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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." 

Abstract:

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

Faculty

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 ( http://hrsonline.isr.umich.edu ).

Last Name: 
Langa

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

Tue, April 25, 2017, 8:30am
Location: 
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

J. Scott Roberts, PhD

Faculty

Scott Roberts, PhD, is Associate Professor of Health Behavior & Health Education at the University of Michigan’s School of Public Health (U-M SPH), where he directs the School’s Public Health Genetics program and teaches a course on public health ethics. A clinical psychologist by training, Dr. Roberts conducts research on the psychosocial implications of genetic testing for adult-onset diseases.

Last Name: 
Roberts

2017 Bishop Lecture featuring Norman Daniels, PhD

Tue, April 25, 2017, 11:15am
Location: 
Great Lakes Room, Palmer Commons, 100 Washtenaw Ave, Ann Arbor, MI 48109

The 2017 Bishop Lecture in Bioethics was presented by Norman Daniels, PhD, Mary B Saltonstall Professor and Professor of Ethics and Population Health in the Department of Global Health and Population at Harvard School of Public Health. Dr. Daniels will present a talk entitled, "Universal Access vs. Universal Coverage: Two models of what we should aim for." The Bishop Lecture served as the keynote address during the CBSSM Research Colloquium.

Abstract: We contrast two models of health care insurance, the Universal Coverage model underlying the Affordable Care Act and the Universal Access model underlying the (now withdrawn) American Health Care Act. Our goal is to evaluate the strongest argument for the Universal Access model. That model suggests that if people have real choices about health care insurance, some will buy it and some will not, and no one should be mandated to buy it. We argue that the Universal Access model presupposes that people can afford insurance, and that means subsidizing it for millions of people as the Universal Coverage model underlying the ACA does. These costs aside, the strongest argument for the Universal Access model is that giving people true choice may make the population level of well-being higher. Some people will have other priorities that they prefer to pursue, especially if they can free ride by enjoying the benefits of a system that provides health care without their contributing to it. If the additional costs that third parties have to pay as a result of the increase in real choice are significant, then the strongest argument for Universal access fails: the benefits of choosing not to be insured are outweighed by the imposed costs on others from these choices.

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.

Leaving the Emergency Room in a Fog (Sep-09)

Consider this scenario:

Alfred made a visit to his local Emergency Room. What was his diagnosis? What did the medical team do for his problem? What was he supposed to do to continue care at home? And what symptoms was he supposed to watch for to alert him to return to the ER?

Alfred woke up at 4 am on Sunday morning with pain in his left foot. That place where his new running shoes had rubbed a raw spot earlier in the week was getting worse. By 9 am, the foot was red and swollen, with a large oozing sore, and Alfred decided to go to the Emergency Room at his local hospital.

Late on Sunday afternoon, Alfred returned home from the ER. He crutched his way into the house and collapsed on the sofa. His teenage son quizzed him.

"What did they say was wrong?"
"Oh, an infection," replied Alfred.
"Well, what did they do for it?"
"I think they cut a chunk out of my foot," said Alfred.
"Whoa! Did they give you any medicine?"
"Yeah, a shot," said Alfred.
"And what’s with the crutches?"
"I’m supposed to use them for a while," said Alfred, looking annoyed.
"How long a while?"
"It’s written down," said Alfred, digging a crumpled sheet of paper out of his pocket.
"Says here you should take some prescription and elevate your left leg for two days."
"Two days? I have to go to work tomorrow," groaned Alfred.
"And you’re supposed to go back to the ER if you have a fever or pain in your leg. Where’s the prescription?"
"Here, look through my wallet. Maybe I stuck it in there," said Alfred.
The good news is that Alfred recovered completely, with some assistance and cajoling from his son. But how common is it for people who go to the Emergency Room to be foggy about what happened and what they should do once they leave the ER?
What do you think is the percentage of ER patients who do not understand at least one of the following: their diagnosis, the emergency care they received, their discharge care, or their return instructions?
 
  • 38%
  • 48%
  • 78%
  • 88%

How do your answers compare?

A recent study in the Annals of Emergency Medicine found that 78% of emergency room patients showed deficient comprehension in at least one of these areas:
 
  • Diagnosis
  • Emergency care that was given
  • Post-ER care needs
  • Symptoms that would require a return to the ER
51% of patients showed deficient comprehension in two or more areas. Only 22% of reports from patients were in complete harmony with what their care teams reported in all four areas. The biggest area of misunderstanding was in patients' post-ER care needs, such as medications, self-care steps, follow-up from their regular doctors, or follow-up with specialists.
 
Even more alarming is that, according to the study, "most patients appear to be unaware of their lack of understanding and report inappropriate confidence in their comprehension and recall." The patients were quite sure of what they knew 80% of the time—even when what they knew was not right.
 
