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A Novel Survey Platform in the Age of COVID-19 to Increase Accuracy and Adoptability While Reducing Selection Bias

Given the ongoing COVID-19 pandemic, secure and distanced data collection platforms are critical for reaching vulnerable populations. Commonly used electronic data collection systems lack a myriad of critical features, including a modern technology stack, new data encryption and security standards,...

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Autores principales: Mendhe, Dinesh, Bergren, Stephanie, Dong, XinQi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7740837/
http://dx.doi.org/10.1093/geroni/igaa057.3415
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author Mendhe, Dinesh
Bergren, Stephanie
Dong, XinQi
author_facet Mendhe, Dinesh
Bergren, Stephanie
Dong, XinQi
author_sort Mendhe, Dinesh
collection PubMed
description Given the ongoing COVID-19 pandemic, secure and distanced data collection platforms are critical for reaching vulnerable populations. Commonly used electronic data collection systems lack a myriad of critical features, including a modern technology stack, new data encryption and security standards, study workflows, and reporting algorithms. Moreover, these systems do not have multilingual mapping functionalities of survey and consent forms. All of these components ultimately increase selection bias while simultaneously reducing the security and quality of the response data. In order to directly address the aforementioned issues, we have developed a multilingual and highly secure data management platform. Our application is built using stable, tested, and modular programming frameworks and design patterns targeted at accommodating intricately complex structures of polyglot mapping, large volume of data, encryption and granular user authorization. The statistical accuracy along with the multilingual mapping are the core highlights of this system. The multilingual function of this platform has the ability to eliminate selection biases while creating a well-balanced cross-section of society. Modern survey design workflows and validation checks ultimately prevent data loss and help reduce data collection errors. The platform design was initiated in April 1, 2020 and has been pilot tested for use in multilingual populations. The currently active application version of the system is capable of supporting in-person and telephone interviews, emailing survey links to every registered participant, building family tree architecture, and online consent management. This platform also has built-in report functionality. Additional features are being explored to improve study coordination and monitoring.
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spelling pubmed-77408372020-12-21 A Novel Survey Platform in the Age of COVID-19 to Increase Accuracy and Adoptability While Reducing Selection Bias Mendhe, Dinesh Bergren, Stephanie Dong, XinQi Innov Aging Abstracts Given the ongoing COVID-19 pandemic, secure and distanced data collection platforms are critical for reaching vulnerable populations. Commonly used electronic data collection systems lack a myriad of critical features, including a modern technology stack, new data encryption and security standards, study workflows, and reporting algorithms. Moreover, these systems do not have multilingual mapping functionalities of survey and consent forms. All of these components ultimately increase selection bias while simultaneously reducing the security and quality of the response data. In order to directly address the aforementioned issues, we have developed a multilingual and highly secure data management platform. Our application is built using stable, tested, and modular programming frameworks and design patterns targeted at accommodating intricately complex structures of polyglot mapping, large volume of data, encryption and granular user authorization. The statistical accuracy along with the multilingual mapping are the core highlights of this system. The multilingual function of this platform has the ability to eliminate selection biases while creating a well-balanced cross-section of society. Modern survey design workflows and validation checks ultimately prevent data loss and help reduce data collection errors. The platform design was initiated in April 1, 2020 and has been pilot tested for use in multilingual populations. The currently active application version of the system is capable of supporting in-person and telephone interviews, emailing survey links to every registered participant, building family tree architecture, and online consent management. This platform also has built-in report functionality. Additional features are being explored to improve study coordination and monitoring. Oxford University Press 2020-12-16 /pmc/articles/PMC7740837/ http://dx.doi.org/10.1093/geroni/igaa057.3415 Text en © The Author(s) 2020. Published by Oxford University Press on behalf of The Gerontological Society of America. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Abstracts
Mendhe, Dinesh
Bergren, Stephanie
Dong, XinQi
A Novel Survey Platform in the Age of COVID-19 to Increase Accuracy and Adoptability While Reducing Selection Bias
title A Novel Survey Platform in the Age of COVID-19 to Increase Accuracy and Adoptability While Reducing Selection Bias
title_full A Novel Survey Platform in the Age of COVID-19 to Increase Accuracy and Adoptability While Reducing Selection Bias
title_fullStr A Novel Survey Platform in the Age of COVID-19 to Increase Accuracy and Adoptability While Reducing Selection Bias
title_full_unstemmed A Novel Survey Platform in the Age of COVID-19 to Increase Accuracy and Adoptability While Reducing Selection Bias
title_short A Novel Survey Platform in the Age of COVID-19 to Increase Accuracy and Adoptability While Reducing Selection Bias
title_sort novel survey platform in the age of covid-19 to increase accuracy and adoptability while reducing selection bias
topic Abstracts
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7740837/
http://dx.doi.org/10.1093/geroni/igaa057.3415
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