May 2010


In a Mar 2010 blog post, I discussed the long transplant wait times faced by kidney patients in the O blood group. In that post, I made some simplifying assumptions regarding the distribution of blood groups. I also ignored incompatibilities due to human leukocyte antigens (HLA). The complete list of seven assumptions are shown in the footnote under Table 1 of that blog post.

I want to eliminate those simplifications and do a deeper analysis. In today’s post, I will start by checking to see if there is any correlation among kidney patients on the UNOS transplant waiting list between ABO blood group and the likelihood that the patient has antibodies to foreign HLAs. In general, the recipient must receive a donor organ for which they do not have antibodies against.

It is believed that people are not born with antibodies to foreign HLAs. They become sensitized to foreign HLAs due to blood transfusions, previous transplants, or pregnancies (May 2008 blog post). These antibodies will cause them to reject organs that are not compatible. One measure of the levels of HLA antibodies a person carries is called calculated panel reactive antibodies (cPRA). Calculated PRA indicates the percent of donors in the U.S. (based on historical U.S. deceased donor data) whose HLAs would react with the patient’s antibodies. If the patient has no HLA antibodies, then the cPRA is 0%. As the number of antibodies in the patient rises, the percentage of donor organs that don’t match rises. Not all HLAs are equally common and that affects the calculated PRA result as well. Any patient with a cPRA of 80% or higher is considered hard-to-match and is accorded extra points to put them closer to the top of the waiting list. The cPRA of patients on the waiting list can rise over time as they get additional transfusions. It can also fall, as HLA antibodies disappear in a process that isn’t well understood.

In order to investigate the relationship between blood type, HLA, and other factors, we need to know if these variables are correlated. Specifically, are people with a particular blood group more likely to have high cPRA percentages? If these two variables are correlated (not independent), then any further data analysis becomes much more complex. In order to test this, I requested some data from UNOS. I want to thank Bruce Shepperson of UNOS for generating this data for me. Any errors in analysis are mine alone.

The cPRA is not reported for all patients. To make data comparable, in the tables below I added a row that excludes the Not Reported patients and calculate an adjusted total. (Note: This exclusion assumes that there is no bias in the Not Reported group. But this may be incorrect. Perhaps transplant centers will be more likely to measure the cPRA of patients whose medical history includes previous blood transfusions, transplants, or pregnancies. If so, then the Not Reported column is more likely to contain patients with low cPRA. In fact, starting on Oct 1, 2009, the UNOS assumes any patients that do not have reported HLA sensitivities have a cPRA of 0%.)

In the tables below, cells with higher than expected counts (at the 0.05 significance level) are shaded green while those with lower than expected counts are shaded yellow. In the rows and columns containing totals, the cells are compared to the corresponding cell in Table 1.

Patients added to the waiting list during a year

The first test is to see if the distribution of blood groups and cPRA has changed over time for patients added to the active and inactive kidney-only waiting lists. An explanation of these waiting lists in a Apr 2010 blog post. Table 1, shows the distribution for patients added in 1995. Patients in the O blood group are more likely to have 1-19% cPRA than expected, but I suspect this is just random noise. (At the 0.05 significance level, you would expect 1 out of 20 significant results to be due to noise.)

Table 1. Blood group by cPRA* for patients added to waiting list in 1995 (n=17,872)

 

0%

1-19%

20-79%

80+%

Not Rpt’d

Row Total

O

24.5%

7.7%

3.7%

1.5%

10.3%

47.7%

A

18.5%

4.8%

2.2%

1.0%

7.5%

34.0%

B

7.7%

2.0%

0.9%

0.5%

3.5%

14.5%

AB

2.1%

0.5%

0.3%

0.1%

0.8%

3.8%

Column Total

52.8%

15.0%

7.0%

3.1%

22.0%

100.0%

Adjusted Total

67.8%

19.2%

9.0%

4.0%

100.0%

  *Measured at time of listing

Table 2 shows the distribution of new patients added to the waiting list 14 years later, in 2009. None of the cells in Table 2 show significant differences. Notice though that the proportion of patients with 0% PRA is higher than in 1995. There may be several explanations for this. First, eliminating the use of human-derived EPO for dialysis patients eliminates that as a source of foreign HLA. Perhaps changes in the way cPRA is calculated over this time (as DNA testing becomes more accurate) may produce more 0% results. Also note the number of new patients nearly doubled from 17,872 to 35,122 over this period. So the proportion of first time transplant patients to repeat patients is probably rising. New patients are probably more likely to have a cPRA of 0%. The incidence of end-stage renal disease is growing very fast. It really is a health care crisis.

