White-Privilege   white-privilege2

Examples of images when searching for “white privilege”

by George Taniwaki

Making people aware of their unconscious bias or stereotypes is an important part of helping them become better critical thinkers.  Many of these prejudices are based on visual cues such as race, gender, and class. They are learned very early in life and are difficult to overcome.

As described by the Kirwan Institute for the Study of Race and Ethnicity, implicit biases are pervasive. They “encompass both favorable and unfavorable assessments, are activated involuntarily and without an individual’s awareness or intentional control.”

Unlike known biases “that individuals may choose to conceal for the purposes of social and/or political correctness, implicit biases are not accessible through introspection.”

To learn more about your own implicit biases regarding race, create an account and take this test.

Teaching about white privilege may be counterproductive

Recently, the term white privilege has come into vogue. The phrase is often used to describe how Americans of European ancestry, even if they do not actively discriminate, enjoy passive advantages over minorities.

However, recent research shows that teaching people about white privilege may have the opposite effect as intended. That’s the conclusion of a paper by Erin Cooley, et al. published in J. Exper. Psy.: General, Apr 2019 (subscription required).

In a May 2019 article on Vice, professor Cooley does a very good job of describing the results of this research, including her own personal connection through her fiancé, a white man who grew up poor. It’s a great article and I strongly recommend reading it.

She says, “Given a career focused on race, I was fixated on the privileges of being a white man. I couldn’t stop myself from mentioning that white male poverty wasn’t exactly the worst injustice out there.

“I had evidence on my side. Poor white men can hide being poor more than Black people can hide being Black. And there are plenty of systemic barriers my fiancé was unlikely to face as he made his way up to being the successful professor he is today. Still, the fact that he was a poor white man had escaped my empathy radar. I wondered whether this might be connected to my liberal worldview.”

How the study was conducted

There were two studies conducted with a total of 1,189 participants. The sample was divided into two groups, social conservatives and social liberals. Within each group, some were provided a lesson on white privilege and some were not.

All participants then read a story about a person and the hardship they underwent. For some participants, the subject of the story was a poor white man. For the others, it was a poor black man. Afterwards, all participants answered a questionnaire to measure the level of sympathy they had for the subject of the story.


Overall, there are a total of eight subsamples. The chart below shows the data, with lines indicating the change before and after receiving the lesson on white privilege (actually with and without the lesson, since they are different samples). Social conservatives have solid red lines and social liberals in dashed blue lines. Story with a poor black man have a solid triangle marker and story with a poor white man have an open square marker.


Figure 1. Effect of reading a lesson on white privilege on sympathy score on story of a poor person

In general, social conservatives reported lower sympathy scores than social liberals. Without reading the lesson on white privilege, social conservatives reported higher sympathy scores when the character was black. Reading a lesson on white privilege causes sympathy to rise for both black and white characters in the story of the poor man. Further, the difference in score is reduced.

Without a lesson, social liberals reported similar sympathy scores for both white and black characters. However , reading the lesson on white privilege causes the sympathy score to fall for the poor white man and rise for the black man, causing the difference to increase.

Thus, providing lessons on white privilege in an effort to help reduce implicit bias against poor blacks may unintentionally harm poor whites.

As prof. Cooley states in Vice, “My prior insensitivity to the experiences of poor white people might be just the type of attitude that contributes to an increasingly polarized US political climate—a climate that ultimately causes further harm to Black people too.”

Since the sympathy scores in this study are self-reported, they need to be treated with skepticism. Further, the reading and sympathy survey were administered immediately after the lesson on white privilege was given. It would be interesting to see if the effects are transient and how long lasting they are.

[Update1: I fixed some typos and a broken hyperlink.]

[Update2: Moved description of my chart to new May 2019 blog post.]

by George Taniwaki

Did you watch the debate on Monday night? I did. But I am also very interested in the post-debate media coverage and analysis. This morning, two articles that combine big data and the debate caught my eye. Both are novel and much more interesting than the tired stories that simply show changes in polls after a debate.

First, the New York Time reports that during the presidential debate (between 9:00 and 10:30 PM EDT) there is high correlation between the Betfair prediction market for who will win the presidential election and afterhours S&P 500 futures prices (see chart 1).


