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Funded by Robert Wood Johnson Foundation

Funding Years: 2014 - 2016.

The Robert Wood Johnson Foundation Clinical Scholars Program at the University of Michigan has established a rigorous curriculum, with enhanced and mentored research practicum and exciting opportunities to engage in community-based and partnered participatory research. The curriculum is based on adult learning theory and integrates research theory and practical applications. This curriculum will fulfill requirements for a Master's Degree in Health and Health Care Research, a degree program that was designed specifically to meet the needs of the Clinical Scholars at the University of Michigan. These above courses make up the central components of the first year of Clinical Scholars Program at the University of Michigan. The second year of the Clinical Scholars Program is primarily devoted to research, with the Scholars' Research Committee continuing as an advisory committee. Education in the second year focuses more closely to each Scholar's specific needs. In the second year the Scholars also participate in a "Work-in-Progress Seminar" led by one of the Program Directors. Throughout all years of the program, Scholars participate in the Clinical Scholars noon health Seminar. This is a weekly 1.5 hour seminar which will alternate between presentation of research findings by Scholars, faculty, or invited guests, and presentations about health policy by Michigan faculty and invited guests. All Scholars are expected to attend the seminar each week, as well as the CSP Leadership, most Core Faculty, and selected guests.

PI(s): Rodney Hayward

Co-I(s): Matthew Davis, Gary Freed, Mary Ellen Heisler, Timothy Hofer, Joel Howell, Theodore Iwashyna, Eve Kerr, Joyce Lee, Richard Lichtenstein, Laurence McMahon Jr, Caroline Richardson, Mary AM Rogers, Sanjay Saint, Antonius Tsai, Michael Volk, Sara Waber

Funded by National Institutes of Health; National Institute of Mental Health

Funding Years: 2012-2017

This project will test a practical intervention that uses low cost technologies to activate depressed patients' existing social networks for self-management support. The intervention links patients with a "CarePartner" (CP), i.e., a non-household family member or close friend who is willing to support the patient in coordination with the clinician and any existing in-home caregiver (ICG). Through weekly automated telemonitoring, patients report their mood and self-management status, and receive tailored guidance on self-management. The CP receives a corresponding update along with guidance on how to best support the patient's self-management efforts, and the primary care team is notified about clinically urgent situations. The intervention will be tested among depressed primary care patients from clinics serving low-income and underinsured patients, whom the intervention was especially designed to benefit. Specific Aim 1 is to conduct a randomized controlled trial to compare the effectiveness of one year of telemonitoring-supported CP for depression versus usual care (control) on depression severity. Specific Aim 2 is to examine key secondary outcomes (response and remission, impairment, well-being, caregiving burden, healthcare costs) and potential moderators. Specific Aim 3 is to use a mixed-methods approach to enrich our interpretation of the statistical associations, and to discover strategies to enhance the intervention's acceptability, effectiveness, and sustainability. If the intervention proves effective without increasing clinician burden or marginal costs, then its subsequent implementation could yield major public health benefits, especially in medically underserved populations.

PI(s): James Aikens

Co-I(s): Michael Fetters, John Piette, Ananda Sen, Marcia Valenstein, Daniel Eisenberg, Daphne Watkins

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.



Does order matter when distributing resources? (Jun-03)

Should people with more severe health problems receive state funding for treatment before people with less severe health problems? See how your opinion compares with the opinions of others.

Imagine that you are a government official responsible for deciding how state money is spent on different medical treatments. Your budget is limited so you cannot afford to offer treatment to everyone who might benefit. Right now, you must choose to spend money on one of two treatments.

  • Treatment A treats a life threatening illness. It saves patients' lives and returns them to perfect health after treatment
  • Treatment B treats a different life threatening illness. It saves patients' lives but is not entirely effective and leaves them with paraplegia after treatment. These patients are entirely normal before their illness but after treatment will have paraplegia.

Suppose the state has enough money to offer Treatment A to 100 patients. How many patients would have to offered Treatment B so that you would have difficulty choosing which treatment to offer?

How do your answers compare?

The average person said that it would become difficult to decide which treatment to offer when 1000 people were offered Treatment B.