Table 2. Blood group by cPRA* for patients added to waiting list in 2009 (n=35,122)

 

0%

1-19%

20-79%

80+%

Not Rpt’d**

Row Total

O

26.1%

3.8%

3.2%

2.0%

13.4%

48.5%

A

17.8%

2.6%

2.2%

1.5%

8.9%

33.0%

B

7.8%

1.2%

1.0%

0.6%

4.0%

14.6%

AB

2.1%

0.3%

0.3%

0.2%

1.0%

3.9%

Column Total

53.9%

7.9%

6.7%

4.2%

27.3%

100.0%

Adjusted Total

74.2%

10.8%

9.2%

5.8%

100.0%

*Measured at time of listing
**
As of 10/1/2009, if no unacceptable antigens are reported, cPRA value defaults to 0%

Overall, we can conclude that there is no correlation between blood group and cPRA for patients entering the UNOS waiting list.

Patients taken off the waiting list during a year

The next test is to see if there is a correlation between blood groups and cPRA among patients who received a transplant. Tables 3 and 4 show the distributions for patients receiving a kidney during 2009 from a live donor and deceased donor respectively.

Table 3 shows no discernible relationship between blood group and cPRA. There is a relationship in Table 4. Among patients receiving a deceased donor transplant, those with O blood and high cPRA are more likely than other blood groups with high cPRA. This is a result of the UNOS allocation rules that favor this combination and so is expected. These patients are the hardest to match, so they are given extra points which helps gets them off the list faster than expected by chance.

Comparing the totals in Table 3 and 4 against the totals in Table 1 shows that patients with type O blood, who can only accept a type O kidney, are less likely to get a transplant than patients with type A or AB. This is expected. (Patients with type B blood are also less likely to get a transplant, but that is because of different issues which I will discuss in a future blog post.)

But there is an unexpected result. Patients with high cPRA are more frequent among those receiving a kidney transplant than those entering the waiting list. This seems counterintuitive since they are harder to match. There are four possible explanations. First, maybe patients with high cPRA are exiting the list faster than patients with low cPRA. We know this is not true, and Table 5 (which we will look at shortly) shows it.

A second explanation requires us to notice the low proportion of Not Reported for patients receiving a transplant from a deceased donor. Perhaps most of the Not Reported in Tables 1 and 2 have high cPRA, which is the opposite of what I posited earlier. Third, cPRA is not static like blood type. Maybe it rises between the time the patient is added to the waiting list to the time of the transplant. Assuming no bias in the Not Reported group, this means about a fourth of all patients have their cPRA score rise significantly during their years on the waiting list. Finally, perhaps the cPRA data collected at the time the patient entered the list was incorrectly low and was updated to a higher value by the time of transplant.

The discrepancy between cPRA for patients entering the waiting list and those leaving it is an issue worth exploring. But it does not affect our conclusion that there is no unexpected correlation between blood type and cPRA among those receiving a transplant.

Table 3. Blood group by cPRA* for patients receiving a live donor transplant during 2009 (n=6,387)

 

0%

1-19%

20-79%

80+%

Not Rpt’d**

Row Total

O

23.7%

5.9%

4.7%

1.6%

9.2%

45.0%

A

20.6%

4.4%

3.4%

1.3%

8.2%

37.9%

B

6.9%

1.4%

1.6%

0.5%

2.8%

13.2%

AB

2.1%

0.5%

0.3%

0.1%

0.9%

3.8%

Column Total

53.3%

12.2%

10.0%

3.4%

21.1%

100.0%

Adjusted Total

67.6%

15.4%

12.7%

4.3%

100.0%

*Measured at time of transplant

**As of 10/1/2009, if no unacceptable antigens are reported, cPRA value defaults to 0%

Table 4. Blood group by cPRA* for patients receiving a deceased donor transplant during 2009 (n=10,442)

 

