Formulating a good research question

On April 16, 2020, I gave a presentation to students from the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Student Chapter at the University of Washington’s Comparative Health Outcomes, Policy, & Economics (CHOICE) Institute. I reviewed some of the ways to think about a research topic, how to narrow the scope of the topic, and how to formulate a specific and testable research question. The presentation was meant to help students develop their own process for developing a good research question for their thesis.

I discussed the FINER criteria for formulating a research question

I also discussed the PICOT format of a research question.

The presentation is available on the CHOICE Institute’s blog: https://choiceblog.org/2020/04/27/best-practices-in-developing-research-questions/

Communicating data effectively with data visualizations: Part 24 (Mortality Curves)

INTRODUCTION

The continual threat of infection by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has ground the world to a metaphysical stop. Economies appear to be under threat of a long recession, political debates have delayed needed relief to citizens, lack of N95 masks for healthcare workers place them at greater risk for doing their jobs, and mortality has increased world-wide. We are undoubtedly experiencing an seminal period in the 21st Century and data analysts have rushed to develop stunning visuals and dashboards such as the ones developed by Johns Hopkins University, “Our World in Data”,1 and the Centers for Disease Control and Prevention to feature the impact the SARS-CoV-2 is having on our world.

MOTIVATING EXAMPLE

In this article, we will replicate the total deaths due to SARS-CoV-2 using data from the European Centre for Disease Control and Prevention (ECDC)2 or from the Our World in Data’s GitHub site.3 Due to the changing nature of SARS-CoV-2 data, this exercise will have used data that would ultimately be updated in the future. Please visit the ECDC site to download the most recent SARS-CoV-2 data.

The figure we will replicate is one posted on the “Our World in Data” website and looks like the following:

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Source: Our World in Data. Total confirmed COVID-19 deaths: How rapidly are they increasing? URL: https://ourworldindata.org/grapher/covid-confirmed-deaths-since-5th-death [last accessed: 17 April 2020].

We will download data from the ECDC and then use Excel to recreate this plot for several countries (we won’t create plots for all the countries, but you can feel free to do so by taking advantage of the available data).

Step 1. Download data from the ECDC.

You can download the raw data from the ECDC’s site here. Alternatively, you can also download the cleaned data for this article here (I cleaned the data and prepared them for use in Excel).

The data has the following format:

Each column represents a country and the rows represents the total number of deaths for each day after the 5th confirmed death.

Step 2. Select all of the data and insert a line chart.

Once all the data have been downloaded, select all of them. Insert a line chart and Excel will automatically generate a figure for you. This figure will need to be edited further, but Excel does a good job of plotting the total number of deaths along the X-axis (time).

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Right-click in the chart region and click on the Select Data option. You want to de-select the “Days since the 5th total confirmed death” because this is not the value of interest. Rather, this represents the values for the X-axis (time).

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Once you de-select the “Days since the 5th total confirmed death,” your line chart should look like the following figure.

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Step 3. Changing the scale on the Y-axis.

In the figure from the Our World in Data, the values for total deaths are plotted using a log-scale. When Excel generates the line chart, it automatically uses the continuous scale on the Y-axis. To change this, you need to right-click on the Y-axis and then select Format Axis...

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Once you make the changes to the Y-axis scale, the line chart should now look similar to the one from Our World in Data.

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Step 4. Adding the axes labels and formatting the lines.

Once the line chart’s Y-axis has been transformed into a log-scale, you can make changes to the axes labels and the line formatting. Select the Design tab to made changes to the Y-axis label. Select the Add Chart Element to open the drop-down menu, then select the Axis Title followed by the Primary Vertical option. This will allow you to make changes to the Y-axis label.

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Change the Y-axis label to read, “Log number of COVID-19 deaths.” Do the same thing for the X-axis label, but change it to read “Days since 5th death occurred.” Your figure should look like the following.

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You can make the lines thinner by right-clicking on one of them (e.g., China), opening up the options. Select Format Data Series… and then adjusting the Width to be 1.5 points. This will make the line easier to see without having to take up space with the thicker lines.

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Repeat this process for all the lines in the chart. Once you have completed that, the line chart should look like the following.

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Step 5. Now all that’s left is changing some of the aesthetics.

The final line chart replicates the figure from Our World in Data and provides the references line for a doubling in the number of deaths for each country. The reference line (“Doubling every 5 days”) was creating using a base of 10 on a log-scale to replicate a doubling of that value every 5 days. The reference line was also placed on a secondary axis to create a continuous line (since creating a line on the same scale as the other countries would have yielded gaps because we’re doubling deaths every 5 days). In the Design tab, you can add the secondary horizontal axis to match that of the primary horizontal axis (time). The secondary vertical and horizontal axes had their font color changed to white to hide them from view and to clean the final figure.

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Gridlines were added along with the label for the reference line, which indicates to point where the total confirmed SARS-CoV-2 deaths are doubling every five days.

Here is the final chart after some formatting changes were made.

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Conclusions

Although we plotted total deaths from SARS-CoV-19 from existing data, these were limited to five countries. More countries can be added using the available data, and it is encouraged that you try to plot all the other countries as an exercise. The reference line provides us with the doubling of deaths on a log-scale and carefully provides the readers with a threshold where certain doubling of deaths would be reported. China seems to have controlled their total number of deaths, but there is a spike at the end of day 87 that shows an increase in deaths. This may be due to reporting error or a change in the definitions of death. The US, including the other European countries, are trailing the rest of the world in containing the SARA-CoV-2 pandemic. Hopefully, this type of data visualization will help inform decision makers to develop policies that would mitigate the impact of SARS-CoV-19 on mortality any parts of the world.

You can download the data and complete exercise here.

References

1. Roser M, Ritchie H, Ortiz-Ospina E, Hasell J. Coronavirus Disease (COVID-19) – Statistics and Research. Our World Data. March 2020. https://ourworldindata.org/coronavirus. Accessed April 17, 2020.

