
==== Front
JMIR Public Health Surveill
JMIR Public Health Surveill
JPH
JMIR Public Health and Surveillance
2369-2960
JMIR Publications Toronto, Canada

v10i1e53580
39226091
10.2196/53580
Original Paper
Original Paper
Impact of Long SARS-CoV-2 Omicron Infection on the Health Care Burden: Comparative Case-Control Study Between Omicron and Pre-Omicron Waves
Mavragani Amaryllis
Boeras Debi
Hassan Muhammad Muneeb
Hoedl Manuela
Valdivieso-Martinez Bernardo PHO 1https://orcid.org/0000-0003-3344-2369

Lopez-Sanchez Victoria PhD 1https://orcid.org/0009-0006-1303-0622

Sauri Inma PharmB 2https://orcid.org/0000-0001-5017-2618

Diaz Javier PhD 2https://orcid.org/0000-0002-5822-3325

Calderon Jose Miguel PhD 2https://orcid.org/0000-0003-3280-9114

Gas-Lopez Maria Eugenia PhD 1https://orcid.org/0000-0003-0924-9665

Lidon Laura MAT 2https://orcid.org/0009-0001-9441-3193

Philibert Juliette FRS 1https://orcid.org/0009-0009-7592-2935

Lopez-Hontangas Jose Luis Prof Dr 1https://orcid.org/0000-0003-1426-3672

Navarro David Prof Dr Med 3https://orcid.org/0000-0003-3010-4110

Cuenca Llanos PhD 4https://orcid.org/0000-0003-3589-4182

Forner Maria Jose Prof Dr Med 3https://orcid.org/0000-0002-6786-6760

Redon Josep Prof Dr Med https://orcid.org/0000-0001-8777-6773
2Hospital Clínico de la Comunidad Valenciana (INCLIVA) Research Institute University of Valencia Menendez y Pelayo, 4 Valencia, 46010 Spain 34 658909676 josep.redon@uv.es

1 Medical Research Institute, Hospital Universitari i Politècnic La Fe Valencia Spain
2 Hospital Clínico de la Comunidad Valenciana (INCLIVA) Research Institute University of Valencia Valencia Spain
3 Hospital Clinico of Valencia Valencia Spain
4 Universitat Politècnica de València Valencia Spain
Corresponding Author: Josep Redon josep.redon@uv.es
2024
3 9 2024
10 e5358011 10 2023
4 3 2024
17 3 2024
23 5 2024
©Bernardo Valdivieso-Martinez, Victoria Lopez-Sanchez, Inma Sauri, Javier Diaz, Jose Miguel Calderon, Maria Eugenia Gas-Lopez, Laura Lidon, Juliette Philibert, Jose Luis Lopez-Hontangas, David Navarro, Llanos Cuenca, Maria Jose Forner, Josep Redon. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 03.09.2024.
2024
https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on https://publichealth.jmir.org, as well as this copyright and license information must be included.

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.

Omicron
long COVID
post–COVID-19
diagnostics
primary care
specialist
emergency department
hospitalization
==== Body
pmcIntroduction

The all-cause disease burden worldwide in 2020 and 2021 was the highest in the past 3 decades, mainly due to the consequences of the SARS-CoV-2 pandemic [1]. In fact, the worldwide population was profoundly affected not only by the acute infection, resulting in increased morbidity and mortality [2], but also by persistent symptoms after the initial acute phase of illness or the impact on organ systems with the emergence of new diseases [3-12]. As a result, health care resource use has escalated among individuals with SARS-CoV-2 infection. Ongoing viral mutations and the introduction of vaccines may result in varying degrees of impact, and periodic assessment of the disease burden may help to better define strategies to mitigate the impact.

Rapid dissemination of various SARS-CoV-2 variants and the introduction of vaccines have raised expectations of altered impacts on long COVID. Subsequent to the prevalence of the Alpha and Delta variants during the initial pandemic waves in the pre-Omicron period, SARS-CoV-2 Omicron (PANGO B.1.1.529) swiftly propagated across Europe from December 2021 to February 2022. Clinically, the Omicron variant induced less severe acute illness than its predecessors, and certain studies reported a reduced incidence of postacute-phase impacts [13-19], both in adults and in children [20-24]. Nevertheless, conflicting data have surfaced in the literature [25], and the potential influence of prior vaccination remains uncertain [26-28]. Evaluating the genuine repercussions of SARS-CoV-2 infection in the realm of long COVID following the acute phase proves intricate due to myriad factors. Discrepancies in defining criteria, observation durations, encompassed medical conditions, and symptom dynamics may account for disparities among reported studies. Assessing the tangible effect on the health care resource burden following the acute infection episode holds paramount significance, given its implications for the resources that health care systems must allocate. One effective approach to gauge this impact is to leverage the data available in electronic health records (EHRs), which serve as a valuable source of information regarding the health care resource demands placed on health systems by encompassing a wealth of data on diagnoses, medication usage, and health care resource requirements [29].

Based on the EHRs of 2 health care departments (HCDs) in the Valencian Community of more than 600,000 inhabitants, this study aimed to compare new diagnoses of long COVID and the demand for health services in the general population after the Omicron variant wave with those observed during the pre-Omicron Alpha and Delta waves using similar assessment protocols for both periods. In addition, it sought to elucidate the potential influence of vaccination in mitigating these effects in different age groups.

Methods

Study Design and Participants

A case-control study with retrospective observation of health care data collected from ABUCASIS, the EHR of the Valencia Community was conducted. Administrative data, diagnoses, all prescriptions and dispensations of subsidized treatments, and the use of health services are linked in a database that integrates all health care interventions. In this study, the data included observations of patients with SARS-CoV-2 infection from a total of 604,000 subjects from 2 different HCDs (centers A and B).

Ethical Considerations

Exemption from obtaining informed consent was permissible based on the 17th additional provision of Spanish Organic Law 3/2018, dated December 5, which pertains to the protection of personal data and the guarantee of digital rights. This provision legalizes the use of pseudonymized personal data for health-related purposes, particularly in the realm of biomedical research. To harness pseudonymized personal data for the objectives of public health and biomedical research, the following criteria were met: A clear demarcation in terms of both technical and functional aspects was maintained between the research team and individuals responsible for executing the pseudonymization process, as well as safeguarding the information that could potentially facilitate reidentification. Data collection and analysis were carried out considering the protection of patients’ privacy by means of a 2-layered method of pseudo anonymization, and the information was managed as aggregated data. Access to pseudonymized data was only permitted for the research team under specific conditions:

An explicit commitment to maintain confidentiality and abstain from any reidentification endeavors was established.

Stringent security measures were instituted to forestall reidentification and unauthorized access by third parties.

The research was conducted in full compliance with the provisions of Regulation (EU) 2016/679 of the European Parliament and of the Council of April 27, 2016, on the protection of individuals with regard to the processing of personal data and on the free movement of such data. The study also complied with the 17th additional provision of Spanish Organic Law 3/2018 of December 5, the corresponding European norms (General Data Protection Regulation [GDPR]) [30], and the applicable sectoral legislation. The information was available for research and pseudo anonymization in accordance with the Spanish Data Protection Act, and the study was approved by the Ethics and Clinical Trials Committee of the Hospital Clinico of Valencia and the Hospital Universitari i Politècnic La Fe of Valencia.

