JMIR Public Health Surveill. 2017 May 04;3(2):e27. doi: 10.2196/publichealth.7150.
GapMap: Enabling Comprehensive Autism Resource Epidemiology.
JMIR public health and surveillance
Nikhila Albert, Jena Daniels, Jessey Schwartz, Michael Du, Dennis P Wall
Affiliations
Affiliations
- Department of PediatricsDivision of Systems MedicineStanford UniversityStanford, CAUnited States.
- Department of Computer SciencePrinceton UniversityPrinceton, NJUnited States.
- Department of Biomedical Data ScienceStanford UniversityStanford, CAUnited States.
PMID: 28473303
PMCID: PMC5438459 DOI: 10.2196/publichealth.7150
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.
©Nikhila Albert, Jena Daniels, Jessey Schwartz, Michael Du, Dennis P Wall. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 04.05.2017.
Keywords: autism; autism spectrum disorder; crowdsourcing; epidemiology; prevalence; resources
References
- Autism Res. 2012 Jun;5(3):160-79 - PubMed
- J Dev Behav Pediatr. 2014 Nov-Dec;35(9):561-9 - PubMed
- J Autism Dev Disord. 2012 Feb;42(2):294-300 - PubMed
- J Am Acad Child Adolesc Psychiatry. 2008 Jun;47(6):719-20; author reply 720-1 - PubMed
- Autism. 2016 Feb;20(2):153-62 - PubMed
- Pediatrics. 2005 Dec;116(6):1480-6 - PubMed
- J Spec Pediatr Nurs. 2009 Jul;14(3):166-72 - PubMed
- Behav Res Methods. 2011 Sep;43(3):800-13 - PubMed
- MMWR Surveill Summ. 2016 Apr 01;65(3):1-23 - PubMed
- J Med Internet Res. 2012 Mar 07;14(2):e46 - PubMed
- PLoS Med. 2010 Dec 07;7(12):e1000376 - PubMed
- J Am Acad Child Adolesc Psychiatry. 2009 May;48(5):474-83 - PubMed
- Environ Health. 2014 Sep 05;13:73 - PubMed
- MMWR Surveill Summ. 2012 Mar 30;61(3):1-19 - PubMed
- Soc Sci Med. 2013 Oct;95:87-96 - PubMed
- J Child Psychol Psychiatry. 2010 Jun;51(6):643-51 - PubMed
- Clin Microbiol Infect. 2014 Jan;20(1):17-21 - PubMed
- Rev Sci Tech. 2007 Dec;26(3):537-49 - PubMed
- J Dev Behav Pediatr. 2008 Oct;29(5):345-50 - PubMed
- J Dev Behav Pediatr. 2006 Apr;27(2 Suppl):S79-87 - PubMed
- Pediatrics. 2016 Feb;137 Suppl 2:S149-57 - PubMed
- PLoS One. 2016 Dec 21;11(12 ):e0168224 - PubMed
- Paediatr Perinat Epidemiol. 2007 Mar;21(2):179-90 - PubMed
- Health Serv Res. 2011 Jun;46(3):768-86 - PubMed
- Child Adolesc Psychiatr Clin N Am. 2010 Oct;19(4):855-67 - PubMed
- Curr Opin Psychiatry. 2014 Mar;27(2):158-65 - PubMed
- Clin Psychol Rev. 2009 Apr;29(3):216-29 - PubMed
Publication Types