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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

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 design...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5353315/
https://www.ncbi.nlm.nih.gov/pubmed/28289044
http://dx.doi.org/10.1136/bmjopen-2016-012996
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author 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
author_facet 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
author_sort Shakya, Holly B
collection PubMed
description 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.
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spelling pubmed-53533152017-03-17 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 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 BMJ Open Global Health 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. BMJ Publishing Group 2017-03-10 /pmc/articles/PMC5353315/ /pubmed/28289044 http://dx.doi.org/10.1136/bmjopen-2016-012996 Text en Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/ This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/
spellingShingle Global Health
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
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
title 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
title_full 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
title_fullStr 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
title_full_unstemmed 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
title_short 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
title_sort 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
topic Global Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5353315/
https://www.ncbi.nlm.nih.gov/pubmed/28289044
http://dx.doi.org/10.1136/bmjopen-2016-012996
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