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Gonçalves BP, Olliaro PL, Horby P, Merson L, Cowling BJ. Interpretations of Studies on SARS-CoV-2 Vaccination and Post-acute COVID-19 Sequelae. Epidemiology 2024; 35:368-371. [PMID: 38630510 DOI: 10.1097/ede.0000000000001720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/19/2024]
Abstract
This article discusses causal interpretations of epidemiologic studies of the effects of vaccination on sequelae after acute severe acute respiratory syndrome coronavirus 2 infection. To date, researchers have tried to answer several different research questions on this topic. While some studies assessed the impact of postinfection vaccination on the presence of or recovery from post-acute coronavirus disease 2019 syndrome, others quantified the association between preinfection vaccination and postacute sequelae conditional on becoming infected. However, the latter analysis does not have a causal interpretation, except under the principal stratification framework-that is, this comparison can only be interpreted as causal for a nondiscernible stratum of the population. As the epidemiology of coronavirus disease 2019 is now nearly entirely dominated by reinfections, including in vaccinated individuals, and possibly caused by different Omicron subvariants, it has become even more important to design studies on the effects of vaccination on postacute sequelae that address precise causal questions and quantify effects corresponding to implementable interventions.
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Affiliation(s)
- Bronner P Gonçalves
- From the ISARIC, Pandemic Sciences Institute, University of Oxford, Oxford, United Kingdom
- Department of Comparative Biomedical Sciences, School of Veterinary Medicine, University of Surrey, Guildford, United Kingdom
| | - Piero L Olliaro
- From the ISARIC, Pandemic Sciences Institute, University of Oxford, Oxford, United Kingdom
| | - Peter Horby
- From the ISARIC, Pandemic Sciences Institute, University of Oxford, Oxford, United Kingdom
| | - Laura Merson
- From the ISARIC, Pandemic Sciences Institute, University of Oxford, Oxford, United Kingdom
| | - Benjamin J Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, People's Republic of China
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Jia JZ, Cohen CA, Gu H, McLean MR, Varadarajan R, Bhandari N, Peiris M, Leung GM, Poon LLM, Tsang T, Chung AW, Cowling BJ, Leung NHL, Valkenburg SA. Influenza antibody breadth and effector functions are immune correlates from acquisition of pandemic infection of children. Nat Commun 2024; 15:3210. [PMID: 38615070 PMCID: PMC11016072 DOI: 10.1038/s41467-024-47590-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 04/04/2024] [Indexed: 04/15/2024] Open
Abstract
Cross-reactive antibodies with Fc receptor (FcR) effector functions may mitigate pandemic virus impact in the absence of neutralizing antibodies. In this exploratory study, we use serum from a randomized placebo-controlled trial of seasonal trivalent influenza vaccination in children (NCT00792051) conducted at the onset of the 2009 H1N1 pandemic (pH1N1) and monitored for infection. We found that seasonal vaccination increases pH1N1 specific antibodies and FcR effector functions. Furthermore, prospective baseline antibody profiles after seasonal vaccination, prior to pH1N1 infection, show that unvaccinated uninfected children have elevated ADCC effector function, FcγR3a and FcγR2a binding antibodies to multiple pH1N1 proteins, past seasonal and avian (H5, H7 and H9) strains. Whereas, children that became pH1N1 infected after seasonal vaccination have antibodies focussed to seasonal strains without FcR functions, and greater aggregated HA-specific profiles for IgM and IgG3. Modeling to predict infection susceptibility, ranked baseline hemagglutination antibody inhibition as the highest contributor to lack of pH1N1 infection, in combination with features that include pH1-IgG1, H1-stem responses and FcR binding to seasonal vaccine and pH1 proteins. Thus, seasonal vaccination can have benefits against pandemic influenza viruses, and some children already have broadly reactive antibodies with Fc potential without vaccination and may be considered 'elite influenza controllers'.
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Affiliation(s)
- Janice Z Jia
- HKU-Pasteur Research Pole, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China
| | - Carolyn A Cohen
- HKU-Pasteur Research Pole, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China
| | - Haogao Gu
- Division of Public Health Laboratory Sciences, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China
| | - Milla R McLean
- Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, Australia
| | | | - Nisha Bhandari
- Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India
| | - Malik Peiris
- Division of Public Health Laboratory Sciences, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China
- Centre for Immunology and Infection (C2i), Hong Kong Science and Technology Park, Hong Kong, SAR, China
| | - Gabriel M Leung
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, Hong Kong, SAR, China
| | - Leo L M Poon
- HKU-Pasteur Research Pole, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China
- Division of Public Health Laboratory Sciences, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China
- Centre for Immunology and Infection (C2i), Hong Kong Science and Technology Park, Hong Kong, SAR, China
| | - Tim Tsang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China
| | - Amy W Chung
- Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, Australia
| | - Benjamin J Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China
| | - Nancy H L Leung
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China
| | - Sophie A Valkenburg
- HKU-Pasteur Research Pole, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China.
- Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, Australia.
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Tsang TK, Du RQR, Fang VJ, Lau EHY, Chan KH, Chu DKW, Ip DKM, Peiris JSM, Leung GM, Cauchemez S, Cowling BJ. Decreased risk of non-influenza respiratory infection after influenza B virus infection in children. Epidemiol Infect 2024; 152:e60. [PMID: 38584132 DOI: 10.1017/s0950268824000542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/09/2024] Open
Abstract
Previous studies suggest that influenza virus infection may provide temporary non-specific immunity and hence lower the risk of non-influenza respiratory virus infection. In a randomized controlled trial of influenza vaccination, 1 330 children were followed-up in 2009-2011. Respiratory swabs were collected when they reported acute respiratory illness and tested against influenza and other respiratory viruses. We used Poisson regression to compare the incidence of non-influenza respiratory virus infection before and after influenza virus infection. Based on 52 children with influenza B virus infection, the incidence rate ratio (IRR) of non-influenza respiratory virus infection after influenza virus infection was 0.47 (95% confidence interval: 0.27-0.82) compared with before infection. Simulation suggested that this IRR was 0.87 if the temporary protection did not exist. We identified a decreased risk of non-influenza respiratory virus infection after influenza B virus infection in children. Further investigation is needed to determine if this decreased risk could be attributed to temporary non-specific immunity acquired from influenza virus infection.
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Affiliation(s)
- Tim K Tsang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, Hong Kong
| | - Richael Q R Du
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong
| | - Vicky J Fang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong
| | - Eric H Y Lau
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, Hong Kong
| | - Kwok Hung Chan
- Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong
| | - Daniel K W Chu
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong
| | - Dennis K M Ip
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong
| | - J S Malik Peiris
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong
- HKU-Pasteur Research Pole, The University of Hong Kong, Hong Kong
- Centre for Immunology and Infection, Hong Kong Science and Technology Park, Hong Kong
| | - Gabriel M Leung
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, Hong Kong
| | - Simon Cauchemez
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris, France
| | - Benjamin J Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, Hong Kong
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Chen D, Cowling BJ, Ainslie KEC, Lin Y, Wong JY, Lau EHY, Wu P, Nealon J. Association of COVID-19 vaccination with duration of hospitalization in older adults in Hong Kong. Vaccine 2024; 42:2385-2393. [PMID: 38448323 DOI: 10.1016/j.vaccine.2024.02.074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 02/21/2024] [Accepted: 02/24/2024] [Indexed: 03/08/2024]
Abstract
INTRODUCTION The association between COVID-19 vaccination and length of hospital stay may provide further insight into vaccination benefits, but few studies have investigated such associations in detail. We aimed to investigate the association between COVID-19 vaccination and length of hospital stay in COVID-19 patients during Omicron waves in Hong Kong, and explore potential predictors. METHODS This retrospective cohort study was conducted on local patients aged ≥60 years who were admitted due to COVID-19 infection in Hong Kong in 2022, from 1 February to 22 November, and with 28 days of follow-up since admission. The exposure was either not vaccinated; or having received 2/3/4 doses of CoronaVac (Sinovac); or 2/3/4 doses of BNT162b2 (BioNTech/Fosun Pharma/Pfizer). Length of stay in hospital was the main outcome. Accelerated failure time models were used to quantify variation in hospital stay for vaccinated compared with unvaccinated patients, accounting for age, sex, comorbidity, type of vaccine and number of doses received, care home residence and admission timing; stratified by age groups and epidemic waves. RESULTS This study included 32,398 patients aged 60 years and above for main analysis, their median (IQR) age was 79 (71-87) years, 53% were men, and 40% were unvaccinated. The patients were stratified by confirmation prior to or since 23 May 2022, resulting in a sample size of 15,803 and 16,595 in those two waves respectively. Vaccinated patients were found to have 13-39% shorter hospital stay compared to unvaccinated patients. More vaccine doses received were associated with shorter hospital stay, and BNT162b2 recipients had slightly shorter hospital stays than CoronaVac recipients. CONCLUSION Vaccination was associated with reduced hospital stay in breakthrough infections. Increased vaccination uptake in older adults may improve hospital bed turnover and public health outcomes especially during large community epidemics.
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Affiliation(s)
- Dongxuan Chen
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region; Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region
| | - Benjamin J Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region; Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region.
| | - Kylie E C Ainslie
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region; Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands
| | - Yun Lin
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region
| | - Jessica Y Wong
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region
| | - Eric H Y Lau
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region; Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region
| | - Peng Wu
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region; Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region
| | - Joshua Nealon
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region
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Cheng SMS, Mok CKP, Li JKC, Chan KKP, Luk KS, Lee BHW, Gu H, Chan KCK, Tsang LCH, Yiu KYS, Ling KKC, Tang YS, Luk LLH, Yu JKM, Pekosz A, Webby RJ, Cowling BJ, Hui DSC, Peiris M. Cross-neutralizing antibody against emerging Omicron subvariants of SARS-CoV-2 in infection-naïve individuals with homologous BNT162b2 or BNT162b2(WT + BA.4/5) bivalent booster vaccination. Virol J 2024; 21:70. [PMID: 38515117 PMCID: PMC10956325 DOI: 10.1186/s12985-024-02335-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Accepted: 03/02/2024] [Indexed: 03/23/2024] Open
Abstract
Since the emergence of SARS-CoV-2, different variants and subvariants successively emerged to dominate global virus circulation as a result of immune evasion, replication fitness or both. COVID-19 vaccines continue to be updated in response to the emergence of antigenically divergent viruses, the first being the bivalent RNA vaccines that encodes for both the Wuhan-like and Omicron BA.5 subvariant spike proteins. Repeated infections and vaccine breakthrough infections have led to complex immune landscapes in populations making it increasingly difficult to assess the intrinsic neutralizing antibody responses elicited by the vaccines. Hong Kong's intensive COVID-19 containment policy through 2020-2021 permitted us to identify sera from a small number of infection-naïve individuals who received 3 doses of the RNA BNT162b2 vaccine encoding the Wuhan-like spike (WT) and were boosted with a fourth dose of the WT vaccine or the bivalent WT and BA.4/5 spike (WT + BA.4/5). While neutralizing antibody to wild-type virus was comparable in both vaccine groups, BNT162b2 (WT + BA.4/BA.5) bivalent vaccine elicited significantly higher plaque neutralizing antibodies to Omicron subvariants BA.5, XBB.1.5, XBB.1.16, XBB.1.9.1, XBB.2.3.2, EG.5.1, HK.3, BA.2.86 and JN.1, compared to BNT162b2 monovalent vaccine. The single amino acid substitution that differentiates the spike of JN.1 from BA.2.86 resulted in a profound antigenic change.
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Affiliation(s)
- Samuel M S Cheng
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Chris K P Mok
- The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China
- Li Ka Shing Institute of Health Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
- SH Ho Research Centre for Emerging Infectious Diseases, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - John K C Li
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Ken K P Chan
- Department of Medicine & Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Kristine S Luk
- Princess Margaret Hospital, Hospital Authority, Hong Kong SAR, China
| | - Ben H W Lee
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Haogao Gu
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Karl C K Chan
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Leo C H Tsang
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Karen Y S Yiu
- Department of Medicine & Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Ken K C Ling
- Department of Medicine & Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Yun Sang Tang
- The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China
- Li Ka Shing Institute of Health Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Leo L H Luk
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Jennifer K M Yu
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Andrew Pekosz
- W. Harry Feinstone Department of Molecular Microbiology and Immunology, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Richard J Webby
- Department of Infectious Diseases, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Benjamin J Cowling
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - David S C Hui
- Department of Medicine & Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
- SH Ho Research Centre for Emerging Infectious Diseases, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Malik Peiris
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
- Centre for Immunology and Infection, Hong Kong Science Park, Hong Kong SAR, China.
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Wong CKH, Lau KTK, Au ICH, Lau EHY, Cowling BJ. Comparison of Bivalent and Monovalent mRNA Vaccine Boosters. Clin Infect Dis 2024; 78:633-636. [PMID: 37647855 DOI: 10.1093/cid/ciad519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 08/17/2023] [Accepted: 08/25/2023] [Indexed: 09/01/2023] Open
Abstract
In this cohort study conducted in Hong Kong where both bivalent and monovalent formulations of BNT162b2 were available, there were no significant differences in the mortality or hospitalization between those who received bivalent and monovalent mRNA as second boosters. Bivalent and monovalent mRNA boosters appear equally protective against clinical outcomes.
