Page 198 - GHES-3-2

Global Health Economics and
Sustainability
Gender inequality and healthcare

than traditionally understood (Macintyre et al., 1996; and insights obtained, we offer some key implications and
Raj, 2011; Read & Gorman, 2010; Roxo et al., 2021). The future direction.
trends and immensity of gender-related disparities in
health varied depending on the prevailing indications 2. Methods
or observations, as well as the stage of the life cycle 2.1. Data and variables
(Macintyre et al., 1996; Palència et al., 2014; Van Wijk
et al., 1996). Gender inequality is persistent during the Data were gathered from the World Bank’s World
entire life cycle for affliction in psychological issues but Development Indicators (WDI) database for 173 countries
less pronounced or contradictory for many physical between 2007 and 2019 (https://databank.worldbank.org/
indications (Macintyre et al., 1996). Research from recent reports.aspx?source=2&country=ARE).
decades increasingly supports the perspective that gender Due to delays in data upload by several countries,
inequalities in healthcare are vested in the social aspects, particularly developing countries, missing values are
while also acknowledging that males have their biological prevalent. Consequently, data from recent years are more
limitations (Madell & Hayward, 2019; Read & Gorman, difficult to access. Preprocessing of data was performed
2010). Gender perceptions have mostly transformed, and to remove rows of data with missing values. In addition,
several of these likely impact gender-related challenges normalization of the data was conducted to ensure that
in health and sickness. An affirming possibility is that values across indicators, data types, and scales could be
gender inequalities in healthcare have changed over time analyzed collectively. Software platforms, including Excel
(Artazcoz & Benach, 2001; Heise et al., 2019; Sen & Ostlin, for coding data, Python for preprocessing, R programming
2007). For example, while there was previously a female languages, and Tableau for statistical analysis and
excess, this disparity has lessened. Gender inequalities refer visualization, were used accordingly. Tables S1 and S2
to the different treatment of men and women, resulting in display the independent variables (i.e., gender inequality)
the systematic empowerment of men, often with adverse and dependent variables (i.e., health). The variables were
effects on women’s health. It is universally recognized that chosen based on the categorization of the indicators in the
while the lifespan of women is longer than that of men in World Bank’s WDI database (Denton et al., 2004; Franklin
advanced countries, women often live with poor health et al., 2021; Milner et al., 2021; Roxo et al., 2021; Sörlin
conditions (Annandale & Hunt, 2000; Espelt et al., 2010; et al., 2011; Sörlin et al., 2012; Weber et al., 2019).
Palència et al., 2014). In other words, gender inequalities
in healthcare stem from inequities in relative financial 2.2. Visualization
situations and power dynamics (Arber & Khlat, 2002), We adopted the descriptive visualization analytical
as well as the division of labor based on sex (Malmusi technique, supported by visual analytics (Börner et al., 2019;
et al., 2012). As Sen & Ostlin (2007) articulated, enhancing Keim, 2001; Keim et al., 2010; Raghupathi & Raghupathi,
gender equity in healthcare and articulating women’s rights 2013; Raghupathi & Raghupathi, 2021; Wong & Thomas,
to healthcare are two key strategies to mitigate overall 2004), to provide insight into the association among and
disparities and ensure fair and equitable healthcare delivery. between gender inequality and health indicators. This
Therefore, to reiterate, the topic of gender differences in data-driven approach, known as descriptive analytics,
healthcare warrants continuous and periodic studies. facilitates the study of historical data as it is. Visual
The aim of this exploratory study is to investigate the analytics is particularly relevant when the data render itself
multi-dimensional relationships between gender inequality to association rather than causal studies, for which control
(e.g., immunization, access to anti-retroviral drugs and groups and experimentation are required (Kohlhammer
school enrollment, self-employment, unemployment, and et al., 2011; Raghupathi & Raghupathi, 2020; Thomas and
women in parliament) and health variables (e.g., fertility Cook, 2005). The dual model of integrating the platforms
rate, incidence of human immunodeficiency virus [HIV], and tools with the modeling capability of visualization
life expectancy, and mortality rate) (Dahlin & Harkonen, helps uncover previously unidentified associations,
2013; Denton et al., 2004; Ekbrand & Hallerod, 2018; King enabling data-driven decision-making (Cao et al., 2018;
et al., 2020; Milner et al., 2021). Using visual analytics, the Singh & Singh, 2020). Visualization renders complex data
study seeks to understand the extensive and interactive into easily understandable charts that are self-explanatory,
dimensions of gender inequality and healthcare. By supporting the idea of “letting the data reveal itself.” Taken
elucidating the aforementioned relationship, we can together, the series of charts form a compelling narrative
shape policies and strategies to bridge the gap in gender (Kohlhammer et al., 2011; Raghupathi & Raghupathi,
inequality and its effect on health. Based on the results 2021; Raghupathi et al., 2023; Zhang et al., 2024).

Volume 3 Issue 2 (2025) 190 https://doi.org/10.36922/ghes.5776