Evidence map›Paper›PMID 35335044›Full record

ArticleVaccines2022

Determinants of COVID-19 Vaccinations among a State-Wide Year-Long Surveillance Initiative in a Conservative Southern State.

Lídia Gual-Gonzalez, Maggie S J McCarter, Kyndall Dye-Braumuller, Stella Self, Connor H Ross, Chloe Rodriguez-Ramos, Virginie G Daguise, Melissa S Nolan

Open access · goldAbstract read
In one paragraph

Article in Vaccines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.6field-weighted citation impact, top 33% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 2 institutions in 1 country.

Lídia Gual-GonzalezDepartment of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC 29208, USA.ORCID 0000-0001-9459-3713
Maggie S J McCarterDepartment of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC 29208, USA.ORCID 0000-0002-5169-9363
Kyndall Dye-BraumullerDepartment of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC 29208, USA.
Stella SelfDepartment of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC 29208, USA.
Connor H RossDepartment of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC 29208, USA.
Chloe Rodriguez-RamosDepartment of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC 29208, USA.
Virginie G DaguiseDepartment of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC 29208, USA.
Melissa S NolanDepartment of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC 29208, USA.ORCID 0000-0001-8579-5372
University of South Carolina · USSouth Carolina Department of Health and Environmental Control · US

Funding

HHS NU50CK000542
6 · The paper itself

Abstract

By the end of 2021, the COVID-19 pandemic resulted in over 54 million cases and more than 800,000 deaths in the United States, and over 350 million cases and more than 5 million deaths worldwide. The uniqueness and gravity of this pandemic have been reflected in the public health guidelines poorly received by a growing subset of the United States population. These poorly received guidelines, including vaccine receipt, are a highly complex psychosocial issue, and have impacted the successful prevention of disease spread. Given the intricate nature of this important barrier, any single statistical analysis methodologically fails to address all convolutions. Therefore, this study utilized different analytical approaches to understand vaccine motivations and population-level trends. With 12,975 surveys from a state-wide year-long surveillance initiative, we performed three robust statistical analyses to evaluate COVID-19 vaccine hesitancy: principal component analysis, survival analysis and spatial time series analysis. The analytic goal was to utilize complementary mathematical approaches to identify overlapping themes of vaccine hesitancy and vaccine trust in a highly conservative US state. The results indicate that vaccine receipt is influenced by the source of information and the population's trust in the science and approval process behind the vaccines. This multifaceted statistical approach allowed for methodologically rigorous results that public health professionals and policy makers can directly use to improve vaccine interventions.

Indexed as

COVID-19GISprincipal component analysisSARS-CoV-2survival analysisvaccine hesitancy

Identifiers

PMID35335044
PMCPMC8949372
OpenAlexW4221108372

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.