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Mapping the ChristianaCare response to COVID-19:: Clinical insights from the Value Institute’s Geospatial Analytics Core
INTRODUCTION: COVID-19 exemplifies the spatial nature of infectious disease in both its mechanism of transmission and the community-level conditions that facilitate its spread. With a long history of use for infectious disease applications, maps and geographic information systems (GIS) have been wid...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Delaware Academy of Medicine / Delaware Public Health Association
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8389823/ https://www.ncbi.nlm.nih.gov/pubmed/34467114 http://dx.doi.org/10.32481/djph.2020.07.018 |
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author | Brooks, Madeline Brown, Chenesia Liu, Wei Siegel, Scott D. |
author_facet | Brooks, Madeline Brown, Chenesia Liu, Wei Siegel, Scott D. |
author_sort | Brooks, Madeline |
collection | PubMed |
description | INTRODUCTION: COVID-19 exemplifies the spatial nature of infectious disease in both its mechanism of transmission and the community-level conditions that facilitate its spread. With a long history of use for infectious disease applications, maps and geographic information systems (GIS) have been widely used in recent months for surveillance and risk prediction mapping. The Value Institute’s Geospatial Analytics Core applied spatial methodologies to inform ChristianaCare’s pandemic response around telehealth, testing disparities, and test site prioritization. METHODS: Descriptive data related to disparities in telehealth utilization were mapped to identify areas in which intervention is needed to increase telehealth access. Cluster detection methodology was used to identify “hot” and “cold” spots for COVID-19 testing by place and race across New Castle County, DE. A composite risk score was created to prioritize communities for testing sites. All analyses took place in Delaware from March-June 2020, with particular emphasis on New Castle County. RESULTS: Parts of northeastern New Castle County and western Sussex County were highlighted for intervention to increase broadband internet access for telehealth utilization. “Cold” spots for COVID-19 testing were found in New Castle County, indicating neighborhoods in which testing levels were significantly lower than expected. Data for testing levels, disease positivity, and socioeconomic risk factors were used to identify communities in northeastern New Castle County that warranted new test sites to mitigate disease spread. PUBLIC HEALTH IMPLICATIONS: Geospatial methodologies can be used to combine electronic health record data and population-level spatial data for pandemic response efforts. This allows health systems to confidently identify areas of need while mitigating disparities in resource allocation. |
format | Online Article Text |
id | pubmed-8389823 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Delaware Academy of Medicine / Delaware Public Health Association |
record_format | MEDLINE/PubMed |
spelling | pubmed-83898232021-08-30 Mapping the ChristianaCare response to COVID-19:: Clinical insights from the Value Institute’s Geospatial Analytics Core Brooks, Madeline Brown, Chenesia Liu, Wei Siegel, Scott D. Dela J Public Health Article INTRODUCTION: COVID-19 exemplifies the spatial nature of infectious disease in both its mechanism of transmission and the community-level conditions that facilitate its spread. With a long history of use for infectious disease applications, maps and geographic information systems (GIS) have been widely used in recent months for surveillance and risk prediction mapping. The Value Institute’s Geospatial Analytics Core applied spatial methodologies to inform ChristianaCare’s pandemic response around telehealth, testing disparities, and test site prioritization. METHODS: Descriptive data related to disparities in telehealth utilization were mapped to identify areas in which intervention is needed to increase telehealth access. Cluster detection methodology was used to identify “hot” and “cold” spots for COVID-19 testing by place and race across New Castle County, DE. A composite risk score was created to prioritize communities for testing sites. All analyses took place in Delaware from March-June 2020, with particular emphasis on New Castle County. RESULTS: Parts of northeastern New Castle County and western Sussex County were highlighted for intervention to increase broadband internet access for telehealth utilization. “Cold” spots for COVID-19 testing were found in New Castle County, indicating neighborhoods in which testing levels were significantly lower than expected. Data for testing levels, disease positivity, and socioeconomic risk factors were used to identify communities in northeastern New Castle County that warranted new test sites to mitigate disease spread. PUBLIC HEALTH IMPLICATIONS: Geospatial methodologies can be used to combine electronic health record data and population-level spatial data for pandemic response efforts. This allows health systems to confidently identify areas of need while mitigating disparities in resource allocation. Delaware Academy of Medicine / Delaware Public Health Association 2020-07-01 /pmc/articles/PMC8389823/ /pubmed/34467114 http://dx.doi.org/10.32481/djph.2020.07.018 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/The journal and its content is copyrighted by the Delaware Academy of Medicine / Delaware Public Health Association (Academy/DPHA). This DJPH site, its contents, and its metadata are licensed under Creative Commons License - CC BY-NC-ND. (Please click to read (https://creativecommons.org/licenses/by-nc-nd/4.0/) common-language details on this license type, or copy and paste the following into your web browser: https://creativecommons.org/licenses/by-nc-nd/4.0/). Images are NOT covered under the Creative Commons license and are the property of the original photographer or company who supplied the image. Opinions expressed by authors of articles summarized, quoted, or published in full within the DJPH represent only the opinions of those authors and do not necessarily reflect the official policy of the Academy/DPHA or the institution with which the authors are affiliated. |
spellingShingle | Article Brooks, Madeline Brown, Chenesia Liu, Wei Siegel, Scott D. Mapping the ChristianaCare response to COVID-19:: Clinical insights from the Value Institute’s Geospatial Analytics Core |
title | Mapping the ChristianaCare response to COVID-19:: Clinical insights from the Value Institute’s Geospatial Analytics Core |
title_full | Mapping the ChristianaCare response to COVID-19:: Clinical insights from the Value Institute’s Geospatial Analytics Core |
title_fullStr | Mapping the ChristianaCare response to COVID-19:: Clinical insights from the Value Institute’s Geospatial Analytics Core |
title_full_unstemmed | Mapping the ChristianaCare response to COVID-19:: Clinical insights from the Value Institute’s Geospatial Analytics Core |
title_short | Mapping the ChristianaCare response to COVID-19:: Clinical insights from the Value Institute’s Geospatial Analytics Core |
title_sort | mapping the christianacare response to covid-19:: clinical insights from the value institute’s geospatial analytics core |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8389823/ https://www.ncbi.nlm.nih.gov/pubmed/34467114 http://dx.doi.org/10.32481/djph.2020.07.018 |
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