Effectiveness of seasonal influenza vaccine in preventing influenza primary care visits and hospitalisation in Auckland, New Zealand in 2015: interim estimates

Bissielo, Ange; Pierse, Nevil; Huang, Q. Sue; Thompson, MG; Kelly, Heath; Vp, Mishin; Turner, Nikki; Shivers,

doi:10.2807/1560-7917.es.2016.21.1.30101

Cited by 13 publications

(6 citation statements)

References 15 publications

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“…In Chile, severe acute respiratory infection (SARI) sentinel surveillance included seven sentinel hospitals distributed across six of 16 administrative regions [4]. In New Zealand, ILI surveillance leverages general practice-registered patients in all 20 district health boards, ca 540,000, while SARI surveillance includes four public hospitals in Auckland and Counties Manukau District Health Boards [5]. Syndromic surveillance data from South Africa came from outpatient presentations to a large private healthcare provider network, based on International Classification of Diseases (ICD-10) codes for pneumonia and influenza (J9-J11) [6,7].…”

Section: Influenza Surveillance Systemsmentioning

confidence: 99%

Heterogeneity in influenza seasonality and vaccine effectiveness in Australia, Chile, New Zealand and South Africa: early estimates of the 2019 influenza season

et al. 2019

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We compared 2019 influenza seasonality and vaccine effectiveness (VE) in four southern hemisphere countries: Australia, Chile, New Zealand and South Africa. Influenza seasons differed in timing, duration, intensity and predominant circulating viruses. VE estimates were also heterogeneous, with all-ages point estimates ranging from 7–70% (I2: 33%) for A(H1N1)pdm09, 4–57% (I2: 49%) for A(H3N2) and 29–66% (I2: 0%) for B. Caution should be applied when attempting to use southern hemisphere data to predict the northern hemisphere influenza season.

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Section: Influenza Surveillance Systemsmentioning

confidence: 99%

Heterogeneity in influenza seasonality and vaccine effectiveness in Australia, Chile, New Zealand and South Africa: early estimates of the 2019 influenza season

et al. 2019

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“…Not only did this approach meet the requirements of influenza surveillance (identification of all currently circulating H3 clades), but it was also successfully applied for testing thousands of viruses collected during the 2013 to 2014 and 2014 to 2015 seasons. Additionally, the assay proved useful in the expedient characterization of respiratory specimens collected for vaccine effectiveness studies during the 2014 to 2015 (32,33) and 2015 to 2016 (unpublished) seasons.…”

Section: Discussionmentioning

confidence: 99%

A Pyrosequencing-Based Approach to High-Throughput Identification of Influenza A(H3N2) Virus Clades Harboring Antigenic Drift Variants