Chart 1. Betfair prediction market for Mrs. Clinton compared to S&P 500 futures. Courtesy of New York Times

Correlation between markets is not a new phenomena. For several decades financial analysts have measured the covariance between commodity prices, especially crude oil, and equity indices. But this is the first time I have seen an article illustrating the covariance between a “fun” market for guessing who will become president against a “real” market. Check out the two graphs above, the similarity in shape is striking, including the fact that both continue to rise for about an hour after the debate ended.

In real-time, while the debate was being broadcast, players on Betfair believed the chance Mrs. Clinton will win the election rose by 5 percent. Meanwhile, the price of S&P 500 futures rose by 0.6%, meaning investors (who may be the same speculators who play on Betfair) believed the stock market prices in November were likely to be higher than before the debates started. There was no other surprise economic news that evening, so the debate is the most likely explanation for the surge. Pretty cool.

If the two markets are perfectly correlated (they aren’t) and markets are perfectly efficient (they aren’t), then one can estimate the difference in equity futures market value between the two candidates. If a 5% decrease in likelihood of a Trump win translates to a 0.6% increase in equity futures values, then the difference between Mr. Trump or Mrs. Clinton being elected (a 100% change in probability) results in about a 12% or $1.2 trillion (the total market cap of the S&P 500 is about $10 trillion) change in market value. (Note that I assume perfect correlation between the S&P 500 futures market and the actual market for the stocks used to calculate the index.)

Further, nearly all capital assets (stocks, bonds, commodities, real estate) in the US are now highly correlated. So the total difference is about $24 trillion (assuming total assets in the US are $200 trillion). Ironically, this probably means Donald Trump would be financially better off if he were to lose the election.


The other article that caught my eye involves Google Trend data. According to the Washington Post, the phrase “registrarse para votar” was the third highest trending search term the day after the debate was broadcast. The number of searches is about four times higher than in the days prior to the debates (see chart 2). Notice the spike in searches matches a spike in Sep 2012 after the first Obama-Romney debate.

The article says that it is not clear if it was the debate itself that caused the increase or the fact that Google recently introduced Spanish-language voting guides to its automated Knowledge Box, which presumably led to more searches for “registrarse para votar”. (This is the problem with confounding events.)

After a bit of research, I discovered an even more interesting fact. The spike in searches did not stop on Sep 27. Today, on Sep 30, four days after the debates, the volume of searches is 10 times higher than on Sep 27, or a total of 40x higher than before the debate (see chart 3). The two charts are scaled to make the data comparable.


Chart 2. Searches for “registrarse para votar” past 5 years to Sep 27. Courtesy of Washington Post and Google Trends


Chart 3. Searches for “registrarse para votar” past 5 years to Sep 30. Courtesy of Google Trends

I wanted to see if the spike was due to the debate or due to the addition of Spanish voter information to the Knowledge Box. To do this, I compared “registrarse para votar” to “register to vote”. The red line in chart 4 shows Google Trend data for “register to vote” scaled so that the bump in Sept 2012 is the same height as in the charts above. I’d say the debate really had an unprecedented effect on interest in voting and the effect was probably bigger for Spanish speaking web users.


Chart 4. Searches for “register to vote” past 5 years to Sep 30. Courtesy of Google Trends

Finally, I wanted to see how the search requests were distributed geographically. The key here is that most Hispanic communities vote Democratic and many states with a large Hispanic population are already blue (such as California, Washington, New Mexico, New Jersey, and New York). The exception is Florida with a large population of Cuban immigrants who tend to vote Republican.


Chart 5. Searches for “registrarse para votar” past 5 years to Sep 30 by county. Courtesy of Google Trends

If you are a supporter of Democrats like Mrs. Clinton, the good news is that a large number of queries are coming from Arizona, and Texas, two states where changes in demographics are slowly turning voting preferences from red to blue.

In Florida, it is not clear which candidate gains from increased number of Spanish-speaking voters. However, since the increase is a result of the debate (during which it was revealed that Mr. Trump had insulted and berated a beauty pageant winner from Venezuela, calling her “miss housekeeping”), I will speculate many newly registered voters are going to be Clinton supporters.

If the Google search trend continues, it may be driven by new reports that Mr. Trump may have violated the US sanctions forbidding business transactions in Cuba. Cuban-Americans searching for information on voter registration after hearing this story are more likely to favor Mrs. Clinton.