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Affiliation(s)
- Carlos K H Wong
- Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region (SAR), China
- Laboratory of Data Discovery for Health (D24H), Hong Kong Special Administrative Region (SAR), China
- Department of Family Medicine and Primary Care, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region (SAR), China
- The Vaccine Confidence Project, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Kristy T K Lau
- Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region (SAR), China
| | - Ivan C H Au
- Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region (SAR), China
| | - Eric H Y Lau
- Laboratory of Data Discovery for Health (D24H), Hong Kong Special Administrative Region (SAR), China
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region (SAR), China
| | - Benjamin J Cowling
- Laboratory of Data Discovery for Health (D24H), Hong Kong Special Administrative Region (SAR), China
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region (SAR), China
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Peng L, Huang X, Wang C, Xin H, Cowling BJ, Wu P, Tsang TK. Comparative epidemiology of outbreaks caused by SARS-CoV-2 Delta and Omicron variants in China. Epidemiol Infect 2024; 152:e43. [PMID: 38500342 DOI: 10.1017/s0950268824000360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/20/2024] Open
Abstract
From 2020 to December 2022, China implemented strict measures to contain the spread of severe acute respiratory syndrome coronavirus 2. However, despite these efforts, sustained outbreaks of the Omicron variants occurred in 2022. We extracted COVID-19 case numbers from May 2021 to October 2022 to identify outbreaks of the Delta and Omicron variants in all provinces of mainland China. We found that omicron outbreaks were more frequent (4.3 vs. 1.6 outbreaks per month) and longer-lasting (mean duration: 13 vs. 4 weeks per outbreak) than Delta outbreaks, resulting in a total of 865,100 cases, of which 85% were asymptomatic. Despite the average Government Response Index being 12% higher (95% confidence interval (CI): 9%, 15%) in Omicron outbreaks, the average daily effective reproduction number (Rt) was 0.45 higher (95% CI: 0.38, 0.52, p < 0.001) than in Delta outbreaks. Omicron outbreaks were suppressed in 32 days on average (95% CI: 26, 39), which was substantially longer than Delta outbreaks (14 days; 95% CI: 11, 19; p = 0.004). We concluded that control measures effective against Delta could not contain Omicron outbreaks in China. This highlights the need for continuous evaluation of new variants' epidemiology to inform COVID-19 response decisions.
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Affiliation(s)
- Liping Peng
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Xiaotong Huang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Can Wang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Hualei Xin
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Benjamin J Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong, China
| | - Peng Wu
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong, China
| | - Tim K Tsang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong, China
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Murphy C, Kwan MYW, Chan ELY, Wong JSC, Sullivan SG, Peiris M, Cowling BJ, Lee SL. Influenza vaccine effectiveness against hospitalizations associated with influenza A(H3N2) in Hong Kong children aged 9 months to 17 years, June-November 2023. Vaccine 2024; 42:1878-1882. [PMID: 38395722 PMCID: PMC10947845 DOI: 10.1016/j.vaccine.2024.02.056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 02/05/2024] [Accepted: 02/17/2024] [Indexed: 02/25/2024]
Abstract
A test negative study was carried out from 13 June through to 15 November 2023 enrolling 3183 children hospitalized with acute respiratory illness in Hong Kong. Influenza A and B viruses were detected in 528 (16.6%) children, among which 419 (79.4%) were influenza A(H3N2). The overall vaccine effectiveness against hospitalization associated with any influenza virus infection was estimated as 22.4% (95% CI: -11.7%, 46.1%), and against influenza A(H3N2) specifically was 14.3% (95% CI: -29.2%, 43.2%). Despite the moderate to low VE estimated here, which could be a result of waning immunity and antigenic drift, influenza vaccination remains an important approach to reduce the impact of influenza in children.
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Affiliation(s)
- Caitriona Murphy
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Mike Y W Kwan
- Department of Paediatrics and Adolescent Medicine, Princess Margaret Hospital, Hong Kong Special Administrative Region, China
| | - Eunice L Y Chan
- Department of Paediatrics and Adolescent Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Joshua S C Wong
- Department of Paediatrics and Adolescent Medicine, Princess Margaret Hospital, Hong Kong Special Administrative Region, China
| | - Sheena G Sullivan
- WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, and Department of Infectious Diseases, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia; Department of Epidemiology, University of California, Los Angeles, California
| | - Malik Peiris
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China; Centre for Immunology & Infection, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
| | - Benjamin J Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China; Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China.
| | - So-Lun Lee
- Department of Paediatrics and Adolescent Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China; Department of Paediatrics and Adolescent Medicine, Queen Mary Hospital, Hong Kong Special Administrative Region
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Loes AN, Tarabi RAL, Huddleston J, Touyon L, Wong SS, Cheng SMS, Leung NHL, Hannon WW, Bedford T, Cobey S, Cowling BJ, Bloom JD. High-throughput sequencing-based neutralization assay reveals how repeated vaccinations impact titers to recent human H1N1 influenza strains. bioRxiv 2024:2024.03.08.584176. [PMID: 38496577 PMCID: PMC10942427 DOI: 10.1101/2024.03.08.584176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
The high genetic diversity of influenza viruses means that traditional serological assays have too low throughput to measure serum antibody neutralization titers against all relevant strains. To overcome this challenge, we have developed a sequencing-based neutralization assay that simultaneously measures titers against many viral strains using small serum volumes via a workflow similar to traditional neutralization assays. The key innovation is to incorporate unique nucleotide barcodes into the hemagglutinin (HA) genomic segment, and then pool viruses with numerous different barcoded HA variants and quantify infectivity of all of them simultaneously using next-generation sequencing. With this approach, a single researcher performed the equivalent of 2,880 traditional neutralization assays (80 serum samples against 36 viral strains) in approximately one month. We applied the sequencing-based assay to quantify the impact of influenza vaccination on neutralization titers against recent human H1N1 strains for individuals who had or had not also received a vaccine in the previous year. We found that the viral strain specificities of the neutralizing antibodies elicited by vaccination vary among individuals, and that vaccination induced a smaller increase in titers for individuals who had also received a vaccine the previous year-although the titers six months after vaccination were similar in individuals with and without the previous-year vaccination. We also identified a subset of individuals with low titers to a subclade of recent H1N1 even after vaccination. This study demonstrates the utility of high-throughput sequencing-based neutralization assays that enable titers to be simultaneously measured against many different viral strains. We provide a detailed experimental protocol (DOI: https://dx.doi.org/10.17504/protocols.io.kqdg3xdmpg25/v1) and a computational pipeline (https://github.com/jbloomlab/seqneut-pipeline) for the sequencing-based neutralization assays to facilitate the use of this method by others.
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Affiliation(s)
- Andrea N Loes
- Howard Hughes Medical Institute, Seattle, WA
- Division of Basic Sciences, Computational Biology Program, and Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA
| | - Rosario Araceli L Tarabi
- Division of Basic Sciences, Computational Biology Program, and Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA
| | - John Huddleston
- Division of Basic Sciences, Computational Biology Program, and Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA
| | - Lisa Touyon
- HKU-Pasteur Research Pole, School of Public Health, The University of Hong Kong, Hong Kong, SAR, China
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong, SAR, China
| | - Sook San Wong
- HKU-Pasteur Research Pole, School of Public Health, The University of Hong Kong, Hong Kong, SAR, China
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong, SAR, China
| | - Samuel M S Cheng
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong, SAR, China
| | - Nancy H L Leung
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong, SAR, China
| | - William W Hannon
- Division of Basic Sciences, Computational Biology Program, and Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA
- Molecular and Cellular Biology Graduate Program, University of Washington, Seattle, WA 98109, USA
| | - Trevor Bedford
- Howard Hughes Medical Institute, Seattle, WA
- Division of Basic Sciences, Computational Biology Program, and Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA
| | - Sarah Cobey
- Department of Ecology and Evolution, University of Chicago, Chicago, IL
| | - Benjamin J Cowling
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong, SAR, China
| | - Jesse D Bloom
- Howard Hughes Medical Institute, Seattle, WA
- Division of Basic Sciences, Computational Biology Program, and Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA
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10
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Lim WW, Shuo F, Wong SS, Sullivan SG, Cowling BJ. Hemagglutination inhibition antibody titers mediate influenza vaccine efficacy against symptomatic influenza A(H1N1), A(H3N2), and B/Victoria infections. J Infect Dis 2024:jiae122. [PMID: 38452179 DOI: 10.1093/infdis/jiae122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 02/24/2024] [Accepted: 03/05/2024] [Indexed: 03/09/2024] Open
Abstract
BACKGROUND The hemagglutination inhibition antibody (HAI) titer mediates only a part of vaccine-induced protection against influenza virus infections. Using causal mediation analysis, we quantified the proportion of vaccine efficacy mediated by post-vaccination HAI titers. METHODS Causal mediation analyses were conducted using data collected for a randomized, active-comparator controlled, phase 3 trial of a candidate inactivated, split-virion seasonal quadrivalent influenza vaccine (QIV) in children aged 3 to 8 years conducted from October 2010 to December 2011 in eight countries. Vaccine efficacy was estimated with a weighted Cox proportional hazards regression model. Estimates were decomposed into the direct and indirect effects mediated by post-vaccination HAI titers. RESULTS The proportions of vaccine efficacy mediated by post-vaccination HAI titers were estimated to be 22% (95% CI: 18%, 47%) for influenza A(H1N1), 20% (95% CI: 16%, 39%) for influenza A(H3N2), and 37% (95% CI: 26%, 85%) for influenza B/Victoria. CONCLUSIONS HAI titers partially mediate influenza vaccine efficacy against influenza A(H1N1), A(H3N2), and B/Victoria. Our estimates were lower than previous studies, possibly reflecting expected heterogeneity in antigenic similarity between vaccine and circulating viruses across seasons. Data from more influenza seasons are needed to understand better the mediating effects of HAI titers on vaccine efficacy.
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Affiliation(s)
- Wey Wen Lim
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong SAR, China
| | - Feng Shuo
- Oxford Vaccine Group, Department of Paediatrics, University of Oxford, Oxford, UK
| | - Sook-San Wong
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- HKU-Pasteur Research Pole, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Sheena G Sullivan
- WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, and Department of Infectious Diseases, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
- Department of Epidemiology, University of California, Los Angeles, USA
| | - Benjamin J Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong SAR, China
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11
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Wong CKH, Lau KTK, Au ICH, Lau EHY, Cowling BJ. Effectiveness of mRNA BNT162b2 and inactivated CoronaVac vaccines against severe COVID-19 outcomes among non-hospitalised children aged 1-3 years with SARS-CoV-2 Omicron infection. Int J Antimicrob Agents 2024; 63:107094. [PMID: 38272281 DOI: 10.1016/j.ijantimicag.2024.107094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 12/05/2023] [Accepted: 01/12/2024] [Indexed: 01/27/2024]
Abstract
OBJECTIVES Clinical evidence on the effectiveness of COVID-19 vaccines for children aged 1-3 years is scarce. The effectiveness of COVID-19 vaccines was evaluated among non-hospitalised children aged 1-3 years with SARS-CoV-2 Omicron infection in Hong Kong. METHODS A retrospective cohort of all non-hospitalised children aged 1-3 years with confirmed SARS-CoV-2 infection between 4 August 2022 and 29 January 2023 in Hong Kong was analysed. Vaccinated group was defined as the recipients of one or more doses of CoronaVac or mRNA vaccine BNT162b2 (original, monovalent) at least 14 days prior to infection. Hazard ratios (HR) with 95% confidence intervals (95% CI) of study outcomes were estimated using Cox regression models. Effectiveness outcomes included 28-day all-cause mortality and COVID-19-related hospitalisation. RESULTS A total of 5552 vaccinated patients and 5552 propensity-score matched controls (unvaccinated patients) were included for analysis. The cumulative incidence of COVID-19-related hospitalisation over 28 days was 2.3% and 2.9% in the vaccinated and control groups, respectively. There were no deaths in both groups. COVID-19 vaccination was associated with a significant reduction in 28-day COVID-19-related hospitalisation risk (HR=0.785, 95% CI=0.626-0.985, P=0.037), particularly for children aged 3 years, those who had received two or more vaccine doses, and those who received CoronaVac as the last dose. CONCLUSION COVID-19 vaccination is associated with a significantly lower risk of 28-day COVID-19-related hospitalisation among infected children aged 1-3 years, particularly those who had received two or more vaccine doses. This observation emphasises the importance of completing the full two-dose or three-dose series to optimise vaccine effectiveness.
