Enhancing participant diversity during recruitment for a national COVID-19 surveillance study using stakeholder engagement.

In May 2020 A COVID-19 Community Research Partnership, a surveillance study aimed at learning about the spread of the COVID-19 virus in local communities and among healthcare workers, began. Recognizing the disproportionate impact of COVID-19 on low-income and racial/ethnic minority groups, recruitment strategies that enabled diverse representation and engagement were used. We describe a community based participatory approach to engage a stakeholder advisory board to guide recruitment and data collection. Study Design was a community based participatory research study. Approximately 30 participants comprise the SAB. The study recruits participants from a large metropolitan area, and Atrium Health, a large, vertically integrated, not-for-profit healthcare system. Population Studied >12,000 community participants Outcome Measures recruitment and demographics of participants Results With SAB guidance we recruited >12,000 participants. The highly engaged stakeholders provided valuable input to guide the development of recruitment materials (¬flyers, emails, social media, websites, videos); use of incentives (free phones to complete the online symptom survey); and community outreach opportunities (primary care practices in underserved neighborhoods; COVID-19 mobile testing units located in underserved communities; mask giveaway events; and the local public school system) The SAB identified groups that the research team then partnered with to share information about the study (Village Heart BEAT, a community-based organization aimed at improving the health of African American and Hispanic populations; and HealthCare System employee affinity groups serving Hispanic/Latino and African American communities). Demographics include 90% White/Caucasian, 5% Black/African American; 3% Hispanic and 2% Asian or Pacific Islander; 38% are healthcare workers; 67% are female. Conclusions Use of a community stakeholder advisory board has enhanced understanding and participation in a COVID-19 Community Research Partnership. Engaging diverse community stakeholders early in the research process was essential for ensuring data collection efforts are patient-centered and tailored to reach diverse communities.

Disparities in Level of Care and Outcomes Among Patients with COVID-19: Associations Between Race/Ethnicity, Social Determinants of Health and Virtual Hospitalization, Inpatient Hospitalization, Intensive Care, and Mortality.

OBJECTIVE: To examine the role of race/ethnicity and social determinants of health on COVID-19 care and outcomes for patients within a healthcare system that provided virtual hospital care. METHODS: This retrospective cohort study included 12,956 adults who received care for COVID-19 within an integrated healthcare system between 3/1/2020 and 8/31/2020. Multinomial models were used to examine associations between race/ethnicity, insurance, neighborhood deprivation measured by Area Deprivation Index (ADI), and outcomes of interest. Outcomes included (1) highest level of care: virtual observation (VOU), virtual hospitalization (VACU), or inpatient hospitalization; (2) intensive care (ICU); and (3) all-cause 30-day mortality. RESULTS: Patients were 41.8% White, 27.2% Black, and 31.0% Hispanic. Compared to White patients, Black patients had 1.86 higher odds of VACU admission and 1.43 higher odds of inpatient hospitalization (vs. VOU). Hispanic patients had 1.24 higher odds of inpatient hospitalization (vs. VOU). In models stratified by race/ethnicity, Hispanic and Black patients had higher odds of inpatient hospitalization (vs. VOU) if Medicaid insured compared to commercially insured. Hispanic patients living in the most deprived neighborhood had higher odds of inpatient hospitalization, compared to those in the least deprived neighborhood. Black and Hispanic patients had higher odds of ICU admission and 30-day mortality after adjustment for other social determinants. CONCLUSIONS: Insurance and ADI were associated with COVID-19 outcomes; however, associations varied by race/ethnicity. Racial/ethnic disparities in outcomes are not fully explained by measured social determinants of health, highlighting the need for further investigation into systemic causes of inequities in COVID-19 outcomes.

Association between self-reported masking behavior and SARS-CoV-2 infection wanes from Pre-Delta to Omicron-predominant periods - North Carolina COVID-19 Community Research Partnership (NC-CCRP).

BACKGROUND: Wearing a face mask is a primary public health method to reduce SARS-CoV-2 transmission. METHODS: We performed a nested case-control analysis within the North Carolina COVID-19 Community Research Partnership (NC-CCRP) of adults who completed daily surveillance surveys, April 2020 - February 2022. We assessed the association between self-reported mask wearing behavior during nonhousehold interactions and COVID-19 infection during 3 pandemic periods using conditional logistic regression models of risk of infection that were adjusted for demographics, vaccination status, and recent known exposure to COVID-19. RESULTS: Among 3,901 cases and 27,813 date-matched controls, there was a significant interaction between mask use and time period (P < .001). Prior to July 2021, the odds of a reported infection were 66% higher (aOR = 1.66, 95% CI = 1.43-1.91) among participants reporting ≥1 day not wearing a mask compared to those who reported no days (1,592 cases, 11,717 controls). During the Delta-predominant period, the results were similar (aOR = 1.53, 95% CI = 1.23-1.89; 659 cases, 4,649 controls). This association was attenuated during the Omicron-predominant period, where odds of an infection was 16% higher (aOR = 1.16, 95% CI = 1.03-1.32; 1,563 cases, 10,960 controls). CONCLUSIONS: While the effect of not wearing a mask remains significant, during the Omicron-predominant period we observed a decrease in the association between self-reported mask wearing and risk of SARS-CoV-2 infection.

Evaluating Racial/Ethnic Differences in Care Escalation Among COVID-19 Patients in a Home-Based Hospital.

