# Social Networks and Child Malnutrition in a Resource Limited Setting

> **NIH NIH R21** · DUKE UNIVERSITY · 2020 · $195,662

## Abstract

Project Summary
Interventions that reduce childhood malnutrition in resource-limited settings around the world are critical, and
recent work suggests that social relationships can affect health-promoting behaviors using social accountability
(SA) interventions. Our project, Social Networks and Health in Uttar Pradesh, India, will study social networks
in 80 villages where the State Government of Uttar Pradesh (UP), India, introduced accountability interventions
to improve delivery of key health and nutrition services. Our general model posits greater information
intervention effectiveness in cohesive, well-connected networks. We propose to examine how (1) network
structure is related to health at baseline, (2) the social network moderates intervention effectiveness at the
village level, and (3) position within the network is associated with household-level health after the intervention.
Starting in 2016, the government implemented SA interventions that included information about rights and
entitlements to publicly provided services and healthcare opportunities. A recent cluster-randomized evaluation
of these efforts found large improvements in average childhood health outcomes, including reductions in
indicators of malnutrition and increases in immunization rates, although it also noted effect heterogeneity
across treated villages and likely unequal effectiveness within villages. Our study will leverage unique social
network data collected in 2016, prior to the SA interventions, along with two waves of household survey data
from before and after the cluster-randomized trial of the intervention. In Aim 1, we will estimate the relationship
between village-level network structure—measured using complete network data and a novel complete-
network clustering technique—and pre-intervention community-level health outcomes. This aim sets the
foundation for explaining between-village effect heterogeneity. In Aim 2, we will determine whether network
characteristics account for observed health outcome heterogeneity and healthcare service use net of baseline,
two years after implementation of SA interventions. This aim tests whether core information and social
pressure related network features can account for the effectiveness of the intervention. For Aim 3, we will use
a hierarchical linear modeling (HLM) framework to evaluate the relationship between individual households’
positions within the village social network and household-level health. This aim helps us identify within-village
heterogeneity to test expectations that those with greater exposure to informed others will be healthier. This
novel project will explore the potential influence of network structure on information diffusion, social position,
and health outcomes within real-world, village-level social networks and build a foundation for future research
on dynamic networks and community-based health interventions that can leverage the growing availability of
large-scale, dynamic, community-bas...

## Key facts

- **NIH application ID:** 10072769
- **Project number:** 1R21HD101268-01A1
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Manoj Mohanan
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $195,662
- **Award type:** 1
- **Project period:** 2020-09-22 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10072769

## Citation

> US National Institutes of Health, RePORTER application 10072769, Social Networks and Child Malnutrition in a Resource Limited Setting (1R21HD101268-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10072769. Licensed CC0.

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