2. European Centre for Disease Control and Prevention. Download today’s data on the geographic distribution of COVID-19 cases worldwide. European Centre for Disease Prevention and Control. https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide. Published April 18, 2020. Accessed April 18, 2020.

3. Our World in Data. GitHub: Owid/Covid-19-Data. Our World in Data; 2020. https://github.com/owid/covid-19-data. Accessed April 18, 2020.

Communicating data effectively with data visualizations: Part 23 (Epidemic Curves)

INTRODUCTION

In December 2019, a novel strain of coronavirus was detected in Wuhan, the capital city of the Hubei province in China. This coronavirus was designated as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This current iteration of the coronavirus has many similar characteristics to its earlier ancestor SARS-CoV-1,[1] which was first detected in 2003 and known simply as SARS. According to a recent study, clinical characteristics of patients in China who were infected with SARS-CoV-2 included fever (up to 88.7% who were hospitalized) and cough (67.8%).[2] The median age of patients infected was 47.0 years (IQR: 35.0-58.0) with a large distribution of 58.3% over the age of 50 years having severe symptoms. Additionally, the case fatality rate was reported to be 1.4%.

In January 2020, the World Health Organization (WHO) in regards to the SARS-CoV-2 outbreak declared a global health emergency.[3] Regardless, as SARS-CoV-2 spread across the globe into a pandemic, many countries started to report the attributable number of cases and deaths. According to the WHO, the global total of confirmed cases is at 191,127 and global deaths is at 7,807 (as reported on March 18, 2020).[4]

One of the most important tools in understanding the SARS-CoV-2 epidemic course is the epidemic curve. Epidemic curves allow epidemiologists to visualize the progression of an outbreak by surveilling the number of cases across time.[5] The epidemic curve informs epidemiologists about the pattern of the outbreak’s spread, magnitude, time to exposure, and outliers. Moreover, the epidemic curve is constantly updated as more data become available.

As the SARS-CoV-2 pandemic spreads to other countries, many data visualizations have been developed to help educate and inform people. Johns Hopkins University has developed a real-time dashboard with epidemic curves on the SARS-CoV-2 pandemic that is an excellent source of global cases and mortality. The Centers for Disease Control and Prevention (CDC) also has a series of data visualizations on the SARS-CoV-2 outbreak in the United States including an epidemic curve.

This article will review the features of an epidemic curve and provide a tutorial on creating one based on the available data from the CDC on the SARS-CoV-2 outbreak in the United States.

EPIDEMIC CURVE

When an outbreak happens, there is an urgency to determine when it first occurred. Epidemiologists carefully, collect data to determine who patient zero is and when the case was first identified. This gives them a starting point for when the outbreak occurred. Epidemic curves provide information on the outbreaks’ spread, magnitude, incubation period, outliers, and time trend. Key features of the epidemic curve include the number of cases on the Y-axis and the date of illness on the X-axis. Figure 1 illustrates the key features of the epidemic curve for a point-source outbreak.

Figure 1. Key features of the epidemic curve for a point-source outbreak.

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Epidemic curves can tell us information about the outbreak’s pattern of spread. Figure 1 illustrates the pattern of spread for a point-source outbreak. In a point-source outbreak, a single source of contamination affects a group of people at a single event (e.g., rotten potato salad at a dinner party). Other patterns include continuous-source and propagated-source outbreaks. Continuous-source outbreak occurs when the group of people are exposed to a source of contamination for a period of time (e.g., lead poisoning in children). Propagated-source outbreak occurs when the contamination is spread from person-to-person (e.g., flu). The SARS-CoV-2 is an example of a mixed-source outbreak where the early outbreak was due to a common-source (e.g., possibly from zoonotic transmission from animal to human) followed by a propagated-source outbreak where the virus is spread from person-to-person via air droplets or physical contact.[6] Based on a recent study, the mean incubation period for SARS-CoV-2 is 5.1 days (95% CI: 4.5 to 5.8 days).[6]

Motivating example

The SARS-CoV-2 outbreak in the United States was first reported in January 14, 2020. Data on the number of cases can be downloaded from the CDC’s SARS-CoV-2 surveillance site (Note: CDC data are updated daily; hence, the data for this exercise will not reflect these changes). We will use these data to create an epidemic curve of the SARS-CoV-2 outbreak in the United States using Excel. You can download the data files used in this exercise here.

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The data are arranged in a wide format where the date (time) is represented by columns and the number of cases is represented by rows. This makes it much easier to generate the epidemic curve in Excel.

Step 1. Highlight the data and Insert a bar chart.

Select the data and insert a clustered column chart. The default version will provide a simple epidemic curve. However, we want to remove the spaces between the bars. To do that, we will need to format the

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Step 2. Changing the size of the bars.

To change the size of the bars, we need to right-click one of them to bring up the editing menu. Then we select the Format Data Series… to bring up the options. We set the Gap Width of the bars to 0% so that their sides are in contact with each other. But to distinguish them, we can change the outline’s color to White. Increasing the width of the border’s color will increase gaps between each bar.

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After a few more changes (e.g., color and labels), the final epidemic curve will represent the CDC’s data on SARS-CoV-2 on March 18, 2020 (Figure 2). Since data are constantly changing and require validation during an outbreak, this epidemic curve will eventually change. It is recommended that you constantly update this exercise’s data in order to have the most recent, accurate, and valid data from the CDC on SARS-CoV-2. You can also compare your findings to those of the CDC at their website.

Figure 2. Cases of SARS-CoV-2 in the United States.

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Conclusions

Epidemic curves are helpful in understanding a disease outbreak in a community. They provide us with a visual representation of the outbreak’s magnitude, pattern, and time period, which will allow us to implement public health policy to stem, reduce, and eventually eradicate the contagion from our population. Although this is a short introduction on epidemic curves, it will, hopefully, be enough for you to review and interpret other epidemic curves in the news or literature.

Files related to this exercise are available here.

