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

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.

Author information

1
Division of Global Public Health, School of Medicine, Center on Gender Equity and Health, University of California San Diego, San Diego, California, USA.
2
CEO, EmpleApp.
3
Department of Sociology, Yale Institute for Network Science, Yale University, New Haven, Connecticut, USA.
4
School of Information, University of California Berkeley, Berkeley, California, USA.
5
Salud Mesoamerica Initiative, Inter-American Development, Washington, District of Columbia, USA.
6
Salud Mesoamerica Initiative, Tegucigalpa, Honduras.
7
Department of Statistics, Harvard University, Cambridge, Massachusetts, USA.
8
Division of Global Public Health, School of Medicine, University of California San Diego, San Diego, California, USA.

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.

KEYWORDS:

SOCIAL MEDICINE

PMID:
28289044
PMCID:
PMC5353315
DOI:
10.1136/bmjopen-2016-012996
[Indexed for MEDLINE]
Free PMC Article

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