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Record W4316686761 · doi:10.2196/44234

The Association Between Pediatric COVID-19 Vaccination and Socioeconomic Position: Nested Case-Control Study From the Pedianet Veneto Cohort

2023· article· en· W4316686761 on OpenAlexvenueno aff
Erich Batzella, Anna Cantarutti, Nicola Caranci, Carlo Giaquinto, Claudio Barbiellini Amidei, Cristina Canova

Bibliographic record

VenueJMIR Public Health and Surveillance · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineVaccinationSocioeconomic statusDemographyLogistic regressionOdds ratioCohortNested case-control studyCohort studyPoisson regressionPediatricsEnvironmental healthPopulationImmunologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The success of pediatric COVID-19 vaccination strongly depends on parents' willingness to vaccinate their children. To date, the role of socioeconomic position (SEP) in pediatric COVID-19 vaccination has not been thoroughly examined. OBJECTIVE: We evaluated the association between COVID-19 vaccination and SEP in a large pediatric cohort. METHODS: A case-control study design nested into a pediatric cohort of children born between 2007 and 2017, living in the Veneto Region and followed up to at least January 1, 2022, was adopted. Data on children were collected from the Pedianet database and linked with the regional COVID-19 registry. Each child vaccinated with at least one dose of any COVID-19 vaccine between July 1, 2021, and March 31, 2022, was matched by sex, year of birth, and family pediatrician to up to 5 unvaccinated children. Unvaccinated children with a positive outcome on the swab test within 180 days before the index date were excluded from the analyses. Children were geo-referenced to determine their area deprivation index (ADI)-a social and material deprivation measure calculated at the census block level and consisting of 5 socioeconomic items. The index was then categorized in quintiles based on the regional ADI level. The association between ADI quintiles and vaccination status was measured using conditioned logistic regression models to estimate odds ratios and the corresponding 95% CIs. Quantile-g-computation regression models were applied to develop a weighted combination of the individual items to estimate how much each component influenced the likelihood of vaccination. All analyses were stratified by age at vaccination (5-11 and 12-14 years). RESULTS: The study population consisted of 6475 vaccinated children, who were matched with 32,124 unvaccinated children. Increasing area deprivation was associated with a lower probability of being vaccinated, with approximately a linear dose-response relationship. Children in the highest deprivation quintile were 36% less likely to receive a COVID-19 vaccine than those with the lowest area deprivation (95% CI 0.59-0.70). The results were similar in the 2 age groups, with a slightly stronger association in 5-11-year-old children. When assessing the effects of the weighted combination of the individual items, a quintile increase was associated with a 17% decrease in the probability of being vaccinated (95% CI 0.80-0.86). The conditions that influenced the probability of vaccination the most were living on rent, being unemployed, and being born in single-parent families. CONCLUSIONS: This study has shown a significant reduction in the likelihood of receiving a COVID-19 vaccine among children living in areas characterized by a lower SEP. Findings were robust among multiple analyses and definitions of the deprivation index. These findings suggest that SEP plays an important role in vaccination coverage, emphasizing the need to promote targeted public health efforts to ensure global vaccine equity.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
models agreeAgreement compares identical category sets and study designs across arms.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.333
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2023
Admission routes1
Has abstractyes

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