References

1. van Doremalen N, Bushmaker T, Morris DH, et al. Aerosol and Surface Stability of SARS-CoV-2 as Compared with SARS-CoV-1. N Engl J Med. 2020;0(0):null. doi:10.1056/NEJMc2004973

2. Guan W, Ni Z, Hu Y, et al. Clinical Characteristics of Coronavirus Disease 2019 in China. N Engl J Med. 2020;0(0):null. doi:10.1056/NEJMoa2002032

3. World Health Organization. Statement on the second meeting of the International Health Regulations (2005) Emergency Committee regarding the outbreak of novel coronavirus (2019-nCoV). https://www.who.int/news-room/detail/30-01-2020-statement-on-the-second-meeting-of-the-international-health-regulations-(2005)-emergency-committee-regarding-the-outbreak-of-novel-coronavirus-(2019-ncov). Accessed March 19, 2020.

4. World Health Organization. Coronavirus Disease 2019 (COVID-19) Situation Report –58.; 2020. https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200318-sitrep-58-covid-19.pdf?sfvrsn=20876712_2. Accessed March 19, 2020.

5. Centers for Disease Control and Prevention. Interpretation of Epidemic (Epi) Curves during Ongoing Outbreak Investigations | Foodborne Outbreaks | Food Safety | CDC. https://www.cdc.gov/foodsafety/outbreaks/investigating-outbreaks/epi-curves.html. Published November 16, 2018. Accessed March 19, 2020.

6. Lauer SA, Grantz KH, Bi Q, et al. The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application. Ann Intern Med. March 2020. doi:10.7326/M20-0504

Communicating data effectively with data visualizations: Part 22 (How to create a double axes figure in Excel)

INTRODUCTION

Displaying two types of information on a single figure is commonly done using double axes. Normally, you would use a single y-axis to reflect the outcome or metric of interest and a single x-axis for a standard set of categories (e.g., time). However, if the scales are different, leveraging a secondary axis can enhance and improve the visualization. In this article, we will demonstrate how to create a secondary axis using Excel.

MOTIVATING EXAMPLE

For example, the total overdose deaths are plotted on the y-axis and the year is plotted on the x-axis (Figure 1).

Figure 1. Total overdose deaths in the United States, 1999 to 2017.

Source: https://www.drugabuse.gov/related-topics/trends-statistics/overdose-death-rates

Total overdose deaths in the United States (US) is large and growing. In 1999 it started at 16,849 deaths, but by 2019, that number rose to over seventy thousand. This is a large number and the scale that is used can impact how the viewer will synthesize this visualization.

However, if you wanted to add another line or data dimension such as the number of deaths due to opioids with a benzodiazepine prescription, that number may not be on the same scale as the total overdose deaths that’s currently represented by the y-axis. Figure 2 illustrates the Total Overdose Deaths and Overdose Deaths due to an Opioid-Benzodiazepine combination. Notice how the Opioid Deaths due to an Opioid-Benzodiazepine combination is plotted on the same scale as the Total Overdose Deaths. Using the same scale makes it difficult to discern the change in deaths associated with an Opioid-Benzodiazepine combination.

Figure 2. Overdose Deaths due to Opioids, 1999 to 2017.

To improve this, we may consider using a secondary y-axis to correctly scale for the number of deaths due to opioids-benzodiazepine co-prescribing.

Step 1. Open the Excel document with the data, which is located here.

Step 2. Review the data. In this example, we have the total overdose deaths and the deaths associated with opioids-benzodiazepine co-prescribing from 1999 to 2017.

Step 3. Select the three columns and use the Excel Insert tab to select the line figure.

Step 4. Review the line graph.

The default line plot that is generated includes all the variables in a single y-axis. This is not what we want.

We need to correct this and select the correct axes for these variables.

Step 5. The Year variable needs to be on the x-axis.

Right-click anywhere on the chart space. A menu of options will appear. You want to click on the Select Data… option to make changes to the data and how they are displayed on the chart.

Then Remove the Year variable from the Legend Entries (Series) panel. This will remove Year from being plotted on the y-axis. Next, you want to edit the Total Overdose Deaths in the Horizontal (Category) Axis Labels panel.

After clicking on Edit, you will be given a small window to select the range of data to display on the x-axis. Use the button in the Axis label range to select the Years 1999 to 2017. This will populate the x-axis with the years corresponding to the Total Overdose Deaths.

Step 6. Selecting the secondary y-axis.

Once the Year has been correctly specified, we can create a secondary y-axis to represent the Deaths due to opioids-bzd since these numbers are much smaller than the Total Overdose Deaths.

Right click on the line that represents Deaths due to opioids-bzd. Select the Format Data Series… option. This will give you a menu with the choice to use the Primary or Secondary axis. Select the Secondary axis.

This will generate a chart that will include two y-axes: (1) Total Overdose Deaths and (2) Deaths due to opioids-bzd.

You can format the figure to improve its appearance with Excel’s other functions. In this example, the line colors were changed to provide a better contrast, the y-axes were properly labelled, and the font colors matched those of the lines.

CONCLUSIONS

Using double axes can improve the narrative of your data visualization. Take advantage of this option when presenting data with the same x-axis, but different measures. In this example, the metric of interest was death, but the scales were different. Total Overdose Deaths were significantly larger than Deaths due to opioid-benzodiazepine co-prescribing. Therefore, to better visualize the increasing number of deaths due to opioid-benzodiazepine co-prescribing, a secondary y-axis was added.

SUPPLEMENTS

Data and Excel file used for this exercise are available here.

Medical Expenditure Panel Survey (MEPS) Guide - Part 1

INTRODUCTION

Medical Expenditure Panel Survey (MEPS) is a publicly available dataset on healthcare expenditures that is representative of the US population.

The MEPS homepage contains vital information about the methods used to validate household responses and guides on how to properly use these data for research or exploration. You can learn about MEPS in its background section.  

MEPS data files are available for download here. The most important file is the Full Year Consolidated Data Files, which contains the data for unique household responses on their characteristics and expenditures. These data are great practice for those interested in learning more about MEPS. Each of the Full Year Consolidated Data Files contain information about the data in the form of Documentation and Code Books. For example, the 2017 Full Year Consolidated Data Files Documentation and Code Book are located here.

