2 Summarising AMR data visually
Research has shown that our brains understand complex information more easily when it is presented in a visual format (Ware, 2012). That is why data are often best communicated visually, using graphs and maps rather than tables or spreadsheets. Visual summaries can be a great way of communicating your findings, particularly to people who may be unfamiliar with the data.
When choosing the most appropriate visual display, ask yourself the following:
- What is the primary aim of my visual analysis? For example, do I want to demonstrate a difference in prevalence of resistant pathogens by geographic area or between two groups? Do I want to highlight a change in antimicrobial use trends over time?
- What do I want to communicate, and with whom am I communicating? Graphs and maps can help you share public health information to people who may not be familiar with the topic.
- What type of data do I have? What is the data’s measurement scale (nominal, ordinal, discrete or continuous)? The data type will influence the kind of visual representation you can use.
Table 4 summarises the different analysis aims and gives examples of the best visual displays for presenting the analysis, described in more detail as we work through this module.
| Analysis aim | What do you want to explore/present | Example | Visual presentation |
|---|---|---|---|
| Distribution | The range of values, central tendency and outliers in your data | Frequency distribution for categorical variable, mean and range of age for male and female patients | Table Histogram Bar chart |
| Composition of a variable | The composition of a set of variables and how different values sets make up a whole of that variable | Percentage of patients that are either inpatients or outpatients Percentage of countries participating in the World Health Organization (WHO) supported | Stacked bar chart Maps |
| Compare value sets/categories | Direct comparison between two or more sets of variables or categories of variables | Percentage of inpatients that are infected with E. coli compared with the percentage of outpatients that are infected with E. coli Percentage of Campylobacter isolates which are ciprofloxacin resistant between regions in a country | Bar chart |
| Trends over time | How variable(s) values are changing over time | Percentage of inpatients who are infected with E. coli each quarter over the last two years Percentage change in ceftriaxone resistance in non-typhoidal Salmonella from chicken sources over ten-year period | Table Line graph |
| Relationships and associations | Any relationships and/or associations between variables | Association between the levels of antibiotic consumption (AMC) and antibiotic resistance in | Line graph |
1.2.2 Plotting variability in data
