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Abstract. Changing social norms, shared beliefs about what is acceptable, is a key focus of global health campaigns aimed at ending intimate partner violen

During the rollout of COVID-19 vaccines, public health officials in the United States called upon vaccinated individuals to encourage reluctant family members to get vaccinated. Due in part to the ...

Caregivers’ health status is important, given its importance for their own wellbeing and capacity to provide quality care. While single item self-rated health questions in surveys are an efficient mea...

Social networks are important sources of influence on many personal, organizational, community, and societal behaviors. Network interventions (NIs) are

BackgroundHIV and Hepatitis C (HCV) are blood borne infections (BBIs) that remain a significant cause of global morbidity and mortality among people who inject drugs (PWID). UNAIDS and WHO have set go...

Social network analysis offers a high-resolution framework for understanding social influences on alcohol use, but full-length assessments confer significant burden, giving rise to brief measures. How...

Adolescents’ non-suicidal self-injury (NSSI) could be influenced by their friends’ NSSI and by their own social position within peer networks. However, prior research has examined these effects separa...

Schools Championing Safe South Africa is an intervention to prevent sexual violence perpetration and HIV/STI risk behavior among teenage boys, focusing on correcting misperceived social norms regardin...

Interprofessional Care and Informal Support Networks of Independent Community-Dwelling Older Adults

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Abstract. Cultural transmission across generations is key to cumulative cultural evolution. While several mechanisms—such as vertical, horizontal, and obli

The bulk of causal inference studies rule out the presence of interference between units. However, in many real-world scenarios, units are interconnected by social, physical, or virtual ties, and the effect of the treatment can spill from one unit to other connected individuals in the network. In this paper, we develop a machine learning method that uses tree-based algorithms and a Horvitz–Thompson estimator to assess the heterogeneity of treatment and spillover effects with respect to individual, neighborhood, and network characteristics in the context of clustered networks and interference within clusters. The proposed network causal tree (NCT) algorithm has several advantages. First, it allows the investigation of the heterogeneity of the treatment effect, avoiding potential bias due to the presence of interference. Second, understanding the heterogeneity of both treatment and spillover effects can guide policymakers in scaling up interventions, designing targeting strategies, and increasing cost-effectiveness. We investigate the performance of our NCT method using a Monte Carlo simulation study and illustrate its application to assess the heterogeneous effects of information sessions on the uptake of a new weather insurance policy in rural China.

Published article 9450 Rural and Remote Health