{"paper_id":"1e756bab-236a-46db-bc91-8411fbdc77d9","body_text":"Among individuals who die of COVID-19, is the percentage who had diabetes actually higher than in those dying of other viral infections? | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report Among individuals who die of COVID-19, is the percentage who had diabetes actually higher than in those dying of other viral infections? Neha V. Reddy, Virginia Pate, Til Stürmer, Rachel Wong, Jeremy Harper, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8808866/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Background Early in the COVID-19 pandemic, mainstream news outlets sensationalized that 30–40% of all coronavirus deaths in the United States occurred among individuals with diabetes. It was unclear why this would be news-worthy because 30–40% is approximately the prevalence of diabetes in older adult, the age group most at risk for mortality from COVID-19. Thus, we sought to quantify the proportion of decedents from COVID-19 who had diabetes. To understand the proportion in context, we also calculated the proportion of decedents from influenza who had diabetes. Methods For assessing COVID-19 decedents who had diabetes, we used the National COVID Cohort Collaborative (N3C) data enclave, a nationally-representative, harmonized, and de-identified electronic health record database. For assessing influenza decedents who had diabetes, we used Medicare data. We restricted the N3C sample to > 65 years to align with Medicare eligibility. Results Among seniors with inpatient mortality due to COVID-19, 46.6% (95% CI: 46.1–47.0) had diabetes. Among seniors with inpatient mortality from influenza, the crude percent with diabetes was 61.2%. When age-standardized to match the N3C COVID-19 data, the percentage of influenza decedents with diabetes was 63.1% (95% CI: 59.1–67.1). Conclusions Among seniors with inpatient mortality from respiratory viruses, a very large proportion had diabetes before infection: 63% of influenza decedents and 47% of COVID-19 decedents. Thus, a high proportion of decedents having diabetes is not new or unique to COVID-19. These findings highlight the value of using available data to contextualize health communication to the public. Figures Figure 1 BACKGROUND In 2022, several mainstream news outlets headlined that 30 to 40% of individuals dying from COVID-19 in the United States had diabetes. 1 It is unclear why this claim made headlines because 30–40% is the approximate prevalence of diabetes in seniors, the group most susceptible to mortality from COVID-19. 2 In seeking to better understand this proportion, we reviewed the references cited to support this statistic, and they did not support the statement that proportion of decedents from COVID-19 with diabetes was high. The first was a CDC report explaining that underlying conditions were unknown for 54.9% of COVID-19 decedents. 3 A meta-analysis of 186 observational studies reporting that diabetes, hypertension, obesity and smoking combined contributed to nearly 30% of COVID-19 deaths, stated that the proportion of COVID-19 deaths attributable to diabetes was 8%. 4 A cohort study at 10 hospitals seeking to quantify risk of death among COVID-19 patients with prediabetes and diabetes reported that the mortality rate among those with diabetes was 27% while it was 15% among those with prediabetes. 5 The fourth reference reported similar COVID-19 mortality in those without diabetes, with prediabetes, or with diabetes that was medication-managed (by self-report) compared to those with diabetes who were not taking diabetes medication. 6 Given these discrepancies between the data cited and presentation of the data in popular media, our objective was to quantify the proportion of decedents from COVID-19 who had diabetes. To understand this proportion in context, we also sought to compare it to the proportion of decedents from influenza who had diabetes. We chose influenza as the comparator respiratory virus because of similar indications for testing and risk factors for severe infection, hospitalization, and death. METHODS This analysis was approved by the University of Minnesota institutional review board (STUDY00011578), which provided a waiver of consent. We used the National COVID Cohort Collaborative (N3C) data enclave, a centralized and harmonized database of de-identified electronic health record data from 78 healthcare systems nationally. For assessing influenza decedents who had diabetes, we used Medicare data. We restricted the N3C sample to > 65 years to align with Medicare eligibility. In both databases, diabetes was defined as any diagnosis code for diabetes or any diabetes medication prescription in the N3C or in Part D claims in the Medicare database with any available look-back. In the N3C database, death due to COVID-19 was defined as inpatient mortality for the encounter in which someone was admitted for COVID-19, from March 2020 to February 2022. In the Medicare database, death due to influenza was defined as inpatient mortality for the encounter in which someone was admitted for influenza, from January 2017 to December 2017 because this was the most recent Medicare data available. As age is an important risk factor for mortality, the age distribution of death due to COVID-19 in the N3C was calculated within 5-year age strata from 65 to 90 + years. These age-specific weights were used to multiply the stratum-specific percentage with diabetes for those with in-patient mortality from influenza in Medicare. The average of these products represents the percent of individuals dying of influenza who had diabetes, if those dying from influenza had the same age distribution as those dying from COVID-19. RESULTS Among seniors with inpatient mortality due to COVID-19, 46.6% (95% CI: 46.1–47.0) had diabetes. Among seniors with inpatient mortality from influenza, the crude percent with diabetes was 61.2%. When age-standardized to match the N3C COVID-19 data, the percentage of influenza decedents with diabetes was 63.1% (95% CI: 59.1–67.1), Figure . << Figure Here >> DISCUSSION This analysis leveraged N3C and Medicare data and found that among seniors with inpatient mortality from respiratory viruses, a very large proportion had diabetes before infection with either virus − 63% of influenza decedents had pre-infection diabetes and 47% of COVID-19 decedents had pre-infection diabetes. Thus, a high proportion of decedents having diabetes is not new or unique to the COVID-19 virus. These findings highlight the importance of using available data to contextualize health communication to the public. The large proportion of decedents having diabetes highlights the importance of diabetes prevention, diagnosis, and access to treatment. A meta-analysis of adults with type 2 diabetes reported two medications had high certainty of evidence for lower associations with COVID-19 mortality, with a summary relative risk (SRR) of 0.69 (95% CI 0.60–0.79) for metformin and 0.83 (0.71–0.97) for glucagon-like-peptide-1 receptor agonists. 7 Another study of adults with diabetes reported that metformin was associated with better survival, OR 0.65 (95% CI 0.45–0.93). 8 Limitations include that this is not a typical analysis – the intent was not to assess diabetes as an independent risk factor for mortality in patients with COVID compared to patients with influenza. Others have shown that diabetes is an independent risk factor for mortality from COVID-19. 9–11 In contrast, we report the proportion of decedents from COVID-19 who had diabetes and the proportion of decedents from influenza who had diabetes. This analysis did not account for potential confounding factors including presence of other comorbid conditions in the N3C and Medicare populations. 12 This analysis should not be considered a causal analysis. CONCLUSION Popular media articles implied that the proportion of COVID-19 decedents who had diabetes was unusually high. However, this analysis demonstrates that among seniors with inpatient mortality after respiratory infection, the proportion of COVID-19 decedents who had diabetes was not unusually high. Diabetes affects approximately 29.2% of adults aged 65 and older in the US and is a serious risk factor for mortality from viral infections. 