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      A review of the quantitative effectiveness evidence synthesis methods used in public health intervention guidelines

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          Abstract

          Background

          The complexity of public health interventions create challenges in evaluating their effectiveness. There have been huge advancements in quantitative evidence synthesis methods development (including meta-analysis) for dealing with heterogeneity of intervention effects, inappropriate ‘lumping’ of interventions, adjusting for different populations and outcomes and the inclusion of various study types. Growing awareness of the importance of using all available evidence has led to the publication of guidance documents for implementing methods to improve decision making by answering policy relevant questions.

          Methods

          The first part of this paper reviews the methods used to synthesise quantitative effectiveness evidence in public health guidelines by the National Institute for Health and Care Excellence (NICE) that had been published or updated since the previous review in 2012 until the 19th August 2019.The second part of this paper provides an update of the statistical methods and explains how they address issues related to evaluating effectiveness evidence of public health interventions.

          Results

          The proportion of NICE public health guidelines that used a meta-analysis as part of the synthesis of effectiveness evidence has increased since the previous review in 2012 from 23% (9 out of 39) to 31% (14 out of 45). The proportion of NICE guidelines that synthesised the evidence using only a narrative review decreased from 74% (29 out of 39) to 60% (27 out of 45).An application in the prevention of accidents in children at home illustrated how the choice of synthesis methods can enable more informed decision making by defining and estimating the effectiveness of more distinct interventions, including combinations of intervention components, and identifying subgroups in which interventions are most effective.

          Conclusions

          Despite methodology development and the publication of guidance documents to address issues in public health intervention evaluation since the original review, NICE public health guidelines are not making full use of meta-analysis and other tools that would provide decision makers with fuller information with which to develop policy. There is an evident need to facilitate the translation of the synthesis methods into a public health context and encourage the use of methods to improve decision making.

          Supplementary Information

          The online version contains supplementary material available at (10.1186/s12889-021-10162-8).

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          Most cited references52

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          Quantifying heterogeneity in a meta-analysis.

          The extent of heterogeneity in a meta-analysis partly determines the difficulty in drawing overall conclusions. This extent may be measured by estimating a between-study variance, but interpretation is then specific to a particular treatment effect metric. A test for the existence of heterogeneity exists, but depends on the number of studies in the meta-analysis. We develop measures of the impact of heterogeneity on a meta-analysis, from mathematical criteria, that are independent of the number of studies and the treatment effect metric. We derive and propose three suitable statistics: H is the square root of the chi2 heterogeneity statistic divided by its degrees of freedom; R is the ratio of the standard error of the underlying mean from a random effects meta-analysis to the standard error of a fixed effect meta-analytic estimate, and I2 is a transformation of (H) that describes the proportion of total variation in study estimates that is due to heterogeneity. We discuss interpretation, interval estimates and other properties of these measures and examine them in five example data sets showing different amounts of heterogeneity. We conclude that H and I2, which can usually be calculated for published meta-analyses, are particularly useful summaries of the impact of heterogeneity. One or both should be presented in published meta-analyses in preference to the test for heterogeneity. Copyright 2002 John Wiley & Sons, Ltd.
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            Conducting Meta-Analyses inRwith themetaforPackage

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              Graphical Tools for Network Meta-Analysis in STATA

              Network meta-analysis synthesizes direct and indirect evidence in a network of trials that compare multiple interventions and has the potential to rank the competing treatments according to the studied outcome. Despite its usefulness network meta-analysis is often criticized for its complexity and for being accessible only to researchers with strong statistical and computational skills. The evaluation of the underlying model assumptions, the statistical technicalities and presentation of the results in a concise and understandable way are all challenging aspects in the network meta-analysis methodology. In this paper we aim to make the methodology accessible to non-statisticians by presenting and explaining a series of graphical tools via worked examples. To this end, we provide a set of STATA routines that can be easily employed to present the evidence base, evaluate the assumptions, fit the network meta-analysis model and interpret its results.
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                Author and article information

                Contributors
                eas24@le.ac.uk
                njc21@leicester.ac.uk
                ajs22@leicester.ac.uk
                kra@le.ac.uk
                sjh62@le.ac.uk
                Journal
                BMC Public Health
                BMC Public Health
                BMC Public Health
                BioMed Central (London )
                1471-2458
                3 February 2021
                3 February 2021
                2021
                : 21
                : 278
                Affiliations
                GRID grid.9918.9, ISNI 0000 0004 1936 8411, Department of Health Sciences, University of Leicester, ; Lancaster Road, Leicester, UK
                Author information
                http://orcid.org/0000-0002-4241-7205
                Article
                10162
                10.1186/s12889-021-10162-8
                7860217
                33535975
                b629e6e9-4ae5-47d5-b0dc-8862e69198c6
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data.

                History
                : 22 September 2020
                : 4 January 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000272, National Institute for Health Research;
                Award ID: DRF-2018-11-ST2-033
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2021

                Public health
                meta-analysis,network meta-analysis,systematic review,public health,decision making,complex interventions,evidence synthesis

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