View in EDS HTML Full Text PDF Full Text

Barriers to Healthcare Predict Reduced Health-Related Quality of Life in Autistic Adults without Intellectual Disability

Saved in:
Bibliographic Details
Title: Barriers to Healthcare Predict Reduced Health-Related Quality of Life in Autistic Adults without Intellectual Disability
Language: English
Authors: Nicole David (ORCID 0000-0001-5233-1977), Pascal Rahlff, Hannah König, Sophia Dückert (ORCID 0000-0002-2953-2487), Petia Gewohn, Frank Erik, Kai Vogeley, Daniel Schöttle, Alexander Konnopka, Holger Schulz, Judith Peth (ORCID 0000-0002-2476-9717)
Source: Autism: The International Journal of Research and Practice. 2025 29(2):476-489.
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Peer Reviewed: Y
Page Count: 14
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Descriptors: Foreign Countries, Autism Spectrum Disorders, Adults, Access to Health Care, Barriers, Quality of Life, Mental Health, Physical Health, Predictor Variables, Individual Characteristics
Geographic Terms: Germany
DOI: 10.1177/13623613241275406
ISSN: 1362-3613
1461-7005
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.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1465388
Database: ERIC
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
    Url: https://content.ebscohost.com/cds/retrieve?content=AQICAHj0k_4E0hTGH8RJwT4gCJyBsGNe_WN95AvKlDbXJGqwxwEslUqNjHq75ysVwfJjxcnAAAAA4jCB3wYJKoZIhvcNAQcGoIHRMIHOAgEAMIHIBgkqhkiG9w0BBwEwHgYJYIZIAWUDBAEuMBEEDIVUWIbgJCht9DGk3gIBEICBmhjPO9vifziPGMKwG8e2ZbSDY5bRbdcwu8-7C5VGFN8HGqk0j46FSbwZjnWeLcA5m3EXFFqUh0zsBI2NV66cLVAQaHAn70KupO4UucLy6ZrwYn6-6z_VS9qH9_-PsqfKaWMKaidxaummGJgCEKX0s-M9qPctR5z4I7CkaXkTeEe6rrbJDOXlhnpEfzZDeRThEOrrgcIj3S-e3LU=
Text:
  Availability: 1
  Value: <anid>AN0183028989;f9d01feb.25;2025Feb17.01:29;v2.2.500</anid> <title id="AN0183028989-1">Barriers to healthcare predict reduced health-related quality of life in autistic adults without intellectual disability </title> <p>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. 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.</p> <p>Keywords: adults; autism spectrum disorders; health services; quality of life</p> <hd id="AN0183028989-2">Introduction</hd> <p>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 ([<reflink idref="bib21" id="ref1">21</reflink>]). Considering the patient's perspective and including these dimensions in the notion of health represented a paradigm shift in healthcare, particularly medicine ([<reflink idref="bib11" id="ref2">11</reflink>]), 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 ([<reflink idref="bib51" id="ref3">51</reflink>]). As such, current guidelines also highlighted HRQOL as an important outcome in autism spectrum disorder (ASD) ([<reflink idref="bib54" id="ref4">54</reflink>]), as autistic adults are often affected in social and work-related domains of life ([<reflink idref="bib24" id="ref5">24</reflink>]; [<reflink idref="bib32" id="ref6">32</reflink>]). 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 ([<reflink idref="bib18" id="ref7">18</reflink>]; [<reflink idref="bib45" id="ref8">45</reflink>]). Compromised health, as indicated by increased rates of somatic and mental illness, has been reported ([<reflink idref="bib16" id="ref9">16</reflink>]; [<reflink idref="bib40" id="ref10">40</reflink>]; [<reflink idref="bib68" id="ref11">68</reflink>]; [<reflink idref="bib73" id="ref12">73</reflink>]). 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 ([<reflink idref="bib23" id="ref13">23</reflink>]).</p> <p>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 ([<reflink idref="bib4" id="ref14">4</reflink>]; [<reflink idref="bib38" id="ref15">38</reflink>]; [<reflink idref="bib50" id="ref16">50</reflink>]; [<reflink idref="bib49" id="ref17">49</reflink>]; [<reflink idref="bib66" id="ref18">66</reflink>]). Overall, most international evidence suggests poorer HRQOL in autistic adults (cf. [<reflink idref="bib56" id="ref19">56</reflink>]). 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 ([<reflink idref="bib35" id="ref20">35</reflink>]). Studies on HRQOL in autistic adults often differ in terms of sample size but also in the outcome measure ([<reflink idref="bib4" id="ref21">4</reflink>]; [<reflink idref="bib25" id="ref22">25</reflink>]). The World Health Organization Quality of Life-BREF ([<reflink idref="bib72" id="ref23">72</reflink>]) and Short-Form Health Survey (SF-36/12/8; [<reflink idref="bib69" id="ref24">69</reflink>]) 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., [<reflink idref="bib33" id="ref25">33</reflink>]; [<reflink idref="bib34" id="ref26">34</reflink>]; [<reflink idref="bib35" id="ref27">35</reflink>]; [<reflink idref="bib50" id="ref28">50</reflink>]; [<reflink idref="bib66" id="ref29">66</reflink>]). Existing meta-analyses, however, strongly suggest that HRQOL can be considered generally reduced in autistic adults regardless of such methodological differences ([<reflink idref="bib66" id="ref30">66</reflink>]).</p> <p>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 ([<reflink idref="bib9" id="ref31">9</reflink>]; [<reflink idref="bib34" id="ref32">34</reflink>]; [<reflink idref="bib37" id="ref33">37</reflink>]; [<reflink idref="bib39" id="ref34">39</reflink>]; [<reflink idref="bib50" id="ref35">50</reflink>]), while age did not result as significant predictor of HRQOL in recent meta-regression analyses ([<reflink idref="bib38" id="ref36">38</reflink>]; [<reflink idref="bib66" id="ref37">66</reflink>]). 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 ([<reflink idref="bib3" id="ref38">3</reflink>]; cf. [<reflink idref="bib50" id="ref39">50</reflink>]). Conversely, evidence on the predictive value of autism severity on HRQOL has also been mixed or weak ([<reflink idref="bib38" id="ref40">38</reflink>]; [<reflink idref="bib43" id="ref41">43</reflink>]; [<reflink idref="bib60" id="ref42">60</reflink>]; [<reflink idref="bib66" id="ref43">66</reflink>]). 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 ([<reflink idref="bib20" id="ref44">20</reflink>]; [<reflink idref="bib39" id="ref45">39</reflink>]; [<reflink idref="bib49" id="ref46">49</reflink>]; [<reflink idref="bib57" id="ref47">57</reflink>]).</p> <p>Despite increased comorbidity burden ([<reflink idref="bib16" id="ref48">16</reflink>]; [<reflink idref="bib40" id="ref49">40</reflink>]; [<reflink idref="bib41" id="ref50">41</reflink>]; [<reflink idref="bib68" id="ref51">68</reflink>]; [<reflink idref="bib73" id="ref52">73</reflink>]) 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; [<reflink idref="bib10" id="ref53">10</reflink>]; [<reflink idref="bib18" id="ref54">18</reflink>]; [<reflink idref="bib19" id="ref55">19</reflink>]; [<reflink idref="bib23" id="ref56">23</reflink>]; [<reflink idref="bib48" id="ref57">48</reflink>]). 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 [<reflink idref="bib26" id="ref58">26</reflink>]). 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 ([<reflink idref="bib22" id="ref59">22</reflink>]). Similarly, Brede and colleagues (2022) suggested that regular mental healthcare services do not adequately support or may even harm autistic adults.</p> <p>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 ([<reflink idref="bib8" id="ref60">8</reflink>]; [<reflink idref="bib14" id="ref61">14</reflink>]; [<reflink idref="bib34" id="ref62">34</reflink>]; [<reflink idref="bib37" id="ref63">37</reflink>]; [<reflink idref="bib43" id="ref64">43</reflink>]; [<reflink idref="bib50" id="ref65">50</reflink>]; [<reflink idref="bib60" id="ref66">60</reflink>]). 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).</p> <p>Thus, the present study aimed to (<reflink idref="bib1" id="ref67">1</reflink>) investigate the current mental and physical HRQOL of autistic adults in Germany and (<reflink idref="bib2" id="ref68">2</reflink>) 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.