Objective: Coronavirus disease 2019 (COVID-19) poses societal challenges that require expeditious data and knowledge sharing. Though organizational clinical data are abundant, these are largely inaccessible to outside researchers. Statistical, machine learning, and causal analyses are most successful with large-scale data beyond what is available in any given organization. Here, we introduce the National COVID Cohort Collaborative (N3C), an open science community focused on analyzing patient-level data from many centers.Materials and Methods: The Clinical and Translational Science Award Program and scientific community created N3C to overcome technical, regulatory, policy, and governance barriers to sharing and harmonizing individual-level clinical data. We developed solutions to extract, aggregate, and harmonize data across organizations and data models, and created a secure data enclave to enable efficient, transparent, and reproducible collaborative analytics.Results: Organized in inclusive workstreams, we created legal agreements and governance for organizations and researchers; data extraction scripts to identify and ingest positive, negative, and possible COVID-19 cases; a data quality assurance and harmonization pipeline to create a single harmonized dataset; population of the secure data enclave with data, machine learning, and statistical analytics tools; dissemination mechanisms; and a synthetic data pilot to democratize data access.Conclusions: The N3C has demonstrated that a multisite collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multiorganizational clinical data for COVID-19 analytics. We expect this effort to save lives by enabling rapid collaboration among clinicians, researchers, and data scientists to identify treatments and specialized care and thereby reduce the immediate and long-term impacts of COVID-19.

As of the middle of December 2020, SARS-CoV-2 had infected more than 70 million people and caused more than 1.6 million deaths worldwide.¹ SARS-CoV-2 can cause COVID-19, a condition characterized by pneumonia, hyperinflammation, hypoxemic respiratory failure, a prothrombotic state, cardiac dysfunction, substantial mortality, and persistent morbidity in some survivors. Few therapeutic interventions authorized by the US Food and Drug Administration are available, and vaccine deployment has been slow. Progress in COVID-19 research has been slowed by a lack of broad access to clinical data. Investigators in the United Kingdom² and Denmark^2,3 have performed person-level analytical analyses across their respective populations to inform health care delivery, medication decisions, and national interventions, but the US has not had this capacity. A large, multicenter, representative clinical data set has been desperately needed by US practitioners, scientists, health care systems, and policymakers to develop predictive and diagnostic computational tools and to inform critical decisions.

OBJECTIVE. The purpose of this study was to describe school food environments and policies in US public schools and how they vary according to school characteristics. METHODS. We analyzed cross-sectional data from the third School Nutrition and Dietary Assessment study by using a nationally representative sample of 395 US public schools in 129 school districts in 38 states. These 2005 data included school reports of foods and beverages offered in the National School Lunch Program and on-site observations, in a subsample of schools, of competitive foods and beverages (those sold in vending machines and a la carte and that are not part of the National School Lunch Program). Seventeen factors were used to characterize school lunches, competitive foods, and other food-related policies and practices. These factors were used to compute the food environment summary score (0 [least healthy] to 17 [most healthy]) of each school. RESULTS. There were vending machines in 17%, 82%, and 97% of elementary, middle, and high schools, respectively, and a la carte items were sold in 71%, 92%, and 93% of schools, respectively. Among secondary schools with vending and a la carte sales, these sources were free of low-nutrient energy-dense foods or beverages in 15% and 21% of middle and high schools, respectively. The food environment summary score was significantly higher (healthier) in the lower grade levels. The summary score was not associated with the percentage of students that was certified for free or reduced-price lunches or the percentage of students that was a racial/ethnic minority. CONCLUSIONS. As children move to higher grade levels, their school food environments become less healthy. The great majority of US secondary schools sell items a la carte in the cafeteria and through vending machines, and these 2 sources often contain low-nutrient, energy-dense foods and beverages, commonly referred to as junk food.

Financial toxicity (FT) encompasses the monetary burden of paying for cancer care (eg, chemotherapy, surgery) and the negative consequences of such treatments on patients’ financial security.¹ Financial toxicity is associated with immediate and long-term consequences including treatment nonadherence, decreased health-related quality of life (HRQoL), bankruptcy, and an increased risk of mortality.^1-4 In 2014, cancer care spending reached $87.8 billion in the US, with $3.9 billion in direct spending by patients related to out-of-pocket expenses.⁵ Older adults (aged ≥70 years) face a unique set of pressures compared with the general population. As of 2020, more than 60% of all individuals with cancer in the US were 65 years or older.⁶ Furthermore, although 93% of the population of older adults has health care coverage through Medicare, many are left with large copayments and deductible minimums.⁷ In 2017, older adults spent 13.1% of total expenditures on health care costs.⁸ The issue of an aging population and increasing cancer prevalence also affects worldwide populations in both high-income and lower-income countries.⁹ Using population projections, it is estimated that by 2035, new cancer diagnoses in those 65 years and older are expected to increase from 6.7 million to 14 million globally.⁹

Background Stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, long COVID is incompletely understood and characterised by a wide range of manifestations that are difficult to analyse computationally. Additionally, the generalisability of machine learning classification of COVID-19 clinical outcomes has rarely been tested. Methods We present a method for computationally modelling PASC phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Our approach defines a nonlinear similarity function that maps from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning. Findings We found six clusters of PASC patients, each with distinct profiles of phenotypic abnormalities, including clusters with distinct pulmonary, neuropsychiatric, and cardiovascular abnormalities, and a cluster associated with broad, severe manifestations and increased mortality. There was significant association of cluster membership with a range of pre-existing conditions and measures of severity during acute COVID-19. We assigned new patients from other healthcare centres to clusters by maximum semantic similarity to the original patients, and showed that the clusters were generalisable across different hospital systems. The increased mortality rate originally identified in one cluster was consistently observed in patients assigned to that cluster in other hospital systems. Interpretation Semantic phenotypic clustering provides a foundation for assigning patients to stratified subgroups for natural history or therapy studies on PASC. Funding NIH (TR002306/OT2HL161847-01/OD011883/HG010860), U.S.D.O.E. (DE-AC02-05CH11231), Donald A. Roux Family Fund at Jackson Laboratory, Marsico Family at CU Anschutz.