Subjects and Procedures

Cases included patients of both sexes diagnosed with acute SARS-CoV-2 infection using reverse transcription polymerase chain reaction (RT-PCR) or antigen tests in the hospital microbiology laboratory during the SARS-CoV-2 Omicron pandemic period regardless of whether the patients were hospitalized. The population was stratified into 2 groups, youths under the age of 18 years and adults aged ≥18 years.

The study design has been previously detailed [31], with a summary provided in Figure 1. In brief, the cases of Omicron infection included individuals with acute infection diagnosed between December 1, 2021, and February 28, 2022. The observation period for the long COVID phase commenced 30 days following the date of diagnosis, either in primary care or after hospital discharge, and extended for up to 6 months, totaling 180 days. During this period, newly occurring diseases and medications, not present prior to the infection, were meticulously documented within the EHR system. Vaccination details, including the number of doses administered and the time elapsed since the last dose before infection, were also ascertained. Throughout the observation period, the collection of data encompassed the identification of newly diagnosed conditions and prescriptions. Additionally, the use of health care resources, comprising the number of patients and visits to primary care physicians, specialists, emergency rooms, and hospitalization, was extracted from administrative records sourced from primary care health care centers, hospital outpatient clinics, emergency departments, and hospitalization units.

Figure 1 Study design. Cases included patients of both sexes diagnosed with acute SARS-CoV-2 infection, and the same number of matched controls without infection were selected using a propensity score matched for age, sex, previous illnesses, and the same time period as the case index. The observation period for new diagnoses and health care resource use was 6 months after 30 days from virus diagnosis or hospital discharge. CKD: chronic kidney disease; COPD: chronic obstructive pulmonary disease; HF: heart failure; ICD-10: International Classification of Diseases, 10th Revision; MI: myocardial infarction; PVD: pulmonary vascular disease.

The analysis of new diagnoses included an equal number of matched controls who had not been infected with SARS-CoV-2. The control group was generated through propensity score matching (PSM) that incorporated variables such as age, sex, the Charlson index, all preexisting chronic diseases (chronic conditions present prior to the pandemic period, including but not limited to myocardial infarction, heart failure, peripheral artery disease, stroke, dementia, chronic obstructive pulmonary disease, rheumatism, peptic ulcer, liver disease, diabetes, chronic kidney disease, tumor, metastatic tumor, and HIV infection), and the corresponding case’s time frame. Data from a prior study conducted using the same methodology during the pre-Omicron period, spanning from March to December 2020, were used to facilitate comparisons regarding the potential impact of Omicron [31].

Statistical Analysis

Data were presented in the form of absolute numbers, incidences per 100 patients per 6 months, and percentages, where applicable. To assess differences in the incidence of new diagnoses among patients with Omicron infection, vaccinated individuals, and unvaccinated patients, as well as in comparison to the pre-Omicron period, statistical analyses were conducted using unpaired Student t tests and chi-squared tests. The potential long-term effects of vaccination, including the number of vaccine doses and the time elapsed since the last dose to the time of infection, were evaluated through logistic regression analysis, adjusted for potential confounding factors, such as age and sex. Furthermore, sensitivity analyses were performed by separately analyzing data from the 2 HCDs and comparing the results between them. All statistical analyses were carried out using R version 6.3.1 (R Foundation for Statistical Computing).

Results

General Characteristics of the Study Population

During the study period corresponding to the Omicron wave, a total of 41,577 patients were diagnosed with positive SARS-CoV-2 using RT-PCR or antigen tests. This patient cohort comprised 33,249 (80%) adults aged ≥18 years and 8328 (20%) youths aged <18 years. Prior to the onset of acute infection, 30,199 (90.8%) adults and 4417 (53%) youths had received the vaccine. The general characteristics of the study population are detailed in Table 1 for adults and in Table 2 for youths. Follow-up of the patients extended from 177 to 179 days after 30 days of confirmed infection. Among them, a total of 833 (2%) patients were hospitalized for acute SARS-CoV-2 Omicron infection, including 662 (79.5%) adults and 75 (9%) youths, with 24 (32%) of the latter being vaccinated.

Table 1 General characteristics of adult (≥18 years) study population cases in the 2 HCDsa during Omicron and pre-Omicron waves.

Characteristic	Omicron (previous vaccine)	Omicron (no previous vaccine)	Pre-Omicron	

	Center A (n=17,441)	Center B (n=12,758)	Center A (n=1040)	Center B (n=2010)	Center A (n=16,382)	Center B (n=14,199)	
Age (years), mean (SD)	46.16 (16.37)	48.78 (18.50)	41.75 (14.81)	40.87 (13.71)	47.63 (17.73)	45.93 (17.10)	
Female, n (%)	9832 (56.4)	7110 (55.7)	570 (54.8)	1168 (58.1)	8861 (54.1)	7671 (54.0)	
Days of observation, mean (SD)	179 (10.93)	179.58 (6.80)	177.98 (16.04)	177.11 (20.15)	176.06 (20.53)	179.00 (7.68)	
Patients hospitalized, n (%)	458 (2.6)	204 (1.6)	78 (7.5)	68 (3.4)	1726 (10.5)	1357 (9.6)	
First vaccination, date	June 6, 2021 10089 (57.95)	January 7, 2021 6668 (52.27)	N/Ab	N/A	N/A	N/A	
Patients with new diagnoses, n (%)	3535 (20.3)	2729 (21.4)	223 (21.4)	392 (19.5)	3082 (18.8)	2883 (20.3)	

	1	2815 (16.1)	2128 (16.7)	171 (16.4)	293 (14.6)	2463 (15.0)	2182 (15.4)	

	2	574 (3.3)	474 (3.7)	42 (3.9)	71 (3.5)	506 (3.1)	544 (3.8)	

	3	121 (0.7)	101 (0.8)	8 (0.8)	20 (1.0)	87 (0.5)	116 (0.8)	

	>3	25 (0.1)	26 (0.2)	2 (0.2)	8 (0.4)	26 (0.2)	41 (0.3)	
aHCD: health care department.

bN/A: not applicable.

Table 2 General characteristics of youth (<18 years) study population cases in the 2 HCDsa during Omicron and pre-Omicron waves.

