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Kulkarni D, Lee B, Ismail NF, Rahman AE, Spinardi J, Kyaw MH, Nair H. Incidence, severity, risk factors and outcomes of SARS-CoV-2 reinfections during the Omicron period: a systematic review and meta-analysis. J Glob Health 2025; 15:04032. [PMID: 39916552 PMCID: PMC11803431 DOI: 10.7189/jogh.15.04032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2025] Open
Abstract
Background Our previous systematic review estimated the cumulative incidence of SARS-CoV-2 reinfections as 1.16% (95% CI = 1.01-1.33%) during the pre-Omicron period. The Omicron variant that emerged in November 2021 was significantly genetically distinct from the previous SARS-CoV-2 variants and thus, more transmissible and posed an increased risk of SARS-CoV-2 reinfections in the population. We, therefore, conducted a fresh systematic review and meta-analysis to estimate the SARS-CoV-2 reinfection burden during the Omicron period. Methods We searched CINAHL, Medline, Global Health, Embase, and WHO COVID-19 in October 2023 for studies reporting the SARS-CoV-2 reinfection incidence during the Omicron period. The quality of the included studies was assessed using the Joanna Briggs Institute checklists. Random effects meta-analyses were conducted to estimate the incidence, and requirement of hospitalisation of SARS-CoV-2 reinfections. Symptomatic severity of reinfections and case fatality rates were analysed narratively. Results Thirty-six studies were included. The reinfection cumulative incidence during the Omicron period was 3.35% (95% CI = 1.95-5.72%) based on data from 28 studies. The cumulative incidence was higher in 18-59-year-old adults (6.62% (95% CI = 3.22-13.12%)) compared to other age groups and in health care workers (9.88% (95% CI = 5.18-18.03%)) compared to the general population (2.48% (95% CI = 1.34-4.54%)). We estimated about 1.81% (95% CI = 0.18-15.87%) of the reinfected cases required hospitalisation based on limited and highly variable data. Conclusions There was an increased risk of reinfections during the Omicron period compared to the pre-Omicron period. The incidence was higher in 18-59-year-old adults and health care workers and generally less severe during the Omicron period. However, data were limited on disease severity and long-term outcomes. Registration PROSPERO: CRD42023482598.
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Affiliation(s)
- Durga Kulkarni
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Bohee Lee
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
- National Heart & Lung Institute, Imperial College, London, UK
| | - Nabihah Farhana Ismail
- Communicable Disease Control Unit, Public Health Department, Johor Bahru, Johor State, Malaysia
| | | | - Julia Spinardi
- Pfizer, Vaccines, Emerging Markets, New York, New York, USA
| | - Moe H Kyaw
- Pfizer, Vaccines, Emerging Markets, New York, New York, USA
| | - Harish Nair
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
- School of Public Health, Nanjing Medical University, Jiangsu, China
- MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
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Lin W, Kung KH, Chan CL, Chuang SK, Au KW. Characteristics and risk factors associated with COVID-19 reinfection in Hong Kong: a retrospective cohort study. Epidemiol Infect 2025; 153:e30. [PMID: 39916599 PMCID: PMC11869080 DOI: 10.1017/s0950268825000172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2024] [Revised: 12/15/2024] [Accepted: 01/27/2025] [Indexed: 02/28/2025] Open
Abstract
We aimed to identify risk factors related to COVID-19 reinfection in Hong Kong. We performed a population-based retrospective cohort study and reviewed case-based data on COVID-19 infections reported to the Centre for Health Protection from 8 January 2020 to 29 January 2023. We analyzed the epidemiology of COVID-19 infections and performed a Cox regression analysis. In this period, 3.32% (103,065/3,106,579) of COVID-19 infections recorded were classified as reinfection. Compared with primarily infected cases, a higher proportion of re-infected cases had chronic diseases (33.54% vs. 27.27%) and were residents of residential care homes (RCH) (10.99% vs. 1.41%). The time interval between the two episodes ranged from 31 to 1,050 days (median 282 days). Cox regression analysis of Omicron cases with the adjustment of covariates showed that being female (Hazard Ratio [HR] 1.12, 95% CI 1.11-1.13), chronic diseases (HR 1.18, 95% CI 1.16-1.20) and RCH residents (HR 6.78, 95% CI 6.61-6.95) were associated with reinfection, while additional vaccination after primary infection was protective (HR 0.80, 95% CI 0.79-0.81). Further analytical studies on the risk factors and protectors of COVID-19 reinfection are needed to guide targeted interventions.
