Long COVID undercoding and prediction from releasable electronic health record summaries in All of Us
Year: 2026
Type: preprint
Abstract: Objective To estimate Long COVID undercoding in electronic health records (EHRs) against federal survey prevalence, and to evaluate a predictor of later coding that anyone can rebuild from counts a research enclave releases. Materials and Methods Among 23,790 All of Us adults with COVID-19, we compared coded shares with Household Pulse Survey prevalence. Among 23,300 adults uncoded at day 90, we predicted a first U09.9 code by day 455. We fitted naive Bayes and linear discriminant models outside the enclave from released counts and compared them with an elastic net fitted inside to the same patients and features. Tests were a random 20% of patients (4,680, 50 coded) and 18 held-out EHR sites (5,760, 60 coded). Results Under survey-comparability assumptions, about 1 in 9 adults who ever had Long COVID carried its code, and the women-to-men coding ratio was 0.79 (95% sensitivity interval, 0.65–0.95). The released models’ areas under the receiver operating characteristic curve (AUROCs) were 0.68 (95% CI, 0.60–0.75) on the patient test and 0.64 (0.55–0.72) at held-out sites, 0.049 (0.001–0.104) above the elastic net. On the patient test, the elastic net had a higher area under the precision-recall curve (difference 0.062, 0.003–0.122). Released probabilities overestimated coding risk at held-out sites. Discussion Fitting from released counts did not consistently lower discrimination. Coded outcomes inform disease discrimination only under stated coding assumptions. Conclusion Long COVID is substantially and unevenly undercoded, and a model rebuilt from public counts predicts its later coding with moderate discrimination.
Source: medRxiv
Authors: Amir Asiaee, Kaveh Aryan
Institutions: Vanderbilt University Medical Center, King's College London
Language: English
Subfield: Clinical Neurology
Field: Medicine
Domain: Health Sciences
Open Access status: green
Funder: National Institutes of Health
Awards: Illinois Precision Medicine Consortium , G1969433312, Precision Medicine Initiative Cohort Program Biobank, New York City Consortium for Precision Medicine, Technology to Empower Changes in Health (TECH) Network Participant Technologies Center, The New England Precision Medicine Consortium of the All of Us Research Program, All of Us Wisconsin , Data and Research Support Center , All of Us Pennsylvania, Trans-America Consortium of the Health Care Systems Research Network for the All of Us Research Program, Southern All of Us Network, Adaptive Platform for Personalized Engagement, California Precision Medicine Research Program Consortium
OpenAlex ID: W7223000017