Characteristic	Omicron (previous vaccine)	Omicron (no previous vaccine)	Pre-Omicron	

	Center A (n=2714)	Center B (n=2243)	Center A (n=1824)	Center B (n=1547)	Center A (n=3981)	Center B (n=3605)	
Age (years), mean (SD)	12.58 (3.41)	12.17 (3.54)	5.31 (3.87)	5.91 (4.30)	10.23 (4.99)	9.42 (5.19)	
Female, n (%)	1377 (50.7)	1123 (50.1)	864 (47.4)	719 (46.5)	1956 (49.1)	1783 (49.0)	
Days of observation, mean (SD)	180 (0)	179.91 (3.76)	180 (0)	179.40 (9.01)	179.11 (4.21)	179.26 (3.89)	
Patients hospitalized, n (%)	12 (0.4)	12 (0.5)	19 (1.0)	32 (2.1)	23 (0.6)	27 (0.7)	
First vaccination, date	October 21, 2021 2243 (82.66)	June 12, 2021 1987 (88.60)	N/Ab	N/A	N/A	N/A	
Patients with new diagnoses, n (%)	511 (18.8)	421 (18.8)	499 (27.4)	404 (26.1)	660 (16.6)	584 (16.2)	

	1	418 (15.4)	339 (15.1)	387 (21.2)	318 (20.6)	544 (13.7)	463 (12.8)	

	2	84 (3.1)	70 (3.1)	97 (5.3)	70 (4.5)	95 (2.4)	90 (2.5)	

	3	8 (0.3)	10 (0.4)	11 (0.6)	13 (0.8)	18 (0.5)	27 (0.7)	

	>3	1 (0)	2 (0.1)	4 (0.2)	3 (0.2)	3 (0.1)	4 (0.1)	
aHCD: health care department.

bN/A: not applicable.

New Diagnostics

The new diagnoses observed during the follow-up period in patients with SARS-CoV-2 and Alpha and Omicron infections and in controls, regardless of their vaccination status, and the corresponding distribution of the International Classification of Diseases, 10th Revision (ICD-10) codes across affected systems are presented in Tables S1-S4 in Multimedia Appendix 1. Among adults, the number of new diagnoses for vaccinated and unvaccinated patients stood at 6642 and 636, respectively, with incidences per 100 patients over 6 months of 27.2 and 25.1, respectively (P=.09). In the case of youths, these numbers were lower, with 1014 diagnoses in vaccinated individuals and 967 in unvaccinated individuals, resulting in incidences of 25.7 and 36.7, respectively (P<.001). In adults, the vaccination status did not appear to significantly influence the number of new diagnoses per patient (P=.09), as indicated by the following percentages: vaccinated (n=4943, 16.4%, had 1 new diagnosis; n=1048, 3.5%, had 2 new diagnoses; n=222, 0.7%, had 3 new diagnoses; and n=51, 0.1%, had more than 3 new diagnoses) and unvaccinated (n=464, 15.5%, had only 1 new diagnosis; n=113, 3.7%, had 2 new diagnoses; n=28, 0.9%, had 3 new diagnoses; and n=10, 0.3%, had more than 3 new diagnoses), as shown in Table 1. Conversely, among youths, a notably higher number of events were observed among the unvaccinated group, with percentages as follows (P<.001): vaccinated (n=757, 15.2%, had 1 new diagnosis; n=154, 3.1%, had 2 new diagnoses; n=18, 0.4%, had 3 new diagnoses; and n=3, 0.1%, had more than 3 new diagnoses in center B) and unvaccinated (n=705, 20.9%, had 1 new diagnosis; n=167, 5%, had 2 new diagnoses; n=24, 0.7%, had 3 new diagnoses; and n=7, 0.2%, had more than 3 new diagnoses), as shown in Table 2. Ultimately, logistic regression analysis revealed a reduced risk of new diagnoses in women (0.39, 95% CI 0.35-0.43, P<.001) and younger patients (0.98, 95% CI 0.98-0.99, P<.001). However, the number of vaccine doses (0.98, 95% CI 0.86-1.11, P=.73) and the time elapsed from the last vaccine dose to infection (1.01, 95% CI 0.99-1.04, P=.42) were not found to be associated with the risk of new diagnoses. Diseases across systems, along with the number and incidence of each disease, are presented in Tables S1 and S2 in Multimedia Appendix 1 for adults and youths, respectively. Incidence data are further detailed in Tables S3 and S4 in Multimedia Appendix 1 for adults and youths, respectively. In the adult population, the most frequently occurring new diagnoses spanned neurophysical, infectious, digestive, respiratory, and musculoskeletal categories. Among the most common diseases were the following: (1) neurophysical conditions included anxiety, insomnia, headache, dizziness, and vertigo; (2) infectious diseases included acute pharyngitis and tonsillitis; (3) digestive conditions included functional dyspepsia, diarrhea, and abdominal pain; (4) respiratory issues included cough; and (5) musculoskeletal ailments included low back pain and weakness. In youths, acute pharyngitis was the most prevalent condition, with no notable difference between vaccinated and unvaccinated individuals.

When comparing the incidence of each new disease between vaccinated and unvaccinated individuals, significant differences were observed in adults for dizziness and giddiness (P=.01) and functional dyspepsia (P=.005). In contrast, among youths, significant differences were found for anxiety disorder (P<.001); dizziness and giddiness (P<.001); headache (P=.025); acute pharyngitis, tonsillitis, and fever (P<.001 for each); dermatitis, unspecified (P=.024); cough (P<.001); functional dyspepsia (P=.009); dysmenorrhea (P=.001); low back pain (P=.004); weakness (P=.033); conjunctivitis (P<.001); and recurrent oral aphthae (P<.001). Further details regarding the statistical significance of each new diagnosis are provided in Table S5 in Multimedia Appendix 1.

Health Care Resources

The use of health care services, encompassing primary care, specialist consultations, emergency visits, and hospital admissions, among both vaccinated and unvaccinated cases and controls in the adult and youth populations is detailed in Tables 3 and 4, respectively. In vaccinated adults, patients exhibited higher health care service use compared to controls (Table 3). This increase was observed across primary care (n=33,672, 25%, more visits in n=7575, 15%, more patients, P<.001), specialist consultations (n=6747, 19%, more visits in n=1875, 17%, more patients, P<.001), and emergency admissions (n=1673, 25%, more visits in n=1026, 23%, more patients, P<.001). The escalation in resource use was even more pronounced in the unvaccinated population, with primary care visits seeing a 53% (n=6250) increase in visits among 46% (n=958) more patients (P<.001), specialist consultations witnessing a 49% (n=1527) surge in visits among 44% (n=413) more patients (P<.001), and emergency services experiencing a 27% (n=177) rise in visits among 19% (n=78) more patients (P=.002). In vaccinated youths, health care service use was also greater among patients with infection compared to controls, although the differences were smaller than in adults (Table 4). Specifically, there was a 24% (n=4348) increase in primary care visits among 13% (n=1501) more patients (P<.001), a 16% (n=417) uptick in specialist consultations among 12% (n=226) more patients (P<.001), and a 24% (n=240) rise in emergency visits among 20% (n=137) more patients (P<.001). Among unvaccinated youths, health care service use was likewise higher among patients with infection compared to controls, with differences again being smaller than in adults: primary care visits increased by 30% (n=5612) among 20% (n=571) more patients (P<.001), specialist consultations surged by 32% (n=667) among 30% (n=262) more patients (P<.001), and emergency visits saw a 32% (n=449) rise among 23% (n=190) more patients (P<.001).

Overall, hospital admissions were influenced by vaccination status in both adults (P<.001) and youths (P=.004), with higher demands observed among unvaccinated patients. Among adults, compared to controls, there were 23% (n=292) more patients in the vaccinated group and 48% (n=63) more patients in the unvaccinated group (P<.001). Among youths, there were 12% (n=9) more patients in the vaccinated group and 41% (n=44) more patients in the unvaccinated group (P<.001 for unvaccinated, P=.55 for vaccinated).

