Predictors of Depression and Anxiety Among Self-Medicating Autistic Adults

Background: We aimed to investigate self-medication prevalence, mental health problems, and their relationship among autistic adults. We also sought to identify key factors associated with anxiety and depression in this population. Methods: An online cross-sectional questionnaire survey was conducted with self-identifying or formally diagnosed autistic adults who reported prior or current substance use. Descriptive statistics, chi-square tests, and backward stepwise logistic regression were applied. Results: A total of 475 participants were recruited. The prevalence of anxiety, depression, and eating disorders among the participants was 87.37%, 75.16%, and 51.16%, respectively. Notably, 80.42% of participants also identified as Attention-Deficit/Hyperactivity Disorder (ADHD). The most commonly reported substances for self-medication were alcohol (92.84%), cannabis (82.32%), and stimulants (81.26%), with 77.69% of participants reporting daily or weekly use. Logistic regression revealed that anxiety, dissociatives, and sedatives were significantly associated with depression, while depression, ADHD, and eating disorders were significantly associated with anxiety. Identifying as female was associated with lower odds of depression. Conclusion: Autistic adults who self-medicate show high rates of mental health problems and substance use. The complex relationship between self-medication, mental health, and co-occurring conditions underscores the urgent need for tailored support services. To reduce harmful self-medication, we must address stigma, promote acceptance, and improve access to appropriate mental health support for autistic adults.

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Publication Details

Journal
Neurodiversity
Published
2025-05-01
DOI
https://doi.org/10.1177/27546330251344453
Primary Topic
Autism Spectrum Disorder Research
Type
article
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article

Predictors of Depression and Anxiety Among Self-Medicating Autistic Adults

Katie Munday, Chris Papadopoulos, Tanya Adkin, David Gray-Hammond
1 links
Neurodiversity
Autism Spectrum Disorder Research
article

Predictors of Depression and Anxiety Among Self-Medicating Autistic Adults

Katie Munday, Chris Papadopoulos, Tanya Adkin, David Gray-Hammond
article en

Abstract

Background: We aimed to investigate self-medication prevalence, mental health problems, and their relationship among autistic adults. We also sought to identify key factors associated with anxiety and depression in this population. Methods: An online cross-sectional questionnaire survey was conducted with self-identifying or formally diagnosed autistic adults who reported prior or current substance use. Descriptive statistics, chi-square tests, and backward stepwise logistic regression were applied. Results: A total of 475 participants were recruited. The prevalence of anxiety, depression, and eating disorders among the participants was 87.37%, 75.16%, and 51.16%, respectively. Notably, 80.42% of participants also identified as Attention-Deficit/Hyperactivity Disorder (ADHD). The most commonly reported substances for self-medication were alcohol (92.84%), cannabis (82.32%), and stimulants (81.26%), with 77.69% of participants reporting daily or weekly use. Logistic regression revealed that anxiety, dissociatives, and sedatives were significantly associated with depression, while depression, ADHD, and eating disorders were significantly associated with anxiety. Identifying as female was associated with lower odds of depression. Conclusion: Autistic adults who self-medicate show high rates of mental health problems and substance use. The complex relationship between self-medication, mental health, and co-occurring conditions underscores the urgent need for tailored support services. To reduce harmful self-medication, we must address stigma, promote acceptance, and improve access to appropriate mental health support for autistic adults.

NeurodiversityVol. 3
University of Bedfordshire (GB), University of Portsmouth (GB)
Openalex Percentile: Top 87%
Autism Spectrum Disorder Research
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