Predicting Covid-19 infection and death rates among E.U. minority populations in the absence of racially disaggregated data through the use of US data comparisons.

BACKGROUND: The E.U.'s lack of racially disaggregated data impedes the formulation of effective interventions, and crises such as Covid-19 may continue to impact minorities more severely. Our predictive model offers insight into the disparate ways in which Covid-19 has likely impacted E.U. minorities and allows for the inference of differences in Covid-19 infection and death rates between E.U. minority and non-minority populations. METHODS: Data covering Covid-19, social determinants of health and minority status were included from 1 March 2020 to 28 February 2021. A systematic comparison of US and E.U. states enabled the projection of Covid-19 infection and death rates for minorities and non-minorities in E.U. states. RESULTS: The model predicted Covid-19 infection rates with 95-100% accuracy for 23 out of 28 E.U. states. Projections for Covid-19 infection and mortality rates among E.U. minority groups illustrate parallel trends to US rates. CONCLUSIONS: Disparities in Covid-19 infection and death rates by minority status likely exist in patterns similar to those observed in US data. Policy Implications: Collecting data by race/ethnicity in the E.U. would help document health disparities and craft more targeted health interventions and mitigation strategies.

A model for harmonizing flow cytometry in clinical trials.

Complexities in sample handling, instrument setup and data analysis are barriers to the effective use of flow cytometry to monitor immunological parameters in clinical trials. The novel use of a central laboratory may help mitigate these issues.