</p> <hd id="AN0183028989-3">Methods</hd> <p></p> <hd id="AN0183028989-4">Participants</hd> <p>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 (<reflink idref="bib1" id="ref69">1</reflink>) were adults, (<reflink idref="bib2" id="ref70">2</reflink>) reported a professionally confirmed diagnosis of ASD with (<reflink idref="bib3" id="ref71">3</reflink>) no co-occurring intellectual disability (IQ > 70) as well as (<reflink idref="bib4" id="ref72">4</reflink>) 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 ([<reflink idref="bib5" id="ref73">5</reflink>]; [<reflink idref="bib17" id="ref74">17</reflink>]). Participation was voluntary and anonymous. Informed consent was obtained prior to participation. No incentives were given for survey participation.</p> <hd id="AN0183028989-5">Data collection and measures</hd> <p>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 ([<reflink idref="bib18" id="ref75">18</reflink>]), 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; [<reflink idref="bib6" id="ref76">6</reflink>]; [<reflink idref="bib70" id="ref77">70</reflink>]) and the Barriers to Healthcare Checklist–Short Form (BHC-SF; [<reflink idref="bib59" id="ref78">59</reflink>]).</p> <p>Table 1. Predictor variables and sample description.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th /><th align="left">M (SD)</th><th align="left">n (%)</th></tr></thead><tbody><tr><td colspan="3"><italic>Demographic</italic></td></tr><tr><td>Age (years)</td><td>38.9 (11.5)</td><td /></tr><tr><td>Gender</td><td /><td><italic>Female</italic>: 172 (55.3%)<italic>Male</italic>: 113 (36.3%)<italic>Diverse</italic>: 26 (8.4%)</td></tr><tr><td>Relationship status</td><td /><td><italic>In a relationship (married/in a relationship)</italic>: 79 (25.4%)<italic>No relationship (single/divorced/ widowed)</italic>: 232 (74.6%)</td></tr><tr><td>Education</td><td /><td><italic>High school or equivalent (e.g., technical diploma)</italic>: 218 (70.1%)<italic>Lower or no degree (</italic>secondary school/elementary school/special school/no degree<italic>)</italic>: 93 (29.9%)</td></tr><tr><td>Employment status</td><td /><td><italic>Employed (employed full-time/part-time/in training)</italic>: 138 (44.4%)<italic>Not employed (unemployed/marginal employment/not applicable)</italic>: 173 (55.6%)</td></tr><tr><td colspan="3"><italic>Clinical</italic></td></tr><tr><td>Age at autism diagnosis (years)</td><td>32.5 (13.0)</td><td /></tr><tr><td>Required support due to social difficulties (DSM-5 interaction)</td><td>3.5 (1.1)</td><td><italic>Note</italic>: rated on a 5-point Likert scale (1 = none, 2 = little, 3 = moderate, 4 = quite, 5 = very)</td></tr><tr><td>Required support due to restrictive behavior and interests (DSM-5 behavior)</td><td>3.3 (1.1)</td><td><italic>Note</italic>: rated on a 5-point Likert scale (1 = none, 2 = little, 3 = moderate, 4 = quite, 5 = very)</td></tr><tr><td>Number of somatic comorbidities (last 6 months)</td><td>1.4 (1.1)</td><td /></tr><tr><td>Number of mental comorbidities (last 6 months)</td><td>1.5 (1.4)</td><td /></tr><tr><td colspan="3"><italic>Healthcare-related</italic></td></tr><tr><td>Outpatient treatment (number of visits last 6 months)</td><td>21.0 (26.5)</td><td /></tr><tr><td>Inpatient treatment (number of days last 6 months)</td><td>4.1 (15.5)</td><td /></tr><tr><td>Emergency treatment (number of contacts last 6 months)</td><td>0.3 (0.9)</td><td /></tr><tr><td>Informal support (number of days last 6 months)</td><td>30.0 (54.2)</td><td /></tr><tr><td>Professional/ formal support (number of contacts last 6 months)</td><td>9.0 (25.2)</td><td /></tr><tr><td colspan="3"><italic>Barriers to Healthcare</italic></td></tr><tr><td>BHC-SF sum score</td><td>8.1 (4.1)</td><td /></tr></tbody></table> </ephtml> </p> <p>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.</p> <hd id="AN0183028989-6">HRQOL</hd> <p>HRQOL was assessed using the German version of the SF-8, for which German normative data were available (<emph>n</emph> = 2552; [<reflink idref="bib6" id="ref79">6</reflink>]). 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 <emph>T</emph>-scores (with M = 50, SD = 10). The SF-8 has good to adequate psychometric properties in terms of overall reliability and validity ([<reflink idref="bib6" id="ref80">6</reflink>]; [<reflink idref="bib74" id="ref81">74</reflink>]). There is also evidence of psychometrically sound applications of the SF-8, SF-12, and SF-36 in autistic adults ([<reflink idref="bib28" id="ref82">28</reflink>]; [<reflink idref="bib36" id="ref83">36</reflink>]; [<reflink idref="bib66" id="ref84">66</reflink>]).</p> <hd id="AN0183028989-7">Demographic variables</hd> <p>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).</p> <hd id="AN0183028989-8">Clinical variables</hd> <p>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; [<reflink idref="bib2" id="ref85">2</reflink>]). 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 (<reflink idref="bib1" id="ref86">1</reflink>)—little (<reflink idref="bib2" id="ref87">2</reflink>)—moderate (<reflink idref="bib3" id="ref88">3</reflink>)—quite (<reflink idref="bib4" id="ref89">4</reflink>)—very (<reflink idref="bib5" id="ref90">5</reflink>)").</p> <hd id="AN0183028989-9">Healthcare-related variables</hd> <p>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; [<reflink idref="bib63" id="ref91">63</reflink>]) and the Questionnaire on the Utilization of Medical and Nonmedical Care Services in Mental Disorders (FIMPsy; [<reflink idref="bib27" id="ref92">27</reflink>]). 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).</p> <hd id="AN0183028989-10">Barriers to healthcare</hd> <p>We used a translated version of the BHC-SF ([<reflink idref="bib59" id="ref93">59</reflink>]; 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 ([<reflink idref="bib59" id="ref94">59</reflink>]). 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).</p> <hd id="AN0183028989-11">Data analysis and selection of predictors</hd> <p>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.</p> <p>We performed three kinds of analyses. First, two-tailed independent <emph>t</emph>-tests were performed (unequal variances assumed, <emph>p</emph> < 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 ([<reflink idref="bib6" id="ref95">6</reflink>]). 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 (<reflink idref="bib1" id="ref96">1</reflink>) examine relationships between HRQOL and demographic, clinical, and healthcare-related variables and (<reflink idref="bib2" id="ref97">2</reflink>) 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 ([<reflink idref="bib53" id="ref98">53</reflink>]), they were excluded from the multiple regression model. Interpretation of effect sizes/correlations was based on the conventions of [<reflink idref="bib15" id="ref99">15</reflink>] (small/weak: <emph>d</emph> = 0.2/<emph>r</emph> = 0.10, medium/moderate: <emph>d</emph> = 0.5/<emph>r</emph> = 0.30, large/high: <emph>d</emph> = 0.8/<emph>r</emph> = 0.50).</p> <p>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 <emph>R</emph>²s between 0.03 and 0.60 ([<reflink idref="bib42" id="ref100">42</reflink>]; [<reflink idref="bib50" id="ref101">50</reflink>]; [<reflink idref="bib60" id="ref102">60</reflink>]), 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 <emph>R</emph>² =.08 (between small and medium effect; [<reflink idref="bib15" id="ref103">15</reflink>]), a statistical power of 0.80, and a significance level of <emph>α</emph> = 0.05, a sample size of <emph>n</emph> = 236 would be sufficient with 16 predictors.</p> <hd id="AN0183028989-12">Community involvement</hd> <p>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.</p> <hd id="AN0183028989-13">Results</hd> <p></p> <hd id="AN0183028989-14">Sample description</hd> <p>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).</p> <hd id="AN0183028989-15">Mental and physical HRQOL compared to the German normative sample</hd> <p>Participants reported reduced mental HRQOL (M = 32.8, SD = 12.2) compared to the German normative sample (M = 53.3, SD = 7.8; [<reflink idref="bib6" id="ref104">6</reflink>]). As these mean scores represent normalized <emph>T</emph>-scores, this result also indicates that mental HRQOL in autistic adults was below average (more than 1.5 standard deviations below <emph>T</emph> = 50). This group difference was significant and corresponded to a large effect size (<emph>t</emph>(<reflink idref="bib342" id="ref105">342</reflink>) = 29.0, <emph>p</emph> < 0.001; Cohen's <emph>d</emph> = 2.0; Figure 1).</p> <p>Graph: Figure 1. Mean scores of the mental and physical components of the SF-8 in our sample compared to the German normative sample ([<reflink idref="bib6" id="ref106">6</reflink>]). 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; [<reflink idref="bib6" id="ref107">6</reflink>]). In particular, mental HRQOL was considerably below average in autistic participants.</p> <p>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; [<reflink idref="bib6" id="ref108">6</reflink>]). This group difference was also significant and corresponded to an almost moderate effect size (<emph>t</emph>(<reflink idref="bib367" id="ref109">367</reflink>) = 7.5, <emph>p</emph> < 0.001; Cohen's <emph>d</emph> = 0.48; Figure 1).</p> <hd id="AN0183028989-16">Correlation analyses</hd> <p>The full correlation matrix is shown in Table 2. <emph>Mental HRQOL</emph> showed moderate negative correlations with mental comorbidities (<emph>r</emph> = −0.404, <emph>p</emph> < 0.001) and the BHC-SF sum score (<emph>r</emph> = −0.416, <emph>p</emph> < 0.001). That is, mental HRQOL decreased with increasing number of mental comorbidities and the amount of self-reported barriers to healthcare. Similarly, <emph>physical HRQOL</emph> showed moderate negative correlations with somatic comorbidities (<emph>r</emph> = −0.462, <emph>p</emph> < 0.001) and the BHC-SF sum score (<emph>r</emph> = −0.420, <emph>p</emph> < 0.001). All other correlations between HRQOL components and the other variables were small or non-significant (Table 2).</p> <p>Table 2. Correlation analyses.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th /><th align="left">1.</th><th align="left">2.</th><th align="left">3.</th><th align="left">4.</th><th align="left">5.</th><th align="left">6.</th><th align="left">7.</th><th align="left">8.</th><th align="left">9.</th><th align="left">10.</th><th align="left">11.</th><th align="left">12.</th><th align="left">13.</th><th align="left">14.</th><th align="left">15.</th><th align="left">16.</th><th align="left">17.</th><th align="left">18.</th></tr></thead><tbody><tr><td>1. Mental HRQOL (MCS)</td><td>-</td><td>0.068</td><td>0.008</td><td>−0.040</td><td>0.016</td><td>−0.052</td><td><bold>0.180</bold>**</td><td>−0.066</td><td><bold>−0.142</bold><xref ref-type="table-fn" rid="tfn3">*</xref></td><td><bold>−0.207</bold><xref ref-type="table-fn" rid="tfn3">***</xref></td><td><bold>−0.135</bold><xref ref-type="table-fn" rid="tfn3">*</xref></td><td><bold>−0.404</bold><xref ref-type="table-fn" rid="tfn3">***</xref></td><td><bold>−0.132</bold><xref ref-type="table-fn" rid="tfn3">*</xref></td><td><bold>−0.213</bold><xref ref-type="table-fn" rid="tfn3">***</xref></td><td><bold>−0.126</bold><xref ref-type="table-fn" rid="tfn3">*</xref></td><td>−0.069</td><td><bold>−0.150</bold><xref ref-type="table-fn" rid="tfn3">**</xref></td><td><bold>−0.416</bold><xref ref-type="table-fn" rid="tfn3">***</xref></td></tr><tr><td>2. Physical HRQOL (PCS)</td><td /><td>-</td><td><bold>−0.221</bold><xref ref-type="table-fn" rid="tfn3">***</xref></td><td>−0.111</td><td>−0.087</td><td>0.100</td><td><bold>0.126</bold><xref ref-type="table-fn" rid="tfn3">*</xref></td><td><bold>−0.231</bold><xref ref-type="table-fn" rid="tfn3">***</xref></td><td><bold>−0.149</bold><xref ref-type="table-fn" rid="tfn3">**</xref></td><td><bold>−0.203</bold><xref ref-type="table-fn" rid="tfn3">***</xref></td><td><bold>−0.462</bold><xref ref-type="table-fn" rid="tfn3">***</xref></td><td><bold>−0.263</bold><xref ref-type="table-fn" rid="tfn3">***</xref></td><td><bold>−0.193</bold><xref ref-type="table-fn" rid="tfn3">**</xref></td><td><bold>−0.128</bold><xref ref-type="table-fn" rid="tfn3">*</xref></td><td>−0.089</td><td><bold>−0.231</bold><xref ref-type="table-fn" rid="tfn3">***</xref></td><td><bold>−0.119</bold><xref ref-type="table-fn" rid="tfn3">*</xref></td><td><bold>−0.420</bold><xref ref-type="table-fn" rid="tfn3">***</xref></td></tr><tr><td>3. Age</td><td /><td /><td>-</td><td>0.039</td><td><bold>0.207</bold><xref ref-type="table-fn" rid="tfn3">**</xref></td><td>0.085</td><td><bold>0.138</bold><xref ref-type="table-fn" rid="tfn3">*</xref></td><td><bold>0.830</bold><xref ref-type="table-fn" rid="tfn3">***</xref></td><td>0.066</td><td>0.003</td><td><bold>0.194</bold><xref ref-type="table-fn" rid="tfn3">**</xref></td><td>−0.067</td><td>0.052</td><td>−0.040</td><td><bold>−0.117</bold><xref ref-type="table-fn" rid="tfn3">*</xref></td><td>0.013</td><td>−0.102</td><td>0.078</td></tr><tr><td>4. Gender</td><td /><td /><td /><td>-</td><td>0.064</td><td>0.105</td><td>−0.017</td><td>0.099</td><td>−0.097</td><td>−0.086</td><td>0.102</td><td>−0.043</td><td>0.026</td><td>−0.054</td><td>−0.095</td><td>0.039</td><td>0.101</td><td>0.111</td></tr><tr><td>5. Relationship status</td><td /><td /><td /><td /><td>-</td><td>0.058</td><td>0.088</td><td><bold>0.274</bold><xref ref-type="table-fn" rid="tfn3">***</xref></td><td>−0.079</td><td>−0.011</td><td>0.031</td><td>−0.059</td><td>−0.089</td><td>−0.066</td><td>−0.005</td><td><bold>0.180</bold><xref ref-type="table-fn" rid="tfn3">**</xref></td><td>−0.106</td><td>−0.008</td></tr><tr><td>6. Education</td><td /><td /><td /><td /><td /><td>-</td><td>0.103</td><td>0.091</td><td>−0.041</td><td><bold>−0.115</bold><xref ref-type="table-fn" rid="tfn3">*</xref></td><td>0.046</td><td>0.025</td><td>0.038</td><td>0.057</td><td>−0.016</td><td>−0.094</td><td>0.000</td><td>−0.013</td></tr><tr><td>7. Employment status</td><td /><td /><td /><td /><td /><td /><td>-</td><td><bold>0.150</bold><xref ref-type="table-fn" rid="tfn3">**</xref></td><td>−0.031</td><td>−0.191<xref ref-type="table-fn" rid="tfn3">**</xref></td><td>−0.097</td><td><bold>−0.137</bold><xref ref-type="table-fn" rid="tfn3">*</xref></td><td>−0.111</td><td>−0.027</td><td>−0.053</td><td>−0.021</td><td><bold>−0.111</bold><xref ref-type="table-fn" rid="tfn3">*</xref></td><td><bold>−0.146</bold><xref ref-type="table-fn" rid="tfn3">**</xref></td></tr><tr><td>8. Diagn. age</td><td /><td /><td /><td /><td /><td /><td /><td>-</td><td>0.057</td><td>0.009</td><td><bold>0.177</bold><xref ref-type="table-fn" rid="tfn3">**</xref></td><td>0.005</td><td>0.065</td><td>−0.022</td><td>−0.104</td><td>−0.092</td><td>−0.025</td><td>−0.074</td></tr><tr><td>9. <italic>DSM</italic>-5 interaction</td><td /><td /><td /><td /><td /><td /><td /><td /><td>-</td><td><bold>0.438</bold><xref ref-type="table-fn" rid="tfn3">***</xref></td><td>−0.031</td><td><bold>0.155</bold><xref ref-type="table-fn" rid="tfn3">**</xref></td><td><bold>0.200</bold><xref ref-type="table-fn" rid="tfn3">***</xref></td><td>0.105</td><td>−0.009</td><td>0.022</td><td>0.087</td><td><bold>0.168</bold><xref ref-type="table-fn" rid="tfn3">**</xref></td></tr><tr><td>10. <italic>DSM</italic>-5 behavior</td><td /><td /><td /><td /><td /><td /><td /><td /><td /><td>-</td><td>0.081</td><td><bold>0.261</bold><xref ref-type="table-fn" rid="tfn3">***</xref></td><td><bold>0.220</bold><xref ref-type="table-fn" rid="tfn3">***</xref></td><td>0.023</td><td>0.076</td><td><bold>0.122</bold><xref ref-type="table-fn" rid="tfn3">*</xref></td><td><bold>0.193</bold><xref ref-type="table-fn" rid="tfn3">**</xref></td><td><bold>0.296</bold><xref ref-type="table-fn" rid="tfn3">***</xref></td></tr><tr><td>11. Somatic