Premium
On Effective Graphic Communication of Health Inequality: Considerations for Health Policy Researchers
Author(s) -
ASADA YUKIKO,
ABEL HANNAH,
SKEDGEL CHRIS,
WARNER GRACE
Publication year - 2017
Publication title -
the milbank quarterly
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 101
eISSN - 1468-0009
pISSN - 0887-378X
DOI - 10.1111/1468-0009.12300
Subject(s) - inequality , bar chart , context (archaeology) , health policy , health equity , computer science , psychology , mathematics , medicine , public health , statistics , nursing , geography , archaeology , mathematical analysis
Policy Points: Effective graphs can be a powerful tool in communicating health inequality. The choice of graphs is often based on preferences and familiarity rather than science. According to the literature on graph perception, effective graphs allow human brains to decode visual cues easily. Dot charts are easier to decode than bar charts, and thus they are more effective. Dot charts are a flexible and versatile way to display information about health inequality. Consistent with the health risk communication literature, the captions accompanying health inequality graphs should provide a numerical, explicitly calculated description of health inequality, expressed in absolute and relative terms, from carefully thought‐out perspectives.Context Graphs are an essential tool for communicating health inequality, a key health policy concern. The choice of graphs is often driven by personal preferences and familiarity. Our article is aimed at health policy researchers developing health inequality graphs for policy and scientific audiences and seeks to (1) raise awareness of the effective use of graphs in communicating health inequality; (2) advocate for a particular type of graph (ie, dot charts) to depict health inequality; and (3) suggest key considerations for the captions accompanying health inequality graphs. Methods Using composite review methods, we selected the prevailing recommendations for improving graphs in scientific reporting. To find the origins of these recommendations, we reviewed the literature on graph perception and then applied what we learned to the context of health inequality. In addition, drawing from the numeracy literature in health risk communication, we examined numeric and verbal formats to explain health inequality graphs. Findings Many disciplines offer commonsense recommendations for visually presenting quantitative data. The literature on graph perception, which defines effective graphs as those allowing the easy decoding of visual cues in human brains, shows that with their more accurate and easier‐to‐decode visual cues, dot charts are more effective than bar charts. Dot charts can flexibly present a large amount of information in limited space. They also can easily accommodate typical health inequality information to describe a health variable (eg, life expectancy) by an inequality domain (eg, income) with domain groups (eg, poor and rich) in a population (eg, Canada) over time periods (eg, 2010 and 2017). The numeracy literature suggests that a health inequality graph's caption should provide a numerical, explicitly calculated description of health inequality expressed in absolute and relative terms, from carefully thought‐out perspectives. Conclusions Given the ubiquity of graphs, the health inequality field should learn from the vibrant multidisciplinary literature how to construct effective graphic communications, especially by considering to use dot charts.