These results suggest that Emergency Room teams need to do a better job of making sure that patients go home with clear information and instructions—and that patients and their loved ones shouldn't leave until they fully comprehend their situation.
 
Lead author Kirsten G. Engel, MD, conducted this study, "Patient Comprehension of Emergency Department Care and Instructions," with Michele Heisler, MD, Dylan M. Smith, PhD , Claire H. Robinson, MPH, Jane H.Forman, ScD, MHS, and Peter A. Ubel, MD, most of whom are affiliated with CBDSM.
 
The researchers carried out detailed interviews with 140 English-speaking patients who visited one of two Emergency Departments in southeast Michigan and were released to go home. These interviews were compared with the patients' medical records, and the comparisons revealed serious mismatches between what the medical teams found or advised and what the patients comprehended.
 
"It is critical that emergency patients understand their diagnosis, their care, and, perhaps most important, their discharge instructions," says Kirsten Engel, a former UM Robert Wood Johnson Clinical Scholar who is now at Northwestern University. "It is disturbing that so many patients do not understand their post-Emergency-Department care, and that they do not even recognize where the gaps in understanding are. Patients who fail to follow discharge instructions may have a greater likelihood of complications after leaving the Emergency Department."
 
Peter A. Ubel, the study's senior author, agrees: "Doctors need to not only ask patients if they have questions, but ask them to explain, in their own words, what they think is wrong with their health and what they can do about it. And patients need to ask their doctors more questions, and even need to explain to their doctors what they think is going on."
 
Read the article:

 

Is your well-being influenced by the guy sitting next to you? (Nov-03)

Rating your satisfaction with your life may not be a completely personal decision. See how your satisfaction rating may be influenced by others.

When answering this question, imagine that there is someone in a wheelchair sitting next to you. They will also be answering this question, but you will not have to share your answers with each other.

How satisfied are you with your life in general?

Extremely satisfied 1       2       3       4       5       6       7       8       9       10 Not at all satisfied

How do you compare to the people surveyed?

You gave your life satisfaction a rating of 1, which means that you are extremely satisfied with your life. In a study done where people with a disabled person sitting next to them wrote down their life satisfaction on a questionnaire, they gave an average life satisfaction rating of 2.4, which means they were very satisfied with their lives.

What if you'd had to report your well-being to another person instead of writing it down?

In the study, half the people had to report their well-being in an interview with a confederate (a member of the research team who was posing as another participant). When the participants had to report in this way, and the confederate was not disabled, the participants rated their well-being as significantly better than those who reported by writing it on the questionnaire in the presence of a non-disabled confederate (2.0 vs. 3.4, lower score means higher well-being). The scores given when reporting to a disabled confederate elicited a well-being score that was no different than that when completing the questionnaire in the presence of a disabled confederate (2.3 vs. 2.4).

Mean life satisfaction ratings, lower score means higher satisfaction
Mode of rating well-being Disabled confederate Non-disabled Confederate
Interview (public) 2.3 2.0
Questionnaire (private) 2.4 3.4
What caused the difference in well-being scores?

When making judgments of well-being, people (at least in this study) tend to compare themselves to those around them. This effect is seen more when well-being was reported in an interview than when the score was privately written down, due to self-presentation concerns. A higher rating was given in public so as to appear to be better off than one may truly feel. Note that the effect was only seen in the case where the confederate was not disabled. While well-being ratings were better overall with a disabled confederate, there was no difference between the private and public ratings. Social comparison led to a better well-being judgment, but it appears that the participants were hesitant to rate themselves too highly in front of the disabled person for fear of making the disabled person feel worse.

Why is this important?

Subjective well-being is a commonly used measure in many areas of research. For example, it is used as one way to look at the effectiveness new surgeries or medications. The above studies show that SWB scores can vary depending on the conditions under which they are given. Someone may give a response of fairly high SWB if they are interviewed before leaving the hospital, surrounded by people more sick than they are. From this, it would appear as though their treatment worked great. But suppose that they are asked to complete a follow-up internet survey a week later. Since they do not have to respond to an actual person face-to-face, and without being surrounded by sick people, they may give a lower rating than previously. Is this because the treatment actually made their SWB worse over the longer term, or simply because a different method was used to get their response? The only way to really know would be to use the same methodology to get all their responses, which might not always be feasible. These are important considerations for researchers to keep in mind when analyzing results of their studies. Are the results they got the true SWB of their participants, or is it an artifact of how the study was done? And is there a way to know which measure is right, or are they both right which would lead to the conclusion that SWB is purely a momentary judgment based on a social context?

For more information see:

Strack F, Schwarz N, Chassein B, Kern D, Wagner D. Salience of comparison standards and the activation of social norms: Consequences for judgements of happiness and their communication. British Journal of Social Psychology. 29:303-314, 1990.

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