comorbidities</td><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td>-</td><td><bold>0.293</bold><xref ref-type="table-fn" rid="tfn3">***</xref></td><td><bold>0.227</bold><xref ref-type="table-fn" rid="tfn3">***</xref></td><td><bold>0.153</bold><xref ref-type="table-fn" rid="tfn3">**</xref></td><td>0.101</td><td>−0.051</td><td><bold>0.179</bold><xref ref-type="table-fn" rid="tfn3">**</xref></td><td><bold>0.218</bold><xref ref-type="table-fn" rid="tfn3">***</xref></td></tr><tr><td>12. Mental comorbidities</td><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td>-</td><td><bold>0.185</bold><xref ref-type="table-fn" rid="tfn3">**</xref></td><td><bold>0.154</bold><xref ref-type="table-fn" rid="tfn3">**</xref></td><td><bold>0.189</bold><xref ref-type="table-fn" rid="tfn3">**</xref></td><td><bold>0.136</bold><xref ref-type="table-fn" rid="tfn3">*</xref></td><td><bold>0.126</bold><xref ref-type="table-fn" rid="tfn3">*</xref></td><td><bold>0.344</bold><xref ref-type="table-fn" rid="tfn3">***</xref></td></tr><tr><td>13. Outpatient treatment</td><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td>-</td><td>0.068</td><td><bold>0.136</bold><xref ref-type="table-fn" rid="tfn3">*</xref></td><td>0.057</td><td><bold>0.211</bold><xref ref-type="table-fn" rid="tfn3">***</xref></td><td><bold>0.160</bold><xref ref-type="table-fn" rid="tfn3">**</xref></td></tr><tr><td>14. Inpatient treatment</td><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td>-</td><td><bold>0.154</bold><xref ref-type="table-fn" rid="tfn3">**</xref></td><td>−0.046</td><td>−0.019</td><td>0.044</td></tr><tr><td>15. Emergency treatment</td><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td>-</td><td>0.069</td><td>0.044</td><td><bold>0.112</bold><xref ref-type="table-fn" rid="tfn3">*</xref></td></tr><tr><td>16. Informal support</td><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td>-</td><td>0.067</td><td><bold>0.206</bold><xref ref-type="table-fn" rid="tfn3">***</xref></td></tr><tr><td>17. Formal support</td><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td>-</td><td><bold>0.211</bold><xref ref-type="table-fn" rid="tfn3">***</xref></td></tr><tr><td>18. BHC-SF</td><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td /><td>-</td></tr></tbody></table> </ephtml> </p> <ulist> <item>2 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<sups>th</sups> edition; BHC-SF: Barriers to Healthcare Checklist–Short Form.</item> <item>3 <emph>p</emph> < 0.05; <sups>**</sups><emph>p</emph> < 0.01; <sups>***</sups><emph>p</emph> < 0.001.</item> </ulist> <p> <emph>Analyses between predictor variables</emph>, and with respect to multicollinearity, revealed that age was significantly and highly correlated with age at diagnosis (<emph>r</emph> = 0.830, <emph>p</emph> < 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 (<emph>r</emph> = 0.438, <emph>p</emph> < 0.001) and between the number of mental comorbidities and the BHC-SF sum score (<emph>r</emph> = 0.344, <emph>p</emph> < 0.001). All other correlations between predictor variables were small or non-significant (Table 2).</p> <p>Table 3. Multiple regression analyses (N = 311).</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th /><th align="left" colspan="5"><italic>Mental HRQOL (MCS)</italic></th><th align="left" colspan="6"><italic>Physical HRQOL (PCS)</italic></th></tr><tr><th /><th align="left"><italic>b</italic></th><th align="left"><italic>SE</italic></th><th align="left"><italic>95% CI</italic></th><th align="left"><italic>ß</italic></th><th align="left"><italic>p</italic></th><th align="left"><italic>b</italic></th><th align="left"><italic>SE</italic></th><th align="left"><italic>95% CI</italic></th><th align="left"><italic>ß</italic></th><th align="left"><italic>p</italic></th><th align="left"><italic>VIF</italic></th></tr></thead><tbody><tr><td colspan="12"><italic>Demographic predictors</italic></td></tr><tr><td>Age</td><td>0.14</td><td>0.09</td><td>[−0.05, 0.33]</td><td>0.13</td><td>0.145</td><td>−0.04</td><td>0.07</td><td>[−0.19, 0.10]</td><td>−0.05</td><td>0.552</td><td>3.42</td></tr><tr><td>Gender</td><td>−0.44</td><td>1.25</td><td>[−2.89, 2.01]</td><td>−0.02</td><td>0.724</td><td>−0.91</td><td>0.95</td><td>[−2.77, 0.96]</td><td>−0.05</td><td>0.339</td><td>1.10</td></tr><tr><td>Relationship status</td><td>0.51</td><td>1.48</td><td>[−2.40, 3.43]</td><td>0.02</td><td>0.728</td><td>−1.35</td><td>1.13</td><td>[−3.56, 0.87]</td><td>−0.06</td><td>0.232</td><td>1.19</td></tr><tr><td>Education</td><td>−1.56</td><td>1.33</td><td>[−4.17, 1.05]</td><td>−0.06</td><td>0.240</td><td><bold>2.70</bold></td><td><bold>1.01</bold></td><td><bold>[0.71, 4.68]</bold></td><td><bold>0.13</bold></td><td><bold>0.008</bold></td><td>1.06</td></tr><tr><td>Employment status</td><td><bold>2.73</bold></td><td><bold>1.25</bold></td><td><bold>[0.27, 5.19]</bold></td><td><bold>0.11</bold></td><td><bold>0.030</bold></td><td>0.95</td><td>0.95</td><td>[−0.92, 2.82]</td><td>0.05</td><td>0.318</td><td>1.11</td></tr><tr><td colspan="12"><italic>Clinical predictors</italic></td></tr><tr><td>Diagnostic age</td><td><bold>−0.17</bold></td><td><bold>0.08</bold></td><td><bold>[−0.34, 0.00]</bold></td><td><bold>−0.18</bold></td><td><bold>0.044</bold></td><td>−0.09</td><td>0.06</td><td>[−0.21, 0.04]</td><td>−0.12</td><td>0.176</td><td>3.49</td></tr><tr><td><italic>DSM</italic>-5 interaction</td><td>−0.26</td><td>0.62</td><td>[−1.49, 0.96]</td><td>−0.02</td><td>0.673</td><td>−0.75</td><td>0.47</td><td>[−1.69, 0.18]</td><td>−0.08</td><td>0.113</td><td>1.33</td></tr><tr><td><italic>DSM</italic>-5 behavior</td><td>−0.30</td><td>0.62</td><td>[−1.52, 0.93]</td><td>−0.03</td><td>0.636</td><td>−0.03</td><td>0.47</td><td>[−0.96, 0.91]</td><td>0.00</td><td>0.957</td><td>1.45</td></tr><tr><td>Somatic comorbidities</td><td>0.45</td><td>0.60</td><td>[−0.74, 1.63]</td><td>0.04</td><td>0.459</td><td><bold>−2.95</bold></td><td><bold>0.46</bold></td><td><bold>[−3.85, −2.05]</bold></td><td><bold>−0.33</bold></td><td><bold><0.001</bold></td><td>1.30</td></tr><tr><td>Mental comorbidities</td><td><bold>−2.06</bold></td><td><bold>0.48</bold></td><td><bold>[−3.00, −1.12]</bold></td><td><bold>−0.24</bold></td><td><bold><0.001</bold></td><td>−0.22</td><td>0.36</td><td>[−0.93, 0.50]</td><td>−0.03</td><td>0.552</td><td>1.36</td></tr><tr><td colspan="12"><italic>Healthcare-related predictors</italic></td></tr><tr><td>Outpatient treatment</td><td>0.00</td><td>0.02</td><td>[−0.05, 0.04]</td><td>−0.01</td><td>0.897</td><td>−0.01</td><td>0.02</td><td>[−0.04, 0.03]</td><td>−0.02</td><td>0.749</td><td>1.19</td></tr><tr><td>Inpatient treatment</td><td><bold>−0.12</bold></td><td><bold>0.04</bold></td><td><bold>[−0.20, −0.04]</bold></td><td><bold>−0.16</bold></td><td><bold>0.002</bold></td><td>−0.04</td><td>0.03</td><td>[−0.10, 0.01]</td><td>−0.07</td><td>0.142</td><td>1.09</td></tr><tr><td>Emergency treatment</td><td>−0.31</td><td>0.72</td><td>[−1.73, 1.11]</td><td>−0.02</td><td>0.666</td><td>−0.20</td><td>0.55</td><td>[−1.28, 0.87]</td><td>−0.02</td><td>0.712</td><td>1.10</td></tr><tr><td>Informal support</td><td>−0.02</td><td>0.01</td><td>[−0.04, 0.01]</td><td>−0.07</td><td>0.199</td><td>−0.01</td><td>0.01</td><td>[−0.03, 0.01]</td><td>−0.06</td><td>0.205</td><td>1.16</td></tr><tr><td>Formal support</td><td>0.02</td><td>0.03</td><td>[−0.03, 0.07]</td><td>0.04</td><td>0.479</td><td><bold>−0.04</bold></td><td><bold>0.02</bold></td><td><bold>[−0.08, 0.00]</bold></td><td><bold>−0.10</bold></td><td><bold>0.045</bold></td><td>1.15</td></tr><tr><td colspan="12"><italic>Barriers to healthcare</italic></td></tr><tr><td>BHC-SF</td><td><bold>−0.87</bold></td><td><bold>0.17</bold></td><td><bold>[−1.19, −0.54]</bold></td><td><bold>−0.29</bold></td><td><bold><0.001</bold></td><td><bold>−0.62</bold></td><td><bold>0.13</bold></td><td><bold>[−0.86, −0.37]</bold></td><td><bold>−0.25</bold></td><td><bold><0.001</bold></td><td>1.32</td></tr></tbody></table> </ephtml> </p> <p>4 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<sups>th</sups> edition; BHC-SF: Barriers to Healthcare Checklist–Short Form.