Evidence map›Paper›PMID 41499770›Full record

ArticleJMIR public health and surveillance2026

A Social Media Campaign to Promote COVID-19 Vaccination: Cost-Effectiveness Analysis.

Michael William Long, Jeffrey B Bingenheimer, Khadidiatou Ndiaye, Dante Donati, Nandan Rao, Selinam Akaba, Sohail Agha, William Douglas Evans

Abstract read
In one paragraph

Article in JMIR public health and surveillance, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Michael William LongDepartment of Prevention and Community Health, Milken Institute School of Public Health, The George Washington University, Washington, DC, United States.ORCID 0000-0002-3953-3424
Jeffrey B BingenheimerDepartment of Prevention and Community Health, Milken Institute School of Public Health, The George Washington University, Washington, DC, United States.ORCID 0000-0002-1427-0402
Khadidiatou NdiayeDepartment of Prevention and Community Health, Milken Institute School of Public Health, The George Washington University, Washington, DC, United States.ORCID 0000-0001-7577-3559
Dante DonatiSchool of Business, Columbia University, New York, NY, United States.ORCID 0000-0001-9661-7299
Nandan RaoVirtual Lab LLC, Corvallis, OR, United States.ORCID 0009-0001-9512-1862
Selinam AkabaDepartment of Prevention and Community Health, Milken Institute School of Public Health, The George Washington University, Washington, DC, United States.ORCID 0000-0003-3327-8618
Sohail AghaBehavioral Insights Lab, Seattle, WA, United States.ORCID 0000-0001-7049-9933
William Douglas EvansDepartment of Prevention and Community Health, Milken Institute School of Public Health, The George Washington University, Washington, DC, United States.ORCID 0000-0002-7559-1592

Funding

Gates Foundation INV-033413
6 · The paper itself

Abstract

backgroundVaccine hesitancy has increased in recent decades internationally, which sets up a critical barrier to the rapid deployment of novel vaccines against infection with SARS-CoV-2.

objectiveThis study used a quasi-experimental design to evaluate the cost-effectiveness of a social media intervention to reduce COVID-19 vaccine hesitancy implemented in Nigeria in 2022.

methodsThe intervention targeted health care providers and adults from the general population who were users of a specific social media platform. We used published estimates from a quasi-experimental evaluation of the campaign's effectiveness compared to the status quo across 6 intervention states and 31 comparison states over a 10-month period. We estimated the cost-effectiveness of the campaign in terms of cost (2022 US dollars) per person vaccinated using a decision tree analysis and probabilistic sensitivity analysis.

resultsOn the basis of the quasi-experimental trial, the campaign led to a crude 6.4-percentage point increase (219/692, 31.6% vs 117/463, 25.3%; P=.045) in vaccination rates and an adjusted 7.8-percentage point increase (95% CI 1.68-14.2; P=.02) controlling for age group, gender, educational level, religion, and occupation among the 20% (1933/9607) of the overall sample who were unvaccinated and in the persuadable middle. Scaled to the overall population, the campaign led to a 1.57-percentage point (95% CI 0.337-2.87; P=.02) increase in the proportion of those vaccinated against COVID-19 among those reached by the social media campaign. The social media campaign resulted in 58.3 million impressions and 1.87 million people reached for a total societal cost of US $1.15 million, or US $0.61 per person reached. This resulted in an incremental cost-effectiveness ratio of US $54.70 (95% uncertainty interval US $20.90-$163) per person vaccinated.

conclusionsA social media-based campaign to address COVID-19 vaccine hesitancy in 6 states in Nigeria resulted in an increase in vaccination rates. The cost-effectiveness of the campaign compared to no campaign is comparable to that of other campaigns promoting COVID-19 vaccine uptake. The cost per person vaccinated due to the social media campaign was 1% to 8% of the estimated cost per life year saved by vaccination against COVID-19 in low- and middle-income countries. Investing in social media campaigns would likely be a cost-effective approach to increase vaccine uptake and save lives.

Indexed as

COVID-19COVID-19 VaccinesHealth PromotionSocial MediaVaccinationVaccination HesitancyAdultCost-Benefit AnalysisCost-Effectiveness AnalysisFemaleHumansMaleMiddle AgedNigeriaCOVID-19 Vaccinescost-effectivenessCOVID-19health promotionsocial mediavaccination

Identifiers

PMID41499770
PMCPMC12824567

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.