GapMap: permettant une épidémiologie complète des ressources autistiques

Aperçu: G.M.

Pour les personnes avec un diagnostic de troubles du spectre de l'autisme (TSA), trouver des ressources peut être un processus long et difficile. La difficulté d'obtenir des données épidémiologiques globales et détaillées sur l'autisme empêche les chercheurs d'étudier rapidement et efficacement les corrélations à grande échelle parmi les TSA, les facteurs environnementaux et les facteurs géographiques et culturels.

L'équipe a créé une application mobile, GapMap, pour recueillir des informations sur les localisations, le diagnostic et l'utilisation des ressources auprès des personnes atteintes d'autisme pour calculer des taux de prévalence précis et mieux comprendre l'épidémiologie des ressources autistiques.

Cette étude a confirmé que les personnes plus proches des services de diagnostic sont plus susceptibles d'être diagnostiquées et proposent GapMap, un moyen de mesurer et d'atténuer les problèmes des centres de diagnostic de plus en plus surchargés et des zones pauvres en ressources où les parents sont dans l'incapacité de diagnostiquer leurs enfants aussi rapidement et facilement que nécessaire. GapMap recueillera des informations qui fourniront des données plus précises pour calculer les ressources et la disponibilité des ressources, en décrivant l'impact de l'épidémiologie des ressources sur l'âge et la probabilité de diagnostic et en regroupant les taux localisés de prévalence de l'autisme. 

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

GapMap: Enabling Comprehensive Autism Resource Epidemiology

Albert N^1,2,3, Daniels J^1,3, Schwartz J^1,3, Du M^1,3, Wall DP^1,3.

Author information

1

Department of Pediatrics, Division of Systems Medicine, Stanford University, Stanford, CA, United States.

2

Department of Computer Science, Princeton University, Princeton, NJ, United States.

3

Department of Biomedical Data Science, Stanford University, Stanford, CA, United States.

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.

PMID: 28473303

DOI: 10.2196/publichealth.7150