Visualizing health inequality data: guidance for selecting and designing graphs and maps

Several resources and tools have been developed for carrying out health inequality analysis, including the preparation of disaggregated data and calculation of summary measures of health inequality. There is also a body of literature that addresses best practices for the preparation of commonly used graphs and maps. There is, however, little guidance for the selection and design of appropriate data visuals for corresponding health inequality data and assessments. This paper aims to fill this gap by describing the types of graphs and maps most frequently used for reporting health inequality analyses and recommending how they can be effectively applied to distinct reporting scenarios. For a range of different data visual types, we assess how they can be designed for effectiveness, attending to the principles of accuracy, utility and efficiency. We assess the suitability of different data visuals for reporting the latest status of inequality, change in inequality over time, inequality assessments across settings and impact of eliminating inequality, considering how different data visualization approaches may be needed for reporting disaggregated data versus summary measures of inequality. Effective visuals can enhance the impact of health inequality reporting and support evidence-informed action to advance health equity.