Risk factors for hospitalisation and death due to COVID-19 during endemic Omicron circulation, a population-based cohort study

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Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Risk factors for hospitalisation and death due to COVID-19 during endemic Omicron circulation, a population-based cohort study Ulrika Marking , Erik Wahlström , Johanna Holm , Sten Walther , View ORCID Profile Håkan Hanberger , View ORCID Profile Kristoffer Strålin doi: https://doi.org/10.1101/2025.05.05.25326987 Ulrika Marking a Public Health Agency of Sweden , Sweden b Department of Clinical Sciences, Karolinska Institutet Danderyd Hospital , Stockholm, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: ulrika.marking{at}folkhalsomyndigheten.se erik.wahlstrom{at}socialstyrelsen.se kristoffer.stralin{at}ki.se Erik Wahlström c National Board of Health and Welfare , Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: ulrika.marking{at}folkhalsomyndigheten.se erik.wahlstrom{at}socialstyrelsen.se kristoffer.stralin{at}ki.se Johanna Holm c National Board of Health and Welfare , Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sten Walther d Swedish Intensive Care Registry, Värmland County Council , Karlstad, Sweden e Department of Cardiothoracic and Vascular Surgery, Heart Centre, Linköping University Hospital , Linköping, Sweden f Department of Health, Medicine and Caring Sciences, Linköping University , Linköping, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Håkan Hanberger g Division of Inflammation and Infection, Department of Biomedical and Clinical Sciences, Faculty of Medicine and Health Sciences, Linköping University , Linköping, Sweden h Department of Infectious Diseases, University Hospital Region Östergötland , Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Håkan Hanberger Kristoffer Strålin i Department of Infectious Diseases, Karolinska University Hospital , Stockholm, Sweden j Department of Medicine, Huddinge, Karolinska Institutet , Stockholm, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kristoffer Strålin For correspondence: ulrika.marking{at}folkhalsomyndigheten.se erik.wahlstrom{at}socialstyrelsen.se kristoffer.stralin{at}ki.se Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Background Knowledge about risk factors for hospitalisation and death due to COVID-19 during the Omicron period, beyond the initial Omicron wave, remains limited. Therefore, to support vaccination strategies and antiviral treatment recommendations, we aimed to investigate the significance of various risk factors and their association with hospitalisation and death from COVID-19 during the Omicron period. Method Nationwide registry data on the Swedish adult population was compiled for the period June 6, 2022, to March 31, 2023. Frequency of hospitalisation with a primary discharge diagnosis of COVID-19 and death with COVID-19 specified as the underlying cause of death on the death certificate were analysed. Individual data were stratified by age, vaccination status, Charlson Comorbidity Index, and specific comorbidities. Poisson regression was used to estimate incidence risk ratios of independent risk factors for hospitalisation and death including age, sex, comorbidities, care dependency and socioeconomic factors. Results Among the more than 8,3 million individuals in the study cohort, 13 941 hospitalisations and 2 309 deaths due to COVID-19 were recorded during the study period. Multivariable analysis revealed that high age was the strongest risk factors for both hospitalisation and death. Advanced comorbidity was the second strongest risk factor for hospitalisation, and care dependency was the second strongest risk factor for death. Among comorbidities, recent chemotherapy, Immune Deficiency, Multiple Sclerosis, and Chronic Kidney Disease stage 5 were the strongest risk factors for hospitalisation and death. Conclusion Also in the current Omicron endemicity, advanced age remains the strongest risk factor for hospitalisation and death due to COVID-19, accompanied by advanced comorbidity and care dependency. These findings can guide recommendations for vaccination strategies and antiviral treatment, helping to protect those most at risk. Introduction Ancestral SARS-CoV-2 were, just like the initially dominant variants of concern alpha and delta more prone to cause severe disease and death than the later emerging Omicron variant ( 1 – 4 ). Omicron appearance coincided with a high vaccine uptake in the population, but also among unvaccinated the Omicron variant exhibited considerably less virulence ( 2 – 5 ). Several explanations have been brought forward, such as a tropism more restricted to the upper respiratory tract than the lungs ( 6 ) and reduced fusogenicity of the spike protein leading to less extensive tissue damage ( 7 ). Regardless