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Affiliation(s)
- Wenhua Lin
- Communicable Disease Branch, Centre for Health Protection, Department of Health, Hong Kong
| | - Kin Hang Kung
- Communicable Disease Branch, Centre for Health Protection, Department of Health, Hong Kong
| | - Chung Lam Chan
- Communicable Disease Branch, Centre for Health Protection, Department of Health, Hong Kong
| | - Shuk Kwan Chuang
- Communicable Disease Branch, Centre for Health Protection, Department of Health, Hong Kong
| | - Ka Wing Au
- Communicable Disease Branch, Centre for Health Protection, Department of Health, Hong Kong
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Zheng H, Wu S, Chen W, Cai S, Zhan M, Chen C, Lin J, Xie Z, Ou J, Ye W. Meta-analysis of hybrid immunity to mitigate the risk of Omicron variant reinfection. Front Public Health 2024; 12:1457266. [PMID: 39253287 PMCID: PMC11381385 DOI: 10.3389/fpubh.2024.1457266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2024] [Accepted: 08/13/2024] [Indexed: 09/11/2024] Open
Abstract
Background Hybrid immunity (a combination of natural and vaccine-induced immunity) provides additional immune protection against the coronavirus disease 2019 (COVID-19) reinfection. Today, people are commonly infected and vaccinated; hence, hybrid immunity is the norm. However, the mitigation of the risk of Omicron variant reinfection by hybrid immunity and the durability of its protection remain uncertain. This meta-analysis aims to explore hybrid immunity to mitigate the risk of Omicron variant reinfection and its protective durability to provide a new evidence-based basis for the development and optimization of immunization strategies and improve the public's awareness and participation in COVID-19 vaccination, especially in vulnerable and at-risk populations. Methods Embase, PubMed, Web of Science, Chinese National Knowledge Infrastructure, and Wanfang databases were searched for publicly available literature up to 10 June 2024. Two researchers independently completed the data extraction and risk of bias assessment and cross-checked each other. The Newcastle-Ottawa Scale assessed the risk of bias in included cohort and case-control studies, while criteria recommended by the Agency for Health Care Research and Quality (AHRQ) evaluated cross-sectional studies. The extracted data were synthesized in an Excel spreadsheet according to the predefined items to be collected. The outcome was Omicron variant reinfection, reported as an Odds Ratio (OR) with its 95% confidence interval (CI) and Protective Effectiveness (PE) with 95% CI. The data were pooled using a random- or fixed-effects model based on the I2 test. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed. Results Thirty-three articles were included. Compared with the natural immunity group, the hybrid immunity (booster vaccination) group had the highest level of mitigation in the risk of reinfection (OR = 0.43, 95% CI:0.34-0.56), followed by the complete vaccination group (OR = 0.58, 95% CI:0.45-0.74), and lastly the incomplete vaccination group (OR = 0.64, 95% CI:0.44-0.93). Compared with the complete vaccination-only group, the hybrid immunity (complete vaccination) group mitigated the risk of reinfection by 65% (OR = 0.35, 95% CI:0.27-0.46), and the hybrid immunity (booster vaccination) group mitigated the risk of reinfection by an additional 29% (OR = 0.71, 95% CI:0.61-0.84) compared with the hybrid immunity (complete vaccination) group. The effectiveness of hybrid immunity (incomplete vaccination) in mitigating the risk of reinfection was 37.88% (95% CI, 28.88-46.89%) within 270-364 days, and decreased to 33.23%% (95% CI, 23.80-42.66%) within 365-639 days; whereas, the effectiveness after complete vaccination was 54.36% (95% CI, 50.82-57.90%) within 270-364 days, and the effectiveness of booster vaccination was 73.49% (95% CI, 68.95-78.04%) within 90-119 days. Conclusion Hybrid immunity was significantly more protective than natural or vaccination-induced immunity, and booster doses were associated with enhanced protection against Omicron. Although its protective effects waned over time, vaccination remains a crucial measure for controlling COVID-19. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/, identifier, CRD42024539682.
