectrum disorder. Journal of Autism and Developmental Disorders, 48(11), 3647–3657. https://doi.org/10.1007/s10803-017-3154-9

62.

Roy

Strate

(2023). Autism spectrum disorders in adulthood—symptoms, diagnosis, and treatment. Deutsches Ärzteblatt International, 120, 87–92. https://doi.org/10.3238/arztebl.m2022.0379

63.

Seidl

Bowles

Bock

J. O.

Brettschneider

Greiner

Konig

H. H.

Holle

(2015). FIMA–Questionnaire for health-related resource use in an elderly population: Development and pilot study [FIMA–Fragebogen zur Erhebung von Gesundheitsleistungen im Alter: Entwicklung und Pilotstudie]. Gesundheitswesen, 77(1), 46–52. https://doi.org/10.1055/s-0034-1372618

64.

Skaletski

E. C.

Bradley

Taylor

Travers

B. G.

Bishop

(2021). Quality-of-life discrepancies among autistic adolescents and adults: A rapid review. American Journal of Occupational Therapy, 75(3), 7503180090. https://doi.org/10.5014/ajot.2021.046391

65.

Taylor

J. L.

Seltzer

M. M.

(2011). Employment and post-secondary educational activities for young adults with autism spectrum disorders during the transition to adulthood. Journal of Autism and Developmental Disorders, 41(5), 566–574. https://doi.org/10.1007/s10803-010-1070-3

66.

van Heijst

B. F. C.

Geurts

H. M

. (2015). Quality of life in autism across the lifespan: A meta-analysis. Autism, 19(2), 158–167. https://doi.org/10.1177/1362361313517053

67.

Vogan

Lake

J. K.

Tint

Weiss

J. A.

Lunsky

(2017). Tracking health care service use and the experiences of adults with autism spectrum disorder without intellectual disability: A longitudinal study of service rates, barriers and satisfaction. Disability and Health Journal, 10(2), 264–270. https://doi.org/10.1016/j.dhjo.2016.11.002

68.

Vohra

Madhavan

Sambamoorthi

(2017). Comorbidity prevalence, healthcare utilization, and expenditures of Medicaid enrolled adults with autism spectrum disorders. Autism, 21(8), 995–1009. https://doi.org/10.1177/1362361316665222

69.

Ware

J. E.

Jr. Sherbourne

C. D.

(1992). The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Medical Care, 30(6), 473–483. https://www.ncbi.nlm.nih.gov/pubmed/1593914

70.

Ware

J. E.

Kosinski

Dewey

J. E.

Gandek

(2001). How to score and interpret single-item health status measures: A manual for users of the SF-8 health survey (Q. Incorpotated, Ed. 3rd ed.).

71.

Weir

Allison

Baron-Cohen

(2022). Autistic adults have poorer quality healthcare and worse health based on self-report data. Molecular Autism, 13(1), 23. https://doi.org/10.1186/s13229-022-00501-w

72.

WHOQOL-Group. (1998). Development of the World Health Organization WHOQOL-BREF quality of life assessment. Psychological Medicine, 28(3), 551–558. https://doi.org/10.1017/s0033291798006667

73.

Williams

Z. J.

Gotham

K. O.

(2022). Current and lifetime somatic symptom burden among transition-aged autistic young adults. Autism Research, 15(4), 761–770. https://doi.org/10.1002/aur.2671

74.

Yiengprugsawan

Kelly

Tawatsupa

(2014). SF-8TM health survey. In Michalos

A. C.

(Ed.), Encyclopedia of quality of life and well-being research (pp. 5940–5942). Springer. https://doi.org/10.1007/978-94-007-0753-5_3664

Barriers to healthcare predict reduced health-related quality of life in autistic adults without intellectual disability

Abstract

Lay abstract

Keywords

Introduction

Methods

Participants

Data collection and measures

HRQOL

Demographic variables

Clinical variables

Healthcare-related variables

Barriers to healthcare

Data analysis and selection of predictors

Community involvement

Results

Sample description

Mental and physical HRQOL compared to the German normative sample

Correlation analyses

Multiple linear regression analyses

Predictors of mental HRQOL

Predictors of physical HRQOL

Discussion

Reduced HRQOL in autistic adults in Germany

Unique predictors of mental or physical HRQOL in autistic adults: demographic, clinical, and healthcare-related variables

Limitations

Conclusions and implications

Footnotes

Declaration of conflicting interests

Funding

ORCID iDs

References