Using social network analysis to identify influential community organizations

We used a social network approach to identify influential community organizations within integrated public health networks in three regions in Texas, US: Houston/Harris County, Cameron County, and Northeast Texas. The study aimed to understand how network structures influence organizational performance in COVID-19 response.

In collaboration with community partners in each region, we administered a snowball sampling network data collection. Initial seed sets for the regions of 23, 25, and 55 organizations identified other organizations that then responded to the survey, which expanded the sets to 268, 145, and 138, respectively. Principal component analysis (PCA) identified key network structures-centrality and brokerage-and we introduced two network-based performance measures: Fulfillment Rate (FR), which measures securing needed resources, and Provision Rate (PR), which measures providing resources to others. We tested hypotheses to examine the associations between these network structures and organizational performance.

Centrality consistently emerged as a predictor of organizational performance across all three regions. Community organizations that are more centrally positioned, rather than broker organizations, play a critical role in meeting resource needs and providing services to other organizations. Centrally positioned organizations conducted more COVID-19 tests, whereas brokers administered fewer vaccines.

This study suggests that public health interventions should prioritize central organizations for resource allocation and distribution. Our network-based performance metrics offer new avenues for evaluating organizational effectiveness in networked public health systems, with broader applicability to future crises and public health planning.