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Autism affects 1 in every 59 children in the United States, according to estimates from the Centers for Disease Control and Prevention’s Autism and Developmental Disabilities Monitoring Network in 2018. Although similar rates of autism are reported in rural and urban areas, rural families report greater difficulty in accessing resources. An overwhelming number of families experience long waitlists for diagnostic and therapeutic services.
The objective of this study was to accurately identify gaps in access to autism care using GapMap, a mobile platform that connects families with local resources while continuously collecting up-to-date autism resource epidemiological information.
After being extracted from various databases, resources were deduplicated, validated, and allocated into 7 categories based on the keywords identified on the resource website. The average distance between the individuals from a simulated autism population and the nearest autism resource in our database was calculated for each US county. Resource load, an approximation of demand over supply for diagnostic resources, was calculated for each US county.
There are approximately 28,000 US resources validated on the GapMap database, each allocated into 1 or more of the 7 categories. States with the greatest distances to autism resources included Alaska, Nevada, Wyoming, Montana, and Arizona. Of the 7 resource categories, diagnostic resources were the most underrepresented, comprising only 8.83% (2472/28,003) of all resources. Alarmingly, 83.86% (2635/3142) of all US counties lacked any diagnostic resources. States with the highest diagnostic resource load included West Virginia, Kentucky, Maine, Mississippi, and New Mexico.
Results from this study demonstrate the sparsity and uneven distribution of diagnostic resources in the United States, which may contribute to the lengthy waitlists and travel distances—barriers to be overcome to be able to receive diagnosis in specific regions. More data are needed on autism diagnosis demand to better quantify resource needs across the United States.
Autism spectrum disorder (ASD), a heterogeneous neurodevelopmental disorder characterized by repetitive and restricted behaviors and interests and social communication impairments, is the fastest growing developmental disorder in the United States [
Early access to diagnostic and therapeutic resources is essential for improving outcomes among children with ASD; however, families of individuals with autism often struggle to secure a diagnosis and find adequate therapeutic services [
Although there are indicators of significant resource shortages for families affected by autism in the United States [
Our preliminary analysis of data derived from GapMap has shown that in the United States, children who live closer to diagnostic centers are more likely to be diagnosed, highlighting a gap in access to care for families in rural communities [
An example of the preliminary mapping interface for GapMap. (1) The landing page shows resources near Stanford University; (2) participants can electronically consent and participate from any desktop or mobile device; (3) after email and zip code submission, resources are shown near the zip code area.
GapMap is connected to a MySQL database that stores autism resource metadata including, but not limited to, program names, addresses, service types, and geo-coordinates. GapMap’s front-end is written in React.js, and the back-end server runs on Amazon Web Services (AWS) application programming interface (API) Gateway, which connects to AWS Lambda and executes JavaScript packages to communicate with our MySQL database hosted on Amazon Relational Database Service. GapMap allows users to search for resources near a given area and populates a map with markers representing each resource that expands into an information box containing its respective program name, address, and website. Due to its large set of resource geo-coordinates, the GapMap database provides the data needed to perform numerical analyses to further our understanding and characterization of resource epidemiology.
The initial resource database for GapMap [
In an effort to increase comprehensiveness of the GapMap resource and to ensure it remains up to date, we used ParseHub [
We assigned resources in the GapMap database to 7 primary resource categories:
Keywords used to allocate resources to the Diagnosis, Therapy, Health, Education, Recreation, and Support categories on GapMap.