</p> <hd id="AN0183028989-17">Multiple linear regression analyses</hd> <p></p> <hd id="AN0183028989-18">Predictors of mental HRQOL</hd> <p>The overall regression model with all 16 predictors was statistically significant (<emph>F</emph>(<reflink idref="bib16" id="ref110">16</reflink>, 294) = 8.08, <emph>p</emph> < 0.001) and explained 26.8% of the variance in the mental HRQOL component (<emph>R</emph><sups>2</sups> = 0.305; adj. <emph>R</emph><sups>2</sups> = 0.268), indicating a high goodness of fit ([<reflink idref="bib15" id="ref111">15</reflink>]). Number of Barriers to healthcare as assessed by the BHC-SF, were awere significant and strongest negative predictors of mental HRQOL (<emph>ß</emph> = −0.29, <emph>p</emph> < 0.001; Table 3), followed by the number of mental comorbidities (<emph>ß</emph> = −0.24, <emph>p</emph> < 0.001), age at diagnosis (<emph>ß</emph> = −0.18, <emph>p</emph> = 0.044) and days of inpatient treatment (<emph>ß</emph> = −0.16, <emph>p</emph> = 0.002). Fourth, employment status was a significant positive predictor of mental HRQOL (i.e., being employed was associated with higher mental HRQOL; <emph>ß</emph> = 0.11, <emph>p</emph> = 0.03).</p> <hd id="AN0183028989-19">Predictors of physical HRQOL</hd> <p>The overall regression model with all 16 predictors was statistically significant (<emph>F</emph>(<reflink idref="bib16" id="ref112">16</reflink>, 294) = 11.85, <emph>p</emph> < 0.001) and explained 35.9% of the variance in the physical HRQOL component (<emph>R</emph><sups>2</sups> = 0.392; adj. <emph>R</emph><sups>2</sups> = 0.359), indicating a high goodness of fit ([<reflink idref="bib15" id="ref113">15</reflink>]). The number of somatic comorbidities was a significant and strongest negative predictor of physical HRQOL (<emph>ß</emph> = −0.33, <emph>p</emph> < 0.001; Table 3), followed by the number of self-reported barriers to healthcare (<emph>ß</emph> = −0.25, <emph>p</emph> < 0.001) and the amount of professional/ formal support (<emph>ß</emph> = −0.10, <emph>p</emph> = 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; <emph>ß</emph> = 0.13, <emph>p</emph> = 0.008).</p> <hd id="AN0183028989-20">Discussion</hd> <p>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.</p> <hd id="AN0183028989-21">Reduced HRQOL in autistic adults in Germany</hd> <p>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 ([<reflink idref="bib16" id="ref114">16</reflink>]; [<reflink idref="bib40" id="ref115">40</reflink>]; [<reflink idref="bib41" id="ref116">41</reflink>]; [<reflink idref="bib68" id="ref117">68</reflink>]) and with previous evidence on particularly compromised (mental) HRQOL in autistic adults using other versions of the SF ([<reflink idref="bib9" id="ref118">9</reflink>]; [<reflink idref="bib29" id="ref119">29</reflink>]; [<reflink idref="bib37" id="ref120">37</reflink>]). 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.</p> <p>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," [<reflink idref="bib45" id="ref121">45</reflink>]).</p> <hd id="AN0183028989-22">Unique predictors of mental or physical HRQOL in autistic adults: demographic, clinical, and...</hd> <p>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.</p> <p> <emph>With respect to demographic predictors</emph>, employment status has previously been associated with HRQOL in autistic adults (e.g., [<reflink idref="bib37" id="ref122">37</reflink>]; [<reflink idref="bib39" id="ref123">39</reflink>]; [<reflink idref="bib50" id="ref124">50</reflink>]). Here, being employed positively predicted mental HRQOL. Mental illness, particularly depression, has been related to unemployment in autistic adults ([<reflink idref="bib57" id="ref125">57</reflink>]). 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 ([<reflink idref="bib34" id="ref126">34</reflink>]; for an overview, [<reflink idref="bib66" id="ref127">66</reflink>]) 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 [<reflink idref="bib50" id="ref128">50</reflink>]). Educational attainment has previously been associated with healthcare utilization and self-care behavior in other physical health conditions (e.g., [<reflink idref="bib1" id="ref129">1</reflink>]). 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).</p> <p> <emph>With respect to clinical predictors</emph>, diagnostic age negatively predicted mental HRQOL, that is, participants who received an ASD diagnosis later in life reported lower mental HRQOL ([<reflink idref="bib3" id="ref130">3</reflink>]). 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 ([<reflink idref="bib12" id="ref131">12</reflink>]; [<reflink idref="bib64" id="ref132">64</reflink>]). 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 ([<reflink idref="bib16" id="ref133">16</reflink>]; [<reflink idref="bib41" id="ref134">41</reflink>]; [<reflink idref="bib46" id="ref135">46</reflink>]; [<reflink idref="bib47" id="ref136">47</reflink>]). It is therefore not surprising that, here, the number of self-reported mental comorbidities (in line with [<reflink idref="bib29" id="ref137">29</reflink>]; [<reflink idref="bib42" id="ref138">42</reflink>]; [<reflink idref="bib50" id="ref139">50</reflink>]) negatively predicted mental HRQOL in our participants (while the number of somatic comorbidities negatively predicted physical HRQOL; compare [<reflink idref="bib14" id="ref140">14</reflink>]).</p> <p> <emph>With respect to healthcare-related predictors</emph>, 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 ([<reflink idref="bib52" id="ref141">52</reflink>]), psychiatric inpatient admission has typically been linked to increased autism severity and mental health comorbidity burden ([<reflink idref="bib61" id="ref142">61</reflink>]). 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 ([<reflink idref="bib10" id="ref143">10</reflink>]) as well as the possibility that utilization of inpatient treatment and associated severe health problems may arise from barriers at the primary care level ([<reflink idref="bib22" id="ref144">22</reflink>]). 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.</p> <p>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., [<reflink idref="bib26" id="ref145">26</reflink>]; [<reflink idref="bib60" id="ref146">60</reflink>]).</p> <p>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 [<reflink idref="bib23" id="ref147">23</reflink>]; [<reflink idref="bib48" id="ref148">48</reflink>]).</p> <p>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 [<reflink idref="bib10" id="ref149">10</reflink>], 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).</p> <p>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., [<reflink idref="bib10" id="ref150">10</reflink>]; [<reflink idref="bib22" id="ref151">22</reflink>]; [<reflink idref="bib26" id="ref152">26</reflink>]; [<reflink idref="bib60" id="ref153">60</reflink>]; [<reflink idref="bib67" id="ref154">67</reflink>]). 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 ([<reflink idref="bib10" id="ref155">10</reflink>]; [<reflink idref="bib58" id="ref156">58</reflink>]), which is further underlined by the recent dramatic finding that autistic adults in the United Kingdom receive poorer quality healthcare than those without autism ([<reflink idref="bib71" id="ref157">71</reflink>]).</p> <p>Barriers to healthcare here were measured using the BHC-SF ([<reflink idref="bib59" id="ref158">59</reflink>]). 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 [<reflink idref="bib55" id="ref159">55</reflink>]). This quantifies and corroborates previous qualitative findings from Germany and other countries ([<reflink idref="bib13" id="ref160">13</reflink>]; [<reflink idref="bib19" id="ref161">19</reflink>]; [<reflink idref="bib23" id="ref162">23</reflink>]) but also points to particular shortcomings of the healthcare system for autistic adults in Germany (possibly similar to the United Kingdom as cited above; [<reflink idref="bib71" id="ref163">71</reflink>]). 