Evidence map›Paper›PMID 40768844›Full record

ArticleVaccine2025

Differences in COVID-19 XBB.1.5 vaccination coverage: A decomposition analysis across racial and ethnic groups in a large U.S. healthcare system.

Stanley Xu, Lina S Sy, Richard Contreras, Daniel A Powers, Lei Qian, Vennis Hong, Bruno Lewin, Kimberly J Holmquist, Bing Han, Katia J Bruxvoort

Abstract read
In one paragraph

Article in Vaccine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Stanley XuDepartment of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA, United States; Department of Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, CA, United States. Electronic address: Stan.Xu@kp.org.
Lina S SyDepartment of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA, United States.
Richard ContrerasDepartment of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA, United States.
Daniel A PowersDepartment of Sociology, Population Research Center, The University of Texas at Austin, Austin, TX, United States.
Lei QianDepartment of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA, United States.
Vennis HongDepartment of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA, United States.
Bruno LewinDepartment of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA, United States; Department of Clinical Science, Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, CA, United States.
Kimberly J HolmquistDepartment of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA, United States.
Bing HanDepartment of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA, United States.
Katia J BruxvoortDepartment of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA, United States; School of Public Health, University of Alabama at Birmingham, Birmingham, AL, United States.

Funding

A risk-varying and perturbed self-controlled case series design for assessing the safety of COVID-19 vaccines in a large health care systemR01AI168209 · NIAID · KAISER FOUNDATION RESEARCH INSTITUTE · PI XU, STANLEY · 2022 to 2025
$2.7M
NIAID NIH HHS R01 AI168209
6 · The paper itself

Abstract

backgroundDifferences in COVID-19 vaccination coverage across racial and ethnic groups have been widely documented in the U.S. We conducted Blinder-Oaxaca decomposition analyses to examine the contribution of 13 risk factors to COVID-19 XBB.1.5 vaccination differences across racial and ethnic groups within a large U.S. healthcare system.

methodsThe retrospective cohort included adult members of Kaiser Permanente Southern California between September 12, 2023, and April 30, 2024. The outcome was COVID-19 XBB.1.5 vaccination. We used multivariable logistic regression to assess the association of demographic factors, socioeconomic status measured by neighborhood deprivation index (NDI), clinical factors, and healthcare utilization with vaccination. Decomposition analyses were performed to quantify the extent to which differences in these factors explained differences in vaccination rates between Hispanic, non-Hispanic Black, and non-Hispanic White individuals.

resultsAmong 2,621,148 members eligible for decomposition analyses, COVID-19 XBB.1.5 vaccination rates were 25.5%, 15.5% and 19.7% for non-Hispanic White, Hispanic, and non-Hispanic Black individuals, respectively. Decomposition analyses showed that 78.9% of the vaccination difference between Hispanic and non-Hispanic White individuals and 86.2% of the difference between non-Hispanic Black and non-Hispanic White individuals were explained by observed risk factors. With non-Hispanic White individuals serving as the reference group, differences in age distribution explained 22.3% of the difference for Hispanic individuals and 11.5% for non-Hispanic Black individuals, and NDI explained 12.0% and 21.2%, respectively; differences in prior season influenza vaccination and prior bivalent COVID-19 vaccination accounted for 10.5% and 29.3% of the difference for Hispanic individuals, and 30.8% and 19.3% for non-Hispanic Black individuals, respectively. When prior vaccination history was excluded, the share of the difference explained by age and NDI increased substantially.

conclusionDifferences in COVID-19 XBB.1.5 vaccination across race/ethnicity were largely explained by differences in age, socioeconomic status, and healthcare utilization. The findings underscore the need for targeted interventions, particularly in younger populations and highly deprived neighborhoods, as well as leveraging outpatient visits to improve vaccine uptake.

Indexed as

COVID-19COVID-19 VaccinesEthnicityRacial GroupsVaccination CoverageAdultAgedCaliforniaFemaleHumansMaleMiddle AgedRetrospective StudiesRisk FactorsSARS-CoV-2United StatesCOVID-19 VaccinesBlinder-Oaxaca decompositionCOVID-19 vaccinationCOVID-19 XBB.1.5Healthcare utilizationRace and ethnicitySocioeconomic status

Identifiers

PMID40768844
PMCPMC13268703

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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.