The novel coronavirus disease 2019 (COVID-19) has infected over 414 million people worldwide with 5.8 million deaths, as of February 2022. Telemedicine-based interventions to expand healthcare systems' capacity and reduce infection risk have rapidly increased during the pandemic, despite concerns regarding equitable access. Atrium Health Hospital at Home (AH-HaH) is a home-based program that provides advanced, hospital-level medical care and monitoring for patients who would otherwise be hospitalized in a traditional setting. Our retrospective cohort study of positive COVID-19 patients who were admitted to AH-HaH aims to investigate whether the rate of care escalation from AH-HaH to traditional hospitalization differed based on patients' racial/ethnic backgrounds. Logistic regression was used to examine the association between care escalation within 14 days from index AH-HaH admission and race/ethnicity. We found approximately one in five patients receiving care for COVID-19 in AH-HaH required care escalation within 14 days. Odds of care escalation were not significantly different for Hispanic or non-Hispanic Blacks compared to non-Hispanic Whites. However, secondary analyses showed that both Hispanic and non-Hispanic Black patients were younger and with fewer comorbidities than non-Hispanic Whites. The study highlights the need for new care models to vigilantly monitor for disparities, so that timely and tailored adaptations can be implemented for vulnerable populations.

Racial/Ethnic Disparities in Health Care Setting Choice for Adults Seeking Severe Acute Respiratory Syndrome Coronavirus 2 Testing.

OBJECTIVES: Equitable access to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing is important for reducing disparities. We sought to examine differences in the health care setting choice for SARS-CoV-2 testing by race/ethnicity and insurance. Options included traditional health care settings and mobile testing units (MTUs) targeting communities experiencing disproportionately high coronavirus disease 2019 (COVID-19) rates. METHODS: We conducted a retrospective, observational study among patients in a large health system in the Southeastern US. Descriptive statistics and multinomial logistic regression analyses were employed to evaluate associations between patient characteristics and health care setting choice for SARS-CoV-2 testing, defined as: (1) outpatient (OP) care; (2) emergency department (ED); (3) urgent care (UC); and (4) MTUs. Patient characteristics included race/ethnicity, insurance, and the existence of an established relationship with the health care system. RESULTS: Our analytic sample included 105,386 adult patients tested for SARS-CoV-2. Overall, 55% of patients sought care at OP, 24% at ED, 12% at UC, and 9% at MTU. The sample was 58% White, 24% Black, 11% Hispanic, and 8% other race/ethnicity. Black patients had a higher likelihood of getting tested through the ED compared with White patients. Hispanic patients had the highest likelihood of testing at MTUs. Patients without a primary care provider had a higher relative risk of being tested through the ED and MTUs versus OP. CONCLUSIONS: Disparities by race/ethnicity were present in health care setting choice for SARS-CoV-2 testing. Health care systems may consider implementing mobile care delivery models to reach vulnerable populations. Our findings support the need for systemic change to increase primary care and health care access beyond short-term pandemic solutions.

Modeling COVID-19 latent prevalence to assess a public health intervention at a state and regional scale

BACKGROUND: Emergence of COVID-19 caught the world off-guard and unprepared, initiating a global pandemic. In the absence of evidence, individual communities had to take timely action to reduce the rate of disease spread and avoid overburdening their healthcare systems. Although a few predictive models have been published to guide these decisions, most have not taken into account spatial differences and have included assumptions that do not match the local realities. Access to reliable information that is adapted to local context is critical for policymakers to make informed decisions during a rapidly evolving pandemic. OBJECTIVE: The goal of this study was to develop an adapted susceptible-infected-removed (SIR) model to predict the trajectory of the COVID-19 pandemic in North Carolina (NC) and the Charlotte metropolitan region and to incorporate the effect of a public health intervention to reduce disease spread, while accounting for unique regional features and imperfect detection. METHODS: Using the software package R, three SIR models were fit to infection prevalence data from the state and the greater Charlotte region and then rigorously compared. One of these models (SIR-Int) accounted for a stay-at-home intervention and imperfect detection of COVID-19 cases. We computed longitudinal total estimates of the susceptible, infected, and removed compartments of both populations, along with other pandemic characteristics (e.g., basic reproduction number). RESULTS: Prior to March 26, disease spread was rapid at the pandemic onset with the Charlotte region doubling time of 2.56 days (95% CI: (2.11, 3.25)) and in NC 2.94 days (95% CI: (2.33, 4.00)). Subsequently, disease spread significantly slowed with doubling times increased in the Charlotte region to 4.70 days (95% CI: (3.77, 6.22)) and in NC to 4.01 days (95% CI: (3.43, 4.83)). Reflecting spatial differences, this deceleration favored the greater Charlotte region compared to NC as a whole. A comparison of the efficacy of intervention, defined as 1 - the hazard ratio of infection, gave 0.25 for NC and 0.43 for the Charlotte region. Also, early in the pandemic, the initial basic SIR model had good fit to the data;however, as the pandemic and local conditions evolved, the SIR-Int model emerged as the model with better fit. CONCLUSIONS: Using local data and continuous attention to model adaptation, our findings have enabled policymakers, public health officials and health systems to proactively plan capacity and evaluate the impact of a public health intervention. Our SIR-Int model for estimated latent prevalence was reasonably flexible, highly accurate, and demonstrated the efficacy of a stay-at-home order at both the state and regional level. Our results highlight the importance of incorporating local context into pandemic forecast modeling, as well as the need to remain vigilant and informed by the data as we enter into a critical period of the outbreak.