The Editors' Notepad

The goal of this blog is to help EPIDEMIOLOGY authors produce papers that clearly and effectively communicate their science.

The views and recommendations of contributors do not necessarily indicate official endorsements or opinions of the Journal, WK, or the ISEE. All views are those of the authors and the authors alone.​​

Monday, February 5, 2018

All papers in EPIDEMIOLOGYexcept for letters, research letters, and commentariesrequire an abstract. After the title, the abstract is the most important public-facing aspect of a paper; it is always freely available, even when the rest of the paper is not. As such, the abstract should convey key methods and results against the background and importance of the study. This goal can be challenging given the strict limit of 250 words, so we have come up with some guidelines.


The abstract serves several purposes. It primarily summarizes your work, both in the print and electronic versions, as well as in indices such as PubMed. It also entices readers to invest time in reading the whole paper. Literature summaries start with reviews of abstracts to select papers that meet inclusion criteria. For these reasons, the abstract might be the most important writing you will do. Unfortunately, the abstract often receives the least attention. Authors often simply cut and paste key phrases from each section of their main text to stitch together an abstract. To write better abstracts, write them, don't cut and paste them. Write a first draft before you have written anything else. Then write a second independent draft after you have drafted the other sections. Compare these drafts and keep the best from both. Then start rewriting. As with all writing, revisions lead to the best text.



The usual structure for an EPIDEMIOLOGY research paper has four sections: Introduction, Methods, Results, and Conclusions. A structured abstract should follow this outline. In the abstract, the Introduction section use a sentence or two to focus on the rationale for the study: what is still unknown in a given area and what the study aims to accomplish. The Methods should briefly describe the study design and population investigated, and main methods used. The Results section should include a key numerical result or two, as this adds interest; however, beware of the temptation to make sentences unwieldy with a lot of risk estimates and confidence intervals. (Have a look at our strategies for presenting numbers in text here.) Finally, the Discussion should provide the take-home message of the study, avoiding (per journal policy) undue causal language or strictly avoiding public health or policy recommendations.

Not all abstracts need be structured; methodology papers, particularly those with less internal structure than a typical outcomes paper, can have an abstract free of the constraint of the sections above.



Although we do not have a goal for a formal level of readability, the abstract should be as readable as practicable for most readers. Our audience mostly has a background in biomedical sciences, but includes non-epidemiologists. We are also a general interest epidemiology journal, so jargon specific to a topic area should be avoided, especially in the abstract. For these reasons, we are somewhat more strict regarding abbreviations (about which you can read more herein brief, use only the most familiar ones, e.g. BMI and CI, but they should be defined on first use and, if possible, avoided entirely, particularly when there is potential ambiguity. More than once, for example, a paper using the expression 'men who have sex with men' has appeared in the same issue as one that used 'marginal structural models,' and both were abbreviated MSM (which has various other meanings as well), so we made sure that this abbreviation was not used at all in either abstract.


Word limit

As mentioned above, our limit of 250 words is strict, primarily to avoid PubMed truncating any excess. If the abstract is too long and/or if your editor spells out an abbreviation, she or he will also adjust elsewhere or ask you to do so. Remember that it is fine to save some details of methods or results for the body of the paper.

Friday, September 1, 2017

More often than not, I will suggest edits to the title of an Epidemiology paper. I say suggest because there are few hard and fast requirements, and because framing the title is a central role of the author; I won’t challenge you on its overall content. That said, the journal has certain preferences and practices, and I would like to help you engage readers right up front if I see an opportunity to do so.

First, in contrast to some biomedical journals, we will challenge titles that give away results, if only to encourage readers to look at least as far as the abstract. The title should thus concisely frame the research question, not its conclusion.

An example of giving away results:

Sardine consumption is associated with postural hypotension in elderly penguins

On the other hand, framing the question looks like:

The association between sardine consumption and postural hypotension in elderly penguins

Speaking of conciseness, we would like titles to be 14 words or fewer. The simplest way to do this, when possible, is to drop the subtitle or the name of the study, though we understand some large studies require the name to be included. The length guideline may be at odds with our general impulse toward clarity if abbreviations are involved that need to be spelled out. I’m not sure what the answer is regarding abbreviations in titles, other than avoiding them.

The example above, plus a subtitle, brings it to 18 words:

The association between sardine consumption and postural hypotension in elderly penguins: A nested case-control study in the ANTARCTIC cohort

The subtitle, though, contains methodologic details that the author could consider saving for the abstract. Without the subtitle (the Framing the question example above), the title is only 11 words.

Titles for letters are a special case. The guidelines above apply to research letters (which include new data) and freestanding comments that don’t refer to a particular published article, so the author gets to write the title. Our title format for letters that do comment on a published article are of the form:

Re. Title of the Previously Published Article in Question

and the title of any response is, simply

The Authors Respond

A letter and its response will publish ahead of print at the same time, and then in sequence in the final version of the journal.


Further topics we’ve included for this space include causal language, the PubMed process, statements about ethical review, and abstracts. Please let us know if there are others you’d like to see. We think the comment section works, and we would love to hear from you.​

Tuesday, May 2, 2017

Because we’ve written recently about figures, it seemed a good time to address the other aspect of data presentation: tables. If you’ve been reading this blog regularly, it won’t surprise you to know that our first priority for tables in Epidemiology is clarity. And if you’ve read from the beginning, you may remember that I once confessed to my rusty, out-of-date knowledge of methodology. 