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Affiliation(s)
- Carlos K H Wong
- Laboratory of Data Discovery for Health (D(2)4H), Hong Kong SAR, China; Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China; Department of Family Medicine and Primary Care, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China; Department of Infectious Disease Epidemiology & Dynamics, Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom.
| | - Kristy T K Lau
- Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Ivan C H Au
- Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China; School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Eric H Y Lau
- Laboratory of Data Discovery for Health (D(2)4H), Hong Kong SAR, China; WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Benjamin J Cowling
- Laboratory of Data Discovery for Health (D(2)4H), Hong Kong SAR, China; WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
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12
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Nealon J, Mefsin YM, McMenamin ME, Ainslie KE, Cowling BJ. Reported effectiveness of COVID-19 monovalent booster vaccines and hybrid immunity against mild and severe Omicron disease in adults: A systematic review and meta-regression analysis. Vaccine X 2024; 17:100451. [PMID: 38379667 PMCID: PMC10877401 DOI: 10.1016/j.jvacx.2024.100451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 01/16/2024] [Accepted: 01/24/2024] [Indexed: 02/22/2024] Open
Abstract
Background Waning of COVID-19 vaccine efficacy/effectiveness (VE) has been observed across settings and epidemiological contexts. We conducted a systematic review of COVID-19 VE studies and performed a meta-regression analysis to improve understanding of determinants of waning. Methods Systematic review of PubMed, medRxiv and the WHO-International Vaccine Access Center database summarizing VE studies on 31 December 2022. Studies were those presenting primary adult VE data from hybrid immunity or third/fourth mRNA COVID-19 monovalent vaccine doses [due to limited data with other vaccines] against Omicron, compared with unvaccinated individuals or individuals eligible for corresponding booster doses but who did not receive them. We used meta-regression models, adjusting for confounders, with weeks since vaccination as a restricted cubic spline, to estimate VE over time since vaccination. Results We identified 55 eligible studies reporting 269 VE estimates. Most estimates (180/269; 67 %) described effectiveness of third dose vaccination; with 48 (18 %) and 41 (15 %) describing hybrid immunity and fourth dose effectiveness, respectively, mostly (200; 74 %) derived from test-negative design studies. Most estimates (176/269; 65 %) reported VE compared with unvaccinated comparison groups. Estimated VE against mild outcomes declined following third dose vaccination from 62 % (95 % CI: 58 % - 66 %) after 4 weeks to 48 % (41 % - 55 %) after 20 weeks. Fourth dose VE against mild COVID-19 declined from 48 % (41 % - 56 %) after 4 weeks to 47 % (19 % - 65 %) after 20 weeks. VE for severe outcomes was higher and declined in the three-dose group from 90 % (87 % - 92 %) after 4 weeks to 70 % (65 - 74 %) after 20 weeks. Conclusions Time-since vaccination is an important determinant of booster dose VE, a finding which may support seasonal COVID-19 booster doses. Integration of VE and immunological parameters - and longer-term data including from other vaccine types - are needed to better-understand determinants of clinical protection.
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Affiliation(s)
- Joshua Nealon
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Yonatan M Mefsin
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Martina E. McMenamin
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Kylie E.C. Ainslie
- Centre for Infectious Disease Control, National Institute for Public Health and Environment (RIVM), Bilthoven, The Netherlands
| | - Benjamin J. Cowling
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
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13
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Young BR, Ho F, Lin Y, Lau EHY, Cowling BJ, Wu P, Tsang TK. Estimation of the Time-Varying Effective Reproductive Number of COVID-19 Based on Multivariate Time Series of Severe Health Outcomes. J Infect Dis 2024; 229:502-506. [PMID: 37815808 DOI: 10.1093/infdis/jiad445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 09/21/2023] [Accepted: 10/06/2023] [Indexed: 10/11/2023] Open
Abstract
The time-varying effective reproduction number (Rt at time t) measures the transmissibility of SARS-CoV-2 and is conventionally based on daily case counts, which may suffer from time-varying ascertainment. We analyzed Rt estimates from case counts and severe COVID-19 (intensive care unit admissions, severe or critical cases, and mortality) across 2022 in Hong Kong's fifth and sixth waves of infection. Within the fifth wave, the severe disease-based Rt (3.5) was significantly higher than the case-based Rt (2.4) but not in the sixth wave. During periods with fluctuating underreporting, data based on severe diseases may provide more reliable Rt estimates.
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Affiliation(s)
- Benjamin R Young
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong
| | - Faith Ho
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong
| | - Yun Lin
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong
| | - Eric H Y Lau
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong
- Laboratory of Data Discovery for Health Ltd, Hong Kong Science and Technology Park, China
| | - Benjamin J Cowling
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong
- Laboratory of Data Discovery for Health Ltd, Hong Kong Science and Technology Park, China
| | - Peng Wu
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong
- Laboratory of Data Discovery for Health Ltd, Hong Kong Science and Technology Park, China
| | - Tim K Tsang
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong
- Laboratory of Data Discovery for Health Ltd, Hong Kong Science and Technology Park, China
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14
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Ofori SK, Schwind JS, Sullivan KL, Chowell G, Cowling BJ, Fung ICH. Modeling the health impact of increasing vaccine coverage and nonpharmaceutical interventions against coronavirus disease 2019 in Ghana. Pathog Glob Health 2024:1-15. [PMID: 38318877 DOI: 10.1080/20477724.2024.2313787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2024] Open
Abstract
Seroprevalence studies assessing community exposure to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Ghana concluded that population-level immunity remained low as of February 2021. Thus, it is important to demonstrate how increasing vaccine coverage reduces the economic and public health impacts associated with SARS-CoV-2 transmission. To that end, this study used a Susceptible-Exposed-Presymptomatic-Symptomatic-Asymptomatic-Recovered-Dead-Vaccinated compartmental model to simulate coronavirus disease 2019 (COVID-19) transmission and the role of public health interventions in Ghana. The impact of increasing vaccination rates and decline in transmission rates due to nonpharmaceutical interventions (NPIs) on cumulative infections and deaths averted was explored under different scenarios. Latin hypercube sampling-partial rank correlation coefficient (LHS-PRCC) was used to investigate the uncertainty and sensitivity of the outcomes to the parameters. Simulation results suggest that increasing the vaccination rate to achieve 50% coverage was associated with almost 60,000 deaths and 25 million infections averted. In comparison, a 50% decrease in the transmission coefficient was associated with the prevention of about 150,000 deaths and 50 million infections. The LHS-PRCC results indicated that in the context of vaccination rate, cumulative infections and deaths averted were most sensitive to vaccination rate, waning immunity rates from vaccination, and waning immunity from natural infection. This study's findings illustrate the impact of increasing vaccination coverage and/or reducing the transmission rate by NPI adherence in the prevention of COVID-19 infections and deaths in Ghana.
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Affiliation(s)
- Sylvia K Ofori
- Department of Biostatistics, Epidemiology and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, Georgia
| | - Jessica S Schwind
- Institute for Health Logistics & Analytics, Georgia Southern University, Statesboro, Georgia
| | - Kelly L Sullivan
- Department of Biostatistics, Epidemiology and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, Georgia
| | - Gerardo Chowell
- Department of Population Health Sciences, School of Public Health, Georgia State University, Atlanta, Georgia
| | - Benjamin J Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong
| | - Isaac Chun-Hai Fung
- Department of Biostatistics, Epidemiology and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, Georgia
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15
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Eales O, Plank MJ, Cowling BJ, Howden BP, Kucharski AJ, Sullivan SG, Vandemaele K, Viboud C, Riley S, McCaw JM, Shearer FM. Key Challenges for Respiratory Virus Surveillance while Transitioning out of Acute Phase of COVID-19 Pandemic. Emerg Infect Dis 2024; 30:e230768. [PMID: 38190760 PMCID: PMC10826770 DOI: 10.3201/eid3002.230768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2024] Open
Abstract
To support the ongoing management of viral respiratory diseases while transitioning out of the acute phase of the COVID-19 pandemic, many countries are moving toward an integrated model of surveillance for SARS-CoV-2, influenza virus, and other respiratory pathogens. Although many surveillance approaches catalyzed by the COVID-19 pandemic provide novel epidemiologic insight, continuing them as implemented during the pandemic is unlikely to be feasible for nonemergency surveillance, and many have already been scaled back. Furthermore, given anticipated cocirculation of SARS-CoV-2 and influenza virus, surveillance activities in place before the pandemic require review and adjustment to ensure their ongoing value for public health. In this report, we highlight key challenges for the development of integrated models of surveillance. We discuss the relative strengths and limitations of different surveillance practices and studies as well as their contribution to epidemiologic assessment, forecasting, and public health decision-making.
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16
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Ali ST, Wu P, He D, Tian L, Cowling BJ. Forecasting influenza epidemics in Hong Kong using multiple streams of syndromic and laboratory surveillance data: abridged secondary publication. Hong Kong Med J 2024; 30 Suppl 1:4-8. [PMID: 38413204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/29/2024] Open
Affiliation(s)
- S T Ali
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - P Wu
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - D He
- Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - L Tian
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - B J Cowling
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
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17
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Bai Y, Du Z, Wang L, Lau EHY, Fung ICH, Holme P, Cowling BJ, Galvani AP, Krug RM, Meyers LA. Public Health Impact of Paxlovid as Treatment for COVID-19, United States. Emerg Infect Dis 2024; 30:262-269. [PMID: 38181800 PMCID: PMC10826746 DOI: 10.3201/eid3002.230835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2024] Open
Abstract
We evaluated the population-level benefits of expanding treatment with the antiviral drug Paxlovid (nirmatrelvir/ritonavir) in the United States for SARS-CoV-2 Omicron variant infections. Using a multiscale mathematical model, we found that treating 20% of symptomatic case-patients with Paxlovid over a period of 300 days beginning in January 2022 resulted in life and cost savings. In a low-transmission scenario (effective reproduction number of 1.2), this approach could avert 0.28 million (95% CI 0.03-0.59 million) hospitalizations and save US $56.95 billion (95% CI US $2.62-$122.63 billion). In a higher transmission scenario (effective reproduction number of 3), the benefits increase, potentially preventing 0.85 million (95% CI 0.36-1.38 million) hospitalizations and saving US $170.17 billion (95% CI US $60.49-$286.14 billion). Our findings suggest that timely and widespread use of Paxlovid could be an effective and economical approach to mitigate the effects of COVID-19.
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18
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Yuan J, Xu Y, Wong IOL, Lam WWT, Ni MY, Cowling BJ, Liao Q. Dynamic predictors of COVID-19 vaccination uptake and their interconnections over two years in Hong Kong. Nat Commun 2024; 15:290. [PMID: 38177142 PMCID: PMC10767005 DOI: 10.1038/s41467-023-44650-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 12/20/2023] [Indexed: 01/06/2024] Open
Abstract
The global rollout of COVID-19 vaccines faces a significant barrier in the form of vaccine hesitancy. This study adopts a dynamic and network perspective to explore the determinants of COVID-19 vaccine uptake in Hong Kong, focusing on multi-level determinants and their interconnections. Following the framework proposed by the Strategic Advisory Group of Experts (SAGE), the study used repeated cross-sectional surveys to map these determinants at multiple levels and investigates their interconnections simultaneously in a sample of 15,179 over two years. The results highlight the dynamic nature of COVID-19 vaccine hesitancy in an evolving pandemic. The findings suggest that vaccine confidence attitudes play crucial roles in vaccination uptake, with their importance shifting over time. The initial emphasis on vaccine safety gradually transitioned to heightened consideration of vaccine effectiveness at a later stage. The study also highlights the impact of chronic condition, age, COVID-19 case numbers, and non-pharmaceutical preventive behaviours on vaccine uptake. Higher educational attainment and being married were associated with primary and booster vaccine uptake and it may be possible to leverage these groups as early innovation adopters. Trust in government acts as a crucial bridging factor linking various variables in the networks with vaccine confidence attitudes, which subsequently closely linked to vaccine uptake. This study provides insights for designing future effective vaccination programmes for changing circumstances.
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Affiliation(s)
- Jiehu Yuan
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Yucan Xu
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Irene Oi Ling Wong
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Wendy Wing Tak Lam
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Li Ka Shing Faculty of Medicine, Jocky Club Institute of Cancer Care, The University of Hong Kong, Hong Kong, China
| | - Michael Y Ni
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China
- Urban Systems Institute, The University of Hong Kong, Hong Kong, China
| | - Benjamin J Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong, China.
| | - Qiuyan Liao
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.
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19
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Cheung YYH, Lau EHY, Yin G, Lin Y, Cowling BJ, Lam KF. Effectiveness of Vaccines and Antiviral Drugs in Preventing Severe and Fatal COVID-19, Hong Kong. Emerg Infect Dis 2024; 30:70-78. [PMID: 38040664 PMCID: PMC10756371 DOI: 10.3201/eid3001.230414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2023] Open
Abstract
We compared the effectiveness and interactions of molnupiravir and nirmatrelvir/ritonavir and 2 vaccines, CoronaVac and Comirnaty, in a large population of inpatients with COVID-19 in Hong Kong. Both the oral antiviral drugs and vaccines were associated with lower risks for all-cause mortality and progression to serious/critical/fatal conditions (study outcomes). No significant interaction effects were observed between the antiviral drugs and vaccinations; their joint effects were additive. If antiviral drugs were prescribed within 5 days of confirmed COVID-19 diagnosis, usage was associated with lower risks for the target outcomes for patients >60, but not <60, years of age; no significant clinical benefit was found if prescribed beyond 5 days. Among patients >80 years of age, 3-4 doses of Comirnaty vaccine were associated with significantly lower risks for target outcomes. Policies should encourage COVID-19 vaccination, and oral antivirals should be made accessible to infected persons within 5 days of confirmed diagnosis.
