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Risk and Protective Factors in the COVID-19 Pandemic: A Rapid Evidence Map
Background: Given the worldwide spread of the 2019 Novel Coronavirus (COVID-19), there is an urgent need to identify risk and protective factors and expose areas of insufficient understanding. Emerging tools, such as the Rapid Evidence Map (rEM), are being developed to systematically characterize la...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7732416/ https://www.ncbi.nlm.nih.gov/pubmed/33330323 http://dx.doi.org/10.3389/fpubh.2020.582205 |
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author | Elmore, Rebecca Schmidt, Lena Lam, Juleen Howard, Brian E. Tandon, Arpit Norman, Christopher Phillips, Jason Shah, Mihir Patel, Shyam Albert, Tyler Taxman, Debra J. Shah, Ruchir R. |
author_facet | Elmore, Rebecca Schmidt, Lena Lam, Juleen Howard, Brian E. Tandon, Arpit Norman, Christopher Phillips, Jason Shah, Mihir Patel, Shyam Albert, Tyler Taxman, Debra J. Shah, Ruchir R. |
author_sort | Elmore, Rebecca |
collection | PubMed |
description | Background: Given the worldwide spread of the 2019 Novel Coronavirus (COVID-19), there is an urgent need to identify risk and protective factors and expose areas of insufficient understanding. Emerging tools, such as the Rapid Evidence Map (rEM), are being developed to systematically characterize large collections of scientific literature. We sought to generate an rEM of risk and protective factors to comprehensively inform areas that impact COVID-19 outcomes for different sub-populations in order to better protect the public. Methods: We developed a protocol that includes a study goal, study questions, a PECO statement, and a process for screening literature by combining semi-automated machine learning with the expertise of our review team. We applied this protocol to reports within the COVID-19 Open Research Dataset (CORD-19) that were published in early 2020. SWIFT-Active Screener was used to prioritize records according to pre-defined inclusion criteria. Relevant studies were categorized by risk and protective status; susceptibility category (Behavioral, Physiological, Demographic, and Environmental); and affected sub-populations. Using tagged studies, we created an rEM for COVID-19 susceptibility that reveals: (1) current lines of evidence; (2) knowledge gaps; and (3) areas that may benefit from systematic review. Results: We imported 4,330 titles and abstracts from CORD-19. After screening 3,521 of these to achieve 99% estimated recall, 217 relevant studies were identified. Most included studies concerned the impact of underlying comorbidities (Physiological); age and gender (Demographic); and social factors (Environmental) on COVID-19 outcomes. Among the relevant studies, older males with comorbidities were commonly reported to have the poorest outcomes. We noted a paucity of COVID-19 studies among children and susceptible sub-groups, including pregnant women, racial minorities, refugees/migrants, and healthcare workers, with few studies examining protective factors. Conclusion: Using rEM analysis, we synthesized the recent body of evidence related to COVID-19 risk and protective factors. The results provide a comprehensive tool for rapidly elucidating COVID-19 susceptibility patterns and identifying resource-rich/resource-poor areas of research that may benefit from future investigation as the pandemic evolves. |
format | Online Article Text |
id | pubmed-7732416 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-77324162020-12-15 Risk and Protective Factors in the COVID-19 Pandemic: A Rapid Evidence Map Elmore, Rebecca Schmidt, Lena Lam, Juleen Howard, Brian E. Tandon, Arpit Norman, Christopher Phillips, Jason Shah, Mihir Patel, Shyam Albert, Tyler Taxman, Debra J. Shah, Ruchir R. Front Public Health Public Health Background: Given the worldwide spread of the 2019 Novel Coronavirus (COVID-19), there is an urgent need to identify risk and protective factors and expose areas of insufficient understanding. Emerging tools, such as the Rapid Evidence Map (rEM), are being developed to systematically characterize large collections of scientific literature. We sought to generate an rEM of risk and protective factors to comprehensively inform areas that impact COVID-19 outcomes for different sub-populations in order to better protect the public. Methods: We developed a protocol that includes a study goal, study questions, a PECO statement, and a process for screening literature by combining semi-automated machine learning with the expertise of our review team. We applied this protocol to reports within the COVID-19 Open Research Dataset (CORD-19) that were published in early 2020. SWIFT-Active Screener was used to prioritize records according to pre-defined inclusion criteria. Relevant studies were categorized by risk and protective status; susceptibility category (Behavioral, Physiological, Demographic, and Environmental); and affected sub-populations. Using tagged studies, we created an rEM for COVID-19 susceptibility that reveals: (1) current lines of evidence; (2) knowledge gaps; and (3) areas that may benefit from systematic review. Results: We imported 4,330 titles and abstracts from CORD-19. After screening 3,521 of these to achieve 99% estimated recall, 217 relevant studies were identified. Most included studies concerned the impact of underlying comorbidities (Physiological); age and gender (Demographic); and social factors (Environmental) on COVID-19 outcomes. Among the relevant studies, older males with comorbidities were commonly reported to have the poorest outcomes. We noted a paucity of COVID-19 studies among children and susceptible sub-groups, including pregnant women, racial minorities, refugees/migrants, and healthcare workers, with few studies examining protective factors. Conclusion: Using rEM analysis, we synthesized the recent body of evidence related to COVID-19 risk and protective factors. The results provide a comprehensive tool for rapidly elucidating COVID-19 susceptibility patterns and identifying resource-rich/resource-poor areas of research that may benefit from future investigation as the pandemic evolves. Frontiers Media S.A. 2020-11-24 /pmc/articles/PMC7732416/ /pubmed/33330323 http://dx.doi.org/10.3389/fpubh.2020.582205 Text en Copyright © 2020 Elmore, Schmidt, Lam, Howard, Tandon, Norman, Phillips, Shah, Patel, Albert, Taxman and Shah. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Public Health Elmore, Rebecca Schmidt, Lena Lam, Juleen Howard, Brian E. Tandon, Arpit Norman, Christopher Phillips, Jason Shah, Mihir Patel, Shyam Albert, Tyler Taxman, Debra J. Shah, Ruchir R. Risk and Protective Factors in the COVID-19 Pandemic: A Rapid Evidence Map |
title | Risk and Protective Factors in the COVID-19 Pandemic: A Rapid Evidence Map |
title_full | Risk and Protective Factors in the COVID-19 Pandemic: A Rapid Evidence Map |
title_fullStr | Risk and Protective Factors in the COVID-19 Pandemic: A Rapid Evidence Map |
title_full_unstemmed | Risk and Protective Factors in the COVID-19 Pandemic: A Rapid Evidence Map |
title_short | Risk and Protective Factors in the COVID-19 Pandemic: A Rapid Evidence Map |
title_sort | risk and protective factors in the covid-19 pandemic: a rapid evidence map |
topic | Public Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7732416/ https://www.ncbi.nlm.nih.gov/pubmed/33330323 http://dx.doi.org/10.3389/fpubh.2020.582205 |
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