1 These findings highlight the value of using available data to contextualize health communication to the public. Declarations AUTHOR CONTRIBUTIONS VP, JH, and SJ performed the data analysis. NR, CB, TS, JR, KW, JB, and RW wrote the main manuscript text. All authors edited the main manuscript text. Data Availability and IRB Approval The analyses described in this publication were conducted with data or tools accessed through the NCATS N3C Data Enclave https://covid.cd2h.org and N3C Attribution & Publication Policy v 1.2-2020-08-25b supported by NCATS U24 TR002306, Axle Informatics Subcontract: NCATS-P00438-B. This research was possible because of the patients whose information is included within the data and the organizations (https://ncats.nih.gov/n3c/resources/data-contribution/data-transfer-agreement-signatories) and scientists who have contributed to the on-going development of this community resource. The N3C data transfer to NCATS is performed under a Johns Hopkins University Reliance Protocol # IRB00249128 or individual site agreements with NIH. The N3C Data Enclave is managed under the authority of the NIH; information can be found at https://ncats.nih.gov/n3c/resources. The Medicare database infrastructure used for this project was funded by the Pharmacoepidemiology Gillings Innovation Lab (PEGIL) for the Population-Based Evaluation of Drug Benefits and Harms in Older US Adults (GIL200811.0010); the Center for Pharmacoepidemiology, Department of Epidemiology, UNC Gillings School of Global Public Health; the CER Strategic Initiative of UNC’s Clinical and Translational Science Award (UL1TR002489); the Cecil G. Sheps Center for Health Services Research, UNC; and the UNC School of Medicine. The N3C Publication committee confirmed that this manuscript is in accordance with N3C data use and attribution policies; however, this content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the N3C program. ACKNOWLEDGEMENTS We gratefully acknowledge the following core contributors to N3C: Adam B. Wilcox, Adam M. Lee, Alexis Graves, Alfred (Jerrod) Anzalone, Amin Manna, Amit Saha, Amy Olex, Andrea Zhou, Andrew E. Williams, Andrew Southerland, Andrew T. Girvin, Anita Walden, Anjali A. Sharathkumar, Benjamin Amor, Benjamin Bates, Brian Hendricks, Brijesh Patel, Caleb Alexander, Carolyn Bramante, Cavin Ward-Caviness, Charisse Madlock-Brown, Christine Suver, Christopher Chute, Christopher Dillon, Chunlei Wu, Clare Schmitt, Cliff Takemoto, Dan Housman, Davera Gabriel, David A. Eichmann, Diego Mazzotti, Don Brown, Eilis Boudreau, Elaine Hill, Elizabeth Zampino, Emily Carlson Marti, Emily R. Pfaff, Evan French, Farrukh M Koraishy, Federico Mariona, Fred Prior, George Sokos, Greg Martin, Harold Lehmann, Heidi Spratt, Hemalkumar Mehta, Hongfang Liu, Hythem Sidky, J.W. Awori Hayanga, Jami Pincavitch, Jaylyn Clark, Jeremy Richard Harper, Jessica Islam, Jin Ge, Joel Gagnier, Joel H. Saltz, Joel Saltz, Johanna Loomba, John Buse, Jomol Mathew, Joni L. Rutter, Julie A. McMurry, Justin Guinney, Justin Starren, Karen Crowley, Katie Rebecca Bradwell, Kellie M. Walters, Ken Wilkins, Kenneth R. Gersing, Kenrick Dwain Cato, Kimberly Murray, Kristin Kostka, Lavance Northington, Lee Allan Pyles, Leonie Misquitta, Lesley Cottrell, Lili Portilla, Mariam Deacy, Mark M. Bissell, Marshall Clark, Mary Emmett, Mary Morrison Saltz, Matvey B. Palchuk, Melissa A. Haendel, Meredith Adams, Meredith Temple-O'Connor, Michael G. Kurilla, Michele Morris, Nabeel Qureshi, Nasia Safdar, Nicole Garbarini, Noha Sharafeldin, Ofer Sadan, Patricia A. Francis, Penny Wung Burgoon, Peter Robinson, Philip R.O. Payne, Rafael Fuentes, Randeep Jawa, Rebecca Erwin-Cohen, Rena Patel, Richard A. Moffitt, Richard L. Zhu, Rishi Kamaleswaran, Robert Hurley, Robert T. Miller, Saiju Pyarajan, Sam G. Michael, Samuel Bozzette, Sandeep Mallipattu, Satyanarayana Vedula, Scott Chapman, Shawn T. O'Neil, Soko Setoguchi, Stephanie S. Hong, Steve Johnson, Tellen D. Bennett, Tiffany Callahan, Umit Topaloglu, Usman Sheikh, Valery Gordon, Vignesh Subbian, Warren A. Kibbe, Wenndy Hernandez, Will Beasley, Will Cooper, William Hillegass, Xiaohan Tanner Zhang. Details of contributions available at covid.cd2h.org/core-contributors The following institutions whose data is released or pending: Available: Advocate Health Care Network — UL1TR002389: The Institute for Translational Medicine (ITM) • Aurora Health Care Inc — UL1TR002373: Wisconsin Network For Health Research • Boston University Medical Campus — UL1TR001430: Boston University Clinical and Translational Science Institute • Brown University — U54GM115677: Advance Clinical Translational Research (Advance-CTR) • Carilion Clinic — UL1TR003015: iTHRIV Integrated Translational health Research Institute of Virginia • Case Western Reserve University — UL1TR002548: The Clinical & Translational Science Collaborative of Cleveland (CTSC) • Charleston Area Medical Center — U54GM104942: West Virginia Clinical and Translational Science Institute (WVCTSI) • Children’s Hospital Colorado — UL1TR002535: Colorado Clinical and Translational Sciences Institute • Columbia University Irving Medical Center — UL1TR001873: Irving Institute for Clinical and Translational Research • Dartmouth College — None (Voluntary) Duke University — UL1TR002553: Duke Clinical and Translational Science Institute • George Washington Children’s Research Institute — UL1TR001876: Clinical and Translational Science Institute at Children’s National (CTSA-CN) • George Washington University — UL1TR001876: Clinical and Translational Science Institute at Children’s National (CTSA-CN) • Harvard Medical School — UL1TR002541: Harvard Catalyst • Indiana University School of Medicine — UL1TR002529: Indiana Clinical and Translational Science Institute • Johns Hopkins University — UL1TR003098: Johns Hopkins Institute for Clinical and Translational Research • Louisiana Public Health Institute — None (Voluntary) • Loyola Medicine — Loyola University Medical Center • Loyola University Medical Center — UL1TR002389: The Institute for Translational Medicine (ITM) • Maine Medical Center — U54GM115516: Northern New England Clinical & Translational Research (NNE-CTR) Network • Mary Hitchcock Memorial Hospital & Dartmouth Hitchcock Clinic — None (Voluntary) • Massachusetts General Brigham — UL1TR002541: Harvard Catalyst • Mayo Clinic Rochester — UL1TR002377: Mayo Clinic Center for Clinical and Translational Science (CCaTS) • Medical University of South Carolina — UL1TR001450: South Carolina Clinical & Translational Research Institute (SCTR) • MITRE Corporation — None (Voluntary) • Montefiore Medical Center — UL1TR002556: Institute for Clinical and Translational Research at Einstein and Montefiore • Nemours — U54GM104941: Delaware CTR ACCEL Program • NorthShore University HealthSystem — UL1TR002389: The Institute for Translational Medicine (ITM) • Northwestern University at Chicago — UL1TR001422: Northwestern University Clinical and Translational Science Institute (NUCATS) • OCHIN — INV-018455: Bill and Melinda Gates Foundation grant to Sage Bionetworks • Oregon Health & Science University — UL1TR002369: Oregon Clinical and Translational Research Institute • Penn State Health Milton S. Hershey Medical Center — UL1TR002014: Penn State Clinical and Translational Science Institute • Rush University Medical Center — UL1TR002389: The Institute for Translational Medicine (ITM) • Rutgers, The State University of New Jersey — UL1TR003017: New Jersey Alliance for Clinical and Translational Science • Stony Brook University — U24TR002306 • The Alliance at the University of Puerto Rico, Medical Sciences Campus — U54GM133807: Hispanic Alliance for Clinical and Translational Research (The Alliance) • The Ohio State University — UL1TR002733: Center for Clinical and Translational Science • The State University of New York at Buffalo — UL1TR001412: Clinical and Translational Science Institute • The University of Chicago — UL1TR002389: The Institute for Translational Medicine (ITM) • The University of Iowa — UL1TR002537: Institute for Clinical and Translational Science • The University of Miami Leonard M. Miller School of Medicine — UL1TR002736: University of Miami Clinical and Translational Science Institute • The University of Michigan at Ann Arbor — UL1TR002240: Michigan Institute for Clinical and Health Research • The University of Texas Health Science Center at Houston — UL1TR003167: Center for Clinical and Translational Sciences (CCTS) • The University of Texas Medical Branch at Galveston — UL1TR001439: The Institute for Translational Sciences • The University of Utah — UL1TR002538: Uhealth Center for Clinical and Translational Science • Tufts Medical Center — UL1TR002544: Tufts Clinical and Translational Science Institute • Tulane University — UL1TR003096: Center for Clinical and Translational Science • The Queens Medical Center — None (Voluntary) • University Medical Center New Orleans — U54GM104940: Louisiana Clinical and Translational Science (LA CaTS) Center • University of Alabama at Birmingham — UL1TR003096: Center for Clinical and Translational Science • University of Arkansas for Medical Sciences — UL1TR003107: UAMS Translational Research Institute • University of Cincinnati — UL1TR001425: Center for Clinical and Translational Science and Training • University of Colorado Denver, Anschutz Medical Campus — UL1TR002535: Colorado Clinical and Translational Sciences Institute • University of Illinois at Chicago — UL1TR002003: UIC Center for Clinical and Translational Science • University of Kansas Medical Center — UL1TR002366: Frontiers: University of Kansas Clinical and Translational Science Institute • University of Kentucky — UL1TR001998: UK Center for Clinical and Translational Science • University of Massachusetts Medical School Worcester — UL1TR001453: The UMass Center for Clinical and Translational Science (UMCCTS) • University Medical Center of Southern Nevada — None (voluntary) • University of Minnesota — UL1TR002494: Clinical and Translational Science Institute • University of Mississippi Medical Center — U54GM115428: Mississippi Center for Clinical and Translational Research (CCTR) • University of Nebraska Medical Center — U54GM115458: Great Plains IDeA-Clinical & Translational Research • University of North Carolina at Chapel Hill — UL1TR002489 and UM1TR004406: North Carolina Translational and Clinical Science Institute • University of Oklahoma Health Sciences Center — U54GM104938: Oklahoma Clinical and Translational Science Institute (OCTSI) • University of Pittsburgh — UL1TR001857: The Clinical and Translational Science Institute (CTSI) • University of Pennsylvania — UL1TR001878: Institute for Translational Medicine and Therapeutics • University of Rochester — UL1TR002001: UR Clinical & Translational Science Institute • University of Southern California — UL1TR001855: The Southern California Clinical and Translational Science Institute (SC CTSI) • University of Vermont — U54GM115516: Northern New England Clinical & Translational Research (NNE-CTR) Network • University of Virginia — UL1TR003015: iTHRIV Integrated Translational health Research Institute of Virginia • University of Washington — UL1TR002319: Institute of Translational Health Sciences • University of Wisconsin-Madison — UL1TR002373: UW Institute for Clinical and Translational Research • Vanderbilt University Medical Center — UL1TR002243: Vanderbilt Institute for Clinical and Translational Research • Virginia Commonwealth University — UL1TR002649: C. Kenneth and Dianne Wright Center for Clinical and Translational Research • Wake Forest University Health Sciences — UL1TR001420: Wake Forest Clinical and Translational Science Institute • Washington University in St. Louis — UL1TR002345: Institute of Clinical and Translational Sciences • Weill Medical College of Cornell University — UL1TR002384: Weill Cornell Medicine Clinical and Translational Science Center • West Virginia University — U54GM104942: West Virginia Clinical and Translational Science Institute (WVCTSI) Submitted: Icahn School of Medicine at Mount Sinai — UL1TR001433: ConduITS Institute for Translational Sciences • The University of Texas Health Science Center at Tyler — UL1TR003167: Center for Clinical and Translational Sciences (CCTS) • University of California, Davis — UL1TR001860: UCDavis Health Clinical and Translational Science Center • University of California, Irvine — UL1TR001414: The UC Irvine Institute for Clinical and Translational Science (ICTS) • University of California, Los Angeles — UL1TR001881: UCLA Clinical Translational Science Institute • University of California, San Diego — UL1TR001442: Altman Clinical and Translational Research Institute • University of California, San Francisco — UL1TR001872: UCSF Clinical and Translational Science Institute Pending: Arkansas Children’s Hospital — UL1TR003107: UAMS Translational Research Institute • Baylor College of Medicine — None (Voluntary) • Children’s Hospital of Philadelphia — UL1TR001878: Institute for Translational Medicine and Therapeutics • Cincinnati Children’s Hospital Medical Center — UL1TR001425: Center for Clinical and Translational Science and Training • Emory University — UL1TR002378: Georgia Clinical and Translational Science Alliance • HonorHealth — None (Voluntary) • Loyola University Chicago — UL1TR002389: The Institute for Translational Medicine (ITM) • Medical College of Wisconsin — UL1TR001436: Clinical and Translational Science Institute of Southeast Wisconsin • MedStar Health Research Institute — None (Voluntary) • Georgetown University — UL1TR001409: The Georgetown-Howard Universities Center for Clinical and Translational Science (GHUCCTS) • MetroHealth — None (Voluntary) • Montana State University — U54GM115371: American Indian/Alaska Native CTR • NYU Langone Medical Center — UL1TR001445: Langone Health’s Clinical and Translational Science Institute • Ochsner Medical Center — U54GM104940: Louisiana Clinical and Translational Science (LA CaTS) Center • Regenstrief Institute — UL1TR002529: Indiana Clinical and Translational Science Institute • Sanford Research — None (Voluntary) • Stanford University — UL1TR003142: Spectrum: The Stanford Center for Clinical and Translational Research and Education • The Rockefeller University — UL1TR001866: Center for Clinical and Translational Science • The Scripps Research Institute — UL1TR002550: Scripps Research Translational Institute • University of Florida — UL1TR001427: UF Clinical and Translational Science Institute • University of New Mexico Health Sciences Center — UL1TR001449: University of New Mexico Clinical and Translational Science Center • University of Texas Health Science Center at San Antonio — UL1TR002645: Institute for Integration of Medicine and Science • Yale New Haven Hospital — UL1TR001863: Yale Center for Clinical Investigation FUNDING Dr. Bramante is funded by the National Institute of Digestive, Diabetes, and Kidney Diseases (NIDDK) K23DK124654; Drs. Bramante, Wong, and Johnson by 3R01DK130351-02S1, National Institutes of Health (NIH). Dr. Buse was funded by the National Center for Advancing Translational Sciences (NCATS) (grant UM1TR004406). Dr Johnson were funded by NCATS grant UL1TR002494 and Dr. Bramante by NCATS grant KL2TR002492. DISCLOSURES Dr. Stürmer receives investigator-initiated research funding and support as Principal Investigator (R01 AG056479) from the National Institute on Aging (NIA) and as Co-Investigator (R01 HL118255, R01MD011680), NIH. Dr. Stürmer also receives salary support as Director of Comparative Effectiveness Research (CER), NC TraCS Institute, UNC (UL1TR002489 and UM1TR004406), co-Director of the Human Studies Consultation Core, NC Diabetes Research Center (P30DK124723), National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), the Center for Pharmacoepidemiology (current members: GlaxoSmithKline, UCB BioSciences, Takeda, AbbVie, and Boehringer Ingelheim), from pharmaceutical companies (Novo Nordisk), and from a generous contribution from Dr. Nancy A. Dreyer to the Department of Epidemiology, University of North Carolina at Chapel Hill. ). Dr. Stürmer does not accept personal compensation of any kind from any pharmaceutical company. He owns stock in Novartis, Roche, and Novo Nordisk. The remaining authors have no conflicts of interest to declare. References Jacobs A, Covid, Diabetes, Colliding in a Public Health Train Wreck. New York Times . 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Unmanaged Diabetes and Elevated Blood Glucose Are Poor Prognostic Factors in the Severity and Recovery Time in Predominantly Hispanic Hospitalized COVID-19 Patients. Front Endocrinol (Lausanne). 2022;13:861385. 10.3389/fendo.2022.861385 . Schlesinger S, Lang A, Christodoulou N, et al. Risk phenotypes of diabetes and association with COVID-19 severity and death: an update of a living systematic review and meta-analysis. Diabetologia Aug. 2023;66(8):1395–412. 10.1007/s00125-023-05928-1 . Smati S, Tramunt B, Wargny M, Gourdy P, Hadjadj S, Cariou B. COVID-19 and Diabetes Outcomes: Rationale for and Updates from the CORONADO Study. Curr Diab Rep Feb. 2022;22(2):53–63. 10.1007/s11892-022-01452-5 . Kristófl R, Bodegard J, Ritsinger V, et al. IDF2022-0622 Mortality and cardiorenal disease in type 1 and type 2 diabetes after COVID-19 and influenza hospitalization in Sweden. Diabetes Res Clin Pract. 2023;2023/03/01:197:110543. https://doi.org/10.1016/j.diabres.2023.110543 . Risk prediction of covid-. 19 related death and hospital admission in adults after covid-19 vaccination: national prospective cohort study. BMJ. 2021;374:n2300. 10.1136/bmj.n2300 . Palaiodimos L, Chamorro-Pareja N, Karamanis D, et al. Diabetes is associated with increased risk for in-hospital mortality in patients with COVID-19: a systematic review and meta-analysis comprising 18,506 patients. Hormones (Athens) Jun. 2021;20(2):305–14. 10.1007/s42000-020-00246-2 . Bechini A, Ninci A, Del Riccio M, et al. Impact of Influenza Vaccination on All-Cause Mortality and Hospitalization for Pneumonia in Adults and the Elderly with Diabetes: A Meta-Analysis of Observational Studies. Vaccines (Basel) May. 2020;30(2). 10.3390/vaccines8020263 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 23 Mar, 2026 Reviews received at journal 18 Mar, 2026 Reviewers agreed at journal 24 Feb, 2026 Reviewers agreed at journal 18 Feb, 2026 Reviews received at journal 14 Feb, 2026 Reviewers agreed at journal 13 Feb, 2026 Reviewers invited by journal 11 Feb, 2026 Editor assigned by journal 11 Feb, 2026 Submission checks completed at journal 11 Feb, 2026 First submitted to journal 06 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-8808866\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Short Report\",\"associatedPublications\":[],\"authors\":[{\"id\":592257437,\"identity\":\"38709027-cde3-4a56-9537-389dc83227e6\",\"order_by\":0,\"name\":\"Neha V. Reddy\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Mayo Medical School\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Neha\",\"middleName\":\"V.\",\"lastName\":\"Reddy\",\"suffix\":\"\"},{\"id\":592257438,\"identity\":\"17aafc14-7be6-4a16-9194-33b9571f78b6\",\"order_by\":1,\"name\":\"Virginia Pate\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of North Carolina at Chapel Hill\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Virginia\",\"middleName\":\"\",\"lastName\":\"Pate\",\"suffix\":\"\"},{\"id\":592257439,\"identity\":\"69d948d4-0abd-4b2f-920f-9cf486404ac8\",\"order_by\":2,\"name\":\"Til Stürmer\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of North Carolina at Chapel Hill\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Til\",\"middleName\":\"\",\"lastName\":\"Stürmer\",\"suffix\":\"\"},{\"id\":592257443,\"identity\":\"584f2305-aa43-40f8-80e7-1974582d7e90\",\"order_by\":3,\"name\":\"Rachel Wong\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Stony Brook University Renaissance School of Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Rachel\",\"middleName\":\"\",\"lastName\":\"Wong\",\"suffix\":\"\"},{\"id\":592257444,\"identity\":\"570ed58e-3697-484c-a45c-5fc21f02df35\",\"order_by\":4,\"name\":\"Jeremy Harper\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Owl Health Works\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jeremy\",\"middleName\":\"\",\"lastName\":\"Harper\",\"suffix\":\"\"},{\"id\":592257445,\"identity\":\"058b501f-6390-40fc-9146-ef8f974767c6\",\"order_by\":5,\"name\":\"Jane E Reusch\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Colorado Denver Anschutz Medical Campus\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jane\",\"middleName\":\"E\",\"lastName\":\"Reusch\",\"suffix\":\"\"},{\"id\":592257451,\"identity\":\"a3002439-8c58-461c-9b0b-e5a921122e44\",\"order_by\":6,\"name\":\"Kenneth J Wilkins\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"National Institute of Diabetes and Digestive and Kidney Disease\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Kenneth\",\"middleName\":\"J\",\"lastName\":\"Wilkins\",\"suffix\":\"\"},{\"id\":592257454,\"identity\":\"56e07fc1-6b47-4c7c-86e1-c4738dcb950b\",\"order_by\":7,\"name\":\"Jena Tronieri\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Pennsylvania Perelman School of Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jena\",\"middleName\":\"\",\"lastName\":\"Tronieri\",\"suffix\":\"\"},{\"id\":592257461,\"identity\":\"5729752e-dd57-4f05-85ae-746e06cbd89d\",\"order_by\":8,\"name\":\"John B Buse\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of North Carolina School of Medicine\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"John\",\"middleName\":\"B\",\"lastName\":\"Buse\",\"suffix\":\"\"},{\"id\":592257463,\"identity\":\"b2bf8ac4-1312-4e17-95e3-ba5fe6408544\",\"order_by\":9,\"name\":\"Steven G. Johnson\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Minnesota Twin Cities\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Steven\",\"middleName\":\"G.\",\"lastName\":\"Johnson\",\"suffix\":\"\"},{\"id\":592257465,\"identity\":\"6713ef49-28f7-487a-bc14-3abfe96795fa\",\"order_by\":10,\"name\":\"Carolyn T. Bramante\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYBACAxiDH0IxE63FgEGygWQtBgeI1WIuffjphp87/sgZ38hOe8BQYZ3YQEiLZV+a2c3eMwbGZjdytxswnEknrMXgDIPZDd42g8RtN3K3STC2HSZGC/u3m3/bDOo3zwBp+UeUFh6z20BbEgwkQFoaiNBi2cNTdlu2zdhwxpm32yQSjqUbE9RizsO+7ebbNjl5/nagLR9qrGUJakEFCaQpHwWjYBSMglGACwAA4x0+fv2FfUUAAAAASUVORK5CYII=\",\"orcid\":\"\",\"institution\":\"University of Minnesota Medical School\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Carolyn\",\"middleName\":\"T.\",\"lastName\":\"Bramante\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-02-06 15:38:20\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-8808866/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-8808866/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":102832388,\"identity\":\"eed236d1-a33e-4cbb-b668-0e82b23a641f\",\"added_by\":\"auto\",\"created_at\":\"2026-02-17 10:12:31\",\"extension\":\"jpg\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":167176,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eVisual summary of methods used to assess the number of influenza and COVID-19 decedents with diabetes, and the results in each database, Medicare and N3C.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFigure Legend: Panel A \\u003c/strong\\u003eis a visual overview of the methods. \\u003cstrong\\u003ePanel B. \\u003c/strong\\u003egives the number and percentage of decedents with diabetes within each 5-year age category row (65-69; 70-74, etc). The left most column is the 5-year age category and applies to the three Influenza, Medicare columns and the three COVID-19, N3C columns. The overall proportion of decedents is given in the Total row, with the weighted percentage in the bottom row. *DM prevalence in decedents in Medicare weighted to the age distribution of deaths observed in N3C\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8808866/v1/1d4549c2730248095fec337c.jpg\"},{\"id\":102832390,\"identity\":\"a02e6b1a-7c70-42be-a64b-5153de100cb0\",\"added_by\":\"auto\",\"created_at\":\"2026-02-17 10:12:35\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":622960,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8808866/v1/57596c2d-d3be-4e8e-a23b-a688c29015be.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Among individuals who die of COVID-19, is the percentage who had diabetes actually higher than in those dying of other viral infections?\",\"fulltext\":[{\"header\":\"BACKGROUND\",\"content\":\"\\u003cp\\u003eIn 2022, several mainstream news outlets headlined that 30 to 40% of individuals dying from COVID-19 in the United States had diabetes.\\u003csup\\u003e\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u003c/sup\\u003e It is unclear why this claim made headlines because 30\\u0026ndash;40% is the approximate prevalence of diabetes in seniors, the group most susceptible to mortality from COVID-19.\\u003csup\\u003e2\\u003c/sup\\u003e\\u003c/p\\u003e\\u003cp\\u003eIn seeking to better understand this proportion, we reviewed the references cited to support this statistic, and they did not support the statement that proportion of decedents from COVID-19 with diabetes was high. The first was a CDC report explaining that underlying conditions were unknown for 54.9% of COVID-19 decedents.\\u003csup\\u003e\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u003c/sup\\u003e A meta-analysis of 186 observational studies reporting that diabetes, hypertension, obesity and smoking combined contributed to nearly 30% of COVID-19 deaths, stated that the proportion of COVID-19 deaths attributable to diabetes was 8%.\\u003csup\\u003e4\\u003c/sup\\u003e A cohort study at 10 hospitals seeking to quantify risk of death among COVID-19 patients with prediabetes and diabetes reported that the mortality rate among those with diabetes was 27% while it was 15% among those with prediabetes.\\u003csup\\u003e\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e\\u003c/sup\\u003e The fourth reference reported similar COVID-19 mortality in those without diabetes, with prediabetes, or with diabetes that was medication-managed (by self-report) compared to those with diabetes who were not taking diabetes medication.\\u003csup\\u003e\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e\\u003cp\\u003eGiven these discrepancies between the data cited and presentation of the data in popular media, our objective was to quantify the proportion of decedents from COVID-19 who had diabetes. To understand this proportion in context, we also sought to compare it to the proportion of decedents from influenza who had diabetes. We chose influenza as the comparator respiratory virus because of similar indications for testing and risk factors for severe infection, hospitalization, and death.\\u003c/p\\u003e\"},{\"header\":\"METHODS\",\"content\":\"\\u003cp\\u003eThis analysis was approved by the University of Minnesota institutional review board (STUDY00011578), which provided a waiver of consent. We used the National COVID Cohort Collaborative (N3C) data enclave, a centralized and harmonized database of de-identified electronic health record data from 78 healthcare systems nationally. For assessing influenza decedents who had diabetes, we used Medicare data. We restricted the N3C sample to \\u0026gt;\\u0026thinsp;65 years to align with Medicare eligibility.\\u003c/p\\u003e\\u003cp\\u003eIn both databases, diabetes was defined as any diagnosis code for diabetes or any diabetes medication prescription in the N3C or in Part D claims in the Medicare database with any available look-back.\\u003c/p\\u003e\\u003cp\\u003eIn the N3C database, death due to COVID-19 was defined as inpatient mortality for the encounter in which someone was admitted for COVID-19, from March 2020 to February 2022. In the Medicare database, death due to influenza was defined as inpatient mortality for the encounter in which someone was admitted for influenza, from January 2017 to December 2017 because this was the most recent Medicare data available.\\u003c/p\\u003e\\u003cp\\u003eAs age is an important risk factor for mortality, the age distribution of death due to COVID-19 in the N3C was calculated within 5-year age strata from 65 to 90\\u0026thinsp;+\\u0026thinsp;years. These age-specific weights were used to multiply the stratum-specific percentage with diabetes for those with in-patient mortality from influenza in Medicare. The average of these products represents the percent of individuals dying of influenza who had diabetes, if those dying from influenza had the same age distribution as those dying from COVID-19.\\u003c/p\\u003e\"},{\"header\":\"RESULTS\",\"content\":\"\\u003cp\\u003eAmong seniors with inpatient mortality due to COVID-19, 46.6% (95% CI: 46.1\\u0026ndash;47.0) had diabetes. Among seniors with inpatient mortality from influenza, the crude percent with diabetes was 61.2%. When age-standardized to match the N3C COVID-19 data, the percentage of influenza decedents with diabetes was 63.1% (95% CI: 59.1\\u0026ndash;67.1), \\u003cb\\u003eFigure\\u003c/b\\u003e.\\u003c/p\\u003e\\u003cp\\u003e\\u0026lt;\\u0026lt;\\u003cb\\u003eFigure Here \\u0026gt;\\u0026gt;\\u003c/b\\u003e\\u003c/p\\u003e\"},{\"header\":\"DISCUSSION\",\"content\":\"\\u003cp\\u003eThis analysis leveraged N3C and Medicare data and found that among seniors with inpatient mortality from respiratory viruses, a very large proportion had diabetes before infection with either virus\\u0026thinsp;\\u0026minus;\\u0026thinsp;63% of influenza decedents had pre-infection diabetes and 47% of COVID-19 decedents had pre-infection diabetes. Thus, a high proportion of decedents having diabetes is not new or unique to the COVID-19 virus. These findings highlight the importance of using available data to contextualize health communication to the public.\\u003c/p\\u003e\\u003cp\\u003eThe large proportion of decedents having diabetes highlights the importance of diabetes prevention, diagnosis, and access to treatment. A meta-analysis of adults with type 2 diabetes reported two medications had high certainty of evidence for lower associations with COVID-19 mortality, with a summary relative risk (SRR) of 0.69 (95% CI 0.60\\u0026ndash;0.79) for metformin and 0.83 (0.71\\u0026ndash;0.97) for glucagon-like-peptide-1 receptor agonists.\\u003csup\\u003e\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e\\u003c/sup\\u003e Another study of adults with diabetes reported that metformin was associated with better survival, OR 0.65 (95% CI 0.45\\u0026ndash;0.93).\\u003csup\\u003e\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e\\u003c/sup\\u003e\\u003c/p\\u003e\\u003cp\\u003eLimitations include that this is not a typical analysis \\u0026ndash; the intent was not to assess diabetes as an independent risk factor for mortality in patients with COVID compared to patients with influenza. Others have shown that diabetes is an independent risk factor for mortality from COVID-19.\\u003csup\\u003e9\\u0026ndash;11\\u003c/sup\\u003e In contrast, we report the proportion of decedents from COVID-19 who had diabetes and the proportion of decedents from influenza who had diabetes. This analysis did not account for potential confounding factors including presence of other comorbid conditions in the N3C and Medicare populations.\\u003csup\\u003e\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e\\u003c/sup\\u003e This analysis should not be considered a causal analysis.\\u003c/p\\u003e\"},{\"header\":\"CONCLUSION\",\"content\":\"\\u003cp\\u003ePopular media articles implied that the proportion of COVID-19 decedents who had diabetes was unusually high. However, this analysis demonstrates that among seniors with inpatient mortality after respiratory infection, the proportion of COVID-19 decedents who had diabetes was not unusually high. Diabetes affects approximately 29.2% of adults aged 65 and older in the US and is a serious risk factor for mortality from viral infections.\\u003csup\\u003e\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u003c/sup\\u003e These findings highlight the value of using available data to contextualize health communication to the public.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAUTHOR CONTRIBUTIONS\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eVP, JH, and SJ performed the data analysis. NR, CB, TS, JR, KW, JB, and RW wrote the main manuscript text. All authors edited the main manuscript text.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData Availability and IRB Approval\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe analyses described in this publication were conducted with data or tools accessed through the NCATS N3C Data Enclave https://covid.cd2h.org and N3C Attribution \\u0026amp; Publication Policy v 1.2-2020-08-25b supported by NCATS U24 TR002306, Axle Informatics Subcontract: NCATS-P00438-B. This research was possible because of the patients whose information is included within the data and the organizations (https://ncats.nih.gov/n3c/resources/data-contribution/data-transfer-agreement-signatories) and scientists who have contributed to the on-going development of this community resource. The N3C data transfer to NCATS is performed under a Johns Hopkins University Reliance Protocol # IRB00249128 or individual site agreements with NIH. The N3C Data Enclave is managed under the authority of the NIH; information can be found at https://ncats.nih.gov/n3c/resources. The Medicare database infrastructure used for this project was funded by the Pharmacoepidemiology Gillings Innovation Lab (PEGIL) for the Population-Based Evaluation of Drug Benefits and Harms in Older US Adults (GIL200811.0010); the Center for Pharmacoepidemiology, Department of Epidemiology, UNC Gillings School of Global Public Health; the CER Strategic Initiative of UNC\\u0026rsquo;s Clinical and Translational Science Award (UL1TR002489); the Cecil G. Sheps Center for Health Services Research, UNC; and the UNC School of Medicine.\\u003c/p\\u003e\\n\\u003cp\\u003eThe N3C Publication committee confirmed that this manuscript is in accordance with N3C data use and attribution policies; however, this content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the N3C program.