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 ([<reflink idref="bib62" id="ref164">62</reflink>]). 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.</p> <p>The present ratings on the BHC-SF ([<reflink idref="bib59" id="ref165">59</reflink>]) 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 ([<reflink idref="bib22" id="ref166">22</reflink>]; [<reflink idref="bib31" id="ref167">31</reflink>]).</p> <hd id="AN0183028989-23">Limitations</hd> <p>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 <emph>cause</emph> 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.</p> <p>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 ([<reflink idref="bib7" id="ref168">7</reflink>]; [<reflink idref="bib10" id="ref169">10</reflink>]). Accordingly, there is now growing evidence that autistic adults without intellectual disabilities often show especially poor health or employment outcomes ([<reflink idref="bib65" id="ref170">65</reflink>]). 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).</p> <hd id="AN0183028989-24">Conclusions and implications</hd> <p>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.</p> <ref id="AN0183028989-25"> <title> References </title> <blist> <bibl id="bib1" idref="ref67" type="bt">1</bibl> <bibtext> Alguwaihes A., Shah B. (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.</bibtext> </blist> <blist> <bibl id="bib2" idref="ref68" type="bt">2</bibl> <bibtext> American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.).</bibtext> </blist> <blist> <bibl id="bib3" idref="ref38" type="bt">3</bibl> <bibtext> Atherton G., Edisbury E., Piovesan A., Cross L. (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</bibtext> </blist> <blist> <bibl id="bib4" idref="ref14" type="bt">4</bibl> <bibtext> Ayres M., Parr J. R., Rodgers J., Mason D., Avery L., Flynn D. (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</bibtext> </blist> <blist> <bibl id="bib5" idref="ref73" type="bt">5</bibl> <bibtext> Baron-Cohen S., Bowen D. C., Holt R. J., Allison C., Auyeung B., Lombardo M. V., Smith P., 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</bibtext> </blist> <blist> <bibl id="bib6" idref="ref76" type="bt">6</bibl> <bibtext> Beierlein V., Morfeld M., Bergelt C., Bullinger M., Brähler E. (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</bibtext> </blist> <blist> <bibl id="bib7" idref="ref168" type="bt">7</bibl> <bibtext> Beresford B., Mukherjee S., Mayhew E., Heavey E., Park A. L., Stuttard L., Allgar V., Knapp M. (2020). Evaluating specialist autism teams' provision of care and support for autistic adults without learning disabilities: The shape mixed-methods study. NIHR Journals Library.</bibtext> </blist> <blist> <bibl id="bib8" idref="ref60" type="bt">8</bibl> <bibtext> Bishop-Fitzpatrick L., Rubenstein E. (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</bibtext> </blist> <blist> <bibl id="bib9" idref="ref31" type="bt">9</bibl> <bibtext> Braden B. B., Pagni B. A., Monahan L., Walsh M. J. M., Dixon M. V., Delaney S., Ballard L., 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</bibtext> </blist> <blist> <bibtext> Brede J., Cage E., Trott J., Palmer L., Smith A., Serpell L., Mandy W., Russell A. (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</bibtext> </blist> <blist> <bibtext> Bullinger M., Quitmann J. (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</bibtext> </blist> <blist> <bibtext> 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</bibtext> </blist> <blist> <bibtext> Calleja S., Kingsley J., Amirul Islam F. M., McDonald R. (2022). Barriers to accessing healthcare: Perspectives from autistic adults and carers. Qualitative Health Research, 32(2), 267–278. https://doi.org/10.1177/10497323211050362</bibtext> </blist> <blist> <bibtext> 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</bibtext> </blist> <blist> <bibtext> Cohen J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.</bibtext> </blist> <blist> <bibtext> Croen L. A., Zerbo O., Qian Y., Massolo M. L., Rich S., Sidney S., Kripke C. (2015). The health status of adults on the autism spectrum. Autism, 19(7), 814–823. https://doi.org/10.1177/1362361315577517</bibtext> </blist> <blist> <bibtext> Daniels A. M., Rosenberg R. E., Anderson C., 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</bibtext> </blist> <blist> <bibtext> David N., Duckert S., Gewohn P., Konig H., Rahlff P., Erik F., Vogeley K., Schottle D., Konnopka A., Schulz H., Peth J. (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</bibtext> </blist> <blist> <bibtext> Dern S., Sappok T. (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</bibtext> </blist> <blist> <bibtext> Deserno M. K., Borsboom D., Begeer S., Agelink van Rentergem J. A., Mataw K., 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</bibtext> </blist> <blist> <bibtext> de Wit M., Hajos T. (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</bibtext> </blist> <blist> <bibtext> Doherty M., Neilson S., O'Sullivan J., Carravallah L., Johnson M., Cullen W., 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</bibtext> </blist> <blist> <bibtext> Duckert S., Gewohn P., Konig H., Schottle D., Konnopka A., Rahlff P., Erik F., Vogeley K., Schulz H., David N., Peth J. (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</bibtext> </blist> <blist> <bibtext> Espeloer J., Proft J., Falter-Wagner C. M., Vogeley K. (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</bibtext> </blist> <blist> <bibtext> Evers K., Maljaars J., Schepens H., Vanaken G. J., Noens I. (2022). Conceptualization of quality of life in autistic individuals. Developmental Medicine & Child Neurology, 64(8), 950–956. https://doi.org/10.1111/dmcn.15205</bibtext> </blist> <blist> <bibtext> Gilmore D., Krantz M., Weaver L., 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</bibtext> </blist> <blist> <bibtext> Grupp H., Konig H. H., Riedel-Heller S., Konnopka A. (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</bibtext> </blist> <blist> <bibtext> Hayakawa N., Okada T., Nomura K., Tsukada T., Nakamura M. (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</bibtext> </blist> <blist> <bibtext> Helles A., Gillberg I. C., Gillberg C., Billstedt E. (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</bibtext> </blist> <blist> <bibtext> Hemmerich W. (2019). StatistikGuru: Poweranalyse und Stichprobenberechnung für Regression[StatistikGuru: Power analysis and sample calculation for regression]. https://statistikguru.de/rechner/poweranalyse-regression.html</bibtext> </blist> <blist> <bibtext> Höfer J., Hoffmann F., Dörks M., Kamp-Becker I., Küpper C., Poustka L., Roepke S., Roessner V., Stroth S., Wolff N., 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</bibtext> </blist> <blist> <bibtext> Howlin P. (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</bibtext> </blist> <blist> <bibtext> Jennes-Coussens M., Magill-Evans J., Koning C. (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</bibtext> </blist> <blist> <bibtext> Kamio Y., Inada N., Koyama T. (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</bibtext> </blist> <blist> <bibtext> Kamp-Becker I., Schröder J., Remschmidt H., 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</bibtext> </blist> <blist> <bibtext> Khanna R., Jariwala K., West-Strum D. (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</bibtext> </blist> <blist> <bibtext> Khanna R., Jariwala-Parikh K., West-Strum D., Mahabaleshwarkar R. (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</bibtext> </blist> <blist> <bibtext> Kim S. Y., Bottema-Beutel K. (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</bibtext> </blist> <blist> <bibtext> Knüppel A., Telléus G. K., Jakobsen H., 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</bibtext> </blist> <blist> <bibtext> Kohane I. S., McMurry A., Weber G., MacFadden D., Rappaport L., Kunkel L., Bickel J., Wattanasin N., Spence S., Murphy S., Churchill S. (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</bibtext> </blist> <blist> <bibtext> Lai M. C., Kassee C., Besney R., Bonato S., Hull L., Mandy W., Szatmari P., 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.