Evidence map›Paper›PMID 36186620›Full record

ArticleHealth equity2022

Racial/Ethnic Disparities in Getting COVID-19 Vaccine: Do Age, Gender, and Education Matter?

Wei Zhang, Yan Yan Wu, Bei Wu

Abstract read
In one paragraph

Article in Health equity, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

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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

3 authors.

Wei ZhangDepartment of Sociology, University of Hawai'i at Mānoa, Honolulu, Hawaii, USA.
Yan Yan WuThompson School of Social Work & Public Health, University of Hawai'i at Mānoa, Honolulu, Hawaii USA.
Bei WuRory Meyers College of Nursing, New York University, New York, New York, USA.ORCID https://orcid.org/0000-0002-6891-244X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: COVID-19 disproportionately affects racial/ethnic minorities and vaccine can help mitigate infection and transition, decrease rate of hospitalization, lower mortality rate, and control the pandemic. This study aims to examine disparities in COVID-19 vaccination rate by age among Whites, Hispanics, Blacks, and Asian Americans, and the modification effects by gender and education. Methods: We used seven waves of biweekly surveys from the Household Pulse Survey collected between July 21, 2021, and October 11, 2021. Results: Asians reported the highest, Blacks reported the lowest vaccination rate, and gender differences were minimal. Increasing age was associated with higher vaccination rate except for the oldest age group. The decline was from 84.4% (70-79 years) to 41.1% (80-88 years: 41.1%) among Hispanics and 92.8% to 69.6% among Asians. Educational effect was the most salient among younger adults with the largest gaps observed in Blacks. Among 18-29-year Black participants, the vaccination rates were 31.1% (confidence interval [95% CI]: 25.7-37.1) for high school or lower, 58.9% (95% CI: 54.2-63.5) for some college or associate degree, and 74.2% (95% CI: 69.4-78.5) for bachelor or higher degrees, leaving a 43.1% gap between the lowest and the highest education levels. The gaps in this age group were 33.7% among Whites, 32.1% among Hispanics, and 20.5% among Asian Americans. Conclusion: Our study advances the existing literature on COVID-19 vaccination by providing empirical evidence on the dynamic race/ethnic-age-education differences across racial/ethnic groups. The findings from our study provide scientific foundation for the development of more strategies to improve vaccination rate for the minority populations.

Indexed as

ageCOVID-19disparitieseducationrace/ethnicityvaccination rate

Identifiers

PMID36186620
PMCPMC9518807

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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.