Rural Public Health Data Challenges During the COVID-19 Pandemic: The Case for Building Better Systems Ahead of a Public Health Crisis.

CONTEXT: Rural public health personnel serve communities that have been particularly susceptible to COVID-19 and yet faced the pandemic with far less well-resourced capacity than their urban counterparts. A critical aspect of addressing local health inequities is access to high-quality population data and the capacity to effectively use data to support decision making. However, much of the data required to investigate inequities are not readily available to rural local health departments and the tools and training to analyze data are often lacking. PROGRAM: The purpose of our effort was to explore rural data challenges related to COVID-19 and provide recommendations for improving rural data access and capacity ahead of future crises. IMPLEMENTATION: We gathered qualitative data in 2 phases, more than 8 months apart, from rural public health practice personnel. Initial data were gathered in October-November 2020 regarding rural public health data needs during the COVID-19 pandemic and then to later identify whether the same findings held true in July 2021 or whether access to and capacity to use data to address the pandemic and related inequities improved as the pandemic progressed. EVALUATION: In our 4-state exploration focused on access and use of data among rural public health systems to promote health equity in the Northwest United States, we found tremendous and ongoing unmet data needs, challenges with communicating data, and a lack of capacity to meet this public health crisis. DISCUSSION: Recommendations for addressing these challenges include increasing dedicated resources specifically to rural public health systems, improving data access and infrastructure, and providing dedicated data-related workforce development.

Using Informatics to Engage Vulnerable Populations in Research: Lessons Learned from the COVID-19 Pandemic.

Social distancing and "lockdown" measures introduced by the COVID-19 pandemic created barriers to recruitment and engagement of community members in research activities. Information technology tools were quickly introduced to allow for virtual participation of stakeholders in research. Vulnerable populations, namely communities with limited access to resources or at a higher risk to experience bias or discrimination, were less likely to engage in such virtual research initiatives. Informatics tools have the potential to support these populations, but existing disparities require a careful consideration of engagement strategies. We discuss three case studies of ongoing research projects targeting vulnerable populations and highlight the role of informatics in facilitating engagement. Target populations include family caregivers of hospice patients, low-income older adults and patients with dementia and their families. We describe strategies to overcome unique challenges introduced by the pandemic, and ways to build a more resilient future.

Challenges and opportunities in conducting research with older adults with dementia during COVID-19 and beyond.

The coronavirus disease 19 (COVID-19) pandemic has created significant and new challenges for the conduct of clinical research involving older adults with Alzheimer's disease and related dementias (ADRD). It has also stimulated positive adaptations in methods for engaging older adults with ADRD in research, particularly through the increased availability of virtual platforms. In this paper, we describe how we adapted standard in-person participant recruitment and qualitative data collection methods for virtual use in a study of decision-making experiences in older adults with ADRD. We describe key considerations for the use of technology and virtual platforms and discuss our experience with using recommended strategies to recruit a diverse sample of older adults. We highlight the need for research funding that supports the community-based organizations on which improving equity in ADRD research participation often depends.

The REMAP-CAP (Randomized Embedded Multifactorial Adaptive Platform for Community-acquired Pneumonia) Study. Rationale and Design.

There is broad interest in improved methods to generate robust evidence regarding best practice, especially in settings where patient conditions are heterogenous and require multiple concomitant therapies. Here, we present the rationale and design of a large, international trial that combines features of adaptive platform trials with pragmatic point-of-care trials to determine best treatment strategies for patients admitted to an intensive care unit with severe community-acquired pneumonia. The trial uses a novel design, entitled "a randomized embedded multifactorial adaptive platform." The design has five key features: 1) randomization, allowing robust causal inference; 2) embedding of study procedures into routine care processes, facilitating enrollment, trial efficiency, and generalizability; 3) a multifactorial statistical model comparing multiple interventions across multiple patient subgroups; 4) response-adaptive randomization with preferential assignment to those interventions that appear most favorable; and 5) a platform structured to permit continuous, potentially perpetual enrollment beyond the evaluation of the initial treatments. The trial randomizes patients to multiple interventions within four treatment domains: antibiotics, antiviral therapy for influenza, host immunomodulation with extended macrolide therapy, and alternative corticosteroid regimens, representing 240 treatment regimens. The trial generates estimates of superiority, inferiority, and equivalence between regimens on the primary outcome of 90-day mortality, stratified by presence or absence of concomitant shock and proven or suspected influenza infection. The trial will also compare ventilatory and oxygenation strategies, and has capacity to address additional questions rapidly during pandemic respiratory infections. As of January 2020, REMAP-CAP (Randomized Embedded Multifactorial Adaptive Platform for Community-acquired Pneumonia) was approved and enrolling patients in 52 intensive care units in 13 countries on 3 continents. In February, it transitioned into pandemic mode with several design adaptations for coronavirus disease 2019. Lessons learned from the design and conduct of this trial should aid in dissemination of similar platform initiatives in other disease areas.Clinical trial registered with www.clinicaltrials.gov (NCT02735707).

