Impact of Long SARS-CoV-2 Omicron Infection on the Health Care Burden: Comparative Case-Control Study Between Omicron and Pre-Omicron Waves
Bibliographic record
Abstract
BACKGROUND: Following the initial acute phase of COVID-19, health care resource use has escalated among individuals with SARS-CoV-2 infection. OBJECTIVE: This study aimed to compare new diagnoses of long COVID and the demand for health services in the general population after the Omicron wave with those observed during the pre-Omicron waves, using similar assessment protocols for both periods and to analyze the influence of vaccination. METHODS: This matched retrospective case-control study included patients of both sexes diagnosed with acute SARS-CoV-2 infection using reverse transcription polymerase chain reaction or antigen tests in the hospital microbiology laboratory during the pandemic period regardless of whether the patients were hospitalized. We included patients of all ages from 2 health care departments that cover 604,000 subjects. The population was stratified into 2 groups, youths (<18 years) and adults (≥18 years). Patients were followed-up for 6 months after SARS-CoV-2 infection. Previous vaccination, new diagnoses, and the use of health care resources were recorded. Patients were compared with controls selected using a prospective score matched for age, sex, and the Charlson index. RESULTS: A total of 41,577 patients with a history of prior COVID-19 infection were included, alongside an equivalent number of controls. This cohort encompassed 33,249 (80%) adults aged ≥18 years and 8328 (20%) youths aged <18 years. Our analysis identified 40 new diagnoses during the observation period. The incidence rate per 100 patients over a 6-month period was 27.2 for vaccinated and 25.1 for unvaccinated adults (P=.09), while among youths, the corresponding rates were 25.7 for vaccinated and 36.7 for unvaccinated individuals (P<.001). Overall, the incidence of new diagnoses was notably higher in patients compared to matched controls. Additionally, vaccinated patients exhibited a reduced incidence of new diagnoses, particularly among women (P<.001) and younger patients (P<.001) irrespective of the number of vaccine doses administered and the duration since the last dose. Furthermore, an increase in the use of health care resources was observed in both adult and youth groups, albeit with lower figures noted in vaccinated individuals. In the comparative analysis between the pre-Omicron and Omicron waves, the incidence of new diagnoses was higher in the former; however, distinct patterns of diagnosis were evident. Specifically, depressed mood (P=.03), anosmia (P=.003), hair loss (P<.001), dyspnea (<0.001), chest pain (P=.04), dysmenorrhea (P<.001), myalgia (P=.011), weakness (P<.001), and tachycardia (P=.015) were more common in the pre-Omicron period. Similarly, health care resource use, encompassing primary care, specialist, and emergency services, was more pronounced in the pre-Omicron wave. CONCLUSIONS: The rise in new diagnoses following SARS-CoV-2 infection warrants attention due to its potential implications for health systems, which may necessitate the allocation of supplementary resources. The absence of vaccination protection presents a challenge to the health care system.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".