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      Cost-Effectiveness and Burden of Disease for Adjuvanted Quadrivalent Influenza Vaccines Compared to High-Dose Quadrivalent Influenza Vaccines in Elderly Patients in Spain

      , , , ,
      Vaccines
      MDPI AG

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          Abstract

          Influenza is a contagious respiratory disease that causes severe illness and death, particularly in elderly populations. Two enhanced formulations of quadrivalent influenza vaccine (QIV) are available in Spain. Adjuvanted QIV (aQIV) is available for those aged 65+ and high-dose QIV (HD-QIV) for those aged 60+. In this study, we used a health economic model to assess the costs and outcomes associated with using aQIV or HD-QIV in subjects aged 65+. Using aQIV instead of HD-QIV to vaccinate an estimated 5,126,343 elderly people results in reductions of 5405 symptomatic cases, 760 primary care visits, 171 emergency room visits, 442 hospitalizations, and 26 deaths in Spain each year. Life-years (LYs) and quality-adjusted LYs (QALYs) increases by 260 and 206, respectively, each year. Savings from a direct medical payer perspective are EUR 63.6 million, driven by the lower aQIV vaccine price and a minor advantage in effectiveness. From a societal perspective, savings increase to EUR 64.2 million. Results are supported by scenario and sensitivity analyses. When vaccine prices are assumed equal, aQIV remains dominant compared to HD-QIV. Potential savings are estimated at over EUR 61 million in vaccine costs alone. Therefore, aQIV provides a highly cost-effective alternative to HD-QIV for people aged 65+ in Spain.

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          The PRISMA 2020 statement: an updated guideline for reporting systematic reviews

          The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, published in 2009, was designed to help systematic reviewers transparently report why the review was done, what the authors did, and what they found. Over the past decade, advances in systematic review methodology and terminology have necessitated an update to the guideline. The PRISMA 2020 statement replaces the 2009 statement and includes new reporting guidance that reflects advances in methods to identify, select, appraise, and synthesise studies. The structure and presentation of the items have been modified to facilitate implementation. In this article, we present the PRISMA 2020 27-item checklist, an expanded checklist that details reporting recommendations for each item, the PRISMA 2020 abstract checklist, and the revised flow diagrams for original and updated reviews.
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            Meta-analysis is of fundamental importance to obtain an unbiased assessment of the available evidence. In general, the use of meta-analysis has been increasing over the last three decades with mental health as a major research topic. It is then essential to well understand its methodology and interpret its results. In this publication, we describe how to perform a meta-analysis with the freely available statistical software environment R, using a working example taken from the field of mental health. R package meta is used to conduct standard meta-analysis. Sensitivity analyses for missing binary outcome data and potential selection bias are conducted with R package metasens. All essential R commands are provided and clearly described to conduct and report analyses. The working example considers a binary outcome: we show how to conduct a fixed effect and random effects meta-analysis and subgroup analysis, produce a forest and funnel plot and to test and adjust for funnel plot asymmetry. All these steps work similar for other outcome types. R represents a powerful and flexible tool to conduct meta-analyses. This publication gives a brief glimpse into the topic and provides directions to more advanced meta-analysis methods available in R.
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              The random-effects model is often used for meta-analysis of clinical studies. The method explicitly accounts for the heterogeneity of studies through a statistical parameter representing the inter-study variation. We discuss several iterative and non-iterative alternative methods for estimating the inter-study variance and hence the overall population treatment effect. We show that the leading methods for estimating the inter-study variance are special cases of a general method-of-moments estimate of the inter-study variance. The general method suggests two new two-step methods. The iterative estimate is statistically optimal and it can be easily calculated on a spreadsheet program, such as Microsoft Excel, available on the desktop of most researchers. The two-step methods approximate the optimal iterative method better than the earlier one-step non-iterative methods.
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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                VBSABP
                Vaccines
                Vaccines
                MDPI AG
                2076-393X
                February 2022
                January 23 2022
                : 10
                : 2
                : 176
                Article
                10.3390/vaccines10020176
                35214635
                bbf89d62-15c2-49b3-99bc-347688b8c58c
                © 2022

                https://creativecommons.org/licenses/by/4.0/

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