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How Do Biases Impact Autism Misdiagnosis Rates? 

Author: Beatrice Holloway, MSc | Reviewed by: Dr. Rebecca Fernandez, MBBS

Bias in autism diagnosis plays a significant role in who gets identified and who doesn’t. This bias shapes outcomes across gender, ethnicity, and socioeconomic groups, leading to overlooked cases or incorrect labels. Implicit and systemic biasness results in inconsistent and unequal recognition of autistic traits. 

Implicit bias: unspoken, unconscious associations lead clinicians to expect autism in males more than females or in Western‑trained patients more than minority groups. This contributes to diagnostic error, especially when subtle presentations don’t fit traditional criteria. Research shows that diagnostic tools like ADOS‐2 can underestimate traits in girls and children of colour, even if the effect size is relatively small. 

Moreover, wider systemic inequality affects outcomes beyond individual assessments. Communities with fewer resources or limited access to specialist services are more likely to experience delays, incorrect referrals, or misdiagnoses. These systemic gaps propagate disparities at scale. 

Patterns That Bias Clinical Outcomes 

Here’s a breakdown of how bias leads to misunderstandings: 

Underdiagnosis in girls and women 

Many studies highlight how autistic females mask symptoms and don’t match male‑focused diagnostic norms. Their strengths at social adaptation often result in other psychiatric labels instead of autism. 

Misreading minority presentations 

Cultural differences in communication style or interaction are frequently misinterpreted. Autistic traits in non-white individuals may instead be viewed through a cultural lens, resulting in either missed or inaccurate diagnoses due to health care bias. 

Diagnostic oversights in resource-limited settings 

Delayed or limited access to autism screening and qualified clinicians leaves many without proper diagnosis. This underrepresentation further skews prevalence patterns and reinforces inequity. 

Addressing these biases requires inclusive training, culturally sensitive tools, and ongoing commitment to equity in healthcare.  

Visit providers like Autism Detect for personal consultations attuned to diversity, gender, and context. 

For a deeper dive into the science, diagnosis, and full treatment landscape, read our complete guide to misdiagnosis and differential diagnosis.

Beatrice Holloway, MSc
Beatrice Holloway, MSc
Author

Beatrice Holloway is a clinical psychologist with a Master’s in Clinical Psychology and a BS in Applied Psychology. She specialises in CBT, psychological testing, and applied behaviour therapy, working with children with autism spectrum disorder (ASD), developmental delays, and learning disabilities, as well as adults with bipolar disorder, schizophrenia, anxiety, OCD, and substance use disorders. Holloway creates personalised treatment plans to support emotional regulation, social skills, and academic progress in children, and delivers evidence-based therapy to improve mental health and well-being across all ages.

All qualifications and professional experience stated above are authentic and verified by our editorial team. However, pseudonym and image likeness are used to protect the author's privacy.

Dr. Rebecca Fernandez
Dr. Rebecca Fernandez, MBBS
Reviewer

Dr. Rebecca Fernandez is a UK-trained physician with an MBBS and experience in general surgery, cardiology, internal medicine, gynecology, intensive care, and emergency medicine. She has managed critically ill patients, stabilised acute trauma cases, and provided comprehensive inpatient and outpatient care. In psychiatry, Dr. Fernandez has worked with psychotic, mood, anxiety, and substance use disorders, applying evidence-based approaches such as CBT, ACT, and mindfulness-based therapies. Her skills span patient assessment, treatment planning, and the integration of digital health solutions to support mental well-being.

All qualifications and professional experience stated above are authentic and verified by our editorial team. However, pseudonym and image likeness are used to protect the reviewer's privacy. 

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