Community Network-driven COVID-19 Testing and Vaccination of Vulnerable Populations in the Central US (C3)
Phase: NA
Conditions: Covid19
Sponsor: University of Chicago

 
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COMMUNITY NETWORK DRIVEN COVID-19 TESTING AND VACCINATION AMONG MOST 
VULNERABLE POPULATIONS IN THE CENTRAL UNITED STATES (C3) 
 
 
Lead Investigators:  
 
JOHN SCHNEIDER, MD, MPH 
ASSOCIATE PROFESSOR 
THE UNIVERSITY OF CHICAGO 
CHICAGO, ILLINOIS 
 
KAVITA BHAVAN, MD  
CHIEF INNOVATION OFFICER 
UNIVERSITY OF TEXAS, SOUTHWESTERN 
PARKLAND HEALTH & HOSPITAL SYSTEM  
DALLAS, TEXAS 
 
JEROME MONTGOMERY   
DIRECTOR  
PROJECT VIDA, 
CHICAGO, ILLINOIS 
 
REV. SAMUEL ASHLEY 
ORDAINED MINISTER 
FELLOWSHIP OF AFFIRMING MINISTRIES 
BATON ROUGE, LOUISIANA 
 
 
 
Protocol Version 2023.09.25 (Appendices not included); University of Chicago IRB Approved 10/19/2023 
 
NCT04743908 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
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List of Appendices 
Appendix A  
Multisite Conduct Details  
Appendix B 
Day 1 Survey 
Appendix C 
Misinformation Correction Messaging & Script Discussion Tool 
Appendix D 
Information Card 
Appendix E 
Day 21 Assessment 
Appendix F 
Covid-19 Screener 
Appendix G 
Recruitment Materials (Social Media, Flyer and Script) 
Appendix H 
C3_ROI  
Appendix  I 
How To Collect Anterior Nasal Specimen for COVID-19  
Appendix J 
What Your Test Results Mean 
Appendix K 
Key Things To Know About COVID-19 Vaccines 
 
 
 

 
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BACKGROUND 
 
Existing COVID-19 testing and prevention strategies are failing many communities in the United States.1 
COVID-19 testing uptake, social distancing, contact tracing, all face significant acceptability and implementation 
challenges among those most impacted in the US.2 COVID-19 testing has had technical and diagnostic 
challenges since its inception; however, these challenges are overshadowed by significant implementation 
challenges. First, messaging of testing importance is complicated because of stigma related to subsequent social 
isolation, employment loss and potential illness and death.3,4 Second, testing has been perceived by many 
community members to be futile. In C3 focus groups, participants have described that it is not safe to go out and 
seek testing without private transportation and further there is no reason to test as there is nothing that can be 
done (July 2020 C3 FGD, Chicago). Third, messages about the importance of testing are most often delivered by 
public health authorities. These messages (and the messenger) do not resonate with community who often are 
facing significant racism/stigma, lack resources to meet basic needs, and structural violence in the form of law 
and immigration enforcement.5 Finally, community members who are disenfranchised from health insurance are 
concerned about costs of testing or that their data will be used by government. Because of these factors, 
community faces significant real and perceived barriers to testing.2,5  
 
The distribution of COVID-19 vaccines faces similar challenges. Although COVID- 19 vaccine acceptance is 
slowly increasing across all demographic groups, vaccine confidence still lags behind in Black and Hispanic 
communities50 First, structural violence, historical trauma, and sustained inequality largely shape medical 
mistrust in those who are experiencing health disparities10. Within the United States’ context, the medical 
establishment has a long history of exploiting people of color - specifically black, Latinx, and Indigenous 
individuals - for the sake of purported medical advancement51 . The case of Henrietta Lacks, the Tuskegee 
Studies, and the experiments of James Marion Sims upon Lucy, Betsy, and Anracha are just a few examples of 
the historical medical trauma exerted upon Black and African American individuals living within the United 
States51A strong predictor of intention to get vaccinated is confidence in vaccine safety and effectiveness49 
Second, there are concerns around vaccine side effects and concerns about getting COVID-19 from the vaccine 
50 Finally, vaccine misinformation ranging from rumors to conspiracy theories is contributing to the mistrust 
around eth COVID-19 vaccines, making it challenging to build confidence around vaccines 52  
 
COVID-19 has disproportionately impacted disenfranchised communities.6 Disenfranchisement is a status that 
results in distrust of public institutions and therefore individuals do not participate in services, resources, and 
benefits.7,8 Disenfranchisement generates, maintains, and propagates vulnerability both directly and 
indirectly.7,9Poverty, limited opportunity for prevention or other health services and racism all contribute to the 
COVID-19 inequities that are similar in magnitude to other socially determined outcomes, such as kidney 
disease, violence and death due to AIDS.10,11Two key disenfranchised populations where significant COVID-19 
transmission occurs is among criminal justice involved (ie. arrest, history of jail/prison, probation/parole) and 
low-income Latinx community members.6,11,12,13,14 Both of these populations, and the overlap between them, 
have some of the highest rates of COVID-19 infection and death in the US.13,15 These stark COVID-19 inequities 
are driven by several factors that the communities share.15 First, both are often disenfranchised from 
employment, social services and health-care, all critical to supporting those impacted most by COVID-19. In 
addition, if employed, most are part of the essential low-wage workforce where transmission is high, and 
frequent outbreaks occur within multi-generational working households.12,16,17 There often can be limited agency 
and self-determination in such contexts (ie. inability to self-isolate) which can be disempowering and thus 
requires interventions that are self-affirming. Finally both CJI and Latinx communities often have considerable 
distrust in institutions including public health18 and lack access to accurate and contextually/linguistically 
appropriate COVID-19 information.19, Misinformation around testing and vaccination impedes efforts to 
effectively engage these communities.10,20,50Therefore, we utilize theory-driven self-affirming message framing 

 
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as well as misinformation correction in order to fully engage communities around COVID-19 testing and 
vaccination.11,21-23 
 
Criminal justice involved populations include diverse non-incarcerated people with history of arrest/jail/ prison, 
community supervision (probation/ parole), mandated drug court attendance and are susceptible to distrust in 
public health institutions. One in five American adults has had justice exposure including arrest, incarceration, 
being on supervision, and/or being subjected to a number of liberty restrictions including the right to vote, right 
to live with family members, and employment limitations.24 CJI populations’ differential experiences with justice 
systems, and other social institutions like child welfare and Medicaid, impact willingness to engage in testing. In 
NIDA’s first JCOIN data product developed by the C3 team we used NORC’s AmeriSpeak Panel,25 a 
probability-based panel of about 35K households designed to be representative of the US to examine COVID-19 
among CJI people. We found that respondents with a history of CJI are 18x more likely to have a household 
member pass away from COVID (9% to 0.5%) than those without such history. Those respondents with a history 
of CJI are also less likely to be able to follow important preventative health measures recommended for COVID-
19 (e.g., wearing a mask, social distancing). Complicating these relationships are that CJI individuals are more 
likely to have higher rates of legal cynicism and perception of procedural misjustice which affect trust in social 
institutions. Legal cynicism often translates into anti-authority attitudes that affect willingness to engage in 
health or self-care activities, and given the higher rates of substance use, mental health, and infectious diseases 
among CJI populations, 26,27 COVID-19 testing and vaccination promotion messaging must be tailored to address 
the justice experience, distrust of justice agencies, and self-efficacy. 
 