</bibtext> </blist> <blist> <bibtext> Lawson L. P., Richdale A. L., Haschek A., Flower R. L., Vartuli J., 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</bibtext> </blist> <blist> <bibtext> Leader G., Barrett A., Ferrari C., Casburn M., Maher L., Naughton K., Arndt S., Mannion A. (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</bibtext> </blist> <blist> <bibtext> LimeSurvey GmbH. (n.d.). LimeSurvey: An open source survey tool. <ulink href="http://www.limesurvey.org">http://www.limesurvey.org</ulink></bibtext> </blist> <blist> <bibtext> Lord C., Charman T., Havdahl A., Carbone P., Anagnostou E., Boyd B., Carr T., de Vries P. J., Dissanayake C., Divan G., Freitag C. M., Gotelli M. M., Kasari C., Knapp M., Mundy P., Plank A., Scahill L., Servili C., Shattuck P...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</bibtext> </blist> <blist> <bibtext> 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</bibtext> </blist> <blist> <bibtext> Mandy W., Midouhas E., Hosozawa M., Cable N., Sacker A., Flouri E. (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</bibtext> </blist> <blist> <bibtext> Mason D., Ingham B., Urbanowicz A., Michael C., Birtles H., Woodbury-Smith M., Brown T., James I., Scarlett C., Nicolaidis C., 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</bibtext> </blist> <blist> <bibtext> Mason D., Mackintosh J., McConachie H., Rodgers J., Finch T., 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</bibtext> </blist> <blist> <bibtext> Mason D., McConachie H., Garland D., Petrou A., Rodgers J., 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</bibtext> </blist> <blist> <bibtext> Megari K. (2013). Quality of life in chronic disease patients. Health Psychology Research, 1(3), Article e27. https://doi.org/10.4081/hpr.2013.e27</bibtext> </blist> <blist> <bibtext> Melvin C. L., Barnoux M., Alexander R., Roy A., Devapriam J., Blair R., Tromans S., Shepstone L., 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</bibtext> </blist> <blist> <bibtext> Menard S. (2001). Applied logistic regression analysis (2nd ed.). Sage.</bibtext> </blist> <blist> <bibtext> National Institute for Health and Care Excellence. (2012). Autism spectrum disorder in adults: Diagnosis and management. https://<ulink href="http://www.nice.org.uk/guidance/cg142">www.nice.org.uk/guidance/cg142</ulink></bibtext> </blist> <blist> <bibtext> Nicolaidis C., Raymaker D., McDonald K., Kapp S., Weiner M., Ashkenazy E., Gerrity M., Kripke C., Platt L., Baggs A. (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</bibtext> </blist> <blist> <bibtext> Oakley B. F., Tillmann J., Ahmad J., Crawley D., San Jose Caceres A., Holt R., Charman T., Banaschewski T., Buitelaar J., Simonoff E., Murphy D., Loth E. (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</bibtext> </blist> <blist> <bibtext> Park S. H., Song Y. J. C., Demetriou E. A., Pepper K. L., Norton A., Thomas E. E., Hickie I. B., Hermens D. F., Glozier N., 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</bibtext> </blist> <blist> <bibtext> Platos M., Pisula E. (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</bibtext> </blist> <blist> <bibtext> Raymaker D. M., McDonald K. E., Ashkenazy E., Gerrity M., Baggs A. M., Kripke C., Hourston S., Nicolaidis C. (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</bibtext> </blist> <blist> <bibtext> Renty J. O., Roeyers H. (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</bibtext> </blist> <blist> <bibtext> Righi G., Benevides J., Mazefsky C., Siegel M., 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</bibtext> </blist> <blist> <bibtext> Roy M., Strate P. (2023). Autism spectrum disorders in adulthood—symptoms, diagnosis, and treatment. Deutsches Ärzteblatt International, 120, 87–92. https://doi.org/10.3238/arztebl.m2022.0379</bibtext> </blist> <blist> <bibtext> Seidl H., Bowles D., Bock J. O., Brettschneider C., Greiner W., Konig H. H., Holle R. (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</bibtext> </blist> <blist> <bibtext> Skaletski E. C., Bradley L., Taylor D., Travers B. G., Bishop L. (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</bibtext> </blist> <blist> <bibtext> 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</bibtext> </blist> <blist> <bibtext> 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</bibtext> </blist> <blist> <bibtext> Vogan V., Lake J. K., Tint A., Weiss J. A., Lunsky Y. (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</bibtext> </blist> <blist> <bibtext> Vohra R., Madhavan S., Sambamoorthi U. (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</bibtext> </blist> <blist> <bibtext> 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://<ulink href="http://www.ncbi.nlm.nih.gov/pubmed/1593914">www.ncbi.nlm.nih.gov/pubmed/1593914</ulink></bibtext> </blist> <blist> <bibtext> Ware J. E., Kosinski M., Dewey J. E., Gandek B. (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.).</bibtext> </blist> <blist> <bibtext> Weir E., Allison C., Baron-Cohen S. (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</bibtext> </blist> <blist> <bibtext> 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</bibtext> </blist> <blist> <bibtext> 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</bibtext> </blist> <blist> <bibtext> Yiengprugsawan V., Kelly M., Tawatsupa B. (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</bibtext> </blist> </ref> <ref id="AN0183028989-26"> <title> Footnotes </title> <blist> <bibtext> The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.</bibtext> </blist> <blist> <bibtext> 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.</bibtext> </blist> <blist> <bibtext> Nicole David</bibtext> </blist> <blist> <bibtext>Graph</bibtext> </blist> <blist> <bibtext>https://orcid.org/0000-0001-5233-1977 Sophia Dückert</bibtext> </blist> <blist> <bibtext>Graph</bibtext> </blist> <blist> <bibtext>https://orcid.org/0000-0002-2953-2487 Judith Peth</bibtext> </blist> <blist> <bibtext>Graph https://orcid.org/0000-0002-2476-9717</bibtext> </blist> </ref> <aug> <p>By Nicole David; Pascal Rahlff; Hannah König; Sophia Dückert; Petia Gewohn; Frank Erik; Kai Vogeley; Daniel Schöttle; Alexander Konnopka; Holger Schulz and Judith Peth</p> <p>Reported by Author; Author; Author; Author; Author; Author; Author; Author; Author; Author; Author</p> </aug> <nolink nlid="nl1" bibid="bib21" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib11" firstref="ref2"></nolink> <nolink nlid="nl3" bibid="bib51" firstref="ref3"></nolink> <nolink nlid="nl4" bibid="bib54" firstref="ref4"></nolink> <nolink nlid="nl5" bibid="bib24" firstref="ref5"></nolink> <nolink nlid="nl6" bibid="bib32" firstref="ref6"></nolink> <nolink nlid="nl7" bibid="bib18" firstref="ref7"></nolink> <nolink nlid="nl8" bibid="bib45" firstref="ref8"></nolink> <nolink nlid="nl9" bibid="bib16" firstref="ref9"></nolink> <nolink nlid="nl10" bibid="bib40" firstref="ref10"></nolink> <nolink nlid="nl11" bibid="bib68" firstref="ref11"></nolink> <nolink nlid="nl12" bibid="bib73" firstref="ref12"></nolink> <nolink nlid="nl13" bibid="bib23" firstref="ref13"></nolink> <nolink nlid="nl14" bibid="bib38" firstref="ref15"></nolink> <nolink nlid="nl15" bibid="bib50" firstref="ref16"></nolink> <nolink nlid="nl16" bibid="bib49" firstref="ref17"></nolink> <nolink nlid="nl17" bibid="bib66" firstref="ref18"></nolink> <nolink nlid="nl18" bibid="bib56" firstref="ref19"></nolink> <nolink nlid="nl19" bibid="bib35" firstref="ref20"></nolink> <nolink nlid="nl20" bibid="bib25" firstref="ref22"></nolink> <nolink