Well, I have another confession: I find it hard to look at big, cluttered tables of numbers. When I was analyzing data for my dissertation, I would have to extract data from SAS crosstab output that looked like thiswere cluttered and complicated, and I would get really anxious. I wasn’t sure which numbers I wanted, I often grabbed the wrong one, and sometimes I would switch digits. I often asked a colleague to check my work. To this day, I find it challenging to check table data versus text when the tables are not laid out clearly.

Reasonable epidemiologists can differ on their general preference for presenting data in figures versus tables. At Epidemiology, though, if a figure and a table convey the data equally effectively, we prefer you use a table to gain the advantage of the exact data. However, for the reason I mentioned just above, the visual characteristics of a table matter, and journal style and statistical requirements play into table presentation as well, so here are our recommendations:

Table size

In general, aim to have a table fit on no more than one whole vertical page; a page in Epidemiology is about 275mm x 200 mm, minus margins. Tables can spill over onto another page, or be printed at 90 degrees to the main text, but such scenarios (for example the short but wide table) often end up wasting space on the page. That said, a full-page table is pretty big, and we always appreciate your willingness to manage the size and number of tables by putting data in supplementary digital content. In a recently published meta-analysis, half of the vertical space in a forest plot (more of a hybrid table-figure) consisted of lettered footnotes indicating adjustment variables. The author was willing to put these footnotes in SDC. 

A main concern when we edit tables is that the authors have been too comprehensive. For example, we often receive tables that show the same estimate of association from several different models (e.g, crude, adjusted for age and sex, and adjusted for a more comprehensive set of control variables). We prefer that authors decide which model is most valid and precise, and present only that result. This brevity may allow a table to be deleted altogether, with results presented only in the text. The detailed information can always be presented in the SDC for readers with interested in a more comprehensive view. The main point is that authors should evaluate the information in their tables to assure that every included result is important for the reader to comprehend the study’s results. Information extraneous to that goal should be deleted or included as SDC.

Abbreviations and footnotes

Because of the limited size of column and row headings, we can often be more flexible about allowing abbreviations that in the main text we would ask you to expand, as long as they are defined in a non-lettered footnote.

Other than the one footnote that defines abbreviations, footnotes should follow our convention of superscript letters - a,b,c - rather than numbers (which may be confused with bibliographic citations) or symbols.

Precision and interpretability

We prefer two significant figures for most numbers, except when substantial statistical power allows more; we will flag significant figures exceeding two, but we do leave the final decision to the author. When we say two significant figures, we mean independent of the decimal, so:

XX, X.X, 0.XX

In other words, significant figures is not equivalent to decimal places. This holds for percentages, descriptive statistics, and risk estimates, including those on a log scale.

A special note for descriptive tables, typically Table 1. We are aiming for tables that don’t mix categorical and continuous data. The most useful statistics for describing the distribution of a variable is quantiles, usually quartiles that include a median. Mixing categorical and continuous data can also make a table confusing to read. For these reasons, we prefer authors report any means and standard deviations (SD) in the text. We understand that, when presenting descriptive statistics within subgroups, it may be unwieldy to move means and SDs to text; a possible solution is to group continuous variables separately from categorical ones, so that the respective statistics can be clearly labeled.

Friday, March 10, 2017

It's safe to say that the 45th President of the United States is not a fan of science. The views he has expressed on climate change have ranged from skepticism to dismissal as a hoax perpetrated by China, and the White House reportedly demanded that the EPA remove all its web pages referring to climate change, though this order was quickly rescinded. The President believes vaccines cause autism and has proposed a commission on vaccine safety led by anti-vaccine activists. He has said that the U.S. National Institutes of Health is "terrible." His newly appointed director of Health and Human Services, which comprises several science agencies among others, had a thin track record on science funding during his tenure in Congress. This journal recently published a Commentary on behalf of the International Society of Environmental Epidemiology, this journal's sponsoring society, advocating against the President's nominee for administrator of the Environmental Protection Agency. The nominee, who was confirmed, seems to hold the view that climate and environmental health science are at best inconvenient. Looking further to the Congress, a current legislative initiative aims to shut down EPA entirely by the end of 2018.

None of this will be news to Epidemiology's readership, but governmental research, government sponsored research, and the voices of all researchers are at risk of being suppressed. Government scientists at a range of agencies have already been warned not to speak directly with legislators or the press. Although requirements for agency clearance around politically sensitive topics are not unprecedented, communications from the White House and agencies have been somewhat messy and contradictory, leading to uncertainty. The landscape changes daily, perhaps intentionally cultivating chaos. In reaction, scientists, programmers, and others are scrambling to download pages and data, especially climate change data, from government websites and servers, lest they be destroyed.

In this political climate, government and government-funded epidemiologists may understandably want to protect the data they collect and the knowledge they create. It may be helpful to our contributors to know that, via the Editorial Manager submission system (administered by Epidemiology's publisher, Lippincott Williams & Wilkins) any articles submitted, even if rejected without review, are archived for one year. This archived material includes all original content, including submitted manuscripts and supplemental digital materials, which may include original data. After one year, the system compiles and saves indefinitely a PDF from the materials, but this may not include all supplemental digital materials. Editors and authors can access the materials using their password-protected account.​

We will continue to monitor the news and to think about ways the journal can support and protect public health science, and we welcome readers' input to that end.


The views and recommendations of contributors do not necessarily indicate official endorsements or opinions of the Journal, Wolters-Kluwer, or the ISEE. All views are those of the authors and the authors alone.



Sunday, January 1, 2017

A well-prepared figure conveys information more effectively than tables or text. A poorly prepared figure discourages readers and uses page space inefficiently. Choosing when to use figures and preparing them well is therefore central to writing an effective manuscript.

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.