by William Carter
Sickle Cell Anemia is a type of hereditary blood disorder; it gets the name Sickle Cell from the characteristic abnormal, rigid, crescent shape the red blood cells take on. The sickling of the red blood cells happens due to a mutation within the hemoglobin in which the cells don’t receive enough oxygen; this lack of oxygen also causes the blood cells to break down faster than normal blood cells. Because of their unique shape, sickling decreases the flexibility of the blood cells and can result in various life-threatening complications. For example, sickled-shaped blood cells can obstruct capillaries and restrict blood flow to organs, which will result in a vaso-occlusive crisis (O’Toole and Miller-Keane, 1997).
Because sickle cell is hereditary, the “conditions have an autosomal recessive pattern of inheritance from parents” (“Diseases and Conditions Sickle Cell Anemia,” Mayo Clinic); it is the same as how a child receives its blood type, hair and eye color, and other traits from its biological parents. The type of hemoglobin a child makes will depend on the type of hemoglobin gene that is inherited from the parents. Depending whether or not each parent has the disease or the heterozygous condition, known as the Sickle Cell Trait, the child will have various probabilities of being born with the condition, which is why sickle cell probability diagrams as well as further education is important for those who may be at risk (“Diseases and Conditions Sickle Cell Anemia,” Mayo Clinic).
FIGURE 1 uses full body pictograms, shades of red to depict the status of the body, and compares the probability various other combinations.
Source: Miller-Keane Encyclopedia and Dictionary of Medicine, Nursing, and Allied Health, Seventh Edition. © 2003 by Saunders, an imprint of Elsevier, Inc. All rights reserved.
The probability chart in Figure 1 is published in the 7th Edition of the Miller-Keane Encyclopedia & Dictionary of Medicine, Nursing, & Allied Health, which was copyrighted, back in 2003, but actually published in 2005. The book is known for its accuracy and usefulness of clinical entries; it also depicts various approaches toward a wide range of current health care topics (“Miller-Keane Encyclopedia & Dictionary of Medicine, Nursing & Allied Health 7th Edition,” Elsevier).
Encyclopedic entries are included for significant topics, such as diseases, disorders, or conditions. These encyclopedic entries include the definition along with a concise overview of the most important information related to Symptoms, Treatment, Patient Care, Prevention, etc.” (“Miller-Keane Encyclopedia & Dictionary of Medicine, Nursing & Allied Health 7th Edition,” Elsevier).
Saunders is the academic publisher that prints the book; the company is officially recognized as an imprint of the parent company, Elsevier. Formally known as the W.B. Saunders Company, it was sold to Harcourt publishing back in 1986 followed by Harcourt getting acquired by Reed Elsevier in 2001. Elsevier is an academic publishing company known for its medical and scientific literature (“Reed Elsevier Timeline,” N.I.U. University Libraries).
Elsevier publishes 250,000 articles a year in 2,000 journals. Its archives contain seven million publications. Total yearly downloads amount to 240 million.” (“At A Glance,” Elsevier).
The company’s health services publications, which includes the Miller-Keane Encyclopedia & Dictionary of Medicine, et al., targets an audience of predominate medical professions, pharmaceuticals, research facilities, and schools; the book also publishes in numerous international languages (“At A Glance,” Elsevier).
Author Edward Tufte explains, “certain methods for displaying and analyzing data are better than others” (Tufte 27) when it comes reasoning about quantitative evidence. He states that superior methods can lead to findings that have more truth, credibility, and be more precise; differences between an analysis that is skillful and one that is erroneous can produces various, sometimes drastic, consequences (Tufte 27).
In accordance to what makes causal explanation “a clear logic of data display and analysis,” a graph should place “the data in an appropriate context for assessing cause and effect” (Tufte 29). In relation to Figure 1, the display of graphics shows the cause/effect relationship possibility. For example, the chart vividly explains if both parents have sickle cell anemia, then there is a four-out-of-four or a 100 percent chance that their child will be born with the same disease. In statistical analysis, the main question will be compared with what? and an effective graph will make quantitative comparisons (Tufte 30). In Figure 1, it compares the probability of a child born with sickle cell with two parents who each have the same disease, a normal parent and a parent with sickle cell, two parents who each have the trait, and various other combinations.
A careful determination of all the facts provided enhances the integrity of a report. While providing explanations of what was found because of the evidence, an illustration is more informative by “considering alternative explanations and contrary cases” (Tufte 32). The explanations within Figure 1 are the possibilities themselves; it not only states that if both parents have the sickle cell trait, there is a 50 percent chance the child will also have the trait; but, it also depicts the rare 25 percent chance that the child may have the disease or be normal. Because of the uncertainty or rarity of circumstances that could cause a deviation from Figure 1, any notes on possible errors cannot be explicitly articulated either through a narrative or illustration; however, graphics should report an “assessment of possible errors in the numbers” (Tufte 34).