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Affiliation(s)
- Huiling Zheng
- Institute of Emergency Response and Epidemic Management, Fujian Provincial Center for Disease Control and Prevention, Fuzhou, China
- School of Public Health, Fujian Medical University, Fuzhou, China
| | - Shenggen Wu
- Institute of Emergency Response and Epidemic Management, Fujian Provincial Center for Disease Control and Prevention, Fuzhou, China
| | - Wu Chen
- Institute of Emergency Response and Epidemic Management, Fujian Provincial Center for Disease Control and Prevention, Fuzhou, China
| | - Shaojian Cai
- Institute of Emergency Response and Epidemic Management, Fujian Provincial Center for Disease Control and Prevention, Fuzhou, China
| | - Meirong Zhan
- Institute of Emergency Response and Epidemic Management, Fujian Provincial Center for Disease Control and Prevention, Fuzhou, China
| | - Cailin Chen
- Institute of Emergency Response and Epidemic Management, Fujian Provincial Center for Disease Control and Prevention, Fuzhou, China
| | - Jiawei Lin
- Institute of Emergency Response and Epidemic Management, Fujian Provincial Center for Disease Control and Prevention, Fuzhou, China
| | - Zhonghang Xie
- Institute of Emergency Response and Epidemic Management, Fujian Provincial Center for Disease Control and Prevention, Fuzhou, China
| | - Jianming Ou
- Institute of Emergency Response and Epidemic Management, Fujian Provincial Center for Disease Control and Prevention, Fuzhou, China
| | - Wenjing Ye
- Institute of Emergency Response and Epidemic Management, Fujian Provincial Center for Disease Control and Prevention, Fuzhou, China
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Wang Y, Liu M, Guo Y, Li M, Guo P, He W, Ma T, Liu P, Guo Y, Ye B, Liu J, Wu G. An online survey among convalescents 5 months post SARS-CoV-2 infection in China. BIOSAFETY AND HEALTH 2024; 6:206-215. [PMID: 40078662 PMCID: PMC11894947 DOI: 10.1016/j.bsheal.2024.06.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2024] [Revised: 06/05/2024] [Accepted: 06/06/2024] [Indexed: 03/14/2025] Open
Abstract
The effects of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection persist months and years after recovery. We conducted an online survey to assess the health condition of convalescents approximately 5 months following the primary infection of SARS-CoV-2. The study recruited 5,510 individuals who were primary infected, 626 participants who had experienced reinfection, and 521 participants who were without infective history. The most common disorders after the primary infection group were fatigue (15.18 %), memory issue (13.13 %), post-exertional malaise (PEM, 11.68 %), and brain fog (11.29 %) at the time of survey. In addition, SARS-CoV-2 infection had an impact on the reproductive systems. In stepwise logistic regression analysis, smoking currently, with background diseases, and outpatient visits in the acute phase could be associated with moderate / severe disorders. Further analysis of different background diseases showed that allergic rhinitis, hyperlipidemia, cardiovascular disease, autoimmune diseases, neurological diseases, and asthma likely increased the risk of moderate/severe disorders. The probability of developing disorders of individuals with SARS-CoV-2 reinfection was higher before the secondary infection than uninfected people. Fatigue, PEM, muscle pain/spasms, chills, joint pain, excessive sweating at rest, headache / dizziness, sore throat or foreign body sensation in the throat, cough, expectoration, dry / painful / watery eyes, loss of appetite and constipation were associated with an increased risk of reinfection. It was essential to undertake further research with enhanced randomization in a larger sample in the community, and to strengthen the validation of the research conclusions. The findings of this study contribute to a deeper understanding of the health recovery process among coronavirus disease 2019 (COVID-19) convalescents. Moreover, the findings help identify characteristic health risk factors associated with convalescents and highlight the risk of moderate / severe disorders and reinfection. Furthermore, the findings also provide valuable guidance and reference for SARS-CoV-2 rehabilitation strategies and the prevention of reinfection, offering insights for scientific recommendations.
