Results from using a new dyadic-dependence model to analyze sociocentric physician networks

• Type of Material: Article
• Authors: Sudeshna Paul, Emory University; Nancy L. Keating, Harvard University; Bruce E. Landon, Harvard University; A. James O'Malley, Dartmouth College
• Language: English
• Date: 2014-09-01
• Publisher: Elsevier
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2014 Elsevier Ltd.
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1016/j.socscimed.2014.07.014
• Title of Journal or Parent Work: Social Science and Medicine
• ISSN: 0277-9536
• Volume: 117
• Start Page: 67
• End Page: 75
• Keywords: Biomedical Social Sciences; CARE; Sociocentric network; DIFFUSION; Life Sciences & Biomedicine; Social Sciences, Biomedical; Science & Technology; Physician influence; Public, Environmental & Occupational Health; Patient sharing; Transitivity; Dyadic independence; Latent variables; Social Sciences
• Research Categories: Sociology, Public and Social Welfare; Health Sciences, Public Health

Abstract

Professional physician networks can potentially influence clinical practices and quality of care. With the current focus on coordinated care, discerning influences of naturally occurring clusters and other forms of dependence among physicians' relationships based on their attributes and care patterns is an important area of research. In this paper, two directed physician networks: a physician influential conversation network (N=33) and a physician network obtained from patient visit data (N=135) are analyzed using a new model that accounts for effect modification of the within-dyad effect of reciprocity and inter-dyad effects involving three (or more) actors. The results from this model include more nuanced effects involving reciprocity and triadic dependence than under incumbent models and more flexible control for these effects in the extraction of other network phenomena, including the relationship between similarity of individuals' attributes (e.g., same-gender, same residency location) and tie-status. In both cases we find extensive evidence of clustering and triadic dependence that if not accounted for confounds the effect of reciprocity and attribute homophily. Findings from our analysis suggest alternative conclusions to those from incumbent models.