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      Synthesising quantitative evidence in systematic reviews of complex health interventions

      systematic-review

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          Abstract

          Public health and health service interventions are typically complex: they are multifaceted, with impacts at multiple levels and on multiple stakeholders. Systematic reviews evaluating the effects of complex health interventions can be challenging to conduct. This paper is part of a special series of papers considering these challenges particularly in the context of WHO guideline development. We outline established and innovative methods for synthesising quantitative evidence within a systematic review of a complex intervention, including considerations of the complexity of the system into which the intervention is introduced. We describe methods in three broad areas: non-quantitative approaches, including tabulation, narrative and graphical approaches; standard meta-analysis methods, including meta-regression to investigate study-level moderators of effect; and advanced synthesis methods, in which models allow exploration of intervention components, investigation of both moderators and mediators, examination of mechanisms, and exploration of complexities of the system. We offer guidance on the choice of approach that might be taken by people collating evidence in support of guideline development, and emphasise that the appropriate methods will depend on the purpose of the synthesis, the similarity of the studies included in the review, the level of detail available from the studies, the nature of the results reported in the studies, the expertise of the synthesis team and the resources available.

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          Most cited references51

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          Sifting the evidence-what's wrong with significance tests?

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            Meta-analysis and subgroups.

            Subgroup analysis is the process of comparing a treatment effect for two or more variants of an intervention-to ask, for example, if an intervention's impact is affected by the setting (school versus community), by the delivery agent (outside facilitator versus regular classroom teacher), by the quality of delivery, or if the long-term effect differs from the short-term effect. While large-scale studies often employ subgroup analyses, these analyses cannot generally be performed for small-scale studies, since these typically include a homogeneous population and only one variant of the intervention. This limitation can be bypassed by using meta-analysis. Meta-analysis allows the researcher to compare the treatment effect in different subgroups, even if these subgroups appear in separate studies. We discuss several statistical issues related to this procedure, including the selection of a statistical model and statistical power for the comparison. To illustrate these points, we use the example of a meta-analysis of obesity prevention.
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              The harvest plot: A method for synthesising evidence about the differential effects of interventions

              Background One attraction of meta-analysis is the forest plot, a compact overview of the essential data included in a systematic review and the overall 'result'. However, meta-analysis is not always suitable for synthesising evidence about the effects of interventions which may influence the wider determinants of health. As part of a systematic review of the effects of population-level tobacco control interventions on social inequalities in smoking, we designed a novel approach to synthesis intended to bring aspects of the graphical directness of a forest plot to bear on the problem of synthesising evidence from a complex and diverse group of studies. Methods We coded the included studies (n = 85) on two methodological dimensions (suitability of study design and quality of execution) and extracted data on effects stratified by up to six different dimensions of inequality (income, occupation, education, gender, race or ethnicity, and age), distinguishing between 'hard' (behavioural) and 'intermediate' (process or attitudinal) outcomes. Adopting a hypothesis-testing approach, we then assessed which of three competing hypotheses (positive social gradient, negative social gradient, or no gradient) was best supported by each study for each dimension of inequality. Results We plotted the results on a matrix ('harvest plot') for each category of intervention, weighting studies by the methodological criteria and distributing them between the competing hypotheses. These matrices formed part of the analytical process and helped to encapsulate the output, for example by drawing attention to the finding that increasing the price of tobacco products may be more effective in discouraging smoking among people with lower incomes and in lower occupational groups. Conclusion The harvest plot is a novel and useful method for synthesising evidence about the differential effects of population-level interventions. It contributes to the challenge of making best use of all available evidence by incorporating all relevant data. The visual display assists both the process of synthesis and the assimilation of the findings. The method is suitable for adaptation to a variety of questions in evidence synthesis and may be particularly useful for systematic reviews addressing the broader type of research question which may be most relevant to policymakers.
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                Author and article information

                Journal
                BMJ Glob Health
                BMJ Glob Health
                bmjgh
                bmjgh
                BMJ Global Health
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2059-7908
                2019
                25 January 2019
                : 4
                : Suppl 1
                : e000858
                Affiliations
                [1 ] departmentPopulation Health Sciences , Bristol Medical School, University of Bristol , Bristol, UK
                [2 ] departmentDepartment of Educational Psychology and Learning Systems, College of Education , Florida State University , Tallahassee, Florida, USA
                [3 ] departmentClinical Epidemiology Program , Ottawa Hospital Research Institute, The Ottawa Hospital , Ottawa, Ontario, Canada
                [4 ] departmentDepartment of Medicine , University of Ottawa , Ottawa, Ontario, Canada
                [5 ] departmentNIHR Collaboration for Leadership in Applied Health Care (CLAHRC) West , University Hospitals Bristol NHS Foundation Trust , Bristol, UK
                [6 ] departmentInstitute for Medical Information Processing , Biometry and Epidemiology, Pettenkofer School of Public Health, LMU Munich , Munich, Germany
                [7 ] departmentEPPI-Centre, Department of Social Science , University College London , London, UK
                Author notes
                [Correspondence to ] Professor Julian P T Higgins; julian.higgins@ 123456bristol.ac.uk
                Article
                bmjgh-2018-000858
                10.1136/bmjgh-2018-000858
                6350707
                30775014
                b9b08de2-acbb-448a-8d2d-9730f398d28d
                ©World Health Organization 2019. Licensee BMJ.

                This is an open access article distributed under the terms of the Creative Commons Attribution-Non commercial IGO License ( CC BY-NC 3.0 IGO), which permits use, distribution, and reproduction for non-commercial purposes in any medium, provided the original work is properly cited. In any reproduction of this article there should not be any suggestion that WHO or this article endorse any specific organization or products. The use of the WHO logo is not permitted. This notice should be preserved along with the article's original URL.

                History
                : 29 March 2018
                : 13 August 2018
                : 14 August 2018
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100004423, World Health Organization;
                Categories
                Analysis
                1506
                Custom metadata
                unlocked

                meta-analysis,complex interventions,systematic reviews,guideline development

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