If you area a Stata user, there are Stata programming statements available to copy and paste into a Stata *.do file. These programming statements are used to transform the MEPS data into a *.dta file that is usable by Stata. Follow the instructions in the programming statement to properly transform the MEPS data. This is similar to an extract-transform-load (ETL) process.

MEPS has a library of reports that uses its data. You can search for topics using their search engine. For example, Report #43 describes the annual opioid usage among adults treated for conditions associated with pain versus other conditions from 2013 to 2015.

Other examples of MEPS data being used in research include the following:

1. Hamad R, Niedzwiecki MJ. The short-term effects of the earned income tax credit on health care expenditures among US adults. Health Serv Res. 2019 Dec;54(6):1295-1304. doi: 10.1111/1475-6773.13204. Epub 2019 Sep 30.

2.  Watanabe JH. Examining the Pharmacist Labor Supply in the United States: Increasing Medication Use, Aging Society, and Evolution of Pharmacy Practice. Pharmacy (Basel). 2019 Sep 19;7(3). pii: E137. doi: 10.3390/pharmacy7030137.

3.  Bounthavong M, Li M, Watanabe JH. An evaluation of health care expenditures in Crohn's disease using the United States Medical Expenditure Panel Survey from 2003 to 2013. Res Social Adm Pharm. 2017 May - Jun;13(3):530-538. doi: 10.1016/j.sapharm.2016.05.042. Epub 2016 May 20.

Communicating data effectively with data visualizations: Part 21 [Examples of famous (and infamous) data visualizations]

FAMOUS (AND INFAMOUS) DATA VISUALIZATIONS

Modern data visualization has a relatively young history compared to other forms of science (e.g., physics, mathematics, chemistry, biology, etc). However, it’s existence can arguably be more historic. Throughout history, we have examples of data visualizations that helped us understand communicable diseases, wartime operations, and the diffusion of technology. Each of these are important in their own respective fields, but making them comprehensive and intuitive would be nearly impossible without creative data visualizations. This article will review several key historical data visualizations from the cholera outbreak to the dawn of the internet and their impact on our society.

JOHN SNOW AND THE CHOLERA OUTBREAK

In the 19th century, little was known about the transmission of disease. The discovery of the germ theory of disease was still in the horizon, and medical knowledge and understanding of its significance had yet to make its way into public health policy. This was true for London during the cholera outbreak of 1854.[1]

John Snow (1813-1858) was an English obstetrician who is considered one of the founders of epidemiology, the study of health and diseases in populations. At the time, diseases were thought to have spread through the air, popularly known as the miasma theory. Snow was one of the first to reject this theory and believed instead that cholera was due to contaminated water that when drunk caused a viscous cycle of diarrhea and dysentery that ultimately led to death. This belief was further supported when Snow discovered that sewage was dumped directly into the Thames River where the city got their drinking water supply. But to prove his theory, Snow had to chart out the outbreak of cholera in Soho, one the London’s suburbs.

Snow meticulously went to the homes of cholera infected patients and learned where they received their drinking water supply. He mapped his findings onto a grid of the city and observed that clusters of outbreaks occurred around specific points in the suburbs, mainly the water pumps (Figure 1).

Figure 1. John Snow’s map of cholera outbreak in Soho, London, 1854.

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Source: John Snow - Published by C.F. Cheffins, Lith, Southhampton Buildings, London, England, 1854 in Snow, John. On the Mode of Communication of Cholera, 2nd Ed, John Churchill, New Burlington Street, London, England, 1855. (This image was originally from en.wikipedia; description page is/was here. Image copied from http://matrix.msu.edu/~johnsnow/images/online_companion/chapter_images/fig12-5.jpg)

Snow also noticed that a large cluster of cholera cases occurred in households near the Broad Street pump (Figure 2). In Figure 2, each bar stack represents the number of cholera cases. In particular, the large number of cholera cases near the Broad Street pump provided further evidence that the drinking water supply was contaminated and was the source of the outbreak.

Figure 2. John Snow’s map of cholera outbreaks near the Broad Street pump.

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To prove his point, John Snow had the Broad Street pump handle removed and water delivered from another source, further away from the contaminated Thames River. As he predicted, the incidence of cholera dropped rapidly and the outbreak was mitigated.

This was an early example of using data visualization for real-time surveillance of an outbreak that led to a public health intervention. Clusters of cases within the proximity of the hypothesized contamination source effectively illustrated the benefits of geospatial data visualization of the cholera outcomes in the Soho suburbs of London. Today, we rely on spatial data analysis to monitor the influenza epidemic as well as several other diseases, which will help us to quickly react and contain potential outbreaks.

Napoleon’s Russian campaign of 1812

During the Summer of 1812, Napoleon Bonaparte raised over 422,000 troops and personnel to invade Russia. This was in response to the Russian tsar’s, Alexander I, decision to leave the French-led trade union, which undermined Napoleon’s ideologies for an economically strong centralized Europe.

Charles Joseph Minard (1781-1870) illustrated Napoleon’s doomed campaign of 1812 in a graph that famously shows the decline of the once Grande Armée as it began in the Summer to its fall in the early Winter (Figure 3). The graph tells two stories. The first is the start of the campaign which began in the Summer of 1812 and is displayed by the brown line going from Left to Right. The width of the line represents the size of Napoleon’s army at the beginning of the campaign, which numbered approximately 422,000 strong (troops and personnel). Also displayed is the route the army took to reach Moscow. During the journey, the width of the brown line thins representing the attrition of troops due to desertions and causalities. When Napoleon reached Moscow (represented in the right part of the graph) he only had a small fraction of his original strength (approximately 100,000 troops).

On the return trip, represented by the black line, the width of the line thins considerably and is correlated with the rapid drop in temperature, which is represented by the bottom chart. Desertions, casualties, and the weather reduced Napoleon’s army to approximately 10,000 troops and personnel (less than 3 percent of his original strength) by the time he reached the Neman River.