et al. 2017

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The rapid evolution of influenza A(H3N2) viruses necessitates close monitoring of their antigenic properties so the emergence and spread of antigenic drift variants can be rapidly identified. Changes in hemagglutinin (HA) acquired by contemporary A(H3N2) viruses hinder antigenic characterization by traditional methods, thus complicating vaccine strain selection. Sequence-based approaches have been used to infer virus antigenicity; however, they are time consuming and midthroughput. To facilitate virological surveillance and epidemiological studies, we developed and validated a pyrosequencing approach that enables identification of six HA clades of contemporary A(H3N2) viruses. The identification scheme of viruses of the H3 clades 3C.2, 3C.2a, 3C.2b, 3C.3, 3C.3a, and 3C.3b is based on the interrogation of five single nucleotide polymorphisms (SNPs) within three neighboring HA regions, namely 412 to 431, 465 to 481, and 559 to 571. Two bioinformatics tools, IdentiFire (Qiagen) and FireComb (developed in-house), were utilized to expedite pyrosequencing data analysis. The assay's analytical sensitivity was 10 focus forming units, and respiratory specimens with threshold cycle (C T ) values of Ͻ34 typically produced good quality pyrograms. When applied to 120 A(H3N2) virus isolates and 27 respiratory specimens, the assay displayed 100% agreement with clades determined by HA sequencing coupled with phylogenetics. The multi-SNP analysis described here was readily adopted by another laboratory with pyrosequencing capabilities. The implementation of this approach enhanced the findings from virological surveillance and epidemiological studies between 2013 and 2016, which examined more than 3,000 A(H3N2) viruses.KEYWORDS A(H3N2), genotyping, influenza, pyrosequencing S ince their introduction into the human population in 1968, influenza A(H3N2) viruses have been responsible for seasonal epidemics and associated with both a prolonged duration of the epidemic season and a greater disease severity (1-4). The hemagglutinin (HA) glycoprotein is the major surface antigen of influenza viruses; it is also responsible for the receptor binding and membrane fusion required for entrance into susceptible host cells (5). Decades ago, this glycoprotein was named "hemagglutinin" for its ability to bind and bridge erythrocytes. The other surface antigen, neuraminidase (NA), is a receptor-destroying enzyme (6). As with other seasonal influenza viruses, A(H3N2) viruses undergo genetic and antigenic changes, called antigenic drift,

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“… Forest plot of the vaccine effectiveness (TND studies, TIV vaccine only, mismatch between vaccine and circulating strains [ 44 , 57 , 59 , 73 , 80 , 83 , 85 , 86 , 88 , 89 , 90 , 91 , 93 , 94 , 95 , 96 , 102 , 103 , 105 ]). …”

Section: Figurementioning

confidence: 99%

Seasonal Influenza Vaccine Effectiveness in Persons Aged 15–64 Years: A Systematic Review and Meta-Analysis

et al. 2023

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Influenza is a respiratory disease caused by the influenza virus, which is highly transmissible in humans. This paper presents a systematic review and meta-analysis of randomized controlled trials (RCTs) and test-negative designs (TNDs) to assess the vaccine effectiveness (VE) of seasonal influenza vaccines (SIVs) in humans aged 15 to 64 years. An electronic search to identify all relevant studies was performed. The outcome measure of interest was VE on laboratory-confirmed influenza (any strain). Quality assessment was performed using the Cochrane risk-of-bias tool for RCTs and the ROBINS-I tool for TNDs. The search identified a total of 2993 records, but only 123 studies from 73 papers were included in the meta-analysis. Of these studies, 9 were RCTs and 116 were TNDs. The pooled VE was 48% (95% CI: 42–54) for RCTs, 55.4% (95% CI: 43.2–64.9) when there was a match between the vaccine and most prevalent circulating strains and 39.3% (95% CI: 23.5–51.9) otherwise. The TNDs’ adjusted VE was equal to 39.9% (95% CI: 31–48), 45.1 (95% CI: 38.7–50.8) when there was a match and 35.1 (95% CI: 29.0–40.7) otherwise. The match between strains included in the vaccine and strains in circulation is the most important factor in the VE. It increases by more than 25% when there is a match with the most prevalent circulating strains. The laboratorial method for confirmation of influenza is a possible source of bias when estimating VE.

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Effectiveness of seasonal influenza vaccine in preventing influenza primary care visits and hospitalisation in Auckland, New Zealand in 2015: interim estimates

Cited by 13 publications

References 15 publications

Heterogeneity in influenza seasonality and vaccine effectiveness in Australia, Chile, New Zealand and South Africa: early estimates of the 2019 influenza season

Heterogeneity in influenza seasonality and vaccine effectiveness in Australia, Chile, New Zealand and South Africa: early estimates of the 2019 influenza season

A Pyrosequencing-Based Approach to High-Throughput Identification of Influenza A(H3N2) Virus Clades Harboring Antigenic Drift Variants

Seasonal Influenza Vaccine Effectiveness in Persons Aged 15–64 Years: A Systematic Review and Meta-Analysis

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