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

Funding Years: 2014 - 2017.

Suicide is a leading cause of death and suicide attempts are a major cause of disability, lost productivity, and health care costs. Suicide prevention is a research priority of the National Institutes of Health, and the US Surgeon General's National Strategy for Suicide Prevention calls for a shift towards recovery-oriented prevention efforts which promote hope and social support. Hopelessness and social isolation are two proximal risk factors for suicide which may be improved via peer mentorship, a form of peer support effective for preventing depression and repeat psychiatric hospitalizations. The primary aims of this study are to develop and pilot test a peer mentorship intervention for psychiatrically hospitalized patients at high risk for suicide. The intervention will be adapted by an expert panel from existing peer support training protocols to target suicide risk factors and to enhance suicide risk management. Protocols for training and supervising peer mentors and measures of intervention fidelity will also be developed. The intervention will then be pilot teste among 60 participants randomly assigned to receive the peer mentorship intervention plus usual care or usual care alone. Participants will be recruited from the inpatient psychiatry unit at the University of Michigan Health System. Inclusion criteria will include medical record documentation of suicidal ideation or suicide attempt at admission, and exclusion criteria will include significant cognitive impairment (according to the Mini-Cog), current receipt of peer support, or determination that peer mentorship may cause distress to the patient or the peer mentor. The peer mentorship intervention will include an in-person visit on the inpatient unit and regular in-person or telephone follow-up for 3 months post-discharge. The intervention will be delivered by peer specialists--individuals in stable recovery from serious mental illness who have received formal training and certification in peer support from the state of Michigan--with at least 6 months of professional peer support experience. The primary outcomes of the pilot study are acceptability and feasibility of the intervention as determined by: 1) >50% of eligible participants enroll in the study, 2) >70% of enrollees complete final follow- up measures at 6 months, and 3) among those assigned to the peer mentorship intervention, >80% complete an inpatient session and the median number of total sessions is at least 4. Peer mentorship sessions will be recorded and rated for fidelity. Measures of suicidal ideation and suicide attempts (the intended primary outcomes of a subsequent efficacy study) and secondary outcomes such as quality of life, functioning, depression, and service use will be obtained at baseline, 3 months, and 6 months post-enrollment by a research assistant blinded to study arm. An exploratory aim will be to measure potential mediators of intervention effectiveness including belongingness, burdensomeness, and hopelessness according to the interpersonal theory of suicide. If acceptability and feasibility are demonstrated, the study will result in a novel recovey-oriented suicide prevention intervention ready for a fully-powered randomized controlled efficacy trial.

PI(s): Paul Pfeiffer

Co-I(s): Mark Ilgen, H. Myra Kim, Cheryl King, Marcia Valenstein

Jacob Solomon, PhD

Alumni

Dr. Jacob Solomon was a CBSSM Postdoctoral Research Fellow, 2015-2017.

Jacob Solomon completed a PhD in Media and Information Studies at Michigan State University in 2015. His research is focused on Human-Computer Interaction and Human Factors Engineering where he studies how the design of interactive systems affects users’ behavior. His research merges methods from social sciences with computer and information science to design, build, and evaluate socio-technical systems.

Last Name: 
Solomon

Michael D. Fetters, MD, MPH, MA, Associate Professor, recently gave a talk at the 38th annual North American Primary Care Research Group (NAPCRG) meeting, held November 13-17, 2010, in Seattle, WA.

Mon, October 02, 2017

Sarah Hawley, Brian Zikmund-Fisher, and Reshma Jagsi are co-authors of a recent study published in Medical Decision Making, which was highlighted in MHealth Lab. Their study found that talking to clinicians is the best way for breast cancer patients to understand their recurrence risk. They also found that clinician discussions about recurrence risk should address uncertainty and the relevance of family and personal history. Kamaria Lee is first author of the article.

How We Can Help

CBSSM offers a variety of resources and tools that have broad applicability.

Please consider attending one of our working group meetings. These meetings provide a forum for project focused discussions and interdisciplinary collaborations. Presenters can receive feedback on a range of issues, from project inception and grant applications to manuscript drafts.

As part of our ongoing research efforts, CBSSM investigators often create methodological tools that have broad applicability beyond the specific research projects for which they were developed. We are pleased to make these tools available to all researchers and non-profit organizations, subject only to appropriate attribution in work products (materials and/or manuscripts).Please explore the following tools:

Fri, September 15, 2017

A study on surgeon influence on double mastectomy co-authored by Sarah Hawley and Reshma Jagsi was recently highlighted in Time Health.  This study found that attending surgeons exerted a substantial amount of influence on the likelihood of receipt of contralateral prophylactic mastectomy after a breast cancer diagnosis. Steven Katz was first author of this study.

Interactive Decision

At CBSSM, we perform the basic and applied scientific research that will improve health care policy and practice to benefit patients and their families, health care providers, third-party payers, policy makers, and the general public.  In our "Interactive Decision" web feature, we turn a recent research finding into an interactive decision that a patient or policy maker might face.  Read, decide, click—and see how your answers compare with our respondents.

Impact of the Vaccine Adverse Event Reporting System on Vaccine Acceptance and Trust (Aug-17)

Patient understanding of blood test results (Feb-17)

Attitudes toward Return of Secondary Results in Genomic Sequencing (Sep-16)

Moral concerns and the willingness to donate to a research biobank (Jun-16)

Liver Transplant Organ Quality Decision Aid: Would you consider a less than perfect liver? (Jan-16)

Blocks, Ovals, or People Icons in Icon Array Risk Graphics? (Sept-15)

Getting ahead of illness: using metaphors to influence medical decision making (May-15)

 

 

How much will chemotherapy really help you? (Dec-08)

After breast cancer surgery, additional treatments such as chemotherapy can reduce the risk of cancer coming back. But do women understand how much (or little) benefit chemotherapy provides? Imagine that you're a woman who has recently been diagnosed with breast cancer and then had the cancerous breast tumor surgically removed. While you're at an appointment about 3 weeks after your surgery, your doctor says the following to you:

"Sometimes cancer cells remain after surgery and start to grow again. To try to prevent your cancer from growing again, you should consider having some additional treatment.