Effect of Hydrocortisone on Mortality and Organ Support in Patients With Severe COVID-19: The REMAP-CAP COVID-19 Corticosteroid Domain Randomized Clinical Trial.

Importance: Evidence regarding corticosteroid use for severe coronavirus disease 2019 (COVID-19) is limited. Objective: To determine whether hydrocortisone improves outcome for patients with severe COVID-19. Design, Setting, and Participants: An ongoing adaptive platform trial testing multiple interventions within multiple therapeutic domains, for example, antiviral agents, corticosteroids, or immunoglobulin. Between March 9 and June 17, 2020, 614 adult patients with suspected or confirmed COVID-19 were enrolled and randomized within at least 1 domain following admission to an intensive care unit (ICU) for respiratory or cardiovascular organ support at 121 sites in 8 countries. Of these, 403 were randomized to open-label interventions within the corticosteroid domain. The domain was halted after results from another trial were released. Follow-up ended August 12, 2020. Interventions: The corticosteroid domain randomized participants to a fixed 7-day course of intravenous hydrocortisone (50 mg or 100 mg every 6 hours) (n = 143), a shock-dependent course (50 mg every 6 hours when shock was clinically evident) (n = 152), or no hydrocortisone (n = 108). Main Outcomes and Measures: The primary end point was organ support-free days (days alive and free of ICU-based respiratory or cardiovascular support) within 21 days, where patients who died were assigned -1 day. The primary analysis was a bayesian cumulative logistic model that included all patients enrolled with severe COVID-19, adjusting for age, sex, site, region, time, assignment to interventions within other domains, and domain and intervention eligibility. Superiority was defined as the posterior probability of an odds ratio greater than 1 (threshold for trial conclusion of superiority >99%). Results: After excluding 19 participants who withdrew consent, there were 384 patients (mean age, 60 years; 29% female) randomized to the fixed-dose (n = 137), shock-dependent (n = 146), and no (n = 101) hydrocortisone groups; 379 (99%) completed the study and were included in the analysis. The mean age for the 3 groups ranged between 59.5 and 60.4 years; most patients were male (range, 70.6%-71.5%); mean body mass index ranged between 29.7 and 30.9; and patients receiving mechanical ventilation ranged between 50.0% and 63.5%. For the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively, the median organ support-free days were 0 (IQR, -1 to 15), 0 (IQR, -1 to 13), and 0 (-1 to 11) days (composed of 30%, 26%, and 33% mortality rates and 11.5, 9.5, and 6 median organ support-free days among survivors). The median adjusted odds ratio and bayesian probability of superiority were 1.43 (95% credible interval, 0.91-2.27) and 93% for fixed-dose hydrocortisone, respectively, and were 1.22 (95% credible interval, 0.76-1.94) and 80% for shock-dependent hydrocortisone compared with no hydrocortisone. Serious adverse events were reported in 4 (3%), 5 (3%), and 1 (1%) patients in the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively. Conclusions and Relevance: Among patients with severe COVID-19, treatment with a 7-day fixed-dose course of hydrocortisone or shock-dependent dosing of hydrocortisone, compared with no hydrocortisone, resulted in 93% and 80% probabilities of superiority with regard to the odds of improvement in organ support-free days within 21 days. However, the trial was stopped early and no treatment strategy met prespecified criteria for statistical superiority, precluding definitive conclusions. Trial Registration: ClinicalTrials.gov Identifier: NCT02735707.