# Leveraging Social Networks to Increase COVID-19 Testing Uptake: A Comparison of Credible Messenger and Chain Referral Recruitment Approaches

> **NIH NIH UG1** · NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC · 2021 · $793,293

## Abstract

Until the advent of treatment or a vaccine, our ability to contain COVID-19 must rely on widespread
identification of (asymptomatic) positive cases, their subsequent quarantine, and contact tracing of those
potentially exposed. Therefore testing efforts must be targeted to those highly vulnerable yet unserved
populations, including individuals who use opioids and other substances. These individuals may have poor
respiratory or pulmonary health due to substance use (e.g. opioids, methamphetamine), which may make them
more susceptible to the virus. Also, these individuals are also more likely to have been incarcerated, or reside
on the street, in shelters or in crowded accommodation, further placing them at risk for transmission. We
propose research to establish efficacy and sustainability of a community-social network outreach model that
partners infectious disease health providers with community based organizations to successfully implement
(reach, uptake, delivery and sustainment) COVID-19 point of service, rapid-testing among a highly vulnerable
and often underserved population, those who use opioids and other substances. Two distinct social network
recruitment strategies with demonstrated efficacy identifying hidden populations and increasing uptake of HIV
testing will be adapted and compared. Guided by the EPIS framework, social cognitive theory, and Andersen’s
model, this study comprises three phases. Phase 1: Adaptation of outreach recruitment strategies, we will
work with our project community advisory board (CAB) to adapt chain-referral and credible messenger strategies
for uptake of COVID-19 testing, to finalize recruitment and on-site testing protocols, and to train the CAB in the
new protocols and in continuous quality improvement strategies (Aim 1). Phase 2: Strategy Efficacy Trial and
Implementation Evaluation, we will compare the two strategies in a cross-over design at two community based
organizations (CBOs) with long standing history of serving hard-to-reach populations in their communities. The
comparison of strategies is not to identify the statistical superiority of one sampling strategy in providing
population estimates over the other, but instead to identify the ability of each recruitment strategy to reach the
target population and increase uptake of COVID-19 tests. We will examine the impact of each strategy on (i)
reach (recruitment of target population), (ii) COVID-19 testing/repeat testing, and (iii) service delivery (i.e.
quarantine, medical care and contact tracing) among those who test positive for COVID-19 (exploratory) (Aim
2). Phase 3: Sustainment, CBOs will implement the strategy proven efficacious based on outcomes, and we
will examine their sustainment of the program (Aim 2). Implementation evaluation will identify participant-, staff-,
and organizational-level factors that influence the feasibility, acceptability, and sustainability of each strategy in
these CBOs. (Aim 3). This investigation will provide much ...

## Key facts

- **NIH application ID:** 10258730
- **Project number:** 3UG1DA050071-02S1
- **Recipient organization:** NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC
- **Principal Investigator:** KATHERINE S ELKINGTON
- **Activity code:** UG1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $793,293
- **Award type:** 3
- **Project period:** 2020-12-01 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10258730

## Citation

> US National Institutes of Health, RePORTER application 10258730, Leveraging Social Networks to Increase COVID-19 Testing Uptake: A Comparison of Credible Messenger and Chain Referral Recruitment Approaches (3UG1DA050071-02S1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10258730. Licensed CC0.

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