Assessment of Digital and Community-Based Outreach Interventions to Encourage COVID-19 Vaccination Uptake in an Underserved Community | Health Disparities | JN Learning | AMA Ed Hub [Skip to Content]
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Assessment of Digital and Community-Based Outreach Interventions to Encourage COVID-19 Vaccination Uptake in an Underserved Community

To identify the key insights or developments described in this article
1 Credit CME

Non-Hispanic Black (hereafter, Black) and Hispanic patients have higher risk for COVID-19 infection and hospitalization,1,2 but have lower rates of COVID-19 vaccination3 because of factors such as limited access to care, lack of outreach, technology and language barriers, and mistrust of health systems.4 To address this, we conducted 2 concurrent interventions at the NewYork-Presbyterian Hospital’s vaccination site at the Armory, located in a racially and ethnically diverse neighborhood in Northern Manhattan: (1) a digital redesign to restrict online self-scheduling for vaccination to local zip codes with underserved racial and ethnic minority patient populations4 and (2) direct outreach to educate and schedule patients through community-based organizations (CBO).4,5 Here we describe changes in race and ethnicity makeup of COVID-19 vaccine recipients before and after these interventions.

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CME Disclosure Statement: Unless noted, all individuals in control of content reported no relevant financial relationships. If applicable, all relevant financial relationships have been mitigated.

Article Information

Accepted for Publication: April 29, 2022.

Published: June 22, 2022. doi:10.1001/jamanetworkopen.2022.17875

Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2022 Diaz D et al. JAMA Network Open.

Corresponding Author: Daniela Diaz, MD, NewYork-Presbyterian Hospital, 610 West 158th St, New York, NY 10032 (dd2446@cumc.columbia.edu).

Author Contributions: Dr Ye had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: All authors.

Acquisition, analysis, or interpretation of data: Diaz, Sperling, Louh, Ye.

Drafting of the manuscript: Chacko, Ye.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Ye.

Administrative, technical, or material support: Chacko, Sperling, Fleck, Louh, Ye.

Supervision: Sperling, Ye.

Other: Fleck.

Conflict of Interest Disclosures: None reported.

References
1.
Magesh  S , John  D , Li  WT ,  et al.  Disparities in COVID-19 outcomes by race, ethnicity, and socioeconomic status: a systematic-review and meta-analysis.   JAMA Netw Open. 2021;4(11):e2134147-e2134147. doi:10.1001/jamanetworkopen.2021.34147PubMedGoogle ScholarCrossref
2.
Ogedegbe  G , Ravenell  J , Adhikari  S ,  et al.  Assessment of racial/ethnic disparities in hospitalization and mortality in patients with COVID-19 in New York City.   JAMA Netw Open. 2020;3(12):e2026881-e2026881. doi:10.1001/jamanetworkopen.2020.26881PubMedGoogle ScholarCrossref
3.
Reitsma  MB , Goldhaber-Fiebert  JD , Salomon  JA .  Quantifying and benchmarking disparities in COVID-19 vaccination rates by race and ethnicity.   JAMA Netw Open. 2021;4(10):e2130343-e2130343. doi:10.1001/jamanetworkopen.2021.30343PubMedGoogle ScholarCrossref
4.
Jean-Jacques  M , Bauchner  H .  Vaccine distribution-equity left behind?   JAMA. 2021;325(9):829-830. doi:10.1001/jama.2021.1205PubMedGoogle ScholarCrossref
5.
Iyasere  J , Garcia  A , Prabhu  D , Procaccino  A , Spaziani  KJ .  Trustworthy and trusted: strategies to improve confidence in Covid-19 vaccines.   NEJM Catal Innov Care Deliv. Published online 2021. doi:10.1056/CAT.21.0046Google Scholar
6.
Press  VG , Huisingh-Scheetz  M , Arora  VM .  Inequities in technology contribute to disparities in COVID-19 vaccine distribution.   JAMA Health Forum. 2021;2(3):e210264-e210264. doi:10.1001/jamahealthforum.2021.0264Google ScholarCrossref
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