Coding long COVID: characterizing a new disease through an ICD-10 lens
University of North Carolina at Chapel Hill · University of Tennessee Health Science Center · +7 more institutions
Abstract
Naming a newly discovered disease is a difficult process; in the context of the COVID-19 pandemic and the existence of post-acute sequelae of SARS-CoV-2 infection (PASC), which includes long COVID, it has proven especially challenging. Disease definitions and assignment of a diagnosis code are often asynchronous and iterative. The clinical definition and our understanding of the underlying mechanisms of long COVID are still in flux, and the deployment of an ICD-10-CM code for long COVID in the USA took nearly 2 years after patients had begun to describe their condition. Here, we leverage the largest publicly available HIPAA-limited dataset about patients with COVID-19 in the US to examine the heterogeneity of adoption and use of U09.9, the ICD-10-CM code for "Post COVID-19 condition, unspecified." METHODS: We undertook a number of analyses to characterize the N3C population with a U09.9 diagnosis code (n = 33,782), including assessing person-level demographics and a number of area-level social determinants of health; diagnoses commonly co-occurring with U09.9, clustered using the Louvain algorithm; and quantifying medications and procedures recorded within 60 days of U09.9 diagnosis. We stratified all analyses by age group in order to discern differing patterns of care across the lifespan.
We established the diagnoses most commonly co-occurring with U09.9 and algorithmically clustered them into four major categories: cardiopulmonary, neurological, gastrointestinal, and comorbid conditions. Importantly, we discovered that the population of patients diagnosed with U09.9 is demographically skewed toward female, White, non-Hispanic individuals, as well as individuals living in areas with low poverty and low unemployment. Our results also include a characterization of common procedures and medications associated with U09.9-coded patients.
Citation impact
- FWCI
- 32.45
- Percentile
- 100%
- References
- 44
Authors
16- EPEmily PfaffCorresponding
University of North Carolina at Chapel Hill
- CMCharisse Madlock‐Brown
University of Tennessee Health Science Center
- JMJohn M. Baratta
University of North Carolina at Chapel Hill
- ABAbhishek Bhatia
University of North Carolina at Chapel Hill
- HDHannah Davis
Patient-Led Research Collaborative
Topics & keywords
- Medicine
- Medical diagnosis
- Population
- Pandemic
- Context (archaeology)
- Disease
- Coronavirus disease 2019 (COVID-19)
- Diagnosis code
- No poverty
Funding
- BABill and Melinda Gates FoundationAwards: UL1TR002649, INV-018455
- YUYale University
- BUBrown University
- CHChildren's Hospital of PhiladelphiaAward: UL1TR001878
- VUVanderbilt University
- UOUniversity of MiamiAward: UL1TR002736
- UOUniversity of Pennsylvania
- OSOhio State UniversityAward: UL1TR002733
- EUEmory University
- TMTufts Medical CenterAwards: UL1TR002544, UL1TR001425
- UOUniversity of Wisconsin-MadisonAwards: UL1TR001881, UL1TR002373
- GWGeorge Washington University
- CCCincinnati Children's Hospital Medical CenterAward: UL1TR001425
- CHChildren's Hospital ColoradoAward: UL1TR002535
- VIVanderbilt Institute for Clinical and Translational ResearchAward: UL1TR002243
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- UOUniversity of MinnesotaAward: UL1TR002494
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- JHJohns Hopkins University
- IFInstitute for Translational Medicine and TherapeuticsAward: UL1TR001878
- IOInstitute of Clinical and Translational SciencesAwards: UL1TR002736, UL1TR002537, UL1TR002378, UL1TR002345, UL1TR001445, UL1TR002243
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- IOInstitute of Translational Health SciencesAwards: UL1TR002319, UL1TR002014
- GCGeorgia Clinical and Translational Science AllianceAwards: UL1TR002378, UL1TR002489, UL1TR002544, UL1TR002243, UL1TR002553, UL1TR002649, UL1TR002529, UL1TR002345, UL1TR002366, UL1TR002001, UL1TR003142, UL1TR001863, UL1TR001442, UL1TR001881, UL1TR001425
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- LMLeonard M. Miller School of Medicine
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- CFCenter for Clinical and Translational Science, Mayo ClinicAward: UL1TR002377
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- AIAccelerated Innovation Research Initiative Turning Top Science and Ideas into High-Impact Values