0%

1-19%

20-79%

80+%

Not Rpt’d**

Row Total

O

24.4%

6.0%

6.4%

7.1%

1.4%

45.3%

A

20.0%

5.1%

5.4%

5.1%

1.0%

36.5%

B

7.1%

1.9%

2.0%

1.4%

0.4%

12.9%

AB

3.1%

0.8%

0.8%

0.6%

0.1%

5.3%

Column Total

54.6%

13.8%

14.6%

14.2%

2.9%

100.0%

Adjusted Total

56.2%

14.2%

15.0%

14.7%

100.0%

*Measured at time of transplant
**As of 10/1/2009, if no unacceptable antigens are reported, cPRA value defaults to 0%

Patients remaining on waiting list at end of a year

The final test is to see if there is a correlation between blood group and cPRA among patients who remain on the waiting list at the end of a year. The data in Table 5 below shows the distribution for the 57,203 patients on the active kidney-only waiting list. Looking at the row totals, as expected, patients with type O blood, who are harder to match, are more likely to be on the waiting list at the end of the year than patients with types A and AB. Similarly, looking at the adjusted column totals, patients with high cPRA are more prevalent than those with low cPRA.

Notice that there is a correlation between blood type and cPRA in this table that is the opposite of Table 4. Among patients with high cPRA, type AB blood group is more prevalent than expected and type O blood group is less prevalent. This is a direct result of the allocation scheme that UNOS has developed to favor patients with type O blood and high cPRA. Among patients with 0% cPRA, those with type AB blood group can accept a kidney from almost anybody, so they are less likely to remain on the list at the end of the year, while those with hard to match type O blood group are more likely to remain on the list.

Table 5. Blood group by cPRA* for patients on waiting list at end of year 2009 (n=57,203)

 

0%

1-19%

20-79%

80+%

Not Rpt’d**

Row Total

O

34.4%

3.4%

7.1%

8.6%

0.0%

53.4%

A

17.1%

1.5%

3.5%

5.1%

0.0%

27.2%

B

10.4%

1.1%

2.4%

2.8%

0.0%

16.7%

AB

1.6%

0.2%

0.4%

0.6%

0.0%

2.7%

Column Total

63.4%

6.1%

13.4%

17.0%

0.0%

100.0%

Adjusted Total

63.4%

6.1%

13.4%

17.0%

100.0%

*Results of latest available test
*
*As of 10/1/2009, if no unacceptable antigens are reported, cPRA value defaults to 0%

Thus, we can conclude that there is no unexpected correlation between patients’ blood type group and their cPRA. However, allocation rules and match probabilities will alter the composition of the waiting list.

[Update: Due to an editing error, some of the cells were shaded incorrectly. The corrected data is now shown. The changes do not affect the conclusion that there are no unexpected correlations between blood type and cPRA.]

As mentioned in an Apr 2010 blog post, Loyola Univ. Medical Center recently had four altruistic kidney donors come through its doors. Rather than keeping them in-house and transplant each donor’s kidney to one of its own patients, Loyola doctors decided to enroll all four donors with a kidney exchange that matches pairs of incompatible donors and recipients, to increase the number of matches and thus help the maximum possible number of patients get kidneys. (For an explanation of how kidney exchanges work, see Mar  2010 blog post.) Afterwards, Loyola UMC announced the formation of a pay-it-forward kidney transplant program to encourage more people to donate through kidney exchanges.

Yesterday, Loyola UMC announced that since starting the program in March, twenty-one additional altruistic donors have stepped forward. Says Loyola kidney transplant surgeon Dr. John Milner,

“We’ve had 50 phone calls from people of all ages and backgrounds who heard about the program and who expressed desires to donate kidneys. Those donors should be commended for helping us to unlock the potential of chains to get more people transplanted who might otherwise never have a chance for a new life.

“That’s one of the advantages of the Pay-it-Forward concept. You can ship a kidney and not a donor. That’s the first time that has ever happened in the Midwest. Donors have the luxury of being in familiar surroundings with their friends and family while they recover. There are already so many disincentives to donation, why add another by making the donor, as well as family members, travel?”

I hope Loyola is able to sustain a rate of a few donors per month after this initial publicity dies down. And if every transplant hospital in the country adopted this program and encouraged more people to become altruistic donors, it could help eliminate the 85,000 person-long waiting list for kidneys. Really! If you are interested in becoming an altruistic donor in an exchange (like me), contact your local transplant center and ask if they participate in one of the regional or national kidney exchanges. The two largest national exchanges are the National Kidney Registry and the Alliance for Paired Donations. Both websites include a list of participating hospitals.

