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01.12.2016 | Original Article | Ausgabe 1/2016 Open Access

Network Modeling Analysis in Health Informatics and Bioinformatics 1/2016

Employing the Gini coefficient to measure participation inequality in treatment-focused Digital Health Social Networks

Zeitschrift:
Network Modeling Analysis in Health Informatics and Bioinformatics > Ausgabe 1/2016
Autoren:
Trevor van Mierlo, Douglas Hyatt, Andrew T. Ching

Abstract

Digital Health Social Networks (DHSNs) are common; however, there are few metrics that can be used to identify participation inequality. The objective of this study was to investigate whether the Gini coefficient, an economic measure of statistical dispersion traditionally used to measure income inequality, could be employed to measure DHSN inequality. Quarterly Gini coefficients were derived from four long-standing DHSNs. The combined data set included 625,736 posts that were generated from 15,181 actors over 18,671 days. The range of actors (8–2323), posts (29–28,684), and Gini coefficients (0.15–0.37) varied. Pearson correlations indicated statistically significant associations between number of actors and number of posts (0.527–0.835, p < .001), and Gini coefficients and number of posts (0.342–0.725, p < .001). However, the association between Gini coefficient and number of actors was only statistically significant for the addiction networks (0.619 and 0.276, p < .036). Linear regression models had positive but mixed R 2 results (0.333–0.527). In all four regression models, the association between Gini coefficient and posts was statistically significant (t = 3.346–7.381, p < .002). However, unlike the Pearson correlations, the association between Gini coefficient and number of actors was only statistically significant in the two mental health networks (t = −4.305 and −5.934, p < .000). The Gini coefficient is helpful in measuring shifts in DHSN inequality. However, as a standalone metric, the Gini coefficient does not indicate optimal numbers or ratios of actors to posts, or effective network engagement. Further, mixed-methods research investigating quantitative performance metrics is required.

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