| Description |
Background COVID-19 survivors report that symptoms flare up unpredictably, that can be debilitating, even 3 months after a positive COVID-19 test. In the multi-project study of symptoms of ‘Long-Haul COVID-19', there is a need to account for clustering at the clinical practice level. This study aims to analyze the prevalence of long-haul COVID-19 symptoms in primary care patients, to compare the burden of symptoms between patients with a positive COVID-19 test result and patients with a negative COVID-19 test result, and investigate possible unmeasured symptoms and latent classes of Long-Haul COVID-19 symptoms. U of U Health Electronic Health record data was combined with Long-haul COVID survey data from the DFPM study with team Primary Investigator: Bernadette Kiraly, with Co-Investigators: Jennifer Leiser, Kirsten Stoesser, Camie Schaefer, and Dominik Ose. This study project is accordant to and exempted by the UofU Institutional Review Board (IRB # 139714). Method For this MSTAT Research Project, multilevel structural equation modeling (MSEM) with bivariate correlations and regression was utilized with real data. This multilevel approach was able to put into consideration potential variances by Health Center, and within-person variances by linking patients to physicians. There were 13 health centers to consider. This approach was important because it brings new focus to the study, answering questions regarding the impact of Health Care interventions. Clinicodemographic breakdowns with data quality reports were created. A Directed Acyclic Graph was created to prepare and address any potential confounders or colliders. Candidate models were assessed with a training data set, and test data set. Additional exploratory analysis was conducted in analyzing the relationship between patient characteristics and hospital visit characteristics. Latent class regression (LCR) analysis was performed to discriminate between untested reinfection symptoms and Long-Haul COVID-19 symptoms. The data uses the UofU Data Science Services (Enterprise Data Warehouse), where there is record data from inpatient and outpatient Electronic Health Records across 13 health centers. Analyses were conducted with respect to the clinic, Covid-19 hospitalization dx, if they died of covid, covid vaccination, primary health insurance, patient county of residence, age, ethnicity/race, Sex/Gender, Smoking Status, Obesity, BMI, or multicomorbidity (Charlson Comorbidity Index). |