What if you had made another comparison before the one you just made?

In the study, some people were asked to make a comparison between saving the lives of otherwise-healthy people and saving the lives of people who already had paraplegia. After they made that comparison, they made the comparison you just completed. The average person in that group said it would take 126 people offered Treatment B to make the decision difficult. The differences are shown in the graph below

Why is this important?

The comparison you made is an example of a person tradeoff (PTO). The PTO is one method used to find out the utilities of different health conditions. These utilities are basically measures of the severities of the conditions. More severe conditions have a lower utility, and less severe conditions have a higher utility, on a scale of 0 to 1. Insurance companies, the government, and other organizations use these utilities as a way to decide which group to funnel money into for treatments.

On the surface, it seems like basing the money division on the severity of a condition is a good and fair method, since theoretically the people who are in the greatest need will be treated first. However, the PTO raises issues of fairness and equity that aren't accounted for in other utility elicitation methods like the time tradeoff (TTO) and rating scale (RS).

For example, when asked to decide how many people with paraplegia would have to be saved to equal saving 100 healthy people, many people say 100; that is, they think it is equally important to save the life of someone with paraplegia and a healthy person. Going by values obtained using the TTO or RS, an insurance company may conclude that 160 people with paraplegia (using a utility of .6) would have to be saved to make it equal to saving 100 healthy people. This would mean that less benefit would be gotten by saving someone with paraplegia, and thus they might not cover expenses for lifesaving treatments for people with paraplegia as much as they would for a healthy person. The PTO shows that many people would not agree with doing this, even though their own responses to other utility questions generated the policy in the first place.

For more information see:

Ubel PA, Richardson J, Baron J. Exploring the role of order effects in person trade-off elicitations. Health Policy, 61(2):189-199, 2002.

CBSSM investigators Holly Witteman, Andrea Fuhrel-Forbis, Angela Fagerlin, and Brian Zikmund-Fisher, along with CBSSM alumni Peter Ubel and Andrea Angott will give a plenary talk at the Society for Medical Decision Making's 32nd Annual Meeting in Toronto on Monday, October 25.  The talk is titled, "Colostomy is Better than Death, but a 4% Chance of Death Might Be Better Than a 4% Chance of Colostomy: Why People Make Choices Seemingly At Odds With Their Stated Preferences." 


Purpose: When asked for their preference between death and colostomy, most people say that they prefer colostomy. However, when given the choice of two hypothetical treatments that differ only in that one has four percent chance of colostomy while the other has four percent additional chance of death, approximately 25% of people who say that they prefer colostomy actually opt for the additional chance of death. This study examined whether probability-sensitive preference weighting may help to explain why people make these types of treatment choices that are inconsistent with their stated preferences.

Method: 1656 participants in a demographically diverse online survey were randomly assigned to indicate their preference by answering either, “If you had to choose, would you rather die, or would you rather have a colostomy?†or, “If you had to choose, would you rather have a 4% chance of dying, or would you rather have a 4% chance of having a colostomy?†They were then asked to imagine that they had been diagnosed with colon cancer and were faced with a choice between two treatments, one with an uncomplicated cure rate of 80% and a 20% death rate, and another with an uncomplicated cure rate of 80%, a 16% death rate, and a 4% rate of colostomy.

Result: Consistent with our prior research, most people whose preferences were elicited with the first question stated that they preferred colostomy (80% of participants) to death (20%), but many then made a choice inconsistent with that preference (59% chose the treatment with higher chance of colostomy; 41% chose the treatment with higher chance of death). Compared to the first group, participants whose preferences were elicited with the 4% question preferred death (31%) over colostomy (69%) more often (Chi-squared = 24.31, p<.001) and their treatment choices were more concordant with their stated preferences (64% chose the treatment with higher chance of colostomy; 36% chose the treatment with higher chance of death, Chi-squared for concordance = 36.92, p<.001).

Conclusion: Our experiment suggests that probability-sensitive preference weighting may help explain why people’s medical treatment choices are sometimes at odds with their stated preferences. These findings also suggest that preference elicitation methods may not necessarily assume independence of probability levels and preference weights.

