Technology is now an important component of the healthcare ecosystem, and both computer and management science are playing a greater role in analyzing data to measure efficiencies (Chaudhry et al. 2006; Harrison et al. 2007; Holden and Karsh 2010). The digital health industry is maturing, and as a result, highly tailored evidence-based behavior change programs are increasingly available to consumers through pharmaceutical companies, non-profit organizations, insurers, private corporations, and government entities.
A component of these programs is Digital Health Social Networks (DHSNs), otherwise known as bulletin boards or peer-to-peer support groups. While there are still no firm conclusions on how to determine their efficaciousness (Eysenbach et al. 2004; Graham et al. 2015), the general consensus is that social support and knowledge sharing increases patient education, enhances self-management, and decreases burden on existing health services (Bender et al. 2013; Brennan et al. 1995; Cobb et al. 2011; Conrad et al. 2016; Ploderer et al. 2013; Takahashi et al. 2009; Wicks et al. 2012; Wright 2002).
Hypothetically, an ideal DHSN would consist of members who are equally engaged. In reality, network participation is unequal. An issue is that other than observing a network’s number of actors and number of posts, there are few metrics that can be used to identify participation inequality.
To address this issue, some research has sought to define actor roles (Carron-Arthur et al. 2015; Cleary and Stanton 2015; Cunningham et al. 2008; Jones et al. 2011; Selby et al. 2010; van Mierlo et al. 2012). By systematically categorizing participants, taxonomies can give insight into how various actors in complex networks function in relation to one another.
Other research has explored network topologies. Some of these studies have employed traditional social network analysis and method that focuses on nodes, ties, density of relationships, and degree centrality (Cobb et al. 2010; Urbanoski et al. 2016). Other streams have examined marketing rules of thumb (van Mierlo 2014), latent semantic analysis (Myneni et al. 2013), natural language processing (Wang et al. 2015), or the phenomenon of power laws (Carron-Arthur et al. 2014; van Mierlo et al. 2015).
As DHSNs shift into mainstream healthcare delivery it will be important to develop metrics that help managers assess growth, sustainability, and participation equality (Healey et al. 2014; Stearns et al. 2014). While the quality of DHSN content is important and is rooted in behavioral science, quantitative methods to analyze the health of a network may come from established theoretical constructs in economics and computer science.
Through measuring 222 quarters of participation from 15,181 actors from four separate DHSNs, this paper investigates whether the Gini coefficient, an economic measure of statistical dispersion traditionally used to measure income distribution in populations, can be employed as a management tool to help measure inequality of member participation over time.
The Lorenz curve
In economics, the Lorenz curve is a popular method that illustrates income distribution (Lorenz 1905). Graphically, the y-axis represents the percentage of income in an economy, and the x-axis represents cumulative income distribution in the total percentage of households (Fig. 1).
In a perfectly equal economy, all citizens share the same income, and the Lorenz curve would resemble the red line in Fig. 1, where y = x. Most economies are not equal. In Fig. 1, the Lorenz curve (blue line) illustrates economic inequality. Here, approximately 20 % of households receive 1 % of income, 40 % receive 3 %, 60 % receive 8 %, and 80 % receive 25 %.
The Gini coefficient
Developed by Corrado Gini in 1912 (Gini 1912), the Gini coefficient is an inequality measure based on the Lorenz curve. Specifically, it measures the distance between the Lorenz curve and perfect equality (Bellu and Liberati 2006). A Gini coefficient of 1 represents an economy where a single individual generates all income, whereas a Gini coefficient of 0 represents an economy where all citizens share the same income.
If translated to DHSNs, a Gini coefficient of 1 would represent a network where one individual created all posts. Alternatively, a Gini coefficient of 0 would represent a social network where all members authored the same number of posts.
To our knowledge, Gini coefficient numerical and visual outputs have yet to be applied to assess participation inequality in DHSNs. However, the method has been utilized in other studies.
For example, a 2005 study accessed U.S. census data to measure Personal Computer (PC) ownership inequalities amongst whites and African Americans at national, regional, and state levels (Chakraborty and Bosman 2005). Results indicated that although decreasing overall, PC ownership inequality is substantially smaller among white households. A strength of the study was the use of the Lorenz curve and Gini coefficient to graphically illustrate variations in income and PC ownership within and between the two groups.
Gini coefficient numerical scores and their visual representations were also utilized in a 2002 Statistics Canada research study depicting the digital divide, or cumulative internet usage amongst differing household income deciles (Sciadas 2002). Results indicate that as time progresses, the Gini coefficient is decreasing and the digital divide is closing. However, graphical outputs show that the shift is mainly attributable to middle-income groups, while lower-income and upper-income groups remain fairly stable.
Although there are other uses in research, a final example is a 2010 study analyzing university rankings. This study employed the Gini coefficient to assess whether academic institutions were becoming increasingly unequal (Halffman and Leydesdorff 2010). Ranking data mainly consisted of weighted contributions of total number of publications and citations, and findings indicate that contrary to popular belief, the 500 universities that publish at greatest frequency were becoming more equal in terms of output.