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      The association between socioeconomic status and pandemic influenza: protocol for a systematic review and meta-analysis

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          Abstract

          Background

          Pandemic mortality rates in 1918 and in 2009 were highest among those with the lowest socioeconomic status (SES). Despite this, low SES groups are not included in the list of groups prioritized for pandemic vaccination, and the ambition to reduce social inequality in health does not feature in international and national pandemic preparedness plans. We describe plans for a systematic review and meta-analysis of the association between SES and pandemic outcomes during the last five pandemics.

          Method

          The planned review will cover studies of pandemic influenza that report associations between morbidity, hospitalization, or mortality with socioeconomic factors such as education and income. The review will include published studies in the English, Danish, Norwegian, and Swedish languages, regardless of geographical location. Relevant records were identified through systematic literature searches in MEDLINE, Embase, Cinahl, SocIndex, Scopus, and Web of Science. Reference lists of relevant known studies will be screened and experts in the field consulted in order to identify other additional sources. Two investigators will independently screen and select studies, and discrepancies will be resolved through discussion until consensus is reached. Covidence will be used. Results will be summarized narratively and using three meta-analytic strategies: coefficients expressing the difference between the highest and lowest socioeconomic groups reported will be pooled using (a) fixed and random effects meta-analysis where studies involve similar outcome and exposure measures and (b) meta-regression where studies involve similar outcome measures. In addition, we will attempt to use all reported estimates for SES differences in (c) a Bayesian meta-analysis to estimate the underlying SES gradient and how it differs by outcome and exposure measure.

          Discussion

          This study will provide the first systematic review of research on the relation between SES and pandemic outcomes. The findings will be relevant for health policy in helping to assess whether people of low socioeconomic status should be prioritized for vaccines in preparedness plans for pandemic influenza. The review will also contribute to the research literature by providing pooled estimates of effect sizes as inputs into power calculations of future studies.

          Systematic review registration

          PROSPERO 87922

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          Most cited references24

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          Conducting Meta-Analyses inRwith themetaforPackage

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            Stan: A Probabilistic Programming Language

            Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectation propagation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can be called from the command line using the cmdstan package, through R using the rstan package, and through Python using the pystan package. All three interfaces support sampling and optimization-based inference with diagnostics and posterior analysis. rstan and pystan also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting.
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              PRESS Peer Review of Electronic Search Strategies: 2015 Guideline Statement.

              To develop an evidence-based guideline for Peer Review of Electronic Search Strategies (PRESS) for systematic reviews (SRs), health technology assessments, and other evidence syntheses.
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                Author and article information

                Contributors
                Svenn-Erik.Mamelund@oslomet.no
                Clare.Shelley-Egan@oslomet.no
                ole.rogeberg@frisch.uio.no
                Journal
                Syst Rev
                Syst Rev
                Systematic Reviews
                BioMed Central (London )
                2046-4053
                4 January 2019
                4 January 2019
                2019
                : 8
                : 5
                Affiliations
                [1 ]ISNI 0000 0000 9151 4445, GRID grid.412414.6, Work Research Institute at OsloMet - Oslo Metropolitan University, ; PO. Box 4, St. Olavs plass, 0130 Oslo, Norway
                [2 ]Frisch Centre, Gaustadalleen 21, 0349 Oslo, Norway
                Author information
                http://orcid.org/0000-0002-3980-3818
                Article
                931
                10.1186/s13643-018-0931-2
                6318944
                30609940
                8af8af6b-1cc8-4919-92e7-d0ed483d8b47
                © The Author(s). 2019

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 4 February 2018
                : 25 December 2018
                Categories
                Protocol
                Custom metadata
                © The Author(s) 2019

                Public health
                pandemic influenza,morbidity,hospitalization,mortality,socioeconomic status,education,income,poverty,occupational social class,housing conditions

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