ArticleFrontiers in public health2026
Predictors of childhood vaccination and recent influenza vaccination in older Brazilian adults: an analysis using conventional regression and machine learning approaches.
Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Vaccination is an important public health intervention across the life course but its determinants may differ by life stage. We aimed to identify and compare predictors of childhood vaccination and recent influenza vaccination among older adults using conventional and machine learning approaches. Methods: We used data from the second wave (2019-2021) of the Brazilian Longitudinal Study of Aging (ELSI-Brazil), a nationally representative cohort of individuals aged 50+. Analyses included 9,211 participants for childhood vaccination and 9,863 for influenza vaccination. Predictors were selected from sociodemographic, environmental, lifestyle, and health domains. Multivariable logistic regression and machine learning algorithms were used, with performance evaluated via area under the receiver operating characteristic curve (AUC). Results: The best-performing machine learning model demonstrated acceptable performance for childhood vaccination (AUC = 0.72, 95% CI: 0.69-0.74) and recent influenza vaccination (AUC = 0.67, 95% CI: 0.64-0.70). For childhood vaccination, both analytical approaches identified age, sex, rural residence, parental education, school attendance at age 10, childhood access to books, and allergy as important predictors. Machine learning additionally identified age at school initiation, childhood economic status, self-rated childhood health, severe diarrhea, and history of childhood infectious diseases. Key predictors for recent influenza vaccination included age, marriage, educational level, employment, and hypertension/diabetes. Machine learning additionally highlighted the importance of vaccination history during childhood, life satisfaction, BMI, and mental health. Conclusion: Early-life socioeconomic and health conditions are important predictors of childhood immunization history, whereas current sociodemographic and health status are key predictors of recent vaccination. Machine learning identified supplementary predictors beyond traditional methods.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.