Social network enhanced behavioral interventions for diabetes and obesity: A 3 arm randomized trial with 2 years follow-up in Jordan.

While obesity and diabetes are rising pandemics, few low-cost and effective prevention and management strategies exist, especially in the Middle East. Nearly 20% of adults in Jordan suffer from diabetes, and over 75% are overweight or obese. Social network-based programs have shown promise as a viable public health intervention strategy to address these growing crises. We evaluated the effectiveness of the Microclinic Program (MCP) via a 6-month multi-community randomized trial in Jordan, with follow-up at 2 years. The MCP leverages existing social relationships to propagate positive health behaviors and information. We recruited participants from 3 community health centers in Amman, Jordan. Participants were eligible for the study if they had diabetes, pre-diabetes, or possessed ≥1 metabolic risk factor along with a family history of diabetes. We randomized participants into three trial arms: (A Group) received the Full MCP with curriculum-activated social network interactions; (B Group) received Basic MCP educational sessions with organic social network interactions; or (C Group-Control) received standard care coupled with active monitoring and parallel screenings. Groups of individuals were randomized as units in a 3:1:1 ratio, with resulting group sizes of n = 540, 186, and 188 in arms A, B, and C, respectively. We assessed the overall changes in body weight, fasting glucose, hemoglobin A1c (HbA1c) and mean arterial blood pressure between study arms in multiple evaluations across 2 years (including at 6-months and 2-years follow-up). We investigated the effectiveness of Full and Basic MCP social network interventions using multilevel models for longitudinal data with hierarchical nesting of individuals within MCP classrooms, within community centers, and within temporal cohorts. We observed significant overall 2-year differences between all 3 groups for changes in body weight (P = 0.0003), fasting blood glucose (P = 0.0015), and HbA1c (P = 0.0004), but not in mean arterial blood pressure (P = 0.45). However, significant changes in mean arterial pressure were observed for Full MCP versus controls (P = 0.002). Weight loss in the Full MCP exceeded (-0.97 kg (P<0.001)) the Basic MCP during the intervention. Furthermore, both Full and Basic MCP yielded greater weight loss compared to the control group at 2 years. The Full MCP also sustained a superior fasting glucose change over 2 years (overall P<0.0001) versus the control group. For HbA1c, the Full MCP similarly led to greater 6-month reduction in HbA1c versus the control group (P<0.001), with attenuation at 2 years. For mean arterial blood pressure, the Full MCP yielded a greater drop in blood pressure versus control at 6 months; with attenuation at 2 years. These results suggest that activated social networks of classroom interactions can be harnessed to improve health behaviors related to obesity and diabetes. Future studies should investigate how public health policies and initiatives can further leverage social network programs for greater community propagation. Trial registration. ClinicalTrials.gov Identifier: NCT01818674.

Correction: Ramadan fasting and weight change trajectories: Time-varying association of weight during and after Ramadan in low-income and refugee populations.

[This corrects the article DOI: 10.1371/journal.pgph.0000371.].

Model-based clustering for random hypergraphs.

A probabilistic model for random hypergraphs is introduced to represent unary, binary and higher order interactions among objects in real-world problems. This model is an extension of the latent class analysis model that introduces two clustering structures for hyperedges and captures variation in the size of hyperedges. An expectation maximization algorithm with minorization maximization steps is developed to perform parameter estimation. Model selection using Bayesian Information Criterion is proposed. The model is applied to simulated data and two real-world data sets where interesting results are obtained.

Ramadan fasting and weight change trajectories: Time-varying association of weight during and after Ramadan in low-income and refugee populations.

Obesity is a significant driver of the global burden of non-communicable diseases. Fasting is one approach that has been shown to improve health outcomes. However, the effects of Ramadan fasting differ in that the type, frequency, quantity, and time of food consumption vary. This phenomenon requires in-depth evaluation considering that 90% of Muslims (~2 billion people) fast during Ramadan. To address this issue, we evaluated the pattern of weight change during and following Ramadan for a total of 52 weeks. The study was conducted in Amman, Jordan. Between 2012 and 2015, 913 participants were recruited as part of a trial investigating the efficacy of a weight loss intervention among those with or at risk for diabetes. Weight was measured weekly starting at the beginning of Ramadan, and changes were analyzed using discrete and spline models adjusted for age, sex, and trial group. Results show slight weight gain within the first two weeks and weight loss in the subsequent weeks. During the first week of Ramadan, the estimate for a weight increase was 0·427 kg, (95% CI: -0·007, 0·861) relative to baseline, compared to an estimated weight reduction of 0·55kg (95% CI: 0·05, 1·05) by the 8th week relative to baseline. There was clear evidence of gradual weight gain from week 8 until week 26 with an estimated weight gain of 2.547 kg (95% CI: 1.567, 3.527) at week 26 relative to baseline. A sharp drop of 2.66kg in weight was observed between the 26th and 28th week before it stabilized. Our results show that weight changes occurred during and after Ramadan. Weight fluctuations may affect health risks, and thus, findings from this study can inform interventions. Public health agencies could leverage this period of dietary change to sustain some of the benefits of fasting. The authors (DEZ, EFD) acknowledge the Mulago Foundation, the Horace W. Goldsmith Foundation, Robert Wood Johnson Foundation, and the World Diabetes Foundation. TRIAL REGISTRATION. Clinicaltrials.gov registry identifier: NCT01596244.

Weighted stochastic block model.

We propose a weighted stochastic block model (WSBM) which extends the stochastic block model to the important case in which edges are weighted. We address the parameter estimation of the WSBM by use of maximum likelihood and variational approaches, and establish the consistency of these estimators. The problem of choosing the number of classes in a WSBM is addressed. The proposed model is applied to simulated data and an illustrative data set.

Multiresolution Network Models.

Many existing statistical and machine learning tools for social network analysis focus on a single level of analysis. Methods designed for clustering optimize a global partition of the graph, whereas projection-based approaches (e.g., the latent space model in the statistics literature) represent in rich detail the roles of individuals. Many pertinent questions in sociology and economics, however, span multiple scales of analysis. Further, many questions involve comparisons across disconnected graphs that will, inevitably be of different sizes, either due to missing data or the inherent heterogeneity in real-world networks. We propose a class of network models that represent network structure on multiple scales and facilitate comparison across graphs with different numbers of individuals. These models differentially invest modeling effort within subgraphs of high density, often termed communities, while maintaining a parsimonious structure between said subgraphs. We show that our model class is projective, highlighting an ongoing discussion in the social network modeling literature on the dependence of inference paradigms on the size of the observed graph. We illustrate the utility of our method using data on household relations from Karnataka, India. Supplementary material for this article is available online.