Evidence map›Paper›PMID 39368124›Full record

ArticleVaccine2024

Using regression tree analysis to examine demographic and geographic characteristics of COVID-19 vaccination trends over time, United States, May 2021-April 2022, National Immunization Survey Adult COVID Module.

Morgan Earp, Lu Meng, Carla L Black, Rosalind J Carter, Peng-Jun Lu, James A Singleton, Terence Chorba

Abstract read
In one paragraph

Article in Vaccine, 2024. 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
–field-weighted citation impact
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

7 authors.

Morgan EarpU.S. Centers for Disease Control and Prevention, National Center for Health Statistics, United States.
Lu MengU.S. Centers for Disease Control and Prevention, National Center for Emerging and Zoonotic Infectious Diseases, United States.
Carla L BlackU.S. Centers for Disease Control and Prevention, National Center for Immunization and Respiratory Diseases, United States.
Rosalind J CarterU.S. Centers for Disease Control and Prevention, National Center for Immunization and Respiratory Diseases, United States.
Peng-Jun LuU.S. Centers for Disease Control and Prevention, National Center for Immunization and Respiratory Diseases, United States.
James A SingletonU.S. Centers for Disease Control and Prevention, National Center for Immunization and Respiratory Diseases, United States.
Terence ChorbaU.S. Centers for Disease Control and Prevention, National Center for HIV, Viral Hepatitis, STD, and TB Prevention, United States. Electronic address: tlc2@cdc.gov.

Funding

Intramural CDC HHS CC999999
6 · The paper itself

Abstract

Using data from the nationally representative National Immunization Survey (NIS), we applied conditional linear regression tree methodology to examine relationships between demographic and geographic factors and propensity of receiving various doses of COVID-19 vaccine over time; these analyses identified temporal changes in these relationships that heretofore had not been identified using conventional logistical regression methodologies. Three regression tree models were built using an R package, Recursive Partitioning for Modeling Survey (rpms), to examine propensities over time of receiving a (1) first dose of a two-dose COVID-19 mRNA primary vaccination series or single dose of the Janssen vaccine (vaccine initiation), (2) primary series completion, and (3) monovalent booster dose, using a conditional linear effect model. Persons ≥50 years were more likely to complete a primary series and receive a first booster dose; persons reporting having received non-COVID-19 vaccines recently were more likely to initiate vaccination, complete the primary series, and get a first booster dose; persons reporting having work or school requirements were more likely to complete the primary series. Persons not reporting having received non-COVID-19 vaccines in 2 years but reporting having work or school vaccination requirements were more likely to initiate vaccination than those without work/school requirements. Among persons not reporting having received non-COVID-19 vaccines in 2 years and not reporting having work or school vaccination requirements, those aged ≥50 years were more likely to initiate vaccination than were younger adults. Propensity of receiving various doses was correlated with age, having recently received non-COVID 19 vaccines, and having vaccination requirements at work or school. Regression tree methodology enabled modeling of different COVID-19 vaccination dose propensities as a linear effect of time, revealed changes in relationships over time between demographic factors and propensity of receipt of different doses, and identified populations that may benefit from vaccination outreach efforts.

Indexed as

COVID-19COVID-19 VaccinesVaccinationAdolescentAdultAgedFemaleHumansImmunization, SecondaryMaleMiddle AgedRegression AnalysisSARS-CoV-2United StatesYoung AdultCOVID-19 VaccinesCOVID-19 / prevention & controlCOVID-19 vaccinationCOVID-19 vaccinesEthnicityMachine learningRegression treeSARS-CoV-2

Identifiers

PMID39368124
PMCPMC12024164

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.