Association of Participation in Medicare's Oncology Care Model With Spending, Utilization, and Quality Outcomes Among Commercially Insured and Medicare Advantage Members.

Purpose: The Oncology Care Model (OCM), a value-based payment model for traditional Medicare beneficiaries with cancer, yielded total spending reductions that were outweighed by incentive payments, resulting in net losses to the Centers for Medicare & Medicaid Services. We studied whether the OCM yielded spillover effects in total episode spending, utilization, and quality among commercially insured and Medicare Advantage (MA) members, who were not targeted by the program.

Patients And Methods: This observational study used administrative claims from a large national payer, yielding 157,189 total patients with commercial insurance or MA with solid malignancies who initiated 229,376 systemic anticancer therapy episodes before (2012-2015) and during (2016-2021) the OCM at 125 OCM-participating practices (a subset of total OCM practices) and a 1:10 propensity-matched set of 860 non-OCM practices.

Improving on polygenic scores across complex traits using select and shrink with summary statistics (S4) and LDpred2.

Background: As precision medicine advances, polygenic scores (PGS) have become increasingly important for clinical risk assessment. Many methods have been developed to create polygenic models with increased accuracy for risk prediction. Our select and shrink with summary statistics (S4) PGS method has previously been shown to accurately predict the polygenic risk of epithelial ovarian cancer.

Prediction of daily new COVID-19 cases - Difficulties and possible solutions.

Epidemiological compartmental models, such as SEIR (Susceptible, Exposed, Infectious, and Recovered) models, have been generally used in analyzing epidemiological data and forecasting the trajectory of transmission of infectious diseases such as COVID-19. Experience shows that accurately forecasting the trajectory of COVID-19 transmission curve is a big challenge for researchers in the field of epidemiological modeling because multiple unquantified factors can affect the trajectory of COVID-19 transmission. In the past years, we used a new compartmental model, l-i SEIR model, to analyze the COVID-19 transmission trend in the United States.