of mechanism, the rapid omicron transmission led to wide-spread infection acquired immunity, significantly shifting the risk landscape attributed to SARS-CoV-2. With increased hybrid immunity from a combination of vaccination and one or multiple infections the risk of severe COVID-19 outcomes is reduced considerable, and interventions to mitigate disease burden may need adaptations. Detailed knowledge of patient factors increasing the risk for severe COVID-19 is essential to inform public health decisions and mitigate risks of infection among the relevant vulnerable groups. This study aims to assess risk factors for hospitalisation and death due to COVID-19 in a setting reflecting the endemic phase of SARS-CoV-2 Omicron circulation in the Swedish population, with study period starting in June 2022, after 6 months of Omicron dominance. Methods Study population and design This observational study included all adult (≥ 18 years) individuals registered as residents in Sweden as of December 31, 2021, according to the Total Population Register, Statistics Sweden. Baseline characteristics were described, and the population was followed from June 10 th 2022 to March 31 st 2023, covering a period of almost 10 months. Outcome data, including hospitalisation for COVID-19 and death due to COVID-19, were registered. Nationwide registry data were compiled by the Swedish National Board of Health and Welfare using the unique national personal identification number as previously described ( 5 ). Briefly, data on comorbidity, care dependency, hospitalisations, mortality, and cause of death were obtained from health registers maintained by the National Board of Health and Welfare, data on SARS-CoV-2 vaccination were obtained from registers held by Public Health Agency of Sweden as previously described, and data on level of education and socio-economic index of residential area was obtained from Statistics Sweden ( 5 ). Covariate data Major comorbidities were identified and defined using 1) diagnoses recorded at discharge from hospitalisation or specialised out-patient care; and/or 2) prescribed drugs for chronic conditions commonly managed in primary care dispensed during the year preceding study start; and/or 3) the National Cancer register (Supplementary Table S1). A 30-day wash-out window prior to index admission date was applied. Codes of the 10 th International Statistical Classification of Diseases (ICD-10) and Anatomic Therapeutic Chemical (ATC) classifications, and health care procedures defining each comorbidity are provided in Supplementary Table S1. Diagnoses were sourced from the National Patient Register (held by the National Board of Health and Welfare) which contains comprehensive data on diagnoses recorded at hospital discharge or specialized out-patient departments. Data on drugs dispensed were obtained from the Swedish Prescribed Drug Register (held by the National Board of Health and Welfare), which contains all prescribed and pharmacy-dispensed medicines in the community classified according to the ATC Classification System. The register has almost complete coverage (data missing < 1%). A Charlson Comorbidity Index (CCI) was calculated for all individuals based on the comorbidities registered in the National Patient Register ( 8 ) in five years prior to index date. In addition, to study specific comorbidities as exposures in relation to the combined comorbidities of patients in the present study, we calculated a modified CCI, in which all comorbidities of the patients except from the specific exposure of interest were included. Vaccination data was obtained from the National Vaccination Register, maintained by the Public Health Agency of Sweden, which has mandatory reporting on COVID-19 vaccination. The majority (88.5%) of the Swedish adult population had received at least one dose vaccine against COVID-19 by June 2022 ( 9 ). Socioeconomic index of area of residency as determined yearly by Statistics Sweden was employed to capture socioeconomic status. The measure is based on 1) proportion of citizens with completed primary school; 2) proportion of citizens with a low economic standard based on household income; and 3) proportion of citizens either unemployed for >6 months or with welfare benefits. Education level was obtained from the Education Register held by Statistics Sweden. Information on care dependency (long-term care facilities for elderly (LTCF), home-based care, or supportive housing for persons with functional impairments) prior to index date was obtained from the Care and Social Services for the Elderly and for Persons with Impairments Register and the National Register of Municipal Support and Service for Persons with Certain Functional Impairment, held by the National Board of Health and Welfare. Outcomes Outcome measures were hospitalisation for COVID-19 