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Last Saturday, Dr. Susan Goold had the highly esteemed task of presenting to the AMA House of Delegates on modernizing the Code of Medical Ethics. As the chair of the AMA Committee on Ethical and Judicial Affairs (CEJA), Dr. Goold is very involved in this massive reformatting and modernization effort. More information can be found in the AMA Wire press release.

Kathryn Moseley served as one of the judges at "The Big Ethical Question Slam 5" hosted by In addition, Naomi Laventhal, Michele Gornick, Christian Vercler, Lauren Smith, and Lauren Wancata served as judges at the "Michigan Highschool Ethics Bowl 2."

Thanks to all the CBSSM folks who contributed their time!

For more information about these events and other great ethics-related activites, go to

A short video about the Highschool Ethics Bowl can be found here.

Bioethics Grand Rounds: Musical Event "When Death Comes Callin"

Wed, October 26, 2016, 12:00pm
UH Ford Amphitheater & Lobby

When Death Comes Callin': Songs and Reflections About Death

Charlotte DeVries, Jeanne Mackey, Merilynne Rush, and friends offer a program of songs and brief readings reflecting various perspectives on death - humorous, sad, thoughtful, and quirky.

Lunch is provided on a first-come, first-served basis.

Funded by the NIH

The overarching goal of our research is to improve opioid analgesic safety and efficacy by optimizing opioid risk recognition, informed analgesic decision-making, and drug storage/disposal behaviors among parents of youth who are prescribed these agents for home use. With this proposal, we aim to demonstrate that our Scenario-Tailored Opioid Messaging Program (STOMP?) will: 1) Improve parents' opioid risk understanding and their analgesic decision-making; 2) Enhance parents' analgesic self-efficacy, analgesic use, storage behaviors and their children's pain outcomes, and 3) To demonstrate that the STOMP? plus provision of a method to get rid of left-over medications will effectively nudge parents to safely dispose of left-over opioid analgesics. For more info:

PI: Terri Lewis-Voepel

CBSSM Co-Is: Brian Zikmund-Fisher & Alan Tait

Mon, June 23, 2014

Brian Zikmund-Fisher was interviewed by Reuters Health for the article "Shared decision making still lacking for cancer screening." He discusses his research and trade-offs in cancer screenings. "What this study does is it shows that despite all of the initiatives and the discussion of shared decision making that has been going on, we don't seem to be moving the needle very much," he states. 

His interview also received press in the Chicago Tribune and New York Daily News.

Mon, June 06, 2016

A recent internet study on the effect of the VAERS (Vaccine Adverse Event Reporting System) on vaccine acceptance and trust was featured in "The Conversation." This study found telling participants about VAERS, without having them read the actual reports, improved vaccine acceptance only very slightly. However, when participants read the detailed reports, both vaccine acceptance and trust in the CDC’s conclusion that vaccines are safe declined significantly. This was true, even though the vast majority of respondents believed that the vaccine caused few or none of the reported deaths and disabilities.

For the original study:

Scherer LD, Shaffer VA, Patel N, Zikmund-Fisher BJ. Can the vaccine adverse event reporting system be used to increase vaccine acceptance and trust?. Vaccine. 2016 May 5;34(21):2424-9.

Research Topics: 

Get it out of me! (Dec-05)

A 5% chance of death or a 10% chance of death:  which would you choose?

Imagine that you have been diagnosed with a slow growing cancer. Right now, the cancer is not causing you to feel sick. For most people, the cancer will grow so slowly it will never cause them any trouble. For others, the cancer will grow to the point that it makes them sick. Untreated, five percent (5 out of 100) will die of the cancer. Your doctor tells you that you have two treatment options: watchful waiting or surgery. Watchful waiting means you will not do any treatment right away, but your doctor will follow your cancer closely and treat any symptoms that you have if it begins to spread. Although it would be too late to be cured, you would be comfortable and free of pain. There are no side effects to watchful waiting, but five percent (5 out of 100) of the people who choose this treatment will develop symptoms and die from their cancer within five years. On the other hand, the surgery would cure your cancer permanently. Following surgery you will feel more tired than usual and will experience stomach upset occasionally for the three months following your surgery. However, surgery has a ten percent (10 out of 100) risk of death during the surgery.

Imagine that both of these treatments are completely covered by your health insurance. Which would you choose?

  •  I would not take the surgery and accept the 5% chance of dying from this cancer.
  •  I would take the surgery and accept the 10% chance of dying from the surgery.

How do your answers compare?

In the real world, cancer patients sometimes choose treatments that may have devastating side effects over less invasive, yet equally or more effective, approaches. One explanation for this is that people may feel a strong need to "get the cancer out" of their bodies. Surgical removal of all potentially cancerous tissues may satisfy this desire so thoroughly that people end up ignoring important statistical information about adverse outcomes.

