Can social network analysis help to include marginalised young women in structural support programmes in Botswana? A mixed methods study

Social networks for health communication in rural Uganda: A mixed-method analysis of Dekabusa Trading Centre, Luwero County

To reach the most vulnerable individuals in under-resourced countries, health communication interventions increasingly move towards the community level. However, little is known about how health information spreads through local social networks. This paper maps the health information network of a rural trading centre in Uganda. As part of a five-year ethnographic study of sustainable community health resources, ego networks were obtained for 231 village residents in March 2014. Using both ethnographic and social network data, we analyze how the village social network is structured, and how this structure may influence the transmission of health information. Results show a network with low average proximity, with a small number of individuals, notably key administrative officials, much closer connected to many other community members than average. However, because of social partitioning in the village network, a number of people are outside the social clusters in which the top influencers are located.

Social capital is associated with lower mosquito vector indices: secondary analysis from a cluster randomised controlled trial of community mobilisation for dengue prevention in Mexico

BACKGROUND

Control of the Aedes aegypti mosquito is central to reducing the risk of dengue, zika, chikungunya, and yellow fever. Randomised controlled trials, including the Camino Verde trial in Mexico and Nicaragua, demonstrate the convincing impact of community mobilisation interventions on vector indices. These interventions might work through building social capital but little is known about the relationship between social capital and vector indices.

METHODS

A secondary analysis used data collected from 45 intervention clusters and 45 control clusters in the impact survey of the Mexican arm of the Camino Verde cluster randomised controlled trial. Factor analysis combined responses to questions about aspects of social capital to create a social capital index with four constructs, their weighted averages then combined into a single scale. We categorised households as having high or low social capital based on their score on this scale. We examined associations between social capital and larval and pupal vector indices, taking account of the effects of other variables in a multivariate analysis. We report associations as odds ratios and 95% confidence intervals.

RESULTS

The four social capital constructs were involvement, participation, investment, and communication. Among the 10,112 households, those in rural communities were much more likely to have a high social capital score (OR 4.51, 95% CIca 3.26-6.26). Households in intervention sites had higher social capital, although the association was not significant at the 5% level. Households with high social capital were more likely to be negative for larvae or pupae (OR 1.38, 95% CIca 1.12-1.69) and for pupae specifically (OR 1.37, 95% CIca 1.08-1.74). There was interaction between intervention status and social capital; in multivariate analysis, a combined variable of intervention/high social capital remained associated with larvae or pupae (ORa l.56, 95% CIca 1.19-2.04) and with pupae specifically (ORa 1.65, 95% CIca 1.20-2.28).

CONCLUSION

This is the first report of an association of high social capital with low vector indices. Our findings support the idea that the Camino Verde community mobilisation intervention worked partly through an interaction with social capital. Understanding such interactions may help to maximise the impact of future community mobilisation interventions.