Duration of Influenza Vaccine Effectiveness: A Systematic Review, Meta-analysis, and Meta-regression of Test-Negative Design Case-Control Studies

Young, Barnaby Edward; Sadarangani, Sapna P.; Jiang, Lili; Wilder‐Smith, Annelies; Chen, Mark

doi:10.1093/infdis/jix632

Cited by 125 publications

(98 citation statements)

References 41 publications

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“…Exploring how gene expression changes with age in immune related genes can help further characterize the disease and improve treatments. For example, age, preexisting health conditions and influenza history (previous infection or vaccination) are all factors that can affect the efficacy of the vaccine (50). There is an on-going effort to improve efficacy of vaccination in the elderly population.…”

Section: Discussionmentioning

confidence: 99%

Microarray Gene Expression Dataset Re-Analysis Reveals Variability in Influenza Infection and Vaccination

Rogers

Campos

Mias

2019

Preprint

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2Influenza, a communicable disease, affects thousands of people worldwide. Young children, 3 elderly, immunocompromised individuals and pregnant women are at higher risk for being 4 infected by the influenza virus. Our study aims to highlight differentially expressed genes in 5 influenza disease compared to influenza vaccination. We also investigate genetic variation 6 due to the age and sex of samples. To accomplish our goals, we conducted a meta-analysis 7 using publicly available microarray expression data. Our inclusion criteria included subjects with 8 influenza, subjects who received the influenza vaccine and healthy controls. We curated 18 9 microarray datasets for a total of 3,481 samples (1,277 controls, 297 influenza infection, 1,907 10 influenza vaccination). We pre-processed the raw microarray expression data in R using packages 11 available to pre-process Affymetrix and Illumina microarray platforms. We used a Box-Cox power 12 transformation of the data prior to our down-stream analysis to identify differentially expressed 13 genes. Statistical analyses were based on linear mixed effects model with all study factors and 14 successive likelihood ratio tests (LRT) to identify differentially-expressed genes. We filtered LRT 15 results by disease (Bonferroni adjusted p-value < 0.05) and used a two-tailed 10% quantile cutoff 16 to identify biologically significant genes. Furthermore, we assessed age and sex effects on the 17 disease genes by filtering for genes with a statistically significant (Bonferroni adjusted p-value < 18 0.05) interaction between disease and age, and disease and sex. We identified 4,889 statistically 19 significant genes when we filtered the LRT results by disease factor, and gene enrichment analysis 20 (gene ontology and pathways) included innate immune response, viral process, defense response 21 to virus, Hematopoietic cell lineage and NF-kappa B signaling pathway. Our quantile filtered gene 22 lists comprised of 978 genes each associated with influenza infection and vaccination. We also 23 identified 907 and 48 genes with statistically significant (Bonferroni adjusted p-value < 0.05) 24 disease-age and disease-sex interactions respectively. Our meta-analysis approach highlights 25 key gene signatures and their associated pathways for both influenza infection and vaccination. 26 1 Rogers LRK et al. Variability in Influenza Infection and VaccinationWe also were able to identify genes with an age and sex effect. This gives potential for improving 27 current vaccines and exploring genes that are expressed equally across ages when considering 28 universal vaccinations for influenza. 29 30

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Section: Discussionmentioning

confidence: 99%

Microarray Gene Expression Dataset Re-Analysis Reveals Variability in Influenza Infection and Vaccination

Rogers

Campos

Mias

2019

Preprint

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“…Potential confounders included the year of the surveillance program, participant's age and ethnicity, type of BMT intake, history of smoking and asthma (including childhood asthma), and receipt of influenza vaccine before the FRI episode. Assessment of whether the participant received MIV or TIV was based on self-reported vaccination history up to 1 year prior to the first FRI episode (since antibody titers and vaccine effectiveness could wane thereafter [17][18][19]), supplemented with Singapore Armed Forces records for vaccines received after enlistment. Since FRI episodes are concentrated differentially during a BMT course, we adjusted for this effect using the FRI incidence rate during successive 2-weekly phases after the time of enlistment for that intake type ( Figure 1B and Supplementary Materials).…”

Section: Study Design Outcomes and Exposures Of Interest Potential mentioning

confidence: 99%

Evidence for Cross-Protection Against Subsequent Febrile Respiratory Illness Episodes From Prior Infections by Different Viruses Among Singapore Military Recruits 2009–2014

