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Offeddu V, Low MSF, Surendran S, Kembhavi G, Tam CC. Acceptance and feasibility of school-based seasonal influenza vaccination in Singapore: A qualitative study. Vaccine 2020; 38:1834-1841. [PMID: 31862193 DOI: 10.1016/j.vaccine.2019.12.020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2019] [Revised: 12/06/2019] [Accepted: 12/10/2019] [Indexed: 11/13/2022]
Abstract
INTRODUCTION Influenza is a major cause of disease in children. School-based seasonal influenza vaccination can be a cost-effective tool to improve vaccine uptake among children, and can bring substantial health and economic benefits to the broader community. The acceptance and feasibility of school-based influenza vaccination are likely to be highly context-specific, but limited data exist from tropical settings with year-round influenza transmission. We conducted a qualitative study to assess acceptability and feasibility of a school-based seasonal influenza vaccination programme in Singapore. METHODS We conducted qualitative in-depth interviews with key stakeholders, including healthcare professionals, representatives of relevant ministries, preschool principals and parents to understand their perspectives on a proposed school-based seasonal influenza vaccination programme. Interviews were transcribed verbatim and analysed using thematic analysis. RESULTS We conducted 40 interviews. Although preschool-aged children are currently the recommended age group for vaccination, stakeholders suggested introducing the programme in primary and/or secondary schools, where existing vaccination infrastructure would facilitate delivery. However, more comprehensive evidence on the local influenza burden and transmission patterns among children is required to develop an evidence-based, locally relevant rationale for a school-based vaccination programme and effectively engage policy-makers, school staff, and parents. Extensive, age-appropriate public education and awareness campaigns would increase the acceptability of the programme among stakeholders. Stakeholders indicated that an opt-out programme with free or subsidised vaccination would be the most likely to achieve high vaccine coverage and make access to vaccination more equitable. CONCLUSIONS Overall, participants were supportive of a free or subsidised school-based influenza vaccination programme in primary and/or secondary schools, although children in this age group are not currently a recommended group for vaccination. However, a better informed, evidence-based rationale to estimate the programme's impact in Singapore is currently lacking. Extensive, age-appropriate public education and awareness campaigns will help ensure full support across key stakeholder groups.
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Affiliation(s)
- Vittoria Offeddu
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, 117549 Singapore.
| | - Mabel Sheau Fong Low
- Harvard T.H. Chan School of Public Health, Harvard University, MA 02138 Cambridge, USA
| | - Shilpa Surendran
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, 117549 Singapore.
| | - Gayatri Kembhavi
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, 117549 Singapore
| | - Clarence C Tam
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, 117549 Singapore; London School of Hygiene & Tropical Medicine, WC1E 7HT London, United Kingdom.
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Kleynhans J, Treurnicht FK, Cohen C, Vedan T, Seleka M, Maki L, von Gottberg A, McCarthy K, Ramkrishna W, McMorrow M, Walaza S. Outbreak of influenza A in a boarding school in South Africa, 2016. Pan Afr Med J 2019; 33:42. [PMID: 31384357 PMCID: PMC6658148 DOI: 10.11604/pamj.2019.33.42.16666] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Accepted: 02/07/2019] [Indexed: 01/09/2023] Open
Abstract
Introduction We investigated an outbreak of influenza-like illness (ILI) at a boarding school in Eastern Cape Province, South Africa. We aimed to confirm the etiological agent, estimate attack rates and identify risk factors for illness. Methods We conducted a retrospective cohort study including senior school boarders (n=308). Students with ILI (cough and fever) were identified through school medical records. We also conducted a questionnaire-based cross-sectional study among senior students including boarders (n=107) and day students (n=45). We collected respiratory specimens for respiratory pathogen testing by real-time polymerase chain reaction from a subset of symptomatic students. We calculated attack rates of medically attended ILI (medILI) and identified factors associated with medILI using logistic regression. We calculated seasonal influenza vaccine effectiveness (VE) against medILI. Results Influenza A (H3N2) virus was detected in 61% (23/38) of specimens. Attack rate for medILI was 13% among boarders (39/308) in the cohort study and 20% in both day students (9/45) and boarders (21/107) in the cross-sectional study. Playing squash was associated with medILI (aOR 5.35, 95% confidence interval [95% CI]: 1.68-17.07). Of the boarders, 19% (57/308) were vaccinated before the outbreak. The adjusted VE against medILI was 18% (aOR 0.82, 95% CI 0.38-1.78). The outbreak led to cancellation of several events and the need for academic remedial sessions. Conclusion We confirmed an influenza A (H3N2) virus outbreak with a high attack rate. The outbreak affected academic and sports activities. Participation in sports and social gatherings while experiencing ILI should be discouraged to reduce viral transmission and impact on school activities.