[Update: A Nov 2010 blog post covers a meeting between one of the pay-it-forward donors and her recipient.]

by George Taniwaki

If a patient with end-stage renal disease (ESRD) is a good candidate for a kidney transplant, nearly all health care professionals agree that they should consider a live donor since the outcomes are better and the wait times shorter than a transplant from a deceased donor. (More details comparing outcomes for different treatment modalities will be provided in a future blog post.) But many patients never find a live donor and instead spend years waiting for a deceased donor kidney. As Harvey Mysel of the Living Kidney Donors Network says, the most important step in finding a live donor is to just tell your story.

“The most common reason people give for not pursuing living kidney donation is the concern they have about asking someone to donate… For many people, the ‘ah ha’ moment occurs when they change their thought process from ‘I need to ask someone to donate’, to ‘I need to let people know about my situation, and educate them about the options that are available.’ The later results in having your donor find you!”

Mr. Mysel offers seminars to help kidney patients gain the confidence to approach their friends and families to discuss their situation. He points to three recent stories that show the importance of telling your story. In each case, the donor wasn’t related to the patient and was not a close friend either. The patient didn’t ask the person to be a donor. The offer was unexpected. And the event changed both people’s lives.

The first story appeared in the Seattle Post Intel. Mar 2008. Annamarie Ausnes works at the Univ. Puget Sound in Tacoma, WA and is a frequent customer at a local Starbucks. She is friendly with a barista there named Sandie Andersen. Ms Ausnes mentions that her kidneys are failing after many years of polycystic kidney disease and she will soon be starting dialysis. Ms Anderson quickly offers to donate one of hers. Says her husband, “If you can save somebody’s life, it’s special. It’s what Sandi wanted to do.”

450starbuckskidney12_virginiamason

Recipient Annamarie Ausnes on left. Photo from Seattle Post Intel.

The second story involves Keri Evans, a single mother in Midland, TX. She regularly takes a taxi to her thrice weekly dialysis sessions. One day, being frustrated that none of her close relatives were matches for a transplant, Ms Evans declares, “I give up, don’t pick me up for dialysis tomorrow.”

That complaint leads the taxi driver, Carol Hambright, to decide to donate her kidney. She isn’t a match for Ms Evans either, but the two of them have joined a kidney exchange at Methodist Specialty and Transplant Hospital in San Antonio. The story was covered by KWES in Midland and also by ABC News Jul 2009.

Hambright

Recipient Keri Evans on right. Photo from KWES

The final story comes from the Chicago Tribune Feb 2010. It involves Myra de la Vega, a clerk at Jewel-Osco in Evanston, IL. She is friendly with Dan Coyne, a regular customer. After learning she was starting dialysis, he offered to donate his kidney. She refused his offer, hoping to receive a kidney from her sister. When they learned she couldn’t, Mr. Coyne repeated his offer and Ms de la Vega accepted. Mr. Coyne’s wife, Emily, had reservations about her husband’s gift. But after seeing how much he wanted to do it, she relented.

delaVega

Recipient Myra de la Vega on left. Photo from Chicago Tribune

[Update: A video featuring the first pair mentioned, Sandie Andersen and Annamarie Ausnes, is described in a Dec 2010 blog post.]

When designing advertisements, creative directors often prepare mock ups of proposed designs to show to clients. Creative directors rely on their experience and training to prepare combinations of images and text that will hopefully engage readers. But they may not be able to articulate why their combination works or is better than another combination. Similarly, clients often accept or reject designs based on subjective and personal criteria. Is there a better way to judge the potential impact of an ad?

One method used by market researchers is eye tracking studies. This technique records a person’s eye position and movement when viewing visual media. You want the audience to be attracted to the image and headline which piques curiosity. Then they will be drawn into the text, and finally look at the company name, logo, tagline, and contact information.

