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Use of and Comorbidities Associated With Diagnostic Codes for COVID-19 in US Health Insurance Claims

Educational Objective
To identify the key insights or developments described in this article
1 Credit CME

Accurate identification of COVID-19 diagnosis in patient medical records is essential for studies using administrative data to examine morbidity, mortality, and risk factors associated with COVID-19.1 Before April 1, 2020, the Centers for Disease Control and Prevention suggested using the existing International Statistical Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) code B97.29 (other coronavirus as the cause of diseases classified elsewhere) as the primary diagnostic code for patients infected with COVID-19.2 On April 1, 2020, a new code U07.1 (2019-nCoV acute respiratory disease) was added to ICD-10-CM3 and was rapidly adopted by hospitals.4 Our study examined how nonhospital and hospital health care professionals have used these diagnostic codes in practice using a national medical claims data set in the US. We analyzed the comorbidities associated with COVID-19 diagnosis to assess the specificity of the legacy code and the importance of using both codes.

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CME Disclosure Statement: Unless noted, all individuals in control of content reported no relevant financial relationships. If applicable, all relevant financial relationships have been mitigated.

Article Information

Accepted for Publication: July 8, 2021.

Published: September 8, 2021. doi:10.1001/jamanetworkopen.2021.24643

Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2021 Yang KC et al. JAMA Network Open.

Corresponding Author: Brea L. Perry, PhD, Department of Sociology, Indiana University-Bloomington, 1020 E Kirkwood Ave, 767 Ballantine Hall, Bloomington, IN 47405 (blperry@indiana.edu).

Author Contributions: Mr Yang and Dr Perry had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Yang, Lee, Ahn.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Yang.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Yang.

Obtained funding: Ahn, Perry.

Administrative, technical, or material support: Yang, Ahn, Perry.

Supervision: Ahn, Perry.

Conflict of Interest Disclosures: None reported.

Funding/Support: This research was funded by grant R01 DA039928 from the National Institute on Drug Abuse (Dr Perry).

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The findings and conclusions in this study are those of the authors and do not necessarily represent the official position of the National Institute on Drug Abuse.

Additional Contributions: We thank Kosali Simon, PhD, and the College of Arts and Sciences at Indiana University-Bloomington for their support. They did not receive financial compensation for their contribution.

References
1.
Schwab  P , Mehrjou  A , Parbhoo  S ,  et al.  Real-time prediction of COVID-19 related mortality using electronic health records.   Nat Commun. 2021;12(1):1058. doi:10.1038/s41467-020-20816-7 PubMedGoogle ScholarCrossref
2.
Centers for Disease Control and Prevention. ICD-10-CM official coding guidelines—supplement coding encounters related to COVID-19 coronavirus outbreak. February 20, 2020. Accessed October 9, 2020. https://www.cdc.gov/nchs/data/icd/ICD-10-CM-Official-Coding-Gudance-Interim-Advice-coronavirus-feb-20-2020.pdf
3.
Centers for Disease Control and Prevention. ICD-10-CM official coding and reporting guidelines: April 1, 2020 through September 30, 2020. Accessed October 9, 2020. https://www.cdc.gov/nchs/data/icd/COVID-19-guidelines-final.pdf
4.
Kadri  SS , Gundrum  J , Warner  S ,  et al.  Uptake and accuracy of the diagnosis code for COVID-19 among US hospitalizations.   JAMA. 2020;324(24):2553-2554. doi:10.1001/jama.2020.20323 PubMedGoogle ScholarCrossref
5.
Wallace  PJ , Shah  ND , Dennen  T , Bleicher  PA , Crown  WH .  Optum Labs: building a novel node in the learning health care system.   Health Aff (Millwood). 2014;33(7):1187-1194. doi:10.1377/hlthaff.2014.0038 PubMedGoogle ScholarCrossref
6.
Ogrinc  G , Davies  L , Goodman  D , Batalden  P , Davidoff  F , Stevens  D .  SQUIRE 2.0 (Standards for Quality Improvement Reporting Excellence): revised publication guidelines from a detailed consensus process.   BMJ Qual Saf. 2016;25(12):986-992. doi:10.1136/bmjqs-2015-004411 PubMedGoogle ScholarCrossref
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