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Wong CKH, Lau KTK, Chung MSH, Au ICH, Cheung KW, Lau EHY, Daoud Y, Cowling BJ, Leung GM. Nirmatrelvir/ritonavir use in pregnant women with SARS-CoV-2 Omicron infection: a target trial emulation. Nat Med 2024; 30:112-116. [PMID: 37913816 DOI: 10.1038/s41591-023-02674-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 10/30/2023] [Indexed: 11/03/2023]
Abstract
To date, there is a lack of randomized trial data examining the use of the antiviral nirmatrelvir/ritonavir in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected pregnant persons. This target trial emulation study aimed to address this gap by evaluating the use of nirmatrelvir/ritonavir in nonhospitalized pregnant women with symptomatic SARS-CoV-2 Omicron variant infection. Among patients diagnosed between 16 March 2022 and 5 February 2023, exposure was defined as outpatient nirmatrelvir/ritonavir treatment within 5 days of symptom onset or coronavirus disease 2019 (COVID-19) diagnosis. Primary outcomes were maternal morbidity and mortality index (MMMI), all-cause maternal death and COVID-19-related hospitalization, while secondary outcomes were individual components of MMMI, preterm birth, stillbirth, neonatal death and cesarean section. One-to-ten propensity-score matching was conducted between nirmatrelvir/ritonavir users and nonusers, followed by cloning, censoring and weighting. Overall, 211 pregnant women on nirmatrelvir/ritonavir and 1,998 nonusers were included. Nirmatrelvir/ritonavir treatment was associated with reduced 28-day MMMI risk (absolute risk reduction (ARR) = 1.47%, 95% confidence interval (CI) = 0.21-2.34%) but not 28-days COVID-19-related hospitalization (ARR = -0.09%, 95% CI = -1.08% to 0.71%). Nirmatrelvir/ritonavir treatment was also associated with reduced risks of cesarean section (ARR = 1.58%, 95% CI = 0.85-2.39%) and preterm birth (ARR = 2.70%, 95% CI = 0.98-5.31%). No events of maternal or neonatal death or stillbirth were recorded. The findings suggest that nirmatrelvir/ritonavir is an effective treatment in symptomatic pregnant women with SARS-CoV-2 Omicron variant infection.
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Affiliation(s)
- Carlos K H Wong
- Laboratory of Data Discovery for Health (D24H), Hong Kong SAR, China.
- Department of Family Medicine and Primary Care, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
- Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
- Vaccine Confidence Project, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK.
| | - Kristy T K Lau
- Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Matthew S H Chung
- Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Department of Medicine, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Ivan C H Au
- Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Ka Wang Cheung
- Department of Obstetrics and Gynaecology, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Eric H Y Lau
- Laboratory of Data Discovery for Health (D24H), Hong Kong SAR, China
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Yasmin Daoud
- Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Benjamin J Cowling
- Laboratory of Data Discovery for Health (D24H), Hong Kong SAR, China
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Gabriel M Leung
- Laboratory of Data Discovery for Health (D24H), Hong Kong SAR, China
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
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21
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Liu X, Wong MKL, Zhang D, Chan DCL, Chan OSK, Chan GPL, Shum MHH, Peng Y, Lai CKC, Cowling BJ, Zhang T, Fukuda K, Lam TTY, Tun HM. Longitudinal monitoring reveals the emergence and spread of bla GES-5-harboring carbapenem-resistant Klebsiella quasipneumoniae in a Hong Kong hospital wastewater discharge line. Sci Total Environ 2023; 903:166255. [PMID: 37574056 DOI: 10.1016/j.scitotenv.2023.166255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 08/10/2023] [Accepted: 08/10/2023] [Indexed: 08/15/2023]
Abstract
Testing hospital wastewater (HWW) is potentially an effective, long-term approach for monitoring trends in antimicrobial resistance (AMR) patterns in health care institutions. Over a year, we collected wastewater samples from the clinical and non-clinical sites of a tertiary hospital and from a downstream wastewater treatment plant (WWTP). We focused on the extent of carbapenem resistance among Enterobacteriaceae isolates given their clinical importance. Escherichia coli and Klebsiella spp. were the most frequently isolated Enterobacteriaceae species at all sampling sites. Additionally, a small number of isolates belonging to ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species), except K. pneumoniae, were detected. Of the 232 Klebsiella spp. isolates, 100 (43.1 %) were multi-drug resistant (MDR), with 46 being carbapenem-resistant. Most of these carbapenem-resistant isolates were K. quasipneumoniae (CRKQ) (n = 44). All CRKQ isolates were isolated from the wastewater of a clinical site that includes intensive care units, which also yielded significantly more multi-drug resistant isolates compared to all other sampling sites. Among the CRKQ isolates, blaGES-5 genes (n = 42) were the primary genetic determinant of carbapenem resistance. Notably, three different CRKQ isolates, collected within the same month in HWW and the influent and effluent flow of the WWTP, shared >99 % sequence similarity between their blaGES-5 genes and between their flanking regions and upstream integron-integrase region. The influent isolate was phylogenetically close to K. quasipnuemoniae isolates from wastewater collected in Japan. Its blaGES-5 gene and surrounding sequences were > 99 % identical to blaGES-24 genes found in the Japanese isolates. Our results suggest that testing samples from sites located closer to hospitals could support antibiotic stewardship programs compared to samples collected further downstream. Moreover, testing samples collected regularly from WWTPs may reflect the local and global spread of pathogens and their resistances.
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Affiliation(s)
- Xin Liu
- The Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China; Microbiota-I Center (MagIC), Hong Kong, China; System Microbiology and Antimicrobial Resistance (SMART) Lab, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Matthew K L Wong
- Microbiota-I Center (MagIC), Hong Kong, China; System Microbiology and Antimicrobial Resistance (SMART) Lab, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Dengwei Zhang
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Darren C L Chan
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Olivia S K Chan
- System Microbiology and Antimicrobial Resistance (SMART) Lab, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China; School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Gary P L Chan
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Marcus Ho-Hin Shum
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China; Laboratory of Data Discovery for Health Limited, 19W Hong Kong Science & Technology Parks, Hong Kong, China
| | - Ye Peng
- The Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China; Microbiota-I Center (MagIC), Hong Kong, China; System Microbiology and Antimicrobial Resistance (SMART) Lab, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Christopher K C Lai
- Department of Microbiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Benjamin J Cowling
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Tong Zhang
- Environmental Microbiome Engineering and Biotechnology Lab, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Hong Kong, China
| | - Keiji Fukuda
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Tommy Tsam-Yuk Lam
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China; Laboratory of Data Discovery for Health Limited, 19W Hong Kong Science & Technology Parks, Hong Kong, China
| | - Hein M Tun
- The Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China; Microbiota-I Center (MagIC), Hong Kong, China; System Microbiology and Antimicrobial Resistance (SMART) Lab, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China.
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Du Z, Shao Z, Zhang X, Chen R, Chen T, Bai Y, Wang L, Lau EHY, Cowling BJ. Nowcasting and Forecasting Seasonal Influenza Epidemics - China, 2022-2023. China CDC Wkly 2023; 5:1100-1106. [PMID: 38125915 PMCID: PMC10728554 DOI: 10.46234/ccdcw2023.206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Accepted: 12/05/2023] [Indexed: 12/23/2023] Open
Abstract
Background Seasonal influenza resurged in China in February 2023, causing a large number of hospitalizations. While influenza epidemics occurred across China during the coronavirus disease 2019 (COVID-19) pandemic, the relaxation of COVID-19 containment measures in December 2022 may have contributed to the spread of acute respiratory infections in winter 2022/2023. Methods Using a mathematical model incorporating influenza activity as measured by influenza-like illness (ILI) data for northern and southern regions of China, we reconstructed the seasonal influenza incidence from October 2015 to September 2019 before the COVID-19 pandemic. Using this trained model, we predicted influenza activities in northern and southern China from March to September 2023. Results We estimated the effective reproduction number R e as 1.08 [95% confidence interval ( CI): 0.51, 1.65] in northern China and 1.10 (95% CI: 0.55, 1.67) in southern China at the start of the 2022-2023 influenza season. We estimated the infection attack rate of this influenza wave as 18.51% (95% CI: 0.00%, 37.78%) in northern China and 28.30% (95% CI: 14.77%, 41.82%) in southern China. Conclusions The 2023 spring wave of seasonal influenza in China spread until July 2023 and infected a substantial number of people.
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Affiliation(s)
- Zhanwei Du
- WHO Collaborating Center for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Zengyang Shao
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Xiao Zhang
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Ruohan Chen
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Tianmu Chen
- State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen City, Fujian Province, China
| | - Yuan Bai
- WHO Collaborating Center for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Lin Wang
- Department of Genetics, University of Cambridge, Cambridge, UK
| | - Eric H. Y. Lau
- Institute for Health Transformation & School of Health & Social Development, Deakin University, Melbourne, Australia
| | - Benjamin J. Cowling
- WHO Collaborating Center for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
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23
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Wong CKH, Mak LY, Au ICH, Cheng WY, So CH, Lau KTK, Lau EHY, Cowling BJ, Leung GM, Yuen MF. Risk of acute liver injury following the nirmatrelvir/ritonavir use. Liver Int 2023; 43:2657-2667. [PMID: 37448114 DOI: 10.1111/liv.15673] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 05/21/2023] [Accepted: 07/01/2023] [Indexed: 07/15/2023]
Abstract
BACKGROUND Elevations in alanine aminotransferase (ALT) and aspartate aminotransferase (AST) were reported as adverse events of nirmatrelvir/ritonavir users in the EPIC-HR trial. AIM To quantify the risk and severity of acute liver injury (ALI) associated with nirmatrelvir/ritonavir use. METHODS This self-controlled case-series study was conducted using electronic medical records of patients with confirmed diagnosis of SARS-CoV-2 infection between 26th February 2022 and 12th February 2023 in Hong Kong. RESULTS Among 2 409 848 patients with SARS-CoV-2 infection during the study period, 153 853 were prescribed with nirmatrelvir/ritonavir, of whom 834 (.5%) had incident ALI (moderate: 30.5%; moderate to severe: 18.9%; severe or fatal: 5.8%). Compared with the non-exposure period, risk of ALI increased significantly during the pre-exposure period (IRR = 38.13, 95% CI = 29.29-49.62) and remained elevated during the five-day nirmatrelvir/ritonavir treatment (IRR = 20.75, 95% CI = 17.06-25.25) and during wash-out period (IRR = 16.27, 95% CI = 13.23-20.01). Compared to the pre-exposure period, risk of ALI was not increased during the five-day nirmatrelvir/ritonavir treatment period (IRR = .54, 95% CI = .43-.70). Compared to 5469 non-nirmatrelvir/ritonavir users with incident ALI, nirmatrelvir/ritonavir users had less severe ALI by the severity index (p < .001) and peak INR (1.7 vs. 2.3; p < .001). ALI cases with nirmatrelvir/ritonavir use had lower risk of all-cause death (29.1% vs. 39.1%; OR = .64; p < .001) and no increase in risk of liver decompensation (1.0% vs. 1.3%; OR = .62; p = .230) compared to non-users. CONCLUSION The risk of ALI associated with nirmatrelvir/ritonavir treatment for COVID-19 was elevated in the pre-exposure period, but not following nirmatrelvir/ritonavir initiation. ALI following nirmatrelvir/ritonavir treatment were mostly mild and less severe than ALI events in non-nirmatrelvir/ritonavir users.
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Affiliation(s)
- Carlos King Ho Wong
- Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China
- Department of Family Medicine and Primary Care, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science and Technology Park, Hong Kong SAR, China
| | - Lung Yi Mak
- Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- State Key Laboratory of Liver Research, The University of Hong Kong, Hong Kong SAR, China
| | - Ivan Chi Ho Au
- Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China
| | - Wing Yiu Cheng
- School of Biomedical Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Ching Hei So
- Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China
| | - Kristy Tsz Kwan Lau
- Department of Pharmacology and Pharmacy, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China
| | - Eric Ho Yin Lau
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science and Technology Park, Hong Kong SAR, China
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Benjamin J Cowling
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science and Technology Park, Hong Kong SAR, China
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Gabriel M Leung
- Laboratory of Data Discovery for Health (D24H), Hong Kong Science and Technology Park, Hong Kong SAR, China
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Man Fung Yuen
- Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- State Key Laboratory of Liver Research, The University of Hong Kong, Hong Kong SAR, China
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24
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Cowling BJ, Sullivan SG. Incremental benefit of booster vaccinations for COVID-19 in the United Kingdom. Lancet Reg Health Eur 2023; 35:100790. [PMID: 38115958 PMCID: PMC10730305 DOI: 10.1016/j.lanepe.2023.100790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 11/06/2023] [Indexed: 12/21/2023]
Affiliation(s)
- Benjamin J. Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
| | - Sheena G. Sullivan
- WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, and Department of Infectious Diseases, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
- Department of Epidemiology, University of California, Los Angeles, California, USA
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25
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Xiong W, Cowling BJ, Tsang TK. Influenza Resurgence after Relaxation of Public Health and Social Measures, Hong Kong, 2023. Emerg Infect Dis 2023; 29:2556-2559. [PMID: 37885047 PMCID: PMC10683823 DOI: 10.3201/eid2912.230937] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2023] Open
Abstract
Soon after a mask mandate was relaxed (March 1, 2023), the first post-COVID-19 influenza season in Hong Kong lasted 12 weeks. After other preventive measures were accounted for, mask wearing was associated with an estimated 25% reduction in influenza transmission. Influenza resurgence probably resulted from relaxation of mask mandates and other measures.
