Author : Michael J. Oravec
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (14 download)
Book Synopsis Incidence Of Post-Acute COVID-19 Sequelae And Predictors For Post-COVID Infection Health Care Utilization In An Integrated Health System Patient Population by : Michael J. Oravec
Download or read book Incidence Of Post-Acute COVID-19 Sequelae And Predictors For Post-COVID Infection Health Care Utilization In An Integrated Health System Patient Population written by Michael J. Oravec and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Background: A growing area of public health concern is the phenomenon of post-acute sequelae of COVID-19 manifesting for months after the initial infection. Patients often report lingering symptoms such as fatigue, brain fog, persistent breathing issues, and reduced health-related quality of life (HRQoL) in the months after acute infection. This phenomenon has gone by various names, including "long COVID," "long haul COVID," and "post-COVID syndrome," and while it lacks a firm clinical definition, is generally characterized by symptoms that persist for at least four weeks past the initial infection. Up to 50 percent of those infected with COVID-19 may experience these lingering symptoms, and post-COVID sequelae are associated with personal and societal impacts including increased mental health issues and work absences/loss of employment. The impacts of post-COVID sequelae on the health care system are not well-established in the current literature, nor is the best approach to treat and mitigate the impacts of post-COVID. Evidence identifying risk factors for post-COVID sequelae and health care utilization patterns for those experiencing these symptoms has begun to emerge but remains limited. The purpose of this study was to synthesize and contribute to the existing knowledge base regarding the impacts of post-COVID sequelae on health care utilization after the acute phase of infection, leveraging a large institutional COVID-19 research database comprised primarily of electronic medical record (EMR) data, from a Northeast Ohio integrated health delivery system. Aims: This study has three primary aims: 1) contribute to the literature base addressing risk factors for long COVID, 2) quantify the impact and burden of long COVID on the health care system in terms of additional utilization and cost, and 3) assess differences in utilization patterns for potentially vulnerable populations to identify potential access barriers or disparities in long COVID management. Methods: This study included all patients with a system primary care provider (PCP) that had a documented diagnosis of acute COVID-19 between March 2020 and February 2022, with follow up through August 2022. A multivariable binomial regression model was used to assess the likelihood of having post-COVID sequelae for included covariates, and multivariable negative binomial regression models were used to assess health care utilization rates for included covariates, including overall presence of post-COVID sequelae and presence of individual symptoms. Results: There were 9,629 patients that met inclusion criteria for the study, of which 2,625 (27.3%) sought care for post-COVID sequelae. More severe acute COVID-19 illness was strongly associated with increased incidence of sequelae. Additionally increasing age, female sex, Black/African-American race, and having Medicaid insurance were independently associated with increased incidence. Presence of post-COVID sequelae was associated with an overall 70% increase in utilization, with long COVID patients using 2.5 times as many ED visits and over 6 times as many pulmonary specialty visits. Persistent fatigue, dyspnea, and neurological symptoms were all associated with increased utilization. Increased ED and reduced specialty utilization was observed for some vulnerable groups, suggesting potential access barriers to health services for long COVID. Discussion: The results of this study contribute to the growing body of literature regarding risk factors for long COVID, though the multivariable model did not demonstrate strong predictive accuracy, indicating that unmeasured factors may be at play. The result of this study are also a first step toward quantifying the overall differences in utilization for patients with long COVID, as well as the influence of specific post-COVID sequelae. The study is limited by the availability and quality of EMR data. Implications for health policy and management reflect the need to better classify, document, and measure outcomes related to long COVID in order to better inform ways to mitigate the impacts of this emerging chronic disease threat, and establish best practices for clinical and population health management.