Low-income Latinx people (250% at or below FPL) have some of the highest rates of COVID-19 in the US and 
require testing interventions that address unique cultural factors and engage familial support systems. Latinx and 
Hispanic people (hereafter Latinx) include a diverse set of communities and several intersectional factors: 
country of origin, duration of time in US, and documented status. Nearly 25% of Latinx people are employed in 
essential service industries 28 (e.g., food service, factory processing) and are exposed to a greater likelihood of 
contracting COVID while working.29 Additional barriers to consider for non-citizens are that they are more 
likely to experience barriers to social distancing due to household size,30 and few roles in the low-wage, essential 
workforce have the option to work fully or even partially remotely. Latinx are also more likely to have three 
generations reside in a household,31 which requires different approaches to promote public health messages for 
testing and preventing the spread of COVID-19 within a household. Latinx who are undocumented or who are in 
mixed-status households are more likely to be uninsured and less likely to access and utilize healthcare services32 
including COVID-19 testing. The existence of multi-generational and other mixed-status households are 
examples of the collectivistic culture that places a prominent role on familismo, which is an important protective 
factor for an individual’s well-being. 33,34 Family network based approaches that integrate household members 
play a critical role in promoting well-being for oneself as well as others.35 The social network strategy is an 
example of a testing approach that can leverage existing family network structures to promote and engage even 
the most marginalized communities in testing and vaccination.  
 
The Social Network Strategy (SNS) was developed for HIV and is adapted to accelerate COVID-19 testing to 
identify networks most at risk. Social network interventions are recognized as highly potent testing interventions 
that move beyond individual-level “risk”36 which are key to COVID-19 elimination efforts. Network 
mobilization/induction is a Type III intervention strategy that stimulates peer-to-peer interaction to create 
behavioral diffusion through existing social pathways among network members.37 This Type III intervention 37 
represents a class of network interventions that have been found to be effective in HIV prevention (i.e., CDC’s 
EBI—Social Network Strategy). 38 Past research has shown that individuals who are members of the same social 
network are more likely to have similar HIV risk potential.39 SNS identifies HIV positive individuals and/or 
individuals at risk for acquiring HIV and motivates them to recruit persons from their social network for testing 
and provides modest compensation for referrals. In so doing, the reach of the testing program increases as does 
the volume of people tested. In a recent study conducted by members of C3,40 the social network strategy (SNS) 

 
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was superior in HIV case identification when compared to standard testing approaches, such as those in health 
care settings (i.e. emergency department) or contact tracing. These findings are not surprising given that social 
network theory, such as homophily (i.e. birds of a feather) 41 suggest that subsequent waves of referred network 
members will resemble an index client’s attributes.  
 
STUDY DESIGN 
The scientific premise of C3 is to engage disenfranchised people in COVID-19 testing and vaccination referral 
through social network referral combined with theory-driven COVID-19 prevention messaging. Eligible CJI and 
Latinx clients (and the overlap between the two) will be enrolled into C3.  
 
Using a two-arm randomized controlled trial design, participants will be enrolled into the SNS arm (involves 
social networking referrals only) or the SNS+messaging arm. The latter includes affirmation/misinformation 
correction messaging (discussion tools and coaching). SNS and SNS+messaging arms will both include an initial 
group of index study participants who will refer their network members into the study and the process will repeat 
itself one more time for a total of 3 waves.  
 
A total of 2400 participants will be enrolled (estimated n=300 index seeds and n=2100 1st and 2nd degree 
network referrals) across sites in the Central US: Dallas County, TX (n=600); East Baton Rouge, LA (n=200); 
Pulaski County, AR. (n=200); Marion County, IN (n=200); Porter County, IN (n=200); and Cook County, IL 
(n=800).  
 
AIMS 
This study aims to evaluate the implementation of a combination Social Network testing Strategy (SNS) with 
COVID-19 prevention messages (SNS+) to engage disenfranchised populations such as criminal justice involved 
(CJI) and low-income Hispanic/Latinx (hereafter Hispanic) community members in COVID-19 testing,  
prevention strategies and vaccination across eight sites in the Central United States. Accordingly, the SNS+ team 
aims to: 
 
Table 1: Breakdown of study sites 
Study Sites 
Site PI 
 
IRB of Record 
Stakeholders 
Key 
Community  
Populations 
Sample 
Size(n) 
Scholarly 
Contribution 
 
University of 
Chicago 
South Cook Cnty., 
IL  
John 
Schneider 
 
University of 
Chicago IRB 
▪ Illinois Dept. of Public 
Health 
▪ Cook County Jail 
Black CJI 
350 
Network science, 
COVID-19 testing 
Howard Brown 
Health 
West Cook Cnty., IL 
  
Aniruddha 
Hazra 
University of 
Chicago IRB 
▪ Chicago Dept. of Public 
Health 
Latinx 
500 
LGBTQ, COVID-19 
testing 
implementation 
Project Vida 
West Cook Cnty., IL  
 
Aniruddha 
Hazra 
  
NA - HBH study 
location only 
▪ Chicago Dept of Public 
Health 
Latinx 
Shared 
sample 
size with 
HBH 
LGBTQ, COVID-19 
testing 
implementation, 
harm reduction 
services 
TCAP, Inc. 
Jackson Cnty., IL  
 
Mai Pho 
University of 
Chicago IRB 
▪ Jackson County. Health 
Dept. 
▪ Jackson County. Jail 
Rural, CJI,  
substance users 
200 
Rural Health  
Indiana University 
Marion Cnty., IN  
 
Matthew 
Aalsma, 
  
Indiana University 
IRB 
▪ Indianapolis Juvenile  
Correction Facility 
▪ Marion Superior Court 
Juvenile CJI  
200 
Law, ethics 
Indiana University 
Porter Cnty., IN  
 
  
Matthew 
Aalsma 
  
Indiana University 
IRB 
▪ Indiana Dept. of Public 
Health 
Juvenile CJI 
100 
Adolescent health 

 
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 Test intervention efficacy (numbers tested - COVID-19 test results will be collected as part of study data; 
numbers vaccinated – COVID-19 vaccine records will be collected as part of the study) and community factors 
that may moderate efficacy. Secondary analysis to compare numbers tested in SNS strategies and COVID-19 
contact and determine whether COVID-19 status within social networks impacts the referral process. 
 
