AuDHD Blog
Why Co-Occurrence Rates Are Messy
A careful guide to why autism+ADHD statistics vary, and how to read headline numbers without panic or dismissal.
Research · · 8 min read
Co-occurrence rates are messy because the studies are not all answering the same question. Some ask how many autistic children meet ADHD criteria. Some ask how many people with ADHD have autistic traits. Some analyze adults, clinical referrals, service records, insurance claims, or community samples. Those are different denominators.
Sample matters
Clinical samples usually include people with higher support needs or clearer reasons for referral, while community samples may include many people who have never been assessed. Child samples also cannot be assumed to describe adults, especially after years of masking, missed diagnosis, compensation, or uneven service access (Craddock, 2024a; Hours et al., 2022).
Method matters
A questionnaire score, a parent report, a clinical interview, and a formal diagnosis do not measure the same thing. Hours et al. (2022) caution that overlap can reflect true co-occurrence, shared mechanisms, or measurement ambiguity. Rommelse et al. (2010) and Leitner (2014) remain useful, but their numbers should be read as context-dependent rather than universal.
Records matter
Real-world databases can show service burden and population patterns, but they inherit the limits of the systems that create records. Zaleski et al. (2025) and Yerys et al. (2025) are valuable partly because they show how autism+ADHD appears in healthcare data, while also reminding us that people outside diagnosis, insurance, or service pathways may be invisible.
When a statistic sounds too clean, ask: who was counted, how were they assessed, and who was likely missed?
How to read a headline rate
The safest reading is: autism and ADHD overlap often enough that integrated assessment and support are necessary, but no headline percentage should be treated as the final truth for every person or setting. Daily challenge data also show why the point is not only counting labels; it is understanding what support is needed in real life (Sainsbury et al., 2024; Young et al., 2020).
Questions to ask of any statistic
A headline number can look authoritative even when it hides a fragile method. Good evidence literacy is not cynicism; it is a way to keep public guidance honest. Hours et al. (2022) are especially useful here because they show that comorbidity language can blend true co-occurrence, shared mechanisms, and measurement overlap.
- Who was included: children, adults, clinic referrals, school samples, service users, claims records, or community participants?
- How was autism or ADHD measured: diagnosis, interview, rating scale, records, traits, or parent report?
- Was dual diagnosis allowed at the time of the study?
- Who might have been missed because of masking, access barriers, race, gender, income, geography, or service eligibility?
- Does the article distinguish co-occurring diagnoses from overlapping traits?
What not to conclude
Do not conclude that messy rates make co-occurrence rare or unimportant. Also do not conclude that every attention difficulty in autism is ADHD or every social difficulty in ADHD is autism. Daily challenge data and clinical guidance point to the same careful middle: assess context, ask better questions, and design support around real functioning (Sainsbury et al., 2024; Young et al., 2020).
A plain-language reading method
You do not need to be a statistician to read co-occurrence claims more safely. A simple method is to slow down at three points: who was counted, how they were measured, and what the author is claiming from the number. That helps preserve the usefulness of the evidence without letting a headline overreach (Hours et al., 2022; Rommelse et al., 2010).
- Counted: children, adults, clinics, claims records, community samples, or self-selected surveys?
- Measured: formal diagnosis, clinical interview, rating scale, trait screen, parent report, or service code?
- Claimed: support need, diagnosis rate, trait overlap, healthcare use, or cause?
- Applied: does the article explain what the number can and cannot mean for an individual reader?
When two true numbers disagree
Two studies can report different rates and both still be useful. One may count autistic children in clinics, another may count adults in healthcare claims, and another may measure traits in a community sample. They are not answering the same question. The careful reader asks what each number was built to measure before deciding whether the findings conflict (Hours et al., 2022; Rommelse et al., 2010).
- If the sample is clinical, expect higher support needs and more referral bias.
- If the sample is community-based, expect more variation and possibly fewer confirmed diagnoses.
- If the data come from claims records, remember they depend on access, coding, insurance, and recognition.
- If the article does not explain the method, treat the headline number as incomplete.
References
- Craddock, E. (2024a). Being a woman is 100% significant to my experiences of attention deficit hyperactivity disorder and autism: Exploring the gendered implications of an adulthood combined autism and attention deficit hyperactivity disorder diagnosis. *Qualitative Health Research, 34*(14), 1442-1455. https://doi.org/10.1177/10497323241253412
- Hours, C., Recasens, C., & Baleyte, J. M. (2022). ASD and ADHD comorbidity: What are we talking about? *Frontiers in Psychiatry, 13*. https://doi.org/10.3389/fpsyt.2022.837424
- Leitner, Y. (2014). The co-occurrence of autism and attention deficit hyperactivity disorder in children: What do we know? *Frontiers in Human Neuroscience, 8*. https://doi.org/10.3389/fnhum.2014.00268
- Rommelse, N. N. J., Franke, B., Geurts, H. M., Hartman, C. A., & Buitelaar, J. K. (2010). Shared heritability of attention-deficit/hyperactivity disorder and autism spectrum disorder. *European Child & Adolescent Psychiatry, 19*(3), 281-295. https://doi.org/10.1007/s00787-010-0092-x
- Sainsbury, W. J., Whitehouse, A. J. O., Carrasco, K. D., & Waddington, H. (2024). Parent-reported areas of greatest challenge for their ADHD and/or autistic children. *Advances in Neurodevelopmental Disorders, 9*(2), 330-337. https://doi.org/10.1007/s41252-024-00417-x
- Yerys, B. E., Tao, S., Shea, L., & Wallace, G. L. (2025). Attention-deficit/hyperactivity disorder in Medicaid-enrolled autistic adults. *JAMA Network Open, 8*(2), e2453402. https://doi.org/10.1001/jamanetworkopen.2024.53402
- Young, S., Hollingdale, J., Absoud, M., Bolton, P., Branney, P., Colley, W., Craze, E., Dave, M., Deeley, Q., Farrag, E., Gudjonsson, G., Hill, P., Liang, H. L., Murphy, C., Mackintosh, P., Murin, M., O'Regan, F., Ougrin, D., Rios, P., ... Woodhouse, E. (2020). Guidance for identification and treatment of individuals with attention deficit/hyperactivity disorder and autism spectrum disorder based upon expert consensus. *BMC Medicine, 18*(1). https://doi.org/10.1186/s12916-020-01585-y
- Zaleski, A. L., Craig, K. J. T., Khan, R., Waber, R., Xin, W., Powers, M., Ramey, U., Verbrugge, D. J., & Fernandez-Turner, D. (2025). Real-world evaluation of prevalence, cohort characteristics, and healthcare utilization and expenditures among adults and children with autism spectrum disorder, attention-deficit hyperactivity disorder, or both. *BMC Health Services Research, 25*(1). https://doi.org/10.1186/s12913-025-13296-2