Literature DB >> 35082975

COVID-19: A Vaccine Priority Index Mapping Tool for Rapidly Assessing Priority Populations in North Carolina.

Gregory D Kearney1, Katherine Jones1, Yoo Min Park2, Rob Howard2, Ray Hylock3, Bennett Wall4, Maria Clay5, Peter Schmidt6, John Silvernail7.   

Abstract

BACKGROUND: The initial limited supply of COVID-19 vaccine in the U.S. presented significant allocation, distribution, and delivery challenges. Information that can assist health officials, hospital administrators and other decision makers with readily identifying who and where to target vaccine resources and efforts can improve public health response.
OBJECTIVE: The objective of this project was to develop a publicly available geographical information system (GIS) web mapping tool that would assist North Carolina health officials readily identify high-risk, high priority population groups and facilities in the immunization decision making process.
METHODS: Publicly available data were used to identify 14 key health and socio-demographic variables and 5 differing themes (social and economic status; minority status and language; housing situation; at risk population; and health status). Vaccine priority population index (VPI) scores were created by calculating a percentile rank for each variable over each N.C. Census tract. All Census tracts (N = 2,195) values were ranked from lowest to highest (0.0 to 1.0) with a non-zero population and mapped using ArcGIS.
RESULTS: The VPI tool was made publicly available (https://enchealth.org/) during the pandemic to readily assist with identifying high risk population priority areas in N.C. for the planning, distribution, and delivery of COVID-19 vaccine. DISCUSSION: While health officials may have benefitted by using the VPI tool during the pandemic, a more formal evaluation process is needed to fully assess its usefulness, functionality, and limitations.
CONCLUSION: When considering COVID-19 immunization efforts, the VPI tool can serve as an added component in the decision-making process. This is an Open Access article. Authors own copyright of their articles appearing in the Journal of Public Health Informatics. Readers may copy articles without permission of the copyright owner(s), as long as the author and OJPHI are acknowledged in the copy and the copy is used for educational, not-for-profit purposes.

Entities:  

Keywords:  COVID-19; Informatics; public health; spatial; vaccine

Year:  2021        PMID: 35082975      PMCID: PMC8765798          DOI: 10.5210/ojphi.v13i3.11617

Source DB:  PubMed          Journal:  Online J Public Health Inform        ISSN: 1947-2579


  7 in total

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Authors:  Barry E Flanagan; Elaine J Hallisey; Erica Adams; Amy Lavery
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2.  Accounting for vulnerable populations in rural hazard mitigation plans: results of a survey of emergency managers.

Authors:  Jennifer A Horney; Mai Nguyen; John Cooper; Matthew Simon; Kristen Ricchetti-Masterson; Shannon Grabich; David Salvesen; Philip Berke
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Authors:  Pui Hing Chau; Michael K Gusmano; Joanna O Y Cheng; Sai Hei Cheung; Jean Woo
Journal:  J Urban Health       Date:  2014-12       Impact factor: 3.671

4.  Validating Resilience and Vulnerability Indices in the Context of Natural Disasters.

Authors:  Laura A Bakkensen; Cate Fox-Lent; Laura K Read; Igor Linkov
Journal:  Risk Anal       Date:  2016-08-30       Impact factor: 4.000

5.  Neighborhood socioeconomic disadvantage and 30-day rehospitalization: a retrospective cohort study.

Authors:  Amy J H Kind; Steve Jencks; Jane Brock; Menggang Yu; Christie Bartels; William Ehlenbach; Caprice Greenberg; Maureen Smith
Journal:  Ann Intern Med       Date:  2014-12-02       Impact factor: 25.391

6.  Area deprivation and widening inequalities in US mortality, 1969-1998.

Authors:  Gopal K Singh
Journal:  Am J Public Health       Date:  2003-07       Impact factor: 9.308

7.  Dose-Optimal Vaccine Allocation over Multiple Populations.

Authors:  Lotty E Duijzer; Willem L van Jaarsveld; Jacco Wallinga; Rommert Dekker
Journal:  Prod Oper Manag       Date:  2017-10-19       Impact factor: 4.965

  7 in total
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1.  An optimized machine learning model for identifying socio-economic, demographic and health-related variables associated with low vaccination levels that vary across ZIP codes in California.

Authors:  George Avirappattu; Alfred Pach Iii; Clarence E Locklear; Anthony Q Briggs
Journal:  Prev Med Rep       Date:  2022-06-10
  1 in total

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