COVID vaccine data quality, cleaning, and management.
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| Title: | COVID vaccine data quality, cleaning, and management. |
|---|---|
| Authors: | Ayres, Kelly1 (AUTHOR), Zhao, Longwen1 (AUTHOR), Megahed, Fadel M.2 (AUTHOR), Jones-Farmer, L. Allison2 (AUTHOR), Burroughs, Thomas E.3 (AUTHOR), Rigdon, Steven E.1 (AUTHOR) steve.rigdon@slu.edu |
| Source: | Quality Engineering. 2026, Vol. 38 Issue 1, p72-86. 15p. |
| Subjects: | Data quality, Data management, COVID-19, Database design, Vaccination status, Vaccine safety, Data scrubbing |
| Abstract: | When COVID-19 vaccines were introduced in late 2020 and widely distributed in early 2021, states were responsible for collecting and managing the data. In the best situation, states kept accurate records of each person who received the vaccine, including the age and the county of residence. States reported the cumulative number of those vaccinated in each county, although there were substantial numbers of vaccine recipients (within a given state) whose county of residence was unknown. Some states have very low numbers of vaccine recipients with unknown county, while other states reported upwards of 50% "unknown county of residence." At the extreme, Texas did not report the county of residence until October 2021, although they did report the state-wide total. There were a number of states that reported a nearly simultaneous jump in the cumulative number of those vaccinated whose county of residence was known and a drop in the number of "unknowns," likely caused by a retrospective analysis and reallocation of those whose county of residence was unknown. A further problem occurs when the cumulative number of vaccines drops. We describe how we created a database for county-level vaccine data that addresses these data quality issues. [ABSTRACT FROM AUTHOR] |
| Copyright of Quality Engineering is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 191012107 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: COVID vaccine data quality, cleaning, and management. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ayres%2C+Kelly%22">Ayres, Kelly</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhao%2C+Longwen%22">Zhao, Longwen</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Megahed%2C+Fadel+M%2E%22">Megahed, Fadel M.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jones-Farmer%2C+L%2E+Allison%22">Jones-Farmer, L. Allison</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Burroughs%2C+Thomas+E%2E%22">Burroughs, Thomas E.</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Rigdon%2C+Steven+E%2E%22">Rigdon, Steven E.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> steve.rigdon@slu.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Quality+Engineering%22">Quality Engineering</searchLink>. 2026, Vol. 38 Issue 1, p72-86. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Data+quality%22">Data quality</searchLink><br /><searchLink fieldCode="DE" term="%22Data+management%22">Data management</searchLink><br /><searchLink fieldCode="DE" term="%22COVID-19%22">COVID-19</searchLink><br /><searchLink fieldCode="DE" term="%22Database+design%22">Database design</searchLink><br /><searchLink fieldCode="DE" term="%22Vaccination+status%22">Vaccination status</searchLink><br /><searchLink fieldCode="DE" term="%22Vaccine+safety%22">Vaccine safety</searchLink><br /><searchLink fieldCode="DE" term="%22Data+scrubbing%22">Data scrubbing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: When COVID-19 vaccines were introduced in late 2020 and widely distributed in early 2021, states were responsible for collecting and managing the data. In the best situation, states kept accurate records of each person who received the vaccine, including the age and the county of residence. States reported the cumulative number of those vaccinated in each county, although there were substantial numbers of vaccine recipients (within a given state) whose county of residence was unknown. Some states have very low numbers of vaccine recipients with unknown county, while other states reported upwards of 50% "unknown county of residence." At the extreme, Texas did not report the county of residence until October 2021, although they did report the state-wide total. There were a number of states that reported a nearly simultaneous jump in the cumulative number of those vaccinated whose county of residence was known and a drop in the number of "unknowns," likely caused by a retrospective analysis and reallocation of those whose county of residence was unknown. A further problem occurs when the cumulative number of vaccines drops. We describe how we created a database for county-level vaccine data that addresses these data quality issues. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Quality Engineering is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=191012107 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/08982112.2025.2567562 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 72 Subjects: – SubjectFull: Data quality Type: general – SubjectFull: Data management Type: general – SubjectFull: COVID-19 Type: general – SubjectFull: Database design Type: general – SubjectFull: Vaccination status Type: general – SubjectFull: Vaccine safety Type: general – SubjectFull: Data scrubbing Type: general Titles: – TitleFull: COVID vaccine data quality, cleaning, and management. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ayres, Kelly – PersonEntity: Name: NameFull: Zhao, Longwen – PersonEntity: Name: NameFull: Megahed, Fadel M. – PersonEntity: Name: NameFull: Jones-Farmer, L. Allison – PersonEntity: Name: NameFull: Burroughs, Thomas E. – PersonEntity: Name: NameFull: Rigdon, Steven E. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 08982112 Numbering: – Type: volume Value: 38 – Type: issue Value: 1 Titles: – TitleFull: Quality Engineering Type: main |
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