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Affiliation(s)
- Jackie Kleynhans
- Centre for Respiratory Diseases and Meningitis (CRDM), National Institute for Communicable Diseases (NICD) of the National Health Laboratory Service (NHLS), Johannesburg, South Africa.,South African Field Epidemiology Training Programme (SA-FETP), NICD of the NHLS, Johannesburg, South Africa
| | - Florette Kathleen Treurnicht
- Centre for Respiratory Diseases and Meningitis (CRDM), National Institute for Communicable Diseases (NICD) of the National Health Laboratory Service (NHLS), Johannesburg, South Africa
| | - Cheryl Cohen
- Centre for Respiratory Diseases and Meningitis (CRDM), National Institute for Communicable Diseases (NICD) of the National Health Laboratory Service (NHLS), Johannesburg, South Africa.,School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Theesan Vedan
- South African Field Epidemiology Training Programme (SA-FETP), NICD of the NHLS, Johannesburg, South Africa
| | - Mpho Seleka
- Centre for Respiratory Diseases and Meningitis (CRDM), National Institute for Communicable Diseases (NICD) of the National Health Laboratory Service (NHLS), Johannesburg, South Africa
| | - Lwando Maki
- Division of Public Health, Surveillance and Response (DPHSR), NICD of the NHLS, Johannesburg, South Africa
| | - Anne von Gottberg
- Centre for Respiratory Diseases and Meningitis (CRDM), National Institute for Communicable Diseases (NICD) of the National Health Laboratory Service (NHLS), Johannesburg, South Africa.,School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Kerrigan McCarthy
- School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.,Division of Public Health, Surveillance and Response (DPHSR), NICD of the NHLS, Johannesburg, South Africa
| | - Wayne Ramkrishna
- South African National Department of Health (NDoH), Pretoria, South Africa
| | - Meredith McMorrow
- Influenza Division, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.,U.S. Centers for Disease Control and Prevention, Pretoria, South Africa
| | - Sibongile Walaza
- Centre for Respiratory Diseases and Meningitis (CRDM), National Institute for Communicable Diseases (NICD) of the National Health Laboratory Service (NHLS), Johannesburg, South Africa.,School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
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Different Repeat Annual Influenza Vaccinations Improve the Antibody Response to Drifted Influenza Strains. Sci Rep 2017; 7:5258. [PMID: 28701762 PMCID: PMC5507920 DOI: 10.1038/s41598-017-05579-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2017] [Accepted: 05/31/2017] [Indexed: 11/16/2022] Open
Abstract
Seasonal influenza vaccine formulas change almost every year yet information about how this affects the antibody repertoire of vaccine recipients is inadequate. New vaccine virus strains are selected, replacing older strains to better match the currently circulating strains. But even while the vaccine is being manufactured the circulating strains can evolve. The ideal response to a seasonal vaccine would maintain antibodies toward existing strains that might continue to circulate, and to generate cross-reactive antibodies, particularly towards conserved influenza epitopes, potentially limiting infections caused by newly evolving strains. Here we use the hemagglutination inhibition assay to analyze the antibody repertoire in subjects vaccinated two years in a row with either identical vaccine virus strains or with differing vaccine virus strains. The data indicates that changing the vaccine formulation results in an antibody repertoire that is better able to react with strains emerging after the vaccine virus strains are selected. The effect is observed for both influenza A and B strains in groups of subjects vaccinated in three different seasons. Analyses include stratification by age and sex.
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Cao K, Yang K, Wang C, Guo J, Tao L, Liu Q, Gehendra M, Zhang Y, Guo X. Spatial-Temporal Epidemiology of Tuberculosis in Mainland China: An Analysis Based on Bayesian Theory. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2016; 13:E469. [PMID: 27164117 PMCID: PMC4881094 DOI: 10.3390/ijerph13050469] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/27/2016] [Revised: 04/06/2016] [Accepted: 04/27/2016] [Indexed: 01/12/2023]
Abstract
OBJECTIVE To explore the spatial-temporal interaction effect within a Bayesian framework and to probe the ecological influential factors for tuberculosis. METHODS Six different statistical models containing parameters of time, space, spatial-temporal interaction and their combination were constructed based on a Bayesian framework. The optimum model was selected according to the deviance information criterion (DIC) value. Coefficients of climate variables were then estimated using the best fitting model. RESULTS The model containing spatial-temporal interaction parameter was the best fitting one, with the smallest DIC value (-4,508,660). Ecological analysis results showed the relative risks (RRs) of average temperature, rainfall, wind speed, humidity, and air pressure were 1.00324 (95% CI, 1.00150-1.00550), 1.01010 (95% CI, 1.01007-1.01013), 0.83518 (95% CI, 0.93732-0.96138), 0.97496 (95% CI, 0.97181-1.01386), and 1.01007 (95% CI, 1.01003-1.01011), respectively. CONCLUSIONS The spatial-temporal interaction was statistically meaningful and the prevalence of tuberculosis was influenced by the time and space interaction effect. Average temperature, rainfall, wind speed, and air pressure influenced tuberculosis. Average humidity had no influence on tuberculosis.
