The goal of this project is to conduct algorithm validation using diagnosis codes to identify potential hospitalized COVID-19 cases and validate the algorithm(s) using laboratory test results.
The workgroup will develop a dataset for validation that contains a) data on presumed hospitalized COVID-19 cases using multiple claims-based algorithms; b) presence or absence of diagnostic laboratory tests; and c) results of tests performed. The overall objective of this work is to calculate positive predictive values for multiple diagnosis code-based algorithms for hospitalized COVID-19 cases where the reference standard is a positive diagnostic laboratory test.
The workgroup will collect the dataset two times over the course of the six-month project period to validate these algorithms in the context of expected changes in coding practices and testing patterns. The dataset will include both unadjudicated claims data sources and adjudicated claims data sources, where possible, for comparison. The workgroup anticipates collaborating with four Data Partners on this activity, including two national claims-based insurers, and two integrated delivery systems partners.
Sarah Dutcher, PhD; Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD
Sheryl Kluberg, PhD; Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA
Robert Ball, MD, MPH, ScM; Monisha Billings, DDS, MPH, PhD; Brian Kit, MD, MPH; Michael Nguyen, MD; Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD
Noelle Cocoros, DSc, MPH; Adee Kennedy, MS, MPH; Darren Toh, ScD; Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA