Long COVID in Elderly Patients: An Epidemiologic Exploration Using a Medicare Cohort

Background Incidence of long COVID in the elderly is difficult to estimate and can be under-reported. While long COVID is sometimes considered a novel disease, many viral or bacterial infections have been known to cause prolonged illnesses. We postulate that some influenza patients might develop residual symptoms that would satisfy the diagnostic criteria for long COVID, a condition we call “long Flu”. In this study, we estimate the incidence of long COVID and long Flu among Medicare patients using the World Health Organization (WHO) consensus definition. We compare the incidence, symptomatology, and healthcare utilization between long COVID and long Flu patients. Methods and Findings This is a cohort study of Medicare (the U.S. federal health insurance program) beneficiaries over 65. ICD-10-CM codes were used to capture COVID-19, influenza and residual symptoms. Long COVID was identified by a) the designated long COVID-19 code B94.8 (code-based definition), or b) any of 11 symptoms identified in the WHO definition (symptom-based definition), from one to 3 months post infection. A symptom would be excluded if it occurred in the year prior to infection. Long Flu was identified in influenza patients from the combined 2018 and 2019 Flu seasons by the same symptom-based definition for long COVID. Long COVID and long Flu were compared in four outcome measures: a) hospitalization (any cause), b) hospitalization (for long COVID symptom), c) emergency department (ED) visit (for long COVID symptom), and d) number of outpatient encounters (for long COVID symptom), adjusted for age, sex, race, region, Medicare-Medicaid dual eligibility status, prior-year hospitalization, and chronic comorbidities. Among 2,071,532 COVID-19 patients diagnosed between April 2020 and June 2021, symptom-based definition identified long COVID in 16.6% (246,154/1,479,183) and 29.2% (61,631/210,765) of outpatients and inpatients respectively. The designated code gave much lower estimates (outpatients 0.49% (7,213/1,479,183), inpatients 2.6% (5,521/210,765)). Among 933,877 influenza patients, 17.0% (138,951/817,336) of outpatients and 24.6% (18,824/76,390) of inpatients fit the long Flu definition. Long COVID patients had higher incidence of dyspnea, fatigue, palpitations, loss of taste/smell and neurocognitive symptoms compared to long Flu. Long COVID outpatients were more likely to have any-cause hospitalization (31.9% (74,854/234,688) vs. 26.8% (33,140/123,736), odds ratio 1.06 (95% CI 1.05-1.08, p<0.001)), and more outpatient visits than long Flu outpatients (mean 2.9(SD 3.4) vs. 2.5(SD 2.7) visits, incidence rate ratio 1.09 (95% CI 1.08-1.10, p<0.001)). There were less ED visits in long COVID patients, probably because of reduction in ED usage during the pandemic. The main limitation of our study is that the diagnosis of long COVID in is not independently verified. Conclusions Relying on specific long COVID diagnostic codes results in significant under-reporting. We observed that about 30% of hospitalized COVID-19 patients developed long COVID. In a similar proportion of patients, long COVID-like symptoms (long Flu) can be observed after influenza, but there are notable differences in symptomatology between long COVID and long Flu. The impact of long COVID on healthcare utilization is higher than long Flu. Why was this study done? What did the researchers do and find? What do these findings mean? ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This research was supported in part by the Intramural Research Program of the National Institutes of Health, National Library of Medicine. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This study was declared not human subject research by the Office of Human Research Protection at the National Institutes of Health and by the CMS’s Privacy Board. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes Concerning data availability, the minimal data set is included in the Supporting information. All data in Supporting information can be used without restriction. This now includes the precise values used to build the long COVID symptom trend graphs (S1 Table) and the detailed statistical data obtained in the logistic and Poisson regressions (S5 Table), from which the odds ratios and incidence rate ratios can be derived. As for raw data, CMS does not let us download (or distribute) any patient level data. The data stay on their machine, and we analyze it with software they provide on their machine. If researchers wish to access the raw data, they can contact the CMS Virtual Research Data Center. However, data access requires the payment of a fee.