[Note: This is the third blog post comparing opt-in and opt-out organ donor registration. The series starts here.]

The previous blog post argued that switching from opt-in to opt-out could increase the number of people on the organ donor registry but could actually reduce the number of organs recovered.

That is because an opt-out process creates ambiguity about the intent of those on the organ donor registry. This would make the Uniform Anatomical Gift Act (UAGA) harder to enforce.

One way to avoid this problem is to couple the use of an opt-out-donor registry with increased training of clerks at the DMV to inform each customer that they will be added to the registry unless they opt-out. In addition to training costs, there will be increased labor costs since each customer transaction may be about a minute longer as the clerk explains what the organ donor registry is and sells the benefits of organ transplantation to the customer.

This extra effort to educate the public is needed to get implicit consent from the driver. Unlike presumed consent where the customer is never told that a decision is being made for them, implicit consent creates a true decision. Unlike mandated choice where the customer is forced to make a cognitively complex choice in a short time span, implicit consent relies on framing to make the default option (the one most people will pick) the one that is most beneficial for society.

Using the same hypothetical data presented in the prior blog posts, I have created a table showing the organ recovery rate when combining opt-out with implicit consent. Assume that an opt-out registration system results in 88% of drivers registering to be organ donors (same rate as in table 2 of that blog post). Of these, the OPO is able to get 99% of families to cooperate (same as in table 1). The OPO does not approach the families of patients who were on the opt-out list (same as in table 2). The overall organ recovery rate is 87%, significantly higher than the 81% rate in the opt-in case or the 79% rate for opt-out without implicit consent. This appears to be a big win.

    Implicit conse nt case             Patient on organ registry
Yes No Row total
agrees to
Yes 87
donation No 1
Col. total 88 12 100

By combining opt-out with implicit consent, 88% of drivers register and 87% of organs are recovered

What impact could the combination of opt-out and implicit consent make in the United States? That is difficult to predict since no state has attempted to implement them together. Legislation was introduced to implement opt-out and presumed consent in New York last year by an assemblyman whose daughter had received two kidney transplants. But the bill never made it out of committee. (See debate in New York Times May 2010.) Similar legislation was introduced in Colorado earlier this year but was withdrawn after public protests and consultation with Donor Alliance, the local OPO.

Why isn’t the combination of opt-out and implicit consent gaining political traction in the U.S.? Most likely it is because the training required to implement implicit consent correctly would be expensive. Even with training, at least one unwilling donor family will probably request an injunction against the OPO. The potential result of this litigation was described in the last blog post. The resulting media coverage and lobbying would likely put pressure on the state legislature to eliminate the opt-out nature of the registry. It could also cause them to revoke the UAGA. This could make it harder for OPOs to recover organs than before the switch to opt-out since they currently can recover organs without consent of the family under opt-in.

Finally, if the driving public feels it is being coerced into becoming donors, it may result in falling donation rates (higher opt-out rates) and reduce trust in the healthcare system. Overall, the combination of opt-out and implicit consent just doesn’t seem like a winning strategy to increase organ recovery rates.

Much thanks to thank Alexandra Glazier, Vice President & General Counsel at The New England Organ Bank, for clearly explaining that adopting an opt-out registration process does not automatically result in adopting presumed consent. Each issue needs to be analyzed separately.

In the previous blog post, I showed how the registration of organ donors using a Boolean variable leads to some drivers to be misclassified. I also showed how requiring drivers to opt-in to the donor registry causes less severe types of misclassifications than opt-out.

Now I will discuss how opt-out can result in uncertainty in the composition of drivers listed in the registry. This uncertainly can impact the behavior of organ procurement coordinators and family members.

Role of certainty in interactions between counselors and family

In states that maintain a donor registry, they share the list of names on the registry with the organ procurement organization (OPO) that is responsible for recovery and distribution of organs for transplant. If a patient dies under conditions that allow the organs to be recovered, an organ recovery coordinator at the OPO will see if the patient’s name is on the organ registry.

Under opt-in, if the patient’s name is on the registry the coordinator can be fairly certain the deceased patient wanted to be a donor (categories 1a and 1b as defined in the previous blog) and can confidently tell the family this and proceed with recovery. Under the Uniform Anatomical Gift Act enacted in most states, a gift by a donor cannot be revoked by the family.