Ken Langa, MD, PhD


Dr. Langa is the Cyrus Sturgis Professor in the Department of Internal Medicine and Institute for Social Research, a Research Scientist in the Veterans Affairs Center for Clinical Management Research, and an Associate Director of the Institute of Gerontology, all at the University of Michigan. He is also Associate Director of the Health and Retirement Study (HRS), a National Institute on Aging funded longitudinal study of 20,000 adults in the United States ( ).

Last Name: 

2017 CBSSM Research Colloquium and Bishop Lecture (Norman Daniels, PhD)

Tue, April 25, 2017, 8:30am
Great Lakes Room, Palmer Commons, 100 Washtenaw Ave, Ann Arbor, MI 48109

The Center for Bioethics and Social Sciences in Medicine (CBSSM) Research Colloquium was held Tuesday, April 25, 2017 at the Great Lakes Room, Palmer Commons, 100 Washtenaw Ave, Ann Arbor, MI 48109.

The CBSSM Research Colloquium featured the Bishop Lecture in Bioethics as the keynote address.  Norman Daniels, PhD presented the Bishop Lecture with a talk entitled: “Universal Access vs Universal Coverage: Two models of what we should aim for."

Norman Daniels, PhD is Mary B. Saltonstall Professor of Population Ethics and Professor of Ethics and Population Health in the Department of Global Health and Population at the Harvard School of Public Health. Formerly chair of the Philosophy Department at Tufts University, his most recent books include Just Health: Meeting Health Needs Fairly (Cambridge, 2008); Setting Limits Fairly: Learning to Share Resources for Health, 2nd edition, (Oxford, 2008); From Chance to Choice: Genetics and Justice (2000); Is Inequality Bad for Our Health? (2000); and Identified versus Statistical Lives (Oxford 2015). He has published 200 peer-reviewed articles and as many book chapters, editorials, and book reviews. His research is on justice and health policy, including priority setting in health systems, fairness and health systems reform, health inequalities, and intergenerational justice. A member of the IOM, a Fellow of the Hastings Center, and formerly on the ethics advisory boards of the CDC and the CIHR, he directs the Ethics concentration of the Health Policy PhD at Harvard and recently won the Everett Mendelsohn Award for mentoring graduate students.

2017 Colloquium Schedule:

  • 8:30     Check in, refreshments
  • 9:05     Welcome
  • 9:10     Presentation 1: “Setting priorities for Medicaid: The views of minority and underserved communities” Susan Goold, MD, MHSA, MA & Zachary Rowe, Executive Director, Friends of Parkside
  • 9:35     Presentation 2: ““How Acceptable Is Paternalism? A Survey-Based Study of Clinician and Non-clinician Opinions on Decision Making After Life Threatening Stroke” Kunal Bailoor, MD Candidate
  • 10:00   Medical Student in Ethics Award
  • 10:10   Presentation 3: “Ethical Challenges Faced by Providers in Pediatric Death: A Qualitative Thematic Analysis” Stephanie Kukora, MD
  • 10:35   Presentation 4: “Capacity for Preferences:  An overlooked criterion for resolving ethical dilemmas with incapacitated patients” Jason Wasserman, PhD & Mark Navin, PhD
  • 11:00   Break
  • 11:15  Bishop Lecture: Norman Daniels, PhD
  • 12:45  Lunch