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Affiliation(s)
- Kai Cao
- School of Public Health, Capital Medical University, No. 10 Xitoutiao, You'anmen Wai, Fengtai District, Beijing 100069, China.
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China.
- Beijing Ophthalmology & Visual Science Key Lab., Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China.
| | - Kun Yang
- School of Public Health, Capital Medical University, No. 10 Xitoutiao, You'anmen Wai, Fengtai District, Beijing 100069, China.
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China.
| | - Chao Wang
- School of Public Health, Capital Medical University, No. 10 Xitoutiao, You'anmen Wai, Fengtai District, Beijing 100069, China.
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China.
- Department of Statistics and Information, Beijing Centers for Disease Control and Prevention, No 16, Hepingli Middle Street, Dongcheng District, Beijing 100013, China.
| | - Jin Guo
- School of Public Health, Capital Medical University, No. 10 Xitoutiao, You'anmen Wai, Fengtai District, Beijing 100069, China.
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China.
| | - Lixin Tao
- School of Public Health, Capital Medical University, No. 10 Xitoutiao, You'anmen Wai, Fengtai District, Beijing 100069, China.
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China.
| | - Qingrong Liu
- School of Public Health, Capital Medical University, No. 10 Xitoutiao, You'anmen Wai, Fengtai District, Beijing 100069, China.
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China.
| | - Mahara Gehendra
- School of Public Health, Capital Medical University, No. 10 Xitoutiao, You'anmen Wai, Fengtai District, Beijing 100069, China.
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China.
| | - Yingjie Zhang
- Chinese Center for Disease Control and Prevention, Beijing 102206, China.
| | - Xiuhua Guo
- School of Public Health, Capital Medical University, No. 10 Xitoutiao, You'anmen Wai, Fengtai District, Beijing 100069, China.
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China.
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Aldridge RW, Hayward AC, Field N, Warren-Gash C, Smith C, Pebody R, Fleming D, McCracken S. Are School Absences Correlated with Influenza Surveillance Data in England? Results from Decipher My Data-A Research Project Conducted through Scientific Engagement with Schools. PLoS One 2016; 11:e0146964. [PMID: 26933880 PMCID: PMC4775053 DOI: 10.1371/journal.pone.0146964] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2015] [Accepted: 12/23/2015] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND School aged children are a key link in the transmission of influenza. Most cases have little or no interaction with health services and are therefore missed by the majority of existing surveillance systems. As part of a public engagement with science project, this study aimed to establish a web-based system for the collection of routine school absence data and determine if school absence prevalence was correlated with established surveillance measures for circulating influenza. METHODS We collected data for two influenza seasons (2011/12 and 2012/13). The primary outcome was daily school absence prevalence (weighted to make it nationally representative) for children aged 11 to 16. School absence prevalence was triangulated graphically and through univariable linear regression to Royal College of General Practitioners (RCGP) influenza like illness (ILI) episode incidence rate, national microbiological surveillance data on the proportion of samples positive for influenza (A+B) and with Rhinovirus, RSV and laboratory confirmed cases of Norovirus. RESULTS 27 schools submitted data over two respiratory seasons. During the first season, levels of influenza measured by school absence prevalence and established surveillance were low. In the 2012/13 season, a peak of school absence prevalence occurred in week 51, and week 1 in RCGP ILI surveillance data. Linear regression showed a strong association between the school absence prevalence and RCGP ILI (All ages, and 5-14 year olds), laboratory confirmed cases of influenza A & B, and weak evidence for a linear association with Rhinovirus and Norovirus. INTERPRETATION This study provides initial evidence for using routine school illness absence prevalence as a novel tool for influenza surveillance. The network of web-based data collection platforms we established through active engagement provides an innovative model of conducting scientific research and could be used for a wide range of infectious disease studies in the future.
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Affiliation(s)
- Robert W. Aldridge
- Institute of Health Informatics, University College London, 222 Euston Road, London, NW1 2DA, United Kingdom
- The Farr Institute of Health Informatics Research, University College London, 222 Euston Road, London, NW1 2DA, United Kingdom
- * E-mail:
| | - Andrew C. Hayward
- Institute of Health Informatics, University College London, 222 Euston Road, London, NW1 2DA, United Kingdom
- The Farr Institute of Health Informatics Research, University College London, 222 Euston Road, London, NW1 2DA, United Kingdom
| | - Nigel Field
- Research Department of Infection and Population Health, University College London, London, United Kingdom
| | - Charlotte Warren-Gash
- Institute of Health Informatics, University College London, 222 Euston Road, London, NW1 2DA, United Kingdom
- The Farr Institute of Health Informatics Research, University College London, 222 Euston Road, London, NW1 2DA, United Kingdom
| | - Colette Smith
- Research Department of Infection and Population Health, University College London, London, United Kingdom
| | - Richard Pebody
- Centre for Infectious Disease Surveillance and Control, Public Health England, London, United Kingdom
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