The human eye and mind are remarkable devices. The human eye is not just a camera that records color and light. It also processes images before sending it to the brain. The eye is very slow at resolving an image. It takes about 1/20th of a second for the eye to generate an image to send to the brain. During that time, the eye must remain fixed on the point of interest, even if your body is in motion. This is called fixation. Then your eye moves quickly to the next point of interest using an action called saccade. During the saccade, your eye can’t create a good image, it is just a jittery blur. It doesn’t send this blurry image to the brain, it sends nothing. Yet you never notice that your entire visual life consists of a series of rapid still images with blackouts in between. In fact, that’s why movies and television (which consist of 24 or 30 still images a second) can fool you into thinking that there is constant motion.

800px-ReadingFixationsSaccades

Fixation and saccade. Image from Wikipedia

Eye tracking studies for advertising have been conducted for years, starting in the 1960s though as equipment improved and fell in price, the practice expanded. A new web service from 3M (Creativepro May 2010) called Visual Attention Service does not require actual consumer testing. Instead, this service uses a database of previous studies to predict what a person will look at and rate the effectiveness of the image and text in holding a person’s attention.

VAS

Visual Attention Service. Video from 3M

I’ve signed up as a user, but haven’t tried the service out myself yet. My guess is that the current tool is quite crude. But the concept makes sense and I can see that Google, Microsoft, and Yahoo! would be interested. Anything that increases the effectiveness of advertising is valuable and worth a lot to advertisers, publishers, and design firms.

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While researching this blog entry, I came across a study by Think Eye tracking, a market research firm in Berkshire, UK, that reports the hilarious eye tracking results of a guy and what he looks at while attending a speed dating event. I think what actually happened is that he was so embarrassed and self-conscious about his appearance while wearing the eye tracking video camera headset that he had to avert his eyes. Yeah, that’s it.

And speaking of research into speed dating, previous research had indicated that women are pickier than men when selecting who to meet again. Two psychologists at Univ. of Penn. found that among 2,650 participants at HurryDate, the average woman was chosen by 49% of the men but the average man was only chosen by 34% of the women. However, this result may be biased because at most speed dating events, the man moves from table to table while the woman remains seated. A study by two psychologists at Northwestern in Psych. Sci. Sep 2009 shows that the gender selectivity difference disappears if women are the ones who rotate and men sit. The act of approaching someone increased self-confidence and reduced selectivity. Research bias can be very subtle.

[Update: Fixed a broken link to VAS YouTube video.]

by George Taniwaki

An interesting story in Tech. Rev. May 2010 describes how a social media site called PatientsLikeMe can be used to quickly run clinical trials at low-cost. PLM is a free site where patients can record their daily health status, find other patients with similar conditions, and share ideas. Knowing that you are not alone and getting support from others like yourself are very powerful forces when dealing with complex and serious medical conditions.

On the flip side, PatientsLikeMe’s operating costs are paid for by healthcare companies that get access to anonymized patient data and permission-based access to the members. Clinicians can recruit volunteers, run clinical trials with them, and compare the outcomes between those that volunteered and those that did not. The results will not meet the standards for a double-blind study, but the ability to compare the attributes of participants and nonparticipants to control for bias is something that is very difficult to do in a typical study. One result reported in 2008 using PatientsLikeMe data showed that generic lithium was not effective at treating amyotrophic lateral sclerosis (ALS). The results appear to refute the conclusions of a study that had been published in Proceed Nat. Acad. Sci. Feb 2008. The PLM data is compelling, but there is a whole list of caveats that need to be considered.

PatientsLikeMe

Effect of lithium on ALS. Image from PatientsLikeMe

Researchers can also present their results to the community as well as engage with members to consider fruitful areas for future research.

Like most healthcare websites, PatientsLikeMe has a privacy policy. But what makes PatientsLikeMe different, is it has an openness philosophy. Part of it reads,

“Currently, most healthcare data is inaccessible due to privacy regulations or proprietary tactics. As a result, research is slowed, and the development of breakthrough treatments takes decades. Patients also can’t get the information they need to make important treatment decisions. But it doesn’t have to be that way. When you and thousands like you share your data, you open up the healthcare system. You learn what’s working for others. You improve your dialogue with your doctors. Best of all, you help bring better treatments to market in record time.”