STUDY SITES 
This multi-site study will be completed at eight sites as shown in Table 1.  Upon IRB approval, these sites will 
participate in research activities as listed in Appendix A, including recruitment, participant engagement and 
interviews.  In-person study activities will be conducted by IRB approved study staff and faculty at the study 
center shown in Table 1, or in a private room at a public library or school.  If the study interaction takes place in 
a public library or school, study activities will be conducted by IRB approved study staff who will ensure that 
subjects have complete privacy including that the room has a door and there are no other people present except 
for the subject and the study staff member, unless the Messaging Intervention (described below, number 4 under 
Study Visit Procedures) is being delivered, which can occur as a small group of consented subjects. 
 
University of Chicago will provide pre-programmed tablets to all study sites. These tablets have been 
programmed by the Research Computing Group within the Department of Public Health Sciences (leadership 
Phil Schumm) at the University of Chicago to ensure data protection and enable data transfer.   All data 
collection will be conducted electronically via tablets and data will be uploaded into databases hosted by 
University of Chicago.  These are similar procedures as have been developed with Schumm for NIDA’s 
Methodology and Advanced Analytics Research Center (PI Schneider). 
 
The University of Chicago BSD IRB will act as IRB of Record for all sites, except Indiana University (due to 
their enrollment of juvenile CJI populations) who will use their own institutional IRB.   
University of 
Arkansas  
for Medical 
Sciences 
Pulaski Cnty., AR  
 
Nickolas 
Zaller 
University of 
Chicago IRB 
▪ Arkansas Dept of Health 
and Central AR Community 
Correction Center 
CJI  
200 
Criminology, faith-
based engagement 
Better Community 
Development (BCD) 
Little Rock, AR  
 
  
Nickolas 
Zaller 
  
University of 
Chicago IRB 
▪ Pulaski County Health Unit 
▪ Central AR community 
Correction Center 
CJI 
Shared 
sample 
size with 
UAMS 
CJI, substance use 
recovery program, 
faith-based 
engagement 
Capitol Area 
Reentry  
Program (CARP) 
Baton Rouge, LA  
 
Russell 
Brewer 
University of 
Chicago IRB 
▪ Orleans Health Dept. 
CJI, substance 
users 
250 
Implementation 
science 
University of Texas 
SW 
Dallas Cnty., TX  
 
Kavita 
Bhavan 
University of 
Chicago IRB 
▪ Parkland Hospital 
Latinx 
600 
Predictive analytics, 
vulnerability index, 
COVID-19 testing 
San Jose State 
University  
Santa Clara Cnty, 
CA 
Moctezuma 
Garcia 
  
University of 
Chicago IRB 
▪ NA 
Latinx 
NA 
Health Inequities, 
Intersectionality, and 
Infectious Diseases 
GMU 
Fairfax, VA 
Faye Taxman 
University of 
Chicago IRB 
▪ NA 
NA 
NA 
Health Inequities, 
Intersectionality, and 
Infectious Diseases 
 
NORC 
Leslie 
Watson 
University of 
Chicago IRB 
▪ NA 
All 
NA 
Study design and 
planning, Site 
management and 
training, Data 
analysis 
 

 
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METHODS 
 
AIM: Test intervention efficacy (numbers tested – COVID-19 test results will be collected as study data; 
numbers vaccinated – COVID-19 vaccine records will be collected as part of the study) and community factors 
that may moderate efficacy. Secondary analysis to compare numbers tested in SNS strategies and COVID-19 
contact and determine whether COVID-19 status within social networks impacts the referral process. 
 
We hypothesize that participants randomized to receive SNS+ (SNS+COVID-19 messaging) will be more likely 
to have their network members successfully tested and vaccinated (study participants will be given COVID-19 
vaccine information and contact information to vaccination sites in their community. Vaccination will depend 
on the network members’ tier/health department eligibility for vaccination). We also hypothesize that both SNS 
and SNS+ will generate more people tested per index as compared to COVID-19 contact tracing. Finally, we 
anticipate that there may be differential intervention effects across sites, by CJI status, prior COVID-19 testing 
history, race/ethnicity, and network composition, and will formally evaluate these differences as part of 
analyses.  
 
Recruitment of Index and Network Member study participants.  
We will enroll index seeds (12% of sample) ages 18 and over who are CJI or low-income Latinx across eight 
collaborative sites. Index clients and their social network referrals will be recruited by local Research Assistants 
(RAs) embedded within community-based agencies and community health care settings that provide a number 
of in person and remote social and care services, including community COVID-19 testing.  
 
Recruitment activities may include: 
• In-person recruitment may be conducted at the study center (Table 1), during regular drop-in and 
community-based services, and events occurring at the study center and its outreach programs.  This 
may include providing a study flyer or contact card so that a client can contact the study team directly 
about participation.  A sign-up sheet may also be provided, where interested clients can provide their 
contact information if they wish to be contacted by the study team about the study. 
• Flyers posted at study centers  
• Social media postings 
• 
Contacting individuals from previous studies who have indicated interest in being contacted for future 
work or from existing community programs. 
 
Subjects will also be recruited through other study participants through compensated referral. Each study 
participant will be given a referral code that will be used for identification purpose. The study participants will 
distribute flyer and the referral code to people who are eligible for the study. People who are eligible and 
provide the referral code can be enrolled in the study. Participants will also be recruited from participants in 
other studies who have agreed to be contacted for future studies. 
 
A two-step referral process will be utilized whereby index seeds will refer 308 first- and second-degree social 
network members (88% of sample). So indexes will refer network members (1st degree) and then those network 
members will refer one more round (2nd degree).  Index seeds will meet the eligibility for index community 
members while the first and second-degree network members will meet the social network member eligibility 
(see inclusion and exclusion criteria below). Based upon previous experiences with SNS recruitment, we expect 
on average two social network members to be referred per study participant.  
 
Each study participant will be given a referral code that will be used for identification purposes. The study 
participants will distribute the referral code to their network members who are eligible for the study. People who 

 
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are eligible and provide the referral code can be enrolled in the study. Network members will also have the 
option to bring others into the study visit with them or link them to the RA at a study site that is recruiting for 
this study.   
 
Site specific details are listed in Appendix A, including recruitment plans, consent activities and overall study 
conduct for each site. 
 
Inclusion and exclusion criteria 
Index community members will be:  
(1) 18 years or older;  
(2) spend majority of their time in the metropolitan area or county where recruited;  
(3) have access to a phone for 21-day follow-up call; and  
(4) primary communication in English or Spanish (based on site chart above) AND at least one of the following:  
(i) ever had CJI (operationalized as any jail, prison, arrest, parole (completed), probation, drug court);  
(ii) ever had negative interaction with police or law enforcement that did not lead to an arrest or 
jail/prison time (operationalized as ever stopped, searched, physically or verbally abused, or had another 
negative interaction with police or law enforcement);  
(iii) ever witnessed a negative interaction with police or law enforcement (operationalized as ever 
witnessed another person being stopped, searched, or physically or verbally abused by police or law 
enforcement); 
(iv) lower-income Latinx (operationalized as at or below 250% of FPL).  
 
