An Elastic Net Regression Model for Identifying Long COVID Patients Using Health Administrative Data: A Population-Based Study
Bibliographic record
Abstract
Background: Long coronavirus disease (COVID) patients experience persistent symptoms after acute severe acute respiratory syndrome coronavirus 2 (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 = 2430) 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 International Classification of Diseases, Tenth Revision, Canada (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, >10 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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".