Keywords | Allocated category |
Diagnosis, Where to get an Autism Diagnosis, Diagnostic, Assessments and Diagnosis | Diagnosis |
Therapy; Early Intervention: Ages Birth-3; Interventions; Related Services; Early Intervention Services; Augmentative and Alternative Communication; Equine Therapy; Music Therapy; Occupational Therapy; Physical Therapy; Sensory Integration; Social Skills; Speech and Language Therapy; Applied Behavior Analysis (ABA); Floortime or DIR; Other Interventions; Picture Exchange Communication (PEC); Relationship Development Intervention (RDI); SCERTS Model; TEACCH; Verbal Behavior; Early Intervention; Related Services (Therapists); Speech/Language Therapy; Therapists; Art Therapy; instruction/intervention; Executive Function; ABA (Applied Behavior Analysis); Therapeutic; Speech | Therapy |
Health, Biomedical Interventions; Health Services; Health and Dental Services; Diet or Nutrition; Other Biomedical Interventions; First Responder Resources; Dentists; Family Practitioners; Gastroenterologists; Inpatient Treatment Care Centers; Neurologists; Other Professionals; Pediatricians: Developmental, Pediatricians: General, Psychiatrists, Psychologists, State Mental Health Centers, Blood Draw or Phlebotomists; Community Mental Health Centers; Crisis Intervention Services; Substance Abuse Treatment; Crisis/Crime Victim Services; Dentist; Mental Health Professional; Other Medical Services; Physician; Doctors | Health |
Education; Preschool Age: Ages 3-5; School Age: Ages 5-22; Post Secondary Education; Schools: Nonpublic (Private); Schools: Residential; Schools: Preschool; Transition to Adult Services; Academic Supports; Private/Non-Public School; Public School System; Job/Vocational; Transition; Living Skills; Schools; Tutoring; Academy; Preschool; Tutor; School; Learn; Teach; Pre-K | Education |
Recreation; Recreational and Leisure Activities; Day Programs; Recreation and Community Activities; Camps; After-School Programs; Camps and Recreation; Social Activities; After School Care: Children; Recreation; Play | Recreation |
Support; Support Groups; Community and Support Network; Advocates; Grandparents; Autism Speaks Communities; Local Autism Events; Local Autism Organizations; Military Family Resources; Online Support Groups; Religious Resources; Support Groups; Autism Society Affiliate (Chapter); Parent Training; Conferences; Other Local Organizations; Faith Community Services; Information and Support; Training; Support/Self Help Groups; Parent Support; Advocate-School District; Advocacy; Volunteer | Support |
As GapMap does not yet collect diagnostic information from users, we represented the population distribution of individuals with autism through a simulation. For each of the 3142 counties in the United States, geo-coordinates were generated in a random distribution inside a county bounding box derived from the 2010 US Census Bureau’s set of county Tiger/Line shapefiles [
All resources in the GapMap database were tagged with their respective geo-coordinates. The Euclidean distance was calculated between each geo-coordinate representing a simulated individual with autism and the geo-coordinate representing the autism resource from the GapMap database closest to the corresponding individual. These distances were used to calculate the average resource distance for each county. The values were then used to rank counties with gaps between autism resources and individuals with autism in descending order. This analysis was repeated for each of the 7 resource categories.
Resource distance is correlated with the number of resources and the proportional distribution of resources in the region, but it is not an effective measure of how well resource demands are being met because of the variations in the number of individuals who can be served between resources. As a result, resource load was calculated. The diagnostic resources in the GapMap database comprises resources that provide medical diagnoses of ASD made by pediatricians, neurologists, and psychiatrists as well as diagnoses that occur through psychological assessments and mental health units. Resource load represents how well diagnostic centers can meet the demands of the individuals in a given county annually. Ideally, this value would be a demand over supply ratio of diagnostic services in a given county annually; however, because of the lack of available data on individuals diagnosed and waitlisted for diagnosis for each state, an approximation of this measure was represented by a resource load formula. In addition, as calculating resource load for each diagnostic center rather than region would lead to overestimated values from the potential recounting of individuals within the proximity of multiple diagnostic resources, the previous resource load formula was adjusted to calculate a comparative value that approximates the demand over supply ratio of autism resources. The new resource load below does not exhibit any meaning as a stand-alone value but can be compared with other state resource load values to estimate how well diagnostic resources can meet individual demand.
The resource load
We originally identified 29,935 autism resources in the United States and removed 8402 resources from the GapMap database through our validation and deduplication process. Additional resources were then extracted from Autism Speaks and Google Places, verified through our validation pipeline, merged with our database, and deduplicated, resulting in the addition of 6470 resources and amounting to a total of 28,003 unique and validated resources in the GapMap database.
The estimated average distance between an individual with autism and the nearest autism resource belonging to any of the 7 resource categories across the United States is 17.12 km.
The number, percent breakdown, and average distance of autism resources for each of the 7 resource categories.
Category | Resources, n (%) | Average distance (km) |
All resources | 28,003 (100.00) | 17.12 |
Therapy | 11,602 (41.43) | 21.84 |
Support | 8153 (29.11) | 21.65 |
Health | 7236 (25.84) | 24.02 |
Education | 5595 (19.98) | 24.17 |
Recreation | 2791 (9.97) | 30.44 |
Diagnosis | 2472 (8.83) | 35.49 |
Other | 5190 (18.53) | 26.80 |
Top 10 states in the United States with the largest and smallest average distances between an individual with autism and the nearest autism resource, respectively.
Top 10 states with largest distance | Top 10 states with smallest distance | ||
State | Distance (km) | State | Distance (km) |
Alaska | 100.66 | New Jersey | 3.74 |
Nevada | 54.16 | Connecticut | 4.67 |
Wyoming | 54.14 | Maryland | 5.65 |
Montana | 49.12 | Massachusetts | 6.42 |
Arizona | 43.69 | New York | 7.07 |
North Dakota | 41.52 | Rhode Island | 7.20 |
New Mexico | 36.75 | Pennsylvania | 7.60 |
South Dakota | 33.24 | Delaware | 8.09 |
Oregon | 27.96 | Virginia | 9.06 |
Idaho | 27.35 | New Hampshire | 9.3 |
A heat map depicting the density and distribution of autism resources across the United States (Alaska and Hawaii not to scale), where red indicates a high density of resources.