Social network referrals will be:  
(1) linked to the index as a “friend, family, coworker or someone you spend time with on a regular basis”;  
(2) visit within two weeks of index visit; 
(3) 18 years or older;  
(4) spend the majority of their time in the metropolitan area or county where recruited;  
(5) have access to a phone for 21-day follow-up call; and  
(6) primary communication in English or Spanish (based on site chart above).  
 
Exclusion criteria include:  
(1) inability to provide informed consent; and  
(2) active COVID-19 symptoms per CDC. Participants with COVID-19 symptoms will be referred for free 
testing at existing partners for each of the study sites. 
(3) currently on parole 
 
Study Visit Procedures  
All study participants (index and referral) will go through the following study procedures (Figure 4) include:  

 
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• 
Informed consent;  
• 
Day 1 survey;  
• 
Randomization;  
• 
Messaging intervention (for SNS+ only);  
• 
COVID-19 testing  
• 
Social Network referral  
• 
Day 21 Follow-up 
 
 
 
 
 
 
 
1. Informed Consent. Eligible participants will be asked to participate in written informed consent using either 
a paper consent or electronic consent. The following consent processes applies to both the study consent 
form and the release of information form.   
 
Paper Consent in-person: 
A study team will provide the subject with a copy of the study consent. The consent document will be 
reviewed and any questions regarding the study will be answered by the study team. If the subject agrees 
to participate in the study, they will be instructed to sign the consent form. The subject will be provided 
with a final signed copy. A signed copy will be stored by the study team. 
 
Electronic Consent in-person: 
Consent may be obtained in-person electronically via RedCAP. Study staff can present the consent to the 
subject on the tablet or on paper depending upon subject preference. The consent document will be 
reviewed and any questions regarding the study will be answered by the study team. If the subject agrees 
to participate in the study, the subject will be instructed to sign the consent form via RedCAP e-consent 
signature. Staff will also sign the consent form and update the RedCAP link to provide the subject with a 
final, signed version of the consent form via email. The final signed consent document will be stored in 
RedCAP. 
 
Electronic Consent Remote, using RedCAP: 
Remote consent would be conducted via telephone or video call (with preference being video call 
whenever possible) using a link through REDCap.  A link to the to the consent in RedCAP will be sent to 
the subject via text and/oremail. The study staff will discuss the consent form with the subject, and any 
questions regarding the study will be answered by the study team.  The subject's identity will be verified 
as described below.  If the subject agrees to participate in the study, he/she will be instructed to sign the 
consent form via RedCAP e-consent signature. Staff will also sign the consent form and update the 
RedCAP link in order to provide the subject with a final, signed version of the consent form. The final 
signed consent document will be stored in RedCAP.   
 
The study team will do due diligence to verify the subject’s identity before beginning the consent process.  
Traditional means of verifying subject identity may be challenging in this subject population.  Most will 
not be patients with local medical records to reference, some will not have formal state IDs, some may 
not have permanent residence, etc. Strict enforcement of state ID could disenfranchise those who may 
well be the most important group to reach and the very population that needs to be studied.  Subject 
identity will be verified using the following methods, in the following order of requirement: 
1) Visually display a driver’s license or state ID 
Figure 4 
 

 
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2) Visually display an alternative photo ID (employee ID, school ID, etc) in combination with a piece of 
mail displaying the subject’s name.  
3) Visually display an alternative photo ID (employee ID, school ID, etc) alone 
4) Visually display one of the following pieces of mail with subject’s name and address: utility bill, 
cellphone bill, correspondence from the secretary of state or other government organization 
5) Visually displaying any piece of mail listing the subject’s name and address 
6) Asking the subject to verbally state their name 
 
2. Day 1 Survey. Following consent, study participants will complete an electronic survey that includes social 
network inventory.  Identifying information of social network members (first name and last initial and cross 
streets/zipcode/neigborhood) is collected through self-report for the purposes of connecting social networks 
of study subjects. Day 1 Survey is found in Appendix B. This will survey will be completed on the day of 
recruitment and will take 60 minutes. The RA can interview participants who have difficulty self-
administering the survey.    
 
3. Randomization.  Will occur following the survey, and participants in the SNS+messaging arm will receive 
the messaging intervention (discussion tools and coaching). Study participants will be randomized to receive 
no messaging (i.e., SNS only) or SNS+messaging with equal probability. Randomization will be performed 
at the participant level, meaning that in general, participants within a recruitment cluster will be assigned to 
different groups. Random assignments will be provided by the University of Chicago via a web-based API, 
with the touchpads programmed to retrieve an assignment at the appropriate time and deliver the messaging 
(or not), as appropriate. 
 
Indexes and first-degree referrals will be randomized as described above. Second-degree referrals will also 
be randomized, however, we will not ask for additional SNS referrals from them if the study sample size has 
been reached.  
 
4. Messaging Intervention. Study staff will be notified of group allocation and provide the in-person message 
reinforcement and will address questions related to messaging. 
a. C3 Study Conditions. Trained community engagement coordinators (CEC) that exist across C3 sites 
will deliver the study conditions. The CECs will have capacity to support multiple participants 
simultaneously in the self-administered portion of the study visit: clarifying any survey items, 
troubleshooting any technology/data collection issues, and transitioning to interviewer administered 
for people with limited literacy. CECs will provide the appropriate scripted messaging strategy based 
upon condition assignment (see conditions below) and will follow with describing and motivating the 
SNS network referral process. 
b. SNS Study Condition. Study participants in the SNS condition will receive the SNS intervention 
training which will take about 30 minutes. This will occur towards the end of the study visit when 
CEC staff describe the SNS, construct a plan for successfully referring network members into the 
study, and discuss compensation for the recruitment efforts. CECs will specifically discuss network 
members of interest: “friend, family, coworker or someone you spend time with on a regular basis”. 
Walkthrough and scenario play will be strategies to assist with planning regarding: (1) how the 
conversation will be raised, (2) how potential barriers to testing will be addressed, (3) how the 
screening by phone or web survey will be conducted, (4)  what information about the study will be 
shared. Information from the client providing referrals will be kept confidential. Participants will be 
compensated $20 per successful test completed per network member and up to six referrals, 
consistent with our previous work.42 
c. SNS+Messaging Condition. The SNS+ condition will include everything described in the SNS 
condition above. In addition, a scripted message (Appendix C)  in the participant’s preferred language 

 
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will be deployed via the tablet in written or  audio format. Training for this additional messaging will 
take about 15 minutes. 
d. Quality Monitoring - A sample (5%) of the trainings will be recorded (video and/or audio) for quality 
improvement purposes.  These recordings may be shared amongst the respective sites-specific study 
team (not shared outside of the subject’s study site). Subjects will be asked whether they are willing 
to have the session recorded and can indicate yes or no on the study consent form. 
 