Table 3 Burden of health care resources (primary care, specialist consultations, emergency room, hospital admissions, and CCUa admissions) in the adult study population in the 2 HCDsb during the 2 study periods.c

Health care resource	Omicron (previous vaccine)	Omicron (no previous vaccine)	Pre-Omicron	

	Center A	Center B	Center A	Center B	Center A	Center B	
Primary care visits	

	Visits	Cases: 76,957
Controls: 56,503
Difference: 20,454 (27%)	Cases: 56,855
Controls: 43,637
Difference: 13,218 (23%)	Cases: 4050
Controls: 1887
Difference: 2163 (53%)	Cases: 7701
Controls: 3614
Difference: 4087 (53%)	Cases: 94,131
Controls: 76,702
Difference: 17,429 (19%)	Cases: 76,902
Controls: 56,563
Difference: 20,339 (26%)	

	Patients	Cases: 13,360
Controls: 11,346
Difference: 6101 (15%)	Cases: 9893
Controls: 8419
Difference: 1474 (15%)	Cases: 735
Controls: 401
Difference: 334 (45%)	Cases: 1347
Controls: 723
Difference: 624 (46%)	Cases: 14,074
Controls: 12,967
Difference: 1107 (8%)	Cases: 11,896
Controls: 9905
Difference: 1991 (17%)	
Specialist visits	

	Visits	Cases: 19,969
Controls: 15,472
Difference: 4497 (23%)	Cases: 15,768
Controls: 13,518
Difference: 2250 (14%)	Cases: 1063
Controls: 542
Difference: 521 (49%)	Cases: 2054
Controls: 1048
Difference: 1006 (49%)	Cases: 21,439
Controls: 16,255
Difference: 5184 (24%)	Cases: 19,892
Controls: 13,540
Difference: 6352 (32%)	

	Patients	Cases: 6298
Controls: 5200
Difference: 1098 (17%)	Cases: 4728
Controls: 3951
Difference: 777 (16%)	Cases: 356
Controls: 167
Difference: 189 (53%)	Cases: 584
Controls: 360
Difference: 224 (38%)	Cases: 6389
Controls: 5106
Difference: 1283 (20%)	Cases: 5331
Controls: 3720
Difference: 1611 (30%)	
Emergency room visits	

	Visits	Cases: 4081
Controls: 3018
Difference: 1063 (26%)	Cases: 2480
Controls: 1870
Difference: 610 (25%)	Cases: 265
Controls: 169
Difference: 96 (36%)	Cases: 389
Controls: 308
Difference: 81 (21%)	Cases: 3503
Controls: 2746
Difference: 1665 (22%)	Cases: 2727
Controls: 1706
Difference: 1021 (37%)	

	Patients	Cases: 2682
Controls: 2060
Difference: 622 (23%)	Cases: 1736
Controls: 1332
Difference: 404 (23%)	Cases: 159
Controls: 95
Difference: 64 (40%)	Cases: 247
Controls: 233
Difference: 14 (6%)	Cases: 2270
Controls: 1838
Difference: 432 (19%)	Cases: 1885
Controls: 1227
Difference: 658 (35%)	
Hospital admissions	

	Admissions	Cases: 850
Controls: 656
Difference: 194 (23%)	Cases: 472
Controls: 374
Difference: 98 (21%)	Cases: 43
Controls: 20
Difference: 23 (53%)	Cases: 88
Controls: 48
Difference: 40 (45%)	Cases: 838
Controls: 792
Difference: 46 (5%)	Cases: 603
Controls: 438
Difference: 165 (27%)	

	Admissions from emergency	Cases: 303
Controls: 307
Difference: –4 (–1%)	Cases: 181
Controls: 118
Difference: 63 (35%)	Cases: 18
Controls: 10
Difference: 8 (44%)	Cases: 42
Controls: 15
Difference: 27 (64%)	Cases: 417
Controls: 476
Difference: –59 (–14%)	Cases: 318
Controls: 208
Difference: 110 (35%)	

	Admissions scheduled	Cases: 246
Controls: 160
Difference: 86 (35%)	Cases: 159
Controls: 136
Difference: 23 (14%)	Cases: 10
Controls: 5
Difference: 5 (50%)	Cases: 21
Controls: 21
Difference: 0	Cases: 196
Controls: 160
Difference: 36 (18%)	Cases: 110
Controls: 96
Difference: 14 (13%)	

	Patients	Cases: 741
Controls: 555
Difference: 186 (25%)	Cases: 436
Controls: 352
Difference: 84 (19%)	Cases: 39
Controls: 18
Difference: 21 (54%)	Cases: 82
Controls: 45
Difference: 37 (45%)	Cases: 672
Controls: 641
Difference: 31 (5%)	Cases: 518
Controls: 366
Difference: 152 (29%)	
CCU admissions	

	Admissions	Cases: 9
Controls: 15
Difference: –6 (–67%)	Cases: 22
Controls: 11
Difference: 11 (50%)	Cases: 1
Controls: 0
Difference: 1 (100%)	Cases: 22
Controls: 11
Difference: 11 (50%)	Cases: 17
Controls: 36
Difference: 19 (–112%)	Cases: 15
Controls: 19
Difference: –4 (–27%)	

	Patients	Cases: 7
Controls: 13
Difference: –6 (–86%)	Cases: 20
Controls: 10
Difference: 10 (50%)	Cases: 1
Controls: 0
Difference: 1 (100%)	Cases: 20
Controls: 10
Difference: 10 (50%)	Cases: 15
Controls: 33
Difference: –18 (–120%)	Cases: 14
Controls: 17
Difference: –3 (–21%)	
aCCU: critical care unit.

bHCD: health care department.

cData show the number of cases and controls. The difference is presented as both a numerical value (n) and a percentage calculated as ([cases – controls]/cases) × 100.

Table 4 Burden of health care resources (primary care, specialist consultations, emergency room, hospital admissions, and CCUa admissions) in the adult study population in the 2 HCDsb during the 2 study periods.c

Health care resource	Omicron (previous vaccine)	Omicron (no previous vaccine)	Pre-Omicron	

	Center A	Center B	Center A	Center B	Center A	Center B	
Primary care visits	

	Visits	Cases: 9525
Controls: 6808
Difference: 2717 (29%)	Cases: 8578
Controls: 6947
Difference: 1631 (19%)	Cases: 10,595
Controls: 7344
Difference: 3251 (31%)	Cases: 8375
Controls: 6014
Difference: 2361 (28%)	Cases: 14,801
Controls: 13,045
Difference: 1756 (12%)	Cases: 14,195
Controls: 9769
Difference: 4426 (31%)	

	Patients	Cases: 2094
Controls: 1758
Difference: 1336 (16%)	Cases: 1814
Controls: 1649
Difference: 165 (9%)	Cases: 1519
Controls: 1248
Difference: 271 (18%)	Cases: 1303
Controls: 1003
Difference: 300 (23%)	Cases: 2988
Controls: 2681
Difference: 307 (10%)	Cases: 2665
Controls: 1952
Difference: 713 (27%)	
Specialist visits	

