National COVID Cohort Collaborative Data Enhancements: A Path for Expanding Common Data Models.

Introduction: To support long COVID research in National COVID Cohort Collaborative (N3C), the N3C Phenotype and Data Acquisition team created data designs to aid contributing sites in enhancing their data. Enhancements include: long COVID specialty clinic indicator; Admission, Discharge, and Transfer (ADT) transactions; patient-level social determinants of health; and in-hospital use of oxygen supplementation.

Methods: For each enhancement, we defined the scope and wrote guidance on how to prepare and populate the data in a standardized way.

Diabetes medication regimens and patient clinical characteristics in the national patient-centered clinical research network, PCORnet.

We used electronic medical record (EMR) data in the National Patient-Centered Clinical Research Network (PCORnet) to characterize "real-world" prescription patterns of Type 2 diabetes (T2D) medications. We identified a retrospective cohort of 613,203 adult patients with T2D from 33 datamarts (median patient number: 12,711) from 2012 through 2017 using a validated computable phenotype. We characterized outpatient T2D prescriptions for each patient in the 90 days before and after cohort entry, as well as demographics, comorbidities, non-T2D prescriptions, and clinical and laboratory variables in the 730 days prior to cohort entry.

Evaluating Potential Racial Inequities in Low-dose Computed Tomography Screening for Lung Cancer.

Background: Lung cancer is the leading cause of cancer death in the US, and significant racial disparities exist in lung cancer outcomes. For example, Black men experience higher lung cancer incidence and mortality rates than their White counterparts. New screening recommendations for low-dose computed tomography (LDCT) promote earlier detection of lung cancer in at-risk populations and can potentially help mitigate racial disparities in lung cancer mortality if administered equitably.