5. COVID-19 Testing. All study participants, irrespective of symptoms, will be tested for COVID-19.  Based on 
routine practices of the site, testing will be either administered by a clinical professional or conducted 
through self-administration according to CDC guidelines (Appendix I).  Testing procedures will follow 
existing SOPs utilized at each site, utilizing FDA-approved tests. COVID testing at these sites is covered by 
public health programs for each site.   
 
Test result provision will be provided by the testing/clinical team. All subjects will be provided with the 
CDC’s information page “What Your Test Results Mean” (Appendix J). 
 
6. COVID-19 Vaccination:  All subjects will be provided with the link to CDC’s COVID-19 vaccine 
information page “Key Things You Need To Know About  COVID-19 Vaccines” (Appendix K) and a link to 
CDC’s “Vaccines for COVID-19” page available at https://www.cdc.gov/coronavirus/2019-
ncov/vaccines/index.html. Access to vaccines will follow routine practices and will depend on a participants’ 
tier/health department eligibility to get the vaccine as well as vaccine availability. Contact information for 
vaccination sites and any referrals will be given according to the normal practices at each site. 
 
All participants will be asked sign release of information forms for study team members to obtain test results 
and vaccination records. Vaccination records will also be collected through participant self-report 
(participants can show study staff their vaccination card). COVID test results and vaccine records will be 
collected as part of study data and will be needed to determine the effectiveness of the intervention.  
 
7. Social Network Referral – Network referrals will be conducted as outlined above (See SNS Study 
condition).  A study information card (Appendix D) will be given to all enrolled subjects to share with their 
network referrals. The information card contains information about the study and study team contact 
information.   Linkages between study participants are collected through a code/code word process that our 
team has used in previous network referral studies. 
 
8. Day 21 Assessment - Study participants will be contacted 21 days following the initial study visit via phone 
or Zoom, depending upon client preference.  Data will be captured electronically by the research coordinator 
using a study tablet. This interview also includes social network inventory (identifying information: first 
name, last initial, cross streets/zipcode/neighborhood) for the purposes of connecting social networks of 
study subjects.  Day 21 Interview is found in Appendix E. The 21day follow-up (Day 21 Interview) has two 
purposes:  
(1) administering perceived message effectiveness surveys and post-intervention COVID-19 knowledge 
assessment; COVID vaccine information and (2) check-in on network member referral (Appendix D). 
 
Subject Compensation. Compensation will be provided for study participation - $50 after the first visit and 
$20 after the 21-day follow-up. Participants will be provided a $20 incentive for each network referrals 
initiated (up to six referrals, up to $120 for referrals). Payments will be in cash, gift cards or e-payments such 
as CashApp, PayPal or Venmo.  
 

 
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C3 Study Conditions. Trained community engagement coordinators (CEC) that exist across C3 sites will 
deliver the study conditions. The CECs will have capacity to support multiple participants simultaneously in the 
self-administered portion of the study visit: clarifying any survey items, troubleshooting any technology/data 
collection issues, and transitioning to interviewer administered for people with limited literacy. CECs will 
provide the appropriate scripted messaging strategy based upon condition assignment (see conditions above) 
and will follow with describing and motivating the SNS network referral process. 
 
Primary Outcomes. We focus on two primary outcomes:  
(1) Total number of tests among network members referred for COVID testing (network tested) - Network 
tested is measured at the participant level by the number of network members that are tested through the SNS 
(2) proportion of network members tested (tested proportion). 
(3) Total number of vaccinations among study participants given COVID vaccine information (number 
vaccinated) - Number vaccinated is measured at the participant level by the number of study participants that 
are vaccinated through the SNS.  
 
Other Important Variables: Age, race, ethnicity, gender, sex at birth, and CJI history; as well as COVID-19 
knowledge, testing history, infection history, substance use history, beliefs around mask efficacy, 
treatment/prevention history, vaccine knowledge/attitudes, prior contact by contact tracer, experiences of 
racism, housing status, food insecurity, employment, experience of violence, workplace resources, PPE 
availability, and known COVID-19 contact. 
 
Analytic overview. Two types of analyses will be performed.  
The first will focus on the likelihood that a participant’s network members are referred for testing. This will be 
modeled using logistic regression, with each network member treated as an observation (tested versus not). A 
three-level hierarchical model will be used, with random effects at the level of the study participant (level two) 
and the recruitment cluster (level three, all participants referred by the same index participant).43 We shall also 
consider adding site as a fourth level to permit us to estimate between-site variability both in the outcomes and 
in the effectiveness of the interventions.  
 
The second analysis will focus on the total number of individuals referred by a participant for testing, including 
both network members and additional referrals not initially named as part of the participant’s network. The total 
number of referrals will be modeled using negative binomial regression.44 A random effect at the level of the 
recruitment cluster will again be included to capture potential within-cluster correlation in the number of 
referrals. To ensure correct inferences even in cases where our models do not fully capture the within-cluster 
correlation in the data, we shall use the clustered version of the robust (i.e., sandwich) variance estimator 
throughout.45  
 
Primary outcome analysis (overall comparison of SNS vs. SNS + messaging). The primary outcome analysis 
will compare the likelihood of testing among network members and the total number of referrals tested between 
those assigned to SNS and those assigned to SNS plus messaging. Our initial analysis will not adjust for 
covariates, relying instead on the randomization to justify the comparison. We shall then incorporate 
covariates—measured at both the level of the network member (e.g., member characteristics and the nature of 
the relationship to the participant) and of the participant—to improve the precision of our estimates and to 
identify demographic and social characteristics associated with differences in referral yield. In addition, we shall 
consider interactions between intervention group (SNS vs. SNS + messaging) and participant characteristics to 
determine whether messaging is more effective in certain groups than others. Since we shall consider several 
possible interactions, we shall use partial pooling within a Bayesian estimation framework 46 to avoid bias and 
address the issue of multiple comparisons.  
 

 
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We will compare the probability of participants being vaccinated in the SNS vs. SNS + messaging arm using 
logistic regression with each participant treated as an observation (vaccinated vs. not). This will be modeled 
with a three-level hierarchical model with random effects at the level of the recruitment cluster and study site. 
We will also consider adjustments for time trends in vaccine availability and participant eligibility.  
 