If the patient’s name is not on the registry, the intent of the patient isn’t known. Perhaps the patient wanted to donate (category 3a), didn’t want to donate (2b or 3b), or wanted the family to decide (2a or 4). The coordinator can say the patient’s wishes were not known and politely request the family to make an organ donation on behalf of the deceased patient.

Under opt-out, there are more categories of drivers included in the registry. This reduces the certainty in the composition of the donor registry. This is true even if no drivers are misclassified (i.e., no drivers fall into categories 3a, 3b, or 4), This uncertainty will have an impact on the behavior of the coordinators.

Specifically, if the deceased patient’s name is on the registry, the coordinator cannot be certain the deceased patient wanted to be a donor. She must rely on presumed consent. However, if the family complains that it was not the deceased patient’s intent to be a donor, then the ambiguous nature of the composition of the registry may lead to a delay, which will make recovery impossible. If the OPO pushes the issue, eventually, a court case may resolve the issue, but if the ruling is in favor of the patient’s family, then the entire registry is placed at risk.

Conversely, if the patient’s name is not on the registry, then having the coordinator approach the family to request a donation is also problematic since a donation would require the family to override the wishes of the deceased. If that is allowed, then the wishes of the deceased should be allowed to be overridden if she is on the organ donor registry as well. Again, if the OPO pushes the issue, the organ donor registry is placed at risk.

A hypothetical example of outcomes

Let’s look at some hypothetical numbers to illustrate a possible outcome. In the first table below, the state has an opt-in registration system and has a 64% registration rate. (This is very high, but is achieved in Washington, the state where I live.) The OPO approaches the family of every patient who dies under conditions that allow the organs to be recovered. For patients on the registry it works to enforce the UAGA and gets 99% of families to cooperate in time. For patients not on the registry, it works hard to persuade the family to donate and gets half to cooperate. Overall 81% of organs are recovered.

Opt-in case            Patient on organ registry
Yes No Row total
agrees to
Yes 63
donation No 1
Col. total 64 36 100

Under opt-in, 64% of drivers register to be donors and 81% of organs are recovered

Now suppose that the state switches to an opt-out registration system and the registration rate rises to 88%. However, the cooperation rate among families drops from 99% to 90%. Also, the OPO does not approach any of the families of patients who were on the opt-out list. Overall, the organ recovery rate drops to 79%, lower than it was before the switch. Naturally, I set the numbers to make my case, but it illustrates that switching from opt-in to opt-out will not on its own automatically ensure that donation rates will increase.

Opt-out case             Patient on organ registry
Yes No Row total
agrees to
Yes 79
donation No 9
Col. total 88 12 100

Under opt-out, 88% of drivers register to be donors but only 79% of organs are recovered

Mandated choice

As mentioned in the previous blog entry, there is another option besides opt-in and opt-out called mandated choice. Under mandated choice, the state wants to eliminate the last categories 3a, 3b, and 4 (driver choice undeclared or driver undecided) that create ambiguity. Thus, the law requires the DMV clerk to ask every driver to declare a choice. (It’s not clear what happens if the driver refuses to make a choice or if the clerk forgets to ask or forgets to record the choice.) Several states have tried it, but have given up and returned to opt-in. Currently, only California is experimenting with it, see Jun 2010 blog post.

Texas, which had about a 15% registration rate with opt-in, increased it to about 20% with mandated choice. Unfortunately, I can’t find any data to show if overall organ recovery rate rose or fell after this change. However, the state has abandoned mandated choice, so my guess is the OPOs in that state either saw a drop in donation rates or feared one would occur and lobbied for the return to opt-in.

How opt-out and mandated choice may reduce donation rates

Why has mandated choice failed, and why could opt-out cause donation rates to fall? I think a lot of it may be because of people’s fear of death. Signing up to be an organ donor while applying for a driver’s license is an admission by the registrant that she may die in an accident and needs to make a decision about the disposition of her organs in the event that happens.

Under the current opt-in process, those who are not afraid of death opt-in. Those who are afraid don’t state their preference. For those who don’t opt-in, the decision to donate is still available later to the family. Under opt-out, people who are willing to donate (or let their family decide) but are not willing to admit they may die will opt-out. This is a firm decision, precluding the family from making the donation later.

In the next blog post we will explore ways to make opt-out compatible with individual choice and consent.

