Figures in Epidemiology usually either convey features of study design or study results. For figures of both types, the first consideration is whether a figure will convey the information more effectively than text or tables used to occupy the same amount of space. Figures occupy a lot of space in a manuscript—we budget 250 words per figure—so if the figure’s information can be conveyed as effectively in that many words or less, then text should be used instead of a figure. Similarly, when presenting study results, tables provide exact results whereas figures do not. If the figure’s information can be conveyed as effectively as a table, then a table should be used to gain the advantage of the exact data.
The advantages offered by a figure over a table are to visually convey changes in information along the figure axes. If there is no compelling change in information along at least one axis of the figure, then a table should convey the information as effectively and more precisely. Compelling figures show changes in information along both axes, and sometimes in a third dimension as well. For example, meta-analyses often include a forest plot in which the point estimates, confidence intervals, and relative weights of each study are plotted along the vertical axis. You can find an example here. While figures of this design are quite common in the meta-analysis literature, the vertical axis has no function other than to separate studies from one another. Simply ranking the point estimates provides additional information, as shown here for a similar set of studies. Additional information along the study-scale can be added by plotting the inverse normal of rank percentile as the vertical axis scale (shown here), instead of equally spacing the ranked studies. This stretches the outlying studies further apart and compresses the studies near the central tendency closer together, which adds information to what is conveyed on the study scale axis. The point is not to advocate for a change to the way meta-analyses are presented, but rather to encourage authors to design figures that convey information along all axes in their figures.
Once the content of the figure and its axes has been decided, preparation of the figure itself comes next. In general, the quality of published figures would improve dramatically if all authors realized and reacted to one fundamental problem: the default settings of most graphics-preparation tools yield figures in which everything is too small. Line and axis thickness, marker size, font size, error bars: these are all too light or too small in default settings. Simply by making everything bigger and heavier, the quality of figures would improve. Try exaggerating these settings, then ratchet back a notch or two.
The space between the axes is valuable real estate – fill it. The space outside the axes is also valuable real estate: so fill that by using large font labels sparsely placed rather than small sparse labels or (even worse) many small labels.
For all text elements of the figure, choose fonts that are easy to read, usually a sans-serif font such as Arial or Helvetica. Slightly moderate font sizes so that the most important information has the largest font and the least important information has the smallest font. Usually that would mean that axis titles have the largest font, axis labels have intermediate font, and legend text and other text elements such as data labels have the smallest font.
Avoid clutter in the figure. Never use figure titles because the caption will suffice. Do not outline the figure or the plotted areas because the axes will suffice. Make sure that every data element is important. For example, in a plot showing results stratified by gender, do not include a line for all genders combined unless that combined information is as important as the gender-specific information. Grid lines should also be avoided. If the location of plotted elements must be so closely inspected as to require grid lines, then the data are probably better suited to a table than to a figure.
Embed legends between the axes, rather than above or below, especially when the distribution of data leaves blank spaces between the axes. Using this empty space for the legend allows the size of the chart to increase because no space is reserved for the legend. Legends are more effective when embedded in the figure than when embedded in the caption. Even better is to label plot elements with text labels directly next to the element, thereby deleting the legend.
Many authors present results in figure panels. These can be quite effective when used judiciously. To start, consider whether a single figure can be used instead of a figure panel by reducing the number of compared categories. If there are so many essential data categories that a figure panel is required, then always keep the scale of all axes constant in all panels of the figure. The point of a figure panel is to visually compare results within and across panels. If the axis scales change in different panels, then the visual comparison across panels will be misleading.
While we encourage authors to embolden their figures by making elements large and thick, we strongly discourage the use of ornamentation such as shadows, shaded backgrounds, and word art. Three dimensional figures are, in general, very difficult to comprehend. Unless a surface must be plotted, it is better to convey the third-dimension information as separate lines within the plot or in a figure panel.
Figure captions are critical to high-quality figures. A figure caption should describe what the reader will find in the figure and from what data it was generated. Readers should be able to picture the figure and understand the study setting in which the data were generated by reading the figure caption. Avoid duplicating the caption information in the main text of the manuscript, but be sure to define any abbreviations, even if they are also defined in the main text. Although Epidemiology disallows almost all abbreviations in the text (see our earlier blog), we will sometimes allow abbreviations in figures (data or axis labels, for instance) that we do not allow in the text.
Epidemiology accepts figures prepared in color. Figures printed in color incur extra charges, as described in the instructions for authors, unless the authors have also paid an Open Access fee. Authors can submit figures in color to appear in only the on-line version, and a gray-scale version to appear in print and in the PDF version. This type of submission incurs no extra charges, but then it is imperative that all figure elements are as easily identifiable in the gray-scale version as in the color version and that the figures are identical except for the color itself. When preparing color figures, authors must choose colors that make the figure content accessible to persons with color vision impairments. Be careful that the graphics software does not gratuitously add color in the form of a pastel background or other elements that add no information.
Our editors examine the quality of figures at different sizes during the editing process, but sometimes when a figure appears in page proofs it looks fuzzy. We will then ask the author to submit a higher-resolution version. The best ways to avoid these last-minute inconveniences are to submit the highest resolution figure that is practical (1200 dpi should be good for most line drawings) and to export the figure directly to a graphics format (.tif, .png, and .pdf work best) rather than re-importing into Word or Powerpoint.
Creating a compelling figure requires a substantial investment of energy and creativity. The guidance above may help to avoid common pitfalls, but the quality of the figure will ultimately be determined primarily by the effort put into the creation.