Numerous publications describe the clinical manifestations of post-acute sequelae of SARS-CoV-2 (PASC or “long COVID”), but they are difficult to integrate because of heterogeneous methods and the lack of a standard for denoting the many phenotypic manifestations. Patient-led studies are of particular importance for understanding the natural history of COVID-19, but integration is hampered because they often use different terms to describe the same symptom or condition. This significant disparity in patient versus clinical characterization motivated the proposed ontological approach to specifying manifestations, which will improve capture and integration of future long COVID studies. The Human Phenotype Ontology (HPO) is a widely used standard for exchange and analysis of phenotypic abnormalities in human disease but has not yet been applied to the analysis of COVID-19. We identified 303 articles published before April 29, 2021, curated 59 relevant manuscripts that described clinical manifestations in 81 cohorts three weeks or more following acute COVID-19, and mapped 287 unique clinical findings to HPO terms. We present layperson synonyms and definitions that can be used to link patient self-report questionnaires to standard medical terminology. Long COVID clinical manifestations are not assessed consistently across studies, and most manifestations have been reported with a wide range of synonyms by different authors. Across at least 10 cohorts, authors reported 31 unique clinical features corresponding to HPO terms; the most commonly reported feature was Fatigue (median 45.1%) and the least commonly reported was Nausea (median 3.9%), but the reported percentages varied widely between studies. Translating long COVID manifestations into computable HPO terms will improve analysis, data capture, and classification of long COVID patients. If researchers, clinicians, and patients share a common language, then studies can be compared/pooled more effectively. Furthermore, mapping lay terminology to HPO will help patients assist clinicians and researchers in creating phenotypic characterizations that are computationally accessible, thereby improving the stratification, diagnosis, and treatment of long COVID. U24TR002306; UL1TR001439; P30AG024832; GBMF4552; R01HG010067; UL1TR002535; K23HL128909; UL1TR002389; K99GM145411 .

Purpose: Rural communities are among the most underserved and resource‐scarce populations in the United States. However, there are limited data on COVID‐19 outcomes in rural America. This study aims to compare hospitalization rates and inpatient mortality among SARS‐CoV‐2‐infected persons stratified by residential rurality.Methods: This retrospective cohort study from the National COVID Cohort Collaborative (N3C) assesses 1,033,229 patients from 44 US hospital systems diagnosed with SARS‐CoV‐2 infection between January 2020 and June 2021. Primary outcomes were hospitalization and all‐cause inpatient mortality. Secondary outcomes were utilization of supplemental oxygen, invasive mechanical ventilation, vasopressor support, extracorporeal membrane oxygenation, and incidence of major adverse cardiovascular events or hospital readmission. The analytic approach estimates 90‐day survival in hospitalized patients and associations between rurality, hospitalization, and inpatient adverse events while controlling for major risk factors using Kaplan‐Meier survival estimates and mixed‐effects logistic regression.Findings: Of 1,033,229 diagnosed COVID‐19 patients included, 186,882 required hospitalization. After adjusting for demographic differences and comorbidities, urban‐adjacent and nonurban‐adjacent rural dwellers with COVID‐19 were more likely to be hospitalized (adjusted odds ratio [aOR] 1.18, 95% confidence interval [CI], 1.16‐1.21 and aOR 1.29, CI 1.24‐1.1.34) and to die or be transferred to hospice (aOR 1.36, CI 1.29‐1.43 and 1.37, CI 1.26‐1.50), respectively. All secondary outcomes were more likely among rural patients.Conclusions: Hospitalization, inpatient mortality, and other adverse outcomes are higher among rural persons with COVID‐19, even after adjusting for demographic differences and comorbidities. Further research is needed to understand the factors that drive health disparities in rural populations.

The Health Information Technology for Economic and Clinical Health Act of 2009 introduced the Meaningful Use program to incentivize the adoption of electronic health records (EHRs) in the U.S. This study investigates the disparities in EHR adoption and interoperability between rural and urban physicians in the context of federal programs like the Medicare Access and CHIP Reauthorization Act of 2015 and the 21st Century Cures Act. A cross-sectional analysis was conducted using the 2021 Quality Payment Program Experience Report Public Use File to compare EHR adoption and Promoting Interoperability scores (PISs) between urban and rural physician participants. Data were linked with the Certified Health IT Product List to assess certified EHR adoption and interoperability. The study included 209,152 physician participants, 12% of whom practiced in rural communities. EHR adoption was significantly higher in urban (74%) than in rural areas (64%). Epic Systems dominated the market in both settings. Multivariable logistic regression indicated lower odds of EHR adoption among rural physicians (OR: 0.79, CI: 0.76–0.82). Rural physicians also had lower PISs (β: –3.5, CI: –4.1 to –3.0). Factors like extreme hardship, small practitioner status, and location in a health professional shortage area significantly impacted EHR adoption and PISs. Significant disparities exist in EHR adoption and interoperability between rural and urban physicians. These disparities highlight the need for targeted interventions to enhance EHR adoption and interoperability in rural settings to ensure equitable access to healthcare technologies and improved patient outcomes across all communities.