PatientsLikeMe currently has communities devoted to a range of common diseases including ALS, epilepsy, HIV/AIDS, multiple sclerosis, and Parkinson’s disease. It also has communities for several rare diseases I am unfamiliar with, like progressive supranuclear palsy. In March, PatientsLikeMe joined with Novartis to announce a community for organ transplant patients. I just joined the organ transplant community, registering as a community member rather than as a patient (there isn’t an option to join as a live organ donor). There are already 1,429 members in the community. I plan to track the site and will report any interesting findings in future blog posts.

As I continue to gather materials for my kidney recipient community outreach effort, I want to learn more about the educational activities of the Northwest Kidney Centers. Today (May 12), I attended the NKC Breakfast of Hope, an annual fundraiser. It was held at the Westin Seattle. More than 800 people participated, raising over $375,000.

Jesse Jones, an Emmy Award winning reporter for King 5 television in Seattle, was the master of ceremonies. He related a story, which I was unaware of, that one day he noticed blood in his urine. He went to the doctor and the next morning his wife answered the phone. It was the doctor who said the tests indicated he had kidney cancer and wondering if he could come in that afternoon to prepare for surgery.

JesseJones

Jesse Jones, master of ceremonies. Photo by Mike Nakamura

Joyce Jackson, the CEO of NKC made some remarks. One particular sentence really stuck in my mind, “There are 273 patients at Northwest Kidney Centers waiting for a kidney transplant. Our goal is to get that to zero.” By stating the problem in this way she makes the goal seem achievable. Almost everyone else who talks about reducing the wait list starts with the 85,000+ people on the national list. That big number makes it seem like an insurmountable goal, and that no single person could ever make an impact. (See a Jun 2010 blog post for more on how framing numbers affects us.)

The keynote speaker was John Piano, CEO of Transplant Connect. This LA-based company provides the software used by organ procurement organizations (OPOs) to facilitate the matching of donor organs with transplant recipients. The company was mentioned at the end of a Mar 2010 blog post.

JohnPiano

John Piano, keynote speaker. Photo by Mike Nakamura

At the breakfast, I was seated next to Cathy Pelzel, an executive assistant at SightLife. This organization, formerly known as Northwest Lions Eye Bank, recovers and places eye tissue for transplants. It turns out she is also a kidney donor, giving a kidney to her niece over 25 years ago. The graft is still functional, showing the real advantage of live donor kidneys.

Cathy pulled out her smartphone and launched the calendar app, highlighting the anniversary date of her donation. I hadn’t really thought about how I will feel about my donation date. Will I mark the occasion every year? Will my recipient? I wonder how other donors and recipients feel about their anniversary date.

****

The breakfast meal itself featured a kidney-friendly menu. Surprisingly, it included items like sausage, crepes, and chocolates. But it excluded orange juice, sugary pastries, and highly salted eggs. Preparing a kidney-friendly diet means being careful, but doesn’t mean bland. (More on that in a June 2010 blog post.)

100512_nkc_0119

A Breakfast of Hope guest discovers that a kidney-friendly menu need not be bland. Photo by Mike Nakamura

The NKC premiered a heartwarming video featuring the story of Dave LeFevre and Bill Hewlett. Dave is a Microsoft employee who donated a kidney to Bill, a fellow church member.

DaveLeFevre

Breakfast of Hope 2010. Video still from NKC

After the breakfast, I introduced myself to Dave and Bill. Dave said that if he was a speaker, he would have said that the people in the room could solve the waiting list just by signing up to be a live donor that day. (Well, except for the many people in the room who only have one kidney because they’ve already donated the other one.)

by George Taniwaki

Every year Mattel introduces several new editions of its ”I can be…” Barbie doll. They include the clothing and accessories necessary for this famous doll to be successful in a particular career. (Me thinks this may have more to do with selling toys than broadening the career sights of little girls, but I digress.) This year, the company decided to let girls vote for their favorite career. The top vote getter would be featured as this fall’s new edition. Mattel heavily promoted the contest on social media sites like Facebook and Twitter. (Not sure how I missed it.)

On Feb 12, at the New York Toy Fair, Mattel announced that the winner of the popular vote for the 125th special edition Barbie was computer engineer. It’s hard to tell from the drawing below, but Barbie is wearing a Bluetooth headset, has a t-shirt with computer code written on it, and has a smartphone strapped on. As my friend Jim Reichle would say, “The heat, the heat.” (It’s an inside joke from Caltech.) But to compensate for that bit of nerdiness, she’s color coordinated with hot pink laptop, glasses, and wristwatch (maybe it’s a revival of a Spot Watch).