	Visits	Cases: 1714
Controls: 1442
Difference: 272 (16%)	Cases: 1624
Controls: 1479
Difference: 145 (9%)	Cases: 1066
Controls: 661
Difference: 405 (38%)	Cases: 988
Controls: 726
Difference: 262 (27%)	Cases: 2493
Controls: 2065
Difference: 428 (17%)	Cases: 2243
Controls: 1710
Difference: 533 (24%)	

	Patients	Cases: 768
Controls: 622
Difference: 146 (19%)	Cases: 649
Controls: 569
Difference: 80 (12%)	Cases: 458
Controls: 314
Difference: 144 (31%)	Cases: 409
Controls: 291
Difference: 118 (29%)	Cases: 1008
Controls: 885
Difference: 123 (12%)	Cases: 847
Controls: 633
Difference: 214 (25%)	
Emergency room visits	

	Visits	Cases: 550
Controls: 400
Difference: 150 (27%)	Cases: 439
Controls: 349
Difference: 90 (21%)	Cases: 796
Controls: 521
Difference: 275 (35%)	Cases: 612
Controls: 439
Difference: 173 (28%)	Cases: 675
Controls: 486
Difference: 189 (28%)	Cases: 610
Controls: 418
Difference: 192 (31%)	

	Patients	Cases: 390
Controls: 300
Difference: 90 (23%)	Cases: 311
Controls: 264
Difference: 47 (15%)	Cases: 479
Controls: 332
Difference: 147 (31%)	Cases: 361
Controls: 318
Difference: 43 (12%)	Cases: 468
Controls: 367
Difference: 101 (22%)	Cases: 431
Controls: 315
Difference: 116 (27%)	
Hospital admissions	

	Admissions	Cases: 32
Controls: 25
Difference: 9 (22%)	Cases: 39
Controls: 52
Difference: –13 (–33%)	Cases: 44
Controls: 23
Difference: 21 (48%)	Cases: 55
Controls: 32
Difference: 23 (42%)	Cases: 62
Controls: 32
Difference: 30 (48%)	Cases: 84
Controls: 57
Difference: 27 (32%)	

	Admissions from emergency	Cases: 15
Controls: 14
Difference: 1 (7%)	Cases: 11
Controls: 14
Difference: –3 (–27%)	Cases: 20
Controls: 10
Difference: 10 (50%)	Cases: 19
Controls: 19
Difference: 0	Cases: 24
Controls: 7
Difference: 17 (71%)	Cases: 39
Controls: 21
Difference: 18 (46%)	

	Admissions scheduled	Cases: 5
Controls: 2
Difference: 3 (60%)	Cases: 17
Controls: 19
Difference: –2 (–12%)	Cases: 7
Controls: 6
Difference: 1 (14%)	Cases: 23
Controls: 10
Difference: 13 (57%)	Cases: 17
Controls: 4
Difference: 13 (76%)	Cases: 26
Controls: 24
Difference: 2 (8%)	

	Patients	Cases: 31
Controls: 23
Difference: 8 (26%)	Cases: 36
Controls: 52
Difference: –16 (–44%)	Cases: 39
Controls: 22
Difference: 17 (44%)	Cases: 51
Controls: 31
Difference: 20 (39%)	Cases: 51
Controls: 31
Difference: 20 (39%)	Cases: 72
Controls: 49
Difference: 23 (32%)	
CCU admissions	

	Admissions	Cases: 0
Controls: 0
Difference: 0	Cases: 2
Controls: 1
Difference: 1 (50%)	Cases: 0
Controls: 0
Difference: 0	Cases: 2
Controls: 1
Difference: 1 (50%)	Cases: 0
Controls: 0
Difference: 0	Cases: 1
Controls: 5
Difference: –4 (–400%)	

	Patients	Cases: 0
Controls: 0
Difference: 0	Cases: 2
Controls: 1
Difference: 1 (50%)	Cases: 0
Controls: 0
Difference: 0	Cases: 2
Controls: 1
Difference: 1 (50%)	Cases: 0
Controls: 0
Difference: 0	Cases: 1
Controls: 4
Difference: –3 (–300%)	
aCCU: critical care unit.

bHCD: health care department.

cData show the number of cases and controls. The difference is presented as both a numerical value (n) and a percentage calculated as ([cases – controls]/cases) × 100.

Comparison With the Data From Pre-Omicron SARS-CoV-2 Waves

The general characteristics of the study population, along with the occurrence of new diagnoses and the health care burden associated with Omicron and pre-Omicron waves, are presented in Tables 3 and 4 for adults and youths, respectively. In adults, the prevalence of certain diagnoses in the pre-Omicron period was higher than in the Omicron period. Specifically, depressed mood (P=.03), anosmia (P=.003), hair loss (P<.001), dyspnea (P<.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. Conversely, cough (P<.001), diarrhea (P=.03), low back pain (P<.001), and conjunctivitis (P<.001) were more prevalent in the Omicron period. In youths, anosmia (P=.003) was found to be more common in the pre-Omicron period. Despite these differences in frequency, the burden of long COVID complaints on health care services was similar between the Omicron and pre-Omicron periods, with the exception of musculoskeletal complaints.

Overall, the use of health care resources was higher during the pre-Omicron period in both adults (primary care, P<.001; specialist, P<.001; emergency, P<.001) and youths (primary care, P<.001; specialist, P<.001; emergency, <.001). Furthermore, the demand for health care services was greater among the unvaccinated population in the Omicron period than in the pre-Omicron period.

Sensitivity Analysis

The concordance of new diagnoses within the 2 HCDs was subjected to analysis. Figure 2 displays the correlation coefficient within each of the Omicron subgroups, comprising vaccinated and unvaccinated adults and youths. The disparities observed between the 2 HCDs were, to some extent, attributed to the distinct protocols used by each HCD. However, these discrepancies were not deemed significant in terms of their overall impact.

Figure 2 Correlation of new diagnoses between 2 health care areas in the study populations aged ≥18 and <18 years old. The black line is the regression line of the number of diagnoses in the 2 HCDs. The blue broken line is the 95% CI. The red line is the reference. HCD: health care department.

Discussion

Principal Findings

The analysis of the long-term impact of SARS-CoV-2 Omicron infection following the acute phase was conducted using EHRs sourced from 2 distinct HCDs, comprising data from the medical records of these HCDs. Notably, vaccinated patients with Omicron infection exhibited a reduced incidence of new diagnoses, particularly among females and younger individuals, irrespective of the number of vaccine doses administered and the interval between the last dose and infection. In the case of youths, vaccination also contributed to a decrease in the incidence of new diagnoses. Additionally, vaccinated patients displayed a reduced demand for health care services compared to their unvaccinated counterparts, a pattern observed across both adult and youth populations.

Furthermore, it was observed that the overall number of new diagnoses was higher during the pre-Omicron period in comparison to the Omicron period. Specific conditions, such as depressed mood, anosmia, alopecia, dyspnea, chest pain, dysmenorrhea, myalgia, weakness, and tachycardia, were more prevalent during the pre-Omicron period, whereas conditions such as diarrhea, conjunctivitis, and low back pain exhibited higher incidence rates in the Omicron period. Despite the increase in health care resource use during the Omicron period, it remained significantly lower when compared to the pre-Omicron period. Notably, among adults, the increment in health care resource usage was more pronounced in unvaccinated patients and during the pre-Omicron period when contrasted with the vaccinated population.