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Affiliation(s)
- Yalan Wang
- NHC Key Laboratory of Biosafety, National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention (China CDC), Beijing 102206, China
| | - Maoshun Liu
- NHC Key Laboratory of Biosafety, National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention (China CDC), Beijing 102206, China
- College of Life Sciences, Beijing Normal University, Beijing 100875, China
| | - Yuanyuan Guo
- NHC Key Laboratory of Biosafety, National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention (China CDC), Beijing 102206, China
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Shandong 250100, China
| | - Min Li
- NHC Key Laboratory of Biosafety, National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention (China CDC), Beijing 102206, China
| | - Peipei Guo
- NHC Key Laboratory of Biosafety, National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention (China CDC), Beijing 102206, China
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Shandong 250100, China
| | - Wenjun He
- NHC Key Laboratory of Biosafety, National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention (China CDC), Beijing 102206, China
- School of Public Health and Management, Shandong First Medical University &Shandong Academy of Medical Sciences, Jinan 250117, China
| | - Tian Ma
- NHC Key Laboratory of Biosafety, National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention (China CDC), Beijing 102206, China
- School of Public Health and Management, Shandong First Medical University &Shandong Academy of Medical Sciences, Jinan 250117, China
| | - Peipei Liu
- NHC Key Laboratory of Biosafety, National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention (China CDC), Beijing 102206, China
| | - Yaxin Guo
- NHC Key Laboratory of Biosafety, National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention (China CDC), Beijing 102206, China
| | - Beiwei Ye
- NHC Key Laboratory of Biosafety, National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention (China CDC), Beijing 102206, China
| | - Jun Liu
- NHC Key Laboratory of Biosafety, National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention (China CDC), Beijing 102206, China
| | - Guizhen Wu
- NHC Key Laboratory of Biosafety, National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention (China CDC), Beijing 102206, China
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Ryu B, Shin E, Kim DH, Lee H, Choi SY, Kim SS, Kim IH, Kim EJ, Lee S, Jeon J, Kwon D, Cho S. Changes in the intrinsic severity of severe acute respiratory syndrome coronavirus 2 according to the emerging variant: a nationwide study from February 2020 to June 2022, including comparison with vaccinated populations. BMC Infect Dis 2024; 24:1. [PMID: 38166696 PMCID: PMC10759357 DOI: 10.1186/s12879-023-08869-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 12/05/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND As the population acquires immunity through vaccination and natural infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), understanding the intrinsic severity of coronavirus disease (COVID-19) is becoming challenging. We aimed to evaluate the intrinsic severity regarding circulating variants of SARS-CoV-2 and to compare this between vaccinated and unvaccinated individuals. METHODS With unvaccinated and initially infected confirmed cases of COVID-19, we estimated the case severity rate (CSR); case fatality rate (CFR); and mortality rate (MR), including severe/critical cases and deaths, stratified by age and compared by vaccination status according to the period regarding the variants of COVID-19 and vaccination. The overall rate was directly standardized with age. RESULTS The age-standardized CSRs (aCSRs) of the unvaccinated group were 2.12%, 5.51%, and 0.94% in the pre-delta, delta, and omicron period, respectively, and the age-standardized CFRs (aCFRs) were 0.60%, 2.49%, and 0.63% in each period, respectively. The complete vaccination group had lower severity than the unvaccinated group over the entire period showing under 1% for the aCSR and 0.5% for the aCFR. The age-standardized MR of the unvaccinated group was 448 per million people per month people in the omicron period, which was 11 times higher than that of the vaccinated group. In terms of age groups, the CSR and CFR sharply increased with age from the 60 s and showed lower risk reduction in the 80 s when the period changed to the omicron period. CONCLUSIONS The intrinsic severity of COVID-19 was the highest in the delta period, with over 5% for the aCSR, whereas the completely vaccinated group maintained below 1%. This implies that when the population is vaccinated, the impact of COVID-19 will be limited, even if a new mutation appears. Moreover, considering the decreasing intrinsic severity, the response to COVID-19 should prioritize older individuals at a higher risk of severe disease.