Making a choice not in their best interest

CBDSM investigators Angela Fagerlin, Brian Zikmund-Fisher, and Peter Ubel hypothesized that people perceive cancer diagnoses as a call to action, and more specifically, a call to get rid of the cancer through surgery, regardless of what statistical information might say to the contrary. Consequently, they predicted that when presented with hypothetical cancer diagnoses, many people would say they would pursue surgery even if such an action would decrease their chance of survival.

To explore the relative frequency of people's willingness to choose surgery when it wasn't in their best interest, the investigators designed a cancer scenario similar to the one you read on the previous page. Participants were presented either a surgery or a medication treatment that would either increase or decrease their chance of survival.

The investigators found that participants who were presented with the opportunity to rid themselves of their cancer through surgery were significantly more inclined to take action than those who were presented with the medication treatment. For example, when the treatment reduced their overall chance of survival, 65% chose the surgery, whereas only 38% chose the medication treatment. This suggests that people's treatment decisions may be based not on the effectiveness of the treatments, but rather on their beliefs about how cancer should be treated. Specifically, cancer diagnoses seem to conjure up a strong desire for active treatment. And people seem to have an intuitive belief that action should not just involve treatment, but surgical removal of the cancer.

Why these findings are important

The results of this study may resonate with many clinicians who have encountered cancer patients who seem to desire treatment for treatment's sake, or who have a preference for surgical intervention even before they learn about the pros and cons of their treatment alternatives. This study should serve to remind clinicians that patients' preference for action can be strong enough, at times, to be a bias. At a minimum, it is important for health care professionals to be aware of the potential for such biases, so they can decide whether to accept patients' preferences at face value, or try to convince patients that aggressively treating a tumor may not be in their best interests.

Read the article:

Cure me even if it kills me: Preferences for invasive cancer treatment.
Fagerlin A, Zikmund-Fisher BJ, Ubel PA. Medical Decision Making 2005;25(6):614-619.

The Importance of First Impressions (Jun-05)

How do your risk estimate and your actual level of risk impact your anxiety? Please answer the following question to the best of your ability:

What is the chance that the average woman will develop breast cancer in her lifetime?

The average lifetime chance of developing breast cancer is actually 13%.

How does this risk of breast cancer (13% or 13 out of 100 women) strike you?
As an extremely low risk 1       2       3       4       5        6        7        8       9       10 As an extremely high risk

How do your answers compare?

Making a risk estimate can change the feel of the actual risk

CBDSM investigators Angela Fagerlin, Brian Zikmund-Fisher, and Peter Ubel designed a study to test whether people react differently to risk information after they have been asked to estimate the risks. In this study, half the sample first estimated the average woman's risk of breast cancer (just as you did previously), while the other half made no such estimate. All subjects were then shown the actual risk information and indicated how the risk made them feel and gave their impression of the size of the risk. The graph below shows what they found:


As shown in the graph above, subjects who first made an estimated risk reported significantly more relief than those in the no estimate group. In contrast, subjects in the no estimate group showed significantly greater anxiety. Also, women in the estimate group tended to view the risk as low, whereas those in the no estimate group tended to view the risk as high.

So what's responsible for these findings? On average, those in the estimate group guessed that 46% of women will develop breast cancer at some point in their lives, which is a fairly large overestimate of the actual risk. It appears, then, that this overestimate makes the 13% figure feel relatively low, leading to a sense of relief when subjects find the risk isn't as bad as they had previously thought.

Why this finding is important

Clinical practice implications - The current research suggests that clinicians need to be very deliberate but very cautious in how they communicate risk information to their patients. These results argue that a physician should consider whether a person is likely to over-estimate their risk and whether they have an unreasonably high fear of cancer before having them make a risk estimation. For the average patient who would overestimate their risk, making a risk estimation may be harmful, leading them to be too relieved by the actual risk figure to take appropriate actions. On the other hand, if a patient has an unreasonably high fear of cancer, having them make such an estimate may actually be instrumental in decreasing their anxiety. Physicians may want to subtly inquire whether their patient is worried about her cancer risk or if she has any family history of cancer to address the latter type of patient.

Research implications - Many studies in cancer risk communication literature have asked participants at baseline about their perceived risk of developing specific cancers. Researchers then implement an intervention to "correct" baseline risk estimates. The current results suggest that measuring risk perceptions pre-intervention will influence people's subsequent reactions, making it difficult to discern whether it was the intervention that changed their attitudes or the pre-intervention risk estimate. Researchers testing out such interventions need to proceed with caution, and may need to add research arms of people who do not receive such pre-tests.