Secondary outcome analyses: We shall use negative binomial regression to compare the average number of 
referrals tested per index client between those recruited to C3 and assigned to the SNS condition and a 
comparison group of clients at the same sites receiving standard contact tracing only. This analysis will be 
performed first using only those contact tracing clients who visited the sites on a day randomized to no C3 
recruitment, since these individuals should, on average, be similar to those recruited into the study. A 
subsequent analysis will be performed using a larger set of non-study clients to increase precision; this will use 
covariates capturing demographic and other client characteristics to adjust for potential differences between the 
C3 SNS group and the non-study comparison group. Analyses will use generalized linear mixed models similar 
to those described above, as appropriate. 
 
Power calculations: In order to estimate our power, we performed the following simulation. We assumed that 
index participants generate network members according to a negative binomial distribution with mean 4 and SD 
2.8 (to match the study design, networks larger than 6 were truncated). Each network member comes in for 
testing with a probability determined by the group to which they are assigned, and we conservatively assume 
intraclass correlations of 0.46 within a participant’s network and 0.37 within a recruitment cluster. If we assume 
that the overall likelihood of testing among network members is 0.44 among the SNS group and 0.52 among the 
SNS plus messaging group, then an overall sample size of 2,400 participants will provide approximately 87% 
power to detect the difference at the 0.05 level (twosided). We shall have approximately 80% power to detect an 
interaction between the intervention and a binary participant characteristic (Groups A and B) such that the 
overall probability of testing for those receiving SNS only (both A and B) is 0.44, the probability of testing for 
those in Group A receiving SNS plus messaging is 0.50, and the probability of testing for those in Group B 
receiving SNS plus messaging is 0.63 (i.e., messaging is more effective among Group B).  
 
Attrition and missing data: All participants enrolled in the study will be included in our analyses; since the 
primary outcomes will be measured based on referrals coming in to get tested which does not require additional 
participation by the participant, we anticipate very little missing outcome data (referrals who come in for testing 
and decline to identify the person who referred them will not be counted). Any missing data will be addressed 
using multiple imputation 47 or by using a fully Bayesian approach to estimate the model48 (in which missing 
data are essentially treated as additional unknown parameters).  
 
POTENTIAL RISKS AND BENEFITS TO SUBJECTS 
Study Risks. Interviewed subjects will be exposed to minimal risk: loss of confidentiality and discomfort from 
the questionnaire. Subjects will be told they do not need to answer any questions that make them uncomfortable.  
Linkages between study participants are collected through a code/code word process that our team has used in 
previous network referral studies.  All survey data will be collected using a tablet and linked through the 
subject’s study code instead of name. Tablets will be encrypted and data will be password-protected and HIPAA 
compliant. At all sites, COVID infection control procedures will be in place. These will include a COVID 
symptom screener (Appendix F), temperature checks, mask-wearing requirement and at least six feet apart 
during in-person interactions. 
 
Benefits: The primary potential benefits of this research will accrue to participants of this study and their 
networks. Participants will be linked to COVID-19 testing and treatment services as needed. The results of this 
study may lead to future interventions which can support COVID-19 prevention messaging, linkage to testing 
and treatment services for disenfranchised populations.  
 

 
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DATA STORAGE AND SHARING 
We shall take several steps to ensure the security of the identifiers and data collected for this study. First, all data 
will be collected using dedicated laptops purchased by the project and configured and certified by BSDIS to meet 
their requirements for portable computers (e.g., timed log-out, whole-disk encryption, use of BigFix and 
antivirus software, etc.). Data will be entered into two applications: REDCap (web-based application hosted by 
the CRI at the University of Chicago) and Network Canvas (developed at Northwestern University, and running 
locally on the laptop). The social network data, including identifiers for social network members, will be entered 
into Network Canvas, which has been specially designed for collection of such data. Following an interview, the 
data will be exported from Network Canvas and the network member identifiers will be split off and placed in a 
separate file which will then be strongly encrypted using RSA via the software GnuPG (an implementation of the 
the OpenPGP standard as defined by RFC4880). This operation will be performed programmatically, using a 
custom utility installed on the laptop. After verification of the encrypted file, the unencrypted file will be deleted.  
 
Before any data transfer, the University of Chicago will encrypt the data both at rest and in transit via a Shared 
File Transfer protocol (SFTP) to provide a high level of security of sending and receiving file transfers. The 
encrypted data products will be securely uploaded to a SFTP server using a cyberinfrastructure service such as 
Globus (https://www.globus.org/) via the Biological Science Division Information Services (BSDIS). Using a 
STFP will ensure high security features that meet authentication and authorization standards for sensitive data 
containing protected health information. Additionally, data management and transferring services for personally 
identifiable data will be with a High Assurance or HIPAA BAA subscription. If an institution is responsible to 
provide data (incoming), they will be responsible for encryption. Thus, the network member identifiers will be 
encrypted both in transit and at rest. 
 
The identifiers of network members will be firewalled from all other data in two ways. First, they will be 
uploaded and stored in a separate, dedicated fileshare. Second and more importantly, an honest broker within the 
University of Chicago Biostatistics Laboratory will hold the encryption key for the identifiers. Only this 
individual will be able to decrypt the files containing the identifiers (i.e., our protocol achieves end-to-end 
encryption between the laptop in the field and the honest broker). He or she will monitor the incoming files to 
ensure that the system is working properly, but the files will continue to be stored in encrypted format. The 
honest broker will not be a member of the research team, and will not have access to any of the other data 
collected by the project. 
 
Finally, the honest broker will provide study subject and network member location identifiers to one or more 
data analysts who will perform the network matching and geocoding needed to generate files for analysis. This 
analyst (or analysts) will be located at the University of Chicago (e.g., CCHE, the Biostatistics Lab, the Center 
for Spatial Data Science, or Argonne), and his or her only role on the project will be to perform the network 
matching and geocoding. Once this operation has been completed and the results have undergone QC, the 
original files containing network member identifiers will be destroyed by both the analyst(s) and the honest 
broker. All network matching and geocoding will take place on a secure compute server maintained by the 
Research Computing Group in the Department of Public Health Sciences. This is the only location where the raw 
files will be decrypted. 
 
After study termination, the de-identified study data may be maintained for up to 10 years at the University of 
Chicago. All recordings of study trainings will be destroyed upon study termination. 
 
UAMS will destroy any raw data received from Arkansas Department of Health upon study completion. 
 
The Duke Clinical Research Institute (DCRI) has been chosen by the National Institute of Health (NIH) to 
serve as the data coordinating center for all of the RADx-UP study centers.  Coded data for all sites 
participating in our RADx-UP C3 study will flow from University of Chicago to DCRI for storage and sharing 

 
Page 16 of 19 Version 2023.09.25 
 
purposes for future COVID 19 research as directed by the NIH (in keeping with the NIH’s policy).  All data 
shared in this manner would be deidentified and could be used indefinitely. 
 