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eACKNOWLEDGEMENTS\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe gratefully acknowledge the following core contributors to N3C:\\u003c/p\\u003e\\n\\u003cp\\u003eAdam B. Wilcox, Adam M. Lee, Alexis Graves, Alfred (Jerrod) Anzalone, Amin Manna, Amit Saha, Amy Olex, Andrea Zhou, Andrew E. Williams, Andrew Southerland, Andrew T. Girvin, Anita Walden, Anjali A. Sharathkumar, Benjamin Amor, Benjamin Bates, Brian Hendricks, Brijesh Patel, Caleb Alexander, Carolyn Bramante, Cavin Ward-Caviness, Charisse Madlock-Brown, Christine Suver, Christopher Chute, Christopher Dillon, Chunlei Wu, Clare Schmitt, Cliff Takemoto, Dan Housman, Davera Gabriel, David A. Eichmann, Diego Mazzotti, Don Brown, Eilis Boudreau, Elaine Hill, Elizabeth Zampino, Emily Carlson Marti, Emily R. Pfaff, Evan French, Farrukh M Koraishy, Federico Mariona, Fred Prior, George Sokos, Greg Martin, Harold Lehmann, Heidi Spratt, Hemalkumar Mehta, Hongfang Liu, Hythem Sidky, J.W. Awori Hayanga, Jami Pincavitch, Jaylyn Clark, Jeremy Richard Harper, Jessica Islam, Jin Ge, Joel Gagnier, Joel H. Saltz, Joel Saltz, Johanna Loomba, John Buse, Jomol Mathew, Joni L. Rutter, Julie A. McMurry, Justin Guinney, Justin Starren, Karen Crowley, Katie Rebecca Bradwell, Kellie M. Walters, Ken Wilkins, Kenneth R. Gersing, Kenrick Dwain Cato, Kimberly Murray, Kristin Kostka, Lavance Northington, Lee Allan Pyles, Leonie Misquitta, Lesley Cottrell, Lili Portilla, Mariam Deacy, Mark M. Bissell, Marshall Clark, Mary Emmett, Mary Morrison Saltz, Matvey B. Palchuk, Melissa A. Haendel, Meredith Adams, Meredith Temple-O\\u0026apos;Connor, Michael G. Kurilla, Michele Morris, Nabeel Qureshi, Nasia Safdar, Nicole Garbarini, Noha Sharafeldin, Ofer Sadan, Patricia A. Francis, Penny Wung Burgoon, Peter Robinson, Philip R.O. Payne, Rafael Fuentes, Randeep Jawa, Rebecca Erwin-Cohen, Rena Patel, Richard A. Moffitt, Richard L. Zhu, Rishi Kamaleswaran, Robert Hurley, Robert T. Miller, Saiju Pyarajan, Sam G. Michael, Samuel Bozzette, Sandeep Mallipattu, Satyanarayana Vedula, Scott Chapman, Shawn T. O\\u0026apos;Neil, Soko Setoguchi, Stephanie S. Hong, Steve Johnson, Tellen D. Bennett, Tiffany Callahan, Umit Topaloglu, Usman Sheikh, Valery Gordon, Vignesh Subbian, Warren A. Kibbe, Wenndy Hernandez, Will Beasley, Will Cooper, William Hillegass, Xiaohan Tanner Zhang. Details of contributions available at covid.cd2h.org/core-contributors\\u003c/p\\u003e\\n\\u003cp\\u003eThe following institutions whose data is released or pending:\\u003c/p\\u003e\\n\\u003cp\\u003eAvailable: Advocate Health Care Network \\u0026mdash; UL1TR002389: The Institute for Translational Medicine (ITM) \\u0026bull; Aurora Health Care Inc \\u0026mdash; UL1TR002373: Wisconsin Network For Health Research \\u0026bull; Boston University Medical Campus \\u0026mdash; UL1TR001430: Boston University Clinical and Translational Science Institute \\u0026bull; Brown University \\u0026mdash; U54GM115677: Advance Clinical Translational Research (Advance-CTR) \\u0026bull; Carilion Clinic \\u0026mdash; UL1TR003015: iTHRIV Integrated Translational health Research Institute of Virginia \\u0026bull; Case Western Reserve University \\u0026mdash; UL1TR002548: The Clinical \\u0026amp; Translational Science Collaborative of Cleveland (CTSC) \\u0026bull; Charleston Area Medical Center \\u0026mdash; U54GM104942: West Virginia Clinical and Translational Science Institute (WVCTSI) \\u0026bull; Children\\u0026rsquo;s Hospital Colorado \\u0026mdash; UL1TR002535: Colorado Clinical and Translational Sciences Institute \\u0026bull; Columbia University Irving Medical Center \\u0026mdash; UL1TR001873: Irving Institute for Clinical and Translational Research \\u0026bull; Dartmouth College \\u0026mdash; None (Voluntary) Duke University \\u0026mdash; UL1TR002553: Duke Clinical and Translational Science Institute \\u0026bull; George Washington Children\\u0026rsquo;s Research Institute \\u0026mdash; UL1TR001876: Clinical and Translational Science Institute at Children\\u0026rsquo;s National (CTSA-CN) \\u0026bull; George Washington University \\u0026mdash; UL1TR001876: Clinical and Translational Science Institute at Children\\u0026rsquo;s National (CTSA-CN) \\u0026bull; Harvard Medical School \\u0026mdash; UL1TR002541: Harvard Catalyst \\u0026bull; Indiana University School of Medicine \\u0026mdash; UL1TR002529: Indiana Clinical and Translational Science Institute \\u0026bull; Johns Hopkins University \\u0026mdash; UL1TR003098: Johns Hopkins Institute for Clinical and Translational Research \\u0026bull; Louisiana Public Health Institute \\u0026mdash; None (Voluntary) \\u0026bull; Loyola Medicine \\u0026mdash; Loyola University Medical Center \\u0026bull; Loyola University Medical Center \\u0026mdash; UL1TR002389: The Institute for Translational Medicine (ITM) \\u0026bull; Maine Medical Center \\u0026mdash; U54GM115516: Northern New England Clinical \\u0026amp; Translational Research (NNE-CTR) Network \\u0026bull; Mary Hitchcock Memorial Hospital \\u0026amp; Dartmouth Hitchcock Clinic \\u0026mdash; None (Voluntary) \\u0026bull; Massachusetts General Brigham \\u0026mdash; UL1TR002541: Harvard Catalyst \\u0026bull; Mayo Clinic Rochester \\u0026mdash; UL1TR002377: Mayo Clinic Center for Clinical and Translational Science (CCaTS) \\u0026bull; Medical University of South Carolina \\u0026mdash; UL1TR001450: South Carolina Clinical \\u0026amp; Translational Research Institute (SCTR) \\u0026bull; MITRE Corporation \\u0026mdash; None (Voluntary) \\u0026bull; Montefiore Medical Center \\u0026mdash; UL1TR002556: Institute for Clinical and Translational Research at Einstein and Montefiore \\u0026bull; Nemours \\u0026mdash; U54GM104941: Delaware CTR ACCEL Program \\u0026bull; NorthShore University HealthSystem \\u0026mdash; UL1TR002389: The Institute for Translational Medicine (ITM) \\u0026bull; Northwestern University at Chicago \\u0026mdash; UL1TR001422: Northwestern University Clinical and Translational Science Institute (NUCATS) \\u0026bull; OCHIN \\u0026mdash; INV-018455: Bill and Melinda Gates Foundation grant to Sage Bionetworks \\u0026bull; Oregon Health \\u0026amp; Science University \\u0026mdash; UL1TR002369: Oregon Clinical and Translational Research Institute \\u0026bull; Penn State Health Milton S. Hershey Medical Center \\u0026mdash; UL1TR002014: Penn State Clinical and Translational Science Institute \\u0026bull; Rush University Medical Center \\u0026mdash; UL1TR002389: The Institute for Translational Medicine (ITM) \\u0026bull; Rutgers, The State University of New Jersey \\u0026mdash; UL1TR003017: New Jersey Alliance for Clinical and Translational Science \\u0026bull; Stony Brook University \\u0026mdash; U24TR002306 \\u0026bull; The Alliance at the University of Puerto Rico, Medical Sciences Campus \\u0026mdash; U54GM133807: Hispanic Alliance for Clinical and Translational Research (The Alliance) \\u0026bull; The Ohio State University \\u0026mdash; UL1TR002733: Center for Clinical and Translational Science \\u0026bull; The State University of New York at Buffalo \\u0026mdash; UL1TR001412: Clinical and Translational Science Institute \\u0026bull; The University of Chicago \\u0026mdash; UL1TR002389: The Institute for Translational Medicine (ITM) \\u0026bull; The University of Iowa \\u0026mdash; UL1TR002537: Institute for Clinical and Translational Science \\u0026bull; The University of Miami Leonard M. Miller School of Medicine \\u0026mdash; UL1TR002736: University of Miami Clinical and Translational Science Institute \\u0026bull; The University of Michigan at Ann Arbor \\u0026mdash; UL1TR002240: Michigan Institute for Clinical and Health Research \\u0026bull; The University of Texas Health Science Center at Houston \\u0026mdash; UL1TR003167: Center for Clinical and Translational Sciences (CCTS) \\u0026bull; The University of Texas Medical Branch at Galveston \\u0026mdash; UL1TR001439: The Institute for Translational Sciences \\u0026bull; The University of Utah \\u0026mdash; UL1TR002538: Uhealth Center for Clinical and Translational Science \\u0026bull; Tufts Medical Center \\u0026mdash; UL1TR002544: Tufts Clinical and Translational Science Institute \\u0026bull; Tulane University \\u0026mdash; UL1TR003096: Center for Clinical and Translational Science \\u0026bull; The Queens Medical Center \\u0026mdash; None (Voluntary) \\u0026bull; University Medical Center New Orleans \\u0026mdash; U54GM104940: Louisiana Clinical and Translational Science (LA CaTS) Center \\u0026bull; University of Alabama at Birmingham \\u0026mdash; UL1TR003096: Center for Clinical and Translational Science \\u0026bull; University of Arkansas for Medical Sciences \\u0026mdash; UL1TR003107: UAMS Translational Research Institute \\u0026bull; University of Cincinnati \\u0026mdash; UL1TR001425: Center for Clinical and Translational Science and Training \\u0026bull; University of Colorado Denver, Anschutz Medical Campus \\u0026mdash; UL1TR002535: Colorado Clinical and Translational Sciences Institute \\u0026bull; University of Illinois at Chicago \\u0026mdash; UL1TR002003: UIC Center for Clinical and Translational Science \\u0026bull; University of Kansas Medical Center \\u0026mdash; UL1TR002366: Frontiers: University of Kansas Clinical and Translational Science Institute \\u0026bull; University of Kentucky \\u0026mdash; UL1TR001998: UK Center for Clinical and Translational Science \\u0026bull; University of Massachusetts Medical School Worcester \\u0026mdash; UL1TR001453: The UMass Center for Clinical and Translational Science (UMCCTS) \\u0026bull; University Medical Center of Southern Nevada \\u0026mdash; None (voluntary) \\u0026bull; University of Minnesota \\u0026mdash; UL1TR002494: Clinical and Translational Science Institute \\u0026bull; University of Mississippi Medical Center \\u0026mdash; U54GM115428: Mississippi Center