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Identification and Quantification of Gaps in Access to Autism Resources in the United States: An Infodemiological Study

BACKGROUND: 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 familie...

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Autores principales: Ning, Michael, Daniels, Jena, Schwartz, Jessey, Dunlap, Kaitlyn, Washington, Peter, Kalantarian, Haik, Du, Michael, Wall, Dennis P
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6652124/
https://www.ncbi.nlm.nih.gov/pubmed/31293243
http://dx.doi.org/10.2196/13094
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author Ning, Michael
Daniels, Jena
Schwartz, Jessey
Dunlap, Kaitlyn
Washington, Peter
Kalantarian, Haik
Du, Michael
Wall, Dennis P
author_facet Ning, Michael
Daniels, Jena
Schwartz, Jessey
Dunlap, Kaitlyn
Washington, Peter
Kalantarian, Haik
Du, Michael
Wall, Dennis P
author_sort Ning, Michael
collection PubMed
description BACKGROUND: 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. OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
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spelling pubmed-66521242019-07-30 Identification and Quantification of Gaps in Access to Autism Resources in the United States: An Infodemiological Study Ning, Michael Daniels, Jena Schwartz, Jessey Dunlap, Kaitlyn Washington, Peter Kalantarian, Haik Du, Michael Wall, Dennis P J Med Internet Res Original Paper BACKGROUND: 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. OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. JMIR Publications 2019-07-10 /pmc/articles/PMC6652124/ /pubmed/31293243 http://dx.doi.org/10.2196/13094 Text en ©Michael Ning, Jena Daniels, Jessey Schwartz, Kaitlyn Dunlap, Peter Washington, Haik Kalantarian, Michael Du, Dennis P Wall. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 10.07.2019. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Ning, Michael
Daniels, Jena
Schwartz, Jessey
Dunlap, Kaitlyn
Washington, Peter
Kalantarian, Haik
Du, Michael
Wall, Dennis P
Identification and Quantification of Gaps in Access to Autism Resources in the United States: An Infodemiological Study
title Identification and Quantification of Gaps in Access to Autism Resources in the United States: An Infodemiological Study
title_full Identification and Quantification of Gaps in Access to Autism Resources in the United States: An Infodemiological Study
title_fullStr Identification and Quantification of Gaps in Access to Autism Resources in the United States: An Infodemiological Study
title_full_unstemmed Identification and Quantification of Gaps in Access to Autism Resources in the United States: An Infodemiological Study
title_short Identification and Quantification of Gaps in Access to Autism Resources in the United States: An Infodemiological Study
title_sort identification and quantification of gaps in access to autism resources in the united states: an infodemiological study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6652124/
https://www.ncbi.nlm.nih.gov/pubmed/31293243
http://dx.doi.org/10.2196/13094
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