Background: Whilst cannabis is known to be toxic to brain function and brain development in many respects it is not known if its increasing availability is associated with the rising US autism rates, whether this contribution is sufficient to effect overall trends and if its effects persist after controlling for other major covariates.

Methods: Longitudinal epidemiological study using national autism census data from the US Department of Education Individuals with Disabilities Act (IDEA) 1991-2011 and nationally representative drug exposure (cigarettes, alcohol, analgesic, and cocaine abuse, and cannabis use monthly, daily and in pregnancy) datasets from National Survey of Drug Use and Health and US Census (income and ethnicity) and CDC Wonder population and birth data. Geotemporospatial and causal inference analysis conducted in R.

Results: 266,950 autistic of a population of 40,119,464 eight year olds 1994-2011. At the national level after adjustment daily cannabis use was significantly related (β-estimate=4.37 (95%C.I. 4.06-4.68), P<2.2x10-16) as was cannabis exposure in the first trimester of pregnancy (β-estimate=0.12 (0.08-0.16), P=1.7x10-12). At the state level following adjustment cannabis use was significant (from β-estimate=8.41 (3.08-13.74), P=0.002); after adjustment for varying cannabis exposure by ethnicity and other covariates (from β-estimate=10.88 (5.97-15.79), P=1.4x10-5). Cannabigerol (from β-estimate=-13.77 (-19.41—8.13), P = 1.8x10-6) and Δ9-tetrahydrocannabinol (from β-estimate=1.96 (0.88-3.04), P=4x10-4) were also significant. Geospatial state-level modelling showed an exponential relationship between ASMR and both Δ9-tetrahydrocannabinol and cannabigerol exposure; effect size calculations reflected this exponentiation. Exponential coefficients for the relationship between modelled ASMR and THC- and cannabigerol- exposure were 7.053 (6.39-7.71) and 185.334 (167.88-202.79; both P<2.0x10-7).

In inverse probability-weighted robust generalized linear models ethnic cannabis exposure (from β-estimate=3.64 (2.94-4.34), P=5.9x10-13) and cannabis independently (β-estimate=1.08 (0.63-1.54), P=2.9x10-5) were significant. High eValues in geospatial models indicated that uncontrolled confounding did not explain these findings. Therefore the demonstrated relationship satified the criteria of causal inference. Dichotomized legal status was geospatiotemporally linked with elevated ASMR.

Conclusions: Data show cannabis use is associated with ASMR, is powerful enough to affect overall trends, and persists after controlling for other major drug, socioeconomic, and ethnic-related covariates. Selected cannabinoids are exponentially associated with ASMR. The cannabis-autism relationship satisfies criteria of causal inference.

This dataset is no longer updated as of April 2023.

Basic Metadata Note: Condition is a new addition to 2017. *Rates per 100,000 population. Age-adjusted rates per 100,000 2000 US standard population.

**Blank Cells: Rates not calculated for fewer than 5 events. Rates not calculated in cases where zip code is unknown.

***API: Asian/Pacific Islander. ***AIAN: American Indian/Alaska Native.

Prepared by: County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics Unit, 2019.

Code Source: ICD-9CM - AHRQ HCUP CCS v2015. ICD-10CM - AHRQ HCUP CCS v2018. ICD-10 Mortality - California Department of Public Health, Group Cause of Death Codes 2013; NHCS ICD-10 2e-v1 2017.

Data Guide, Dictionary, and Codebook: https://www.sandiegocounty.gov/content/dam/sdc/hhsa/programs/phs/CHS/Community%20Profiles/Public%20Health%20Services%20Codebook_Data%20Guide_Metadata_10.2.19.xlsx

Purpose: We aim to determine the prevalence and characteristics of developmental disabilities among the clinical population of children who receive hearing health care in the United States.Method: Using electronic health records of 131,709 children (0–18 years), we identified those with a diagnosis of attention deficit/hyperactivity disorder, autism spectrum disorder, vision differences, cerebral palsy, chromosomal abnormalities, delayed milestones, Down syndrome, or intellectual disability. We determined prevalence, age of first audiology encounter, age of diagnosis for the developmental disability, and hearing status based on the specific disability and the number of diagnoses. Binomial and multinomial logistic regressions were performed.Results: One in four children had a diagnosed developmental disability. The most common disabilities were delayed milestones (11.3%), vision differences (7.4%), attention-deficit/hyperactivity disorder (6.6%), and autism spectrum disorder (6.2%). Half of the children with developmental disabilities had at least one diagnosis before their first audiology encounter. Children with developmental disabilities were more likely to have a reduced hearing or an unknown hearing status than children without developmental diagnoses. For children with reduced hearing, those with developmental disabilities had higher rates of bilateral configurations and poorer hearing severity levels.Conclusions: Developmental disabilities are common among children who seek hearing health care. Moreover, developmental disabilities often co-occur with reduced hearing. Further research and advocacy efforts are critical for creating clinical practices that are inclusive of, and equitable for, children with complex and diverse developmental profiles.Supplemental Material S1. ICD-9/10 umbrella mappings for the specific developmental disabilities used in the study.Supplemental Material S2. Binomial logistic regression results for if a diagnosis of attention deficit/hyperactivity disorder (ADHD) was known at the time of the first audiology encounter.Supplemental Material S3. Binomial logistic regression results for if a diagnosis of autism spectrum disorder was known at the time of the first audiology encounter.Supplemental Material S4. Binomial logistic regression results for if a diagnosis of cerebral palsy was known at the time of the first audiology encounter.Supplemental Material S5. Binomial logistic regression results for if a diagnosis of a chromosomal abnormality was known at the time of the first audiology encounter.Supplemental Material S6. Binomial logistic regression results for if a diagnosis of delayed milestones was known at the time of the first audiology encounter.Supplemental Material S7. Binomial logistic regression results for if a diagnosis of Down syndrome was known at the time of the first audiology encounter.Supplemental Material S8. Binomial logistic regression results for if a diagnosis of an intellectual disability was known at the time of the first audiology encounter.Supplemental Material S9. Binomial logistic regression results for if a diagnosis of a vision difference was known at the time of the first audiology encounter.Bonino, A. Y., Goodwich, S. F., & Mood, D. (2025). Prevalence and characteristics of developmental disabilities among children who receive hearing health care. American Journal of Audiology, 34(1), 60–71. https://doi.org/10.1044/2024_AJA-24-00118