Digital Health Communication and Vaccine Confidence in Mexico Using Aggregated Randomized Brand Lift Studies: Secondary Analysis
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".