Figure 3. Charles J. Minard’s graph depicted Napoleon’s Grande Armée ill-fated Russian Campaign of 1812.

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Source: Charles Joseph Minard's famous graph showing the decreasing size of the Grande Armée as it marches to Moscow (brown line, from left to right) and back (black line, from right to left) with the size of the army equal to the width of the line. Temperature is plotted on the lower graph for the return journey (multiply Réaumur temperatures by 1¼ to get Celsius, e.g. −30 °R = −37.5 °C). Published November 20, 1869. (This image was originally from en.wikipedia; description page is/was here. Image copied from https://en.wikipedia.org/wiki/French_invasion_of_Russia#/media/File:Minard.png)

Minard’s graph shows many data elements highlighting the potential for multiple dimensions incorporated onto a two-dimensional canvas. The lines (both brown and black) denote the route of the army and its strength. At the very bottom of the graph, the temperature of the return journey dropped to below freezing temperatures highlighting the misery of the French troops during the long retreat to France (Figure 4). The creative use of space allowed Minard to include many data dimensions to tell the horribly tragic story of Napoleon’s disastrous Russian campaign. To date, Minard’s graphic is a reminder of the devastating defeat of Napoleon’s ambitions in Europe and the effective use of data visualizations to tell a compelling story.[2]

Figure 4. Temperatures on the return journey (Right to Left).

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CARNA BOTNET MAP

In what is now called the Internet Census of 2012, an anonymous hacker produced one of the most important and invaluable data visualization of the diffusion of internet traffic across the globe.[3] Using a botnet and taking advantage of vulnerabilities in network systems, this anonymous hacker was able to penetrate the securities of these networks and then ping these IP addresses to yield a census of active internet networks across the world. The botnet was called Carna, named after the Roman goddess of the door hinge (but she is also known as the goddess of the body). The Carna botnet captured over 1.3 billion IP addresses in the world.

The Carna botnet map is an animated Graphic Interchange Format (GIF) file that provides a 24-hour cycle of internet use around the globe (Figure 5). It was first published sometime in June to October 2012 by the anonymous hacker who wanted to illustrate internet use around the world with all the data that was available. To this day, no one knows the identity of the hacker.

Figure 5. 24-hour world map of IP addresses observed using IP ping requests.

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Source: World map of 24 hour relative average utilization of IPv4 addresses observed using ICMP ping requests. Carna Botnet, * Internet Census 2012: Port scanning /0 using insecure embedded devices, Carna Botnet, June - October 2012. 16 March 2013.

The author of this animated GIF uses colors and contrast ratio effectively to deliver a powerful narrative of the daily cycle of internet use. The warm colors represent internet usage during the day and the cool colors represent internet usage after sunset. The nightly cycle moves from Right to Left giving the impression that the world is rotating from being asleep to being awake. More importantly, the image of the world provides the audience with a reference that is recognizable and easy to understand. The data that were used to generate this animated GIF continue to be used by researchers to study their implications on internet security and ethics.[4,5]

It is highly recommended that you download and view the GIF on your own to appreciate the animation.

CONCLUSIONS

Data visualization is an effective tool to tell complicated stories; sometimes, it’s the only way. Historically, we have been doing this without the aid of personal computers and visual software. In most cases, data visualization was something that was done by hand and carefully illustrated like a piece of art. In these examples, stories from the cholera outbreak, failed military ambition, and an illegal comprehensive internet census have provided us with a better understanding of how our world operates and the impact of these data on our society.

REFERENCES

1. Johnson S. The Ghost Map: The Story of London’s Most Terrifying Epidemic—And How It Changed Science, Cities and the Modern World. New York, NY, USA: Riverhead Books; 2006.

2. Joyce H. Minard and Napoleon’s march on Moscow. Significance. 2008;5(3):133-134. doi:10.1111/j.1740-9713.2008.00311.x

3. Internet Census 2012. http://census2012.sourceforge.net/paper.html. Accessed December 12, 2019.

4. Krenc T, Hohlfeld O, Feldmann A. An Internet Census Taken by an Illegal Botnet: A Qualitative Assessment of Published Measurements. SIGCOMM Comput Commun Rev. 2014;44(3):103–111. doi:10.1145/2656877.2656893

5. Dittrich D, Carpenter K, Karir M. The Internet Census 2012 Dataset: An Ethical Analysis. IEEE Technology and Society Magazine. June 2015:40-46. doi:10.1109/MTS.2015.2425592
   
   

Communicating data effectively with data visualizations: Part 20 (Enhance your data visualization with labels and contrast)

USING LABELS TO ENHANCE YOUR DATA VISUALIZATIONS

Labeling objects (data points, categories, axes, etc.) in your data visualizations is an important part of telling a good story. Without proper labels the figures in your presentation will leave out important elements of the narrative. Labels provide information about the data points or the categories in the figure. We normally use labels to provide information about the axes of the figure (e.g., horizontal and vertical). This is crucial because it tells our audience what the data visualization is measuring. But labels can also be used to provide a richer and informative description of your data visualization that enhances the narrative of your data-driven story.

Take a look at the two figures below. Which one tells you a better story?

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The obvious answer is the right figure because it contains labels for the lines that reflect the sales of hardware and software products between 2010 and 2019. We easily see the sales growth from 2010 to 2019 because the labels identify these two products. Additionally, the labels are color coordinated with the line colors so that these are explicitly clear what lines the labels represent. Without these labels, we would have no idea what the lines represent.

Take a look at the next set of figures, what’s different about them? Are they better than the figures above?

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The figure on the left removes the Y-axis and tells us that the growth of hardware sales was greater than software. However, we don’t know the magnitude of the difference in the sales. The figure on the right is more efficient in presenting the hardware and software sales because it includes the values from 2010 to 2019. In other words, the right figure removes the unnecessary values from the X-axis and provides the values that are relevant, in particular, those from 2010 and 2019. (This is a return to Tufte’s principle of the data-ink ratio where we want to maximize the information the ink provides in terms of the data.)  