defined as hospitalisation with a COVID-19 diagnosis as the primary diagnosis at discharge; and death from COVID-19, defined as death with COVID-19 specified as the underlying cause of death on the death certificate. Both outcomes were binary. Statistical analysis Poisson regression was used to estimate Incidence rate ratios (IRR) for a range of factors associated to one or both outcomes (hospitalisation and death due to COVID-19) using different models. Separate models were created for each exposure – outcome combination. We estimated the effect of each comorbidity in models adjusted for age (in 5-year intervals), sex (male/female), time since most recent SARS-CoV-2 vaccination (<6 months, 6-12 months, 13-24 months, unvaccinated (including vaccination more than 24 months prior to start of study period)), modified CCI (0, 1-2, 3-5, 6+), calendar time (in 7-day intervals), socioeconomic index of area of residency (5 categories), education (primary school, secondary school, university/college 3 yrs) and care dependency , all modelled as main effects. To assess the IRR associated to risk factors included in all models, specific models were created without a specific comorbidity variable but with the unmodified CCI for both study outcomes. Crude analysis of the outcomes (hospitalisation for COVID-19 and death due to COVID-19) are reported as Incidence rate per 1000 person-years. Registry data were complete on all variables except for education (2.1%), and complete case analysis was applied in all regression models. Sensitivity analyses were performed comparing adjusted IRR when including cases without education data and treating them as an additional category. All data management and statistical analyses was performed using SAS Software SAS Enterprise Guide version 8.3 (SAS institute, Cary, NC, USA). Ethical permission The study was approved by the Swedish Ethics Review Authority, Uppsala, Sweden (Dnr 2020-04278). Results Among more than 8,3 million individuals in the study cohort, 13 941 hospitalisations and 2 309 deaths due to COVID-19 were recorded during the study period. Table 1 shows characteristics of individuals with or without hospitalisation for COVID-19 and with or without death due to COVID-19 during the study period. As noted, both hospitalisation and death were more frequent among males than females and among individuals ≥ 80 years of age compared with younger individuals. View this table: View inline View popup Download powerpoint Table 1. Distribution of covariates and sociodemographic characteristics of study cohort per 1000 person-years. Time since most recent vaccination varied over the study period and is presented in Figure 1A . Rates of hospitalisation due to COVID-19 during the study period are presented in Figure 1B . Download figure Open in new tab Figure 1. (A) Time since most recent vaccination in study cohort and (B) number of patients hospitalized during study period as a surrogate of SARS-CoV-2 transmission. In multivariable regression analyses, age remained a significant risk factor, with individuals aged 80–84 exhibiting a 7.1-fold increased risk of hospitalisation (95% CI: 6.5–7.9) ( Figure 2A ) and a 15.6-fold higher risk of death (95% CI: 10.8–22.6) compared to those aged 60–64 (Figure 3A). A high Charlson Comorbidity Index (CCI) of 3–5 compared to 0, was also a strong predictor of hospitalisation (IRR 4.9 [95% CI 4.7–5.2]), though its association with death was comparatively weaker (IRR 2.7 [95% CI 2.5–3.1]). However, care dependency was strongly associated with death (IRR 12.2 [95% CI 10.8–13.7] for nursing home and IRR 9.5 [95% CI 5.1–17.8] for supportive housing). Download figure Open in new tab Download figure Open in new tab Download figure Open in new tab Download figure Open in new tab Figure 2. Forest plots of incidence rate ratios with 95% confidence intervals, estimated from multivariable regression models for hospitalisation (A, B) and death (C, D) due to COVID-19. The overall models (A, C) include vaccine status, sex, age, Charlson Comorbidity Index (CCI), care dependency, education, and socioeconomic index (SEI). The disease specific models (B, D) include adjustment for age, sex, vaccination status, modified CCI (total CCI apart from the score rendered by the specific disease), calendar time, care dependency, education, and socioeconomic index, all modelled as main effects. CCI; Charlson Comorbidity Index, SEI; socioeconomic Index Among specific diseases, recent chemotherapy, Immune Deficiency, Multiple Sclerosis (MS), and Chronic Kidney Disease stage 5 were the strongest risk factors for both hospitalisation (IRR 2.9-5.4) and death (IRR 2.5-5.4) ( Figure 2B , 2D and Table 2 ). View this table: View inline View popup Download powerpoint Table 2. Crude incidence rates for hospitalisation and death due to COVID-19, per 1000 person-years, stratified