Of the 3142 US counties surveyed, our analysis found that 2635 counties (83.86%) did not have a single diagnostic resource.
Top 10 states in the United States with the highest and lowest resource load, respectively.
Top 10 states with the highest load | Top 10 states with the lowest load | ||
State | Resource load | State | Resource load |
West Virginia | 5.71 | Montana | 1 |
Kentucky | 4.26 | Connecticut | 1.09 |
Maine | 4.15 | Colorado | 1.10 |
Mississippi | 3.73 | Rhode Island | 1.10 |
New Mexico | 3.71 | New Jersey | 1.14 |
Oklahoma | 3.49 | Pennsylvania | 1.18 |
South Carolina | 3.26 | Massachusetts | 1.25 |
Nevada | 2.82 | Wisconsin | 1.26 |
Tennessee | 2.68 | Maryland | 1.32 |
Nebraska | 2.64 | New York | 1.32 |
In this study, we utilized GapMap to measure autism resource accessibility and availability across the United States. GapMap’s database contains 28,003 unique, validated, and categorized autism resources. As shown in
The average distance between the nearest resource belonging to any resource category and a family affected by autism is 17.12 km. As resources that belong to an individual resource category are a subset of the total number of resources, the average distances corresponding to each individual category will be greater than 17.12 km, as the average distance is negatively correlated with the number of resources. The
Of the 3142 US counties, only 507 have 1 or more diagnostic resources, suggesting that centers are not only being overloaded by its county population but also by individuals outside of the county with no access to diagnostic resources locally. This observation aligns with the historical lack of mental and behavioral health resources in rural communities [
In a study of several countries including the United States, Williams et al found urban locations reported higher prevalence estimates than those reported in rural and mixed locations [
Although we made significant programmatic and manual effort to ensure the accuracy and completeness of the autism resources contained in GapMap, the true number of autism resources in the United States may be above or below the estimated value we derived through our analysis. As we do not have a method to ensure homogeneous reporting of resources by the primary databases from which we extracted our resources, the GapMap database is prone to population biases as a result of increased probability of reporting in densely crowded areas. Due to the lack of complete locational data on the population affected by autism in the United States, we assumed a randomly distributed simulation of the autism population. The geographic boundaries used to simulate the population are rectangular bounding boxes, which do not perfectly capture the actual geographic boundaries that exist in the US counties; however, we found that this primarily affects the relatively small number of counties near bodies of water. Furthermore, the resource distances are calculated with Euclidean distance, which may underestimate the true resource distance. Finally, because of a lack of annual data on the number of individuals who received an autism diagnosis and who were waitlisted for diagnosis for each state, we used a comparative resource load value, allowing only for comparison within this analysis.
These results show an uneven distribution of autism resources throughout the United States and that diagnostic resources are the most underrepresented out of all autism resource categories analyzed. Demand for diagnostic resources varies throughout the United States, contributing to the diagnostic bottleneck, long waitlists spanning several months, and large travel distances for families in rural or underserved locations. The discrepancy in access to services across the United States could be attributed to many factors. We will attempt to parse such sources further as more granular population data are collected from families affected by autism.
Consequently, GapMap will now endeavor to collect geocoded location data and self-reported diagnoses from recruited families. Over time, this will enable a more detailed and accurate representation of gaps in access to care by enabling us to replace simulated family locations with self-reported locations. Euclidean distances between individuals and resources will be replaced with more accurate road distances that factor in street network using Google Maps API. In addition, we plan to incorporate several new features to the GapMap platform (
Depiction of the proposed rating- and review-based features. (1) Landing page; (2) allows families to rate and review resources; (3) allows families to join local autism communities created by other families.
application programming interface
Autism Spectrum Disorder
Amazon Web Services
The work was supported in part by funds to DPW from the National Institutes of Health (NIH; 1R01EB025025-01 & 1R21HD091500-01), The Hartwell Foundation, Bill and Melinda Gates Foundation, Coulter Foundation, Lucile Packard Foundation, and grants from Stanford’s Human Centered Artificial Intelligence Program, Precision Health and Integrated Diagnostics Center (PHIND), Beckman Center, Bio-X Center, Predictives and Diagnostics Accelerator (SPADA) Spectrum, Spark Program in Translational Research, and the Wu Tsai Neurosciences Institute Neuroscience: Translate Program. All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.
None declared.