CONTRAST RATIO

According to the Web Content Accessibility Guidelines (WCAG), the minimum contrast ratio between the text and background is 4.5:1. This meets Section 508 requirements from the Rehabilitation Act (29 U.S.C. 794d) that was amended by the Workforce Rehabilitation Act of 1973, which requires that all electronic content purchased by any Federal Agency be accessible to people with disabilities. These requirements are in place to assist those who have difficulties seeing the full color spectrum.

Take a look at the following figures below. Which one has a better contrast ratio?

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The left figure has a contrast ratio of 2.35:1, which is below Section 508 requirements. The right figure has a contrast ratio of 7.36:1, which is above Section 508 requirements. It’s clear that the data labels are much easier to see in the right figure compared to the left figure. Having a good contrast ratio is critical to telling your narrative with data, but it is also a considerable advantage when presenting using slides where colors can be washed out by different projectors or bright rooms. Make sure to use high contrast ratio to have your data be more effective for your audience. (Note: For large-scale text (≥ 18 point font or ≥ 14 bold point font), you can use a contrast ratio of 3:1.)

You can check the contrast ratio using online tools such as the one here (developed by WebAIM). However, you will need to get the hex triplet color number from your data visualization. The hex triplet is a six-digit hexadecimal code used for web-based design and reflects the 24-bit RGB color spectrum.

To get the hex triplet color number from your data visualization in Excel (we are using Excel as an example, but this can work with other products that use a color palette), go to color format window and select the “More Colors…” option.

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Use the eye dropper to select the color from your file (e.g., Excel, Word). The hex triplet color number will automatically populate in the “Hex Color #” field. Use this on the following website to determine the contrast ratio. (Remember, you want to have a contrast ratio of ≥ 4.5:1.)

CONSISTENCY

Labels should be consistent throughout your data visualization. If you decide to use Arial font in your labels, make sure that you consistently use them for the same label type.

Compare the two figures below. The figure in the bottom panel uses different fonts for the data labels, but the figure in the top panel has a font that is consistent. Having different fonts can be distracting, so it’s best to be consistent with the font (and size) that you use in your data visualizations.

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Another point about consistency is the case rule for labels. Normal sentence case is the preferred method for providing labels according to the US Data Visualization Standards. However, I believe you are the best judge for when to use sentence case or other case rules for your data visualizations.

Compare the two figures below. The left figure has a legend that uses a sentence case where each word is capitalized (e.g., “Prevalence Of Deaths in 2015”). The right figure’s legend uses a normal sentence case (e.g.,  “Prevalence of deaths in 2015”). Which is better?

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For me, having each word capitalized looks awkward (see below). I prefer to use a legend with a normal sentence case, but you may choose to use something different. I encourage you to experiment and find the right rules for your specific scenarios.

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The top panel has the sentence case where all the words are capitalized. The bottom panel has normal sentence case.

CONCLUSIONS

Including data labels can enhance your data visualizations and strengthen your narrative. But you need to make sure that you are consistent and apply high contrast to be effective with your presentation. In this article, we introduce the importance of using the correct contrast ratios according to the WCAG and standardizing your font style. However, it is also important to incorporate your own creativity into your data visualization. Some rules should be broken in order to improv the narrative. So, be adventurous!

REFERENCES

WebAIM is a site that provides a contrast ratio tool, that checks the contrast ratio for your projects. WebAIM a non-profit organization that is based at the Center for Persons with Disabilities in the University of Utah. Their mission is to “…empower organizations to make their web content accessible to people with disabilities.”

The US Data Visualization Standards (DVS) is a great site for rules that the US Government uses for their data visualizations and web tools.

Web Content Accessibility Guidelines are a great resource for learning more about standardizing your data visualization. Although the WCAG was meant for web content and design, it can be generalized to your presentations, publications, and other data visualization tools.

Communicating data effectively with data visualizations—Part 19 (Doughnut charts)

INTRODUCTION

When comparing proportions or prevalence, the pie chart has been used as a favorite among Excel users due to its ease and familiarity. However, Tufte and other data visualization pioneers lament its use and recommend other graphical representations as alternatives.1 Doughnut charts are similar to pie charts except that the center is removed. Unlike pie charts which do not provide a good comparison of the proportional slices to one another, the doughnut chart focuses on the use of the length of the arcs for comparisons, which limit the potential for errors. By comparing the arc’s length of a doughnut chart to each other, you avoid the problem of comparing proportions between the slices in a pie chart.

MOTIVATING EXAMPLE

We will use data from the Centers for Disease Prevention and Control (CDC) to illustrate the number of tobacco products used among Middle and High-School students in the United States. You can download the Excel document here.

Source: Flavored Tobacco Product Use Among Middle and High School Students—United States, 2014–2018. url: link [Accessed on 17 October 2019]

In 2018, there were 73.98 students per 100 population who reported using e-cigarettes. This was followed by a vastly lower prevalence of 28.66 students per 100 population who reported using Menthol cigarettes and 26.63 students per 100 population who reported using cigars. The CDC undertook this investigation to assess the types of tobacco products used by students. Based on this data, e-cigarettes appears to be a popular tobacco product. 

We will generate a doughnut chart to illustrate the prevalence of different tobacco product use in students.

CREATING A DOUGHNUT CHART

We want to create a side-by-side comparison between the different types of tobacco use in students using the prevalence, number of students per 100 population. This will allow us to make easy comparisons using the circumference of the doughnut charts.

Step 1. Set up the data

Since the prevalence is the number of students per 100 population, we can define our denominator as 100. Therefore, if 73.98 students reported using e-cigarettes, then 26.02 students did not (100 – 73.98). The table below provides the calculations to estimate the remainder column.

Once the remainder column has been calculated, we can Insert the doughnut chart onto our Excel worksheet.

Step 2. Insert donut chart

Select the prevalence and remainder data for the e-cigarette row.

Then Insert the doughnut chart using the Excel ribbon.

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Once you select the doughnut chart, Excel will generate a default chart for you.