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Zhao J, Schooling CM, Au Yeung SL, Cowling BJ, Baccarelli A. Environment-wide and epigenome-wide association study of adiposity in 'Children of 1997' birth cohort: abridged secondary publication. Hong Kong Med J 2023; 29 Suppl 7:9-13. [PMID: 38148649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2023] Open
Affiliation(s)
- J Zhao
- School of Public Health, The University of Hong Kong, Hong Kong SAR, China
| | - C M Schooling
- School of Public Health, The University of Hong Kong, Hong Kong SAR, China
| | - S L Au Yeung
- School of Public Health, The University of Hong Kong, Hong Kong SAR, China
| | - B J Cowling
- School of Public Health, The University of Hong Kong, Hong Kong SAR, China
| | - A Baccarelli
- Mailman School of Public Health, Columbia University, United States
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27
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Valkenburg S, Cowling BJ, Leung NHL. Influenza ADCC-antibody responses in vaccinated and infected children as a correlate of protection: abridged secondary publication. Hong Kong Med J 2023; 29 Suppl 7:39-40. [PMID: 38148656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2023] Open
Affiliation(s)
- S Valkenburg
- Department of Public Health, The University of Hong Kong, Hong Kong SAR, China
| | - B J Cowling
- Department of Public Health, The University of Hong Kong, Hong Kong SAR, China
| | - N H L Leung
- Department of Public Health, The University of Hong Kong, Hong Kong SAR, China
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28
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Wu P, Cowling BJ, Chiu SS, Wong IOL, Yeung WKY. Cost-effectiveness of prophylaxis with palivizumab among high-risk children in Hong Kong: abridged secondary publication. Hong Kong Med J 2023; 29 Suppl 7:37-38. [PMID: 38148655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2023] Open
Affiliation(s)
- P Wu
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - B J Cowling
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - S S Chiu
- Department of Paediatrics and Adolescent Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - I O L Wong
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - W K Y Yeung
- Department of Paediatrics and Adolescent Medicine, Pamela Youde Nethersole Eastern Hospital, Hong Kong SAR, China
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29
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Cowling BJ, Kwan MYW, Murphy C, Chan ELY, Wong JSC, Sullivan SG, Peiris M, Lee SL. Influenza Vaccine Effectiveness Against Influenza-Associated Hospitalization in Hong Kong Children Aged 9 Months to 17 Years, March-June 2023. J Pediatric Infect Dis Soc 2023; 12:586-589. [PMID: 37818976 PMCID: PMC10687596 DOI: 10.1093/jpids/piad083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 10/09/2023] [Indexed: 10/13/2023]
Abstract
In March-June 2023, we conducted a test-negative study in 1671 children who were hospitalized with acute respiratory illness in Hong Kong. Two hundred and eighty-six children (17.2%) were tested positive for influenza virus including 188 with A(H1N1). We estimated influenza vaccine effectiveness against influenza-associated hospitalization as 69.6% (95% confidence interval: 49.3%, 81.7%).
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Affiliation(s)
- Benjamin J Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
| | - Mike Y W Kwan
- Department of Paediatrics and Adolescent Medicine, Princess Margaret Hospital, Hong Kong Special Administrative Region, China
| | - Caitriona Murphy
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Eunice L Y Chan
- Department of Paediatrics and Adolescent Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Joshua S C Wong
- Department of Paediatrics and Adolescent Medicine, Princess Margaret Hospital, Hong Kong Special Administrative Region, China
| | - Sheena G Sullivan
- WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, and Department of Infectious Diseases, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
- Department of Epidemiology, University of California, Los Angeles, California, USA
| | - Malik Peiris
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Centre for Immunology & Infection, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
| | - So-Lun Lee
- Department of Paediatrics and Adolescent Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Department of Paediatrics and Adolescent Medicine, Queen Mary Hospital, Hong Kong Special Administrative Region, China
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30
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Zhong S, Ng TWY, Skowronski DM, Iuliano AD, Leung NHL, Perera RAPM, Ho F, Fang VJ, Tam YH, Ip DKM, Havers FG, Fry AM, Aziz-Baumgartner E, Barr IG, Peiris M, Thompson MG, Cowling BJ. Standard-dose versus MF59-adjuvanted, high-dose or recombinant-hemagglutinin influenza vaccine immunogenicity in older adults: comparison of A(H3N2) antibody response by prior season's vaccine status. J Infect Dis 2023:jiad497. [PMID: 37950884 DOI: 10.1093/infdis/jiad497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Revised: 10/19/2023] [Accepted: 11/08/2023] [Indexed: 11/13/2023] Open
Abstract
BACKGROUND Annual influenza vaccination is recommended for older adults but repeated vaccination with standard-dose influenza vaccine has been linked to reduced immunogenicity and effectiveness, especially against A(H3N2) viruses. METHODS Community-dwelling Hong Kong adults aged 65-82 years were randomly allocated to receive 2017/18 standard-dose quadrivalent, MF59-adjuvanted trivalent, high-dose trivalent, and recombinant-HA quadrivalent vaccination. Antibody response to unchanged A(H3N2) vaccine antigen was compared among participants with and without self-reported prior year (2016/17) standard-dose vaccination. RESULTS Mean fold rise (MFR) in antibody titers from Day 0 to Day 30 by hemagglutination inhibition and virus microneutralization assays were lower among 2017/18 standard-dose and enhanced vaccine recipients with (range, 1.7-3.0) vs. without (range, 4.3-14.3) prior 2016/17 vaccination. MFR was significantly reduced by about one half to four fifths for previously vaccinated recipients of standard-dose and all three enhanced vaccines (β range, 0.21-0.48). Among prior-year vaccinated older adults, enhanced vaccines induced higher 1.43 to 2.39-fold geometric mean titers and 1.28 to 1.74-fold MFR vs. standard-dose vaccine by microneutralization assay. CONCLUSIONS In the context of unchanged A(H3N2) vaccine strain, prior-year vaccination was associated with reduced antibody response among both standard-dose and enhanced influenza vaccine recipients. Enhanced vaccines improved antibody response among older adults with prior-year standard-dose vaccination.
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Affiliation(s)
- Shuyi Zhong
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Tiffany W Y Ng
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Danuta M Skowronski
- British Columbia Centre for Disease Control, Vancouver, Canada
- University of British Columbia, Vancouver, Canada
| | - A Danielle Iuliano
- Influenza Division, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Nancy H L Leung
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
| | - Ranawaka A P M Perera
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Faith Ho
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Vicky J Fang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Yat Hung Tam
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Dennis K M Ip
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Fiona G Havers
- Division of Viral Diseases, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Alicia M Fry
- Influenza Division, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | | | - Ian G Barr
- World Health Organization Collaborating Centre for Reference and Research on Influenza, Melbourne, Australia
- Department of Microbiology and Immunology, University of Melbourne, Victoria, Australia
| | - Malik Peiris
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Centre of Immunology and Infection, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Mark G Thompson
- Influenza Division, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Benjamin J Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
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Wong JY, Cheung JK, Lin Y, Bond HS, Lau EHY, Ip DKM, Cowling BJ, Wu P. Intrinsic and Effective Severity of Coronavirus Disease 2019 Cases Infected With the Ancestral Strain and Omicron BA.2 Variant in Hong Kong. J Infect Dis 2023; 228:1231-1239. [PMID: 37368235 DOI: 10.1093/infdis/jiad236] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 05/25/2023] [Accepted: 06/23/2023] [Indexed: 06/28/2023] Open
Abstract
BACKGROUND Understanding severity of infections with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its variants is crucial to inform public health measures. Here we used coronavirus disease 2019 (COVID-19) patient data from Hong Kong to characterize the severity profile of COVID-19. METHODS Time-varying and age-specific effective severity measured by case hospitalization risk and hospitalization fatality risk was estimated with all individual COVID-19 case data collected in Hong Kong from 23 January 2020 through 26 October 2022 over 6 epidemic waves. The intrinsic severity of Omicron BA.2 was compared with the estimate for the ancestral strain with the data from unvaccinated patients without previous infections. RESULTS With 32 222 COVID-19 hospitalizations and 9669 deaths confirmed over 6 epidemic waves, the time-varying hospitalization fatality risk dramatically increased from <10% before the largest fifth wave of Omicron BA.2 to 41% during the peak of the fifth wave when hospital resources were severely constrained. The age-specific fatality risk in unvaccinated hospitalized Omicron cases was comparable to the estimates for unvaccinated cases with the ancestral strain. During epidemics predominated by Omicron BA.2, fatality risk was highest among older unvaccinated patients. CONCLUSIONS Omicron has comparable intrinsic severity to the ancestral Wuhan strain, although the effective severity is substantially lower in Omicron cases due to vaccination.
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Affiliation(s)
- Jessica Y Wong
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Justin K Cheung
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Yun Lin
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Helen S Bond
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Eric H Y Lau
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
| | - Dennis K M Ip
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Benjamin J Cowling
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
| | - Peng Wu
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
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Du Z, Wang L, Bai Y, Pei Y, Wu P, Cowling BJ. Mitigation of respiratory syncytial virus epidemics by RSVpreF vaccines after the COVID-19 pandemic in the UK: a modelling study. Lancet 2023; 402 Suppl 1:S39. [PMID: 37997080 DOI: 10.1016/s0140-6736(23)02113-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 09/07/2023] [Accepted: 09/22/2023] [Indexed: 11/25/2023]
Abstract
BACKGROUND The RSVpreF vaccines have breakthrough progress. The respiratory syncytial virus (RSV) vaccine for older adults from GlaxoSmithKline was the first RSV vaccine approved by the US Food and Drug Administration (FDA) in early May 2023, followed by the subsequent FDA approval of Pfizer's RSV vaccines for older adults and pregnant women. We aimed to estimate the public health impact of the potential population-level administrations of the RSVpreF vaccine in the UK. METHODS In this modelling study, we used national census and contact survey data to construct an individual-based mathematical model, with interpersonal connections characterising household structure, social settings, and age-specific contact patterns. We considered both within-host viral-load dynamics and between-host RSV transmission. We modelled the coverages of RSV vaccines for older people (age ≥60 years) and pregnant women, using influenza vaccination data from the 2018-19 season. We explored a range of possible transmissibility and estimated the health burden averted by RSVpreF vaccine over a 300-day period as compared with the control scenario without vaccines. FINDINGS In a low-transmission scenario (Re=1·2), RSVpreF would avert a total population of 2·35 (95% credible interval [CrI] 1·24-3·77) million infections, 12.80 (95% CrI 8·60-17·06) thousand hospital admissions, and 0·93 (95% CrI 0·69-1·25) thousand deaths, with 1·82 (1·41-2·33) million infections, 12·44 (8·50-16·38) thousand hospital admissions, and 0·93 (0·67-1·23) thousand deaths averted for people aged 60 years and older. In a high-transmission scenario (Re=2·0), RSVpreF would avert 2·01 (1·37-2·68) million infections, 14·67 (10·05-18·33) thousand hospital admissions, and 1·12 (0·80-1·35) thousand deaths. The majority averted would still be among older adults. INTERPRETATION Our mathematical models will help improve the vaccine schedules of RSVpreF. Future work will address several limitations when data become available, including the incorporation of population immunity, potential vaccine hesitancy, and other factors affecting vaccine uptake and effectiveness. FUNDING Government of the Hong Kong Special Administrative Region, the European Research Council, and Ministry of Science and Technology of the People's Republic of China.
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Affiliation(s)
- Zhanwei Du
- WHO Collaborating Center for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China; Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Lin Wang
- Department of Genetics, University of Cambridge, Cambridge, UK
| | - Yuan Bai
- WHO Collaborating Center for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China; Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Yao Pei
- WHO Collaborating Center for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China; Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Peng Wu
- WHO Collaborating Center for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China; Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Benjamin J Cowling
- WHO Collaborating Center for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China; Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China.
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Murphy C, Lim WW, Mills C, Wong JY, Chen D, Xie Y, Li M, Gould S, Xin H, Cheung JK, Bhatt S, Cowling BJ, Donnelly CA. Effectiveness of social distancing measures and lockdowns for reducing transmission of COVID-19 in non-healthcare, community-based settings. Philos Trans A Math Phys Eng Sci 2023; 381:20230132. [PMID: 37611629 PMCID: PMC10446910 DOI: 10.1098/rsta.2023.0132] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 05/23/2023] [Indexed: 08/25/2023]
Abstract
Social distancing measures (SDMs) are community-level interventions that aim to reduce person-to-person contacts in the community. SDMs were a major part of the responses first to contain, then to mitigate, the spread of SARS-CoV-2 in the community. Common SDMs included limiting the size of gatherings, closing schools and/or workplaces, implementing work-from-home arrangements, or more stringent restrictions such as lockdowns. This systematic review summarized the evidence for the effectiveness of nine SDMs. Almost all of the studies included were observational in nature, which meant that there were intrinsic risks of bias that could have been avoided were conditions randomly assigned to study participants. There were no instances where only one form of SDM had been in place in a particular setting during the study period, making it challenging to estimate the separate effect of each intervention. The more stringent SDMs such as stay-at-home orders, restrictions on mass gatherings and closures were estimated to be most effective at reducing SARS-CoV-2 transmission. Most studies included in this review suggested that combinations of SDMs successfully slowed or even stopped SARS-CoV-2 transmission in the community. However, individual effects and optimal combinations of interventions, as well as the optimal timing for particular measures, require further investigation. This article is part of the theme issue 'The effectiveness of non-pharmaceutical interventions on the COVID-19 pandemic: the evidence'.
