Neighborhood-level COVID vaccination and booster disparities : A population-level analysis across California
© 2023 The Authors..
Objectives: To describe vaccine and booster uptake by neighborhood-level factors in California.
Methods: We examined trends in COVID-19 vaccination up to September 21, 2021, and boosters up to March 29, 2022 using data from the California Department of Public Health. Quasi-Poisson regression was used to model the association between neighborhood-level factors and fully vaccinated and boosted among ZIP codes. Sub-analyses on booster rates were compared among the 10 census regions.
Results: In a minimally adjusted model, a higher proportion of Black residents was associated with lower vaccination (HR = 0.97; 95%CI: 0.96-0.98). However, in a fully adjusted model, proportion of Black, Hispanic/Latinx, and Asian residents were associated with higher vaccination rates (HR = 1.02; 95%CI: 1.01-1.03 for all). The strongest predictor of low vaccine coverage was disability (HR = 0.89; 95%CI: 0.86-0.91). Similar trends persisted for booster doses. Factors associated with booster coverage varied by region.
Conclusions: Examining neighborhood-level factors associated with COVID-19 vaccination and booster rates uncovered significant variation within the large and geographically and demographically diverse state of California. Equity-based approaches to vaccination must ensure a robust consideration of multiple social determinants of health.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:22 |
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| Enthalten in: |
SSM - population health - 22(2023) vom: 28. Juni, Seite 101366 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Oh, Debora L [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Revised 07.03.2023 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1016/j.ssmph.2023.101366 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM353791423 |
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| 100 | 1 | |a Oh, Debora L |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Neighborhood-level COVID vaccination and booster disparities |b A population-level analysis across California |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
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| 500 | |a Date Revised 07.03.2023 | ||
| 500 | |a published: Electronic-eCollection | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a © 2023 The Authors. | ||
| 520 | |a Objectives: To describe vaccine and booster uptake by neighborhood-level factors in California | ||
| 520 | |a Methods: We examined trends in COVID-19 vaccination up to September 21, 2021, and boosters up to March 29, 2022 using data from the California Department of Public Health. Quasi-Poisson regression was used to model the association between neighborhood-level factors and fully vaccinated and boosted among ZIP codes. Sub-analyses on booster rates were compared among the 10 census regions | ||
| 520 | |a Results: In a minimally adjusted model, a higher proportion of Black residents was associated with lower vaccination (HR = 0.97; 95%CI: 0.96-0.98). However, in a fully adjusted model, proportion of Black, Hispanic/Latinx, and Asian residents were associated with higher vaccination rates (HR = 1.02; 95%CI: 1.01-1.03 for all). The strongest predictor of low vaccine coverage was disability (HR = 0.89; 95%CI: 0.86-0.91). Similar trends persisted for booster doses. Factors associated with booster coverage varied by region | ||
| 520 | |a Conclusions: Examining neighborhood-level factors associated with COVID-19 vaccination and booster rates uncovered significant variation within the large and geographically and demographically diverse state of California. Equity-based approaches to vaccination must ensure a robust consideration of multiple social determinants of health | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a ACS, American Community Survey | |
| 650 | 4 | |a CDPH, California Department of Public Health | |
| 650 | 4 | |a COVID-19 | |
| 650 | 4 | |a COVID-19, Coronavirus Disease 2019 | |
| 650 | 4 | |a Health disparities | |
| 650 | 4 | |a LEP, Limited English Proficiency | |
| 650 | 4 | |a Neighborhood | |
| 650 | 4 | |a Population health | |
| 650 | 4 | |a SDOH, Social Determinants of Health | |
| 650 | 4 | |a Social determinants of health | |
| 650 | 4 | |a UCSF, University of California, San Francisco | |
| 650 | 4 | |a VEM, Vaccine Equity Metric | |
| 650 | 4 | |a Vaccination | |
| 650 | 4 | |a ZCTA, Zip Code Tabulation Area | |
| 700 | 1 | |a Kemper, Kathryn E |e verfasserin |4 aut | |
| 700 | 1 | |a Meltzer, Dan |e verfasserin |4 aut | |
| 700 | 1 | |a Canchola, Alison J |e verfasserin |4 aut | |
| 700 | 1 | |a Bibbins-Domingo, Kirsten |e verfasserin |4 aut | |
| 700 | 1 | |a Lyles, Courtney R |e verfasserin |4 aut | |
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