 
 
 
 
REFERENCES 
1. Lieberman-Cribbin W, Tuminello S, Flores RM, Taioli E. Disparities in COVID-19 Testing and Positivity 
in New York City. American Journal of Preventive Medicine. Published online June 
2020:S0749379720302634. doi:10.1016/j.amepre.2020.06.005 
2. Artiga S, Garfield R, Orgera K. Communities of Color at Higher Risk for Health and Economic 
Challenges due to COVID-19. Communities of Color at Higher Risk for Health and Economic Challenges 
due to COVID-19. Published April 7, 2020. Accessed August 5, 2020. https://www.kff.org/coronavirus-
covid19/issue-brief/communities-of-color-at-higher-risk-for-health-and-economic-challenges-due-to-
covid-19/ 
3. Fischer LS, Mansergh G, Lynch J, Santibanez S. Addressing Disease-Related Stigma During Infectious 
Disease Outbreaks. Disaster med public health prep. 2019;13(5-6):989-994. doi:10.1017/dmp.2018.157 
4. Social Stigma associated with COVID-19. Published online February 24, 2020. Accessed August 5, 
2020. https://www.who.int/docs/default-source/coronaviruse/covid19-stigma-guide.pdf  
5. 7. Double Jeopardy: COVID-19 And Behavioral Health Disparities For Black And Latino Communities In 
The U.S. (Submitted By OBHE). SAMHSA - The Substance Abuse Mental Health Services 
Administration. Published 2020. Accessed August 5, 2020. 
https://www.samhsa.gov/sites/default/files/covid19- behavioral-health-disparities-black-latino-
communities.pdf  
6. Laster Pirtle WN. Racial Capitalism: A Fundamental Cause of Novel Coronavirus (COVID-19) Pandemic 
Inequities in the United States. Health Educ Behav. 2020;47(4):504-508. 
doi:10.1177/1090198120922942  
7. Straub AM, Gray BJ, Ritchie LA, Gill DA. Cultivating disaster resilience in rural Oklahoma: Community 
disenfranchisement and relational aspects of social capital. Journal of Rural Studies. 2020;73:105-113. 
doi:10.1016/j.jrurstud.2019.12.010   
 
8. Whetten K, Leserman J, Whetten R, et al. Exploring Lack of Trust in Care Providers and the 
Government as a Barrier to Health Service Use. Am J Public Health. 2006;96(4):716-721. 
doi:10.2105/AJPH.2005.063255  
 
9. Adams G, Tormala TT, O’Brien LT. The effect of self-affirmation on perception of racism. Journal of 
Experimental Social Psychology. 2006;42(5):616-626. doi:10.1016/j.jesp.2005.11.001  
 
10. Jaiswal J, LoSchiavo C, Perlman DC. Disinformation, Misinformation and Inequality-Driven Mistrust in 
the Time of COVID-19: Lessons Unlearned from AIDS Denialism. AIDS Behav. Published online May 
21, 2020:s10461-020-02925-y. doi:10.1007/s10461-020-02925-y  
 
11. Boutin-Foster C, Scott E, Rodriguez A, et al. The Trial Using Motivational Interviewing and Positive 
Affect and Self-Affirmation in African-Americans with Hypertension (TRIUMPH): From theory to clinical 
trial implementation. Contemporary Clinical Trials. 2013;35(1):8-14. doi:10.1016/j.cct.2013.02.002  
 
12. Brayne S. Surveillance and System Avoidance: Criminal Justice Contact and Institutional Attachment. 
Am Sociol Rev. 2014;79(3):367-391. doi:10.1177/0003122414530398  
 

 
Page 17 of 19 Version 2023.09.25 
 
13. Hawks L, Woolhandler S, McCormick D. COVID-19 in Prisons and Jails in the United States. JAMA 
Intern Med. 2020;180(8):1041. doi:10.1001/jamainternmed.2020.1856  
 
14. Tai DBG, Shah A, Doubeni CA, Sia IG, Wieland ML. The Disproportionate Impact of COVID-19 on 
Racial and Ethnic Minorities in the United States. Clinical Infectious Diseases. Published online June 20, 
2020:ciaa815. doi:10.1093/cid/ciaa815  
 
15. Solis J, Franco-Paredes C, Henao-Martínez AF, Krsak M, Zimmer SM. Structural Vulnerability in the 
U.S. Revealed in Three Waves of COVID-19. The American Journal of Tropical Medicine and Hygiene. 
2020;103(1):25-27. doi:10.4269/ajtmh.20-0391  
 
16. Dorn A van, Cooney RE, Sabin ML. COVID-19 exacerbating inequalities in the US. The Lancet. 
2020;395(10232):1243-1244. doi:10.1016/S0140-6736(20)30893-X  
 
17. Kantamneni N. The impact of the COVID-19 pandemic on marginalized populations in the United States: 
A research agenda. Journal of Vocational Behavior. 2020;119:103439. doi:10.1016/j.jvb.2020.103439  
 
18. Palmer-Wackerly AL, Chaidez V, Wayment C, Baker J, Adams A, Wheeler LA. Listening to the Voices of 
Community Health Workers: A Multilevel, Culture-Centered Approach to Overcoming Structural Barriers 
in U.S. Latinx Communities. Qual Health Res. 2020;30(3):423-436. doi:10.1177/1049732319855963  
 
19. Macias Gil R, Marcelin JR, Zuniga-Blanco B, Marquez C, Mathew T, Piggott DA. COVID-19 Pandemic: 
Disparate Health Impact on the Hispanic/Latinx Population in the United States. The Journal of 
Infectious Diseases. Published online July 30, 2020:jiaa474. doi:10.1093/infdis/jiaa474  
 
20.  Pazzanese C. Social media used to spread, create COVID-19 falsehoods. Harvard Gazette. Published 
May 8, 2020. Accessed August 5, 2020. https://news.harvard.edu/gazette/story/2020/05/social-
mediaused-to-spread-create-covid-19-falsehoods/  
 
21. Wileman V, Farrington K, Chilcot J, et al. Evidence That Self-Affirmation Improves Phosphate Control in 
Hemodialysis Patients: A Pilot Cluster Randomized Controlled Trial. ann behav med. 2014;48(2):275-
281. doi:10.1007/s12160-014-9597-8  
 
22. Harkness A, Gattamorta KA, Estrada Y, et al. Latinx Health Disparities Research During COVID-19: 
Challenges and Innovations. Annals of Behavioral Medicine. Published online July 27, 2020:kaaa054. 
doi:10.1093/abm/kaaa054  
 
23. Steele CM. The Psychology of Self-Affirmation: Sustaining the Integrity of the Self. In: Advances in 
Experimental Social Psychology. Vol 21. Elsevier; 1988:261-302. doi:10.1016/S0065-2601(08)60229-4 
 
24. Friedman M. Just Facts: As Many Americans Have Criminal Records as College Diplomas. Brennan 
Center for Justice. Published November 17, 2015. Accessed August 7, 2020. 
https://www.brennancenter.org/our-work/analysis-opinion/just-facts-many-americans-have-
criminalrecords-college-diplomas  
 
