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利用社会影响力扩大母婴健康领域的人群行为改变:洪都拉斯农村地区网络靶向算法随机对照试验的研究方案

Exploiting social influence to magnify population-level behaviour change in maternal and child health: study protocol for a randomised controlled trial of network targeting algorithms in rural Honduras.

作者信息

Shakya Holly B, Stafford Derek, Hughes D Alex, Keegan Thomas, Negron Rennie, Broome Jai, McKnight Mark, Nicoll Liza, Nelson Jennifer, Iriarte Emma, Ordonez Maria, Airoldi Edo, Fowler James H, Christakis Nicholas A

机构信息

Division of Global Public Health, School of Medicine, Center on Gender Equity and Health, University of California San Diego, San Diego, California, USA.

CEO, EmpleApp.

出版信息

BMJ Open. 2017 Mar 13;7(3):e012996. doi: 10.1136/bmjopen-2016-012996.

Abstract

INTRODUCTION

Despite global progress on many measures of child health, rates of neonatal mortality remain high in the developing world. Evidence suggests that substantial improvements can be achieved with simple, low-cost interventions within family and community settings, particularly those designed to change knowledge and behaviour at the community level. Using social network analysis to identify structurally influential community members and then targeting them for intervention shows promise for the implementation of sustainable community-wide behaviour change.

METHODS AND ANALYSIS

We will use a detailed understanding of social network structure and function to identify novel ways of targeting influential individuals to foster cascades of behavioural change at a population level. Our work will involve experimental and observational analyses. We will map face-to-face social networks of 30 000 people in 176 villages in Western Honduras, and then conduct a randomised controlled trial of a friendship-based network-targeting algorithm with a set of well-established care interventions. We will also test whether the proportion of the population targeted affects the degree to which the intervention spreads throughout the network. We will test scalable methods of network targeting that would not, in the future, require the actual mapping of social networks but would still offer the prospect of rapidly identifying influential targets for public health interventions.

ETHICS AND DISSEMINATION

The Yale IRB and the Honduran Ministry of Health approved all data collection procedures (Protocol number 1506016012) and all participants will provide informed consent before enrolment. We will publish our findings in peer-reviewed journals as well as engage non-governmental organisations and other actors through venues for exchanging practical methods for behavioural health interventions, such as global health conferences. We will also develop a 'toolkit' for practitioners to use in network-based intervention efforts, including public release of our network mapping software.

TRIAL REGISTRATION NUMBER

NCT02694679; Pre-results.

摘要

引言

尽管在许多儿童健康指标方面全球都取得了进展,但发展中世界的新生儿死亡率仍然很高。有证据表明,通过家庭和社区环境中的简单、低成本干预措施可以实现大幅改善,特别是那些旨在改变社区层面知识和行为的措施。利用社会网络分析来识别具有结构影响力的社区成员,然后针对他们进行干预,有望实现可持续的全社区行为改变。

方法与分析

我们将通过深入了解社会网络的结构和功能,确定针对有影响力个体的新方法,以促进人群层面的行为改变级联反应。我们的工作将包括实验性和观察性分析。我们将绘制洪都拉斯西部176个村庄中30000人的面对面社会网络,然后对一种基于友谊的网络靶向算法与一系列成熟的护理干预措施进行随机对照试验。我们还将测试目标人群的比例是否会影响干预措施在整个网络中的传播程度。我们将测试可扩展的网络靶向方法,这些方法未来无需实际绘制社会网络,但仍能提供快速识别公共卫生干预有影响力目标的前景。

伦理与传播

耶鲁大学机构审查委员会和洪都拉斯卫生部批准了所有数据收集程序(协议编号1506016012),所有参与者在入组前将提供知情同意书。我们将在同行评审期刊上发表研究结果,并通过全球健康会议等行为健康干预实用方法交流平台,与非政府组织和其他行为者进行互动。我们还将为从业者开发一个“工具包”,供其用于基于网络的干预工作,包括公开发布我们的网络映射软件。

试验注册号

NCT02694679;预结果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8365/5353315/9affe16a3640/bmjopen2016012996f01.jpg

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