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Affiliation(s)
- Boyeong Ryu
- Epidemiological Investigation and Analysis Task Force, Central Disease Control Headquarters, Korea Disease Control and Prevention Agency (KDCA), 187, Osongsaengmyeong 2-Ro, Osong-Eup, Heungdeok-Gu, Cheongju, Korea
- Department of Public Health Science, Graduate School of Public Health, Seoul National University, 1 Gwanak-Ro, Gwanak-Gu, Seoul, 08826, Republic of Korea
| | - Eunjeong Shin
- Epidemiological Investigation and Analysis Task Force, Central Disease Control Headquarters, Korea Disease Control and Prevention Agency (KDCA), 187, Osongsaengmyeong 2-Ro, Osong-Eup, Heungdeok-Gu, Cheongju, Korea
| | - Dong Hwi Kim
- Epidemiological Investigation and Analysis Task Force, Central Disease Control Headquarters, Korea Disease Control and Prevention Agency (KDCA), 187, Osongsaengmyeong 2-Ro, Osong-Eup, Heungdeok-Gu, Cheongju, Korea
| | - HyunJu Lee
- Epidemiological Investigation and Analysis Task Force, Central Disease Control Headquarters, Korea Disease Control and Prevention Agency (KDCA), 187, Osongsaengmyeong 2-Ro, Osong-Eup, Heungdeok-Gu, Cheongju, Korea
| | - So Young Choi
- Epidemiological Investigation and Analysis Task Force, Central Disease Control Headquarters, Korea Disease Control and Prevention Agency (KDCA), 187, Osongsaengmyeong 2-Ro, Osong-Eup, Heungdeok-Gu, Cheongju, Korea
| | - Seong-Sun Kim
- Epidemiological Investigation and Analysis Task Force, Central Disease Control Headquarters, Korea Disease Control and Prevention Agency (KDCA), 187, Osongsaengmyeong 2-Ro, Osong-Eup, Heungdeok-Gu, Cheongju, Korea
| | - Il-Hwan Kim
- Division of Emerging Infectious Diseases, Bureau of Infectious Diseases Diagnosis Control, Korea Disease Control and Prevention Agency (KDCA), 187, Osongsaengmyeong 2-Ro, Osong-Eup, Heungdeok-Gu, Cheongju, Korea
| | - Eun-Jin Kim
- Division of Emerging Infectious Diseases, Bureau of Infectious Diseases Diagnosis Control, Korea Disease Control and Prevention Agency (KDCA), 187, Osongsaengmyeong 2-Ro, Osong-Eup, Heungdeok-Gu, Cheongju, Korea
| | - Sangwon Lee
- Epidemiological Investigation and Analysis Task Force, Central Disease Control Headquarters, Korea Disease Control and Prevention Agency (KDCA), 187, Osongsaengmyeong 2-Ro, Osong-Eup, Heungdeok-Gu, Cheongju, Korea
| | - Jaehyun Jeon
- Department of Infectious Diseases, Clinical Infectious Disease Research Center, National Medical Center, 245, Eulji-ro, Jung-gu, Seoul, Korea
| | - Donghyok Kwon
- Epidemiological Investigation and Analysis Task Force, Central Disease Control Headquarters, Korea Disease Control and Prevention Agency (KDCA), 187, Osongsaengmyeong 2-Ro, Osong-Eup, Heungdeok-Gu, Cheongju, Korea.
| | - Sungil Cho
- Department of Public Health Science, Graduate School of Public Health, Seoul National University, 1 Gwanak-Ro, Gwanak-Gu, Seoul, 08826, Republic of Korea.
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