OverallVote

Winner of the popular vote, computer engineer. Image from Mattel

You can preorder a computer engineer Barbie from the Mattel web site. I’m sure it will soon become a popular collectible among a certain crowd here in Redmond.

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I thought it was rather odd that computer engineer would beat out the other careers that Mattel offered girls to choose from: architect, environmentalist, news anchor, and surgeon. I didn’t think many girls consider computer science as an attractive career. After all, it isn’t very popular among women entering college. And it turns out that computer engineer wasn’t the first choice of the girls, news anchor was.

An article in Wall St. J. Apr 9. reveals that a viral campaign started by computer engineers hijacked the voting for Barbie’s new career. Computer engineer Barbie became a cause célèbre among the digerati. For instance, a writer for SQLblog encouraged his followers to vote. The influential GeekGirlCamp ran an appeal asking readers to “Please help us in getting Barbie to get her Geek On!”

In the end, Mattel realized the power of social media cuts both ways and decided to have two winners. News anchor was declared the winner of the girls’ vote while computer engineer was the winner of the popular vote. Mattel will release an anchorwoman Barbie in time for this year’s holiday season.

GirlsVote

Winner of the girl’s vote, news anchor. Image from Mattel

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Why did computer engineer Barbie attract so much attention? Well, I think part of it may be the odd sense of duty (or sense of humor) that geeks have toward promoting their culture. (Had I known about this contest, I certainly would have voted for computer engineer.) But part of it may have been to actually raise awareness of computer science as an attractive career for women.

Many formerly male dominated professions such as law, accounting, mathematics, medicine, and biological science are now much more gender balanced, or in some cases becoming female dominated. However, engineering, physical science, and computer science are not.

In fact, the proportion of men in many of these professions never fell below 70% and are actually on the rise again. A Wall St. J. blog post states that the number of women in computer science has been falling while the total number of workers has been growing, causing a steep rise in the male-to-female ratio. (And I don’t think it was caused by girls hearing Barbie say, “Math class is tough.” A study published in Science Jul 2008 shows that the gender gap in math achievement as measured by standardized tests has disappeared. So it is likely something else is causing it. The article points to a problem with standardized tests themselves and the pernicious effect of the No Child Left Behind legislation. But I digress again.)

Most of my own college education and work experience has been in heavily male dominated fields. My freshman year was spent at California Institute of Technology, where in 1977 fewer than 10% of the undergraduates were female. I transferred to the Colorado School of Mines where the proportion of females was about double that. At both schools there were almost no female graduate students or professors. Even after almost 30 years, a Amer. Assoc. Univ. Profes. 2006 report cites Caltech as the doctorate-level school with the lowest proportion of female full professors (14%) in the U.S. The next lowest school? Mines at 16%.

In its first 100 years (from 1874 to 1973), Mines graduated a total of 14 women. The percentage grew quickly thereafter and was still rising while I was attending. But then it stopped. A recent story in Mines Magazine shows that the proportion of women at the school has remain steady for the last twenty years at about 25%. However, the type of student may be changing as women now hold about half of the student leadership positions.

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A professor at Mines, a woman named Tracy Camp, authored a paper that appeared in Comm. ACM Oct 1997 that highlighted the falling enrollment of women in computer science programs and warned of its consequences to the U.S. economy and global competitiveness. She urged action to identify and counteract the forces that were, and still are, leading fewer women to seek degrees in computer science and careers in the IT industry.

I wondered if Dr. Camp was one of the adults who voted for computer engineer Barbie. When asked, she said, “Yes, I voted for the computer engineer Barbie. I also sent an announcement out on my networks, which helped add a lot more votes.” She doesn’t feel bad at all about adults hijacking the vote, “Research has shown that we need to change the image of computing to get more girls interested. Barbie may help.” (One of the great things about writing a blog is that I can send impertinent emails to busy people and they respond, but I digress.)

So there you have it. Computer engineer Barbie is a child’s toy, a collectible, a role model for career-minded girls, an Internet meme that provides a lesson in how social media is changing marketing, a symbol of U.S. economic competitiveness, and a partial solution to the gender gap in engineering. Who knew?

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