Chen

Loh

Chuah

et al. 2019

The Journal of Infectious Diseases

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Background Few studies have evaluated the relative cross-protection conferred by infection with different groups of viruses through studies of sequential infections in humans. We investigated the presence of short-lived relative cross-protection conferred by specific prior viral infections against subsequent febrile respiratory illness (FRI). Methods Men enlisted in basic military training between December 2009 and December 2014 were recruited, with the first FRI as the study entry point. ResPlex II assays and real-time polymerase chain reaction assays were used to detect viral pathogens in nasal wash samples, and survival analyses were performed to determine whether infection with particular viruses conferred short-lived relative cross-protection against FRI. Results Prior infection with adenovirus (hazard ratio [HR], 0.24; 95% confidence interval [CI], .14–.44) or influenza virus (HR, 0.52; 95% CI, .38–.73) conferred relative protection against subsequent FRI episode. Results were statistically significant even after adjustment for the interval between enlistment and FRI (P < .001). Adenovirus-positive participants with FRI episodes tended to be protected against subsequent infection with adenovirus, coronavirus, enterovirus/rhinovirus, and influenza virus (P = .062–.093), while men with influenza virus–positive FRI episodes tended be protected against subsequent infection with adenovirus (P = .044) and influenza virus (P = .081). Conclusion Prior adenovirus or influenza virus infection conferred cross-protection against subsequent FRI episodes relative to prior infection due to other circulating viruses.

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“…Based on the antigenic distance hypothesis put forth by Smith et al, negative or positive interference can result from prior season vaccination depending on differences in the antigenic distances between prior and current vaccine strains and the current epidemic strain [7]. Most studies to date incorporated only a single previous season when examining the impact of repeated vaccination [8][9][10][11][12][13]. Meta-analyses of these studies found substantial heterogeneity in repeated vaccination effects [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

The impact of repeated vaccination using 10-year vaccination history on protection against influenza in older adults: a test-negative design study across the 2010/11 to 2015/16 influenza seasons in Ontario, Canada

et al. 2020

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Introduction Annual influenza vaccination is recommended for older adults, but evidence regarding the impact of repeated vaccination has been inconclusive. Aim We investigated vaccine effectiveness (VE) against laboratory-confirmed influenza and the impact of repeated vaccination over 10 previous seasons on current season VE among older adults. Methods We conducted an observational test-negative study in community-dwelling adults aged > 65 years in Ontario, Canada for the 2010/11 to 2015/16 seasons by linking laboratory and health administrative data. We estimated VE using multivariable logistic regression. We assessed the impact of repeated vaccination by stratifying by previous vaccination history. Results We included 58,304 testing episodes for respiratory viruses, with 11,496 (20%) testing positive for influenza and 31,004 (53%) vaccinated. Adjusted VE against laboratory-confirmed influenza for the six seasons combined was 21% (95% confidence interval (CI): 18 to 24%). Patients who were vaccinated in the current season, but had received no vaccinations in the previous 10 seasons, had higher current season VE (34%; 95%CI: 9 to 52%) than patients who had received 1–3 (26%; 95%CI: 13 to 37%), 4–6 (24%; 95%CI: 15 to 33%), 7–8 (13%; 95%CI: 2 to 22%), or 9–10 (7%; 95%CI: −4 to 16%) vaccinations (trend test p = 0.001). All estimates were higher after correcting for misclassification of current season vaccination status. For patients who were not vaccinated in the current season, residual protection rose significantly with increasing numbers of vaccinations received previously. Conclusions Although VE appeared to decrease with increasing numbers of previous vaccinations, current season vaccination likely provides some protection against influenza regardless of the number of vaccinations received over the previous 10 influenza seasons.

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Duration of Influenza Vaccine Effectiveness: A Systematic Review, Meta-analysis, and Meta-regression of Test-Negative Design Case-Control Studies

Abstract: Exploration of new influenza vaccination strategies must be a priority for influenza control, particularly in tropical countries with year-round influenza virus activity.

Cited by 125 publications

References 41 publications

Microarray Gene Expression Dataset Re-Analysis Reveals Variability in Influenza Infection and Vaccination

Microarray Gene Expression Dataset Re-Analysis Reveals Variability in Influenza Infection and Vaccination

Evidence for Cross-Protection Against Subsequent Febrile Respiratory Illness Episodes From Prior Infections by Different Viruses Among Singapore Military Recruits 2009–2014

The impact of repeated vaccination using 10-year vaccination history on protection against influenza in older adults: a test-negative design study across the 2010/11 to 2015/16 influenza seasons in Ontario, Canada

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