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Affiliation(s)
- Caitriona Murphy
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Wey Wen Lim
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Cathal Mills
- Department of Statistics, University of Oxford, Oxford, UK
| | - Jessica Y. Wong
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Dongxuan Chen
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong, People's Republic of China
| | - Yanmy Xie
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Mingwei Li
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong, People's Republic of China
| | - Susan Gould
- Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK
- Tropical and Infectious Disease Unit, Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
| | - Hualei Xin
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Justin K. Cheung
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
| | - Samir Bhatt
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Kobenhavn, Denmark
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, Imperial College London, London, UK
| | - Benjamin J. Cowling
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong, People's Republic of China
| | - Christl A. Donnelly
- Department of Statistics, University of Oxford, Oxford, UK
- MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine, Imperial College London, London, UK
- Pandemic Sciences Institute, University of Oxford, Oxford, UK
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Lun P, Ning K, Wang Y, Ma TSW, Flores FP, Xiao X, Subramaniam M, Abdin E, Tian L, Tsang TK, Leung K, Wu JT, Cowling BJ, Leung GM, Ni MY. COVID-19 Vaccination Willingness and Reasons for Vaccine Refusal. JAMA Netw Open 2023; 6:e2337909. [PMID: 37856125 PMCID: PMC10587797 DOI: 10.1001/jamanetworkopen.2023.37909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 08/22/2023] [Indexed: 10/20/2023] Open
Abstract
Importance Hong Kong was held as an exemplar for pandemic response until it recorded the world's highest daily COVID-19 mortality, which was likely due to vaccine refusal. To prevent this high mortality in future pandemics, information on underlying reasons for vaccine refusal is necessary. Objectives To track the evolution of COVID-19 vaccination willingness and uptake from before vaccine rollout to mass vaccination, to examine factors associated with COVID-19 vaccine refusal and compare with data from Singapore, and to assess the population attributable fraction for vaccine refusal. Design, Setting, and Participants This cohort study used data from randomly sampled participants from 14 waves of population-based studies in Hong Kong (February 2020 to May 2022) and 2 waves of population-based studies in Singapore (May 2020 to June 2021 and October 2021 to January 2022), and a population-wide registry of COVID-19 vaccination appointments. Data were analyzed from February 23, 2021, to May 30, 2022. Exposures Trust in COVID-19 vaccine information sources (ie, health authorities, physicians, traditional media, and social media); COVID-19 vaccine confidence on effectiveness, safety, and importance; COVID-19 vaccine misconceptions on safety and high-risk groups; political views; and COVID-19 policies (ie, workplace vaccine mandates and vaccine pass). Main Outcomes and Measures Primary outcomes were the weighted prevalence of COVID-19 vaccination willingness over the pandemic, adjusted incidence rate ratios, and population attributable fractions of COVID-19 vaccine refusal. A secondary outcome was change in daily COVID-19 vaccination appointments. Results The study included 28 007 interviews from 20 waves of longitudinal data, with 1114 participants in the most recent wave (median [range] age, 54.2 years [20-92] years; 571 [51.3%] female). Four factors-mistrust in health authorities, low vaccine confidence, vaccine misconceptions, and political views-could jointly account for 82.2% (95% CI, 62.3%-100.0%) of vaccine refusal in adults aged 18 to 59 years and 69.3% (95% CI, 47.2%-91.4%) of vaccine refusal in adults aged 60 years and older. Workplace vaccine mandates were associated with 62.2% (95% CI, 9.9%-139.2%) increases in daily COVID-19 vaccination appointments, and the Hong Kong vaccine pass was associated with 124.8% (95% CI, 65.9%-204.6%) increases in daily COVID-19 vaccination appointments. Conclusions and Relevance These findings suggest that trust in health authorities was fundamental to overcoming vaccine hesitancy. As such, engendering trust in health care professionals, experts, and public health agencies should be incorporated into pandemic preparedness and response.
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Affiliation(s)
- Phyllis Lun
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Ke Ning
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Yishan Wang
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Tiffany S. W. Ma
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Francis P. Flores
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Xiao Xiao
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Mythily Subramaniam
- Research Division, Institute of Mental Health, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | | | - Linwei Tian
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Tim K. Tsang
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science Park, Hong Kong Special Administrative Region, China
| | - Kathy Leung
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science Park, Hong Kong Special Administrative Region, China
| | - Joseph T. Wu
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science Park, Hong Kong Special Administrative Region, China
| | - Benjamin J. Cowling
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science Park, Hong Kong Special Administrative Region, China
| | - Gabriel M. Leung
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science Park, Hong Kong Special Administrative Region, China
| | - Michael Y. Ni
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Urban System Institute, The University of Hong Kong, Hong Kong Special Administrative Region, China
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Parag KV, Cowling BJ, Lambert BC. Angular reproduction numbers improve estimates of transmissibility when disease generation times are misspecified or time-varying. Proc Biol Sci 2023; 290:20231664. [PMID: 37752839 PMCID: PMC10523088 DOI: 10.1098/rspb.2023.1664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 09/04/2023] [Indexed: 09/28/2023] Open
Abstract
We introduce the angular reproduction number Ω, which measures time-varying changes in epidemic transmissibility resulting from variations in both the effective reproduction number R, and generation time distribution w. Predominant approaches for tracking pathogen spread infer either R or the epidemic growth rate r. However, R is biased by mismatches between the assumed and true w, while r is difficult to interpret in terms of the individual-level branching process underpinning transmission. R and r may also disagree on the relative transmissibility of epidemics or variants (i.e. rA > rB does not imply RA > RB for variants A and B). We find that Ω responds meaningfully to mismatches and time-variations in w while mostly maintaining the interpretability of R. We prove that Ω > 1 implies R > 1 and that Ω agrees with r on the relative transmissibility of pathogens. Estimating Ω is no more difficult than inferring R, uses existing software, and requires no generation time measurements. These advantages come at the expense of selecting one free parameter. We propose Ω as complementary statistic to R and r that improves transmissibility estimates when w is misspecified or time-varying and better reflects the impact of interventions, when those interventions concurrently change R and w or alter the relative risk of co-circulating pathogens.
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Affiliation(s)
- Kris V. Parag
- MRC Centre for Global Infectious Disease Analysis, Imperial College London, London, UK
- NIHR Health Protection Research Unit in Behavioural Science and Evaluation, University of Bristol, Bristol, UK
| | - Benjamin J. Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong Hong Kong
| | - Ben C. Lambert
- Department of Mathematics, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK
- Department of Statistics, University of Oxford, Oxford, UK
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Bai Y, Du Z, Wang L, Lau EHY, Fung ICH, Holme P, Cowling BJ, Galvani AP, Krug RM, Meyers LA. The public health impact of Paxlovid COVID-19 treatment in the United States. medRxiv 2023:2023.06.16.23288870. [PMID: 37732213 PMCID: PMC10508801 DOI: 10.1101/2023.06.16.23288870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
The antiviral drug Paxlovid has been shown to rapidly reduce viral load. Coupled with vaccination, timely administration of safe and effective antivirals could provide a path towards managing COVID-19 without restrictive non-pharmaceutical measures. Here, we estimate the population-level impacts of expanding treatment with Paxlovid in the US using a multi-scale mathematical model of SARS-CoV-2 transmission that incorporates the within-host viral load dynamics of the Omicron variant. We find that, under a low transmission scenario R e ∼ 1.2 treating 20% of symptomatic cases would be life and cost saving, leading to an estimated 0.26 (95% CrI: 0.03, 0.59) million hospitalizations averted, 30.61 (95% CrI: 1.69, 71.15) thousand deaths averted, and US$52.16 (95% CrI: 2.62, 122.63) billion reduction in health- and treatment-related costs. Rapid and broad use of the antiviral Paxlovid could substantially reduce COVID-19 morbidity and mortality, while averting socioeconomic hardship.
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Affiliation(s)
- Yuan Bai
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Zhanwei Du
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Lin Wang
- Department of Genetics, University of Cambridge, Cambridge, UK
| | - Eric H. Y. Lau
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Isaac Chun-Hai Fung
- Department of Biostatistics, Epidemiology and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA 30460, USA
| | - Petter Holme
- Department of Computer Science, Aalto University, Espoo, FI 00076, Finland
- Center for Computational Social Science, Kobe University, Nada, Kobe 657-8501, Japan
| | - Benjamin J. Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Alison P. Galvani
- Center for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, CT, USA
| | - Robert M. Krug
- Department of Molecular Biosciences, John Ring LaMontagne Center for Infectious Disease, Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, TX 78712, USA
| | - Lauren Ancel Meyers
- Department of Integrative Biology, University of Texas at Austin, Austin, TX 78712, USA
- Santa Fe Institute, Santa Fe, NM 87507, USA
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Leung NHL, Cheng SMS, Cohen CA, Martín-Sánchez M, Au NYM, Luk LLH, Tsang LCH, Kwan KKH, Chaothai S, Fung LWC, Cheung AWL, Chan KCK, Li JKC, Ng YY, Kaewpreedee P, Jia JZ, Ip DKM, Poon LLM, Leung GM, Peiris JSM, Valkenburg SA, Cowling BJ. Comparative antibody and cell-mediated immune responses, reactogenicity, and efficacy of homologous and heterologous boosting with CoronaVac and BNT162b2 (Cobovax): an open-label, randomised trial. Lancet Microbe 2023; 4:e670-e682. [PMID: 37549680 PMCID: PMC10528748 DOI: 10.1016/s2666-5247(23)00216-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 07/05/2023] [Accepted: 07/07/2023] [Indexed: 08/09/2023]
Abstract
BACKGROUND Few trials have compared homologous and heterologous third doses of COVID-19 vaccination with inactivated vaccines and mRNA vaccines. The aim of this study was to assess immune responses, safety, and efficacy against SARS-CoV-2 infection following homologous or heterologous third-dose COVID-19 vaccination with either one dose of CoronaVac (Sinovac Biotech; inactivated vaccine) or BNT162b2 (Fosun Pharma-BioNTech; mRNA vaccine). METHODS This is an ongoing, randomised, allocation-concealed, open-label, comparator-controlled trial in adults aged 18 years or older enrolled from the community in Hong Kong, who had received two doses of CoronaVac or BNT162b2 at least 6 months earlier. Participants were randomly assigned, using a computer-generated sequence, in a 1:1 ratio with allocation concealment to receive a (third) dose of CoronaVac or BNT162b2 (ancestral virus strain), stratified by types of previous COVID-19 vaccination (homologous two doses of CoronaVac or BNT162b2). Participants were unmasked to group allocation after vaccination. The primary endpoint was serum neutralising antibodies against the ancestral virus at day 28 after vaccination in each group, measured as plaque reduction neutralisation test (PRNT50) geometric mean titre (GMT). Surrogate virus neutralisation test (sVNT) mean inhibition percentage and PRNT50 titres against omicron BA.1 and BA.2 subvariants were also measured. Secondary endpoints included geometric mean fold rise (GMFR) in antibody titres; incidence of solicited local and systemic adverse events; IFNγ+ CD4+ and IFNγ+ CD8+ T-cell responses at days 7 and 28; and incidence of COVID-19. Within-group comparisons of boost in immunogenicity from baseline and between-group comparisons were done according to intervention received (ie, per protocol) by paired and unpaired t test, respectively, and cumulative incidence of infection was compared using Kaplan-Meier curves and a proportional hazards model to estimate hazard ratio. The trial is registered with ClinicalTrials.gov, NCT05057169. FINDINGS We enrolled participants from Nov 12, 2021, to Jan 27, 2022. We vaccinated 219 participants who previously received two doses of CoronaVac, including 101 randomly assigned to receive CoronaVac (CC-C) and 118 randomly assigned to receive BNT162b2 (CC-B) as their third dose; and 232 participants who previously received two doses of BNT162b2, including 118 randomly assigned to receive CoronaVac (BB-C) and 114 randomly assigned to receive BNT162b2 (BB-B) as their third dose. The PRNT50 GMTs on day 28 against ancestral virus were 109, 905, 92, and 816; against omicron BA.1 were 9, 75, 8, and 86; and against omicron BA.2 were 6, 80, 6, and 67 in the CC-C, CC-B, BB-C, and BB-B groups, respectively. Mean sVNT inhibition percentages on day 28 against ancestral virus were 83%, 96%, 87%, and 96%; against omicron BA.1 were 15%, 58%, 19%, and 69%; and against omicron BA.2 were 43%, 85%, 50%, and 90%, in the CC-C, CC-B, BB-C, and BB-B groups, respectively. Participants who had previously received two doses of CoronaVac and a BNT162b2 third dose had a GMFR of 12 (p<0·0001) compared with those who received a CoronaVac third dose; similarly, those who had received two doses of BNT162b2 and a BNT162b2 third dose had a GMFR of 8 (p<0·0001). No differences in CD4+ and CD8+ T-cell responses were observed between groups. We did not identify any vaccination-related hospitalisation within 1 month after vaccination. We identified 58 infections when omicron BA.2 was predominantly circulating, with cumulative incidence of 15·3% and 15·4% in the CC-C and CC-B groups, respectively (p=0·93), and 16·7% and 14·0% in the BB-C and BB-B groups, respectively (p=0·56). INTERPRETATION Similar levels of incidence of, presumably, omicron BA.2 infections were observed in each group despite very weak antibody responses to BA.2 in the recipients of a CoronaVac third dose. Further research is warranted to identify appropriate correlates of protection for inactivated COVID-19 vaccines. FUNDING Health and Medical Research Fund, Hong Kong. TRANSLATION For the Chinese translation of the abstract see Supplementary Materials section.
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Affiliation(s)
- Nancy H L Leung
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China; Takemi Program in International Health, Harvard T H Chan School of Public Health, Harvard University, Boston, MA, USA; Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Samuel M S Cheng
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Carolyn A Cohen
- HKU-Pasteur Research Pole, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Mario Martín-Sánchez
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Niki Y M Au
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Leo L H Luk
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Leo C H Tsang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Kelvin K H Kwan
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Sara Chaothai
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Lison W C Fung
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Alan W L Cheung
- HKU-Pasteur Research Pole, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Karl C K Chan
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - John K C Li
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Yvonne Y Ng
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Prathanporn Kaewpreedee
- HKU-Pasteur Research Pole, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Janice Z Jia
- HKU-Pasteur Research Pole, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Dennis K M Ip
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Leo L M Poon
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China; HKU-Pasteur Research Pole, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China; Centre for Immunology and Infection, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Gabriel M Leung
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China; Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - J S Malik Peiris
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China; HKU-Pasteur Research Pole, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China; Centre for Immunology and Infection, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Sophie A Valkenburg
- HKU-Pasteur Research Pole, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China; Department of Microbiology and Immunology, Peter Doherty Institute of Infection and Immunity, The University of Melbourne, Melbourne, VIC, Australia
| | - Benjamin J Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China; Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China.