Background: Coronavirus disease 2019 (COVID‐19) is now the third leading cause of death in the United States. Malnutrition in hospitalized patients increases risk of complications. However, the effect of malnutrition on outcomes in patients infected is unclear. This study aims to identify the impact of malnutrition on mortality and adverse hospital events in patients hospitalized with COVID‐19.Methods: This study used data from the National COVID Cohort Collaborative (N3C), a COVID‐19 repository containing harmonized, longitudinal electronic health record data from US health systems. Malnutrition was categorized into three groups based on condition diagnosis: (1) none documented, (2) history of malnutrition, and (3) hospital‐acquired malnutrition. Multivariable logistic regression was performed to determine whether malnutrition was associated with mortality and adverse events, including mechanical ventilation, acute respiratory distress syndrome, extracorporeal membrane oxygenation, and hospital‐acquired pressure injury, in hospitalized patients with COVID‐19.Results: Of 343,188 patients hospitalized with COVID‐19, 11,206 had a history of malnutrition and 15,711 had hospital‐acquired malnutrition. After adjustment for potential confounders, odds of mortality were significantly higher in patients with a history of malnutrition (odds ratio [OR], 1.71; 95% confidence interval [CI], 1.63–1.79; P < 0.001) and hospital‐acquired malnutrition (OR, 2.5; 95% CI, 2.4–2.6; P < 0.001). Adjusted odds of adverse hospital events were also significantly elevated in both malnutrition groups.Conclusions: Results indicate the risk of mortality and adverse inpatient events in adults with COVID‐19 is significantly higher in patients with malnutrition. Prevention, diagnosis, and treatment of malnutrition could be a key component in improving outcomes in these patients.

The National COVID Cohort Collaborative (N3C) is a public–private–government partnership established during the Coronavirus pandemic to create a centralized data resource called the “N3C data enclave.” This resource contains individual-level health data from participating healthcare sites nationwide to support rapid collaborative analytics. N3C has enabled analytics within a cloud-based enclave of data from electronic health records from over 17 million people (with and without COVID-19) in the USA. To achieve this goal of a shared data resource, N3C implemented a shared governance strategy involving stakeholders in decision-making. The approach leveraged best practices in data stewardship and team science to rapidly enable COVID-19-related research at scale while respecting the privacy of data subjects and participating institutions. N3C balanced equitable access to data, team-based scientific productivity, and individual professional recognition – a key incentive for academic researchers. This governance approach makes N3C research sustainable and effective beyond the initial days of the pandemic. N3C demonstrated that shared governance can overcome traditional barriers to data sharing without compromising data security and trust. The governance innovations described herein are a helpful framework for other privacy-preserving data infrastructure programs and provide a working model for effective team science beyond COVID-19.

Postacute sequelae after the coronavirus disease (COVID) of 2019 (PASC) is increasingly recognized, although data on solid organ transplant (SOT) recipients (SOTRs) are limited. Using the National COVID Cohort Collaborative, we performed 1:1 propensity score matching (PSM) of all adult SOTR and nonimmunosuppressed/immunocompromised (ISC) patients with acute COVID infection (August 1, 2021 to January 13, 2023) for a subsequent PASC diagnosis using International Classification of Diseases, 10th Revision, Clinical Modification codes. Multivariable logistic regression was used to examine not only the association of SOT status with PASC, but also other patient factors after stratifying by SOT status. Prior to PSM, there were 8769 SOT and 1 576 769 non-ISC patients with acute COVID infection. After PSM, 8756 SOTR and 8756 non-ISC patients were included; 2.2% of SOTR (n = 192) and 1.4% (n = 122) of non-ISC patients developed PASC (P value < .001). In the overall matched cohort, SOT was independently associated with PASC (adjusted odds ratio [aOR], 1.48; 95% confidence interval [CI], 1.09-2.01). Among SOTR, COVID infection severity (aOR, 11.6; 95% CI, 3.93-30.0 for severe vs mild disease), older age (aOR, 1.02; 95% CI, 1.01-1.03 per year), and mycophenolate mofetil use (aOR, 2.04; 95% CI, 1.38-3.05) were each independently associated with PASC. In non-ISC patients, only depression (aOR, 1.96; 95% CI, 1.24-3.07) and COVID infection severity were. In conclusion, PASC occurs more commonly in SOTR than in non-ISC patients, with differences in risk profiles based on SOT status.