To evaluate the potential impact of the viral infection itself on newly diagnosed cases and health care resource use, the inclusion of a control group becomes essential. This is because many of the newly diagnosed conditions or symptoms may not be directly caused by the virus but could instead result from factors such as stress, anxiety, or pandemic-related restrictions, affecting both individuals with and without infection to varying degrees. This study used a control group selection method based on a stringent PSM approach, which closely resembled the methodology used in a previous study conducted by our research team during the pre-Omicron pandemic waves. This earlier study used data extracted from EHRs [31] and served as a basis for comparison between the Omicron and pre-Omicron periods.

The results of this study suggest that the impact on new diagnoses and health care resource use following a less severe acute infection with the Omicron variant decreases when compared to previous pandemic waves involving other SARS-CoV-2 variants, aligning with findings from prior research [32]. However, complaints requiring health care services remain comparable, with potential differences in musculoskeletal symptoms [33]. Several factors may contribute to the reduced risk of sequelae associated with Omicron, including variances in the inherent characteristics of different SARS-CoV-2 variants in causing long-term health issues, the severity of acute infection [34], and variations in vaccination coverage and population immunity to SARS-CoV-2 [19,34,35]. Previous studies have indicated that COVID-19 vaccination and prior infections are associated with a lower risk of developing long COVID events [29,34]. Notably, the population vaccinated before the onset of acute infection exhibits fewer new diagnoses and requires fewer health care resources than its unvaccinated counterpart [36]. Currently, the effect of vaccination in reducing the risk of long COVID appears to be more prominent among women and younger individuals. Furthermore, in this study, the time elapsed between the last vaccine dose and infection did not appear to significantly impact the risk reduction. It is worth noting that individuals with a previous infection prior to the case index were excluded from the study.

The specific effects on youths have been investigated in various studies. A meta-analysis included [37] a wide range of symptoms, including fatigue, headache, loss of smell, cough, and neurological symptoms. These data were corroborated using a report from the United Kingdom, where persistent symptoms were frequently observed in English schoolchildren regardless of their SARS-CoV-2 test results. Additionally, specific symptoms, such as loss of smell and taste, were more commonly reported among those with a positive test history [38]. Furthermore, specific to youths, some studies have underscored that symptomatology can vary depending on the viral variant. This observation was reaffirmed in this study, where the impact of pre-Omicron variants was more pronounced compared to Omicron [39]. Notably, the influence of the immune response stimulated by vaccination was significant in this youth population, as differences between vaccinated and unvaccinated individuals were much more apparent than in adults.

An essential aspect that has received insufficient attention in the majority of long COVID studies pertains to the use of outpatient and hospital resources [34,40]. In this analysis, it was imperative not only to document patient numbers but also to evaluate resource use within a controlled PSM framework. The observed escalation in the health care burden, comparing cases to controls, manifested in both adults and youths, albeit with lower figures among the latter group. Across both periods, Omicron and pre-Omicron, an upsurge in visits to general practitioners, specialists, and emergency departments, as well as an increase in the necessity for hospital admissions, were noted. Although the demand for resources was more substantial in the pre-Omicron period than in the Omicron period, the demand increment in the Omicron period was also noteworthy when compared to the control group.

Several pathogenesis models have been proposed to elucidate the persistence of symptoms or the emergence of new diagnoses after SARS-CoV-2 infection. One hypothesis suggests that the persistence of the virus or a viral component [41] might exacerbate the immune response, leading to elevated levels of proinflammatory cytokines. This could potentially explain organ damage and the enduring presence of symptoms such as fatigue, headache, and olfactory dysfunction [37,42]. Furthermore, another proposed mechanism involves molecular mimicry between autoantigens and spike epitopes [42]. Nevertheless, distinguishing between functional complaints attributable to the virus and those resulting from social limitations poses a challenge in many long COVID sequelae.

Strengths and Limitations

Both the strengths and limitations of the study merit consideration. The assessment of new diagnoses in both youth and adult cases within the general population, alongside propensity score–matched controls, facilitated the measurement of the impact of COVID-19 infection. Robust comparator data for the assessment of new diagnoses and treatments were obtained, not only through the selection of cases and controls, but also through the identification of prior diagnoses. It is important to note that the study did not encompass a clinical evaluation of the new diagnoses; however, characterizing these new diagnoses was not the primary objective of this study. Finally, it is worth acknowledging the limitations inherent in EHRs. Despite efforts to minimize these limitations, the study was restricted to patients with the necessary records for analysis.

Conclusion

In conclusion, attention must be paid to the emergence of new diagnoses after Omicron infection, in preparation for potential future waves of SARS-CoV-2 infection. The virus’s successive mutations introduce the possibility of new waves, which may vary in terms of incidence within the general population or specific risk groups. Regardless of the severity of acute infections and the reinforced immunological status achieved through vaccination or prior infections or both, the potential repercussions for long COVID, and, subsequently, for the demands placed on the health care system, may necessitate additional resources. Vaccination plays a crucial role in mitigating the challenges faced by the health care system.

The study was funded by the Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital (grant number GVA-COVID19/2021/065).

Multimedia Appendix 1 Supplementary tables.

Abbreviations

CCU critical care unit

EHR electronic health record

HCD health care department

ICD-10 International Classification of Diseases, 10th Revision

PSM propensity score matching

RT-PCR reverse transcription polymerase chain reaction

Data Availability

The data presented in this study are available in the paper and multimedia appendices.

Authors' Contributions: BV-M and JR were responsible for conceptualization; BV-M, JR, IS, JMC, and VLS for methodology; JP, JMC, MEG, and JD for software; JLL-H, DN, and MJF for validation; JP, JMC, MEG, LL, and JD for formal analysis; and BV-M, MEG-L, and JR for writing—review and editing All authors have read and agreed to the published version of the manuscript.