on Charlson Comorbidity Index (CCI), time since most recent vaccination, and age. * Investigated exposure do not contribute to CCI in stratification. CDK; Chronic Kidney Disease, compl.; complication. Vaccination within 6 months was associated to a reduced risk of hospitalisation in multivariable regression analyses, whereas individuals with more distant (13-24 months) vaccination had a 1.9-fold [95% CI 1.8-2.0] increased risk of hospitalisation and a 2.1-fold [95% CI 1.8 - 2.5] increased risk of death. In univariate analysis (crude incidence rates), recent vaccination was associated with reduced rates of hospitalisation across the three age groups of the study ( Table 2 ). The present study found limited impact of socioeconomic factors for hospitalisation and death due to COVID-19 ( Figure 2A , 2C). Discussion The emergence of the Omicron variant of SARS-CoV-2 has significantly altered the risk landscape attributed to COVID-19. Accumulation of immunity through vaccination and natural infection, along with the reduced virulence of the Omicron strain has led to a decline in overall hospitalisation and death rates as compared to earlier phases of the pandemic. However, certain groups remain vulnerable to severe disease and may benefit from antiviral treatment and vaccination. This study presents risk factors associated to hospitalisation and death for COVID-19 in the Swedish population after six months with Omicron circulation, mirroring the decreased risk pattern seen after the transition from pandemic to endemic SARS-CoV-2 circulation. In the present study, increasing age was found to be the single most important predictor of both hospitalisation and death in a period with widespread population-level immunity. This finding is in line with other studies on disease severity of COVID-19 during the Omicron period ( 10 – 12 ). The present study also found that advanced comorbidity played the second most critical role in explaining hospitalisation risk. This aligns with reports by Scott et al. ( 13 ), who noted that >98% of hospitalised Omicron patients in their cohort had two or more high-risk conditions. Although Omicron may exhibit lower virulence in a general sense, individuals with significant comorbidity appear to remain highly susceptible to develop severe COVID-19 also in the Omicron era. Notably, we observed recent chemotherapy, Immune Deficiency, MS, and Chronic Kidney Diseases stage 5 to be especially potent contributors to both hospitalisation and death. These findings concur with prior work from Ofer et al. ( 14 ), who showed that active cancer treatment or immunosuppressive states are consistently associated with worse COVID-19 outcomes in the Omicron period. Our data suggest that severe immunocompromising conditions and advanced organ dysfunction collectively confer an elevated vulnerability in these populations. Regarding MS, it should be noted that these patients frequently are B-cell suppressed, due to rituximab treatment that cannot be detected in our current national health registers. We hypothesise that the risk estimates associated with a MS diagnosis likely reflect the impact of rituximab treatment. For prediction of death, care dependency emerged as the second most influential risk factor in the present study. Care dependency may reflect functional impairment and frailty, which in turn correlate with poorer resilience against infectious stressors. This observation parallels Spreco et al. ( 12 ), who reported that most fatal COVID-19 cases involved patients with very low functional levels, often dependent on nursing home care. Similarly, other studies, such as Hippisley-Cox et al. ( 11 ), have highlighted the added prognostic value of frailty indices and performance status in predicting outcome. The present study was not designed to evaluate the effect of vaccination in specific subgroups of individuals, although our unadjusted analysis (crude incidence rates, Table 2 ) showed generally lower rates of hospitalisation and death in recently vaccinated individuals. Tomioka et al. ( 15 ), found that recent vaccination was particularly beneficial for individuals ≥ 80 years of age. However, several factors may negatively impact vaccine response, including immunosuppressive medication and renal failure, both of which are linked to an increased risk of hospitalisation and death due to COVID-19 in the presented material. Therefore, we suggest that additional interventions, such as early antiviral treatment should be considered for the patient groups with increased risk of severe COVID-19. The study has some strengths. First, we have used a large sample size, improving accuracy in our estimates. Second, the use of national health registers with mandatory reporting of all cases has further improved our accuracy and minimised selection biases. Furthermore, high-coverage