Comparison of COVID-19 and Influenza-Related Outcomes in the United States during Fall–Winter 2022–2023: A Cross-Sectional Retrospective Study
Bibliographic record
Abstract
Abstract Background Three years into the pandemic, SARS-COV-2 remains a significant burden in comparison to other respiratory illnesses; however, many of the monitoring tools available during the early phase of the COVID-19 pandemic have been phased out, making it more difficult to track the current burden of outpatient medical encounters and hospitalizations, especially for at-risk groups. The objective of this analysis was to characterize the frequency and severity of medically-attended COVID-19 and influenza during peak influenza activity in the pediatric (0-17), adult (18-64), and older adult (65+) populations and characterize the prevalence of underlying medical conditions among patients hospitalized with COVID-19. Methods This was a cross-sectional analysis of individuals in the Veradigm Health Insights EHR Database linked to Komodo claims data with a medical encounter of claim between October 1, 2022, and March 31, 2023. We captured age, sex, and underlying medical conditions associated with higher risk for severe COVID-19 during a 12-month baseline period. We identified patients with medical encounters with a diagnosis of COVID-19 or influenza between October 1, 2022, and March 31, 2023, and stratified them into 5 mutually exclusive categories based on the highest level of care received with that diagnosis during the season (intensive care unit [ICU] > hospitalization without ICU > emergency department > urgent care > other outpatient). Results Among the 23,526,196 individuals in the dataset, 5.0% had a COVID-19-related medical encounter, and 3.0% had an influenza-related medical encounter during the 6 month observation period. The incidence of hospitalizations with a COVID-19 diagnosis was 4.6 times higher than the incidence of hospitalizations with an influenza diagnosis. Hospitalizations with COVID-19 were higher in all age groups. Nearly all adults hospitalized with COVID-19 had at least one underlying medical condition, but 25.8% of 0-5-year-olds and 18.3% of 6-17-year-olds had no underlying medical conditions. Conclusions COVID-19 continues to place a heavy burden on the United States healthcare system and was associated with more medical encounters in all age groups, including hospitalizations, than influenza during a 6-month period that included the 2022-2023 peak influenza activity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".