25.  AmeriSpeak. Accessed August 4, 2020. https://www.amerispeak.org/  
 
26. Sampson RJ, Bartusch DJ. Legal Cynicism and (Subcultural?) Tolerance of Deviance: The 
Neighborhood Context of Racial Differences. Law & Society Review. 1998;32(4):777. 
doi:10.2307/827739  
 

 
Page 18 of 19 Version 2023.09.25 
 
27. Wright KA, Gifford FE. Legal Cynicism, Antisocial Attitudes, and Recidivism: Implications for a 
Procedurally Just Community Corrections. Victims & Offenders. 2017;12(4):624-642. 
doi:10.1080/15564886.2016.1179238  
 
28. Labor force characteristics by race and ethnicity, 2018 : BLS Reports: U.S. Bureau of Labor Statistics. 
Accessed August 3, 2020. https://www.bls.gov/opub/reports/race-and-ethnicity/2018/home.htm  
 
29. Rho HJ, Fremstad S, Brown H. A Basic Demographic Profile of Workers in Frontline Industries. :10.  
 
30. Langellier BA. Policy Recommendations to Address High Risk of COVID-19 Among Immigrants. Am J 
Public Health. 2020;110(8):1137-1139. doi:10.2105/AJPH.2020.305792  
 
31. Lee H, Kim Y. Living in three-generation family households and body mass index trajectories in Hispanic 
adolescents: Different associations by immigrant status. Children and Youth Services Review. 
2019;107:104508. doi:10.1016/j.childyouth.2019.104508  
 
32. Ortega AN, McKenna RM, Kemmick Pintor J, et al. Health Care Access and Physical and Behavioral 
Health Among Undocumented Latinos in California: Medical Care. 2018;56(11):919-926. 
doi:10.1097/MLR.0000000000000985  
 
33. Ayón C, Marsiglia FF, Bermudez-Parsai M. Latino family mental health: exploring the role of 
discrimination and familismo. J Community Psychol. 2010;38(6):742-756. doi:10.1002/jcop.20392  
 
34. Elder JP, Ayala GX, Parra-Medina D, Talavera GA. Health Communication in the Latino Community: 
Issues and Approaches. Annu Rev Public Health. 2009;30(1):227-251. 
doi:10.1146/annurev.publhealth.031308.100300  
 
35. Davila YR, Reifsnider E, Pecina I. Familismo: influence on Hispanic health behaviors. Applied Nursing 
Research. 2011;24(4):e67-e72. doi:10.1016/j.apnr.2009.12.003  
 
36. Valente TW. Social Networks and Health: Models, Methods, and Applications. Oxford University Press; 
2010.  
 
37. Valente TW. Network Interventions. Science. 2012;337(6090):49-53. doi:10.1126/science.1217330 
 
38.  Pagkas-Bather J, Young LE, Chen Y-T, Schneider JA. Social Network Interventions for HIV 
Transmission Elimination. Curr HIV/AIDS Rep. Published online July 28, 2020. doi:10.1007/s11904-020-
00524-z  
 
39. Kimbrough LW, Fisher HE, Jones KT, Johnson W, Thadiparthi S, Dooley S. Accessing Social Networks 
With High Rates of Undiagnosed HIV Infection: The Social Networks Demonstration Project. Am J Public 
Health. 2009;99(6):1093-1099. doi:10.2105/AJPH.2008.139329 
 
40.  Skaathun B, Pho M, Pollack H, et al. Comparison of effectiveness and cost for different HIV screening 
strategies implemented at large urban medical centre in the United States. Journal of the International 
AIDS Society. 2020;23(e25554). doi:10.1002/jia2.25554  
 
41. Wasserman S, Faust K. Social Network Analysis: Methods and Applications. Cambridge University 
Press; 1994.  
 

 
Page 19 of 19 Version 2023.09.25 
 
42. Schneider JA, Cornwell B, Ostrow D, et al. Network Mixing and Network Influences Most Linked to HIV 
Infection and Risk Behavior in the HIV Epidemic Among Black Men Who Have Sex With Men. Am J 
Public Health. 2013;103(1):e28-e36. doi:10.2105/AJPH.2012.301003  
 
43. Mcculloch CE, Neuhaus JM. Generalized Linear Mixed Models. In: Balakrishnan N, Colton T, Everitt B, 
Piegorsch W, Ruggeri F, Teugels JL, eds. Wiley StatsRef: Statistics Reference Online. John Wiley & 
Sons, Ltd; 2014:stat07540. doi:10.1002/9781118445112.stat07540  
 
44. Cameron AC, Trivedi PK. Regression Analysis of Count Data. Second edition. Cambridge University 
Press; 2013.  
 
45. Rogers W. Regression standard errors in clustered samples. Stata Technical Bulletin. 1994;3(13):88–94.  
 
46. Gelman A, Hill J. Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge 
University Press; 2006. doi:10.1017/CBO9780511790942  
 
47. Harel O, Zhou X-H. Multiple imputation: review of theory, implementation and software. Statist Med. 
2007;26(16):3057-3077. doi:10.1002/sim.2787  
 
48. Gelman A. Bayesian Data Analysis. Third edition. CRC Press; 2014.  
 
49. )“Coronavirus Vaccine Hesesitancy in Black and Latinx Communities.” n.d. Covid Collaborative. Accessed 
April 9, 2021. https://www.covidcollaborative.us/content/vaccine-treatments/coronavirus-vaccine-
hesitancy-in-black-and-latinx-communities. 
50.  Published: and 2021. 2021. “Most Americans Now Say They’ve Gotten At Least One Dose of a COVID-19 
Vaccine or Want to Get Vaccinated As Soon As Possible, with Enthusiasm Rising Across Racial, Ethnic 
and Partisan Groups.” KFF (blog). February 26, 2021. https://www.kff.org/coronavirus-covid-19/press-
release/most-americans-now-say-theyve-gotten-at-least-one-dose-of-a-covid-19-vaccine-or-want-to-get-
vaccinated-as-soon-as-possible-with-enthusiasm-rising-across-racial-ethnic-and-partisan-groups/. 
51. Ramšak, Mojca. 2020. “Medical Racism.” European Journal of Bioethics 11 (1): 9–36. 
https://doi.org/10.21860/j.11.1.1. 
52. Schoch-Spana, Monica, Emily K. Brunson, Rex Long, Alexandra Ruth, Sanjana J. Ravi, Marc Trotochaud, 
Luciana Borio, et al. 2020. “The Public’s Role in COVID-19 Vaccination: Human-Centered 
Recommendations to Enhance Pandemic Vaccine Awareness, Access, and Acceptance in the United 
States.” Vaccine. https://doi.org/10.1016/j.vaccine.2020.10.059.