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Isaac H. Michaels, MPH1; Sylvia J. Pirani, MS, MPH2; Alvaro Carrascal, MD, MPH1 (View author affiliations)

Suggested citation for this article: Michaels IH, Pirani SJ, Carrascal A. Visualizing 50 Years of Cancer Mortality Rates Across the US at Multiple Geographic Levels Using a Synchronized Map and Graph Animation. Prev Chronic Dis 2020;17:190286. DOI: http://dx.doi.org/10.5888/pcd17.190286external icon.

PEER REVIEWED

Static display of the change in US cancer mortality rates from 1968 to 2017. [A text version of this figure is also available.]

We developed the synchronized map and graph animation to visualize changes over time in yearly, age-adjusted, cancer mortality rates at the county, state, and national geographic levels for the United States from 1968 through 2017. The goal was to enable viewers to select trends of interest for a particular state, region, or time interval.

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Background

Cancer is the second leading cause of death in the United States (1). An estimated 42% of all cancer cases and nearly one-half of all cancer deaths in the United States are attributable to modifiable risk factors (2). Health officials and stakeholders need visualizations of data on cancer deaths to target prevention and treatment efforts optimally.

One way of showing changes over time in spatial data is to present side-by-side maps, each map representing data for a different time during the period (3). Animations have been used to present changes over time more granularly than static maps (4). To improve this method and accommodate data for multiple geographic levels, our project proposed a novel technique for visualizing temporal trends in spatial data — presenting an animated choropleth (thematic) map alongside a synchronized animated horizontal bar chart. We demonstrated the method by using data on cancer deaths from 1968 through 2017 in the United States at the county, state, and national geographic levels.

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Data Sources and Map Logistics

Age-adjusted mortality rates, stratified by year, for counties, individual states, and the United States were obtained from the CDC WONDER (Wide-ranging Online Data for Epidemiologic Research) website, including its Compressed Mortality File 1968 through 1978, its Compressed Mortality File 1979 through 1998, and its Multiple Cause of Death file for 1999 through 2017 (5). Cancer deaths were defined as deaths with any malignant cancer listed as the underlying cause. Malignant cancer was indicated by the International Classification of Diseases (ICD-8) codes 140–207 during 1968 through 1978, by ICD-9 codes 140–208 and 238.6 during 1979 through 1998, and by ICD-10 codes C00–C97 during 1999 through 2017 (6). Three ICD case definitions were necessary to compare data for all years during 1968 through 2017 (Table). The possibility of sensitivity or specificity differing among the case definitions is, therefore, a limitation of this project.

We developed and animated a horizontal bar graph and a choropleth map in R version 3.6.0 (R Foundation for Statistical Computing) (7), by using the albersusa (8), ggplot2 (9), viridis (10), ggthemes (11), gganimate (12), and magick (13) packages. Graph and map animations were rendered separately as graphics interchange format (GIF) images, and then combined. The open-source FFmpeg software suite was used to convert the animated GIF image into an MP4 formatted video (14).

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Highlights

The animation has a short duration, which facilitates consecutive viewings. The animation also leverages interactive features of MP4 video players, such as play, pause, vary playback speed, advance frame-by-frame, rewind, fast forward, and jump to specific places. These constitute a partial menu of possible interactive features that a data visualization might incorporate. We acknowledge that, in this sense, our animated data visualization has limitations. The combined animation’s interactivity, open layout, and high placement of the title; however, are consistent with common design practices for animated maps online that generally conform to cartographic standards (15).

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Action

Our animated data visualization presents cancer mortality rates spatially and temporally and illustrates that despite the overall decrease nationally in the age-adjusted rate from 1968 through 2017, disparities persisted among states and counties. This visualization can be used to improve public health resource targeting and evidence-based intervention efforts for states and counties with emerging or persistently high cancer mortality rates. Health officials, policy makers, and stakeholders can use data animation to inform policies and practices that influence cancer outcomes. For example, animation can focus attention on counties throughout the Mississippi Delta and Appalachia, where declines in cancer mortality have lagged compared with national declines — a known pattern that would be difficult to discern from a static data visualization.

Animated choropleth mapping is a novel visualization method for health data. Our project is the first, of which we are aware, to combine an animated graph and animated choropleth map. Although specific data, such as maximum and minimum values, might be difficult to convey by animated visualizations, animation can be effective for adding another dimension of information, particularly time, to static data visualizations. In doing so, animation can enable some data visualizations to convey patterns and relationships that are not apparent from static visualizations, especially across geographic levels. Therefore, we encourage data analysts to consider synchronized graph and choropleth map animations as an option for communicating data to public health researchers, practitioners, and policy makers.

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Acknowledgments

No financial support was received for this work. No copyrighted surveys, instruments, or tools were used. The authors have no conflicts of interest.

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Author Information

Corresponding Author: Isaac H. Michaels, MPH, 3100 Rosendale Road, Niskayuna, NY 12309. Telephone: 347-687-9719. Email: [email protected].

