Evidence map›Paper›PMID 36626835›Full record

ArticleJMIR public health and surveillance2023

Using Location Intelligence to Evaluate the COVID-19 Vaccination Campaign in the United States: Spatiotemporal Big Data Analysis.

Qingfeng Li, James Cheng Peng, Diwakar Mohan, Brennan Lake, Alex Ruiz Euler, Brian Weir, Lena Kan, Cui Yang, Alain Labrique

Abstract read
In one paragraph

Article in JMIR public health and surveillance, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Observational
  4. 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

9 authors.

Qingfeng LiDepartment of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.ORCID 0000-0002-6390-6921
James Cheng PengDepartment of Applied Mathematics and Statistics, Johns Hopkins Whiting School of Engineering, Baltimore, MD, United States.ORCID 0000-0002-8020-0509
Diwakar MohanDepartment of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.ORCID 0000-0002-7532-366X
Brennan LakeCuebiq - Intelligence in Action, New York, NY, United States.ORCID 0000-0001-6047-082X
Alex Ruiz EulerCuebiq - Intelligence in Action, New York, NY, United States.ORCID 0000-0003-1284-0982
Brian WeirDepartment of Health, Behavior and Society, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.ORCID 0000-0003-1634-218X
Lena KanDepartment of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.ORCID 0000-0003-1910-577X
Cui YangDepartment of Health, Behavior and Society, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.ORCID 0000-0003-0144-2575
Alain LabriqueDepartment of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.ORCID 0000-0003-2502-7819

Funding

The Hopkins Population CenterP2CHD042854 · NICHD · JOHNS HOPKINS UNIVERSITY · PI Alison Gemmill · 2021 to 2026
$3.0M
NICHD NIH HHS P2C HD042854
6 · The paper itself

Abstract

backgroundHighly effective COVID-19 vaccines are available and free of charge in the United States. With adequate coverage, their use may help return life back to normal and reduce COVID-19-related hospitalization and death. Many barriers to widespread inoculation have prevented herd immunity, including vaccine hesitancy, lack of vaccine knowledge, and misinformation. The Ad Council and COVID Collaborative have been conducting one of the largest nationwide targeted campaigns ("It's Up to You") to communicate vaccine information and encourage timely vaccination across the United States. More than 300 major brands, digital and print media companies, and community-based organizations support the campaigns to reach distinct audiences.

objectiveThe goal of this study was to use aggregated mobility data to assess the effectiveness of the campaign on COVID-19 vaccine uptake.

methodsCampaign exposure data were collected from the Cuebiq advertising impact measurement platform consisting of about 17 million opted-in and deidentified mobile devices across the country. A Bayesian spatiotemporal hierarchical model was developed to assess campaign effectiveness through estimating the association between county-level campaign exposure and vaccination rates reported by the Centers for Disease Control and Prevention. To minimize potential bias in exposure to the campaign, the model included several control variables (eg, age, race or ethnicity, income, and political affiliation). We also incorporated conditional autoregressive residual models to account for apparent spatiotemporal autocorrelation.

resultsThe data set covers a panel of 3104 counties from 48 states and the District of Columbia during a period of 22 weeks (March 29 to August 29, 2021). Officially launched in February 2021, the campaign reached about 3% of the anonymous devices on the Cuebiq platform by the end of March, which was the start of the study period. That exposure rate gradually declined to slightly above 1% in August 2021, effectively ending the study period. Results from the Bayesian hierarchical model indicate a statistically significant positive association between campaign exposure and vaccine uptake at the county level. A campaign that reaches everyone would boost the vaccination rate by 2.2% (95% uncertainty interval: 2.0%-2.4%) on a weekly basis, compared to the baseline case of no campaign.

conclusionsThe "It's Up to You" campaign is effective in promoting COVID-19 vaccine uptake, suggesting that a nationwide targeted mass media campaign with multisectoral collaborations could be an impactful health communication strategy to improve progress against this and future pandemics. Methodologically, the results also show that location intelligence and mobile phone-based monitoring platforms can be effective in measuring impact of large-scale digital campaigns in near real time.

Indexed as

COVID-19Bayes TheoremCOVID-19 VaccinesData AnalysisHumansImmunization ProgramsIntelligenceUnited StatesCOVID-19 Vaccinesbarriersbig dataCOVID-19COVID-19 vaccineeffectivenesshealth informationhesitancyuptakevaccinationvaccinevaccine campaign

Identifiers

PMID36626835
PMCPMC9937108

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.