Funded by National Institutes of Health; National Insitute on Drug Abuse

Funding Years: 2012-2017

This application seeks a five-year continuation of the panel data collections of the Monitoring the Future (MTF) study, an ongoing epidemiological and etiological research and reporting project begun in 1975. In addition to being a basic research study, MTF has become one of the nation's most relied upon sources of information on trends in illicit drug, alcohol, and tobacco use among American adolescents, college students, and young and middle-aged adults. This application seeks continuation of the mail follow-up surveys of high school graduates (augmented with internet options) at modal ages 19-30, 35, 40, 45, 50, and now 55. The companion main application seeks to continue the in-school data collections and to support the analysis of all of the data in the study, including past and future panel data. (NIDA requests that the study seek continuation funding through two separate applications, as it has done in the last two rounds.)
The study's cohort-sequential longitudinal design permits the measurement and differentiation of three types of change: age (developmental), period (historical), and cohort. Each has different determinants, and all three types of change have been shown by MTF to occur for most drugs. Factors that may explain historical trends and cohort differences also are monitored. MTF is designed to document the developmental history and consequences of drug use and related attitudes from adolescence through middle adulthood, and to determine the individual and contextual characteristics and social role transitions that affect use and related attitudes. Research on risk and protective behaviors for the transmission of HIV/AIDS among adults ages 21-40 also will be continued. All of this work will be extended to new years, cohorts, and ages under this application and the companion main application. The study will examine the importance of many hypothesized determinants of drug use (including attitudes and beliefs and access), as well as a range of potential consequences (including physical and psychological health, status attainment, role performance, and drug abuse and dependence). Impacts of some policy changes on adolescents and young adults will be evaluated, including those of the new FDA cigarette labeling requirements. MTF will experiment with the use of internet response methods and pursue several new approaches to making its panel data more accessible to other investigators.
The study's very broad measurement covers (a) initiation, use, and cessation for over 50 categories and sub-categories of licit and illicit drugs, including alcohol and tobacco; (b) attitudes and beliefs about many of them, perceived availability, and peer norms; (c) other behaviors and individual characteristics; (d) aspects of key social environments (home, work, school) and social role statuses and transitions; and (e) risk and protective behaviors related to the spread of HIV/AIDS. Results will continue to elucidate drug use from adolescence through middle adulthood (including the introduction of new drugs) with major implications for the policy, research, prevention, and treatment agendas.

PI(s): Mick Couper

Co-I(s): Lloyd Johnston, Patrick O'Malley, John Schulenberg, Megan Patrick, Richard Miech

Funded by Health and Human Services, Department of-National Institutes of Health

Funding Years: 2013 - 2015.

With the aging of society and restructuring of families, it is increasingly important to understand how individuals become disabled. New disability is associated with increased mortality, substantial increases in medical costs (often borne by public payers), and a heavy burden on families and caregivers. While the disablement process?as theorized by Verburgge & Jette and their successors?has traditionally been seen as chronic and gradual, there is increasing recognition that acute events play a critical role in disability. Medical illnesses are not the only potentially disabling events. NIA & NINR recently posted PA-11-265, calling for ?Social and Behavioral Research on the Elderly in Disasters? in recognition that natural disasters are common, but we know little about their impact on health and disability. The National Research Council?s Committee on Population published a report in 2009 documenting not only our ignorance in this area, but, importantly, the potential value of studying disasters to understand fundamental processes in disability and health.
Our long-term research agenda is (a) to test the hypothesis that natural disasters cause enduring morbidity for survivors that is not fully addressed by existing health and welfare programs, and (b) to discover remediable mechanisms that generate that enduring morbidity. Here we propose a nationwide test of the association of living in a disaster area with individuals? long-term disability and health care use. To perform this test, we will combine the unique longitudinal resources of over 16,000 respondents in the linked Health and Retirement Study (HRS) / Medicare files with a newly constructed mapping of all FEMA disaster declarations between 1998 and 2012. We will address key gaps in the existing literature of detailed single-disaster studies with a generalizable perspective across time and space via these Specific Aims:
AIM 1: Quantify the association between the extent of a disaster ? measured as the repair cost to public infrastructure and increases in level of disability among survivors. We will follow respondents for an average of 5 years after the disaster. AIM 2: Quantify the association between the extent of a disaster and increases in the likelihood of hospitalization among survivors. AIM 3: Test the hypothesis that increases in level of disability and likelihood of hospitalization after disasters are worse for those living in counties with higher levels of poverty.
This proposal is specifically responsive to PA-11-265. This proposal is innovative because long-term effects of disasters, particularly for vulnerable older Americans, have been systematically neglected in previous research. It is significant because it will address the public health consequences of a relatively common but understudied exposure. Further, a key contribution of this R21 will be to evaluate the feasibility of the National Research Council conjecture that natural disasters can be studied as exogenous shocks to the environment, and that we can thereby test and elaborate usually endogenous mechanisms in the development of disability.

PI(s): Theodore Iwashyna

Co-I(s): Kenneth Langa, Yun Li, Anne Sales