Exposure, hazard, and survival analysis of diffusion on social networks.

Sociologists, economists, epidemiologists, and others recognize the importance of social networks in the diffusion of ideas and behaviors through human societies. To measure the flow of information on real-world networks, researchers often conduct comprehensive sociometric mapping of social links between individuals and then follow the spread of an "innovation" from reports of adoption or change in behavior over time. The innovation is introduced to a small number of individuals who may also be encouraged to spread it to their network contacts. In conjunction with the known social network, the pattern of adoptions gives researchers insight into the spread of the innovation in the population and factors associated with successful diffusion. Researchers have used widely varying statistical tools to estimate these quantities, and there is disagreement about how to analyze diffusion on fully observed networks. Here, we describe a framework for measuring features of diffusion processes on social networks using the epidemiological concepts of exposure and competing risks. Given a realization of a diffusion process on a fully observed network, we show that classical survival regression models can be adapted to estimate the rate of diffusion, and actor/edge attributes associated with successful transmission or adoption, while accounting for the topology of the social network. We illustrate these tools by applying them to a randomized network intervention trial conducted in Honduras to estimate the rate of adoption of 2 health-related interventions-multivitamins and chlorine bleach for water purification-and determine factors associated with successful social transmission.

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 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.

Intimate partner violence norms cluster within households: an observational social network study in rural Honduras.

BACKGROUND: Intimate partner violence (IPV) is a complex global problem, not only because it is a human rights issue, but also because it is associated with chronic mental and physical illnesses as well as acute health outcomes related to injuries for women and their children. Attitudes, beliefs, and norms regarding IPV are significantly associated with the likelihood of both IPV experience and perpetration. METHODS: We investigated whether IPV acceptance is correlated across socially connected individuals, whether these correlations differ across types of relationships, and whether social position is associated with the likelihood of accepting IPV. We used sociocentric network data from 831 individuals in rural Honduras to assess the association of IPV acceptance between socially connected individuals across 15 different types of relationships, both within and between households. We also investigated the association between network position and IPV acceptance. RESULTS: We found that having a social contact that accepts IPV is strongly associated with IPV acceptance among individuals. For women the clustering of IPV acceptance was not significant in between-household relationships, but was concentrated within households. For men, however, while IPV acceptance was strongly clustered within households, men's acceptance of IPV was also correlated with people with whom they regularly converse, their mothers and their siblings, regardless of household. We also found that IPV was more likely to be accepted by less socially-central individuals, and that the correlation between a social contact's IPV acceptance was stronger on the periphery, suggesting that, as a norm, it is held on the periphery of the community. CONCLUSION: Our results show that differential targeting of individuals and relationships in order to reduce the acceptability and, subsequently, the prevalence of IPV may be most effective. Because IPV norms seem to be strongly held within households, the household is probably the most logical unit to target in order to implement change. This approach would include the possible benefit of a generational effect. Finally, in social contexts in which perpetration of IPV is not socially acceptable, the most effective strategy may be to implement change not at the center but at the periphery of the community.

Social network targeting to maximise population behaviour change: a cluster randomised controlled trial.

BACKGROUND: Information and behaviour can spread through interpersonal ties. By targeting influential individuals, health interventions that harness the distributive properties of social networks could be made more effective and efficient than those that do not. Our aim was to assess which targeting methods produce the greatest cascades or spillover effects and hence maximise population-level behaviour change. METHODS: In this cluster randomised trial, participants were recruited from villages of the Department of Lempira, Honduras. We blocked villages on the basis of network size, socioeconomic status, and baseline rates of water purification, for delivery of two public health interventions: chlorine for water purification and multivitamins for micronutrient deficiencies. We then randomised villages, separately for each intervention, to one of three targeting methods, introducing the interventions to 5% samples composed of either: randomly selected villagers (n=9 villages for each intervention); villagers with the most social ties (n=9); or nominated friends of random villagers (n=9; the last strategy exploiting the so-called friendship paradox of social networks). Participants and data collectors were not aware of the targeting methods. Primary endpoints were the proportions of available products redeemed by the entire population under each targeting method. This trial is registered with ClinicalTrials.gov, number NCT01672580. FINDINGS: Between Aug 4, and Aug 14, 2012, 32 villages in rural Honduras (25-541 participants each; total study population of 5773) received public health interventions. For each intervention, nine villages (each with 1-20 initial target individuals) were randomised, using a blocked design, to each of the three targeting methods. In nomination-targeted villages, 951 (74·3%) of 1280 available multivitamin tickets were redeemed compared with 940 (66·2%) of 1420 in randomly targeted villages and 744 (61·0%) of 1220 in indegree-targeted villages. All pairwise differences in redemption rates were significant (p<0·01) after correction for multiple comparisons. Targeting nominated friends increased adoption of the nutritional intervention by 12·2% compared with random targeting (95% CI 6·9-17·9). Targeting the most highly connected individuals, by contrast, produced no greater adoption of either intervention, compared with random targeting. INTERPRETATION: Introduction of a health intervention to the nominated friends of random individuals can enhance that intervention's diffusion by exploiting intrinsic properties of human social networks. This method has the additional advantage of scalability because it can be implemented without mapping the network. Deployment of certain types of health interventions via network targeting, without increasing the number of individuals targeted or the resources used, could enhance the adoption and efficiency of those interventions, thereby improving population health. FUNDING: National Institutes of Health, The Bill & Melinda Gates Foundation, Star Family Foundation, and the Canadian Institutes of Health Research.