Conflicts of Interest: None declared. The funders played no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
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1 GBD 2021 Diseases and Injuries Collaborators Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021 Lancet 2024 05 18 403 10440 2133 2161 10.1016/S0140-6736(24)00757-8 38642570 S0140-6736(24)00757-8 38642570
2 Zhu N Zhang D Wang W Li X Yang B Song J Zhao X Huang B Shi W Lu R Niu P Zhan F Ma X Wang D Xu W Wu G Gao GF Tan W A novel coronavirus from patients with pneumonia in China, 2019 N Engl J Med 2020 02 20 382 8 727 733 10.1056/nejmoa2001017 31978945
3 Daugherty SE Guo Y Heath K Dasmariñas MC Jubilo KG Samranvedhya J Lipsitch M Cohen K Risk of clinical sequelae after the acute phase of SARS-CoV-2 infection: retrospective cohort study BMJ 2021 05 19 373 n1098 10.1136/bmj.n1098 34011492 34011492
4 Ladds E Rushforth A Wieringa S Taylor S Rayner C Husain L Greenhalgh T Persistent symptoms after COVID-19: qualitative study of 114 "long Covid" patients and draft quality principles for services BMC Health Serv Res 2020 12 20 20 1 1144 10.1186/s12913-020-06001-y 33342437 10.1186/s12913-020-06001-y 33342437
5 Lopez-Leon S Wegman-Ostrosky T Perelman C Sepulveda R Rebolledo P Cuapio A Villapol S More than 50 long-term effects of COVID-19: a systematic review and meta-analysis Res Sq 2021 03 01 11 1 16144 10.21203/rs.3.rs-266574/v1 33688642 rs.3.rs-266574
6 Al-Aly Z Xie Y Bowe B High-dimensional characterization of post-acute sequelae of COVID-19 Nature 2021 06 594 7862 259 264 10.1038/s41586-021-03553-9 33887749 10.1038/s41586-021-03553-9 33887749
7 Ghosn J Piroth L Epaulard O Le Turnier P Mentré F Bachelet D Laouénan C French COVID Cohort Study Investigators Groups Persistent COVID-19 symptoms are highly prevalent 6 months after hospitalization: results from a large prospective cohort Clin Microbiol Infect 2021 07 27 7 1041.e1 1041.e4 10.1016/j.cmi.2021.03.012 34125067 S1198-743X(21)00147-6
8 Datta SD Talwar A Lee JT A proposed framework and timeline of the spectrum of disease due to SARS-CoV-2 infection: illness beyond acute infection and public health implications JAMA 2020 12 08 324 22 2251 2252 10.1001/jama.2020.22717 33206133 2773338 33206133
9 Tenforde MW Kim SS Lindsell CJ Billig Rose E Shapiro NI Files DC Gibbs KW Erickson HL Steingrub JS Smithline HA Gong MN Aboodi MS Exline MC Henning DJ Wilson JG Khan A Qadir N Brown SM Peltan ID Rice TW Hager DN Ginde AA Stubblefield WB Patel MM Self WH Feldstein LR IVY Network InvestigatorsCDC COVID-19 Response Team Symptom duration and risk factors for delayed return to usual health among outpatients with COVID-19 in a multistate health care systems network - United States, March-June 2020 MMWR Morb Mortal Wkly Rep 2020 07 31 69 30 993 998 10.15585/mmwr.mm6930e1 10.15585/mmwr.mm6930e1 32730238 32730238
10 Nalbandian A Sehgal K Gupta A Madhavan MV McGroder C Stevens JS Cook JR Nordvig AS Shalev D Sehrawat TS Ahluwalia N Bikdeli B Dietz D Der-Nigoghossian C Liyanage-Don N Rosner GF Bernstein EJ Mohan S Beckley AA Seres DS Choueiri TK Uriel N Ausiello JC Accili D Freedberg DE Baldwin M Schwartz A Brodie D Garcia CK Elkind MSV Connors JM Bilezikian JP Landry DW Wan EY Post-acute COVID-19 syndrome Nat Med 2021 04 27 4 601 615 10.1038/s41591-021-01283-z 33753937 10.1038/s41591-021-01283-z 33753937
11 Carfì A Bernabei R Landi F Gemelli Against COVID-19 Post-Acute Care Study Group Persistent symptoms in patients after acute COVID-19 JAMA 2020 08 11 324 6 603 605 10.1001/jama.2020.12603 32644129 2768351 32644129
12 Golzardi M Hromić-Jahjefendić A Šutković J Aydin O Ünal-Aydın P Bećirević T Redwan EM Rubio-Casillas A Uversky VN The aftermath of COVID-19: exploring the long-term effects on organ systems Biomedicines 2024 04 20 12 4 913 10.3390/biomedicines12040913 38672267 biomedicines12040913 38672267
13 Antonelli M Pujol JC Spector TD Ourselin S Steves CJ Risk of long COVID associated with delta versus omicron variants of SARS-CoV-2 Lancet 2022 06 399 10343 2263 2264 10.1016/s0140-6736(22)00941-2 35717982
14 Wise J Covid-19: long covid risk is lower with omicron than delta, researchers find BMJ 2022 06 17 377 o1500 10.1136/bmj.o1500 35714995 35714995
15 Wilson C Long covid risk with omicron may be half that of delta New Scientist 2022 06 254 3392 12 10.1016/s0262-4079(22)01099-5 35782298
16 Morioka S Tsuzuki S Suzuki M Terada M Akashi M Osanai Y Kuge C Sanada M Tanaka K Maruki T Takahashi K Saito S Hayakawa K Teruya K Hojo M Ohmagari N Post COVID-19 condition of the Omicron variant of SARS-CoV-2 J Infect Chemother 2022 11 28 11 1546 1551 10.1016/j.jiac.2022.08.007 35963600 S1341-321X(22)00233-1 35963600
17 Liao X Guan Y Liao Q Ma Z Zhang L Dong J Lai X Zheng G Yang S Wang C Liao Z Song S Yi H Lu H Long-term sequelae of different COVID-19 variants: the original strain versus the Omicron variant Glob Health Med 2022 12 31 4 6 322 326 10.35772/ghm.2022.01069 36589219 36589219
18 Bhandari S Rankawat G Joshi S Tiwaskar M Lohmror A Bhandari S Post-COVID syndrome: the stranger ghost of culprit COVID-19 J Assoc Physicians India 2023 02 1 71 2 11 12 10.5005/japi-11001-0193 37354471
19 Couzin-Frankel J New long Covid cases decline with Omicron Science 2023 03 24 379 6638 1174 1175 10.1126/science.adh9054 36952419 36952419
20 Lokanuwatsatien T Satdhabudha A Tangsathapornpong A Bunjoungmanee P Sinlapamongkolkul P Chaiyakulsil C Sritipsukho P Tantiyavarong P Prevalence and associating factors of long COVID in pediatric patients during the Delta and the Omicron variants Front Pediatr 2023 5 24 11 1127582 10.3389/fped.2023.1127582 37292374 37292374
21 Buonsenso D Morello R Mariani F De Rose C Mastrantoni L Zampino G Valentini P Risk of long Covid in children infected with Omicron or pre-Omicron SARS-CoV-2 variants Acta Paediatr 2023 06 30 112 6 1284 1286 10.1111/apa.16764 36938946 36938946
22 Chen H Zhang L Zhang Y Chen G Wang D Chen X Wang Z Wang J Che X Horita N Seki N Prevalence and clinical features of long COVID from omicron infection in children and adults J Infect 2023 04 86 4 e97 e99 10.1016/j.jinf.2023.02.015 36803675