"One of our test results shows that you have a type of cancer that is estrogen receptive (ER) positive. This means that your cancer needs the hormone estrogen in order to grow.

"Because you have an ER-positive tumor, you should have hormonal therapy to block estrogen and make it harder for any remaining cancer cells to grow. Hormonal therapy is usually in pill form. It does not cause hair loss or fatigue and generally has very few short-term side effects. You'll start to take hormonal therapy after all other treatments are finished and continue to take it for at least 5 years.

"Although it's clear that you should have hormonal therapy, you'll still need to make a choice about chemotherapy treatments. You could decide to have additional chemotherapy treatments for several months before starting the hormonal therapy. Sometimes, adding chemotherapy can make a big difference in decreasing the risk of dying from cancer. Other times, there's almost no benefit from adding chemotherapy.

"If you decide to have chemotherapy, you'll have 2 to 4 months of fatigue, nausea, hair loss, and other side effects. You'll also face a small risk (less than 1% or less than 1 in 100) of getting a serious infection, a bleeding problem, heart failure, or leukemia. Only you can decide if the benefit of adding chemotherapy to hormonal therapy is worth the risks and side effects."

Next, your doctor shows you a graph that may help you to decide about chemotherapy.

Your doctor says, "The graph below may help you decide if the risk reduction you would get from adding chemotherapy is worth the side effects and risks that the chemotherapy would cause.

  • The green part shows the chance that you'll be alive in 10 years.
  • The red part shows the chance that you'll die because of cancer.
  • The blue part shows the chance that you'll die from other causes.
  • The yellow part shows how much your chance of being alive in 10 years would increase if you add a therapy.
"Remember, given your situation, I think you should definitely take hormonal therapy. What you need to decide is whether to take both chemotherapy and hormonal therapy."
 
In interpreting this graph, imagine that there are two groups of 100 women each. All of these women have the same type of cancer as your hypothetical cancer.
  • The first group all decides to take hormonal therapy only.
  • The second group all decides to take both chemotherapy and hormonal therapy

How many fewer women will die from cancer in the second group, as compared with the first group?

Your doctor continues, "Now, here is another graph that shows the same information in a different way. As before,

  • The green part shows the chance that you'll be alive in 10 years.
  • The red part shows the chance that you'll die because of cancer.
  • The blue part shows the chance that you'll die from other causes.
  • The yellow part shows how much your chance of being alive in 10 years would increase if you add a therapy.
Now we asked you to consider the following question:
How many fewer women will die from cancer in the second group, as compared with the first group?
Do you want to change your answer?
 

About the study

Many participants who saw this graph in a study conducted by CBDSM researchers had similar problems. However, when study participants saw GRAPH B (with the two pictographs), many more were able to correctly calculate the difference.

The CBDSM study compared tools intended to help cancer patients make informed decisions about additional therapies (also called "adjuvant" therapies). The 4 horizontal stacked bars were taken from an online tool called "Adjuvant!" that is often used by physicians to explain risk to cancer patients. The researchers compared comprehension of risk statistics from horizontal bars and from a pictograph format.

They found that study participants who viewed a 2-option pictograph version (GRAPH B in this Decision of the Month) were more accurate in reporting the risk reduction achievable from adding chemotherapy to hormonal therapy for the hypothetical cancer scenario. With GRAPH B, 77% of participants could identify that 2 fewer women out of 100 would die from cancer with both chemotherapy and hormonal therapy. With the 4 horizontal bars (GRAPH A), only 51% of participants could make this calculation. Participants who saw GRAPH B were also much faster at answering this question than participants who saw GRAPH A.
In addition, participants in this study strongly preferred the format of the pictograph you saw (GRAPH B) to the bar graphs you saw (GRAPH A).
The researchers comment:
"While decision support tools such as Adjuvant! use graphical displays to communicate the mortality risks that patients face with different adjuvant therapy options, our research shows that women had difficulty interpreting the 4-option horizontal bar graph format currently used by Adjuvant!. Two simple changes, displaying only risk information related to treatment options that included hormonal therapy...and using pictographs instead of horizontal bars, resulted in significant improvements in both comprehension accuracy and speed of use in our demographically diverse sample....The results...support the concept that simpler information displays can make it easier for decision makers to implement optimal decision strategies. Specifically, focusing patients' attention on those treatment options currently under consideration while removing information related to options which have been already eliminated from consideration (for medically appropriate reasons) may be particularly beneficial. In the context of adjuvant therapy decisions, such an approach would imply that clinicians should discuss the decision in two stages: A first stage in which hormonal therapy is considered and a second stage in which the incremental benefit of chemotherapy is evaluated...Adjuvant! and other online risk calculators enable oncologists and patients to receive individually tailored estimates of mortality and recurrence risks, information that is essential to informed decision making about adjuvant therapy questions. Yet, the full potential of these modeling applications cannot be realized if users misinterpret the statistics provided."
 
Read the article:
Zikmund-Fisher BJ, Fagerlin A, Ubel PA. Cancer 2008;113(12):3382-3390.

 

Mon, May 15, 2017

In light of advancing fetal diagnostic capabilities, Naomi Laventhal and Stephanie Kukora and colleagues are working to improve the decision-making process for families facing complex decisions about their unborn child’s care. For more details check out the MHealth Lab story.

 

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