Author Affiliations: 1Department of Epidemiology and Biostatistics, University at Albany School of Public Health, Rensselaer, New York. 2Health Resources and Services Administration Region 2 Public Health Training Center, Columbia University Mailman School of Public Health, Department of Sociomedical Sciences, New York, New York.

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References

  1. Murphy SL, Xu JQ, Kochanek KD, Arias E. Mortality in the United States, 2017. https://www.cdc.gov/nchs/products/databriefs/db328.htm. Accessed June 16, 2019
  2. Islami F, Goding Sauer A, Miller KD, Siegel RL, Fedewa SA, Jacobs EJ, et al. . Proportion and number of cancer cases and deaths attributable to potentially modifiable risk factors in the United States. CA Cancer J Clin . 2018;68(1):31–54.
  3. Centers for Disease Control and Prevention. Vital signs: state-specific obesity prevalence among adults — United States, 2009. MMWR Morb Mortal Wkly Rep 2010;59(30):951–5.
  4. FiveThirtyEight. 35 Years of American death: mortality rates for leading causes of death in every US county from 1980 to 2014. https://projects.fivethirtyeight.com/mortality-rates-united-states/cancer2/#2014. Accessed July 15, 2019.
  5. Centers for Disease Control and Prevention. CDC WONDER, 2019. http://wonder.cdc.gov. Accessed May 25, 2019.
  6. National Cancer Institute. SEER cause of death recode 1969+ (04/16/2012). https://seer.cancer.gov/codrecode/1969_d04162012/index.html. Accessed July 8, 2019.
  7. R Core Team 2019. The R Project for Statistical Computing. https://www.R-project.org/. Accessed July 8, 2019.
  8. Rudis B. Tools, shapefiles and data to work with an “Albersusa” composite projection in R. R package version 0.3.1. https://github.com/hrbrmstr/albersusa. Accessed January 21, 2020.
  9. Wickham H. Elegant graphics for data analysis. New York (NY): Springer-Verlag Inc; 2016.
  10. Garnier S. Default color maps from ‘matplotlib.’ R package version 0.5.1. https://CRAN.R-project.org/package=viridis. Accessed January 21, 2020.
  11. Arnold JB. Extra themes, scales and Geoms for ‘ggplot2.’ R package version 4.2.0. https://CRAN.R-project.org/package=ggthemes. Accessed January 21, 2020.
  12. Pedersen TL, Robinson D. A grammar of animated graphics. R package version 1.0.5. https://CRAN.R-project.org/package=gganimate. Accessed January 21, 2020.
  13. Ooms J. Advanced graphics and image-processing in R. R package version 2.0. https://CRAN.R-project.org/package=magick. Accessed January 21, 2020.
  14. FFmpeg. ffmpeg tool version 4.1.4. http://ffmpeg.org/. Accessed January 21, 2020.
  15. Cybulski P. Design rules and practices for animated maps online. J Spat Sci 2016;61(2):461–71. CrossRefexternal icon

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Table

Table. Cancer Deaths, United States, 1968–2017a
Year Deaths Age-Adjusted Rate per 100,000 Population
2017 599,108 152.5
2016 598,038 155.8
2015 595,930 158.5
2014 591,700 161.2
2013 584,881 163.2
2012 582,623 166.5
2011 576,691 169.0
2010 574,743 172.8
2009 567,628 173.5
2008 565,469 176.4
2007 562,875 179.3
2006 559,888 181.8
2005 559,312 185.1
2004 553,888 186.8
2003 556,902 190.9
2002 557,271 194.3
2001 553,768 196.5
2000 553,091 199.6
1999 549,838 200.8
1998 541,582 200.8
1997 539,615 203.5
1996 539,593 206.8
1995 538,505 209.9
1994 534,353 211.8
1993 529,951 213.5
1992 520,616 213.5
1991 514,705 215.2
1990 505,366 216.0
1989 496,202 214.2
1988 485,082 212.5
1987 476,965 211.8
1986 469,411 211.6
1985 461,606 211.3
1984 453,530 210.8
1983 443,020 209.1
1982 433,833 208.4
1981 422,132 206.4
1980 416,525 207.9
1979 403,427 204.0
1978 395,149 203.9
1977 384,905 202.5
1976 375,687 201.6
1975 364,111 199.2
1974 358,961 200.6
1973 349,597 199.2
1972 344,130 199.4
1971 336,005 198.5
1970 329,433 197.8
1969 321,804 197.7
1968 317,465 198.1

a Data sources: Centers for Disease Control and Prevention, National Center for Health Statistics, Compressed Mortality File 1968–1978 and Compressed Mortality File 1979–1998 and CDC WONDER Online Database, Multiple Cause of Death Files 1999–2017 (5). International Classification of Diseases (ICD) definitions: ICD-8 codes 140–207 during 1968–1978; ICD-9 codes 140–208 and 238.6 during 1979–1998; and ICD-10 codes C00–C97 during 1999–2017 (6).

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Source of original article: Centers for Disease Control and Prevention (CDC) / Preventing Chronic Disease (tools.cdc.gov).
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