dbGaP Study Accession: phs003374

NIH Institute/Center: NIAID

RADx Data Program: RADx-UP

Release Date: 04/11/2024

DOI: 10.60773/pp0p-gj15

Study Description: The novel Severe Acute Respiratory Syndrome (SARS) Coronavirus 2019 (SARS-CoV-2 or COVID-19) emerged as a new viral infection in December 2019. Now a global pandemic, COVID-19 has presently sickened more than one million Americans in just three months with more than 50,000 deaths as of April 27, 2020. COVID-19 presents as a mild dry cough with or without fever and sore throat in the majority of people, yet progression to acute respiratory distress syndrome is possible but the symptom profile appears to be evolving. Initially, data emerging from China suggested the virus had the greatest morbidity and mortality in populations over the age of 70, however, recent data suggest that over 40% of individuals ill enough to require hospitalization are under the age of 50. As with prior coronavirus episodes, wide clinical variation from asymptomatic carriage to acute respiratory distress is unfolding. As with any rapidly changing viral outbreak, data is emerging daily, and little is known about duration of transmissibility, risk of transmission from asymptomatic carriers, environmental contamination and the dynamics of transmission within the home, shelter, or other residential community setting. Further, the long-term sequela of COVID-19 survivors, including their long-term immunity, lung function and other clinical sequela as well as the potential for re-infection is unknown. Effective control requires changes in human behavior including reducing mobility, adoption of non-pharmaceutical interventions including masking and social distancing and testing and isolation of those infected or suspected to be infected. However, there are multilevel barriers to optimal testing in the population; these barriers are complex, dynamic and recursive (Figure 1). The framework portrays these multilevel determinants including the environment, population characteristics, and health behaviors that impact testing utilization and ultimately, health outcomes, including feedback loops indicating that downstream factors may negatively or positively reinforce upstream factors. The framework posits that there are environmental/health care system factors (e.g., testing supplies) and population predisposing, enabling, and need factors, all of which influence testing utilization and subsequent health outcomes. Predisposing factors include sociodemographic and other behavioral characteristics (employment and income). Enabling resources (or lack of) may include factors such as transportation access (e.g., public, personal), concentrated areas of poverty, and testing access (i.e., testing deserts). Need factors include general concerns about the pandemic and personal risk for infection. Health and healthcare inequities will exacerbate poor testing access. Latinx and Blacks are almost three and two times, respectively, more likely to be uninsured compared to non-Latinx whites. Blacks of all ages are also more likely to report not being able to see a doctor in the past year because of cost, which has direct implications for testing. In addition, longstanding issues of institutional (i.e., medical, research, public health) racism, mistrust and distrust, language barriers, and the cost of missing work all decrease the likelihood of testing among these subgroups. Blacks also experience higher rates of chronic conditions at earlier ages and at higher death rates, placing them at increased risk for COVID-19 illness severity and mortality and limiting their ability to seek testing. Racism, stigma, and systemic inequities at multiple levels, including healthcare systems, undermine prevention efforts, increase levels of chronic and toxic stress, and ultimately, sustain these inequities. Community-based studies will be critical for understanding disparities that underpin testing access and uptake. This study was able to define the optimal testing modality in order to maximize testing acceptance, uptake and timeliness of results. In Baltimore City currently, few sites offer testing to patients without symptoms and most still require both a state-level identification and a medical order prior to testing. Efforts to penetrate testing deserts often translate into weekly or monthly pop-up testing sites that are highly unpredictable for community members. Clinics dedicated to low-income residents have signs declaring limited capacity with testing offered while supplies last. This intervention sought to overcome these multi-faceted barriers. Beyond increasing understanding of testing, this study helped to characterize the total spectrum of SARS-CoV-2 infection. COVID-19 is associated with a wide range of symptoms as well as spectrum of clinical illness. Little is known among patients who, ultimately, develop evidence of antibody demonstrating history of infection, yet who do not progress to hospitalization or Acute Respiratory Distress Syndrome (ARDS). For patients who do develop symptoms severe enough to warrant hospitalization, clinical evaluation includes multiple measures of inflammation IL-6, LDH, ESR, CRP, CBC with differential. Additionally, clinical evaluation includes liver function testing and coagulopathy with D-Dimer. This study sought to explore the extent of dysregulation among these parameters in individuals whose symptom spectrum is either prior to or does not lead to acute hospitalization. Moreover, this study provided critical information on community and specifically household transmission of SARS-CoV-2. Household transmission of both viral and bacterial pathogens is common and well established. In the outbreaks of SARS, the Middle Eastern Respiratory Syndrome (MERS) and Avian Influenza (H1N1), household transmission ranged between 5% in MERS, 6.2% in SARS, and 81% in H1N1. The transmissibility of COVID-19 is estimated to be higher than both MERS and SARS, yet lower than H1N1. Sub-clinical transmission within households has also been identified between MERS index cases and household contacts. With most of the household/residential studies, it is unclear whether direct contact with the index case or contact with a fomite within the residence is the source of transmission. Thus, this study helped define the optimal SARS-CoV-2 testing modality for maximizing testing uptake through a randomized comparative effectiveness trial. The three testing modalities were: (1) standard-of-care (SOC) or fixed site testing, (2) community-based, mobile van testing, and (3) self-collected, home-based testing. Population-based sample of households within Baltimore city with randomization to SARS-CoV-2 testing modality and follow-up for up to 12 months. The overall study is divided into two Phases: Phase I and Phase II. Phase I involved informed consent and a baseline survey only. Phase II involved randomization and SARS-CoV-2 testing. At consent into Phase II, participants were randomized as a part of the comparative effectiveness trial and thus, only those who consent to Phase II of the study were included in primary and secondary analyses.

Updated Date: 04/17/2024

Principal Investigator: Farley, Jason

Has Data Files: Yes

Study Domain: At-Home or Over-the-Counter (OTC) Self-Testing; Community Outreach Programs; Mobile Unit Testing; Social Determinants of Health; Testing Rate or Uptake; Virological Testing

Data Collection Method: Antibody or Adaptive Immune Response Testing; Antigen Testing Device; Molecular Nucleic Acid or PCR Testing Device; Survey

Keywords: Aggregate Data; Clinical Symptoms; Testing Modality

Study Design: Longitudinal Cohort

Multi-Center Study: No

Data Types: Behavioral; Clinical; Electronic Medical Records; Family History; Psychological; Questionnaire or Survey; Social

Study Start Date: 09/23/2020

Study End Date: 12/31/2022

Species: Human

Estimated Cohort Size: 2772

Study Population Focus: Adults; African Americans; Children; Hispanics or Latinos; Older Adults or Elderly; Racial or Ethnic Minorities

ClinicalTrials.gov URL: https://clinicaltrials.gov/study/NCT04673292

Acknowledgement Statement: This study was supported through funding, 2P30AI094189-11, for the National Institute of Allergy and Infectious Diseases (NIAID) as part of the RADx-UP program. We acknowledge Johns Hopkins University Center for AIDS Research for their services and mentorship. Approved users should acknowledge the provision of data access by dbGaP for accession phs003374.v1.p1, and the NIH RADx Data Hub. Approved users should also acknowledge the specific version(s) of the dataset(s) obtained from the NIH RADx Data Hub.

Funding Opportunity Announcement (FOA) Number: PAR-20-106

NIH Grant or Contract Number(s): 2P30AI094189-11

Consent/Data Use Limitations: Use of the data is limited to health/medical/biomedical purposes; Use of the data is limited to not-for-profit organizations; IRB required