for Clinical and Translational Research (CCTR) \\u0026bull; University of Nebraska Medical Center \\u0026mdash; U54GM115458: Great Plains IDeA-Clinical \\u0026amp; Translational Research \\u0026bull; University of North Carolina at Chapel Hill \\u0026mdash; UL1TR002489 and UM1TR004406: North Carolina Translational and Clinical Science Institute \\u0026bull; University of Oklahoma Health Sciences Center \\u0026mdash; U54GM104938: Oklahoma Clinical and Translational Science Institute (OCTSI) \\u0026bull; University of Pittsburgh \\u0026mdash; UL1TR001857: The Clinical and Translational Science Institute (CTSI) \\u0026bull; University of Pennsylvania \\u0026mdash; UL1TR001878: Institute for Translational Medicine and Therapeutics \\u0026bull; University of Rochester \\u0026mdash; UL1TR002001: UR Clinical \\u0026amp; Translational Science Institute \\u0026bull; University of Southern California \\u0026mdash; UL1TR001855: The Southern California Clinical and Translational Science Institute (SC CTSI) \\u0026bull; University of Vermont \\u0026mdash; U54GM115516: Northern New England Clinical \\u0026amp; Translational Research (NNE-CTR) Network \\u0026bull; University of Virginia \\u0026mdash; UL1TR003015: iTHRIV Integrated Translational health Research Institute of Virginia \\u0026bull; University of Washington \\u0026mdash; UL1TR002319: Institute of Translational Health Sciences \\u0026bull; University of Wisconsin-Madison \\u0026mdash; UL1TR002373: UW Institute for Clinical and Translational Research \\u0026bull; Vanderbilt University Medical Center \\u0026mdash; UL1TR002243: Vanderbilt Institute for Clinical and Translational Research \\u0026bull; Virginia Commonwealth University \\u0026mdash; UL1TR002649: C. Kenneth and Dianne Wright Center for Clinical and Translational Research \\u0026bull; Wake Forest University Health Sciences \\u0026mdash; UL1TR001420: Wake Forest Clinical and Translational Science Institute \\u0026bull; Washington University in St. Louis \\u0026mdash; UL1TR002345: Institute of Clinical and Translational Sciences \\u0026bull; Weill Medical College of Cornell University \\u0026mdash; UL1TR002384: Weill Cornell Medicine Clinical and Translational Science Center \\u0026bull; West Virginia University \\u0026mdash; U54GM104942: West Virginia Clinical and Translational Science Institute (WVCTSI)\\u2028\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eSubmitted: Icahn School of Medicine at Mount Sinai \\u0026mdash; UL1TR001433: ConduITS Institute for Translational Sciences \\u0026bull; The University of Texas Health Science Center at Tyler \\u0026mdash; UL1TR003167: Center for Clinical and Translational Sciences (CCTS) \\u0026bull; University of California, Davis \\u0026mdash; UL1TR001860: UCDavis Health Clinical and Translational Science Center \\u0026bull; University of California, Irvine \\u0026mdash; UL1TR001414: The UC Irvine Institute for Clinical and Translational Science (ICTS) \\u0026bull; University of California, Los Angeles \\u0026mdash; UL1TR001881: UCLA Clinical Translational Science Institute \\u0026bull; University of California, San Diego \\u0026mdash; UL1TR001442: Altman Clinical and Translational Research Institute \\u0026bull; University of California, San Francisco \\u0026mdash; UL1TR001872: UCSF Clinical and Translational Science Institute\\u2028\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003ePending: Arkansas Children\\u0026rsquo;s Hospital \\u0026mdash; UL1TR003107: UAMS Translational Research Institute \\u0026bull; Baylor College of Medicine \\u0026mdash; None (Voluntary) \\u0026bull; Children\\u0026rsquo;s Hospital of Philadelphia \\u0026mdash; UL1TR001878: Institute for Translational Medicine and Therapeutics \\u0026bull; Cincinnati Children\\u0026rsquo;s Hospital Medical Center \\u0026mdash; UL1TR001425: Center for Clinical and Translational Science and Training \\u0026bull; Emory University \\u0026mdash; UL1TR002378: Georgia Clinical and Translational Science Alliance \\u0026bull; HonorHealth \\u0026mdash; None (Voluntary) \\u0026bull; Loyola University Chicago \\u0026mdash; UL1TR002389: The Institute for Translational Medicine (ITM) \\u0026bull; Medical College of Wisconsin \\u0026mdash; UL1TR001436: Clinical and Translational Science Institute of Southeast Wisconsin \\u0026bull; MedStar Health Research Institute \\u0026mdash; None (Voluntary) \\u0026bull; Georgetown University \\u0026mdash; UL1TR001409: The Georgetown-Howard Universities Center for Clinical and Translational Science (GHUCCTS) \\u0026bull; MetroHealth \\u0026mdash; None (Voluntary) \\u0026bull; Montana State University \\u0026mdash; U54GM115371: American Indian/Alaska Native CTR \\u0026bull; NYU Langone Medical Center \\u0026mdash; UL1TR001445: Langone Health\\u0026rsquo;s Clinical and Translational Science Institute \\u0026bull; Ochsner Medical Center \\u0026mdash; U54GM104940: Louisiana Clinical and Translational Science (LA CaTS) Center \\u0026bull; Regenstrief Institute \\u0026mdash; UL1TR002529: Indiana Clinical and Translational Science Institute \\u0026bull; Sanford Research \\u0026mdash; None (Voluntary) \\u0026bull; Stanford University \\u0026mdash; UL1TR003142: Spectrum: The Stanford Center for Clinical and Translational Research and Education \\u0026bull; The Rockefeller University \\u0026mdash; UL1TR001866: Center for Clinical and Translational Science \\u0026bull; The Scripps Research Institute \\u0026mdash; UL1TR002550: Scripps Research Translational Institute \\u0026bull; University of Florida \\u0026mdash; UL1TR001427: UF Clinical and Translational Science Institute \\u0026bull; University of New Mexico Health Sciences Center \\u0026mdash; UL1TR001449: University of New Mexico Clinical and Translational Science Center \\u0026bull; University of Texas Health Science Center at San Antonio \\u0026mdash; UL1TR002645: Institute for Integration of Medicine and Science \\u0026bull; Yale New Haven Hospital \\u0026mdash; UL1TR001863: Yale Center for Clinical Investigation\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFUNDING\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eDr. Bramante is funded by the National Institute of Digestive, Diabetes, and Kidney Diseases (NIDDK) K23DK124654; Drs. Bramante, Wong, and Johnson by 3R01DK130351-02S1, National Institutes of Health (NIH).\\u0026nbsp;Dr. Buse was funded by the National Center for Advancing Translational Sciences (NCATS) (grant UM1TR004406). Dr Johnson were funded by NCATS grant\\u0026nbsp;UL1TR002494 and Dr. Bramante by NCATS grant KL2TR002492.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eDISCLOSURES\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eDr. St\\u0026uuml;rmer receives investigator-initiated research funding and support as Principal Investigator (R01 AG056479) from the National Institute on Aging (NIA) and as Co-Investigator (R01 HL118255, R01MD011680), NIH. Dr. St\\u0026uuml;rmer also receives salary support as Director of Comparative Effectiveness Research (CER), NC TraCS Institute, UNC (UL1TR002489 and UM1TR004406), co-Director of the Human Studies Consultation Core, NC Diabetes Research Center (P30DK124723), National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), the Center for Pharmacoepidemiology (current members: GlaxoSmithKline, UCB BioSciences, Takeda, AbbVie, and Boehringer Ingelheim), from pharmaceutical companies (Novo Nordisk), and from a generous contribution from Dr. Nancy A. Dreyer to the Department of Epidemiology, University of North Carolina at Chapel Hill. ). Dr. St\\u0026uuml;rmer does not accept personal compensation of any kind from any pharmaceutical company. He owns stock in Novartis, Roche, and Novo Nordisk. The remaining authors have no conflicts of interest to declare.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eJacobs A, Covid, Diabetes, Colliding in a Public Health Train Wreck. \\u003cem\\u003eNew York Times\\u003c/em\\u003e. Accessed 12/2/2024. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.nytimes.com/2022/04/03/health/diabetes-covid-deaths.html\\u003c/span\\u003e\\u003cspan address=\\\"https://www.nytimes.com/2022/04/03/health/diabetes-covid-deaths.html\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eNational Diabetes Statistics Report. 2023. Accessed November 5, 2024. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e\\u003c/span\\u003e\\u003cspan address=\\\"http://www.cdc.gov/diabetes/data/statistics-report/index.html\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eWortham JMLJ, Althomsons S et al. \\u003cem\\u003eCharacteristics of Persons Who Died with COVID-19. United States, February 12\\u0026ndash;May 18\\u003c/em\\u003e, 2020. Vol. 69. 2020:923\\u0026ndash;929. \\u003cem\\u003eMMWR Morb Mortal Wkly Rep\\u003c/em\\u003e. May 18, 2020. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.15585/mmwr.mm6928e1\\u003c/span\\u003e\\u003cspan address=\\\"10.15585/mmwr.mm6928e1\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eMahamat-Saleh Y, Fiolet T, Rebeaud ME, et al. Diabetes, hypertension, body mass index, smoking and COVID-19-related mortality: a systematic review and meta-analysis of observational studies. BMJ Open Oct. 2021;25(10):e052777. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1136/bmjopen-2021-052777\\u003c/span\\u003e\\u003cspan address=\\\"10.1136/bmjopen-2021-052777\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eSourij H, Aziz F, Br\\u0026auml;uer A, et al. COVID-19 fatality prediction in people with diabetes and prediabetes using a simple score upon hospital admission. Diabetes Obes Metab Feb. 2021;23(2):589\\u0026ndash;98. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1111/dom.14256\\u003c/span\\u003e\\u003cspan address=\\\"10.1111/dom.14256\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eBajpeyi S, Mossayebi A, Kreit H, et al. Unmanaged Diabetes and Elevated Blood Glucose Are Poor Prognostic Factors in the Severity and Recovery Time in Predominantly Hispanic Hospitalized COVID-19 Patients. Front Endocrinol (Lausanne). 2022;13:861385. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.3389/fendo.2022.861385\\u003c/span\\u003e\\u003cspan address=\\\"10.3389/fendo.2022.861385\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eSchlesinger S, Lang A, Christodoulou N, et al. Risk phenotypes of diabetes and association with COVID-19 severity and death: an update of a living systematic review and meta-analysis. Diabetologia Aug. 2023;66(8):1395\\u0026ndash;412. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1007/s00125-023-05928-1\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s00125-023-05928-1\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eSmati S, Tramunt B, Wargny M, Gourdy P, Hadjadj S, Cariou B. COVID-19 and Diabetes Outcomes: Rationale for and Updates from the CORONADO Study. Curr Diab Rep Feb. 2022;22(2):53\\u0026ndash;63. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1007/s11892-022-01452-5\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s11892-022-01452-5\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eKrist\\u0026oacute;fl R, Bodegard J, Ritsinger V, et al. IDF2022-0622 Mortality and cardiorenal disease in type 1 and type 2 diabetes after COVID-19 and influenza hospitalization in Sweden. Diabetes Res Clin Pract. 2023;2023/03/01:197:110543. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.diabres.2023.110543\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.diabres.2023.110543\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eRisk prediction of covid-. 19 related death and hospital admission in adults after covid-19 vaccination: national prospective cohort study. BMJ. 2021;374:n2300. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1136/bmj.n2300\\u003c/span\\u003e\\u003cspan address=\\\"10.1136/bmj.n2300\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003ePalaiodimos L, Chamorro-Pareja N, Karamanis D, et al. Diabetes is associated with increased risk for in-hospital mortality in patients with COVID-19: a systematic review and meta-analysis comprising 18,506 patients. Hormones (Athens) Jun. 2021;20(2):305\\u0026ndash;14. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.1007/s42000-020-00246-2\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s42000-020-00246-2\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003cli\\u003e\\u003cspan\\u003eBechini A, Ninci A, Del Riccio M, et al. Impact of Influenza Vaccination on All-Cause Mortality and Hospitalization for Pneumonia in Adults and the Elderly with Diabetes: A Meta-Analysis of Observational Studies. Vaccines (Basel) May. 2020;30(2). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e10.3390/vaccines8020263\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/vaccines8020263\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":false,\"email\":\"\",\"identity\":\"cardiovascular-diabetology-endocrinology-reports\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"\",\"title\":\"Cardiovascular Diabetology – Endocrinology Reports\",\"twitterHandle\":\"\",\"acdcEnabled\":false,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"Unsupported Journal\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":false},\"keywords\":\"\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8808866/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8808866/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e \\u003cp\\u003eEarly in the COVID-19 pandemic, mainstream news outlets sensationalized that 30\\u0026ndash;40% of all coronavirus deaths in the United States occurred among individuals with diabetes. It was unclear why this would be news-worthy because 30\\u0026ndash;40% is approximately the prevalence of diabetes in older adult, the age group most at risk for mortality from COVID-19. Thus, we sought to quantify the proportion of decedents from COVID-19 who had diabetes. To understand the proportion in context, we also calculated the proportion of decedents from influenza who had diabetes.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003eFor assessing COVID-19 decedents who had diabetes, we used the National COVID Cohort Collaborative (N3C) data enclave, a nationally-representative, harmonized, and de-identified electronic health record database. For assessing influenza decedents who had diabetes, we used Medicare data. We restricted the N3C sample to \\u0026gt;\\u0026thinsp;65 years to align with Medicare eligibility.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eAmong seniors with inpatient mortality due to COVID-19, 46.6% (95% CI: 46.1\\u0026ndash;47.0) had diabetes. Among seniors with inpatient mortality from influenza, the crude percent with diabetes was 61.2%. When age-standardized to match the N3C COVID-19 data, the percentage of influenza decedents with diabetes was 63.1% (95% CI: 59.1\\u0026ndash;67.1).\\u003c/p\\u003e\\u003ch2\\u003eConclusions\\u003c/h2\\u003e \\u003cp\\u003eAmong seniors with inpatient mortality from respiratory viruses, a very large proportion had diabetes before infection: 63% of influenza decedents and 47% of COVID-19 decedents. Thus, a high proportion of decedents having diabetes is not new or unique to COVID-19. These findings highlight the value of using available data to contextualize health communication to the public.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Among individuals who die of COVID-19, is the percentage who had diabetes actually higher than in those dying of other viral infections?\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-02-17 10:12:18\",\"doi\":\"10.21203/rs.3.rs-8808866/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2026-03-23T22:48:41+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-03-18T18:14:55+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"324179600718498336756476105442472429753\",\"date\":\"2026-02-24T21:03:13+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"11682187084516392489876240566595978874\",\"date\":\"2026-02-18T07:20:57+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-02-14T15:39:03+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"64605260418125306758793960478742445119\",\"date\":\"2026-02-13T14:22:27+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2026-02-11T13:15:39+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2026-02-11T11:52:52+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2026-02-11T11:46:32+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Cardiovascular Diabetology – Endocrinology Reports\",\"date\":\"2026-02-06T15:26:07+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":false,\"email\":\"\",\"identity\":\"cardiovascular-diabetology-endocrinology-reports\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"\",\"title\":\"Cardiovascular Diabetology – Endocrinology Reports\",\"twitterHandle\":\"\",\"acdcEnabled\":false,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"Unsupported Journal\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":false}}],\"origin\":\"\",\"ownerIdentity\":\"ee78a842-3fc1-4221-9cc2-81a1f4d30dd1\",\"owner\":[],\"postedDate\":\"February 17th, 2026\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"in-revision\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-03-23T22:54:00+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-02-17 10:12:18\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-8808866\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-8808866\",\"identity\":\"rs-8808866\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}