One of the important characteristics of Figure 1 is its use of color. In dealing with the hereditary passing of a blood disorder, the color red works best in the context of Figure 1. The use of color can instantly “convey additional dimensions inside a unit of space” (Steele 63). The chart uses different brightness or shades of red to depict the status of the body: deep red for sickle cell anemia, a very light red or pink for the sickle cell trait, and grey for normality. This example of luminosity,
Determines a color’s visual impact. Bright colors pop, and dark colors recede…luminosity along with hue will highlight data points…making it suitable for mapping to quantitative dimensions of data” (Steele 63).
Color in data visualization can be neglected and abused by making poor color choices or relying on default color sets (Steele 63); in this context, can the color use of the figure, or others like it, effectively express the analytical information?
FIGURE 2 uses head silhouettes, shades of blue to show status of the body, but depicts the probability of only one combination.
Source: National Institutes of Health. Web. 28 September 2012. http://www.nhlbi.nih.gov/health/health-topics/topics/sca/causes.html
In comparison to Figure 1, Figure 2 and Figure 3 attempts to tell the same information. Following Tufte’s description of clear logic of data display and analysis, Figure 2 and Figure 3 shows the cause and effect relationship and explains the possibilities of contrary cases within the chart. Similar to Figure 1, both charts cannot note on the possible errors of illustration; however, unlike Figure 1, they don’t make any quantitative comparisons. While Figure 1 compares the probability of various other parent combinations, Figure 2 and Figure 3 only shows the probability between two parents with the trait; therefore, how could someone, on their own, make and put together the probabilities of the many other combinations?
Figure 3 uses full body pictograms, the colors blue, red, and purple to represent status of normal, sickle cell anemia, and sickle cell trait respectively, and doesn’t depict any quantitative comparisons, similar to Figure 2.
Source: Colin M.L. Burnett. 25 March 2007. Web. http://en.wikipedia.org/wiki/User:Cburnett.
Figure 2 and Figure 3 both display a unique color scheme; however, does the use of color in these chart map quantitative dimensions of data as described by Steele? Figure 2 uses shades of blue within oversimplified head silhouettes. While the different shades of blue are categorized in the key, can the analytical information be expressed or even hinted at with just the use of this particular color scheme? Figure 3 uses red, blue, and purple in full body pictograms. With some insight, one could assume that the purple pictograms are the combination of the blue and red pictograms since those two colors make the third. But, what other information can be assessed?
Information of this nature needs to be carefully and effectively explained as well as displayed because an essential component of care is the education of patients and their family members. Not understanding the difference between the sickle cell disease and the trait or how its genetically transmitted, parents and grandparents tend to blame each other for affecting the child. Correcting misinformation or a lack thereof can prevent expectations of unrealistic treatment or cures and avoid guilt or anger of the parents. Adequate counseling and instruction of genetics such as in Figure 1 can relieve much anxiety or marital discord that is associated with the blood disorder (O’Toole and Miller-Keane 1482).
Work Cited
“At A Glance.” Elsevier. 2014. Web. 25 March 2014.
http://www.elsevier.com/about/at-a-glance
“Diseases and Conditions Sickle Cell Anemia.” Mayo Clinic.org. 26 March 2011. Web. 25 March 2014.
http://www.mayoclinic.org/diseases-conditions/sickle-cell-anemia/basics/causes/con-20019348
“Miller-Keane Encyclopedia & Dictionary of Medicine, Nursing & Allied Health — Revised Reprint, 7th Edition.” Elsevier. 2014. Web. 25 March 2014.
http://www.us.elsevierhealth.com/dictionary/miller-keane-encyclopedia-dictionary-of-medicine-nursing-allied-health-revised-
reprint-hardcover/9781416026044/
O’Toole, Marie T. and Miller-Keane. Miller-Keane Encyclopedia and Dictionary of Medicine, Nursing, and Allied Health 6th Ed. Philadelphia, Pennsylvania: W.B. Saunders Company, 1997. Print.
“Reed Elsevier Timeline.” N.I.U. University Libraries. n.d. Web. 25 March 2014.
http://www.ulib.niu.edu/publishers/ReedElsevier.htm
Steele, Julie. Beautiful Visualization. Sebastopol, California: O’Reilly Media, Inc., 2010. Print.
Tufte, Edward R. Visual Explanations. Cheshire, Connecticut: Graphics Press, 1997. Print.