Dan Spratt will be speaking about a project to evaluate the possible impact of a change in the grading system for prostate cancer on patient decisions to pursue active surveillance versus treatment. They are developing an online survey that randomizes an internet panel of participants to seeing various scenarios.
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Reshma Jagsi, MD, DPhil, is the lead author on a new study showing that breast cancer patients who have had mastectomies and need radiation are less likely to receive these treatments than patients who have had lumpectomies. The article appears in the Journal of Clinical Oncology (online March 29, 2010). Additional authors are Paul Abrahamse, Sarah T. Hawley, Jennifer J. Griggs, Steven J. Katz, Monica Morrow, John J. Graff, and Ann S. Hamilton. Read a press release about the research here.
Funded by: NIH
Funding Years: 2016-2021
There is a fundamental gap in understanding how Mild Cognitive Impairment (MCI) influences treatment and Decision Making for serious illnesses, like Cardiovascular disease (CVD), in older patients. Poor understanding of Clinical Decision Making is a critical barrier to the design of interventions to improve the quality and outcomes of CVD care of in older patients with MCI. The long-term goal of this research is to develop, test, and disseminate interventions aimed to improve the quality and outcomes of CVD care and to reduce CVD-related disability in older Americans with MCI. The objective of this application is to determine the extent to which people with MCI are receiving sub-standard care for the two most common CVD events, Acute myocardial infarction (AMI) and acute ischemic stroke, increasing the chance of mortality and morbidity in a population with otherwise good quality of life, and to determine how MCI influences patient preferences and physician recommendations for treatment. AMI and acute ischemic stroke are excellent models of serious, acute illnesses with a wide range of effective therapies for acute management, Rehabilitation, and secondary prevention. Our central hypothesis is that older Adults with MCI are undertreated for CVD because patients and physicians overestimate their risk of dementia and underestimate their risk of CVD. This hypothesis has been formulated on the basis of preliminary data from the applicants' pilot research. The rationale for the proposed research is that understanding how patient preferences and physician recommendations contribute to underuse of CVD treatments in patients with MCI has the potential to translate into targeted interventions aimed to improve the quality and outcomes of care, resulting in new and innovative approaches to the treatment of CVD and other serious, acute illnesses in Adults with MCI. Guided by strong preliminary data, this hypothesis will be tested by pursuing two specific aims: 1) Compare AMI and stroke treatments between MCI patients and cognitively normal patients and explore differences in Clinical outcomes associated with treatment differences; and 2) Determine the influence of MCI on patient and surrogate preferences and physician recommendations for AMI and stroke treatment. Under the first aim, a health services research approach- shown to be feasible in the applicants' hands-will be used to quantify the extent and outcomes of treatment differences for AMI and acute ischemic stroke in older patients with MCI. Under the second aim, a multi-center, mixed-methods approach and a national physician survey, which also has been proven as feasible in the applicants' hands, will be used to determine the influence of MCI on patient preferences and physician recommendations for AMI and stroke treatment. This research proposal is innovative because it represents a new and substantially different way of addressing the important public health problem of enhancing the health of older Adults by determining the extent and causes of underuse of effective CVD treatments in those with MCI. The proposed research is significant because it is expected to vertically advance and expand understanding of how MCI influences treatment and Decision Making for AMI and ischemic stroke in older patients. Ultimately, such knowledge has the potential to inform the development of targeted interventions that will help to improve the quality and outcomes of CVD care and to reduce CVD-related disability in older Americans.
PI: Deborah Levine
CO(s): Darin Zahuranec, Lewis Morgenstern & Ken Langa
Jeff Kullgren's editorial "Injecting Facts Into the Heated Debates Over Medicaid Expansion" was recently published in the Annals of Internal Medicine. In this editorial, Dr. Kullgren reviews Wherry and Miller's study on the effects of ACA on coverage, access, utilization, and health.
Link to IHPI article.
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:
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|
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.
This will be the first year that CBSSM will be participating in Researchpalooza. Please come and enjoy the fun!
Wednesday, August 27, 2014
11:00 a.m. - 2:00 p.m.
Circle Drive in front of Med Sci I
All UMHS employees from the Hospitals and Health Centers and Medical School are invited to celebrate this annual event.
Stop by the University Hospital Courtyard and Medical School Circle Drive for:
- Ice Cream sundaes and sugar-free alternatives
- Karaoke and musical entertainment
- Festival Games
- Department and vendor tables with information and giveaways
Beth Tarini and Scott Roberts spoke at the Michigan State Medical Society’s 17th Annual Conference on Bioethics, "Putting the Me in Medicine: The Ethics of Personalized Medical Care"
For more information on the conference, you can visit its website here.
Beth Tarini and Scott Roberts spoke at the Michigan State Medical Society’s 17th Annual Conference on Bioethics, "Putting the Me in Medicine: The Ethics of Personalized Medical Care" The conference examined moral and ethical issues which face physicians and other health care professionals daily.
For more information on the conference, you can visit its website here.
Kathryn Moseley served as one of the judges at "The Big Ethical Question Slam 5" hosted by a2ethics.org. 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 a2ethics.org.
A short video about the Highschool Ethics Bowl can be found here.