Step 3. Change the size of the doughnut chart

The default size is not balanced. We want to make the height and width the same size. To do that, we start by clicking on the Format tab and then going to the dimensions box to change the defaults to 4 inches by 4 inches.

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Step 4. Change the size of the doughnut ring

The current doughnut ring is too thin. We can change this by right-clicking on the doughnut and selecting the Format Data Series. It will open a window with options to modify Doughnut Hole Size. Change this from the default to 65%. 

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Step 5. Add data labels

To add data labels, right-click on the doughnut and select Add Data Labels. The data labels will populate both segments of the doughnut.

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Step 6. Change font size and color palette

We can improve the aesthetics of the doughnut chart by increasing the font size and changing the color palette. In this example, I changed the font to Arial size 14 and I used the Blue Monochromatic Palette #1.

Repeat this for the other types of tobacco use and you can generate a series of doughnut charts that are easily comparable to each other.

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CONCLUSIONS

Doughnut charts are better alternatives to pie charts because they use the arc of the circle to represent the proportion of the population. You can navigate through the differences quickly with the doughnut charts and see how different the prevalence of e-cigarettes are compared to other forms of tobacco use. This indicates that there is a huge popularity among students to use e-cigarettes as a favorite tobacco product. The implications of students using e-cigarettes let along any type of tobacco products are under investigation, but the data reported here highlight the popularity of e-cigarettes among students in the United States.  

REFERENCE

1. Tufte ER. The Visual Display of Quantitative Information. Second. Cheshire, CT: Graphics Press, LLC.; 2001.

Biography: Florence Nightingale

INTRODUCTION

Although data visualization has established itself as an important part of any scientific report and presentation, it has largely depended on the contributions of unique individuals. Several of these individuals have been mentioned throughout this data visualization series such as Edward R. Tufte, William S. Cleveland, and Cole Nussbaumer Knaflic. Each of these individuals have advanced the field of data visualization by sharing their philosophy and style to improve how data can be visualized easily and thoughtfully. But one person in history made the greatest advancements with data visualization in a time when war and public health became important partners in improving health care—Florence Nightingale.

Source: Duyckinick, Evert A. Portrait Gallery of Eminent Men and Women in Europe and America. New York: Johnson, Wilson & Company, 1873. [Link]

CAREER

Florence Nightingale (1820–1910) was a nurse, statistician, and social reformer who is famously known for treating British troops during the Crimean War. During the conflict where nations from Britain, France, Sardinia, Russia, and the Ottoman Empire mobilized for war between 1853-1856, more than 21,000 British troops died; only 5,000 deaths were attributable to actual battle. Most troops died not because of combat, but due to common camp diseases such as cholera, dysentery, and typhoid. Nightingale’s reforms helped to reduce non-combat related mortality in the British Army and earned her the accolade of Henry Wadsworth Longfellow who immortalized to her as “The Lady with the Lamp” in one of his poems.

When she was appointed Superintendent of the Female Nurses in the Hospitals in the East by Sydney Herbert, the Secretary of War, in 1854, she brought with her a team of 38 volunteer nurses and an innovative and determined mind.[1] Armed with her classical training and determination to get thing done, Nightingale began implementing reforms in the British Military Hospital Barracks. She instituted sterilized laundry and hand washing sanitation protocols, raised funds, and improved hospital administration. Moreover, during her tour in the Crimean War, Nightingale collected an impressive collection of data about mortality in the army, which were later used in several reports to the Royal Commission on the Health of the Army and Queen Victoria.

When Nightingale returned from the war, she created the Nightingale Training School at St Thomas’ Hospital (now called the Florence Nightingale Faculty of Nursing and Midwifery and & Palliative Care at King’s College London) to train a new generation of nurses using her ideas and philosophies in 1860.

DATA VISUALIZATION

In addition to her accomplishments in nursing, public health, and social reform, Nightingale has been hailed as a pioneer in using statistics and data visualization to maximum effect and changed policies regarding how soldiers were cared for in military hospitals. Using data she collected, Nightingale went about describing them in visual detail. She is famous for creating a new type of diagram that was meant to fuel the narrative she was arguing called the Nightingale rose or wedge diagram (Figure 1). (Other names for the rose diagram include the coxcomb and polar area diagrams.)

Figure 1. Florence Nightingale rose diagram illustrated the causes of death in the British Army. 1858. Source: [Link]

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The rose diagrams were generated using the following table from Nightingales report to the Royal Commission on the Health of the Army (Figure 2). The rose diagram takes advantage of the radii of the segments or petals in addition to their length from the center to generate areas that reflected the scale and size of the different months. Each petal (segment) represented a a month and the estimated mortality rate (deaths per 1000 population). From each petal of the rose diagram, a reader can discern the scale of the mortality by month relative to other months based on the area. This type of visual aid prompted to military to review how the soldiers were being treated and reformed how the military operated.

Figure 2. Estimated Average Monthly Strength of the Army; and the deaths and Annual Rate of Mortality per 1000 in each month, from April 1854, to March 1856.

Source: Mortality of the British Army, At Home, At Home and Abroad, and During the Russian War, As Compared with the Mortality of the Civil Population in England. 1858. Harrison and Sons, St. Martin's Lane. [Link] [Accessed September 11, 2019].

Legacy

Nightingale was relentless in her pursuits; she stood up and challenged the establishment of British male dominance in the military and at the hospitals. In doing so, she brought about reform that saved lives and changed the way we used and viewed data. Among her many accomplishments, she was the first female member of the Royal Statistical Society and an honorary member of the American Statistical Association. In her book, Nightingale extolled the partnership between people and government in establishing public health measures as necessary and ethical:

“Let the people only see how much they can do for themselves in improving their surface drainage, in keeping their water supply free from pollution, in cleansing inside and out.

Let the Government see how much they can do for the people in introducing and stimulating better agriculture; irrigation, combined with drainage works in water-logged districts; for the two must never be separated there.

There is not a country in the world for which so much might be done as for India.

There is not a country in the world for which there is so much hope.

Only let us do it.

