Evidence map›Paper›PMID 36513535›Full record

ArticleVaccine2023

Trusted information sources in the early months of the COVID-19 pandemic predict vaccination uptake over one year later.

Carl Latkin, Lauren Dayton, Jacob Miller, Evan Eschliman, Jingyan Yang, Amelia Jamison, Xiangrong Kong

Abstract read
In one paragraph

Article in Vaccine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Observational
  8. 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

7 authors.

Carl LatkinDepartment of Health, Behavior and Society, Bloomberg School of Public Health, Johns Hopkins University, United States; Division of Infectious Diseases, Johns Hopkins University, School of Medicine, United States. Electronic address: clatkin@jhsph.edu.
Lauren DaytonDepartment of Health, Behavior and Society, Bloomberg School of Public Health, Johns Hopkins University, United States.
Jacob MillerDepartment of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, United States.
Evan EschlimanDepartment of Health, Behavior and Society, Bloomberg School of Public Health, Johns Hopkins University, United States.
Jingyan YangDepartment of Political Science, Columbia University, United States.
Amelia JamisonDepartment of Health, Behavior and Society, Bloomberg School of Public Health, Johns Hopkins University, United States.
Xiangrong KongWilmer Eye Institute, Johns Hopkins University, School of Medicine, United States.

Funding

A geospatial analysis of hotspots and targeted injection settings pilot intervention for HIV prevention among people who inject drugs in Baltimore, MarylandR01DA050470 · NIDA · JOHNS HOPKINS UNIVERSITY · PI LATKIN, CARL A · 2020 to 2025
$4.1M
The Effects of Drug Use, Depression, and Social Factors on HIV/HCV Care Continuum among PWID in Urban Areas: A Social-Network PerspectiveR01DA040488 · NIDA · JOHNS HOPKINS UNIVERSITY · PI LATKIN, CARL A, TOBIN, KARIN E · 2015 to 2020
$4.0M
NIDA NIH HHS R01 DA040488NIDA NIH HHS R01 DA050470
6 · The paper itself

Abstract

introductionCOVID-19 vaccine uptake has been a major barrier to stopping the pandemic in many countries with vaccine access. This longitudinal study examined the capability to predict vaccine uptake from data collected early in the pandemic before vaccines were available.

methods493 US respondents completed online surveys both at baseline (March 2020) and wave 6 (June 2021), while 390 respondents completed baseline and wave 7 (November 2021) surveys. The baseline survey assessed trust in sources of COVID-19 information, social norms, perceived risk of COVID-19, skepticism about the pandemic, prevention behaviors, and conspiracy beliefs. Multivariable logistic models examined factors associated with the receipt of at least one COVID-19 vaccine dose at the two follow-ups.

resultsIn the adjusted model of vaccination uptake at wave 6, older age (aOR = 1.02, 95 %CI = 1.00-1.04) and greater income (aOR = 1.69, 95 %CI = 1.04-2.73) was associated with positive vaccination status. High trust in state health departments and mainstream news outlets at baseline were positively associated with vaccination at wave 6, while high trust in the Whitehouse (aOR = 0.42, 95 %CI = 0.24-0.74) and belief that China purposely spread the virus (aOR = 0.66, 95 %CI = 0.46-0.96) at baseline reduced the odds of vaccination. In the adjusted model of vaccination uptake at wave 7, increased age was associated with positive vaccination status, and Black race (compared to white) was associated with negative vaccination status. High trust in the CDC and mainstream news outlets at baseline were both associated with being vaccinated at wave 7, while high trust in the Whitehouse (aOR = 0.24, 95 %CI = 0.11-0.51) and belief that the virus was spread purposefully by China (aOR = 0.60, 95 %CI = 0.39-0.93) were negatively associated with vaccination.

conclusionsThese findings indicated that vaccine uptake could be predicted over a year earlier. Trust in specific sources of COVID-19 information were strong predictors, suggesting that future pandemic preparedness plans should include forums for news media, public health officials, and diverse political leaders to meet and develop coherent plans to communicate to the public early in a pandemic so that antivaccine attitudes do not flourish and become reinforced.

Indexed as

COVID-19COVID-19 VaccinesHumansInformation SourcesLongitudinal StudiesPandemicsTrustVaccinationCOVID-19 VaccinesCovid-19Health BehaviorsInformation SourcesSARS-CoV-2TrustVaccine

Identifiers

PMID36513535
PMCPMC9722679

What OpenQuestion holds

Textmetadata
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.