Gapmap: Enabling Comprehensive Autism Resource Epidemiology
ASD has been attracting more interest recently as a result of skyrocketing prevalence rates of 1 in 68 children. Comprehensive regional autism prevalence rates would be extremely helpful for determining the true prevalence of autism and correlating genetic and environmental factors with higher standards of significance. In particular, comparing geographic trends in prevalence rates to autism resource epidemiology would be invaluable in invaluable in revealing patient care deficits. Finding these resource gaps, regions in which there exist limited diagnostic or treatment resources with respect to the demand, can back up pushes for congressional change with hard data, allocate resources more efficiently, and provide information to emerging organizations and businesses to let them know where their services are most needed. These efforts can help reduce the time to diagnosis and increase the chances that speech and behavioral therapies are started during critical periods when they are maximally impactful.
The specific aims of this study were to: 1) obtain an early approximation of the disconnect between autism resources and diagnosed individuals by determining the average distance between an individual with autism and the nearest diagnostic center, 2) define useful metrics that can be used to determine if a center is overloaded or if a region is underserved, and 3) create an online tool to collect information pertaining to geographic variations of autism prevalence and the geographic resource utilization of autism resources.
The Wall Lab created an application, GAPMap, to collect locational, diagnostic, and resource use information from individuals with autism in order to compute accurate prevalence rates and better understand autism resource epidemiology. The metrics resource load, resource gap, and resource availability were defined to aid in this purpose, and estimates were calculated with limited datasets.
The average distance from an individual in the United States to the nearest diagnostic center is approximately 182 kilometers (50 miles), with a standard deviation of 235 kilometers (146 miles). The dataset for the United States was comprised of 47,622 individuals with autism and 840 developmental and diagnostic medical centers.
While these analyses and metrics highlight the lack of resources in much of the United States and the overburdening of many centers, they are not enough. We have built GAPMap as a tool to collect important information and visually display the results. The collected location information, diagnosis, diagnostic tools, and co-morbid conditions will be used obtain both widespread and highly localizable autism prevalence rates. Date of diagnosis and age will be aggregated and used to obtain localizable average age of diagnosis, a measure that correlates with difficulty obtaining a diagnosis and can be used to help approximate geographic differences in resource accessibility. Ratings and local services will be used to estimate resource usage trends with respect to geography and resource density. Prevalence rates and local service usage will also be used to calculate resource load and availability for different resource types, such as behavioral therapy.