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Tsang TK, Huang X, Fong MW, Wang C, Lau EHY, Wu P, Cowling BJ. Effects of School-Based Preventive Measures on COVID-19 Incidence, Hong Kong, 2022. Emerg Infect Dis 2023; 29:1850-1854. [PMID: 37490926 PMCID: PMC10461670 DOI: 10.3201/eid2909.221897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2023] Open
Abstract
We show that school closures reduced COVID-19 incidence rates in children by 31%-46% in Hong Kong in 2022. After school reopening accompanied by mask mandates, daily rapid testing, and vaccination requirements, school-reported cases correlated with community incidence rates. Safe school reopening is possible when appropriate preventive measures are used.
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Lin Y, Wu P, Tsang TK, Wong JY, Lau EHY, Yang B, Leung GM, Cowling BJ. Viral kinetics of SARS-CoV-2 following onset of COVID-19 in symptomatic patients infected with the ancestral strain and omicron BA.2 in Hong Kong: a retrospective observational study. Lancet Microbe 2023; 4:e722-e731. [PMID: 37659420 DOI: 10.1016/s2666-5247(23)00146-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 05/05/2023] [Accepted: 05/05/2023] [Indexed: 09/04/2023]
Abstract
BACKGROUND Assessment of viral kinetics of SARS-CoV-2 can inform on host immune responses to the virus and on the viral transmission potential. We aimed to characterise viral shedding kinetics by age, virus type, and clinical outcome, and to examine the potential effect of vaccination on viral shedding. METHODS In this retrospective observational study, we analysed longitudinal data on cycle threshold (Ct) values of reverse-transcription quantitative PCR (RT-qPCR) assays of upper respiratory tract samples from symptomatic patients with COVID-19. Patients who were confirmed with COVID-19 with at least one Ct value of the RT-qPCR test available within 28 days after symptom onset, and discharged or died at the time of the analysis, were included in the study. Patients were isolated in hospitals in Hong Kong during three major epidemic waves dominated by the ancestral strain or omicron BA.2. We modelled the temporal trajectories of viral burden in these patients. Electronic medical records of the patients with COVID-19 were retrieved and linked to the patients' epidemiological information obtained from contact tracing. Patients who were infected outside Hong Kong, infected with variants other than the ancestral strain or omicron BA.2, not reporting any COVID-19 related symptoms, still hospitalised at the time of analysis, missing information on age, time of symptom onset, infection severity, vaccination or clinical outcome, infected more than once, or treated with nirmatrelvir-ritonavir or molnupiravir were excluded from analysis. The main outcome of this study is the temporal change of SARS-CoV-2 viral burden measured by Ct values of RT-qPCR tests in symptomatic patients with COVID-19. FINDINGS Among 22 461 symptomatic patients with COVID-19 confirmed from July 1, 2020, to May 22, 2022, the estimated viral burden from a random-effects model indicated a longer duration of viral shedding in patients with more severe outcomes of infection (mean difference 13·1 days, 95% CI 12·9-13·3, for fatal vs mild-to-moderate) and in older patients (5·2, 5·0-5·5, for age ≥80 years vs 0-18 years). Vaccinated individuals with a breakthrough infection with the omicron BA.2 variant had a generally lower viral burden and shorter durations of viral shedding (mean difference of 2-4 days) over 4 weeks after onset than unvaccinated individuals infected with omicron BA.2, particularly in patients whose last dose of COVID-19 vaccine was received ≤90 days before symptom onset. Marginal differences in viral burden following symptom onset and the duration of viral shedding were observed between unvaccinated individuals infected with the ancestral strain and omicron BA.2. INTERPRETATION The viral kinetics since symptom onset characterised for symptomatic patients with COVID-19 in our study show that previously vaccinated or younger individuals, or those with a milder infection, shed fewer viruses in a shorter period, implying possible transmission dynamics of SARS-CoV-2 and protective mechanisms of vaccination against infection and severe outcomes. FUNDING Hong Kong Health and Medical Research Fund and Hong Kong Collaborative Research Fund.
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Affiliation(s)
- Yun Lin
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Peng Wu
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China; Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China.
| | - Tim K Tsang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Jessica Y Wong
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Eric H Y Lau
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China; Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
| | - Bingyi Yang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Gabriel M Leung
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China; Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
| | - Benjamin J Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China; Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
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Dong M, Ip DKM, Yuan J, So HC, Cowling BJ, Liao Q. Assessing the longitudinal effects of the continuation and discontinuation of the school-located influenza vaccination programme on parental vaccine hesitancy in Hong Kong. J Public Health (Oxf) 2023; 45:e501-e509. [PMID: 37002942 DOI: 10.1093/pubmed/fdad018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 02/01/2023] [Accepted: 02/07/2023] [Indexed: 04/03/2023] Open
Abstract
BACKGROUND School-located influenza vaccination programme (SIVP) can effectively promote childhood seasonal influenza vaccination (SIV). However, the longitudinal effects of continuation and discontinuation of the SIVP on parents' vaccine hesitancy remained unknown. METHODS A two-wave longitudinal study recruited adult parents who had at least one child attending a kindergarten or primary school using random-digital-dialled telephone interviews. Generalized estimating equation and structural equation modelling were used to examine the impact of changes in schools' SIVP participation status on parents' vaccine-related attitudes, and childhood SIV acceptance over 2 years in Hong Kong. RESULTS Children's SIV uptake varied by the schools' SIVP participation status. The highest SIV uptake was found in schools that consistently participated in SIVP (Consistent participation group) (2018/2019: 85.0%; 2019/2020: 83.0%) but lowest in the Consistent non-Participation group (2018/2019: 45.0%; 2019/2020: 39.0%). SIV uptake increased in the Late Initiation group but declined in the Discontinuation group. An increasing trend of parental vaccine-hesitant attitudes was observed in the Consistent non-Participation group. CONCLUSIONS Initiation and continuation of the SIVP can reduce parental vaccine hesitancy to achieve a high childhood SIV uptake. Conversely, discontinuation of the SIVP or persistent resistance to the implementation of SIVP can increase parental vaccine hesitancy and reduce childhood SIV uptake.
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Affiliation(s)
- Meihong Dong
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Dennis Kai Ming Ip
- World Health Organization Collaborating Center for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Jiehu Yuan
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Hau Chi So
- World Health Organization Collaborating Center for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Benjamin J Cowling
- World Health Organization Collaborating Center for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong, China
| | - Qiuyan Liao
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
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Ling L, Zhang JZ, Chang LC, Chiu LCS, Ho S, Ng PY, Dharmangadan M, Lau CH, Ling S, Man MY, Fong KM, Liong T, Yeung AWT, Au GKF, Chan JKH, Tang M, Liu YZ, Wu WKK, Wong WT, Wu P, Cowling BJ, Lee A, Rhee C. Population sepsis incidence, mortality, and trends in Hong Kong between 2009-2018 using clinical and administrative data. Clin Infect Dis 2023:ciad491. [PMID: 37596856 DOI: 10.1093/cid/ciad491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 07/26/2023] [Accepted: 08/16/2023] [Indexed: 08/20/2023] Open
Abstract
BACKGROUND Sepsis surveillance using electronic health record (EHR)-based data may provide more accurate epidemiologic estimates than administrative data, but experience with this approach to estimate population-level sepsis burden is lacking. METHODS This was a retrospective cohort study including all adults admitted to publicly-funded hospitals in Hong Kong between 2009-2018. Sepsis was defined as clinical evidence of presumed infection (clinical cultures and treatment with antibiotics) and concurrent acute organ dysfunction (≥2 point increase in baseline SOFA score). Trends in incidence, mortality, and case fatality risk (CFR) were modelled by exponential regression. Performance of the EHR-based definition was compared with 4 administrative definitions using 500 medical record reviews. RESULTS Among 13,550,168 hospital episodes during the study period, 485,057 (3.6%) had sepsis by EHR-based criteria with 21.5% CFR. In 2018, age- and sex-adjusted standardized sepsis incidence was 759 per 100,000 (relative +2.9%/year [95%CI 2.0, 3.8%] between 2009-2018) and standardized sepsis mortality was 156 per 100,000 (relative +1.9%/year [95%CI 0.9,2.9%]). Despite decreasing CFR (relative -0.5%/year [95%CI -1.0, -0.1%]), sepsis accounted for an increasing proportion of all deaths (relative +3.9%/year [95%CI 2.9, 4.9%]). Medical record reviews demonstrated that the EHR-based definition more accurately identified sepsis than administrative definitions (AUC 0.91 vs 0.52-0.55, p < 0.001). CONCLUSIONS An objective EHR-based surveillance definition demonstrated an increase in population-level standardized sepsis incidence and mortality in Hong Kong between 2009-2018 and was much more accurate than administrative definitions. These findings demonstrate the feasibility and advantages of an EHR-based approach for widescale sepsis surveillance.
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Affiliation(s)
- Lowell Ling
- Department of Anaesthesia and Intensive Care, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Jack Zhenhe Zhang
- Department of Anaesthesia and Intensive Care, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Lok Ching Chang
- Department of Anaesthesia and Intensive Care, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Lok Ching Sandra Chiu
- Department of Anaesthesia and Intensive Care, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Samantha Ho
- Department of Anaesthesia and Intensive Care, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Pauline Yeung Ng
- Critical Care Medicine Unit, The University of Hong Kong, Hong Kong SAR, China
- Department of Adult Intensive Care, Queen Mary Hospital, Hong Kong SAR, China
| | | | - Chi Ho Lau
- Department of Intensive Care, North District Hospital, Hong Kong SAR, China
| | - Steven Ling
- Department of Intensive Care, Tuen Mun Hospital, Hong Kong SAR, China
| | - Man Yee Man
- Department of Intensive Care, Pamela Youde Nethersole Eastern Hospital, Hong Kong SAR, China
| | - Ka Man Fong
- Department of Intensive Care, Queen Elizabeth Hospital, Hong Kong SAR, China
| | - Ting Liong
- Department of Intensive Care, United Christian Hospital, Hong Kong SAR, China
| | - Alwin Wai Tak Yeung
- Department of Medicine & Geriatrics, Ruttonjee and Tang Shiu Kin Hospitals, Hong Kong SAR, China
| | - Gary Ka Fai Au
- Department of Intensive Care, Kwong Wah Hospital, Hong Kong SAR, China
| | | | - Michele Tang
- Department of Medicine and Geriatrics, Caritas Medical Centre, Hong Kong SAR, China
| | - Ying Zhi Liu
- Department of Anaesthesia and Intensive Care, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - William Ka Kei Wu
- Department of Anaesthesia and Intensive Care, The Chinese University of Hong Kong, Hong Kong SAR, China
- State Key Laboratory of Digestive Diseases, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
- Institute of Digestive Disease, The Chinese University of Hong Kong, Hong Kong SAR, China
- CUHK Shenzhen Research Institute, Shenzhen, China
- Peter Hung Pain Research Institute, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Wai Tat Wong
- Department of Anaesthesia and Intensive Care, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Peng Wu
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong SAR, China
| | - Benjamin J Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong SAR, China
| | - Anna Lee
- Department of Anaesthesia and Intensive Care, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Chanu Rhee
- Department of Population Medicine, Harvard Medical School/Harvard Pilgrim Health Care Institute, Boston, MA, USA
- Division of Infectious Diseases, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
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Young BR, Yang B, Wu P, Adam DC, Wong JY, Ho F, Gao H, Lau EHY, Leung GM, Cowling BJ. Residential Clustering of Coronavirus Disease 2019 Cases and Efficiency of Building-Wide Compulsory Testing Notices as a Transmission Control Measure in Hong Kong. J Infect Dis 2023; 228:426-430. [PMID: 37094371 DOI: 10.1093/infdis/jiad107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 04/06/2023] [Accepted: 04/14/2023] [Indexed: 04/26/2023] Open
Abstract
We described the frequency of residential case clusters and the efficiency of compulsory testing in identifying cases using buildings targeted in compulsory testing and locally infected coronavirus disease 2019 (COVID-19) cases matched by residence in Hong Kong. Most of the buildings (4246 of 7688, 55.2%) with COVID-19 cases identified had only 1 reported case, and 13% of the daily reported cases were detected through compulsory testing. Compulsory testing notices could be essential in attempting to eliminate infections ("zero COVID") and have an impact early in an epidemic, but they appear to be relatively inefficient in response to sustained community transmission.
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Affiliation(s)
- Benjamin R Young
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Bingyi Yang
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Peng Wu
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
| | - Dillon C Adam
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Jessica Y Wong
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Faith Ho
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Huizhi Gao
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Eric H Y Lau
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
| | - Gabriel M Leung
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
| | - Benjamin J Cowling
- World Health Organization Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
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Tsang TK, Wang C, Fang VJ, Perera RAPM, So HC, Ip DKM, Leung GM, Peiris JSM, Cauchemez S, Cowling BJ. Reconstructing household transmission dynamics to estimate the infectiousness of asymptomatic influenza virus infections. Proc Natl Acad Sci U S A 2023; 120:e2304750120. [PMID: 37549267 PMCID: PMC10436695 DOI: 10.1073/pnas.2304750120] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 07/15/2023] [Indexed: 08/09/2023] Open
Abstract
There has long been controversy over the potential for asymptomatic cases of the influenza virus to have the capacity for onward transmission, but recognition of asymptomatic transmission of COVID-19 stimulates further research into this topic. Here, we develop a Bayesian methodology to analyze detailed data from a large cohort of 727 households and 2515 individuals in the 2009 pandemic influenza A(H1N1) outbreak in Hong Kong to characterize household transmission dynamics and to estimate the relative infectiousness of asymptomatic versus symptomatic influenza cases. The posterior probability that asymptomatic cases [36% of cases; 95% credible interval (CrI): 32%, 40%] are less infectious than symptomatic cases is 0.82, with estimated relative infectiousness 0.57 (95% CrI: 0.11, 1.54). More data are required to strengthen our understanding of the contribution of asymptomatic cases to the spread of influenza.
