Background Information and behaviour can spread through interpersonal ties. By targeting influential individuals, health interventions that harness the distributive properties of social networks may be made more effective and efficient than those that do not. Methods In this block-randomised trial of network targeting methods, we delivered two dissimilar public health interventions to 32 villages in rural Honduras (22–541 participants each; total study population of 5,773): chlorine for water purification, and multivitamins for micronutrient deficiencies. We blocked villages on the basis of network size, socioeconomic status, and baseline rates of water purification. We then randomised villages, separately for each intervention, to one of three targeting methods, introducing the interventions to 5% samples composed either of: (1) randomly selected villagers (n=9 villages for each intervention), (2) villagers with the most social ties (n=9), or (3) nominated friends of random villagers (n=9; the last strategy exploiting the “friendship paradox” of social networks). Primary endpoints were the proportion of available products redeemed by the entire population under each targeting method. Participants and data collectors were not aware of the targeting methods. The trial is registered with ClinicalTrials.gov (NCT01672580). Findings For each intervention, 9 villages (each with 1–20 initial target individuals) were randomised to each of the three targeting methods. Targeting the most highly connected individuals produced no greater adoption of the interventions than random targeting. Targeting nominated friends, however, increased adoption of the nutritional intervention by 12·2% compared to random targeting (95% CI, 6·9 to 17·9). Interpretation Introducing 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. Deploying certain types of health interventions via network targeting, without increasing the number of individuals targeted or the resources used, may enhance the adoption and efficiency of those interventions, thereby improving population health. Funding NIH, Bill and Melinda Gates Foundation, Star Family Foundation, and the Canadian Institutes of Health Research. We thank The Clorox Company and Tishcon Corporation for their donations of supplies used in the study in Honduras.

BackgroundIntimate 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.MethodsWe 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.ResultsWe 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.ConclusionOur 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12889-016-2893-4) contains supplementary material, which is available to authorized users.

IntroductionDespite 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 analysisWe 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 disseminationThe 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 numberNCT02694679; Pre-results.

The masticatory pattern of meat and meat products, varying from red meat to sausages of frankfurter type, was registered by ten strain gauges attached to a prosthetic appliance. The deformation rates measured could be as high as 200 ‐ 400 cm/min, which should be compared to the deformation rates of 20 cm/min usually used in instrumental analysis of meat texture. Among the instrumental records of the masticatory pattern the number of chewing cycles was the best single indicator of the sensory impression of meat texture. A highly significant correlation with the sensory evaluations was achieved when a toughness index was formed as a product of the loading rate and the number of chewing cycles. The relevance of this is discussed in the light of failure mechanics of food and a hypothetical neuromuscular control of mastication.

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.