nlid="nl21" bibid="bib72" firstref="ref23"></nolink> <nolink nlid="nl22" bibid="bib69" firstref="ref24"></nolink> <nolink nlid="nl23" bibid="bib33" firstref="ref25"></nolink> <nolink nlid="nl24" bibid="bib34" firstref="ref26"></nolink> <nolink nlid="nl25" bibid="bib37" firstref="ref33"></nolink> <nolink nlid="nl26" bibid="bib39" firstref="ref34"></nolink> <nolink nlid="nl27" bibid="bib43" firstref="ref41"></nolink> <nolink nlid="nl28" bibid="bib60" firstref="ref42"></nolink> <nolink nlid="nl29" bibid="bib20" firstref="ref44"></nolink> <nolink nlid="nl30" bibid="bib57" firstref="ref47"></nolink> <nolink nlid="nl31" bibid="bib41" firstref="ref50"></nolink> <nolink nlid="nl32" bibid="bib10" firstref="ref53"></nolink> <nolink nlid="nl33" bibid="bib19" firstref="ref55"></nolink> <nolink nlid="nl34" bibid="bib48" firstref="ref57"></nolink> <nolink nlid="nl35" bibid="bib26" firstref="ref58"></nolink> <nolink nlid="nl36" bibid="bib22" firstref="ref59"></nolink> <nolink nlid="nl37" bibid="bib14" firstref="ref61"></nolink> <nolink nlid="nl38" bibid="bib17" firstref="ref74"></nolink> <nolink nlid="nl39" bibid="bib70" firstref="ref77"></nolink> <nolink nlid="nl40" bibid="bib59" firstref="ref78"></nolink> <nolink nlid="nl41" bibid="bib74" firstref="ref81"></nolink> <nolink nlid="nl42" bibid="bib28" firstref="ref82"></nolink> <nolink nlid="nl43" bibid="bib36" firstref="ref83"></nolink> <nolink nlid="nl44" bibid="bib63" firstref="ref91"></nolink> <nolink nlid="nl45" bibid="bib27" firstref="ref92"></nolink> <nolink nlid="nl46" bibid="bib53" firstref="ref98"></nolink> <nolink nlid="nl47" bibid="bib15" firstref="ref99"></nolink> <nolink nlid="nl48" bibid="bib42" firstref="ref100"></nolink> <nolink nlid="nl49" bibid="bib342" firstref="ref105"></nolink> <nolink nlid="nl50" bibid="bib367" firstref="ref109"></nolink> <nolink nlid="nl51" bibid="bib29" firstref="ref119"></nolink> <nolink nlid="nl52" bibid="bib12" firstref="ref131"></nolink> <nolink nlid="nl53" bibid="bib64" firstref="ref132"></nolink> <nolink nlid="nl54" bibid="bib46" firstref="ref135"></nolink> <nolink nlid="nl55" bibid="bib47" firstref="ref136"></nolink> <nolink nlid="nl56" bibid="bib52" firstref="ref141"></nolink> <nolink nlid="nl57" bibid="bib61" firstref="ref142"></nolink> <nolink nlid="nl58" bibid="bib67" firstref="ref154"></nolink> <nolink nlid="nl59" bibid="bib58" firstref="ref156"></nolink> <nolink nlid="nl60" bibid="bib71" firstref="ref157"></nolink> <nolink nlid="nl61" bibid="bib55" firstref="ref159"></nolink> <nolink nlid="nl62" bibid="bib13" firstref="ref160"></nolink> <nolink nlid="nl63" bibid="bib62" firstref="ref164"></nolink> <nolink nlid="nl64" bibid="bib31" firstref="ref167"></nolink> <nolink nlid="nl65" bibid="bib65" firstref="ref170"></nolink>
Header DbId: eric
DbLabel: ERIC
An: EJ1465388
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Barriers to Healthcare Predict Reduced Health-Related Quality of Life in Autistic Adults without Intellectual Disability
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Nicole+David%22">Nicole David</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-5233-1977">0000-0001-5233-1977</externalLink>)<br /><searchLink fieldCode="AR" term="%22Pascal+Rahlff%22">Pascal Rahlff</searchLink><br /><searchLink fieldCode="AR" term="%22Hannah+König%22">Hannah König</searchLink><br /><searchLink fieldCode="AR" term="%22Sophia+Dückert%22">Sophia Dückert</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-2953-2487">0000-0002-2953-2487</externalLink>)<br /><searchLink fieldCode="AR" term="%22Petia+Gewohn%22">Petia Gewohn</searchLink><br /><searchLink fieldCode="AR" term="%22Frank+Erik%22">Frank Erik</searchLink><br /><searchLink fieldCode="AR" term="%22Kai+Vogeley%22">Kai Vogeley</searchLink><br /><searchLink fieldCode="AR" term="%22Daniel+Schöttle%22">Daniel Schöttle</searchLink><br /><searchLink fieldCode="AR" term="%22Alexander+Konnopka%22">Alexander Konnopka</searchLink><br /><searchLink fieldCode="AR" term="%22Holger+Schulz%22">Holger Schulz</searchLink><br /><searchLink fieldCode="AR" term="%22Judith+Peth%22">Judith Peth</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-2476-9717">0000-0002-2476-9717</externalLink>)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Autism%3A+The+International+Journal+of+Research+and+Practice%22"><i>Autism: The International Journal of Research and Practice</i></searchLink>. 2025 29(2):476-489.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 14
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2025
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Autism+Spectrum+Disorders%22">Autism Spectrum Disorders</searchLink><br /><searchLink fieldCode="DE" term="%22Adults%22">Adults</searchLink><br /><searchLink fieldCode="DE" term="%22Access+to+Health+Care%22">Access to Health Care</searchLink><br /><searchLink fieldCode="DE" term="%22Barriers%22">Barriers</searchLink><br /><searchLink fieldCode="DE" term="%22Quality+of+Life%22">Quality of Life</searchLink><br /><searchLink fieldCode="DE" term="%22Mental+Health%22">Mental Health</searchLink><br /><searchLink fieldCode="DE" term="%22Physical+Health%22">Physical Health</searchLink><br /><searchLink fieldCode="DE" term="%22Predictor+Variables%22">Predictor Variables</searchLink><br /><searchLink fieldCode="DE" term="%22Individual+Characteristics%22">Individual Characteristics</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Germany%22">Germany</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1177/13623613241275406
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 1362-3613<br />1461-7005
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2025
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1465388
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1465388
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1177/13623613241275406
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 14
        StartPage: 476
    Subjects:
      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: Autism Spectrum Disorders
        Type: general
      – SubjectFull: Adults
        Type: general
      – SubjectFull: Access to Health Care
        Type: general
      – SubjectFull: Barriers
        Type: general
      – SubjectFull: Quality of Life
        Type: general
      – SubjectFull: Mental Health
        Type: general
      – SubjectFull: Physical Health
        Type: general
      – SubjectFull: Predictor Variables
        Type: general
      – SubjectFull: Individual Characteristics
        Type: general
      – SubjectFull: Germany
        Type: general
    Titles:
      – TitleFull: Barriers to Healthcare Predict Reduced Health-Related Quality of Life in Autistic Adults without Intellectual Disability
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Nicole David
      – PersonEntity:
          Name:
            NameFull: Pascal Rahlff
      – PersonEntity:
          Name:
            NameFull: Hannah König
      – PersonEntity:
          Name:
            NameFull: Sophia Dückert
      – PersonEntity:
          Name:
            NameFull: Petia Gewohn
      – PersonEntity:
          Name:
            NameFull: Frank Erik
      – PersonEntity:
          Name:
            NameFull: Kai Vogeley
      – PersonEntity:
          Name:
            NameFull: Daniel Schöttle
      – PersonEntity:
          Name:
            NameFull: Alexander Konnopka
      – PersonEntity:
          Name:
            NameFull: Holger Schulz
      – PersonEntity:
          Name:
            NameFull: Judith Peth
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 02
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 1362-3613
            – Type: issn-electronic
              Value: 1461-7005
          Numbering:
            – Type: volume
              Value: 29
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: Autism: The International Journal of Research and Practice
              Type: main
ResultId 1