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. 2017 May 4;3(2):e27.
doi: 10.2196/publichealth.7150.

GapMap: Enabling Comprehensive Autism Resource Epidemiology

Nikhila Albert  1   2   3 , Jena Daniels #  1   3 , Jessey Schwartz  1   3 , Michael Du  1   3 , Dennis P Wall  1   3
Affiliations

GapMap: Enabling Comprehensive Autism Resource Epidemiology

Nikhila Albert et al. JMIR Public Health Surveill. .

Abstract

Background: For individuals with autism spectrum disorder (ASD), finding resources can be a lengthy and difficult process. The difficulty in obtaining global, fine-grained autism epidemiological data hinders researchers from quickly and efficiently studying large-scale correlations among ASD, environmental factors, and geographical and cultural factors.

Objective: The objective of this study was to define resource load and resource availability for families affected by autism and subsequently create a platform to enable a more accurate representation of prevalence rates and resource epidemiology.

Methods: We created a mobile application, GapMap, to collect locational, diagnostic, and resource use information from individuals with autism to compute accurate prevalence rates and better understand autism resource epidemiology. GapMap is hosted on AWS S3, running on a React and Redux front-end framework. The backend framework is comprised of an AWS API Gateway and Lambda Function setup, with secure and scalable end points for retrieving prevalence and resource data, and for submitting participant data. Measures of autism resource scarcity, including resource load, resource availability, and resource gaps were defined and preliminarily computed using simulated or scraped data.

Results: The average distance from an individual in the United States to the nearest diagnostic center is approximately 182 km (50 miles), with a standard deviation of 235 km (146 miles). The average distance from an individual with ASD to the nearest diagnostic center, however, is only 32 km (20 miles), suggesting that individuals who live closer to diagnostic services are more likely to be diagnosed.

Conclusions: This study confirmed that individuals closer to diagnostic services are more likely to be diagnosed and proposes GapMap, a means to measure and enable the alleviation of increasingly overburdened diagnostic centers and resource-poor areas where parents are unable to diagnose their children as quickly and easily as needed. GapMap will collect information that will provide more accurate data for computing resource loads and availability, uncovering the impact of resource epidemiology on age and likelihood of diagnosis, and gathering localized autism prevalence rates.

Keywords: autism; autism spectrum disorder; crowdsourcing; epidemiology; prevalence; resources.

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Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
GapMap features an interactive Google heatmap, comparing resource availability to families with a diagnosed individual with autism. The red coloring on the heatmap shows high autism resource prevalence, while purple coloring shows moderate autism resource prevalence, blue coloring shows low autism resource prevalence, and no coloring shows that, based on our calculations, there are very limited autism-resources available.
Figure 2
Figure 2
Example of the mapping interface and home page for GapMap (gapmap.stanford.edu). Participants can electronically consent and participate from any mobile device by clicking on the yellow “Add yourself to the map!” button, as well as toggle between country-level and state-level prevalence of diagnosed autism cases.
Figure 3
Figure 3
GapMap’s technical architecture. GapMap is hosted on AWS S3, running on a React and Redux front-end framework. The backend framework is comprised of an AWS API Gateway and Lambda Function setup, with secure and scalable end points for retrieving prevalence and resource data, and for submitting participant data. Database 1: unencrypted and stores prevalence rates and resource data; Database 2: encrypted and stores submitted diagnostic information; Database 3: encrypted and stores user login information, location, and action-item status; and Database 4: encrypted and stores the users’ questionnaires.
Figure 4
Figure 4
Equation for the resource load for a given resource. Where: r = a given resource; RLr = resource load for a given resource (r); N = the number of individuals nearby; p = the proportion of individuals who are are in need of resources; s = the number of specialists; and o = the number of individuals a specialist can attend yearly.
Figure 5
Figure 5
Equation for the resource availability for a given location. Where: l = given location; RAl= resource availability for a given location (l); R = the pool of resource options, where r is one such resource; d(r,l)= the distance between the resource r and the given location (l); RLr = the resource load for the resource r; and z = the average distance an individual is willing to travel for a resource.
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