Background
Long COVID patients experience persistent symptoms after acute SARS-CoV-2 infection. Healthcare utilization data could provide critical information on the disease burden of long COVID for service planning, however, not all patients are diagnosed or assigned long COVID diagnostic codes. We developed an algorithm to identify individuals with long COVID using population-level health administrative data from British Columbia (BC), Canada.
Methods
An elastic net penalized logistic regression model was developed to identify long COVID patients based on demographic characteristics, pre-existing conditions, COVID-19-related data, and all symptoms/conditions recorded 28-183 days after the COVID-19 symptom onset/reported (index) date of known long COVID patients (N = 2,430) and a control group (N = 24,300); selected from all adult COVID-19 cases in BC with an index date on/before October 31, 2021 (N = 168,111). Known long COVID cases were diagnosed in a clinic and/or had the ICD-10-CA code for “Post COVID-19 condition” in their records.
Results
The algorithm retained known symptoms/conditions associated with long COVID, demonstrating high sensitivity (86%), specificity (86%), and area under the receiver operator curve (93%). It identified 25,220 (18%) long COVID patients among the remaining 141,381 adult COVID-19 cases; over ten times the number of known cases. Known and predicted long COVID patients had comparable demographic and health-related characteristics.
Conclusions
Our algorithm identified long COVID patients with a high level of accuracy. This large cohort of long COVID patients will serve as a platform for robust assessments on the clinical course of long COVID, and provide much needed concrete information for decision-making.
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