Nearly every state in the U.S. maintains a registry of people willing to become deceased organ donors. The intent of an individual to be a donor is stored as a Boolean value (meaning only yes or no responses are allowed) within the driver’s license database. Nearly all states use what is called an opt-in registration process. That is, the states start with the assumption that drivers do not want to participate in the registry (default=no) and require them to declare their desire (called explicit consent) to be a member of the registry either in-person, via a website, or in writing.

One of the frequent proposals to increase the number of deceased organ donors is to switch the registration of donors from an opt-in system to an opt-out system. In an opt-out system, all drivers are presumed to want to participate (default=yes) and people who do not wish to participate must state their desire not to be listed.

Let’s look at the logical and ethical issues this change would present.

Not just a framing problem

Several well-known behavioral economists have stated that switching from opt-in to opt-out is simply a framing problem. For instance, see chapter 11 of Richard Thaler and Cass Sunstein’s book Nudge and a TED 2008 talk by Dan Ariely using data from papers by his colleagues Eric Johnson et al., in Transpl. Dec 2004 and Science Nov 2003 (subscription required).

The basic argument is that deciding whether to donate organs upon death is cognitively complex and emotionally difficult. When asked to choose between difficult options, most people will just take the default option. In the case of an opt-in donor registration, this means they will not be on the organ donor registry. By switching to an opt-out process, the default becomes being a donor. Thus, any person who refuses to make an active decision will automatically become a registered organ donor (this is called presumed consent). This will increase the number of people in the donor registry without causing undue hardship since drivers can easily state a preference when obtaining a driver’s license.

However, these authors overlook two important practical factors. First, switching from opt-in to opt-out doesn’t just reframe the decision the driver must make between two options. It will actually recategorize some drivers.

Second, it changes the certainty of the decision of those included in the organ registry, which affects the interaction between the organ recovery coordinators at the organ procurement organization (OPO) and the family member of a deceased patient.

There are more than two states for drivers regarding their decision to donate

Note that the status of a driver’s intent to be an organ donor is not just a simple two-state Boolean value (yes, no). There are actually at least three separate states related to the intension to be an organ donor. First, upon the driver’s death, if no other family members would be affected, would she like to be an organ donor (yes, no, undecided). Second, has she expressed her decision to the DMV and have it recorded (yes, no). Finally, would she like her family to be able to override her decision (yes, no, undecided). The table below shows the various combinations of these variables.


Driver would like to be organ donor
Driver tells DMV of decision
Driver would permit family to override decision


1a Yes Yes No Strong desire
1b Yes Yes Yes or Undecided Weak desire
2a No Yes Yes or Undecided Weak reject
2b No Yes No Strong reject
3a Yes No Yes, No, or Undecided Unrecorded desire
3b No No Yes, No, or Undecided Unrecorded reject
4 Undecided Yes or No Yes* Undecided

*No or Undecided options make no sense in this context

Opt-in incorrectly excludes some drivers from the donor registry

Now let’s sort these people into two groups, one that we will call the organ donor registry and the other not on the registry.

Under the opt-in process, only drivers in categories 1a and 1b are listed on the organ registry. These drivers have given explicit consent to being on the registry. Drivers in categories 2a, 2b, 3a, 3b, and 4 are excluded from the registry. Thus, we can be quite certain that everyone on the registry wants to be a donor. (There is always a small possibility that the driver accidentally selected the wrong box, changed their mind between the time they obtained their driver’s license and the time of death, or a computer error occurred.)

In most states the drivers not on the organ registry are treated as if they have not decided (i.e., as if they were in the fourth category). When drivers not on the registry die under conditions where the organs can be recovered, the families are asked to decide on behalf of the deceased.

Under an opt-in process, drivers in category 2a are miscategorized. They don’t want to be donors and didn’t want their family to override that decision, but the family is still allowed to decide. The drivers in categories 3a and 3b are miscategorized as well. The ones who don’t want to be donors (3b) are also forced to allow their families to decide. The ones who want to be donors (3a) are now left to let their families decide.

Opt-out incorrectly includes some drivers in the donor registry

Under an opt-out process, drivers in categories 1a, 1b, 3a, 3b, and 4 are grouped together and placed on the organ registry. If the donor registry is binding and the family is not allowed to stop the donation, then the process is called presumed consent. (Note that many authors use opt-out and presumed consent interchangeably. However, they are distinct ideas. Opt-in is a mechanical process of deciding which driver names are added to the registry. Presumed consent is a legal condition that avoids the need to ask the family for permission to recover the organs.)