— Florence Nightingale [2] ”

It only seems fitting that Florence Nightingale has been immortalized by Henry Wadsworth Longfellow in his poem “Santa Filomena”:

“A lady with a lamp shall stand
In the great history of the land,
A noble type of good,
Heroic womanhood.”

REFERENCES

1. Fee E, Garofalo ME. Florence Nightingale and the Crimean War. Am J Public Health. 2010 September; 100(9): 1591. [Link]

2. Nightingale F. Life and Death in India. 1874. Spottiswoode & Co. New Street Square, London. [Link] [Accessed: September 10, 2019].

There are countless articles and sites on Florence Nightingale that you can find online. However, I found the following to be helpful in writing this article:

Andrews RJ. Florence Nightingale is a Design Hero. July 15, 2019. [Link] [Accessed: September 10, 2019].

Mathematics of Florence Nightingales’ rose diagram. [Link] [Accessed: September 11, 2019]

Communicating data effectively with data visualizations—Part 18 (Histograms)

BACKGROUND

Inspecting your data is an important part of data analysis preparation. Data, like all things, should behave according to some reasonable expectation. For example, if we randomly sampled a group of people in the U.S., we would reasonably expect to get 50% males and 50% females. Similarly, if we examined the age distribution of this sample, we would expect to have a normal distribution.

At the macro level, we may only be interested if the mean and standard deviations are representative of the population distribution. Since we sample from the population (randomly), we would expect to get similar means (and medians). This can be accomplished using simple Excel functions (or commands in statistical packages) to generate a descriptive summary. Table 1 describes the summary statistics for the total fat consumed by a sample of 8,327 responders to the National Health and Nutrition Examination Survey (NHANES) survey.

We can see that the mean and the median are different, which is an indicator that the distribution is not normal. However, we may be interested in learning more about the distribution or behavior of this variable. Are there any outliers? How skewed is the distribution?

HISTOGRAMS

To visualize this, we will need to generate a histogram. A histogram is a visual representation (bars) of the distribution of data (usually continuous). It uses spacings called “bins” to count the number of times a value falls into that bin. A histogram looks like a bar chart, but the key difference is that in the histogram the adjacent bars are touching each other rather than having a space between them. Another difference is that histograms plot the frequency (or density) of a value or a range of values for a continuous data type; whereas, bar charts plot the count of a discrete data type (Figure 1).

Figure 1. Comparisons between histogram and bar chart.

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Keep in mind that the number of bins for histograms should be just enough to make out the distribution and not too small to be too much information. This is Grice’s maxim of quantity where data are presented in an informative manner without overwhelming the audience with too much information.[1] Creating smaller bins to increase the resolution of the histogram is unnecessary when all you want is a general visualization of the data’s distribution.

MOTIVATING EXAMPLE

We will use data from the NHANES survey (2015-2016) to generate a histogram in Excel. The data can be downloaded from my Dropbox folder here. I cleaned the file so that all missing data were dropped. In total, there are three variables:

·      seqn = subject identifier

·      drqsdiet = special diet (Yes/No/Don’t know)

·      dr1ttfat = amount of total fat (gm) consumed

We will create a histogram to visualize the distribution of total fat consumed by the subjects. To start, let’s select the data and insert a histogram chart from the Insert Tab.

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A histogram will be inserted near where your data are located on the worksheet. Excel automatically selects the bin sizes for you. But you can customize this to your needs.

Right click anywhere x-axis and select Format Axis. You should see a column on the right side appear with options to modify the bin sizes.

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You can modify the bin width, number of bins, the overflow bin, and underflow bin.

The bin width can be larger or smaller depending on how much resolution you want. You should balance this out with the appropriate number of bins you want to show. According Grice’s maxim of quantity, you don’t want to overwhelm your audience. In Excel, you can only modify either the bin width or the number of bins; never both.

The overflow bin indicates what the last bin should be. If anything is over the overflow bin value (X), then Excel will collapse those frequencies into that last bin. For example, if I wanted the overflow bin to be 137 grams or greater, I enter “137” into the overflow bin field. You can do the same thing on the other end of the x-axis with the underflow bin value.

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Once you’ve figure out how to change the number of bins, let’s change the number of bins from 66 to 100, 75, 50, and 25 to observe how the histogram changes.

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Notice that the histogram with a bin size of 100 is really fine whereas the bin size of 25 is blocky. We can tell from all of these figures that there is a right skew to the distribution due to a few outliers. There are 3 subjects who consume more than 400 grams of total fat compared to 19 subjects who consume between 300 and 399 grams of total fat. The higher resolution doesn’t really help us determine that the total fat consumption is right skewed compared to the figures with bin sizes of 75 and 50. If I were presenting to an audience or publishing an appendix, I would select either the figure with a bin size of 75 or 50. These two histograms illustrate the peak at the mean and the right-skewed distribution without violating Grice’s maxim of quantity. However, different situations will require you to make different choices, so I encourage you to explore the design features on Excels’ histogram.

STEM-AND-LEAF HISTOGRAM

The stem-and-leaf display is an alternative histogram that uses the prefix of number to assign positions into the bins. The following figure is a randomly selected number of subjects from our NHANES data. The first subject consumed 14 grams of total fat which is indicated by the 1* | 4. The 1* represents the first digit of “14” and the “|” separates the next digit. Similarly, there is one subject who consumed 22 grams of total fat indicated by the 2* | 2 and another subject consumed 24 grams of total fat (2* | 4).

CONCLUSIONS

Histograms are a great visualization tool to quickly check whether your continuous data are normally distributed. You can identify whether the mean is close to the median or whether there are left or right skewness to your data. Moreover, you can change the bin sizes of a histogram to become more refined or less so. But according to Grice’s maxim of quantity, it is best to present enough data that will get the information across to your audience without overwhelming them with unnecessary details.

REFERENCES

Grice, H. P. Logic and Conversation.  In Cole P. and Morgan J. (Eds), Syntax and Semantics: Vol 3, Speech Acts. Academic Press, New York, pp.43-58, 1975.