The harvest plot: A method for synthesising evidence about the differential effects of interventions
The harvest plot: A method for synthesising evidence about the differential effects of interventions
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DOI：
10.1186/1471-2288-8-8
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发表时间：
2008-02-25
影响因子：
4
通讯作者：
Worthy, Gill
中科院分区：
文献类型：
--
作者：
Ogilvie, David;Fayter, Debra;Worthy, Gill
背景：荟萃分析的一个吸引人的地方是森林图，它是系统评价和总体“结果”中包含的基本数据的紧凑概述。然而，荟萃分析并不总是适合于综合可能影响更广泛的健康决定因素的干预措施效果的证据。作为对人口水平烟草控制干预对吸烟社会不平等影响的系统回顾的一部分，我们设计了一种新的综合方法，旨在将森林图的图形直接性方面用于综合来自复杂和多样化研究群体的证据问题。方法：我们根据两个方法学维度（研究设计的适用性和执行质量）对纳入的研究（n = 85）进行了编码，并提取了根据多达六个不同不平等维度（收入、职业、教育、性别、种族或民族和年龄）分层的影响数据，区分了“硬”（行为）和“中间”（过程或态度）结果。采用假设检验方法，我们评估了三个相互竞争的假设（正社会梯度、负社会梯度或无梯度）中哪一个最能得到每个研究对每个不平等维度的支持。结果：我们将每个干预类别的结果绘制在矩阵（“收获图”）上，根据方学标准对研究进行加权，并将其分布在相互竞争的假设之间。这些矩阵构成分析过程的一部分，有助于概括产出，例如提请注意提高烟草产品价格可能更有效地阻止低收入和低职业群体吸烟的发现。结论：采收小区是一种新颖而有用的方法，可用于综合人群水平干预措施差异效应的证据。它通过纳入所有相关数据，有助于最大限度地利用所有现有证据这一挑战。视觉展示有助于综合和吸收发现的过程。该方法适用于适应证据综合中的各种问题，对于处理可能与决策者最相关的更广泛类型的研究问题的系统综述可能特别有用。
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.