Drivers in category 3a who wanted to be registered are now correctly placed on the registry. But any drivers in category 3b who don’t want to be on the registry are now assumed to want to be donors, a completely incorrect categorization. Similarly, all drivers in the fourth category who were undecided are now members of the definite donor group and the family no longer has a say.

Only drivers in category 2a and 2b are excluded from the registry. We can be quite certain these people do not want to be donors. But some (category 2a) were willing to let the family decide. Now they are combined with the group of drivers who explicitly do not want to donate.

The distribution of categories into the registry under the opt-in and opt-out process and how they are treated are shown in the table below.

Categories added to donor registry
Categories not added to donor registry


Opt-in process 1a, 1b both treated as if in category 1a (explicit consent) 2a, 2b, 3a, 3b, 4 all treated as if in category 4 (family choice) Drivers in registry are nearly certain to want to be donors. Actual desire of drivers not on registry is ambiguous
Opt-out process 1a, 1b, 3a, 3b,4 all treated as if in category 1a (presumed consent) or 1b (family choice) 2a, 2b both treated as if in category 2b (explicit reject) Drivers not in registry are nearly certain to not want to be donors. Actual desire of drivers on registry is ambiguous


Ethical implications of misclassification

If there are no drivers in categories 3a, 3b, and 4, then switching from opt-in to opt-out will have no impact on the size of the donor registry. However, if there are any drivers in these categories, then some will be incorrectly categorized regardless of whether opt-in or opt-out is used. This miscategorization will lead to some ethical problems.

Under opt-in, there may exist cases where the drivers has made a decision to donate (category 3a) or not (categories 2a or 3b) but family members overrules it. These errors are hard to avoid because they are caused by the lack of agreement between the drivers and other family members.

However, under opt-out combined with presumed consent, there may exist cases where neither the driver (category 3b) nor the family want to donate, but cannot stop it. Similarly, the driver may want to let the family choose whether to donate (category 4) and the family does not want to donate but cannot stop it.

It appears that from an ethical perspective, opt-in is less likely to create a situation where the respect for individual’s right to make decisions about how the body should be treated is denied. For further discussion of the ethical issues see  J. Med. Ethics Jun 2011, and J. Med. Ethics Oct 2011 (subscription required).

Next we will look at the impact switching from opt-in to opt-out will have on the interaction between the organ recovery coordinator and the family. See Part 2 here.

[Update: This blog post was significantly modified to clarify the “decision framing” issue.]

by George Taniwaki

Patients with end-stage renal disease (ESRD) often wait many years for a transplant. There are currently over 85,000 people in the U.S. waiting for a kidney transplant and the number grows each year. The average wait time is over three years. The mortality rate for those with ESRD on dialysis is over 15% per year, meaning that almost half of the patients die and never get a transplant.

Eliminating the waiting list for kidney transplants is a complex problem. But I see four separate solutions. They are reduce the incidence rate of ESRD, increase the supply of deceased donor organs, increase the supply of live donor organs, and apply new technologies to enhance or replace human organs. These solutions are not mutually exclusive and should each be investigated and instituted by the appropriate organizations. In fact, I don’t believe any one of these solutions will eliminate the list on its own, and so possibly all of them will need to be pursued.

I will illustrate the various pieces of this problem with the four flow charts shown below and then discuss each of the four solution areas in future blog posts. The text in orange boxes represent actions that can be taken. The text in green boxes indicate the intended results of those actions.

Access to healthcare

For blog posts related to patient access to preventative care, patient education on treatment modalities, or dialysis treatment, see entries tagged with Access To Healthcare or Dialysis.

Note that in the right side of Figure 1, educating patients about the advantages of transplant therapy will increase the demand for transplants, which will make the waiting list longer if other steps are not taken to reduce the incidence of ESRD or increase the supply of organs.


Figure 1. Actions that may reduce the incidence of ESRD (left) and increase demand for transplant therapy (right)

Deceased donor transplants

For blog posts related to deceased donor transplants, including patient evaluation and experience, see entries tagged with Deceased Donor.