23 Pinto Pereira SM Nugawela MD Stephenson T Foret-Bruno P Dalrymple E Xu L Whittaker E Heyman I Ford T Segal T Chalder T Ladhani SN Mensah AA McOwat K Simmons R CLoCk ConsortiumShafran R Post-Covid-19 condition (long Covid) in children and young people 12 months after infection or reinfection with the Omicron variant: a prospective observational study Sci Rep 2024 04 30 14 1 9957 10.1038/s41598-024-60372-4 10.1038/s41598-024-60372-4 38693285 10.1038/s41598-024-60372-4 38693285
24 Noij L Blankestijn J Lap C van Houten MA Biesbroek G Maitland-van der Zee A-H Abdel-Aziz M van Goudoever JB Alsem M Brackel C Oostrom K Hashimoto S Terheggen-Lagro S Clinical-based phenotypes in children with pediatric post-COVID-19 condition World J Pediatr 2024 04 25 Online ahead of print 10.1007/s12519-024-00805-2 38664324 10.1007/s12519-024-00805-2
25 Du M Ma Y Deng J Liu M Liu J Comparison of long COVID-19 caused by different SARS-CoV-2 strains: a systematic review and meta-analysis Int J Environ Res Public Health 2022 11 30 19 23 16010 10.3390/ijerph192316010 36498103 ijerph192316010 36498103
26 Hernández J Dehesa-Canseco F Vázquez-López AB Reséndiz-Sandoval M Caire-Juvera G Solís-Hernández M Valenzuela O Gómez-Gil B Mata-Haro V Neutralization of Omicron BA.1, BA.5.1.6, BQ.1.3 and XBB1.1 induced by heterologous vaccination Ad5-nCoV and mRNA-1273 Signal Transduct Target Ther 2023 04 29 8 1 174 10.1038/s41392-023-01447-y 10.1038/s41392-023-01447-y 37120638 10.1038/s41392-023-01447-y 37120638
27 Luo J Zhang J Tang HT Wong HK Lyu A Cheung CH Bian Z Prevalence and risk factors of long COVID 6-12 months after infection with the Omicron variant among nonhospitalized patients in Hong Kong J Med Virol 2023 06 19 95 6 e28862 10.1002/jmv.28862 37334978 37334978
28 AlBahrani S AlBarrak A AlGubaisi N Alkurdi H Alburaiki D AlGhamdi A AlOthaiqy M Tayeb S Tayeb N Abdraboh S Khairi A Alshareef L AlHarbi A AlRabeeah S Alqahtani AS Alqahtani JS Hakami FH Al-Maqati TN Alkhrashi SA Almershad MM AlAbbadi A Hakami MH Faqihi O Altawfiq KJA Jebakumar AZ Al-Tawfiq JA Self-reported long COVID-19 symptoms are rare among vaccinated healthcare workers J Infect Public Health 2023 08 16 8 1276 1280 10.1016/j.jiph.2023.05.037 37315430 S1876-0341(23)00201-0 37315430
29 Brannock MD Chew RF Preiss AJ Hadley EC Redfield S McMurry JA Leese PJ Girvin AT Crosskey M Zhou AG Moffitt RA Funk MJ Pfaff ER Haendel MA Chute CG N3CRECOVER Consortia Long COVID risk and pre-COVID vaccination in an EHR-based cohort study from the RECOVER program Nat Commun 2023 05 22 14 1 2914 10.1038/s41467-023-38388-7 10.1038/s41467-023-38388-7 37217471 10.1038/s41467-023-38388-7 37217471
30 General Data Protection Regulation: GDPR General Data Protection Regulation 2024-07-23 https://gdpr-info.eu/
31 Valdivieso-Martínez B Sauri I Philibert J Calderon J Gas M Diaz J López-Hontangas J Navarro D Forner M Redon J Impact of long-COVID on health care burden: a case-control study JCM 2023 09 05 12 18 5768 10.3390/jcm12185768 10.3390/jcm12185768
32 Hedberg P Nauclér P Post-COVID-19 condition after SARS-CoV-2 infections during the Omicron surge vs the Delta, Alpha, and wild type periods in Stockholm, Sweden J Infect Dis 2024 01 12 229 1 133 136 10.1093/infdis/jiad382 37665981 7259851 37665981
33 Magnusson K Kristoffersen DT Dell'Isola A Kiadaliri A Turkiewicz A Runhaar J Bierma-Zeinstra S Englund M Magnus PM Kinge JM Post-COVID medical complaints following infection with SARS-CoV-2 Omicron vs Delta variants Nat Commun 2022 11 30 13 1 7363 10.1038/s41467-022-35240-2 10.1038/s41467-022-35240-2 36450749 10.1038/s41467-022-35240-2 36450749
34 Hedberg P Granath F Bruchfeld J Askling J Sjöholm D Fored M Färnert A Naucler P Post COVID-19 condition diagnosis: a population-based cohort study of occurrence, associated factors, and healthcare use by severity of acute infection J Intern Med 2023 02 07 293 2 246 258 10.1111/joim.13584 36478477 36478477
35 Diexer S Klee B Gottschick C Xu C Broda A Purschke O Binder M Frese T Girndt M Hoell JI Moor I Gekle M Mikolajczyk R Association between virus variants, vaccination, previous infections, and post-COVID-19 risk Int J Infect Dis 2023 11 136 14 21 10.1016/j.ijid.2023.08.019 37634619 S1201-9712(23)00702-6 37634619
36 Hammel IS Tosi DM Tang F Pott H Ruiz JG Frailty as a risk factor for post-acute sequelae of COVID-19 among US veterans during the Delta and Omicron waves J Am Geriatr Soc 2023 12 19 71 12 3826 3835 10.1111/jgs.18584 37725480 37725480
37 Izquierdo-Pujol J Moron-Lopez S Dalmau J Gonzalez-Aumatell A Carreras-Abad C Mendez M Rodrigo C Martinez-Picado J Post COVID-19 condition in children and youths: an emerging problem Front Pediatr 2022 5 11 10 894204 10.3389/fped.2022.894204 35633949 35633949
38 Warren-Gash C Lacey A Cook S Stocker D Toon S Lelii F Ford B Ireland G Ladhani SN Stephenson T Nguipdop-Djomo P Mangtani P COVID-19 Schools Infection Survey 2 Study Group Post-COVID-19 condition and persisting symptoms in English schoolchildren: repeated surveys to March 2022 BMC Infect Dis 2023 04 05 23 1 201 10.1186/s12879-023-08203-1 37020190 10.1186/s12879-023-08203-1 37020190
39 Yildirim Arslan S Avcu G Sahbudak Bal Z Arslan A Ozkinay FF Kurugol Z Evaluation of post-COVID symptoms of the SARS-CoV-2 Delta and Omicron variants in children: a prospective study Eur J Pediatr 2023 10 01 182 10 4565 4571 10.1007/s00431-023-05134-6 37526704 10.1007/s00431-023-05134-6 37526704
40 Leahy J Bajracharya R Altonen B Ferreira-Ortiz M Silvera L Astua A Post-discharge healthcare usage and costs from March 2020 through the Omicron surge for individuals hospitalized with COVID-19 Cureus 2023 12 15 12 e50663 10.7759/cureus.50663 38229792 38229792
41 Buonsenso D Piazza M Boner AL Bellanti JA Long COVID: a proposed hypothesis-driven model of viral persistence for the pathophysiology of the syndrome Allergy Asthma Proc 2022 05 01 43 3 187 193 10.2500/aap.2022.43.220018 35524358 35524358
42 L'Huillier AG Pagano S Baggio S Meyer B Andrey DO Nehme M Guessous I Eberhardt CS Huttner A Posfay-Barbe KM Yerly S Siegrist C Kaiser L Vuilleumier N Autoantibodies against apolipoprotein A-1 after COVID-19 predict symptoms persistence Eur J Clin Invest 2022 10 02 52 10 e13818 10.1111/eci.13818 35598178 35598178