registers and investigation of the entire population ensures high external validity. The study has several limitations. First, hospitalisation due to COVID-19 does not always indicate severe illness, as very frail individuals may require hospital admission even for mild disease. Additionally, a potential bias may have been introduced in the hospitalisation analysis, as patients with immunosuppressive conditions are more likely to be admitted for Remdesivir treatment, and hospital admission is standard practice for patients on chemotherapy that present with fever. However, this bias would not affect death analyses, which generally showed patterns similar to those of the hospitalisation analyses. Second, the use of individual models for each assessed comorbidity to better assess confounding, may have introduce some variation in the underlying risk of the reference group. Third, we do not have access to data on immunosuppressive treatments given at hospital apart from chemotherapy, for example to patients with MS and Rheumatoid Arthritis. However, regardless of the underlying mechanisms, the presented estimates accurately reflect the real-world situation patients with the investigated diagnoses. Forth, as there was no public testing for SARS-CoV-2 during the study period, COVID-19 deaths outside of hospitals may have been underreported. In conclusion, although the Omicron variant generally exhibits lower virulence than previous SARS-CoV-2 variants, vulnerable individuals remain at elevated risk of developing severe COVID-19. Our study showed that advanced age is still the strongest risk factor for hospitalisation and death due to COVID-19, followed by advanced comorbidity and care dependency. These findings can guide recommendations for vaccination strategies and antiviral treatment, helping to protect those most at risk. Data availability The data underlying this article cannot be shared publicly due to regulations under Swedish law. According to the Swedish Ethics Review Act, the General Data Protection Regulation, and the Public Access to Information and Secrecy Act, patient data can only be made available, after legal review, to researchers who meet the criteria for access to this type of confidential data. Requests regarding data in this report may be made to the corresponding author. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding The study was funded and in part carried out by the National Board of Health and Welfare. Data Availability The data underlying this article cannot be shared publicly due to regulations under Swedish law. According to the Swedish Ethics Review Act, the General Data Protection Regulation, and the Public Access to Information and Secrecy Act, patient data can only be made available, after legal review, to researchers who meet the criteria for access to this type of confidential data. Requests regarding data in this report may be made to the corresponding author. View this table: View inline View popup Supplementary Table S1. Categorisation of health conditions identified through codes of the 10 th International Statistical Classification of Diseases (ICD-10) classification, Anatomic Therapeutic Chemical (ATC) classification, and health care procedures (“KVÅ” codes), in national health registers. View this table: View inline View popup Table S2. STROBE Statement—Checklist of items that should be included in reports of cohort studies References 1. ↵ Wolter N , Jassat W , Walaza S , Welch R , Moultrie H , Groome M , et al. Early assessment of the clinical severity of the SARS-CoV-2 omicron variant in South Africa: a data linkage study . Lancet . 2022 ; 399 ( 10323 ): 437 – 46 . OpenUrl CrossRef PubMed 2. ↵ Bager P , Wohlfahrt J , Bhatt S , Stegger M , Legarth R , Møller CH , et al. Risk of hospitalisation associated with infection with SARS-CoV-2 omicron variant versus delta variant in Denmark: an observational cohort study . Lancet Infect Dis . 2022 ; 22 ( 7 ): 967 – 76 . OpenUrl CrossRef PubMed 3. Nyberg T , Ferguson NM , Nash SG , Webster HH , Flaxman S , Andrews N , et al. Comparative analysis of the risks of hospitalisation and death associated with SARS-CoV-2 omicron (B.1.1.529) and delta (B.1.617.2) variants in England: a cohort study . Lancet . 2022 ; 399 ( 10332 ): 1303 – 12 . OpenUrl PubMed 4. ↵ Lewnard JA , Hong VX , Patel MM , Kahn R , Lipsitch M , Tartof SY . Clinical outcomes associated with SARS-CoV-2 Omicron (B.1.1.529) variant and BA.1/BA.1.1 or BA.2 subvariant infection in Southern California . Nat Med. 2022 ; 28 ( 9 ): 1933 – 43 . OpenUrl CrossRef PubMed 5. ↵ Wahlström E , Bruce D , Bennet-Bark AM , Walther S , Hanberger H , Strålin K . Severity outcomes of SARS-CoV-2 infection in the Omicron and pre-Omicron periods, in unvaccinated first-time test positive adults less than 65 years old without comorbidity, in Sweden . J Infect Public Health . 