Figure 2. Actions that may increase supply of deceased donor kidneys

Live donor transplants

For blog posts related to live donor transplants, see entries tagged with Live Donor or Kidney Exchange. (For more on the live donor evaluation process, see entries tagged with Donor Story.)


Figure 3. Actions that may increase supply of live donor kidneys

New technologies

For blog posts related to alternatives to current transplant therapy, see entries tagged with Artificial Organs, Stem Cells, and New Therapies.


Figure 4. New technologies that may someday replace standard transplant therapy

Disclosure note: I am a community member of the Organ Donation Legislative Workgroup in Washington state. I am also a volunteer for several organizations that provide healthcare services to patients with ESRD. However, the opinions in this blog post are my own and do not represent those of any group.

All images by George Taniwaki

[Update1: I modified Figure 3]

[Update2: I added links to tagged blog posts]

In a Dec 2009 blog post, I wrote that too many patients with end-stage renal disease (ESRD) are waiting for a deceased donor kidney. They would have a much shorter wait and experience better outcomes if they could find a live kidney donor. I am currently working with Harvey Mysel and the Living Kidney Donors Network to set up a program in Seattle to provide training to patients to give them the tools and the confidence to find a donor.

Part of my effort includes learning as much as I can about working with patients. I have plenty of experience in public speaking, having been a market research consultant. But in that case the audience consists of highly driven business executives. I have some experience working with disadvantaged populations, having been a volunteer tutor in an adult literacy program. But I do not have any experience working with medical patients. How does one instruct and motivate kidney patients who are quite ill? Even more concerning to me, can I effectively work with patients who have behavioral or emotional problems that make me uncomfortable? What about physical appearance? The leading causes of kidney failure are diabetes mellitus and hypertension, both of which are highly correlated with obesity. Will I consciously or even unconsciously blame overweight patients for their disease? Hopefully, just knowing I have a potential bias may help prevent me from allowing it to affect my ability to help.

While pondering this, my wife forwarded an article entitled “How clinicians make (or avoid) moral judgments of patients: implications of the evidence for relationships and research” that appeared in Philosophy, Ethics, and Humanities in Medicine Jul 2010. It is a review of 141 articles on how clinicians form moral judgments regarding patients and how those evaluations affect empathy, level of care, and the clinician’s own well-being. Just reading the list of references to the article is an eye opener. Below are some selected quotes from the article.

“The paucity of attention to moral judgment, despite its significance for patient-centered care, communication, empathy, professionalism, health care education, stereotyping, and outcome disparities, represents a blind spot that merits explanation and repair… Clinicians, educators, and researchers would do well to recognize both the legitimate and illegitimate moral appraisals that are apt to occur in health care settings.”

“[T]he treatment of medically unexplained symptoms… varied by patient ethnicity, physician specialty, the spatial layout of the clinic, and the path sequence of patient contact with physicians and ancillary personnel.”

“[N]urses judged dying patients by their perceived social loss, often giving ‘more than routine care’ to higher status patients and ‘less than routine care’ to the unworthy. People dying from a Friday night knife fight, or the adolescent on the verge of death who has killed others in a wild car drive, have their own social loss reinforced by an ‘it’s their own fault’ rationale.”

“The patients and physicians were able to gauge whether the other liked them, and that perception predicted whether they themselves liked the other. Physicians liked their healthier patients more than their sick patients, and healthier patients liked their physicians more. Physician liking predicted patient satisfaction a year later.”

“Poor patients belong to outgroups of particular interest in healthcare. Public hospitals serving these groups comprise only 2% of acute care hospitals in the United States but train 21% of doctors and 36% of allied health professionals. Primary care physicians serving poor communities are often troubled by what they perceive as their patients’ inadequate motivation and dysfunctional behavioral characteristics.”

“One of the factors that may prevent clinicians from triggering moral appraisals is interest, often equated with curiosity… Good teachers have stressed the value of curiosity for clinical care… ‘One of the essential qualities of the clinician is interest in humanity, for the secret of the care of the patient is in caring for the patient.’”

“Once a stimulus–or perhaps patient, for our purposes–appears beyond one’s comprehension and ability to manage, interest wanes. These appraisals mediate individual personality differences in curiosity and the experience of interest… [W]e can use interest to self-regulate our motivation. When intrinsic motivation lags, we can activate strategies to engage our interest and thereby remain motivated for the task.”