Using Network Analysis to Characterize Interagency Connections for Developing Community-Based Prevention for Immigrant Worker Health

Introduction: Innovative prevention approaches are needed to address health disparities endured by immigrant workers worldwide. In the US, immigrants experience disparities related to worksite hazard exposures and associated health/safety problems as most are employed in low-wage, high health risk jobs. For complex reasons, commonly used worksite approaches for delivering prevention are limited in their effectiveness for immigrant workers. Academic-community partnerships represent a promising strategy to improve participation in prevention efforts. However, methods are needed to strategically identify and engage diverse community agencies in the process of promoting immigrant worker health. This presentation describes an innovative application of network analytic methods to identify interagency connections needed to design and deliver comprehensive community-based worker health prevention.

Methods: Community agencies pertinent to Chinese immigrant worker health were identified using community information sources and nominations made by ≥ 2 participating agencies. Using a joint interview, two representatives from each agency provided data on organization characteristics and cross-agency connections related to: information sharing, resource sharing, referrals, joint programs, joint political actions, and service contracts specifically for Chinese immigrants. With social network analysis (UCINET 6), we characterized the community agency network structure and the relative positions of agencies in the network.

Results: The sample represented 42 community-based organizations (CBOs), faith-based organizations (FBOs), unions, and public agencies. Chinese FBOs and unions had the fewest links with other agencies, whereas Chinese, pan-Asian and nonprofit CBOs and public agencies had the most interagency connections. Density analysis revealed agencies connected primarily for information sharing (d=.26), referrals (d=.16) and resource sharing (d=.16). These relations were centered (degree centralization=.68, .58 & .53, respectively) around a few agencies. A small number of agencies mediated links between many other agencies for referrals and joint programs (betweenness centralization=.36 & .32, respectively). Degree centrality and betweenness centrality were high for three community agencies, indicating their central as well as gatekeeper positions, rendering them relevant nodes for disseminating worker health prevention.

Conclusions: Application of network analysis methods generated critical understanding of interagency networks within and across community sectors. This extensive knowledge will guide strategic choices to facilitate community-based partnership development and dissemination of comprehensive, sustainable prevention programs for immigrant workers.