2024 ; 17 ( 9 ): 102502 . OpenUrl PubMed 6. ↵ Hui KPY , Ho JCW , Cheung MC , Ng KC , Ching RHH , Lai KL , et al. SARS-CoV-2 Omicron variant replication in human bronchus and lung ex vivo . Nature . 2022 ; 603 ( 7902 ): 715 – 20 . OpenUrl CrossRef PubMed 7. ↵ Suzuki R , Yamasoba D , Kimura I , Wang L , Kishimoto M , Ito J , et al. Attenuated fusogenicity and pathogenicity of SARS-CoV-2 Omicron variant . Nature . 2022 ; 603 ( 7902 ): 700 – 5 . OpenUrl CrossRef PubMed 8. ↵ Ludvigsson JF , Appelros P , Askling J , Byberg L , Carrero JJ , Ekström AM , et al. Adaptation of the Charlson Comorbidity Index for Register-Based Research in Sweden . Clin Epidemiol . 2021 ; 13 : 21 – 41 . OpenUrl CrossRef PubMed 9. ↵ European Center for Disease Prevention and Control . COVID-19 Vaccine Tracker [Available from: https://vaccinetracker.ecdc.europa.eu/public/extensions/COVID-19/vaccine-tracker.html#uptake-tab . 10. ↵ Cogill S , Nallamshetty S , Fullenkamp N , Heberer K , Lynch J , Lee KM , et al. Predicting clinical outcomes of SARS-CoV-2 infection during the Omicron wave using machine learning . PLoS One . 2024 ; 19 ( 4 ): e0290221 . OpenUrl PubMed 11. ↵ Hippisley-Cox J , Khunti K , Sheikh A , Nguyen-Van-Tam JS , Coupland CAC . Risk prediction of covid-19 related death or hospital admission in adults testing positive for SARS-CoV-2 infection during the omicron wave in England (QCOVID4): cohort study . Bmj . 2023 ; 381 : e072976 . OpenUrl Abstract / FREE Full Text 12. ↵ Spreco A , Andersson C , Sjödahl R , Niward K . 60-day mortality and the role of SARS-CoV-2 in hospital admissions of immunocompromised patients during later Omicron period: a population-based study in Sweden . Infect Dis (Lond) . 2025 : 1 – 13 . 13. ↵ Scott A , Puzniak L , Murphy MV , Benjumea D , Rava A , Benigno M , et al. Assessment of clinical characteristics and mortality in patients hospitalized with SARS-CoV-2 from January 2022 to November 2022, when Omicron variants were predominant in the United States . Curr Med Res Opin . 2025 ; 41 ( 1 ): 71 – 82 . OpenUrl PubMed 14. ↵ Ofer J , Drozdinsky G , Basharim B , Turjeman A , Eliakim-Raz N , Stemmer SM . Mortality and Hospitalization Risks in Patients With Cancer and the SARS-CoV-2 Omicron Variant . JAMA Oncol . 2024 ; 10 ( 1 ): 137 – 8 . OpenUrl PubMed 15. ↵ Tomioka K , Uno K , Yamada M . Risk of severe COVID-19 in unvaccinated patients during the period from wild-type to Omicron variant: real-world evidence from Japan . Environ Health Prev Med . 2024 ; 29 : 10 . OpenUrl PubMed View the discussion thread. Back to top Previous Next Posted May 06, 2025. Download PDF Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. 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Share Risk factors for hospitalisation and death due to COVID-19 during endemic Omicron circulation, a population-based cohort study Ulrika Marking , Erik Wahlström , Johanna Holm , Sten Walther , Håkan Hanberger , Kristoffer Strålin medRxiv 2025.05.05.25326987; doi: https://doi.org/10.1101/2025.05.05.25326987 Share This Article: Copy Citation Tools Risk factors for hospitalisation and death due to COVID-19 during endemic Omicron circulation, a population-based cohort study Ulrika Marking , Erik Wahlström , Johanna Holm , Sten Walther , Håkan Hanberger , Kristoffer Strålin medRxiv 2025.05.05.25326987; doi: https://doi.org/10.1101/2025.05.05.25326987 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Infectious Diseases (except HIV/AIDS) Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (300) Cardiovascular Medicine (4435) Dentistry and Oral Medicine (444) Dermatology (382) Emergency Medicine (608) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1509) Epidemiology (15229) Forensic Medicine (30) Gastroenterology (1124) Genetic and Genomic Medicine (6600) Geriatric Medicine (668) Health Economics (997) Health Informatics (4536) Health Policy (1368) Health Systems and Quality Improvement (1613) Hematology (541) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15916) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (146) Nephrology (667) Neurology (6599) Nursing (346) Nutrition (998) Obstetrics and Gynecology (1144) Occupational and Environmental Health (957) Oncology (3332) Ophthalmology (974) Orthopedics (369) Otolaryngology (420) Pain Medicine (436) Palliative Medicine (130) Pathology (663) Pediatrics (1693) Pharmacology and Therapeutics (691) Primary Care Research (711) Psychiatry and Clinical Psychology (5447) Public and Global Health (9232) Radiology and Imaging (2198) Rehabilitation Medicine and Physical Therapy (1370) Respiratory Medicine (1196) Rheumatology (593) Sexual and Reproductive Health (712) Sports Medicine (530) Surgery (712) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a00ae102e9cd73e4',t:'MTc3OTYxMDgwMw=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();