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Affiliation(s)
- Tim K. Tsang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
| | - Can Wang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Vicky J. Fang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Ranawaka A. P. M. Perera
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- HKU-Pasteur Research Pole, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Hau Chi So
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Dennis K. M. Ip
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Gabriel M. Leung
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
| | - J. S. Malik Peiris
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Simon Cauchemez
- Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, Université Paris Cité, UMR2000, CNRS, 75015Paris, France
| | - Benjamin J. Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
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Sullivan SG, Khvorov A, Huang X, Wang C, Ainslie KEC, Nealon J, Yang B, Cowling BJ, Tsang TK. The need for a clinical case definition in test-negative design studies estimating vaccine effectiveness. NPJ Vaccines 2023; 8:118. [PMID: 37573443 PMCID: PMC10423262 DOI: 10.1038/s41541-023-00716-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 08/01/2023] [Indexed: 08/14/2023] Open
Abstract
Test negative studies have been used extensively for the estimation of COVID-19 vaccine effectiveness (VE). Such studies are able to estimate VE against medically-attended illness under certain assumptions. Selection bias may be present if the probability of participation is associated with vaccination or COVID-19, but this can be mitigated through use of a clinical case definition to screen patients for eligibility, which increases the likelihood that cases and non-cases come from the same source population. We examined the extent to which this type of bias could harm COVID-19 VE through systematic review and simulation. A systematic review of test-negative studies was re-analysed to identify studies ignoring the need for clinical criteria. Studies using a clinical case definition had a lower pooled VE estimate compared with studies that did not. Simulations varied the probability of selection by case and vaccination status. Positive bias away from the null (i.e., inflated VE consistent with the systematic review) was observed when there was a higher proportion of healthy, vaccinated non-cases, which may occur if a dataset contains many results from asymptomatic screening in settings where vaccination coverage is high. We provide an html tool for researchers to explore site-specific sources of selection bias in their own studies. We recommend all groups consider the potential for selection bias in their vaccine effectiveness studies, particularly when using administrative data.
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Affiliation(s)
- Sheena G Sullivan
- WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, and Department of Infectious Diseases, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia.
- Department of Epidemiology, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, USA.
| | - Arseniy Khvorov
- WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, and Department of Infectious Diseases, University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
| | - Xiaotong Huang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Can Wang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Kylie E C Ainslie
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, the Netherlands
| | - Joshua Nealon
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Bingyi Yang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Benjamin J Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
| | - Tim K Tsang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
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Yuan J, Lam WWT, Xiao J, Cowling BJ, Ni MY, Dong M, Liao Q. Unravelling disparity in age-specific acceptance of COVID-19 vaccination: the contextual and psychosocial influences. Psychol Health 2023:1-20. [PMID: 37491766 DOI: 10.1080/08870446.2023.2239279] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2023]
Abstract
BACKGROUND High COVID-19 vaccination uptake rates across all age groups are important for achieving herd immunity. However, age disparity in vaccination acceptance was consistently identified. OBJECTIVE Taking cues from tenets of socioemotional selectivity theory, this study examined how the contextual and psychosocial factors contributed to age-specific COVID-19 vaccination acceptance. METHOD Four rounds of population-based cross-sectional surveys were conducted before and after the COVID-19 vaccination programme started in Hong Kong (n = 3527). Participants' vaccination acceptance, trust in government, social norms, vaccine confidence and risk perception of COVID-19 were obtained. Vaccine-related news headlines were collected in the same timeframe. RESULT Sentiment analysis found that the impact of negative news sentiment on vaccine hesitancy was greater among older people. The path analyses found that older people had greater trust in government, perceived greater influence of social norms, and had greater vaccine confidence which all in turn were associated with greater vaccination acceptance. However, older people were found to have less worry about contracting COVID-19, which somewhat lowered their vaccination acceptance. CONCLUSION Communication to promote older people's vaccination uptake should focus on promoting the government's timely response to the negative news reports about vaccines and increasing the positive influences of social norms on their vaccination acceptance.
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Affiliation(s)
- Jiehu Yuan
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Wendy Wing Tak Lam
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Jingyi Xiao
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- World Health Organization Collaborating Center for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Benjamin J Cowling
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- World Health Organization Collaborating Center for Infectious Disease Epidemiology and Control, School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
| | - Michael Y Ni
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Healthy High Density Cities Lab, HKUrbanLab, The University of Hong Kong, Hong Kong Special Administrative Region, China
- The State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Meihong Dong
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Qiuyan Liao
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
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Tsang TK, Gostic KM, Chen S, Wang Y, Arevalo P, Lau EHY, Cobey S, Cowling BJ. Investigation of the Impact of Childhood Immune Imprinting on Birth Year-Specific Risk of Clinical Infection During Influenza A Virus Epidemics in Hong Kong. J Infect Dis 2023; 228:169-172. [PMID: 36637115 PMCID: PMC10345470 DOI: 10.1093/infdis/jiad009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 01/04/2023] [Accepted: 01/11/2023] [Indexed: 01/14/2023] Open
Abstract
Influenza imprinting reduces risks of influenza A virus clinical infection by 40%-90%, estimated from surveillance data in western countries. We analyzed surveillance data from 2010 to 2019 in Hong Kong. Based on the best model, which included hemagglutinin group-level imprinting, we estimated that individuals imprinted to H1N1 or H2N2 had a 17% (95% confidence interval [CI], 3%-28%) lower risk of H1N1 clinical infection, and individuals imprinted to H3N2 would have 12% (95% CI, -3% to 26%) lower risk of H3N2 clinical infection. These estimated imprinting protections were weaker than estimates in western countries. Identifying factors affecting imprinting protections is important for control policies and disease modeling.
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Affiliation(s)
- Tim K Tsang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Special Administrative Region, China
| | - Katelyn M Gostic
- Department of Ecology and Evolution, University of Chicago, Chicago, Illinois, USA
| | - Sijie Chen
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Yifan Wang
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Philip Arevalo
- Department of Ecology and Evolution, University of Chicago, Chicago, Illinois, USA
| | - Eric H Y Lau
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Special Administrative Region, China
| | - Sarah Cobey
- Department of Ecology and Evolution, University of Chicago, Chicago, Illinois, USA
| | - Benjamin J Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health, Hong Kong Special Administrative Region, China
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47
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Eccles R, Boivin G, Cowling BJ, Pavia A, Selvarangan R. Treatment of COVID-19 symptoms with over the counter (OTC) medicines used for treatment of common cold and flu. Clin Infect Pract 2023; 19:100230. [PMID: 37197288 PMCID: PMC10163789 DOI: 10.1016/j.clinpr.2023.100230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 04/12/2023] [Accepted: 04/24/2023] [Indexed: 05/19/2023] Open
Abstract
Persons suffering from acute upper respiratory tract viral infections (URTI) commonly use over the counter (OTC) medicines to relieve symptoms such as fever, muscle aches, cough, runny nose, sore throat and nasal congestion. At present OTC medicines are only licensed for treatment of common cold and flu symptoms and not for treatment of the same symptoms associated with COVID-19. The innate immune response responsible for the mechanisms of the symptoms of URTI is the same for all respiratory viruses including SARS-CoV-2 and these symptoms can be relieved by treatment with the same OTC medicines as available for treatment of colds and flu. This review provides scientific information that OTC treatments for common cold and flu-like illness caused by respiratory viruses are safe and effective treatments for the same symptoms associated with COVID-19.
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Affiliation(s)
| | - Guy Boivin
- Université Laval, Quebec City, QC, Canada
| | - Benjamin J Cowling
- School of Public Health, The University of Hong Kong, Hong Kong Special Administrative Region
| | - Andrew Pavia
- Division of Pediatric Infectious Diseases, University of Utah, Salt Lake City, UT, USA
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48
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Feng S, Lin E, Cowling BJ. Number needed to vaccinate for COVID-19 booster doses: a valuable metric to inform vaccination strategies. Lancet Reg Health Am 2023; 23:100548. [PMID: 37397875 PMCID: PMC10304837 DOI: 10.1016/j.lana.2023.100548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 06/16/2023] [Accepted: 06/19/2023] [Indexed: 07/04/2023]
Affiliation(s)
- Shuo Feng
- Oxford Vaccine Group, Department of Paediatrics, University of Oxford, Oxford, UK
| | - E Lin
- Department of Surgical Sciences, Faculty of Medicine, Uppsala University, Sweden
| | - Benjamin J Cowling
- WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Laboratory of Data Discovery for Health Limited, Hong Kong Science and Technology Park, New Territories, Hong Kong Special Administrative Region, China
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49
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Pather S, Madhi SA, Cowling BJ, Moss P, Kamil JP, Ciesek S, Muik A, Türeci Ö. Corrigendum: SARS-CoV-2 Omicron variants: burden of disease, impact on vaccine effectiveness and need for variant-adapted vaccines. Front Immunol 2023; 14:1232965. [PMID: 37377964 PMCID: PMC10292686 DOI: 10.3389/fimmu.2023.1232965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 06/02/2023] [Indexed: 06/29/2023] Open
Abstract
[This corrects the article DOI: 10.3389/fimmu.2023.1130539.].
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Affiliation(s)
| | - Shabir A. Madhi
- South African Medical Research Council Vaccines and Infectious Diseases Analytics Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Benjamin J. Cowling
- School of Public Health, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Paul Moss
- Institute of Immunology and Immunotherapy, University of Birmingham, Birmingham, United Kingdom
| | - Jeremy P. Kamil
- Department of Microbiology and Immunology, Louisiana State University Health Sciences Center Shreveport, Shreveport, LA, United States
| | - Sandra Ciesek
- Institute for Medical Virology, University Hospital Frankfurt, Goethe University Frankfurt, Frankfurt, Germany
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50
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Yung L, Leung LY, Lee KH, Morrell S, Fong MW, Yan Fung NH, Cheng KL, Kaewpreedee P, Li Y, Cowling BJ, Lau EH, Hui DS, Graham CA, Yen HL. A longitudinal environmental surveillance study for SARS-CoV-2 from the emergency department of a teaching hospital in Hong Kong. J Hosp Infect 2023:S0195-6701(23)00181-0. [PMID: 37315806 PMCID: PMC10259104 DOI: 10.1016/j.jhin.2023.05.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 05/25/2023] [Accepted: 05/27/2023] [Indexed: 06/16/2023]
Abstract
BACKGROUND Understanding factors associated with SARS-CoV-2 exposure risk in the hospital setting may help improve infection control measures for prevention. AIM To monitor SARS-CoV-2 exposure risk among healthcare workers and to identify risk factors associated with SARS-CoV-2 detection. METHODS Surface and air samples were collected longitudinally over 14 months spanning 2020-2022 at the Emergency Department (ED) of a teaching hospital in Hong Kong. SARS-CoV-2 viral RNA was detected by real-time reverse-transcription polymerase chain reaction. Ecological factors associated with SARS-CoV-2 detection were analysed by logistic regression. A sero-epidemiological study was conducted in January to April 2021 to monitor SARS-CoV-2 seroprevalence. A questionnaire was used to collect information on job nature and use of personal protective equipment (PPE) of the participants. FINDINGS SARS-CoV-2 RNA was detected at low frequencies from surfaces (0.7%, n=2562) and air samples (1.6%, n=128). Crowding was identified as the main risk factor, as weekly ED attendance (OR=1.002, p=0.04) and sampling after peak-hours of ED attendance (OR=5.216, p=0.03) were associated with the detection of SARS-CoV-2 viral RNA from surfaces. The low exposure risk was corroborated by the zero seropositive rate among 281 participants by April 2021. CONCLUSION Crowding may introduce SARS-CoV-2 into ED through increased attendances. Multiple factors may have contributed to the low contamination of SARS-CoV-2 at the ED, including hospital infection control measures for screening ED attendees, high PPE compliance among healthcare workers, and various public health and social measures implemented to reduce community transmission in Hong Kong where a dynamic zero COVID-19 policy was adopted.
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Affiliation(s)
- Louise Yung
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Ling Yan Leung
- Accident and Emergency Medicine Academic Unit, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong Special Administrative Region, China
| | - Kwok Hung Lee
- Accident and Emergency Department, Prince of Wales Hospital, Hospital Authority, Hong Kong Special Administrative Region, China
| | - Steven Morrell
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Min Whui Fong
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Nikki Ho Yan Fung
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Kit Ling Cheng
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Prathanporn Kaewpreedee
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Yuguo Li
- Department of Mechanical Engineering, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Benjamin J Cowling
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Eric Hy Lau
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - David Sc Hui
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong Special Administrative Region, China
| | - Colin A Graham
- Accident and Emergency Medicine Academic Unit, The Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong Special Administrative Region, China; Accident and Emergency Department, Prince of Wales Hospital, Hospital Authority, Hong Kong Special Administrative Region, China.
| | - Hui-Ling Yen
- School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China.
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