For more details: Fagerlin A, Zikmund-Fisher BJ, Ubel PA. How making a risk estimate can change the feel of that risk: shifting attitudes toward breast cancer risk in a general public survey. Patient Educ Couns. 2005 Jun;57(3):294-9.



How bad would it be? (May-05)

For certain diseases, receiving treatment can disrupt daily life considerably. How would this disruption affect your happiness?

Think about your average mood during a typical week. How would you rate your average mood?

  • very pleasant
  • slightly pleasant
  • neutral
  • slightly unpleasant
  • very unpleasant
Now imagine you have end-stage renal disease, a condition in which your kidneys fail to perform their normal function of cleaning and filtering the blood. Treatment consists of a procedure called hemodialysis, in which your blood is filtered through a machine. You require treatment three times per week for about three hours each time. Discomfort is minor, and you can read, write, talk, eat, sleep, or watch TV during the treatment. Your lifestyle includes most normal activities, including work, exercise, and leisure; however, you feel fatigued if you miss treatment for several days. Also, you must follow a strict diet that involves reducing salt intake, consuming relatively little meat, and drinking only small amounts of fluids. Imagine, you have been on hemodialysis for a year.
Now imagine your average mood during a typical week if you had end-stage renal disease as described above. If you had end-stage renal disease, how do you think you would rate your average mood?
  • very pleasant
  • slightly pleasant
  • neutral
  • slightly unpleasant
  • very unpleasant

How do your answers compare?

The discrepancy between Patients and Non-patients

Past research has shown that there are serious health conditions that do not seem to be as badly experienced by the people living with them as healthy people would expect. Although the existence of this discrepancy is well established at this point, its cause is not. One possibility is that patients are exaggerating their well-being. They may be focusing on periods of positive mood even though they actually experience lengthy periods of negative mood. On the other hand, patients might be as happy as they report and healthy people might very much be overestimating the negative impact of the illness. A related explanation comes from evidence that healthy people tend to underestimate their own past moods, recalling negative times more readily than positive times. This would then make them more likely to also understate the well-being of other people as well, and this could contribute to the discrepancy.

Which explanation is correct?

Jason Riis, a researcher at the University of Michigan, teamed up with investigators from CBDSM and the University of Pennsylvania to conduct a study with the goal of finding out which of the above explanations is accountable for the discrepancy. To accomplish this, they set out to measure mood in two ways. One way is to ask individuals to estimate their average mood. The other way is to measure mood on a momentary basis, asking individuals at frequent intervals to indicate their mood at the moment, and then taking the average of these responses. This latter way of assessing mood is less influenced by biased recall than just asking subjects to estimate overall mood.

The investigators recruited 49 end-stage renal patients receiving hemodialysis treatment three times per week and 49 healthy controls who were matched to the patients on age, race, sex, and education. Subjects were first given an entry interview during which they estimated their average mood. They were then asked to carry around Palm Pilots for a week that beeped at random intervals, prompting them to indicate their mood at the moment. After carrying the Palm Pilots around for a week, subjects completed an exit interview that asked them to recall their average mood in the last week and to again estimate their average mood in general. Healthy subjects also estimated what they thought their average mood would be if they were a hemodialysis patient.

The investigators found that patients' average momentary moods were no lower than their estimated average mood, thus finding no evidence that patients exaggerate their mood. In fact, they failed to find any evidence that patients experience lower moods than healthy controls. In appears, then, that hemodialysis patients do largely adapt to their condition. On the other hand, healthy controls did rate that their average mood would be lower if they were homodialysis patients. Thus, the previously observed tendency of healthy people to underestimate the reported quality of life of people with various health conditions does seem to be due, in large part, to their misperception of the extent to which people can adapt to such conditions. In this study, healthy people also underestimated their own average mood. This could also account for some of the discrepancy, but the effect was not very large.

Why this is important

Ignorance of adaptation can have negative consequences for decision making. It can cause individuals to opt for unnecessarily risky surgeries and policymakers to invest in programs that have a minimal impact on people's well-being. This is not to say that research and treatment of kidney disease should not continue to be priorities, but in making difficult policy decisions, consideration of the moods experienced by patients may influence priorities between serious conditions such as, for example, paraplegia and depression. The results of this study suggest that policy makers should proceed with caition because healthy people's apparent exaggeration of the influence of illness on mood can lead to incorrect perceptions of how illness will influence quality of life.

Read the article:

Ignorance of hedonic adaptation to hemo-dialysis: a study using ecological momentary assessment.
Riis J, Loewenstein G, Baron J, Jepson C, Fagerlin A, Ubel PA. Journal of Experimental Psychology: General 2005;134:3-9.