Digital Health Communication and Vaccine Confidence in Mexico Using Aggregated Randomized Brand Lift Studies: Secondary Analysis
Notice bibliographique
Résumé
BACKGROUND: Digital vaccination campaigns are increasingly used to address declining vaccine confidence, yet evidence from large-scale, real-world interventions in middle-income countries is limited. Meta's Brand Lift Studies (BLS), which use randomized test-control exposure, provide Bayesian esti-mates of attitudinal shifts resulting from digital content. Mexico, with over 88.6 million active internet users, provides a setting to evaluate the impact of targeted campaigns on vaccine atti-tudes. OBJECTIVE: This study evaluated the impact of five digital vaccination campaigns implemented by the Aso-ciación Mexicana de Vacunología (@Vacunologia) on Facebook and Instagram in Mexico be-tween 2021 and 2022 on key attitudinal constructs related to COVID-19 vaccine confidence. METHODS: This study used a retrospective ecological design, we analyzed aggregated BLS results for five campaigns targeting different audiences and vaccination themes. Measured outcomes included standard ad recall, perceived importance, perceived safety, perceived efficacy, and concerns about side effects. Statistical significance within the BLS framework was defined as an incre-mental lift of ≥2 percentage points with ≥90% posterior probability of replication-a threshold consistent with Meta's operational Bayesian approach. Exploratory comparisons across cam-paigns were conducted using one-way analysis of variance (ANOVA), unpaired t-tests, and Fish-er's Exact Tests. RESULTS: Campaigns reached 84.9 million accounts and generated 179.4 million impressions with a total investment of USD 215,600. All campaigns produced statistically significant improvements in at least one attitudinal outcome (Bayesian threshold ≥90%). Standard ad recall increased in four campaigns (ANOVA, P < .001), and concerns about side effects decreased in two campaigns (t-test, P = .049; P = .006). Perceived safety, importance, and efficacy improved in selected audi-ences, with stronger effects observed among younger users and women (ANOVA, P = .005). No direct behavioral outcomes (eg, vaccination uptake) were measured; therefore, the findings reflect attitudinal rather than behavioral change. However, these constructs are recognized as proximal predictors of vaccine decision-making and constitute health-related outcomes. CONCLUSIONS: Large-scale digital vaccination campaigns can meaningfully strengthen attitudinal determinants of vaccine confidence in a middle-income context. This social media advertising campaigns effec-tively increased standard ad recall and improved perceptions of vaccine importance and safety, particularly among younger audiences and women in urban areas. However, changes in efficacy perceptions and concerns about side effects were limited. The innovation and implications of this study lie in evaluating large-scale, real-world digital vaccine campaigns in Latin America using experimental BLS data. Findings highlight that audience segmentation yield stronger perceptual shifts, suggesting that tailored digital strategies can complement traditional public health com-munication. While BLS does not measure behavioral endpoints, the observed attitudinal im-provements represent foundational steps toward influencing vaccine-related behaviors. Future work should link digital attitudinal metrics with vaccination and epidemiological data to assess real-world health impact.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,032 | 0,056 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,007 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».